{"data":[{"created_on":"2024-08-21T19:47:19.461669","updated_on":"2024-08-21T19:47:19.461675","dataset":"aqueduct_crop_baseline_2020","is_downloadable":true,"metadata":{"created_on":"2024-08-21T19:47:19.479439","updated_on":"2024-08-21T19:47:19.479446","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":"TBD","title":"Aqueduct Crop Baseline (2020)","subtitle":null,"source":"TBD","license":"TBD","data_language":"en","overview":"TBD","function":"TBD","cautions":"TBD","key_restrictions":null,"tags":["aqueduct"],"why_added":null,"learn_more":"TBD","id":"4305d21c-a410-45a0-a5e6-fcd135891f49"},"versions":["v1.1","v1.0","v1.4","v1.8","v1.3","v1.5","v1.10","v1.11","v1.12","v1.9","v1.2","v1.6"]},{"created_on":"2022-12-23T21:05:55.985410","updated_on":"2023-02-13T17:33:07.567718","dataset":"arg_native_forest_land_plan","is_downloadable":false,"metadata":{"created_on":"2023-05-04T13:11:58.897345","updated_on":"2025-01-15T20:49:34.327734","spatial_resolution":null,"resolution_description":"30 x 30m","geographic_coverage":"Argentina","update_frequency":"Annual","scale":null,"citation":"The OTBN is composed of 23 shapefiles, each corresponding to one of the 23 provinces of Argentina. Each province is responsible for updating their OTBN data every 5 years, as established in [the legal article](https://www.argentina.gob.ar/sites/default/files/resolucion_no_236_-_anexo_i_-_pautas_metodologicas_para_las_actualizaciones_de_los_otnb_-_2012_2.pdf)\n\nTo obtain the most up to date OTBN shapefiles, it is necessary to make an official request to the Argentina Ministry of Environment and Sustainable Development via their [online portal](http://snmb.ambiente.gob.ar/develop/). Once approved, the office responsible will send the proper data documentation, methods used, and a shapefile for each of the 23 provinces in a zip file.","title":"Ordenamiento Territorial de Bosques Nativos","subtitle":"(Argentina, 30m, 2008-2022)","source":"[Ministerio de Ambiente y Desarrollo Sostenible](https://snmb.ambiente.gob.ar)","license":"Not for commercial use","data_language":null,"overview":"Native Forest Classification<br>According to the Constitution of Argentina, the management of natural resources is under the jurisdiction of each individual province, but these must abide by minimum standards defined at the national level. With the purpose of protecting forested landscapes, in 2007, Argentina enacted the National Law N. 26.331 on Minimum Standards of Environmental Protections of Native Forests. This piece of legislation proposes the Territorial Planning of Native Forests (OTBN by its Spanish acronym) as the central tool of forest management. <br>This legislation defines native forests as “all natural forest ecosystems composed predominantly by mature trees of native species, with diverse associated flora and fauna, and their respective environmental components – soil, subsoil, hydric system, atmospheric system – which together form an interdependent system with unique characteristics and multiple functions, which in its natural state is in a dynamic equilibrium and that offers diverse ecosystem services in addition to a diverse set of natural resources for economic use. This definition considers both primary native forests, where there has been no human intervention, as well as secondary forests, forest systems formed after deforestation events, as well as those resulting from restauration activities.”<br>This definition of native forests includes forest ecosystems in different stages of development. Additionally, palm groves are also considered native forests. The following terms are defined as:<br>a) Native mature tree species: A woody plant species native to its respective region with a central trunk that branches out above the ground.<br><br>The 230/12 Resolution of the COFEMA, which defines the guidelines for the consideration, identification, and mapping of native forests for the OTBN, states:<br>Observation # 1.1: Within this definition and mapping of native forest land cover classes that were non-forested were included due to the functional interdependency they have with forested ecosystems, as well as their function as buffer areas, and/or because they provide ecosystem services like those of a native forest.<br>Guideline # 1.1: Use the definition of forest presented in Law N° 26.331 and its regulatory decree to identify and exclude from the OTBN land classes of non-forested vegetation. <br>Guideline # 1.2: The thresholds of minimum size, height and tree cover that determine a native forest are:<br>• 0.5 hectares of continuous forested land<br>• 3m in height<br>• 20% tree canopy cover.<br>Article 6 of the 26.331 Law determines that each province must realize their own implementation of OTBN guidelines via a democratic process that assigns native forests within their territory into a conservation category: <br>- **Category I (Red)**: Very high conservation value. These lands must not be transformed. The use of these lands is limited to being indigenous community territories and areas of scientific research. These zones may undergo activities such as maintenance, protection, data recollection, and others that do not later their intrinsic characteristics. This includes sustainable tourism activities, which must be carried out under Conservation Plans. Additionally, these zones could be targets of ecological restauration programs when faced with natural or human-caused disturbances. <br>- **Category II (Yellow)**: Median conservation value zones, that, although might have some degradation, can have high conservation value with the implementation of restauration activities. Their use is limited to sustainable resource management, tourism, data recollection and scientific research. Deforestation activities are not permitted.<br>- **Category III (Green)**: These are zones of low conservation value that can be partially or completely transformed in line with the standards of this legislation.<br>Uncategorized forests: Within this category are forested lands that do not fall into any of the classifications above. As a general rule, deforestation must be avoided in these areas, unless there is proper authorization. <br>According to what is established in articles 16 and 17 of the Law 26.331, and article 9 of its regulatory decree 91/09, all intervention in native forests must be backed by a detailed planification of all activities under development. This planification must be include a sustainable resource management plan, conservation plan, and plans for the management and conservation of a land category, or plans for land use change. The potential of developing different types of plans depends on the conservation categories assigned to native forests.","function":"Displays Argentine forests by OTBN category (Territorial Planning of Native Forests)","cautions":"- The original data, provided as vector shapefile data, was rasterized to 30 m for visualization purposes. Raster data sets are made up of a grid of square pixels, while vector data is made up of lines and curves (or paths). For more information, [see this guide](http://gif.berkeley.edu/documents/GIS_Data_Formats.pdf).<br>- Shapefile data is provided by the 23 provinces, as each is responsible for carrying out an update of OTBN areas (5 years). Please refer to the respective provincial agency for inquiries regarding data quality or future updates.<br>Full methodology is available in the citation below<br>","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"d09ad995-cbcb-4006-9f0a-90d44b1d453e"},"versions":["v202212"]},{"created_on":"2023-12-03T03:31:54.407615","updated_on":"2023-12-03T03:31:54.407621","dataset":"arg_otbn_forest_loss","is_downloadable":false,"metadata":{"created_on":"2023-12-03T03:31:54.415358","updated_on":"2026-08-05T14:42:53.072428","spatial_resolution":null,"resolution_description":"30 x 30 meters","geographic_coverage":"Argentina","update_frequency":"Annual","scale":"national","citation":"Ministry of the Environment and Sustainable Development - National Forests Directorate. “National Monitoring System of Native Forests”. Accessed through Global Nature Watch on 5 Dec 2023","title":"Argentinian National Monitoring System of Native Forests","subtitle":"2007-2022, 30m, Argentina, Ministery of the Environment and Sustainable Development","source":"[Ministry of Environment and Sustainable Development – National Forest Directorate] (https://www.argentina.gob.ar/ambiente/bosques/monitoreo-bosques-nativos)","license":"Public","data_language":"English","overview":"The Argentinian National Monitoring System of Native Forests quantifies deforestation in Argentina’s native forests since 2007. The dataset was initially created to contribute to the implementation of Argentina’s Native Forest Law (Law N°26331), which seeks to establish the minimum environmental protections for the conservation, restoration, and management of Argentina’s native forests. Additionally, the National Monitoring System of Native Forests aids in ensuring Argentina’s compliance with international agreements on climate change and forest protection.","function":"Displays deforestation in Argentinean native forests from 2007-2022 as defined by governmental sources.","cautions":"The original data of the National Monitoring System of Native Forests is not completely annualized and reports forest loss for certain Argentinean regions pre-2017 in a series of date ranges that span multiple years. To visualize this data, multi-year ranges were transformed into annualized values by assigning the deforestation total to the final year of each range. Annual deforestation data from 2007-2017 should be interpreted with caution. The original data files can be accessed on the [Ministry of the Environment and Sustainable Development’s website] (https://www.argentina.gob.ar/ambiente/bosques/monitoreo-bosques-nativos)","key_restrictions":null,"tags":["Conservation"],"why_added":"Required for analyses that comply with Argentina’s Native Forest Law (Law N°26331)","learn_more":"https://www.argentina.gob.ar/ambiente/bosques/monitoreo-bosques-nativos","id":"34c9541b-3b1a-4b83-90ea-e755f1d85e07"},"versions":["v2022"]},{"created_on":"2021-11-04T19:25:59.326377","updated_on":"2021-11-04T19:25:59.326383","dataset":"berkeley_earth_temp_anomaly_2000_2020","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897187","updated_on":"2023-05-04T13:11:58.897189","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Annual","scale":"","citation":"Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-259, in review, 2020.  Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).","title":"Annual Surface Temperature Anomalies","subtitle":null,"source":"Berkeley Earth","license":"[Creative Commons 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)","data_language":"en","overview":"This dataset shows the average annual surface air temperature anomaly from 1850 to present, compared to a 1951-1980 baseline time period. In other words, this dataset shows how much cooler or warmer each year is compared to the average surface air temperature from 1951-1980. This dataset is available at 1 degree (°) spatial resolution and  covers increasing amounts of the Earth’s surface: approximately 57% in 1850, 75% in 1880, 95% in 1960, and 99.9% by 2015. The dataset is shown in degrees Celsius (°C).Surface air temperature is defined as the temperature of the air near the surface of the earth and is generally measured by weather stations. Surface air temperatures are influenced by solar radiation, weather, surface materials, topography, and climate. Surface air temperature anomalies are used to track the change in surface air temperature from a baseline, in this case a 30 year average temperature from 1951-1980. Short-term changes in surface air temperature can have an impact on plants, animals, and humans in those areas. Long-term changes in surface air temperature are used to measure climate change and global warming ([Berkeley Earth](https://essd.copernicus.org/preprints/essd-2019-259/)). Climate change is a long-term change in the average weather patterns that have come to define Earth’s local, regional and global climates. Global warming is the long-term heating of Earth’s climate system observed since the pre-industrial period (between 1850 and 1900) due to human activities, primarily fossil fuel burning, which increases heat-trapping greenhouse gas levels in Earth’s atmosphere ([NASA](https://climate.nasa.gov/resources/global-warming-vs-climate-change/)).This dataset was created by combining the Berkeley Earth [monthly land surface air temperature estimates](http://static.berkeleyearth.org/papers/Methods-GIGS-1-103.pdf)  with the Hadley Centre Sea Surface Temperature 3 dataset ([HadSST3](https://www.eea.europa.eu/data-and-maps/data/external/hadsst-4-global-sea-surface)). Berkeley Earth was founded in 2010 with the goal of addressing the major concerns of climate change skeptics regarding global warming and the land surface temperature record. Berkeley Earth uses this dataset to measure changes in global air surface temperature and climate change.","function":"Annual average surface air temperature anomaly, compared to the 1951-1980 average","cautions":"- Anomaly fields are highly smoothed due to the homogenization and reconstruction methods, despite being gridded at 1° spatial resolutionThe homogenization approach may not perform well in areas of rapid local temperature change, leading to overestimates of warming at coastal locations and underestimates at inland locations\n- The Berkeley Earth algorithm may not detect the seasonally varying biases in temperature readings prior to the introduction of Stevenson screens in the mid-19th century\n","key_restrictions":"Creative Commons 4.0","tags":["geospatial","historical","global","raster","temperature","sea_surface_temperature"],"why_added":"Adding to MapBuilder","learn_more":"http://berkeleyearth.org/ ","id":"efc4215f-aa5f-4040-9765-abe7c59aa677"},"versions":["v20211015"]},{"created_on":"2020-07-22T02:48:33.789746","updated_on":"2025-01-27T20:45:12.063963","dataset":"birdlife_alliance_for_zero_extinction_sites","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897355","updated_on":"2026-08-05T14:42:53.601380","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global","update_frequency":"Every 5 years","scale":"global","citation":"Please use the following credit when these data are displayed:  \n\"Alliance for Zero Extinction Sites\". Alliance for Zero Extinction. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/)  \n\nPlease use the following credit when these data are cited:  \n“Alliance for Zero Extinction Sites”. Alliance for Zero Extinction. 2020. [www.zeroextinction.org](https://zeroextinction.org/)\n","title":"Alliance for Zero Extinction sites","subtitle":"2020, global, AZE","source":"Alliance for Zero Extinction","license":"Not for commercial use","data_language":"English","overview":"Created by the Alliance for Zero Extinction (AZE), this data set shows 587 sites for 920 species of mammals, birds, amphibians, reptiles, conifers, and reef-building corals. The species found within these sites have extremely small global ranges and populations; any change to habitat within a site may lead to the extinction of a species in the wild. To meet AZE Extinction Site status, a site must:<br>- Contain at least one Endangered or Critically Endangered species<br>- Be the sole area where an Endangered or Critically Endangered species occurs<br>- Contain greater than 95% of either the known resident population of the species or 95% of the known population of one life history segment (e.g. breeding or wintering) of the species<br>- Have a definable boundary (e.g., species range, extent of contiguous habitat, etc.)<br><br>Launched in 2005, the Alliance for Zero Extinction (AZE) engages 83 non-governmental biodiversity conservation organizations working to prevent species extinctions. The AZE identifies and safeguards places where species evaluated to be Endangered or Critically Endangered by the [International Union for Conservation of Nature](http://www.iucn.org/) are restricted to single remaining sites.","function":"Displays critical sites for conservation that contain endangered species with limited ranges and populations found nowhere else on the planet","cautions":null,"key_restrictions":"Unclear - they agreed to CC BY 4.0, but want to make people request for data","tags":["Conservation"],"why_added":"More on biodiversity at risk","learn_more":"http://www.zeroextinction.org/","id":"c2d773af-0c52-49b4-9029-15e40bf546b6"},"versions":["v20190816","v20200725"]},{"created_on":"2021-04-05T20:05:22.767844","updated_on":"2025-01-27T20:45:28.486161","dataset":"birdlife_biodiversity_intactness","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897500","updated_on":"2026-08-05T14:42:53.946766","spatial_resolution":1000,"resolution_description":"1 km × 1 km","geographic_coverage":"Forested Biomes Globally","update_frequency":"As new data becomes available","scale":"global","citation":"Use the following credit when this data is displayed:  \n“Forest Biodiversity Intactness.” UNEP-WCMC and Natural History Museum. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/)  \n\nUse the following credit when this data is cited:   \nHill, S.L.L., A. Arnell, C. Maney, S.H.M. Butchart, C. Hilton-Taylor, C. Ciciarelli, C. Davis, E. Dinerstein, A. Purvis, and N.D. Burgess. 2019. “Measuring Forest Biodiversity Status and Changes Globally.” Frontiers in Forests and Global Change 2 (November). [doi:10.3389/ffgc.2019.00070](https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2019.00070/full). \n","title":"Biodiversity Intactness","subtitle":"2018, 1 km, global, UNEP-WCMC/NHM","source":"Hill, S.L.L., A. Arnell, C. Maney, S.H.M. Butchart, C. Hilton-Taylor, C. Ciciarelli, C. Davis, E. Dinerstein, A. Purvis, and N.D. Burgess. 2019. “Measuring Forest Biodiversity Status and Changes Globally.” Frontiers in Forests and Global Change 2 (November). [doi:10.3389/ffgc.2019.00070](https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2019.00070/full)\n","license":"Not for commercial use","data_language":"English","overview":"This layer quantifies the impact humans have had on the intactness of species communities. Anthropogenic pressures such as land use conversion have caused dramatic changes to the composition of species communities and this layer illustrates these changes by focusing on the impact of forest change on biodiversity intactness. The maximum value indicates no human impact, while lower values indicate that intactness has been reduced. The [PREDICTS database](https://www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html) comprises over 3 million records of geographically and taxonomically representative data of land use impacts to local biodiversity (Hudson et al. 2017). A subset of the PREDICTS database, including data pertaining to forested biomes only, is employed to model the impacts of land use change and human population density on the intactness of local species communities. To produce the land use map, all forested biomes are selected and each 30 x 30 m pixel within the biome is assigned a land use category based upon inputs from the GNW forest change database and a downscaled land use map (Hoskins et al 2016). The modelled results of biodiversity intactness derived from the PREDICTS database are projected onto the land use and human population density maps, and the final product is aggregated to match the resolution of the downscaled land use map (Hoskins et al 2016). The final output models the impacts of forest change on local biodiversity intactness within forested biomes.","function":"Displays the impacts of forest change on local biodiversity intactness","cautions":"1. The metric assumes that the biodiversity found in a perfectly intact site is equivalent to the biodiversity that would be present without human interference<br>2. Human impacts on biodiversity intactness are quantified through models that extrapolate results from site-specific studies across large areas and there is always a degree of uncertainty in such extrapolations<br>3. Plantation forests were not distinguished from natural forests in this analysis, and as such, plantation areas may be counted as intact biodiversity areas. <br>","key_restrictions":"Commercial use restrictions","tags":["Conservation"],"why_added":"To give further context to the forest change pixels","learn_more":"https://www.frontiersin.org/articles/10.3389/ffgc.2019.00070/full","id":"ad0019cd-6a2d-42e3-9919-2e952db5d551"},"versions":["v201909"]},{"created_on":"2021-04-05T20:05:05.363776","updated_on":"2025-01-27T20:45:21.154927","dataset":"birdlife_biodiversity_significance","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897363","updated_on":"2026-08-05T14:42:54.344253","spatial_resolution":1000,"resolution_description":"1 km × 1 km","geographic_coverage":"Forested Biomes Globally","update_frequency":"As new data becomes available","scale":"global","citation":"Use the following credit when the data is displayed:  \n“Forest Biodiversity Significance”. IUCN, BirdLife International, and UNEP-WCMC Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/)  \n\nUse the following credit when the data is cited:  \nHill, S.L.L., A. Arnell, C. Maney, S.H.M. Butchart, C. Hilton-Taylor, C. Ciciarelli, C. Davis, E. Dinerstein, A. Purvis, and N.D. Burgess. 2019. “Measuring Forest Biodiversity Status and Changes Globally.” Frontiers in Forests and Global Change 2 (November). [doi:10.3389/ffgc.2019.00070](https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2019.00070/full). \n","title":"Biodiversity Significance","subtitle":"2018, 1 km, global, IUCN/BirdLife International/UNEP-WCMC","source":"Hill, S.L.L., A. Arnell, C. Maney, S.H.M. Butchart, C. Hilton-Taylor, C. Ciciarelli, C. Davis, E. Dinerstein, A. Purvis, and N.D. Burgess. 2019. “Measuring Forest Biodiversity Status and Changes Globally.” Frontiers in Forests and Global Change 2 (November). [doi:10.3389/ffgc.2019.00070](https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2019.00070/full)\n","license":"Not for commercial use","data_language":"English","overview":"This layer shows the significance of each forest location for biodiversity in terms of the relative contribution of each pixel to the global distributions of all forest-dependent mammals, birds, amphibians and conifers worldwide. To calculate it, species that are coded in the IUCN Red List as forest dependent are selected and their distribution maps are clipped by their known altitudinal ranges (note amphibians’ altitudinal range have not been assessed) using a digital elevation model (DEM) dataset, and overlapped with the layer of forest cover. For each species, the relative “significance” of each forest pixel in their range is calculated as one divided by the total number of pixels of forest in their range. These values are summed for all species occurring within the pixel to give an overall value to the pixel. This metric is also sometimes termed ‘range rarity’.","function":"Displays the relative importance of each pixel in terms of its aggregate contribution to the distribution of forest-dependent species of mammals, birds, amphibians, and conifers.","cautions":"1. There are many different ways to define biodiversity significance. This layer is based on one particular approach.<br>2. Only Birds, Mammals, Amphibians and Conifers are included in the analysis<br>3. Only forest-dependent species are included. <br>4. The individual species range maps upon which this layer is based show distributional boundaries, not occupancy, and so contain commission errors. However, when >15,000 species ranges are combined into this single layer, such errors become largely irrelevant.<br>5. Historical ranges are excluded. Hence the value of each pixel is related to the global loss of species richness if the pixel is deforested.<br>6. Locations of high species richness do not necessarily have high scores if most of the species in the location have large global distributions <br>7. All species are treated equally, so the evolutionary distinctiveness of different taxa is not considered<br>8. When overlain with maps of forest loss, forest gain is ignored. It is assumed that tree cover gain over the analysis period is unlikely to translate into significant gain in forest-dependent species, given the natural time-lags in regeneration of forest ecosystems.<br>9. The layer provides a broad picture of variation in biodiversity significance of different forests globally. It is not intended to be used in isolation for priority setting or decision making, for which additional information is typically needed (e.g. on threats, costs etc)<br>10. The underlying species maps come from the [IUCN Red List](https://www.iucnredlist.org/resources/spatial-data-download) and [BirdLife International](http://datazone.birdlife.org/species/requestdis). Integrated data from the [IUCN Red List](https://www.iucnredlist.org/), [World Database of Key Biodiversity Areas](http://www.keybiodiversityareas.org/home) and [World Database on Protected Areas](https://www.protectedplanet.net/) are available in the [Integrated Biodiversity Assessment Tool](https://www.ibat-alliance.org/).","key_restrictions":"Commercial Use Restriction","tags":["Conservation, Land Cover"],"why_added":"Give further context to forest loss pixels","learn_more":"https://www.frontiersin.org/articles/10.3389/ffgc.2019.00070/full","id":"70611539-adcf-45df-b7eb-311a74528bce"},"versions":["v201909"]},{"created_on":"2021-05-06T12:56:52.855693","updated_on":"2025-01-27T20:44:59.254578","dataset":"birdlife_endemic_bird_areas","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897302","updated_on":"2026-08-05T14:42:54.788872","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global","update_frequency":"When new data is available","scale":"global","citation":"Use the following credit when these data are displayed:  \n“Endemic Bird Areas”. BirdLife International. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/)  \n\nUse the following credit when these data are cited:  \nStattersfield, A.J., Crosby, M.J., Long, A.J. and Wege, D.C. (1998) “Endemic Bird Areas of the World. Priorities for biodiversity conservation.” BirdLife Conservation Series 7. Cambridge: BirdLife International.   \n","title":"Endemic Bird Areas","subtitle":"2014, global, BirdLife International","source":"BirdLife International","license":"[Terms of use](https://datazone.birdlife.org/info/dataterms)","data_language":"English","overview":"While many bird species are widespread, over 2,500 are endemic and restricted to an area smaller than 5 million hectares (restricted-range species). BirdLife International has mapped every restricted-range species using geo-referenced locality records. Through this process, they identified regions of the world—known as “Endemic Bird Areas” (EBAs)—where the distributions of two or more of these species overlap.<br><br>Half of all restricted-range species are globally threatened or near-threatened, and the other half remain vulnerable to loss or degradation of habitat. The majority of EBAs are also important for the conservation of restricted-range species from other animal and plant groups. The unique landscapes where these bird species occur, amounting to just 4.5% of the earth's land surface, are high priorities for broad-scale ecosystem conservation.<br><br>Geographically, EBAs are often islands or mountain ranges, and vary considerably in size, from a few hundred hectares to more than 10,000,000 hectares. EBAs also vary in the number of restricted-range species that they support (from two to 80). EBAs are found around the world, but most (77%) of them are located in the tropics and subtropics.","function":"Displays areas where the geographic range of two or more endemic bird species overlaps","cautions":"","key_restrictions":"No redistribution - requests to download should be made to science@birdlife.org\n\nNo commercial use","tags":["Conservation"],"why_added":"Shows where important birds are?","learn_more":"http://www.birdlife.org/datazone/eba","id":"28a44745-8e98-4d14-8efd-9352af02c55f"},"versions":["v2014"]},{"created_on":"2020-07-22T02:58:22.077634","updated_on":"2025-01-27T20:45:05.534153","dataset":"birdlife_key_biodiversity_areas","is_downloadable":false,"metadata":{"created_on":"2023-05-04T13:11:58.897129","updated_on":"2026-08-05T14:42:55.121721","spatial_resolution":null,"resolution_description":" ","geographic_coverage":"Global, terrestrial, freshwater and marine. ","update_frequency":"Twice Per Year","scale":"global","citation":"Use the following credit when these data are displayed: “\nKey Biodiversity Areas”. Birdlife International. Accessed from Global Nature Watch on 22/01/2026. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/map/www.globalnaturewatch.org).  \n\nUse the following credit when these data are cited: \nBirdLife International (2024) World Database of Key Biodiversity Areas. Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, American Bird Conservancy, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Re:wild, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. September 2024 version. Available at <https://www.keybiodiversityareas.org/request-gis-data>.  \n","title":"Key Biodiversity Areas","subtitle":"2024, Global, BirdLife International","source":"[World Database of Key Biodiversity Areas](https://www.keybiodiversityareas.org/)\n","license":"Non-commercial use only. Full terms of use available at [https://www.keybiodiversityareas.org/terms-service](https://www.keybiodiversityareas.org/terms-service). Commercial access is available at [https://ibat-alliance.org](https://ibat-alliance.org).","data_language":"English","overview":"Full details of each KBA, including information on their biodiversity importance, can be viewed at [http://www.keybiodiversityareas.org](http://www.keybiodiversityareas.org).  The criteria by which KBAs are identified are described in the Global Standard for the Identification of Key Biodiversity Areas (IUCN 2016). Sites qualify as global KBAs if they meet one or more of 11 criteria, clustered into five categories: threatened biodiversity; geographically restricted biodiversity; ecological integrity; biological processes; and, irreplaceability. The KBA criteria can be applied to species and ecosystems in terrestrial, inland water and marine environments. Although not all KBA criteria may be relevant to all elements of biodiversity, the thresholds associated with each of the criteria may be applied across all taxonomic groups (other than micro-organisms) and ecosystems. The KBA identification process is a highly inclusive, consultative and bottom-up exercise. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, consultation with stakeholders at the national level (both non-governmental and governmental organizations) is required during the proposal process. \n\nThe KBA Partnership brings together 13 of the world’s largest international conservation organisations to map, monitor and conserve the most important places for life on earth. \n\nFor further information, visit [http://www.keybiodiversityareas.org](http://www.keybiodiversityareas.org). \n","function":"Displays Key Biodiversity Areas, defined as 'sites contributing significantly to the global persistence of biodiversity'.","cautions":"Note that KBAs have not yet been identified comprehensively in every country for all species groups and biodiversity features. See further details at [http://www.keybiodiversityareas.org](http://www.keybiodiversityareas.org/)\n","key_restrictions":"Non-downloadable","tags":["Conservation"],"why_added":"This is information about biodiversity on a global scale","learn_more":"https://www.keybiodiversityareas.org/request-gis-data","id":"edbef072-be6f-4637-a904-3b277ef445a7"},"versions":["v202102","v202106","v20191211","v20240903","v20250911"]},{"created_on":"2025-04-25T02:13:01.860364","updated_on":"2025-04-25T02:13:01.860370","dataset":"carbonflux_adm1_change","is_downloadable":true,"metadata":{},"versions":["v20260327","v20250515"]},{"created_on":"2025-04-25T02:12:32.415571","updated_on":"2025-04-25T02:12:32.415576","dataset":"carbonflux_adm1_summary","is_downloadable":true,"metadata":{},"versions":["v20260327","v20250515"]},{"created_on":"2025-04-25T02:14:25.571984","updated_on":"2025-04-25T02:14:25.571990","dataset":"carbonflux_adm2_change","is_downloadable":true,"metadata":{},"versions":["v20260327","v20250515"]},{"created_on":"2025-04-25T02:13:41.538811","updated_on":"2025-04-25T02:13:41.538816","dataset":"carbonflux_adm2_summary","is_downloadable":true,"metadata":{},"versions":["v20260327","v20250515"]},{"created_on":"2025-04-25T02:11:48.211916","updated_on":"2025-04-25T02:11:48.211921","dataset":"carbonflux_iso_change","is_downloadable":true,"metadata":{},"versions":["v20260327","v20250515"]},{"created_on":"2025-04-25T02:10:45.005031","updated_on":"2025-04-25T02:10:45.005035","dataset":"carbonflux_iso_summary","is_downloadable":true,"metadata":{},"versions":["v20250515","v20260327"]},{"created_on":"2021-09-08T14:24:12.804410","updated_on":"2021-09-08T14:24:12.804416","dataset":"cartocritica_mex_protected_areas_2016","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897176","updated_on":"2026-08-05T14:42:55.469335","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Mexico","update_frequency":" ","scale":"national","citation":"CartoCrítica. “Mexico protected areas.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org. ","title":"Mexico protected areas","subtitle":null,"source":"Compiled by [CartoCrítica](http://www.cartocritica.org.mx/) from [CONANP]( http://www.conanp.gob.mx/) (national) and Bezaury-Creel J.E., J.Fco. Torres-Origel, L.M. Ochoa-Ochoa, Marco Castro-Campos, N. Moreno-Díaz. 2012. Áreas Naturales Protegidas Estales, Municipales y Voluntarias. The Nature Conservancy / Comisión Nacional para el Conocimiento y Uso de la Biodiversidad / Comisión Nacional de Áreas Naturales Protegidas (state, municipal, and voluntary)","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"Espanol","overview":"This data set shows several different types of protected areas in Mexico. National protected areas are managed by CONANP, while state and municipal protected areas are managed by state and municipal agencies. Voluntary conservation areas are lands that private property owners have voluntarily certified as protected for a minimum of 15 years.","function":"Shows the location of Mexico’s national, state, and municipal protected areas as well as voluntary conservation areas","cautions":"This data set is a combination of data from multiple sources, with varying quality","key_restrictions":"CC BY 4.0","tags":["Country data"],"why_added":"Mexico is a priority country, WDPA is out of date","learn_more":"","id":"3dd5cf8c-e085-45ae-8f89-8b8e6b7e10c7"},"versions":null},{"created_on":"2021-05-06T12:55:21.502409","updated_on":"2025-01-27T20:44:52.736053","dataset":"ci_biodiversity_hotspots","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897529","updated_on":"2026-08-05T14:42:55.899049","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global (land only)","update_frequency":" ","scale":"global","citation":"Use the following credit when these data are displayed:   \n\"Biodiversity hotspots\". Conservation International. Accessed from Global Nature Watch on [date]. \\[www.globalnaturewatch.org]\\(https://www.globalnaturewatch.org/)  \n\nUse the following credit when these data are cited:  \nMichael Hoffman, Kellee Koenig, Gill Bunting, Jennifer Costanza, and Kristen J. Williams. “Biodiversity Hotspots (version 2016.1)”. Zenodo, April 25, 2016. <https://doi.org/10.5281/zenodo.3261807>\n","title":"Biodiversity hotspots","subtitle":"2016, global, Conservation International","source":"Conservation International","license":"[CC BY SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)","data_language":"English","overview":"First defined in 1988 by scientist Norman Myers, biodiversity hotspots are areas characterized by high levels of endemic plants coupled with significant habitat loss. Specifically, a region must meet the following criteria to achieve Conservation International’s hotspot classification:<br><br>- At least 1,500 species of vascular plants (>0.5% of the world’s total) are endemic<br>- At least 70% of the original natural vegetation has been lost<br><br>When Myers first defined the term, he identified 10 tropical forest hotspots. The need to pinpoint priority conservation regions led Conservation International (CI) to adopt the term and reassess the hotspot concept. In this process, CI introduced quantitative thresholds (see above) and added additional regions. At that time, there were 25 hotspots. Because of the constant change in environmental threats and the improved understanding of biodiversity, CI has since revisited the hotspots to refine boundaries, update information, and add new regions. This process produced an additional 10 hotspots, bringing the total to 35.","function":"Displays Conservation International’s biodiversity hotspots—defined regions around the world where biodiversity conservation is most urgent because of high levels of endemism and human threat","cautions":"This layer only displays the land-based portion of biodiversity hotspots, although some hotspots extend offshore","key_restrictions":"","tags":["Conservation"],"why_added":"Show important areas for biodiversity globally","learn_more":"","id":"c592bc24-3755-46fa-a3e6-79d82c27c6db"},"versions":["v2016"]},{"created_on":"2021-03-23T18:22:35.439313","updated_on":"2021-03-23T18:22:35.439319","dataset":"cifor_peatlands","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897489","updated_on":"2023-05-04T13:11:58.897491","spatial_resolution":231,"resolution_description":null,"geographic_coverage":"Tropics and Subtropics","update_frequency":null,"scale":null,"citation":null,"title":"Tropical and Subtropical Peatland Distribution","subtitle":null,"source":"Gumbricht, T.; Román-Cuesta, R.M.; Verchot, L.V.; Herold, M.; Wittmann, F; Householder, E.; Herold, N.; Murdiyarso, D., 2017, 'Tropical and Subtropical Wetlands Distribution version 2', https://doi.org/10.17528/CIFOR/DATA.00058, Center for International Forestry Research (CIFOR), V3, UNF:6:Bc9aFtBpam27aFOCMgW71Q== [fileUNF]","license":"CC-BY-4.0","data_language":"english","overview":"Distribution of peatland that covers the tropics and sub tropics, excluding small islands. It was mapped in 231 meters spatial resolution. Peat is here defined as any soil having at least 30cm of decomposed or semi-decomposed organic material with at least 50% of organic matter. This corresponds to 29% of carbon content using 1.72 as the transformation factor. The peatland map is produced by adding the peat forming wetlands: mangrove (20), swamp/bog (30), Fen (40), riverine (50), and floodswamps (60) (note: the number in parentheses refer to pixel code of each class in Wetlands dataset). Our map of peatlands was contrasted against n=275 geo-positioned soil profiles containing peat, with 65% of agreement. Further fieldwork is however needed to validate our map. Mangroves are here considered to host the thresholds of depth and organic matter content needed for peat definition, although mineral soil may prevail. Mangroves contribute with ca. 180,000 km2 to the 1.7 million km2 of peatlands (11%), which would need further ground validation (i.e. in areas like Indonesian Papua have large extents of mangrove that contribute to peat, which would need ground-truthing to validate if they contain peat as defined here).","function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://data.cifor.org/dataset.xhtml?persistentId=doi:10.17528/CIFOR/DATA.00058","id":"cab88b43-b499-4589-9a15-ec516227ef5f"},"versions":["v2"]},{"created_on":"2023-07-07T05:50:14.282393","updated_on":"2023-07-07T06:44:53.605648","dataset":"cities_boundaries_test","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2022-03-31T15:21:21.793180","updated_on":"2022-03-31T15:21:21.793186","dataset":"clark_labs_tropical_pond_aquaculture_1999","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897278","updated_on":"2023-05-04T13:11:58.897279","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Clark labs Tropical Pond Aquaculture (1999)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"2b2cd7bf-349c-4995-87d4-c38bc7833459"},"versions":["v202203"]},{"created_on":"2022-03-31T15:21:28.580828","updated_on":"2022-03-31T15:21:28.580833","dataset":"clark_labs_tropical_pond_aquaculture_2014","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897320","updated_on":"2023-05-04T13:11:58.897322","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Clark labs Tropical Pond Aquaculture (1999)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"aaf45790-0867-4090-aa88-b72e904958d0"},"versions":["v202203"]},{"created_on":"2022-03-31T15:21:31.548857","updated_on":"2022-03-31T15:21:31.548862","dataset":"clark_labs_tropical_pond_aquaculture_2018","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897323","updated_on":"2023-05-04T13:11:58.897324","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Clark labs Tropical Pond Aquaculture (1999)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"e873159c-4021-42c6-86f8-18589ac27d28"},"versions":["v202203"]},{"created_on":"2022-03-31T16:15:18.725934","updated_on":"2022-03-31T16:15:18.725939","dataset":"clark_labs_tropical_pond_aquaculture_change_1999_2014","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897534","updated_on":"2023-05-04T13:11:58.897535","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Clark Labs Tropical Pond Aquaculture Change (1999-2014)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"e0a5159d-01e9-486d-bf03-da4e70306e87"},"versions":["v202203"]},{"created_on":"2022-03-31T16:15:14.413593","updated_on":"2022-03-31T16:15:14.413600","dataset":"clark_labs_tropical_pond_aquaculture_change_1999_2018","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897387","updated_on":"2023-05-04T13:11:58.897388","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Clark Labs Tropical Pond Aquaculture Change (1999-2018)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"6f8b94c1-f3e8-4c71-907a-bc793e15dc4a"},"versions":["v202203"]},{"created_on":"2022-03-31T16:15:07.786034","updated_on":"2022-03-31T16:15:07.786039","dataset":"clark_labs_tropical_pond_aquaculture_change_2014_2018","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897352","updated_on":"2023-05-04T13:11:58.897354","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Clark Labs Tropical Pond Aquaculture Change (2014-2018)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"85936749-afc0-4c12-84b4-d66716abaec4"},"versions":["v202203"]},{"created_on":"2024-05-30T22:10:20.890127","updated_on":"2024-05-30T22:10:20.890132","dataset":"col_frontera_agricola","is_downloadable":false,"metadata":{"created_on":"2024-05-30T22:10:20.905384","updated_on":"2024-05-30T22:10:20.905388","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Colombia","update_frequency":"Yearly","scale":null,"citation":null,"title":"Frontera Agricola Nacional","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":["Conservation"],"why_added":null,"learn_more":null,"id":"6759e519-082d-461d-b099-b1666280093e"},"versions":["v2024"]},{"created_on":"2021-09-08T14:48:21.263064","updated_on":"2021-09-08T14:48:21.263072","dataset":"conafor_mex_forest_zoning","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897304","updated_on":"2026-08-05T14:42:56.210861","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Mexico","update_frequency":" ","scale":"national","citation":"Registro Agrario Nacional. “Mexico land rights.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org. ","title":"Mexico land rights","subtitle":null,"source":"[Registro Agrario Nacional](http://catalogo.datos.gob.mx/dataset/perimetrales-de-los-nucleos-agrarios-certificados)","license":"Unknown","data_language":"Espanol","overview":"In Mexico, land is divided into public, private, and social ownership. This layer shows all social lands in Mexico, which together cover 53% of the country. Social lands are further split into ejidos and community lands, comprising 43% and 10% of the entire country, respectively.\n\nEjidos are communal lands that were granted for agriculture after the Mexican Revolution. Each ejido is zoned into areas for human settlement, individual land parcels, and common-use. Ejidos have their own governing bodies for decision making (Asamblea), representation (Comisariado), and control (Consejo de vigilancia). \n\nCommunity lands are created through recognition of colonial titles or ancestral possession. Communities also have their own governing bodies.","function":"Shows the location of ejidos and community lands in Mexico","cautions":"","key_restrictions":"Unknown","tags":["Country data"],"why_added":"Important country & category of data","learn_more":"http://catalogo.datos.gob.mx/dataset/perimetrales-de-los-nucleos-agrarios-certificados","id":"89dd02d4-8b54-40c8-8251-e290d37bc767"},"versions":["v2011"]},{"created_on":"2021-11-04T19:32:29.656679","updated_on":"2021-11-04T19:32:29.656684","dataset":"dtu_wb_wind_speed_potential_2001_2010","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897315","updated_on":"2023-05-04T13:11:58.897316","spatial_resolution":1000,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":"","citation":"(Data/information/map obtained from the) Global Wind Atlas 2.0, a free, web-based application developed, owned and operated by the Technical University of Denmark (DTU) in partnership with the World Bank Group, utilizing data provided by Vortex, with funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalwindatlas.info. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).","title":"Wind Speed Potential","subtitle":null,"source":"DTU/World Bank Group/ESMAP","license":"[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)","data_language":"en","overview":"Additional Information  Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.","function":"Mean wind speed for any location on Earth","cautions":"The GWA uses two major modeling components that can introduce uncertainty into the calculations. These components are the mesoscale modeling and the microscale modeling.","key_restrictions":"Creative Commons Attribution 4.0 International","tags":["raster","wind_energy","wind","renewable_energy","SDG_7_Affordable_and_Clean_Energy","energy","historical","geospatial","global","energy_production"],"why_added":"Adding to MapBuilder","learn_more":"https://www.globalwindatlas.info/about/introduction","id":"fc7d43fc-df1a-4e3c-8e63-98d8961d817f"},"versions":["v20211015"]},{"created_on":"2021-07-22T18:25:43.506782","updated_on":"2025-02-11T16:12:03.385187","dataset":"esa_land_cover_2015","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897171","updated_on":"2026-08-05T14:42:56.560065","spatial_resolution":300,"resolution_description":"300 m","geographic_coverage":"Global","update_frequency":"Not updated, represents the year 2015.","scale":"global","citation":"ESA Climate Change Initiative, Land Cover - led by UC Louvain. “2015 global land cover.” Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: [maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf](maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf). Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Land cover","subtitle":"2015, 300 m, global, ESA/UCLouvain","source":"© ESA Climate Change Initiative - Land Cover led by UCLouvain (2017)","license":"[Terms of Use](http://maps.elie.ucl.ac.be/CCI/viewer/download.php)","data_language":"English","overview":"This data set (version 2.07) was created as part of the Climate Change Initiative (CCI), an initiative of the European Space Agency to create long-term, consistent, global data for the purposes of climate modelling. The CCI Land Cover project delivers consistent global land cover maps at 300 m spatial resolution on an annual basis from 1992 to 2015. The Global Nature Watch platform only displays the 2015 land cover data.<br><br>To ensure consistency from year to year, land cover maps for each year are derived from a single baseline land cover map. The baseline map was created using the full record of MERIS images from 2003 to 2012, using unsupervised classification as well as a machine learning algorithm over multiple years of imagery. Changes are then detected between individual years at 1 km resolution, using AVHRR data from 1992 to 1999, SPOT-VGT data from 1999 to 2013, and PROVA-V data from 2014 and 2015. Changes must be consistent for two consecutive years in order to be counted, with the exception of forest changes in 2014 and 2015 which are assumed to be well detected. The 1 km changes are then combined with the baseline land cover map and delineated to 300 meters for 2004 onward (when MERIS and PROVA-V data are available).<br><br>The resulting data have a total of 22 global land cover classes. For the sake of better visualization, Global Nature Watch shows only a set of simplified classes, based on the IPCC (agriculture, forest, grassland, wetland, settlement, shrubland, sparse vegetation, bare area, water, and permanent ice and snow). The full set of classes as well as annual land cover maps back to 1992 are available on the [ESA/CCI viewer](http://maps.elie.ucl.ac.be/CCI/viewer/).","function":"Shows the global distribution of land cover in 2015","cautions":"A [full accuracy assessment](http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf) is available from the CCI. In general, land cover classes such as rainfed and irrigated croplands, broadleaved evergreen forest, urban areas, bare areas, water bodies and permanent snow are found quite accurately mapped. On the other hand, classes such as lichens and mosses, sparse vegetation and flooded forest with fresh water can be affected by errors.<br><br>Data quality varies by region, particularly as related to the coverage of MERIS imagery for creation of the baseline map. Areas with less coverage include the western part of the Amazon basin, Chile and the southern part of Argentina, the western part of Congo basin as well as the gulf of Guinea, the eastern part of Russia, and the eastern coast of China and Indonesia.","key_restrictions":"Terms of Use: http://maps.elie.ucl.ac.be/CCI/viewer/download.php. Advertising/ commercial production with the data must be approved by the team","tags":["Land Cover"],"why_added":"Replace our global land cover data with something more recent","learn_more":"http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf ","id":"7ef53170-f931-44a9-bb75-7d3214daf7a6"},"versions":["v2016"]},{"created_on":"2021-07-21T20:30:37.665416","updated_on":"2021-07-21T20:30:37.665421","dataset":"fao_ecozones","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897476","updated_on":"2023-05-04T13:11:58.897477","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"FAO Global Ecological Zones","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"0c118508-4841-4264-a349-42ff638609de"},"versions":["v2000","v2010"]},{"created_on":"2023-10-18T16:24:11.312449","updated_on":"2023-10-18T16:24:11.312455","dataset":"fao_forest_change","is_downloadable":true,"metadata":{"created_on":"2023-10-18T16:24:11.338241","updated_on":"2023-10-18T16:24:11.338247","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"FAO Forest 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C/ha)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"2b632bf9-9dc2-48c8-8a17-a41ac54dd047"},"versions":["v20230222"]},{"created_on":"2021-04-01T18:45:19.069706","updated_on":"2025-01-30T22:42:36.832204","dataset":"gfw_emerging_hot_spots","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897495","updated_on":"2026-08-05T14:42:57.893312","spatial_resolution":null,"resolution_description":"N/A","geographic_coverage":"Tropics","update_frequency":"Annual","scale":"global","citation":"Harris et al. (2017). Emerging Hot Spots. Accessed on [date] from Global Nature Watch.\n","title":"Emerging Hot Spots","subtitle":"2002-2025 tropics, WRI ","source":"Harris, Nancy L., Elizabeth Goldman, Christopher Gabris, et al. 2017. “Using spatial statistics to identify emerging hot spots of forest loss.” _Environmental Research Letters_ 12 (2): 024012. \n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"Due to the increasing size and complexity of global forest monitoring data sources, analysis and interpretation tools for this data are ever more important for intervention efforts, allowing for the quick identification and interpretation of significant forest loss. The emerging hot spots data set identifies the most significant clusters of primary forest loss between 2002-2025 at a country level basis, on a tropical scale. The term ‘hot spot’ is defined as an area that exhibits statistically significant clustering in the spatial patterns of loss. In this analysis, observed patterns of primary forest loss are likely to be attributable to underlying, as opposed to random, spatial processes. The different categories of hot spots are described below: \nNew: A location that is a statistically significant hot spot only for the year 2025 and has never been a hot spot before. \nSporadic: A location that is an on-again then off-again hot spot. Less than 22 of the 24 years have been statistically significant hot spots. \nIntensifying: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%), including the most recent year (2025). In addition, the intensity of clustering of high counts in each year is increasing. \nPersistent: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%), with no discernible trend indicating an increase or decrease in the intensity of clustering over time. \nDiminishing: A location that has been a statistically significant hot spot for more than 21 of the 24 years (>90%). In addition, the intensity of clustering of high counts in each year is decreasing, or the most recent year (2025) is not hot. \nThe emerging hot spots analysis uses the annual Hansen et al 2013 tree cover loss data set between the years 2002 – 2025, the Turubanova et al. 2018 primary forest extent data set for the year 2001, and the ESRI ArcGIS Emerging Hot Spot Analysis geoprocessing tool. In this analysis, primary forest is defined as mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history. Forest loss is defined as ‘stand replacement disturbance,’ or the complete removal of tree cover canopy at the Landsat pixel scale. The emerging hot spots analysis tool uses a combination two statistical measures, the Getis-Ord Gi\\* statistic to identify the location and degree of spatial clustering of forest loss, and the Mann-Kendall trend test to evaluate the temporal trend over time. \nThe forest loss data used in this analysis has a user’s accuracy of 87% and a producer’s accuracy of 83.1% across the tropical biome. Additionally, because this analysis was run for individual countries, results are relative to the patterns and amount of loss in each country. Results should not be directly compared between countries - please use caution when viewing layer at a global scale. \n","function":"To identify statistically significant clusters of primary forest loss on a country level basis","cautions":"This analysis was run for individual countries and therefore results are relative to the patterns and amount of loss in each country. Results should not be directly compared between countries - please use caution when viewing layer at a global scale","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":"https://www.researchgate.net/publication/312543231_Using_spatial_statistics_to_identify_emerging_hot_spots_of_forest_loss","id":"21a1c3f2-afb1-47a7-81d3-d493bed0ca8b"},"versions":["v2025","v2023","v2021","v2022","v2024","v2020"]},{"created_on":"2022-03-23T21:10:38.552095","updated_on":"2022-03-23T21:10:38.552103","dataset":"gfw_forest_age","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897241","updated_on":"2026-08-05T14:42:58.203378","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Forest Age","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"0dc66ad2-66ac-44ce-9cb6-c9c4936a4297"},"versions":["v20220309","v20210621"]},{"created_on":"2021-05-14T19:52:10.254744","updated_on":"2021-05-14T19:52:10.254750","dataset":"gfw_forest_age_category","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897384","updated_on":"2026-08-05T14:42:58.609818","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Carbon Model - Forest Age Category","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":"Contextual layer for the GNW forest carbon model","cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"c405868a-858e-4bdd-babe-9fbf4becbb3e"},"versions":["v20210223"]},{"created_on":"2021-01-27T17:08:23.653673","updated_on":"2021-04-27T19:05:57.618729","dataset":"gfw_forest_carbon_gross_emissions","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897442","updated_on":"2026-08-05T14:42:58.940683","spatial_resolution":30,"resolution_description":"30 m","geographic_coverage":"Global","update_frequency":"Annual","scale":"global","citation":"Harris et al. (2021). Global maps of 21st century forest carbon fluxes. Accessed on [date] from Global Nature Watch.","title":"Forest greenhouse gas emissions","subtitle":"(2001-2025, 30m, Harris et al. 2021, Gibbs et al. 2025)","source":"Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. [https://doi.org/10.1038/s41558-020-00976-6](https://doi.org/10.1038/s41558-020-00976-6)<br><br>Gibbs, D. A., Rose, M., Grassi, G., Melo, J., Rossi, S., Heinrich, V., & Harris, N. L. 2025. Revised and updated geospatial monitoring of 21st century forest carbon fluxes. Earth System Science Data. [https://doi.org/10.5194/essd-17-1217-2025](https://doi.org/10.5194/essd-17-1217-2025)\n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This emissions layer is part of the forest carbon flux model described in [Harris et al. (2021)](https://www.nature.com/articles/s41558-020-00976-6). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2e emissions/ha, between 2001 and 2025). Emissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil organic carbon) and greenhouse gases (CO<sub>2</sub>, NH<sub>4</sub>, N<sub>2</sub>O). Emissions estimates for each pixel are calculated following IPCC Guidelines for [national greenhouse gas inventories](https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/) where stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of [Hansen et al. (2013)](https://www.science.org/doi/10.1126/science.1244693). The carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance.<br><br>Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included. Instead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for in the companion [forest carbon removals layer](https://data.globalforestwatch.org/datasets/forest-carbon-removals). The fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event, whether the disturbance occurred on peat, and more. All emissions are assumed to occur in the year of disturbance. Emissions can be assigned to a specific year using the Hansen tree cover loss data; separate rasters for emissions for each year are not available from GNW. All input layers were resampled to a common resolution of 0.00025 × 0.00025 degrees each to match Hansen et al. (2013).<br><br>We have made several updates to the model since its [original release](https://doi.org/10.1038/s41558-020-00976-6). For documentation through the current version, please refer to [this blog](https://www.globalnaturewatch.org/blog/data-and-tools/whats-new-carbon-flux-monitoring/). For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to [Gibbs et al. (2025)](https://doi.org/10.5194/essd-17-1217-2025).<br><br>Three variations of emissions rasters are available for download:<br>   1) megagrams CO2e emissions/ha in pixels with >30% tree cover density (TCD) in 2000 or tree cover gain: Used for visualizing (mapping) emissions according to the default GNW TCD threshold because it represents the density of emissions per hectare. You would use this if you want to only include emissions in pixels that are more conservatively defined as forest.<br>   2) megagrams CO2e emissions/pixel in pixels with >30% TCD in 2000 or tree cover gain: Used for calculating the emissions in an area of interest (AOI) according to the default GNW TCD threshold because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. You would use this if you want to only include emissions in pixels that are more conservatively defined as forest.<br>   3) megagrams CO2e emissions/pixel in pixels with any amount of tree cover in 2000 or tree cover gain: Used for calculating the emissions in an area of interest (AOI) without any TCD threshold because the values of the pixels in the AOI can be summed to obtain the total emissions for that area. This would represent the total emissions from tree cover loss in the AOI without applying a TCD threshold. You would use this if you want to include emissions in pixels that have low (<30%) TCD in 2000.<br>The values in the megagrams CO2e/pixel layers were calculated by adjusting the emissions per hectare by the size of each pixel, which varies by latitude. Tree cover density in 2000 is according to Hansen et al. (2013) and tree cover gain between 2000 to 2020 is according to [Potapov et al. (2022)](https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full).<br><br>Download data from [here](https://data.globalforestwatch.org/datasets/forest-greenhouse-gas-emissions).<br><br>Access data on GEE [here](https://code.earthengine.google.com/?asset=projects/wri-datalab/gfw-data-lake/v1-4-3-2001-2025/gross-emissions-forest-extent-per-ha/gross-emissions-global-forest-extent-per-ha-2001-2025)\n","function":"Displays forest greenhouse gas emissions from stand-replacing disturbances","cautions":"- Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.\n- Values are applicable to forest areas only (canopy cover >30 percent and >5 m height or areas with tree cover gain). See [Harris et al. (2021)](https://www.nature.com/articles/s41558-020-00976-6) for further information on the forest definition used in the analysis.\n- Although emissions in each pixel are associated with a specific year of disturbance, emissions over an area of interest reflect the total over the model period of 2001-2025. Thus, values must be divided by 25 to calculate average annual emissions.\n- Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation.\n- Emissions reflect a gross estimate, i.e., carbon removals from any regrowth that occurs after disturbance are not included. Instead, gross carbon removals are accounted for in the companion forest carbon removals layer.\n- Emissions data contain temporal inconsistencies. Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer [here](https://www.globalnaturewatch.org/blog/data/20-years-global-tree-cover-loss-data-trends/) for additional information.\n- Forest carbon emissions do not reflect carbon transfers from ecosystem carbon pools to the harvested wood products (HWP) pool.\n- This dataset has been updated since its original publication. See Overview for more information.","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":"https://essd.copernicus.org/articles/17/1217/2025/","id":"66f6d125-d553-422c-a7ee-ac50050880b7"},"versions":["v20230331","v20240308","v20240402","v20231114","v20230407","v20250430","v20260327"]},{"created_on":"2026-05-07T16:32:00.436133","updated_on":"2026-05-07T16:32:00.436140","dataset":"gfw_forest_carbon_gross_emissions_test","is_downloadable":true,"metadata":{},"versions":null},{"created_on":"2021-01-27T17:08:09.189379","updated_on":"2021-04-27T19:08:14.499118","dataset":"gfw_forest_carbon_gross_removals","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897445","updated_on":"2026-08-05T14:42:59.272456","spatial_resolution":30,"resolution_description":"30 m ","geographic_coverage":"Global ","update_frequency":"Annual ","scale":"global","citation":"Harris et al. (2021). Global maps of 21st century forest carbon fluxes. Accessed on [date] from Global Nature Watch. \n","title":"Forest carbon removals","subtitle":"(2001-2025, 30m, Harris et al. 2021, Gibbs et al. 2025)","source":"Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. [https://doi.org/10.1038/s41558-020-00976-6](https://doi.org/10.1038/s41558-020-00976-6)<br><br>Gibbs, D. A., Rose, M., Grassi, G., Melo, J., Rossi, S., Heinrich, V., & Harris, N. L. 2025. Revised and updated geospatial monitoring of 21st century forest carbon fluxes. Earth System Science Data. [https://doi.org/10.5194/essd-17-1217-2025](https://doi.org/10.5194/essd-17-1217-2025)\n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)  ","data_language":"English","overview":"This carbon removals layer is part of the forest carbon flux model described in [Harris et al. (2021)](https://www.nature.com/articles/s41558-020-00976-6). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2025. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for [national greenhouse gas inventories](https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/) where forests existed in 2000 or were established between 2000 and 2020 according to [Potapov et al. (2022)](https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full). Atmospheric carbon removed in each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2025) and must be divided by 25 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match [Hansen et al. (2013)](https://www.science.org/doi/10.1126/science.1244693).<br><br>We have made several updates to the model since its [original release](https://doi.org/10.1038/s41558-020-00976-6). For documentation through the current version, please refer to [this blog](https://www.globalnaturewatch.org/blog/data-and-tools/whats-new-carbon-flux-monitoring/). For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to [Gibbs et al. (2025)](https://doi.org/10.5194/essd-17-1217-2025).<br><br>Removals are available for download in two different area units over the model duration: 1) megagrams of CO2 removed/ha, and 2) megagrams of CO2 removed/pixel. The first is appropriate for visualizing (mapping) removals because it represents the density of removals per hectare. The second is appropriate for calculating the removals in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total removals for that area. The values in the latter were calculated by adjusting the removals per hectare by the size of each pixel, which varies by latitude. When estimating removals occurring over a defined number of years between 2001 and 2025 to compare to emissions, divide total carbon removals by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020.<br><br>Download data from [here](https://data.globalforestwatch.org/datasets/forest-carbon-removals).<br><br>Access data on GEE [here](https://code.earthengine.google.com/?asset=projects/wri-datalab/gfw-data-lake/v1-4-3-2001-2025/gross-removals-forest-extent-per-ha/gross-removals-global-forest-extent-per-ha-2001-2025)\n","function":"Displays forest carbon removals by forest sinks","cautions":"- Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.\n- Values are applicable to forest areas (canopy cover >30 percent and >5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis.\n- Carbon removals reflect the total removals over the model period of 2001-2025, not an annual time series from which a trend can be derived. Thus, values must be divided by 25 to calculate average annual removals.\n- Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors.\n- Carbon removals reflect a gross estimate, i.e., carbon emissions from previous or subsequent loss of tree cover are not included. Instead, gross carbon emissions are accounted for in the companion forest carbon emissions layer.\n- Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2025.\n- Forest carbon removals reflect those occurring only within forest ecosystems and do not reflect carbon stock increases in the harvested wood products (HWP) pool.\n- Large jumps in removals along some boundaries are due to the use of ecozone-specific removal factors. The changes in removals occur at ecozone boundaries, where different removal factors are applied on each side.\n- This dataset has been updated since its original publication. See Overview for more information.","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":"https://essd.copernicus.org/articles/17/1217/2025/","id":"2588666f-8fe6-4e1f-be0c-2744bca5387f"},"versions":["v20230331","v20230407","v20240308","v20231114","v20260327","v20250416"]},{"created_on":"2021-01-27T17:07:51.869115","updated_on":"2021-04-27T19:10:22.178836","dataset":"gfw_forest_carbon_net_flux","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897447","updated_on":"2026-08-05T14:42:59.553965","spatial_resolution":30,"resolution_description":"30 m","geographic_coverage":"Global","update_frequency":"Annual","scale":"global","citation":"Harris et al. (2021). Global maps of 21st century forest carbon fluxes. Accessed on [date] from Global Nature Watch.","title":"Forest greenhouse gas net flux","subtitle":"(2001-2025, 30m, Harris et al. 2021, Gibbs et al. 2025)","source":"Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. [https://doi.org/10.1038/s41558-020-00976-6](https://doi.org/10.1038/s41558-020-00976-6)<br><br>Gibbs, D. A., Rose, M., Grassi, G., Melo, J., Rossi, S., Heinrich, V., & Harris, N. L. 2025. Revised and updated geospatial monitoring of 21st century forest carbon fluxes. Earth System Science Data. [https://doi.org/10.5194/essd-17-1217-2025](https://doi.org/10.5194/essd-17-1217-2025)\n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This net flux layer is part of the forest greenhouse gas flux model described in [Harris et al. (2021)](https://www.nature.com/articles/s41558-020-00976-6). This paper introduces a geospatial monitoring framework for estimating global forest fluxes which can assist a variety of actors and organizations with tracking greenhouse gas (GHG) fluxes from forests and in decreasing emissions or increasing removals by forests. Net forest flux represents the exchange of GHGs between forests and the atmosphere, calculated as the difference between GHGs emitted by forests and carbon removed by (or sequestered by) forests during the model period. Net carbon flux is calculated by subtracting average gross removals from annual gross emissions in each forested pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of GHGs between 2001 and 2025. Net fluxes are calculated following IPCC Guidelines for [national greenhouse gas inventories](https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/) in each pixel where forests existed in 2000 or were established between 2000 and 2020 according to [Potapov et al. 2022](https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full). This layer reflects the cumulative net flux during the model period (2001-2025) and must be divided by 25 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match [Hansen et al. (2013)](https://www.science.org/doi/10.1126/science.1244693).<br><br>We have made several updates to the model since its [original release](https://doi.org/10.1038/s41558-020-00976-6). For documentation through the current version, please refer to [this blog](https://www.globalnaturewatch.org/blog/data-and-tools/whats-new-carbon-flux-monitoring/). For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to [Gibbs et al. (2025)](https://doi.org/10.5194/essd-17-1217-2025).<br><br>Net flux is available for download in two different area units over the model duration: 1) megagrams of CO2e emissions/ha, and 2) megagrams of CO2e emissions/pixel. The first is appropriate for visualizing (mapping) net flux because it represents the density of GHG fluxes per hectare. The second is appropriate for calculating the net flux in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total net flux for that area. The values in the latter were calculated by adjusting the net flux per hectare by the size of each pixel, which varies by latitude. When estimating net flux occurring over a defined number of years between 2001 and 2025, divide the values by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020.<br><br>Download data from [here](https://data.globalforestwatch.org/datasets/forest-greenhouse-gas-net-flux).<br><br>Access data on GEE [here](https://code.earthengine.google.com/?asset=projects/wri-datalab/gfw-data-lake/v1-4-3-2001-2025/net-flux-forest-extent-per-ha/net-flux-global-forest-extent-per-ha-2001-2025)\n","function":"Displays the net flux of greenhouse gases from forests, calculated as the difference between gross forest greenhouse gas emissions from stand-replacing forest disturbances and gross carbon removals from forest growth","cautions":"- Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.\n- Net flux reflects the total over the model period of 2001-2025, not an annual time series from which a trend can be derived. Thus, values must be divided by 25 to calculate average annual net flux.\n- Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. These uncertainties are propagated to the uncertainty in net flux.\n- Values are applicable to forest areas only (canopy cover >30 percent and >5 m height or areas with tree cover gain). See [Harris et al. (2021)](https://www.nature.com/articles/s41558-020-00976-6) for further information on the forest definition used in the analysis.\n- Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation.\n- Activity data used as the basis of the estimates contain temporal inconsistencies:\n    - Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2025.\n    - Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer [here](https://www.globalnaturewatch.org/blog/data/20-years-global-tree-cover-loss-data-trends/) for additional information.\n- Large jumps in net flux along some boundary are due to the use of ecozone-specific removal factors. The changes in net flux occur at ecozone boundaries, where different removal factors are applied on each side.\n- This dataset has been updated since its original publication. See Overview for more information.\n","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":"https://essd.copernicus.org/articles/17/1217/2025/","id":"4a339545-2a0c-4f65-beaa-fae4b0c7034d"},"versions":["v20230407","v20240402","v20240308","v20250430","v20231114","v20260327","v20230331"]},{"created_on":"2023-11-30T22:25:06.937247","updated_on":"2023-11-30T22:25:06.937253","dataset":"gfw_forest_flux_aboveground_carbon_stock_in_emissions_year","is_downloadable":true,"metadata":{"created_on":"2023-11-30T22:25:06.942965","updated_on":"2023-11-30T22:25:06.942971","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Aboveground carbon stock in forests in year of tree cover loss (Mg 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extent)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"1b6618c6-a0ac-4278-85d9-bb752ad1417c"},"versions":["v20230331","v20230407","v20240308","v20220316"]},{"created_on":"2024-09-04T22:10:36.170232","updated_on":"2024-09-04T22:10:36.170238","dataset":"gfw_indigenous_community_and_indicative_lands","is_downloadable":false,"metadata":{"created_on":"2024-09-04T22:10:36.184713","updated_on":"2026-08-05T14:43:03.333404","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Periodic","scale":"global","citation":"LandMark, 2019. 'Indigenous and Community Lands.' www.landmarkmap.org. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Indigenous, community, and indicative lands","subtitle":null,"source":"Please see the complete list of data providers at [LandMark](https://www.landmarkmap.org/data/). ","license":"Varies by source. Click [here]( https://www.landmarkmap.org/data/#data-6) for more detailed information on access to LandMark data.  ","data_language":"English","overview":"Merge of the Indigenous and Community Lands dataset and the Indicative Lands dataset from Landmark.","function":"Depicts indigenous, community, and indicative lands for select countries around the world, classified by legal recognition status.  ","cautions":"This data has been assembled from a variety of contributors and sources. Only data from governments, individual experts, and established civil society organizations in the land rights community are displayed on this platform. Even though a country may not have national or community level data included in this data set, Indigenous Peoples and communities may still hold or use land in that country. The absence of data does not indicate the absence of indigenous or community land.","key_restrictions":"Varies by data source","tags":[""],"why_added":null,"learn_more":"https://www.landmarkmap.org","id":"c1158d05-42c0-4958-976c-9a51a6467052"},"versions":["v202408"]},{"created_on":"2021-09-28T19:18:49.154526","updated_on":"2025-02-20T18:16:23.266647","dataset":"gfw_integrated_alerts","is_downloadable":true,"metadata":{"created_on":"2024-07-11T15:09:28.641874","updated_on":"2026-08-05T14:43:03.646675","spatial_resolution":null,"resolution_description":"10 × 10 m","geographic_coverage":"30°N to 30°S","update_frequency":"Daily","scale":null,"citation":"Source: \"Integrated Deforestation Alerts\". UMD/GLAD and WUR, accessed through Global Nature Watch on [date] \n","title":"Integrated deforestation alerts","subtitle":"daily, 10 m, tropics, UMD/GLAD and WUR","source":"**GLAD-L Alerts**:  Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3). https://dx.doi.org/10.1088/1748-9326/11/3/034008 \n\n **GLAD-S2 Alerts**:  Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland.  \n\n **RADD Alerts**:  Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. [https://doi.org/10.1088/1748-9326/abd0a8](https://doi.org/10.1088/1748-9326/abd0a8) ","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated. \n\n The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30 m GLAD Landsat-based alerts are up-sampled to match the 10 m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels.\n\n Alerts are classified as high confidence when detected twice by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or three) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system.\n\n A study conducted by [Wageningen University](https://www.wur.nl/en/Research-Results/Chair-groups/Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/Research/Sensing-measuring/Radar-Remote-Sensing.htm) in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. \n Learn more: [https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82](https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82) \n\n The integrated deforestation alerts are available on **Google Earth Engine** with asset ID: projects/forma-250/assets/gfw_integrated_alerts/default_latest \n\n **Download** the raster tiles here: [https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about](https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about)","function":"Monitor forest disturbance in near-real-time using integrated alerts from three alerting systems","cautions":"- Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. **This product does not distinguish between human-caused and other disturbance types.** Where alerts are detected within plantation forests (more likely to happen in the GLAD-L system), alerts may indicate timber harvesting operations, without a conversion to a non-forest land use. \n - The term deforestation is used because these are **potential** deforestation events, and alerts could be further investigated to determine this. \n - We do not recommend using deforestation alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. Additionally, updates to the methodologies, differing number of systems (in the case of the integrated alerts), and variation in cloud cover between months and years pose additional risks to using deforestation alerts for inter/intra-annual comparison. \n - The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations are provided in the Places to Watch data layer. \n\n\n\n The three alert systems have different definitions of forest/tree cover, and forest/tree cover disturbances: \n \n - GLAD-L: alerts are within “tree cover” which is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations. “Tree cover loss” indicates the canopy removal of at least half a pixel and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation. \n - GLAD-S2: alerts are within the primary forest mask of [Turubanova et al (2018)](https://doi.org/10.1088/1748-9326/aacd1c) in the Amazon river basin, with 2001-present forest loss from [Hansen et al. (2013)](https://doi.org/10.1126/science.1244693) removed.  \n - RADD: alerts are within primary humid forests. Forest loss is defined as complete or partial removal of tree cover within a pixel, and a minimum-mapping unit of 0.1 ha is used (equivalent to 10 Sentinel-1 pixels)\n\n The input alert systems do not have the same spatial and temporal coverage: \n \n - GLAD-L: Operating in the entire tropics (30°N to 30°S) from January 1, 2018 to the present, and from 2015 to the present (although paused for a period during 2022) for select countries in the Amazon, Congo Basin, and insular Southeast Asia. Due to a re-processing effort of Landsat imagery, the available collection of GLAD-L alerts spans from Jan 1, 2021 to the present. The alert coverage area (mask) is defined by the presence of tree cover as of the year 2000, with a canopy cover threshold of ≥10%, as well as areas of tree cover gain (per [Hansen et al. 2013](http://earthenginepartners.appspot.com/science-2013-global-forest)). Areas that experienced tree cover loss from 2014 to 2021—based on the latest available loss data at the time of the method update—are excluded from the mask.\n - GLAD-S2: Operating in the primary humid tropical forest areas of South America from January 2019 to the present. Areas that experienced tree cover loss (Hansen et al. 2013) from 2001 to 2019 are excluded.\n - RADD: Operating in the primary humid tropical forest areas of South America, sub-Saharan Africa and Southeast Asia with coverage from January 2019 to the present for Africa and January 2020 to the present for South America, Central America, and Southeast Asia. Forest disturbances are mapped only within the primary humid tropical forest mask from [Turubanova et al (2018)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c) with annual (Africa: 2001 - 2018; Other geographies: 2001-2019) forest loss [Hansen et al. 2013](https://www.science.org/doi/10.1126/science.1244693) and mangroves [Bunting et al. 2018](https://www.mdpi.com/2072-4292/10/10/1669) removed.\n - In order to integrate the three alerting systems on a common grid, GLAD-L is resampled from a 30 m spatial resolution to 10 m to match GLAD-S2 and RADD. As a result, a single 30 m GLAD-L pixel will become multiple 10 m pixels in the integrated layer. Users should use caution when comparing the analysis results of individual systems to the integrated alert layer, as the number of integrated alerts will be much greater than the number of native GLAD-L alerts. In addition, pixels in the integrated layer may not exactly align on the map with pixels in the individual GLAD-L layer as a result of this resampling. \n - Each pixel in the integrated layer preserves the earliest date of detection from any alerting system, even if multiple systems have reported an alert in that pixel. In some situations, this may lead to inconsistent visualizations when switching from the integrated layer to individual alerting system layers. It is advisable to use the integrated layer when you are interested in the earliest date of detection by any alerting system. However, it is better to use the individual alerting system layers if you are interested in a specific alert type. \n - The ‘Highest confidence: detected by multiple alert systems’ level can only be achieved in the integrated alert layer, in areas and for time periods where more than one alert system was in operation for that region. \n\n Each system has its own method of determining confidence: \n - For GLAD-L alerts, every new alert starts out as 'low confidence' when loss is first detected (e.g. one anomalous result is detected). Alerts are then classified as high confidence when forest loss has also been identified at that location in a second satellite image within four additional (5 total) cloud-free observations.\n - For GLAD-S2, it's the same process as GLAD-L, except alerts are classified as high confidence when forest loss has also been identified in a second satellite observation within three additional (4 total) cloud-free images. \n -For RADD, researchers use 2 years of data to create historical image metrics showing previous forest condition, preprocess every new Sentinel-1 image, and apply a forest disturbance detection algorithm which calculates the probability that a pixel is disturbed. If the probability of disturbance is greater than 0.85, it becomes a low confidence alert. Subsequent observations within the next 90 days are used to update the probability that the forest was disturbed. When the probability reaches above 0.975, the alert becomes classified as high confidence.\n - The confidence level may change retroactively as source data is updated. GLAD-L and GLAD-S2 alerts that have not become high confidence within 180 days are removed from the dataset.  The RADD alert system removes low confidence alerts after 90 days.\n - Once an alert pixel reaches high confidence, forest loss will not be detected by the same alert system at that location again; however, pixel locations where low confidence alerts have been removed from the database are subject to being alerted again.\n - Accuracies vary across the coverage of the integrated alerts, due to different characteristics of the three alert systems – Radar (RADD) alerts for example may have more false detections in swamp forests due to the high sensitivity of short wavelength C-band radar to moisture variation. \n - When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.","key_restrictions":null,"tags":null,"why_added":"3","learn_more":"https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about","id":"172c233b-9781-413c-925b-1a199c0507f9"},"versions":["v20260830","v20260825","v20260918","v20260916","v20260914","v20211002","v20260909","v20260903","v20220331","v20260701","v20260829","v20260819","v20260902","v20260907","v20260905","v20260908","v20251001","v20260831","v20260915","v20260913","v20260910","v20250701","v20260912","v20260827","v20260823","v20260904","v20260820","v20260821","v20260911","v20250401","v20260101","v20250101","v20260901","v20260824","v20260822","v20260906","v20260826","v20260917","v20260828","v20260401","v20231001","v20220702","v20230704","v20220101","v20230101","v20240701","v20240102","v20241001","v20230401","v20221001","v20240401"]},{"created_on":"2025-10-15T17:41:26.006872","updated_on":"2025-12-16T17:22:11.481566","dataset":"gfw_integrated_dist_alerts","is_downloadable":true,"metadata":{"created_on":"2025-10-15T17:41:26.016876","updated_on":"2026-08-05T14:43:03.925325","spatial_resolution":null,"resolution_description":"10 × 10 m","geographic_coverage":"Global","update_frequency":"Daily","scale":null,"citation":"Source: \"Global Integrated Disturbance Alerts\". UMD/GLAD and WUR, accessed through Global Nature Watch on [date] \n","title":"Global integrated disturbance alerts","subtitle":"daily, 10 m, global, UMD/GLAD and WUR","source":"DIST-ALERT: \n\n Hansen, M.. OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1). 2024, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center, [https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) \n\n Pickens, A.H., Hansen, M.C., Song, Z. et al. Rapid monitoring of global land change. Nat Commun 16, 8948 (2025). [https://doi.org/10.1038/s41467-025-64014-9](https://doi.org/10.1038/s41467-025-64014-9) \n\n GLAD-L Alerts: \n\n Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3). [https://dx.doi.org/10.1088/1748-9326/11/3/034008](https://dx.doi.org/10.1088/1748-9326/11/3/034008) \n\n GLAD-S2 Alerts: \n\n Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. \n\n RADD Alerts: \n\n Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. [https://doi.org/10.1088/1748-9326/abd0a8](https://doi.org/10.1088/1748-9326/abd0a8)","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from four alert systems (DIST-ALERT, GLAD-L, GLAD-S2, RADD) into a single, integrated disturbance alert layer. This integration allows users to detect deforestation and other vegetation loss faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated.  \n\n The source alert systems are derived from satellites of varying spectral and spatial resolutions. The 30 m GLAD Landsat-based alerts and DIST-ALERTs are up-sampled to match the 10 m spatial resolution of purely Sentinel-based alerts (GLAD-S2, RADD). Using the same 10 m grid avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels.  \n\n Alerts are classified as high confidence when detected multiple times (two or more times for GLAD-L, GLAD-S2 and RADD, and four or more times for DIST-ALERT) by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or more) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. \n\n Alerts from multiple systems which are within 180 days (6 months) of each other are considered a single event. In this case, the earliest date within that 180 day window will appear as the alert date in the integrated alert product. These will be considered highest confidence alerts. The date of the alert remains until another new alert (which is not within 6 months) in the same pixel is registered.  \n\n A study conducted by Wageningen University in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. Learn more: [https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82](https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82)  ","function":"Monitor vegetation disturbance in near-real-time using integrated alerts from multiple alerting systems","cautions":" \n- These alerts detect vegetation, forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests (more likely to happen in the GLAD-L and DIST-ALERT systems), alerts may indicate timber harvesting operations, without a conversion to a non- forest land use. \n\n- Some of these systems use the term “deforestation alerts” because these detect potential deforestation events, and alerts could be further investigated to determine this.   \n\n- Regional boundary analysis (GADM) and alert subscriptions are currently limited to the tropics. Outside the tropics, custom geometries support analysis but not alert subscriptions. Subscriptions are available for alerts within “tree cover” which is defined as all vegetation greater than 3 meters in height (2020) with greater than 30% canopy cover (2010), and may take the form of natural forests or plantations. Annual tree cover loss after 2021 is masked out. \n\n- We do not recommend using deforestation alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. In fact, alerts can be removed in as little as 16 days in the case of GLAD-L  alerts which are removed from the dataset after four consecutive observations or more than 180 days if they are not classified as high confidence. Four consecutive observations can occur in 16 days if the pixel is in an area where the satellite paths overlap. Additionally, updates to the methodologies, differing number of systems (in the case of the integrated alerts), and variation in cloud cover between months and years pose additional risks to using deforestation alerts for inter/intra-annual comparison.  \n\n- Alert numbers can appear to jump suddenly after a cloudy period, in the case of the optical systems which are unable to pick up new alerts where clouds or smoke obscure the images. \n\n- The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations are provided in the Places to Watch data layer.   \n\nThe four alert systems have different definitions of forest/tree cover, and forest/tree cover disturbances:   \n\n- DIST-ALERTS: Although the system operates in all vegetation, users can toggle to view alerts within “tree cover” which is defined as all vegetation greater than 3 meters in height (2020) with greater than 30% canopy cover (2010), and may take the form of natural forests or plantations. Annual tree cover loss after 2021 is masked out.    \n\n- Due to the differences in baseline or forest mask in the products, it is possible that alerts outside the forest mask come from one of the forest disturbance alerting systems (GLAD-L, GLAD-S2, or RADD).  \n\n- GLAD-L: alerts are within “tree cover” which is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations. “Tree cover loss” indicates the canopy removal of at least half a pixel and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.   \n\n- GLAD-S2: alerts are within the primary forest mask of [Turubanova et al (2018)](https://doi.org/10.1088/1748-9326/aacd1c) in the Amazon river basin, with 2001-present forest loss from [Hansen et al. (2013)](https://doi.org/10.1126/science.1244693) removed.    \n\n- RADD: alerts are within primary humid forests. Forest loss is defined as complete or partial removal of tree cover within a pixel, and a minimum-mapping unit of 0.1 ha is used (equivalent to 10 Sentinel-1 pixels).  \n\nThe input alert systems do not have the same spatial and temporal coverage:   \n\n- DIST-ALERT is global, and although it has been operating since January 2023, Global Nature Watch has data available since December 2023 (a few alerts may be visible in the system between January and November 2023).  \n\n- GLAD-L: Operating in the entire tropics (30°N to 30°S) from January 1, 2018 to the present, and from 2015 to the present (although paused for a period during 2022) for select countries in the Amazon, Congo Basin, and insular Southeast Asia. Due to a re-processing effort of Landsat imagery, the available collection of GLAD-L alerts spans from Jan 1, 2021 to the present. The alert coverage area (mask) is defined by the presence of tree cover as of the year 2000, with a canopy cover threshold of ≥10%, as well as areas of tree cover gain (per Hansen et al. 2013). Areas that experienced tree cover loss from 2014 to 2021—based on the latest available loss data at the time of the method update—are excluded from the mask.  \n\n- GLAD-S2: Operating in the primary humid tropical forest areas of South America from January 2019 to the present. Areas that experienced tree cover loss (Hansen et al. 2013) from 2001 to 2019 are excluded.  \n\n- RADD: Operating in the primary humid tropical forest areas of South America, sub-Saharan Africa and Southeast Asia with coverage from January 2019 to the present for Africa and January 2020 to the present for South America, Central America, and Southeast Asia. Forest disturbances are mapped only within the primary humid tropical forest mask from Turubanova et al (2018) with annual (Africa: 2001 - 2018; Other geographies: 2001-2019) forest loss Hansen et al. 2013 and mangroves Bunting et al. 2018 removed.  \n\n- In order to integrate the four alerting systems on a common grid, DIST-ALERT and GLAD-L are resampled from a 30 m spatial resolution to 10 m to match GLAD-S2 and RADD. As a result, a single 30 m DIST-ALERT or GLAD-L pixel will become multiple 10 m pixels in the integrated layer. Users should use caution when comparing the analysis results of individual systems to the integrated alert layer, as the number of integrated alerts will be much greater than the number of native DIST-ALERT or GLAD-L alerts. In addition, pixels in the integrated layer may not exactly align on the map with pixels in the individual DIST-ALERT or GLAD-L layer as a result of this resampling.  Due to the resampling of the 30-meter datasets to 10 meters, including the tree cover filter datasets, some DIST-ALERT pixels at the edges of tree cover may not appear in the integrated layer and analysis.  \n\n- Each pixel in the integrated alert layer displays the earliest date of detection reported by any contributing alerting system within a 180-day window. When multiple alerting systems detect a disturbance in the same pixel within this period, it is treated as a single event and receives the highest confidence label, and the earliest detection date is preserved. With the addition of the DIST-ALERT system, alert dates can be overwritten if a more recent disturbance is detected more than 180 days after the earlier alert. Therefore alert dates and visualizations of the integrated layer may differ from those of the individual alert system layers. \n\n- The “Highest confidence: detected by multiple alert systems” level can only be achieved in the integrated alert layer, in areas and for time periods where more than one alert system was in operation for that region.   \n\n Each system has its own method of determining confidence.   \n\n- For DIST-ALERTS, the system determines confidence level by the number of anomalous observations, with more observations meaning a higher confidence level. That is, two to three anomalies detected result in a low confidence alert, whereas four or more mean a high confidence alert. Low confidence alerts are currently not available on GNW for DIST-ALERT.  \n\n- For GLAD-L alerts, every new alert starts out as 'low confidence' when loss is first detected (e.g. one anomalous result is detected). Alerts are then classified as high confidence when forest loss has also been identified at that location in a second satellite image within four additional (5 total) cloud-free observations.  \n\n- For GLAD-S2, it's the same process as GLAD-L, except alerts are classified as high confidence when forest loss has also been identified in a second satellite observation within three additional (4 total) cloud-free images. \n\n- For RADD, researchers use 2 years of data to create historical image metrics showing previous forest condition, preprocess every new Sentinel-1 image, and apply a forest disturbance detection algorithm which calculates the probability that a pixel is disturbed. If the probability of disturbance is greater than 0.85, it becomes a low confidence alert. Subsequent observations within the next 90 days are used to update the probability that the forest was disturbed. When the probability reaches above 0.975, the alert becomes classified as high confidence.  \n\n- The confidence level may change retroactively as source data is updated. GLAD-L and GLAD-S2 alerts that have not become high confidence within 180 days are removed from the dataset.  The RADD alert system removes low confidence alerts after 90 days. The DIST-ALERT layer on GNW does not currently show low confidence alerts.  \n\n- DIST-ALERT has the ability to detect repeated alerts in the same pixel location, unlike the other alert systems (GLAD-L, GLAD-S2, RADD) where once an alert pixel reaches high confidence, forest loss will not be detected by the same alert system at that location again. However, pixel locations where low confidence (GLAD-L, GLAD-S2, RADD) alerts have been removed from the database are subject to being alerted again by the same system.  \n\n- Accuracies vary across the coverage of the integrated alerts, due to different characteristics of the three alert systems – Radar (RADD) alerts for example may have more false detections in swamp forests due to the high sensitivity of short wavelength C-band radar to moisture variation. The optical systems (DIST-ALERT, GLAD-L, GLAD-S2) are more susceptible to errors from cloud interference.  \n\n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.      \n\n","key_restrictions":null,"tags":["Conservation"],"why_added":"3","learn_more":"https://www.globalnaturewatch.org/blog/data-and-tools/integrated-deforestation-alerts/","id":"ec1b9819-fa1a-4dba-b047-a2489c37f350"},"versions":["v20260917","v20260905","v20260906","v20260916","v20260914","v20260829","v20260913","v20260910","v20260831","v20260828","v20260907","v20260830","v20260912","v20260901","v20260903","v20260915","v20260827","v20260909","v20260904","v20260911","v20260908","v20260902","v20251101"]},{"created_on":"2021-07-26T19:41:26.512859","updated_on":"2021-07-26T19:41:26.512865","dataset":"gfw_land_rights","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897518","updated_on":"2026-08-05T14:43:06.179057","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Land Rights","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"957d01af-f251-4964-b515-4c9e7378cab3"},"versions":["v2019"]},{"created_on":"2021-05-14T19:51:51.318409","updated_on":"2021-05-14T19:51:51.318413","dataset":"gfw_litter_carbon","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897461","updated_on":"2026-08-05T14:43:06.725284","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Carbon Model - Litter Carbon","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":"Contextual layer for the GNW forest carbon model","cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"47b8d006-8190-414d-941d-617702a77d34"},"versions":["v20210223","v20200824"]},{"created_on":"2023-11-30T22:33:20.771088","updated_on":"2023-11-30T22:33:20.771095","dataset":"gfw_litter_carbon_stock_2000","is_downloadable":true,"metadata":{"created_on":"2023-11-30T22:33:20.823618","updated_on":"2023-11-30T22:33:20.823623","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Litter carbon stock in forests in 2000 (Mg C/ha)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"73b1d43e-4fc9-4d5c-9ab7-3c7cffcf9592"},"versions":["v20230222"]},{"created_on":"2021-04-29T20:23:09.431016","updated_on":"2025-02-13T22:56:38.688445","dataset":"gfw_logging","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897368","updated_on":"2026-08-05T14:43:07.086006","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Logging concessions","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b6e3c16f-4c0b-431e-a3e6-776a877bb99a"},"versions":["v2020","v202106"]},{"created_on":"2021-04-29T20:26:14.494260","updated_on":"2025-02-13T22:56:42.658958","dataset":"gfw_logging_download","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897452","updated_on":"2026-08-05T14:43:07.422250","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Logging concessions (downloadable)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"503670f6-049d-4174-860c-6a1e64b7db56"},"versions":["v2020"]},{"created_on":"2021-06-30T18:22:23.650663","updated_on":"2025-02-13T22:56:46.800654","dataset":"gfw_managed_forests","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897217","updated_on":"2026-08-05T14:50:49.450959","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Cambodia, Cameroon, Canada, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Indonesia, Liberia, Malaysia (Sarawak, Sabah), Peru, Republic of the Congo, and Suriname","update_frequency":"Varies by source","scale":"global","citation":"“Logging concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.\n","title":"Logging concessions","subtitle":"2010-2025, vector, select countries, sources vary","source":"Cambodia: Cambodian League for the Promotion and Defense of Human Rights (LICADHO)  \n\nCameroon: Ministry of Forestry and Wildlife & WRI  \n\nCanada: Global Witness  \n\nCentral African Republic: Ministère des Eaux, Forêts, Chasse et Pêche & WRI  \n\nDemocratic Republic of the Congo: Ministère de l’Environnement et Développement Durable & WRI  \n\nEquatorial Guinea: Ministry of Agriculture and Forests & WRI  \n\nGabon: Ministere des Eaux et Forests & WRI  \n\nIndonesia: Ministry of Forestry  \n\nLiberia: Liberia Forest Development Authority & WRI  \n\nMalaysia (Sarawak, Sabah): Earthsight Investigations & Global Witness  \n\nPeru: Supervisory Body for Forest and Wildlife Resources (OSINFOR)  \n\nRepublic of the Congo: Ministry of Forest Economy & WRI  \n\nSuriname: Suriname Foundation for Forest Management and Production Control (SBB), Ministry of Physical Planning, Land and Forest Management (RGB), Central Office for Aerial Mapping (CBL)  \n\n","license":"CC BY 4.0 (excluding Indonesia and Gabon)","data_language":"","overview":"Logging concessions refers to areas allocated by a government for harvesting timber and other wood products in a public forest. Logging concessions are distinct from wood fiber concessions, where tree plantations are established for the exclusive production of pulp and paper products. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber and other wood products from public forests; terminology varies at the national level, however, and includes \"forest permits,\" \"tenures,\" \"licenses,\" and other terms.\n\nThis data set displays logging concessions as a single layer assembled by aggregating data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source.\n\nAs available: additional data source information is linked below:\n- [Cambodia](<https://www.licadho-cambodia.org/land_concessions/>)\n- [Cameroon](https://data-mefcp.opendata.arcgis.com/datasets/mefcp::permis-dexploitation-et-dam%C3%A9nagement-/explore)\n- [Democratic Republic of the Congo](https://cod-data.forest-atlas.org/datasets/medd::concession-forestiere2025/about)\n- [Gabon](https://gab.forest-atlas.org/)\n- [Liberia](https://lbr.forest-atlas.org/pages/map)\n- [Suriname](https://gonini.sbb.sr/)\n\nIf you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org).\n","function":"Displays boundaries of forested areas allocated by governments to companies for harvesting timber and other wood products.","cautions":"This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source and may not reflect the most current data available. This layer may not be comprehensive of all existing concessions in a country, and the location of certain concessions can be inaccurate.\n\nDisclaimer: Indonesia logging concessions data were obtained from the Ministry of Forestry in 2021, a period when the Ministry opened access to this data. Public download of this data is no longer available, therefore we have restricted this dataset to view only. Public download is also unavailable for Gabon logging concessions due to government policies and is therefore restricted to view only. \n","key_restrictions":"I believe all of them can be downloaded","tags":["Land Use"],"why_added":"Gives indication of land zoned for logging","learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_global_logging_concessions_v2025_public.zip","id":"fd710a54-0ff9-49c8-8ba6-029b63b43a40"},"versions":["v202106","v2025"]},{"created_on":"2020-12-07T16:17:47.666444","updated_on":"2025-02-13T22:56:07.486386","dataset":"gfw_mining_concessions","is_downloadable":false,"metadata":{"created_on":"2023-05-04T13:11:58.897291","updated_on":"2026-08-05T14:50:49.836913","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Brazil, Cambodia, Cameroon, Canada, Colombia, Democratic Republic of the Congo, Gabon, Indonesia, Mexico, Malaysia, Peru, Republic of the Congo, Suriname, and Zambia","update_frequency":"Varies by source","scale":"global","citation":"“Mining concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.\n","title":"Mining concessions","subtitle":"2014-2025, vector, select countries, sources vary","source":"Brazil: Sistema de Informações Geográficas da Mineração (SIGMINE)  \n\nCambodia: Open Development Cambodia  \n\nCameroon: Ministère des Forêts et de la Faune & WRI  \n\nCanada: Alberta Metallic and Industrial Minerals Agreements, British Columbia MTA - Mineral Tenures, Manitoba Mineral Dispositions, New Brunswick Energy and Resource Development, Newfoundland and Labrador GeoScience Atlas Online, Northwest Territories Mineral Tenures, Nova Scotia Mineral Rights Database, Nunavut Mineral Tenure, Ontario Tenure Spatial Data, Quebec Mining Rights Digital Data, Saskatchewan Mineral Dispositions, and Yukon Mining Recorder  \n\nColombia: Tierra Minada  \n\nDemocratic Republic of the Congo: Ministère de l’Environnement et Développement Durable & WRI  \n\nGabon: Democratic Republic of the Congo Ministry of Mines Mining Registry (CAMI)  \n\nIndonesia: Ministry of Energy and Mineral Resources  \n\nMalaysia: RimbaWatch  \n\nMexico: Secretaria de Economia  \n\nPeru: Instituto Geológico, Minero y Metalúrgico (INGEMMET)  \n\nRepublic of the Congo: Republic of the Congo Ministry of Mines and Geology & WRI  \n\nSuriname: GONINI National Land Monitoring System of Suriname  \n\nZambia: Zambia Ministry of Mines  \n\n","license":"CC BY 4.0 (excluding Indonesia)","data_language":"varies","overview":"“Mining concession” refers to an area allocated by a government or other body for the extraction of minerals. The terminology for these areas varies from country to country. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract minerals from public lands; terminology varies at the national level, however, and includes mineral or mining \"permits,\" \"tenures,\" \"licenses,\" and other terms.\n\nThis data set displays mining concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and vary by date and source. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source.\n\nAs available, additional data source information is linked below:\n- [Brazil](https://dados.gov.br/dados/conjuntos-dados/sistema-de-informacoes-geograficas-da-mineracao-sigmine)\n- [Cameroon](https://data-minfof.opendata.arcgis.com/documents/9a3c4d1e65db401f9fd5b14bf50977de/about)\n- [Cambodia](https://data.opendevelopmentcambodia.net/en/dataset/mining-license-in-cambodia-1995-2014-type-dataset)\n- [Colombia](https://sites.google.com/site/tierraminada/)\n- [Democratic Republic of the Congo](https://cod-data.forest-atlas.org/datasets/medd::mining-permits/about)\n- [Malaysia](https://rimbawatchmy.com/forestTracker)\n- [Peru](https://geocatmin.ingemmet.gob.pe/geocatmin/)\n- [Suriname](https://gonini.sbb.sr/)\n- [Zambia](www.znsdi.net)\n\n\nIf you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org). \n","function":"Displays boundaries of areas allocated by governments to companies for extraction of minerals","cautions":"This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.\n\nThe Colombia mining concessions data provided by Tierra Minada includes special concessions zones for groups such as Indigenous and community groups.\n\nDisclaimer: Indonesia mining concessions data were obtained from the Ministry of Energy and Mineral Resources in 2021, a period when the Ministry opened access to this data. Public download of this data is no longer available, therefore we have restricted this dataset to view only.\n","key_restrictions":"Most unknown. DRC perhaps do not make downloadable? But confusing","tags":["Land Use"],"why_added":"Show where land is zoned for mining - potential driver of deforestation","learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_global_mining_concessions_v2025_public.zip","id":"5f4d65e6-f457-4a42-9899-404f9012ccd3"},"versions":["v202408","v2025","v20201214","v202106","v202203"]},{"created_on":"2021-04-29T20:27:51.157646","updated_on":"2025-01-28T15:40:29.503821","dataset":"gfw_oil_gas","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897244","updated_on":"2026-08-05T14:50:50.191343","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Argentina, Brazil, Colombia, Democratic Republic of the Congo, Ecuador, and Peru","update_frequency":"Varies by source","scale":"regional","citation":"“Oil and Gas Concessions”. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.\n","title":"Oil and gas concessions","subtitle":"2015-2025, vector, select countries, sources vary","source":"Argentina: Ministry of Energy and Mining, Secretary of Coordination of Energy Planning, National Directorate of Energy Information  \n\nBrazil: Agência Nacional do Petróleo, Gás Natural e Biocombustíveis  \n\nColombia: Agencia Nacional de Hidrocarburos (ANH)  \n\nDemocratic Republic of the Congo: Ministère de l’Environnement et Développement Durable & WRI  \n\nEcuador: HUB Amazonia, Instituto de Geografía, Universidad San Francisco de Quito  \n\nPeru: Perupetro  \n\n","license":"CC BY 4.0","data_language":"English, Spanish, Portuguese","overview":"“Oil and gas concessions” refer to areas allocated by the government to companies who explore for and produce oil, natural gas and other hydrocarbons. The terminology for these areas varies from country to country. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract oil and natural gas from public lands; terminology varies at the national level, however, and includes mining or mining “permits,” “tenures,” “licenses,” and other terms.\n\nThis data set displays oil and gas concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and source. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source.\n\nAs available, additional data source information is linked below:\n- [Brazil](https://geomaps.anp.gov.br/)\n- [Colombia](https://www.anh.gov.co/en/hidrocarburos/oportunidades-disponibles/mapa-de-tierras/)\n- [Democratic Republic of the Congo](https://cod-data.forest-atlas.org/datasets/medd::oil-permits/about)\n- [Ecuador](https://hubamazonia-ig-usfq-geocentro.hub.arcgis.com/datasets/geocentro%3A%3Abloques-petroleros/about)\n- [Peru](https://geoportal.perupetro.com.pe/home\\_i/)\n\n\nIf you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org). \n","function":"Displays boundaries of areas allocated by governments to companies for the exploration of oil, gas, and other hydrocarbons.","cautions":"This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.\n","key_restrictions":"","tags":["Land Use"],"why_added":"To better understand locations of oil and gas concessions.","learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_global_oil_gas_concession_v2025_public.zip","id":"e7400c43-c7b0-4634-ae60-115c1e0bd090"},"versions":["v20221024","v20190321","v2025"]},{"created_on":"2020-07-23T04:23:50.142406","updated_on":"2025-02-13T22:56:13.760640","dataset":"gfw_oil_palm","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897395","updated_on":"2026-08-05T14:50:50.837432","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Cambodia, Cameroon, Democratic Republic of the Congo, Indonesia, Liberia, Malaysia (Sarawak), Peru, and Republic of the Congo","update_frequency":"Varies by source","scale":"global","citation":"“Oil palm concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.\n","title":"Oil palm concessions","subtitle":"2010-2025, vector, select countries, sources vary","source":"Cambodia: Cambodian League for the Promotion and Defense of Human Rights (LICADHO)  \n\nCameroon: Ministère des Forêts et de la Faune & WRI  \n\nDemocratic Republic of the Congo: Direction d'Inventaire et Aménagement Forestier (DIAF) & WRI  \n\nIndonesia: Ministry of Forestry  \n\nLiberia: Global Witness  \n\nMalaysia: SADIA, Aidenvironment & Earthsight Investigations (Sarawak only), RimbaWatch  \n\nPeru: Supervisory Body for Forest and Wildlife Resources (OSINFOR)  \n\nRepublic of Congo: WRI & Ministry of Agriculture  \n\n","license":"CC BY 4.0 (excluding Indonesia)","data_language":"varies","overview":"“Oil palm concession” refers to an area allocated by a government or other body for industrial-scale oil palm plantations.\n\nThis data set displays oil palm concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source.\n\nAs available, additional data source information is linked below:\n- [Cambodia](https://www.licadho-cambodia.org/land\\_concessions/)\n- [Cameroon](https://data-minfof.opendata.arcgis.com/documents/3fd45b4e10b7472181be604127ca1517/about)\n- [Democratic Republic of the Congo](https://cod-data.forest-atlas.org/datasets/medd::plantations-3/about)\n- [Malaysia](https://rimbawatchmy.com/forestTracker) \n\n\nIf you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org).\n","function":"Displays boundaries of areas allocated by governments to companies for oil palm plantations.","cautions":"This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.\n\nDisclaimer: Indonesia oil palm concessions data were obtained from the Ministry of Forestry in 2012, a period when the Ministry opened access to this data. Public download of this data is no longer available, therefore we have restricted this dataset to view only.\n","key_restrictions":"Unknown for all","tags":["Land Use"],"why_added":"Shows where land is allocated to oil palm - potentially a driver of deforestation","learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_global_oil_palm_concessions_v2025_public.zip","id":"60d1b96f-95a3-40ac-a397-a89f54e668be"},"versions":["v2025","v20191031"]},{"created_on":"2021-04-29T20:27:27.805436","updated_on":"2022-12-07T19:21:39.024154","dataset":"gfw_peatlands","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897531","updated_on":"2026-08-05T14:43:09.358441","spatial_resolution":30,"resolution_description":"Variable, but down to 30m","geographic_coverage":"Global","update_frequency":"Sporadic","scale":null,"citation":"“Global peatland extent.” Accessed on [date] from Global Nature Watch","title":"Global peatland extent","subtitle":"(Variable resolution, global, multiple data sources)","source":"- Crezee et al. 2022 (Congo basin)\n- Gumbricht et al. 2017 (between 40 deg N and rest of southern hemisphere)\n- Hastie et al. 2022 (Amazonian lowland Peru)\n- Miettinen et al. 2016 (Indonesia and Malaysia)\n- Xu et al. 2018 (temperate/boreal, north of 40 deg N)","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"- This data set delineates peatlands and other organic soils globally using five layers. Miettinen et al. 2016 was used for Indonesia and Malaysia, Hastie et al. 2022 was used in lowland Peru, Crezee et al. 2022 was used in the Congo basin, and Gumbricht et al. 2017 was used for all land between 40 degrees north and 60 degrees south (including areas covered by the aforementioned data sets). Xu et al. 2018 was used for all land above 40 degrees north. Miettinen et al. 2016 and Xu et al. 2018 were rasterized to ~30x30 m resolution while Gumbricht et al. 2017, Crezee et al. 2022, and Hastie et al. 2022 were resampled from their native resolutions to ~30x30 m resolution in order to align with the Global Forest Change maps from Hansen et al. 2013. All layers were combined, i.e. Gumbricht et al. 2017 was also used in Indonesia/Malaysia, the Peruvian Amazon, and the Congo basin. All data sources have different methods for peatland delineation, which are described in their original publications.\n- Crezee, B. et al. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. Nature Geoscience 15: 639-644 (2022). [https://www.nature.com/articles/s41561-022-00966-7](https://www.nature.com/articles/s41561-022-00966-7). Data downloaded from [https://congopeat.net/maps/](https://congopeat.net/maps/), using classes 4 and 5 only (peat classes).\n- Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Glob. Change Biol. 23, 3581–3599 (2017). [https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13689](https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13689)\n- Hastie, A. et al. Risks to carbon storage from land-use change revealed by peat thickness maps of Peru. Nature Geoscience 15: 369-374 (2022). [https://www.nature.com/articles/s41561-022-00923-4](https://www.nature.com/articles/s41561-022-00923-4)\n- Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67– 78 (2016). [https://www.sciencedirect.com/science/article/pii/S2351989415300470](https://www.sciencedirect.com/science/article/pii/S2351989415300470)\n- Xu et al. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. CATENA 160: 134-140 (2018). [https://www.sciencedirect.com/science/article/pii/S0341816217303004](https://www.sciencedirect.com/science/article/pii/S0341816217303004)\n","function":"Delineates extent of peatlands","cautions":"- This is a composite layer comprised of five data sets, each with their own methods and strengths and weaknesses. Refer to the original publications for each data set to learn more about specific cautions for each.\n- All input layers have been converted from vector data or resampled from coarser raster data.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"add770ea-4fa4-4dc2-bc2b-69d650f60e3a"},"versions":["v20230302","v20200807","v20230315"]},{"created_on":"2021-07-20T02:25:54.619042","updated_on":"2021-07-20T02:25:54.619047","dataset":"gfw_pixel_area","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897471","updated_on":"2024-09-26T17:57:24.728442","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Pixel Area","subtitle":null,"source":"UMD GLAD Lab\n","license":"DEPCREATED","data_language":null,"overview":null,"function":"Geodesic area of pixel in square meter","cautions":"DEPRECATED, refer to umd_pixel_area_2013/v1.10","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"6b771e1a-5f47-4abb-951d-22de842026e0"},"versions":["v20150327"]},{"created_on":"2024-11-01T17:00:04.655322","updated_on":"2025-02-20T18:18:45.911106","dataset":"gfw_places_to_watch","is_downloadable":true,"metadata":{"created_on":"2024-11-01T17:00:04.672543","updated_on":"2026-08-05T14:50:51.226405","spatial_resolution":null,"resolution_description":"5 × 5 km","geographic_coverage":"30°N to 30°S ","update_frequency":"Quarterly","scale":null,"citation":"Source: “Places to Watch”.  World Resources Institute. Accessed through Global Nature Watch on [date] www.globalnaturewatch.org. \n","title":"Places to Watch","subtitle":"quarterly, 5 km, tropics, UMD/GLAD, WUR, and Mongabay","source":"Carter, S., Berger, A., Weisse, M.J., Petersen, R., Sargent, S., Gibbes, S., Terry, J. 2024. “Places to Watch: Identifying High-Priority Forest Disturbance from Near–Real Time Satellite Data.” Technical Note. Washington, DC: World Resources Institute. Available online at [[www.wri.org/publication/places-to-watch](http://www.wri.org/publication/places-to-watch)]([www.wri.org/publication/places-to-watch)](http://www.wri.org/publication/places-to-watch). \n","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"en","overview":"The Places to Watch (PTW) initiative is an automated workflow to identify high-priority areas of deforestation alerts each quarter, based on the intersection of the integrated deforestation alerts with other datasets. There are three separate alert filtering approaches which result in the three PTW types in the legend: Mongabay reporting, Oil Palm, and Soy.  \n\n#Mongabay Reporting  \nThese locations were generated by the Places to Watch method which filters the deforestation alerts to those in protected areas and primary forest or intact forest landscapes and identifies the 5x5 km grid cells containing the most alerts in each tropical region. These locations are selected as potential Places to Watch and, after a curation process to identify alert drivers and provide further context, are shared with Global Nature Watch’s “Action Network” of environmental journalists. The goal is to get this information into the hands of journalists who can sound the alarm on new frontiers of deforestation and inspire intervention. These organizations have contact with journalists or activists on the ground, who can then investigate further. If there are articles that are written as a result of sharing the top PTW with the media, the articles are added to the GNW map. The locations and articles on the map are regularly replaced with new articles from more recent curations which may highlight new frontiers of deforestation or provide updates on previous PTW. PTW has proven to be useful in providing a starting point for investigative journalists, contributing to numerous articles reporting on the causes and context of deforestation across the globe. More information on the methodology and curation process involved in Places to Watch is available here: [<https://www.wri.org/publication/places-to-watch>](<https://www.wri.org/publication/places-to-watch)> \n\n \n#Places to Watch – Commodities (Oil Palm and Soy) \n\nSimilarly, these locations are the result of filtering recent deforestation alerts within key datasets. The PTW – Commodities (PTWC) workflow a descendant of the quarterly, global Places to Watch analysis, which provides inputs for curated stories about emerging deforestation. For PTWC, these alerts are related to current palm and soy plantation areas that are likely to be in noncompliance with zero-deforestation commitments made by large companies if planted with oil palm in Southeast Asia, specifically Indonesia and Malaysia, and if planted with soy in various countries in South America. In particular, the PTWC – Soy  method picks up large clearance events most likely caused by industrial palm concessions or large soy farms. The analysis is an automated process that is executed once a quarter with the curated results displayed as a layer on the Global Nature Watch flagship platform. The main differences between PTWC and global PTW is that PTWC focuses only on potential oil palm expansion in Southeast Asia and potential soy expansion in South America rather than on any deforestation in the entire tropics.  \nPTWC – Oil Palm: The palm approach filters alerts that intersect either peat, protected peat, or primary forest or intact forest in protected area, and fall within 50 km from palm oil mills or 10 km from oil palm plantations. \n \nPTWC – Soy: The soy approach filters alerts that fall within a 10 km buffer around existing soy plantations. \nMore information on the Places to Watch – Commodities methodology is available here: \n\n[<https://www.wri.org/research/places-watch-palm-and-soy-identifying-high-priority-forest-disturbances-related-palm> \n](<https://www.wri.org/research/places-watch-palm-and-soy-identifying-high-priority-forest-disturbances-related-palm)> \n\n \nIf you have additional information to share about a Place to Watch, or are interested in joining our Action Network to provide more information about places near you, please email us at gfw@wri.org. \n\nSign up here to receive each Places to Watch curation in your email, and share these places on social media using the hashtag #PlacestoWatch! \n","function":"Identify areas of high-priority deforestation alerts on a quarterly basis and inform investigative journalism. Sign up for the newsletter [here](https://www.globalnaturewatch.org/subscribe/). ","cautions":"- Places to Watch is an automated algorithm, and as such, may miss areas that some would consider “high priority.” Additionally, the algorithm does not consider the legality of identified Places. \n- We assume that for journalists and activists, who rely heavily on storytelling and public support, the most concerning clearing takes place in remote, undisturbed areas with high ecological value. We acknowledge that this method's design to identify priority alert locations is subjective, based on the data inputs that our team and audience deemed priority regions for conservation. \n- WRI makes every effort to ensure that information distributed as part of Places to Watch is accurate, but some information comes from an Action Network of on the ground partners and is not possible for WRI to verify. If you do notice any inaccuracies, please email us at gnw@wri.org. \n","key_restrictions":null,"tags":[],"why_added":null,"learn_more":"https://www.wri.org/research/places-watch-identifying-high-priority-forest-disturbance-near-real-time-satellite-data","id":"991d8b01-7e8b-49ff-adb3-917d8935c223"},"versions":["v0","v20241101"]},{"created_on":"2021-04-29T20:22:25.546054","updated_on":"2021-04-29T20:22:36.278988","dataset":"gfw_plantations","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897450","updated_on":"2026-08-05T14:43:10.083843","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"GNW Plantations","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"9bc97a55-4366-48cb-9eea-b8aa8b728e64"},"versions":["v2014"]},{"created_on":"2020-12-07T16:17:45.611627","updated_on":"2025-01-28T15:40:13.573967","dataset":"gfw_planted_forests","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897418","updated_on":"2026-08-05T14:43:10.458647","spatial_resolution":null,"resolution_description":"Raster version: 30 meters (Vector version: Scale varies by country)","geographic_coverage":"158 countries","update_frequency":"Periodic as new data becomes available","scale":"global","citation":"Richter, J., Goldman, E., Harris, N., Gibbs, D., Rose, M., Peyer, S., Richardson, S., and H. Velappan. 2024. “Spatial Database of Planted Trees (SDPT Version 2.0).” Accessed through Global Nature Watch on [Date]. www.globalnaturewatch.org. \n","title":"Tree plantations","subtitle":"2020, 30m, near-global, GNW","source":"Richter, J., Goldman, E., Harris, N., Gibbs, D., Rose, M., Peyer, S., Richardson, S., and H. Velappan. 2024. “Spatial Database of Planted Trees (SDPT Version 2.0).” Washington, DC: World Resources Institute.\n","license":"CC BY 4.0","data_language":"English","overview":"The Spatial Database of Planted Trees (SDPT) was compiled by Global Nature Watch using data obtained from national governments, non-governmental organizations and independent researchers. In version 2.0 (v2.0) data were compiled for 158 countries around the world, with most country maps originating from supervised classification or manual polygon delineation of Landsat, SPOT or RapidEye satellite imagery. \n“Planted trees” in the SDPT includes planted forests, stand of planted trees - other than tree crops - grown for wood and wood fiber production or for ecosystem protection against wind and/or soil erosion. Planted forests can efficiently produce high quantities of wood products that may alleviate pressure on natural forests, create jobs that support rural development, and/or provide a range of ecosystem services, especially when established on degraded lands. The data set also includes perennial tree crops, such as rubber, oil palm, coffee, coconut, cocoa and orchards. The SDPT makes it possible to identify planted forests and tree crops apart from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover. \nThe SDPT v2.0 contains 264 million hectares of planted forest and 65 million hectares of agricultural trees, or approximately 90% of the world’s total planted forest area in 2020 (FAO 2020). The SDPT was compiled through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table. \nFor more detailed information, please refer to the [Technical Note](https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2).\nData is available for download in all countries except Papua New Guinea.\nIf you are aware of any additional planted trees data, please let us know by filling out this [form](https://docs.google.com/forms/d/e/1FAIpQLSccWSCQXrbWUIKedy9vDvt5rAcugNjX2bygfmBB6upl717qYg/viewform?usp=sf\\_link). \n","function":"Identifies planted forests and tree crops on a near-global scale.","cautions":"This dataset is a compilation of plantation data from a variety of countries and sources. As a result, there are definitional and temporal inconsistencies within the database, as well as an absence of a uniform accuracy assessment. Please be aware that some countries do not have planted forest data available, only tree crop data, so exercise caution when interpreting plantation statistics using this dataset.\n","key_restrictions":"","tags":["Land Cover"],"why_added":"","learn_more":"https://gfw2-data.s3.amazonaws.com/plantations/sdpt/sdpt_v2_v11282023_public.gdb.zip","id":"b91b5f9e-ca22-4a65-8117-08bce716bf83"},"versions":["v20231009","v20201209","v20230911","v20239998","v20221107","v20231128","v20230415"]},{"created_on":"2023-12-12T18:30:18.676664","updated_on":"2023-12-12T18:30:18.676670","dataset":"gfw_planted_forests_palm_oil_buffered_10km","is_downloadable":true,"metadata":{},"versions":["v20230911"]},{"created_on":"2023-12-29T17:33:44.913086","updated_on":"2023-12-29T17:33:44.913091","dataset":"gfw_planted_forests_whitelist","is_downloadable":true,"metadata":{"created_on":"2023-12-29T17:33:44.927657","updated_on":"2023-12-29T17:33:44.927664","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"SDPT Whitelist (iso)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":"Whitelist for v20230911 of SDPT (v2.0)","cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"a8dafab8-f104-4a7d-beb3-1bfaaaa41997"},"versions":["v20231229"]},{"created_on":"2025-03-17T21:14:03.601685","updated_on":"2025-03-17T21:14:03.601690","dataset":"gfw_pre_2000_plantations","is_downloadable":false,"metadata":{"created_on":"2025-03-17T21:14:03.611709","updated_on":"2025-03-17T21:14:03.611713","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":"Periodic","scale":null,"citation":null,"title":null,"subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"ccccf6e8-db80-4ef7-afb0-6a77e3f430c6"},"versions":["v20200724"]},{"created_on":"2021-04-29T20:22:08.790376","updated_on":"2021-04-29T20:22:08.790383","dataset":"gfw_primary_forests","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897366","updated_on":"2026-08-05T14:43:10.781892","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Primary Forests","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"990f3b8e-1b84-400e-b70d-4784529a0179"},"versions":["v201901"]},{"created_on":"2021-02-10T16:59:42.440238","updated_on":"2021-02-10T16:59:42.440246","dataset":"gfwpro_forest_change_regions","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897431","updated_on":"2023-05-04T13:11:58.897432","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GFW PRO Forest Change Regions","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":"Used to select Forest Change Analytics results of GFW Pro.","cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"eebd9f70-d9bc-4826-9ed9-f8709d3d8be2"},"versions":["v20210129","v20220228","v20230216","v20240529","v20230207","v20220225","v20220224"]},{"created_on":"2023-07-28T20:09:59.023445","updated_on":"2023-07-28T20:09:59.023452","dataset":"gfwpro_negligible_risk_analysis","is_downloadable":true,"metadata":{"created_on":"2023-07-28T20:09:59.038977","updated_on":"2024-10-04T16:27:53.760237","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global","update_frequency":"Annual","scale":null,"citation":null,"title":"Negligible Risk","subtitle":null,"source":"Accountability Framework Initiative. 2022. \nDeforestation- and conversion-free \nsupply chains and land use change emissions: A guide to aligning corporate \ntargets, accounting, and disclosure. [Link](https://accountability-framework.org/news-events/news/deforestation-and-conversion-free-supply-chains-and-land-use-change-emissions-a-guide-to-aligning-corporate-targets-accounting-and-disclosure/)\n","license":null,"data_language":"English","overview":"This map was developed to support [reporting guidance](https://accountability-framework.org/news-events/news/deforestation-and-conversion-free-supply-chains-and-land-use-change-emissions-a-guide-to-aligning-corporate-targets-accounting-and-disclosure/) published by the Accountability Framework Initiative (AFI), in partnership with the Science Based Targets Initiative (SPTi) and the Greenhouse Gas (GHG) Protocol which outlines recommended indicators. Companies using this new workflow are encouraged to report on the volumes produced on landscapes where deforestation risk is considered negligible utilizing a number of different methods. <br>Sub-national jurisdictions classified as negligible risk are those that have collectively experienced less than 1 percent of cumulative natural forest loss relative to the entire country. This process is adopted from a similar process utilized by Trase to assess deforestation risk associated with cattle production in Brazil.","function":"Classifies sub-national jurisdictions as negligible or non-negligible risk, based on \ncumulative natural forest loss from 2021 onward","cautions":"This map was developed to support reporting guidance published by the Accountability Framework Initiative (Afi), which includes reporting on volumes produced on landscapes where deforestation risk is negligible. It is not an indicator of legal compliance, nor does it account for non-forest ecosystems at risk of conversion.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"338a53ec-3747-412b-bd4c-5ef757b00e55"},"versions":["v20241021","v20250514","v20260417","v20230726"]},{"created_on":"2021-07-12T20:41:34.406743","updated_on":"2021-07-12T20:41:34.406747","dataset":"gfwpro_peatlands","is_downloadable":true,"metadata":{},"versions":["v2019"]},{"created_on":"2021-08-18T20:01:04.540720","updated_on":"2021-08-18T20:01:04.540726","dataset":"gfw_reforestable_extent_aboveground_carbon_potential_sequestration","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897168","updated_on":"2026-08-05T14:43:11.209736","spatial_resolution":null,"resolution_description":"1 km","geographic_coverage":"Global, within reforestation extent of Griscom et al. 2017 (which excludes the boreal, grassy biomes, and croplands)","update_frequency":null,"scale":null,"citation":"Cook-Patton et al. 2020. Carbon accumulation potential from natural forest regrowth in potentially reforestable areas. Accessed on [date] from Global Nature Watch. \n","title":"Carbon accumulation potential from natural forest regrowth in reforestable areas","subtitle":"(1 km, global, Cook-Patton et al. 2020) ","source":"Cook-Patton, S.C., S.M. Leavitt, D. Gibbs, N.L. Harris, K. Lister, K.J. Anderson-Teixeira, R.D. Briggs, R.L. Chazdon, T.W. Crowther, P.W. Ellis, H.P. Griscom, V. Herrmann, K.D. Holl, R.A. Houghton, C. Larrosa, G. Lomax, R. Lucas, P. Madsen, Y. Malhi, A. Paquette, J.D. Parker, K. Paul, D. Routh, S. Roxburgh, S. Saatchi, J.van den Hoogen, W.S. Walker, C.E. Wheeler, S.A. Wood, L. Xu, B.W. Griscom. 2020. Mapping carbon accumulation potential from natural forest regrowth. Nature, in press. [https://www.nature.com/articles/s41586-020-2686-x](https://www.nature.com/articles/s41586-020-2686-x). This work resulted from a collaboration between The Nature Conservancy, World Resources Institute, and 18 other institutions. \n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":null,"overview":"This map shows the rate at which forests could capture carbon from the atmosphere and store it in aboveground live biomass over the first 30 years of natural forest regrowth. It was created by combining ground-based measurements at thousands of locations around the world with 66 co-located environmental covariate layers in a machine learning model to produce a wall-to-wall map. Forest plot data used to train the model are sourced from published literature, which can be found in the Forest Carbon database (ForC, maintained by the Smithsonian Institute ([https://github.com/forc-db](https://github.com/forc-db)), as well as georeferenced data from publicly available national forest inventories. Although rates were estimated over all forest and savanna biomes globally, they are filtered here by “reforestable” area, as defined in Griscom et al. 2017 (PNAS). Reforestable areas exclude areas of native grasslands and croplands to safeguard the production of food and fiber and habitat for biological diversity. \n","function":"Estimates the rate at which carbon could be sequestered in aboveground live biomass during the first thirty years of natural forest regrowth in potentially reforestable areas (Mg carbon/ha/yr). ","cautions":"- Values represent best estimates but contain uncertainty. Accuracy of results depends on data availability for model training, which is concentrated in ten countries. The uncertainty map associated with this data layer can be downloaded from GNW’s Open Data Portal. \n- Carbon accumulation rates are applicable to natural forest regrowth only, and do not apply to other active restoration methods (agroforestry, plantations, etc.). \n- Carbon accumulation rates are linear and averaged over the first 30 years of regrowth. Extending beyond 30 years will over-estimate sequestration. \n- Rates reflect carbon accumulation in aboveground live biomass only. Accumulation in belowground biomass, dead organic matter and soil organic carbon are not included but a belowground carbon accumulation map is available upon request. \n- In savannas, rates only apply to forested portions of these grassland-forest matrices. \n- These data are not a substitute for detailed site-level assessments of forest regrowth potential. \n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"75c298ab-db20-4c97-9781-32885bb3983c"},"versions":["v2020"]},{"created_on":"2021-08-18T20:00:50.215463","updated_on":"2021-08-18T20:00:50.215470","dataset":"gfw_reforestable_extent_belowground_carbon_potential_sequestration","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897214","updated_on":"2023-05-04T13:11:58.897215","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Belowground Carbon Potential Sequestration (Reforestable Extent)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"08c55cc7-c206-43bf-aa55-87023e29ac97"},"versions":["v2020"]},{"created_on":"2021-07-26T20:01:42.044235","updated_on":"2021-07-26T20:01:42.044241","dataset":"gfw_resource_rights","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897521","updated_on":"2026-08-05T14:43:11.624587","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Currently available for Cameroon, Equatorial Guinea, Liberia and Namibia","update_frequency":null,"scale":"global","citation":"“Resource rights.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Resource rights","subtitle":"(select countries)","source":"*Cameroon:* WRI Congo Basin Forest Atlas\n*Equatorial Guinea:* WRI Congo Basin Forest Atlas\n*Liberia:* USAID-Liberia PROSPER\n*Namibia:* Namibian Association of Community Based Natural Resource Management (CBNRM) Support Organisations (NACSO)","license":null,"data_language":"varies","overview":"“Resource Rights” refers to areas over which indigenous peoples or local communities enjoy rights to certain resources and a limited right to access the land, whether legally recognized or not, in order to exercise their resource rights. The exact nature of these resource rights varies among tenure type and country.<br><br>The resource rights data on GNW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.<br><br>Resource rights data displayed on the GNW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations. See the [Open Data Portal](http://data.globalforestwatch.org/datasets?q=resource+rights&sort_by=relevance) for details on specific data sets.<br><br>If you are aware of resource rights data for additional countries, please email us [here](mailto:gfw@wri.org).","function":"Displays boundaries of areas over which indigenous peoples or local communities enjoy rights to certain resources and a limited right to access the land","cautions":"Some data sets displayed on Global Nature Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.","key_restrictions":"unknown","tags":null,"why_added":"Show where there are community forests or other resource rights","learn_more":null,"id":"3e038f19-d244-4571-acb0-edc1d44a2177"},"versions":["v2018"]},{"created_on":"2023-04-04T19:18:39.685089","updated_on":"2023-04-04T19:18:39.685095","dataset":"gfw_soil_carbon","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897336","updated_on":"2026-08-05T14:43:12.014360","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Soil Carbon (for TCL carbon model)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"236a233d-0480-4d9f-871f-b8433a3c3c2c"},"versions":["v20230322","v20230322.1","v20230322.2","v20230322.3"]},{"created_on":"2023-11-30T22:33:55.725962","updated_on":"2023-11-30T22:33:55.725968","dataset":"gfw_soil_carbon_stock_2000","is_downloadable":true,"metadata":{"created_on":"2023-11-30T22:33:55.734989","updated_on":"2023-11-30T22:33:55.734994","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Soil organic carbon stock in forests in 2000 (Mg C/ha)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"9e01dd0f-479d-4825-8650-deff8d789020"},"versions":["v20231108"]},{"created_on":"2021-04-19T21:12:41.850514","updated_on":"2021-04-19T21:12:41.850525","dataset":"gfw_soil_carbon_stocks","is_downloadable":true,"metadata":{},"versions":["v20200724"]},{"created_on":"2021-08-20T20:35:10.156184","updated_on":"2021-08-25T19:10:25.775415","dataset":"gfw_tiger_landscapes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897145","updated_on":"2026-08-05T14:43:12.322447","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Bangladesh, Bhutan, Cambodia, China, India, Indonesia, Laos, Malaysia, Myanmar, Nepal, Russia, Thailand and Vietnam","update_frequency":"Updated annually","scale":"regional","citation":"WWF and RESOLVE. \"Tiger Conservation Landscapes.\" Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"Tiger Conservation Landscapes","subtitle":null,"source":"*Tiger Conservation Landscapes*\nDinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007. “The fate of wild tigers.” BioScience 57 (June 2007): 508-14. Tx2 Tiger Conservation Landscapes Wikramanayake, E., Dinerstein, E., Seidensticker, J., Lumpkin, S., Pandav, B., Shrestha, M., Mishra, H., Ballou, J., Johnsingh, A.J.T., Chestin, I., Sunarto, S., Thinley, P., Thapa, K., Jiang, G., Elagupillay, S., Kafley, H., Pradhan, N.M.B., Jigme, K., Teak, S., Cutter, P., Aziz, Md. A., Than, U. 2011. A landscape-based conservation strategy to double the wild tiger population. Conservation Letters, 4 (3):219-227.","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"These two data sets, produced by WWF and RESOLVE, show the location of current tiger habitat and priority areas for habitat conservation.\n\n*Tiger Conservation Landscapes: *\nTiger Conservation Landscapes (TCLs) are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat.\n\n*Tx2 Tiger Conservation Landscapes: *\nThis data set displays 29 Tx2 Tiger Conservation Landscapes (Tx2 TCLs), defined areas that could double the wild tiger population through proper conservation and management by 2020.","function":"These layers show the location of current tiger habitats, areas of habitat expansion, and critical tiger corridors.","cautions":"Tiger Conservation Landscapes were created under the assumption that suitable habitat depends on quality and size of land cover and prey base.\n\nLand cover data was problematic in certain geographies due to the presence of tree plantations. In some cases, forest cover was overestimated or underestimated.\n\nThe tiger location database, on which this data set was built, is incomplete for some regions, and the data comes from a variety of sources and research methods.","key_restrictions":"","tags":["Conservation"],"why_added":"","learn_more":"","id":"03f031d4-7a55-4343-b59b-0412fe63ec78"},"versions":["v201904"]},{"created_on":"2020-09-22T18:26:28.012221","updated_on":"2025-01-29T19:15:24.942983","dataset":"gfw_universal_mill_list","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897405","updated_on":"2026-08-05T14:43:12.750066","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global","update_frequency":"Every 6 months","scale":"global","citation":"World Resources Institute, Rainforest Alliance, Proforest, Daemeter, Trase, Earthworm, Auriga, CIFOR, Transitions, Jason Benedict, Robert Heilmayr, Kim Carlson“Universal Mill List.” June 2022. Accessed through Global Nature Watch on [date].www.globalforestwatch.org","title":"Palm Oil Mills","subtitle":"Universal Mill List","source":"World Resources Institute, Rainforest Alliance, Proforest, Daemeter, Trase, Earthworm, Auriga, CIFOR, Transitions, Jason Benedict, Robert Heilmayr, Kim Carlson","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"The Universal Mill List (UML) is a collection of palm oil mill locations across the worldwith associated group, company, and mill names, RSPO certification status and unique “universal IDs”. The UML is based on data contributed to the authors from palm oil buyer companies, the Roundtable on Sustainable Palm Oil (RSPO), and FoodReg, as well as data gathered from government records and through extensive supply chain research. The objective of the UML is to provide a comprehensive, common dataset for the palm oil industry that can be used to easily identify mills across various platforms and reporting efforts. <br><br>The harmonization of data and the verification of each of the mill locations was a joint effort from a consortium of partners at the World Resources Institute (WRI), Rainforest Alliance, Proforest, Daemeter, Trase, Earthworm Foundation, Auriga, CIFOR, Transitions, the University of California, Santa Barbara, and the University of Hawai’i. The UML is created and updated according to a standardized process, as detailed in the “Universal Mill List Methodology” (WRI and Rainforest Alliance in press, update pending). All mill locations are manually verified using high resolution satellite imagery according to criteria for mill infrastructure, including the presence of buildings, settling ponds, and nearby palm oil plantations. Duplicate mills are identified and removed according to an analysis to identify exact and nearby GPS location duplicates. Verified, unique mill locations are assigned “universal IDs” and added to the UML. Mill name, parent company, and group name information are based on the best available data as gathered by Trase (for Indonesia) and Earthworm Foundation (for most other mills) but may still have gaps or potential inaccuracies. Great care is taken to ensure no duplication of attribute information in the database. The RSPO certification status is the status of each mill at a specific cut-off date, available in the attribute data. RSPO certification statuses are updated every day, so current statuses may be different than the ones listed in the attribute table. Please check for up-to-date mill certification status at RSPO Palm Trace, [here](https://www.rspo.org/certification/certified-growers-search).<br><br>The dataset is updated roughly every 6 months as new data becomes available.","function":"Displays the location of palm oil mills.","cautions":"This data set is not a complete representation of all palm oil mills in the world. The global data currently includes mills within the supply chains of companies submitting mill data to the authors. In Indonesia, this list of mills is supplemented with additional data on additional mills using information from government reports. The resulting data provide a robust and rigorously verified collection of mills in major supply chains. Mill name, company, and group name information are based on the best available data and may have potential inaccuracies.","key_restrictions":"","tags":["Land Use"],"why_added":"Provide up to date mill locations with a standardized format","learn_more":"","id":"48e7eae5-ac54-42e5-a788-667cbc972386"},"versions":["v20230115","v20231001","v20210621","v20210319","v20220531","v202410","v202508","v202106"]},{"created_on":"2023-12-15T00:13:01.645478","updated_on":"2023-12-15T00:13:01.645486","dataset":"gfw_universal_mill_list_buffered_50_km","is_downloadable":true,"metadata":{},"versions":["v20231001"]},{"created_on":"2024-02-02T21:40:42.957233","updated_on":"2024-02-07T17:37:25.830451","dataset":"gfw_west_africa_cocoa_deforestation_risk","is_downloadable":false,"metadata":{"created_on":"2024-02-02T21:40:42.972985","updated_on":"2024-10-04T16:27:55.924007","spatial_resolution":null,"resolution_description":"30 × 30m","geographic_coverage":"Côte d'Ivoire and Ghana","update_frequency":null,"scale":null,"citation":"Schneider, M., C. Winchester, E. Goldman, and Y. Shao. 2023. “Mapping cocoa and assessing deforestation risk for the cocoa sector in Côte d’Ivoire and Ghana.” Technical Note. Washington, DC: World Resources Institute. Available online at: doi.org/10.46830/writn.21.00011.","title":"West Africa Cocoa Deforestation Risk Assessment","subtitle":null,"source":"World Resources Institute","license":null,"data_language":null,"overview":"The Cocoa Deforestation Risk Assessment (Cocoa DRA) is a map layer that identifies the risk of future deforestation events linked to cocoa in Côte d’Ivoire and Ghana. Cocoa and chocolate companies, as well as other stakeholders in this sector can use this map to identify and prioritize interventions in cocoa supply chains.<br>Multiple geospatial datasets were brought together to create the Cocoa DRA, which represent landscape features known to influence the probability of deforestation, including explanatory variables such as recent loss, terrain suitability for cocoa production, and human accessibility. We also included the West Africa Cocoa dataset as a variable, which we created by collecting and collating cocoa plot data contributed by 19 cocoa and chocolate companies. All variables were first analyzed at a 30-m resolution and then rescaled to a 1-km resolution to better capture broader landscape trends and ease visual interpretation. Finally, the results were reclassified to 5 priority classes which communicate the level of risk relative to the remainder of the study area.<br><br>1-km pixel values are encoded as follows:<br>1 = high risk, 2 = mid-high risk, 3 = medium risk, 4 = mid-low risk, 5 = low risk, 0 = no forest<br><br>The Cocoa DRA is not geared toward a specific type of infrastructure or level of traceability, meaning that it can be used to assess areas of interest as small as a cocoa plot or as large as a landscape. However, the type of areas of interest must be comparable; for example, comparing the risk associated with a cluster of farms to the risk associated with the sourcing area of a cooperative is an inappropriate use of the Cocoa DRA.<br>Users should note that for the Cocoa DRA, low risk does not immediately translate to low prioritization for interventions. It shows the likelihood of forest loss occurring, but on its own, does not show where forest loss has potential to do the greatest harm. Risk assessments should include additional data that show the presence of high-interest features such as primary forest or boundaries of protected areas. For example, Taï National Park in Côte d’Ivoire and Bia National Park in Ghana have experienced a low amount of recent tree cover loss and as a result deforestation risk is low within these forests. <br>The map layer currently on the Open Data Portal is version 2 and shows priority landscapes in 2023. The Cocoa DRA will be updated annually alongside updates annual tree cover loss data; for the 2023 version, recent loss as a variable includes forest loss that occurred between 2018 and 2022. Other inputs will be updated periodically as improved data becomes available. A full description of the methods can be found in this [technical note](https://www.wri.org/research/mapping-cocoa-assessing-deforestation-risk-cocoa-cote-divoire-ghana).","function":"Displays the density of mapped cocoa plots across Côte d'Ivoire and Ghana in 2021","cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"87ecbbfc-9529-4882-ae57-17da5052fe50"},"versions":["v202312"]},{"created_on":"2024-01-26T18:41:39.565179","updated_on":"2024-02-07T17:37:42.192875","dataset":"gfw_west_africa_cocoa_plot_density","is_downloadable":false,"metadata":{"created_on":"2024-01-26T18:41:39.581513","updated_on":"2026-08-05T14:43:13.149235","spatial_resolution":null,"resolution_description":"30 × 30m","geographic_coverage":"Côte d'Ivoire and Ghana","update_frequency":null,"scale":null,"citation":"Schneider, M., C. Winchester, E. Goldman, and Y. Shao. 2023. “Mapping cocoa and assessing deforestation risk for the cocoa sector in Côte d’Ivoire and Ghana.” Technical Note. Washington, DC: World Resources Institute. Available online at: [doi.org/10.46830/writn.21.00011](https://www.wri.org/research/mapping-cocoa-assessing-deforestation-risk-cocoa-cote-divoire-ghana).","title":"West Africa Cocoa Plot Heat Map","subtitle":null,"source":"World Resources Institute","license":null,"data_language":"English","overview":"The West Africa Cocoa dataset (WAC) is a database of mapped cocoa plots in the direct supply chains of 19 cocoa and chocolate companies operating in Côte d'Ivoire and Ghana. WAC plots are represented by polygons, drawn by connecting coordinates collected in person along the boundary of a plot. A plot is distinct from a cocoa farm in that a plot may be a subset of the entire farm. <br>To create the WAC, the World Resources Institute developed data sharing and collation protocols. These outlined the requirements for data contributed and the procedure for cleaning and aggregating all data received. As part of this process, a legal and ethics review was carried out to minimize unintended consequences for farmers and ensure alignment with data privacy laws in West Africa. To make the WAC a public good, the polygons have been summarized on Global Nature Watch as a cocoa plot density map which shows the distribution of the original polygon data but does not show the precise boundaries of each plot in the dataset. <br>Contributors to the WAC were required to submit single-part polygon features which represented the location of an active plot in 2021. Individual company datasets were first cleaned and then aggregated to identify unique cocoa plots. In total, 840,000 cocoa plots were identified, covering a land area of approximately 1.5 million hectares. This data was a key input for developing a complementary method of assessing deforestation risk: The [Cocoa Deforestation Risk Assessment](https://data.globalforestwatch.org/documents/gfw::cocoa-deforestation-risk-assessment/about). Both products are the result of an unprecedented collaboration between the World Resources Institute, World Cocoa Foundation, and 19 cocoa and chocolate companies.","function":"Displays the density of mapped cocoa plots across Côte d'Ivoire and Ghana in 2021","cautions":"- This map layer is derived from the West Africa Cocoa dataset, a database of mapped cocoa plot polygons in the direct supply chains of 19 cocoa and chocolate companies. Due to data privacy concerns, the original polygons are not publicly available. <br>- The West Africa Cocoa dataset does not capture the entire extent of cocoa production in Côte d'Ivoire and Ghana; just the extent of cocoa that has been mapped by the 19 cocoa and chocolate companies who contributed their data. <br><br><br>","key_restrictions":"not downloadable","tags":null,"why_added":null,"learn_more":"https://www.wri.org/research/mapping-cocoa-assessing-deforestation-risk-cocoa-cote-divoire-ghana","id":"efda8c33-65c6-4640-967b-7cf1b34a1f7c"},"versions":["v202401"]},{"created_on":"2020-07-28T01:33:11.832219","updated_on":"2025-01-28T15:40:19.323419","dataset":"gfw_wood_fiber","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897397","updated_on":"2026-08-05T14:50:51.777312","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Cambodia, Indonesia, Republic of the Congo, and Malaysia","update_frequency":"Varies by source","scale":"global","citation":"“Wood fiber concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.\n","title":"Wood fiber concessions","subtitle":"2016-2025, vector, select countries, sources vary","source":"Cambodia: Cambodian League for the Promotion and Defense of Human Rights (LICADHO)  \n\nIndonesia: Ministry of Environment and Forestry  \n\nRepublic of the Congo: WRI & Ministry of Agriculture  \n\nMalaysia: RimbaWatch  \n\n","license":"CC BY 4.0 (excluding Indonesia)","data_language":"varies","overview":"“Wood fiber concession” refers to an area allocated by a government or other body for establishment of fast-growing tree plantations for the production of timber and wood pulp for paper and paper products.\n\nThis data set displays wood fiber concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and source.\n\nAs available, additional source information is linked below:\n- [Cambodia](https://www.licadho-cambodia.org/land\\_concessions/)\n- [Malaysia](https://rimbawatchmy.com/forestTracker)\n\n\nIf you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org).\n","function":"Displays boundaries of areas allocated by governments to private companies for tree plantations for production of timber and wood pulp for paper and paper products.","cautions":"This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.\n\nDisclaimer: Indonesia wood fiber concessions data were obtained from the Ministry of Environment and Forestry in 2021, a period when the Ministry opened access to this data. Public download of this data is no longer available, therefore we have restricted this dataset to view only.\n","key_restrictions":"Unknown for all","tags":["Land Use"],"why_added":"Shows areas allocated for wood fiber concessions - potential driver of deforestation","learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_global_wood_fiber_concessions_v2025_public.zip","id":"b3597c25-f067-4a48-bd61-c980c293a1ac"},"versions":["v2025","v20200725","v202106"]},{"created_on":"2021-12-10T16:57:52.700483","updated_on":"2025-01-28T15:40:23.905190","dataset":"gfw_wood_fiber_downloadable","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897190","updated_on":"2026-08-05T14:43:13.863704","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GNW Wood Fiber Concessions (downloadable)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"d756910c-d2fa-49d3-b7c5-ccc5deb2da5f"},"versions":["v2021"]},{"created_on":"2025-02-25T14:17:20.835018","updated_on":"2025-02-25T14:17:20.835023","dataset":"global_water_watch_anomalies","is_downloadable":true,"metadata":{"created_on":"2025-02-25T14:17:21.230017","updated_on":"2025-02-25T14:17:21.230022","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Water Watch Anomalies","subtitle":"By Deltares, WRI, WWF","source":"https://www.globalwaterwatch.earth/about","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"en","overview":"Developed by Deltares, WRI, and WWF, with support from Google.org, the Water, Peace, and Security Partnership, and the European Space Agency, the platform monitors thousands of water bodies worldwide. Using advanced Earth Observation data and algorithms, it monitors water availability globally, and provides critical insights to help decision-makers manage climate risks and extreme weather events.","function":null,"cautions":null,"key_restrictions":null,"tags":["Global Water Watch"],"why_added":null,"learn_more":"https://www.globalwaterwatch.earth/","id":"b5c8df3b-10dc-4eae-8e2f-0f8f2224c1bd"},"versions":["v2021","v2022","v2025","v2023","v2020","v2019","v20250424","v20250425","v20220101","v20250422","v20250226","v2018","v2024","v20200101"]},{"created_on":"2025-04-22T07:23:58.929355","updated_on":"2025-04-22T07:23:58.929359","dataset":"global_water_watch_anomalies2","is_downloadable":true,"metadata":{"created_on":"2025-04-22T07:23:58.944933","updated_on":"2025-04-22T07:23:58.944938","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Water Watch Anomalies 2","subtitle":"By Deltares, WRI, WWF","source":"https://www.globalwaterwatch.earth/about","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"en","overview":"Developed by Deltares, WRI, and WWF, with support from Google.org, the Water, Peace, and Security Partnership, and the European Space Agency, the platform monitors thousands of water bodies worldwide. Using advanced Earth Observation data and algorithms, it monitors water availability globally, and provides critical insights to help decision-makers manage climate risks and extreme weather events.","function":null,"cautions":null,"key_restrictions":null,"tags":["Global Water Watch"],"why_added":null,"learn_more":"https://www.globalwaterwatch.earth/","id":"0f24b650-9660-4e2e-aa37-042d52a51c61"},"versions":["v20250421"]},{"created_on":"2023-01-27T19:25:45.030877","updated_on":"2025-02-13T22:56:19.239412","dataset":"gmw_global_mangrove_extent","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897307","updated_on":"2026-08-05T14:43:14.195875","spatial_resolution":null,"resolution_description":"25 meters","geographic_coverage":"Global","update_frequency":null,"scale":"global","citation":"Global Mangrove Watch, 2022. \"Global Mangrove Extent (v3.0).\" https://www.globalmangrovewatch.org. Accessed through Global Nature Watch on [Date]. www.globalnaturewatch.org.","title":"Global Mangrove Extent","subtitle":"2020, 25 m, global, GMW","source":"Global Mangrove Watch, v3.0 \n\nBunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, N.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L.-M. Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sens. 2022, 14, 3657. [https://doi.org/10.3390/rs14153657](https://doi.org/10.3390/rs14153657)","license":"[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This dataset was generated within the framework of the [Global Mangrove Watch (GMW)](https://www.globalmangrovewatch.org/), an initiative convened by [Aberystwyth University](https://www.aber.ac.uk/en/dges/), [soloEO](https://www.soloeo.com/), [The Nature Conservancy](https://www.nature.org/) and [Wetlands International](https://www.wetlands.org/).\nThe larger dataset (v3.0) depicts the global extent of mangrove forests for the years 1996, 2007-2010, and 2015-2020, derived by L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA), thus developing a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. \nThe classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from ocean water. The habitat mask was initially developed in GMW v2.0 and has been revised in subsequent versions.\n","function":"Global coverage of mangroves for 2020","cautions":"Mangrove extent maps in v3.0 have an estimated accuracy of 87.4% (95th CI: 86.2 - 88.6%). Noted confusion exists in fragmented areas of mangroves, including around aquaculture ponds. Additional cautions should be exercised if accessing net change, or individual gain and loss data from the v3.0 dataset. A source of error in v3.0 included a misregistration in the SAR mosaic datasets, resulting in the omission of known change events and commissions where change was known not to have occurred. This was partially corrected for using tie points automatically generated via the method of [Bunting et al. 2010](https://doi.org/10.1016/j.imavis.2009.12.005). The changes associated with misregistration errors are considered to be similar in terms of gain and loss due to the random nature of the input data, so it is recommended that the observed net change statistics are used for analysis rather than individual gain and loss statistics. [Country statistics](https://zenodo.org/record/6894273/files/gmw_v3_country_statistics_ha.xlsx?download=1) have been provided directly by GMW and include corrections for commission and omission errors.  ","key_restrictions":null,"tags":null,"why_added":"Most robust global mangrove layer available","learn_more":"www.globalmangrovewatch.org.","id":"1b525089-9b90-492e-95e8-64d122e9e13f"},"versions":["v3"]},{"created_on":"2021-07-26T20:57:57.142881","updated_on":"2021-07-26T20:57:57.142886","dataset":"gmw_global_mangrove_extent_1996","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897523","updated_on":"2023-05-04T13:11:58.897525","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"GMW Global Mangrove Extent 1996","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"4ef140eb-c9fa-4e25-ae43-a04a13e8c284"},"versions":["v2"]},{"created_on":"2020-12-07T16:17:43.281923","updated_on":"2020-12-07T16:17:43.281930","dataset":"gmw_global_mangrove_extent_2016","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897415","updated_on":"2026-08-05T14:43:14.513906","spatial_resolution":100,"resolution_description":null,"geographic_coverage":"Global","update_frequency":" ","scale":"global","citation":"Global Mangrove Watch, 2018. 'Global Mangrove Extent (v2.0).' www.globalmangrovewatch.org. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org. ","title":"Global Mangrove Extent","subtitle":null,"source":"Global Mangrove Watch, v2.0  Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). [The Global Mangrove Watch - a New 2010 Global Baseline of Mangrove Extent](https://www.mdpi.com/2072-4292/10/10/1669). Remote Sensing, 2018, 10, 1669; doi:10.3390/rs10101669","license":"[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This data set was generated  by [Aberystwyth University](https://www.aber.ac.uk/en/dges/) and [soloEO](http://www.soloeo.com/) within the framework of the [Global Mangrove Watch (GMW) project](http://www.eorc.jaxa.jp/ALOS/en/kyoto/mangrovewatch.htm), which is a part of the Japan Aerospace Exploration Agency's (JAXA) Kyoto & Carbon Initiative  and the Mangrove Capital Africa Programme coordinated by [Wetlands International](http://www.wetlands.org/) and financed by [DOB Ecology](http://dobecology.nl/). The map (v2.0) depicts the global extent of mangrove forests for the year 2010, derived by Random Forest Classification of a combination of L-band radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data. All satellite data and software used to derive the GMW mangrove maps are available in the public domain.  Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (1 x 1 degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and sub-tropical coastlines in the Americas, Africa, Asia and Oceania.  The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from ocean water. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling 38 million points using the mangrove masks (for the year 2000) of [Giri et al. (2011)](http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2010.00584.x/abstract) and [Spalding et al. (2010)](https://www.routledge.com/World-Atlas-of-Mangroves/Spalding-Kainuma-Collins/p/book/9781844076574) and the water occurrence layer defined by [Pekel et al. (2017)](https://www.nature.com/articles/nature20584).  The data set is available for download at [http://data.unep-wcmc.org/datasets/45](http://data.unep-wcmc.org/datasets/45)","function":"Global coverage of mangroves for select years from 1996 to 2016","cautions":"The Landsat-7 scan-line error affects the classification in certain areas, resulting in striping artefacts in the data.   Classification accuracy was assessed with over 53,800 randomly sampled points across 20 randomly selected regions. Overall accuracy was 95.25 %, while User's and Producer's accuracies for the mangrove class were estimated at 97.5% and 94.0%, respectively. Users should be aware that it is a global-scale dataset, generated with a single methodology applied over all regions, and as such, the accuracy of the map may vary between locations. Factors such as satellite data availability (due to clouds, cloud shadows and Landsat-7 scan-line error), mangrove species composition and level of degradation all influence the local accuracy. The mangrove seaward border is generally also more accurately defined than the landward side, where distinction between mangrove and certain terrestrial vegetation species can be unclear.  Areas known to be missing in this version (v2.0) of the dataset: Bermuda (U.K.); Europa Island (France); Fiji, east of longitude 180°0E; Guam and Saipan (U.S.); Kiribati; Maldives; Peru, south of latitude S4°0; and Wallis and Futuna Islands (France).","key_restrictions":"","tags":["Land Cover"],"why_added":"Most robust global mangrove layer available ","learn_more":"www.globalmangrovewatch.org.","id":"bdea831e-c20e-4c7c-adb2-964cc45e6715"},"versions":["v20201210"]},{"created_on":"2021-11-04T19:31:19.953688","updated_on":"2021-11-04T19:31:19.953693","dataset":"gpcc_total_precipitation_2000_2019","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897225","updated_on":"2023-05-04T13:11:58.897226","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Annual","scale":"","citation":"Schneider, Udo; Becker, Andreas; Finger, Peter; Rustemeier, Elke; Ziese, Markus (2020): GPCC Full Data Monthly Product Version 2020 at 0.25°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data. DOI: 10.5676/DWD_GPCC/FD_M_V2020_025. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).","title":"Annual Cumulative Precipitation","subtitle":null,"source":"Global Precipitation Climatology Centre (GPCC)","license":"[Creative Commons 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)","data_language":"en","overview":"This dataset provides annual cumulative precipitation derived from monthly land-surface precipitation from the Global Global Precipitation Climatology Centre (GPCC) dataset. The GPCC data comes from rain-gauges built on GTS-based and historical data from January 1891 through December 2019, with ~85,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~15,000 (before 6000) to more than 50,000 stations. The data is available globally at 0.25 × 0.25 degree resolution. The Global Precipitation Climatology Centre (GPCC) is operated by Deutscher Wetterdienst (DWD) under the auspices of the World Meteorological Organization (WMO).","function":"Annual cumulative precipitation","cautions":"The two major error sources are: (1) The systematic measuring error that results from evaporation out of the gauge and aerodynamic effects, when droplets or snowflakes are drifted by the wind across the gauge funnel, and (2) The stochastic sampling error due to a sparse network density.","key_restrictions":"Creative Commons 4.0","tags":["geospatial","historical","global","raster","precipitation"],"why_added":"Adding to MapBuilder","learn_more":"http://berkeleyearth.org/ ","id":"9c582933-f853-4bc4-94a0-9383b09a5657"},"versions":["v20211015"]},{"created_on":"2026-07-23T21:09:39.932068","updated_on":"2026-07-23T21:09:39.932074","dataset":"gpw_grasslands_2020","is_downloadable":true,"metadata":{"created_on":"2026-07-23T21:09:39.938705","updated_on":"2026-07-23T21:09:39.938709","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Pasture Watch - 2020 grassland class","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"4c22fe8a-5c08-4eda-9ce0-a02e74db772a"},"versions":["v1.1"]},{"created_on":"2026-07-23T21:10:20.724593","updated_on":"2026-07-23T21:10:20.724602","dataset":"gpw_grasslands_2021","is_downloadable":true,"metadata":{"created_on":"2026-07-23T21:10:20.759395","updated_on":"2026-07-23T21:10:20.759401","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Pasture Watch - 2021 grassland class","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"6d9546b8-32c8-4154-9d6e-25ca3572ac54"},"versions":["v1.1"]},{"created_on":"2026-07-23T21:11:02.724469","updated_on":"2026-07-23T21:11:02.724474","dataset":"gpw_grasslands_2022","is_downloadable":true,"metadata":{"created_on":"2026-07-23T21:11:02.727916","updated_on":"2026-07-23T21:11:02.727918","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Pasture Watch - 2022 grassland class","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b5f92838-7752-4a38-a878-b3cb920a6bfb"},"versions":["v1.1"]},{"created_on":"2026-07-23T21:11:26.107910","updated_on":"2026-07-23T21:11:26.107914","dataset":"gpw_grasslands_2023","is_downloadable":true,"metadata":{"created_on":"2026-07-23T21:11:26.110405","updated_on":"2026-07-23T21:11:26.110408","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Pasture Watch - 2023 grassland class","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"6be86f3d-15f3-45e2-aebf-d2fcf7dc54b7"},"versions":["v1.1"]},{"created_on":"2026-07-23T21:11:47.094627","updated_on":"2026-07-23T21:11:47.094632","dataset":"gpw_grasslands_2024","is_downloadable":true,"metadata":{"created_on":"2026-07-23T21:11:47.100777","updated_on":"2026-07-23T21:11:47.100781","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Global Pasture Watch - 2024 grassland class","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"70321477-2dfe-4e37-b894-03c69c550880"},"versions":["v1.1"]},{"created_on":"2021-09-08T14:25:56.353777","updated_on":"2021-09-08T14:25:56.353782","dataset":"haka_idn_leuser","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897219","updated_on":"2026-08-05T14:43:14.897520","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Leuser Ecosystem","update_frequency":" ","scale":"national","citation":"HAkA. \"Indonesia Leuser Ecosystem.\" Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"Indonesia Leuser Ecosystem","subtitle":null,"source":"Hutan Alam dan Lingkungan Aceh (HAkA)","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"The Leuser Ecosystem spans the provinces of Aceh and North Sumatra on the island of Sumatra in Indonesia. Over 35 times the size of Singapore, this majestic and ancient ecosystem covers more than 2.6 million hectares of lowland rainforests, peat swamps, montane and coastal forests and alpine meadows. Globally recognized as one of the richest expanses of tropical rainforest found anywhere in Southeast Asia, the Leuser Ecosystem is also one of Asia’s largest carbon sinks. The Leuser Ecosystem is the last place on earth where orangutans, rhinos, elephants, and tigers co-exist in the wild. All four of these species are now classified by the International Union for Conservation of Nature (IUCN) as Critically Endangered. The Leuser Ecosystem is the only remaining habitat left in Sumatra large enough to sustain viable populations of these species. A publication in leading international journal Science listed the Leuser Ecosystem as “one of the world’s foremost irreplaceable areas.”<br><br>The Leuser Ecosystem is an essential asset for the economic development of Aceh, providing a total economic value of at least 350 million US dollars per year. The Leuser Ecosystem acts as a life-support system for approximately four million people in Aceh. The primary ecosystem services are fresh water provision and disaster mitigation. The forests of the Leuser Ecosystem act as a sponge, soaking up the downpours of the rainy season and spreading out the release of water downstream more evenly across the months. Deforestation of this environmentally sensitive area is having a dramatic impact by increasing the damage caused by flooding and landslides, and causing economic damage to communities and downstream industry. Locally and globally, the Leuser Ecosystem also has immense environmental value due to its role in climate regulation and carbon storage. Efforts to conserve the Leuser Ecosystem date as far back as the early 19th century, when the traditional leaders of Aceh lobbied the colonial government to protect their natural heritage, ranging from the mountains all the way down to the coast. More recent laws have served to strengthen the protection of the Leuser Ecosystem and placed the responsibility for managing its protection and restoration with the Aceh Provincial Government (Article 150 of National Law on Governing Aceh No. 11/2006). Furthermore, the Leuser Ecosystem in Aceh has special legal status as a National Strategic Area for its Environmental Protection Function (26 of 2007 juncto 26/2008), prohibiting any activities that reduce that function, including cultivation and infrastructure development.","function":"Displays the boundary of the Leuser Ecosystem, a National Strategic Area for its Environmental Protection Function.","cautions":"This data is not official data from the Indonesian Ministry of Environmental and Forestry. As a result, this data may differ from official data and there may be inaccuracies.","key_restrictions":"","tags":["Country data"],"why_added":"The Leuser ecosystem is a critically important ecosystem in Indonesia, but the boundaries displayed on the WDPA protected areas website were inaccurate. Thus, we added this data set in response to many NGOs in Indonesia requesting accurate boundaries.","learn_more":"","id":"1224063b-94dc-4465-afb9-2a2d1653eaf2"},"versions":["v2015"]},{"created_on":"2021-04-12T19:42:31.878786","updated_on":"2021-04-12T19:42:31.878794","dataset":"ibge_bra_biomes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897508","updated_on":"2026-08-05T14:43:15.237146","spatial_resolution":null,"resolution_description":"1: 5,000,000","geographic_coverage":"Continental Brazil","update_frequency":" ","scale":"national","citation":"MMA/IGBE. \"Brazil biomes.\" Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Brazil biomes","subtitle":null,"source":"Ministerio do Meio Ambiente / IGBE","license":"","data_language":"English","overview":"This data set displays the boundaries of six Brazilian continental biomes: the Amazônia, Cerrado, Caatinga, Mata Atlântica, Pantanal and Pampa. “Biome” is defined as a collection of life (plant and animal) constituted by the grouping of contiguous vegetation types identifiable on a regional scale with similar geoclimatic conditions and shared history, which results in a unique biological diversity. The names used were the most common and popular in general associated with the predominant type of vegetation or relief, as in the case of Pantanal biome, which is the highest provincial flooded surface of the world.<br><br>The Amazon Biome is defined by the climatic region, forest physiognomy and geographic location. The Atlantic Forest biome, which occupies the entire Brazilian continental east Atlantic coast and stretches inland in the Southeast and South, is defined by the predominant forest vegetation and diverse relief. The Pampa, restricted to Rio Grande do Sul, is defined by a set of field vegetation in plain relief. The predominant vegetation in the Cerrado biome in Brazil, second in size, extends from the Maranhão coast to the Midwest and the Caatinga Biome, typical of semi-arid climate of the northeastern backlands. The map is a result of a partnership between the Brazilian Ministry of Environemnt (MMA) and the Brazilian Institute of Geography and Statistics (IBGE).","function":"Shows the boundaries of the six Brazilian continental biomes – Amazônia, Cerrado, Caatinga, Mata Atlântica, Pantanal, and Pampa","cautions":"","key_restrictions":"","tags":["Country data"],"why_added":"To allow people to visualize and analyze tree cover loss by biome in Brazil","learn_more":"http://www.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm","id":"93e691f1-b972-463d-bdcf-4b89c43f412c"},"versions":["v2019","v2004"]},{"created_on":"2021-09-07T01:07:57.445321","updated_on":"2021-09-07T01:07:57.445328","dataset":"icf_hnd_forest_type_2013","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897148","updated_on":"2026-08-05T14:43:15.576504","spatial_resolution":5,"resolution_description":null,"geographic_coverage":"Honduras","update_frequency":" ","scale":"national","citation":"National Institute of Conservation and Forest Development, Protected Areas, and Wildlife (ICF). “Honduras forest type”. Accessed through Global Nature Watch on [date] www.globalnaturewatch.org","title":"Honduras forest type","subtitle":null,"source":"National Institute of Conservation and Forest Development, Protected Areas, and Wildlife ([ICF](http://www.icf.gob.hn/))","license":"","data_language":"Espanol","overview":"This data set is Honduras’ first high-resolution forest and land cover map, produced by the Honduran government agency ICF (National Institute of Conservation and Forest Development, Protected Areas, and Wildlife) with the technical and financial support of [Project REDD/CCAD-GIZ](http://www.reddccadgiz.org/). RapidEye satellite imagery from 2013 was acquired and analyzed to produce a map of the spatial distribution of forest types within the country. In addition to mapping of eight different forest types, the map also portrays the extent and distribution of fifteen categories of human land use.\n\nIn contrast to earlier forest maps of Honduras, this version expands on other categories of wooded vegetation, including secondary vegetation, coffee and agroforestry areas, and information on trees outside of forests. Pastures and agricultural fields are also aggregated into one category and comprise the second largest land cover class.","function":"Identifies forest type and land cover in Honduras","cautions":"The validation of the map was done using field data as well as through a grid of control points spread 3 km apart for the entire country (15,777 points total). The overall accuracy of the map was determined to be 90.9% over all 26 categories, with an accuracy of 95% for the eight forest classes.\n\nThe administrative boundaries of the basemap are from Open Street Map and only used to coordinate with other layers in the map.","key_restrictions":"","tags":["Country data"],"why_added":"Understanding the spatial distribution of forest type is important for monitoring deforestation, as defined by national governments","learn_more":null,"id":"9a334aee-b4b9-4655-88df-13a2fd1bf4db"},"versions":["v2013"]},{"created_on":"2022-02-24T22:30:10.511970","updated_on":"2022-02-24T22:30:10.511977","dataset":"icmbio_bra_federal_protected_areas","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897195","updated_on":"2023-05-04T13:11:58.897197","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"ICMBIO Brazil Federal Protected Areas","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"9d7fc0b0-23f8-40ff-be57-1fe606e80401"},"versions":["v202006"]},{"created_on":"2020-07-23T04:07:23.497276","updated_on":"2020-07-23T04:07:23.497284","dataset":"idn_forest_area","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897392","updated_on":"2026-08-05T14:43:15.893516","spatial_resolution":null,"resolution_description":"1:250,000","geographic_coverage":"Indonesia","update_frequency":null,"scale":"national","citation":"Ministry of Environment and Forestry of Indonesia (Kementerian Lingkungan Hidup dan Kehutanan). “Kawatan hutan Indonesia / Indonesia forest area.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"Kawasan hutan Indonesia / Indonesia forest area","subtitle":null,"source":"[Ministry of Environment and Forestry](http://www.menlhk.go.id/) (Kementerian Lingkungan Hidup dan Kehutanan). Accessed from http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK","license":"Accessed from the Ministry of Environment and Forestry’s (Kementerian Lingkungan Hidup dan Kehutanan) [data portal](http://geoportal.menlhk.go.id/arcgis/home/)","data_language":"English/ Indonesian","overview":null,"function":"Indicates the Indonesian government’s designation of legal forest area","cautions":"These data only represent the legal designation of forest area. They do not show actual forest cover.","key_restrictions":null,"tags":["Country data"],"why_added":null,"learn_more":"http://www.peraturan.go.id/inc/view/11e44c4e59caba908671313231333332.html","id":"979a81c6-72c3-4f27-a07d-19831790d60a"},"versions":["v201709"]},{"created_on":"2020-09-23T17:22:11.205275","updated_on":"2020-09-23T17:22:11.205284","dataset":"idn_forest_moratorium","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897408","updated_on":"2024-10-04T16:27:35.561054","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Indonesia","update_frequency":"Every 6 months","scale":"national","citation":"Ministry of Environment and Forestry Indonesia (Kementerian Lingkungan Hidup dan Kehutanan). PIPPIB_2020_Periode1. Accessed from http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK on August 2020.","title":"Indonesia forest moratorium","subtitle":null,"source":"2020 Period I version of the PIPPIB Primary Natural Forest and Peat Land map by the Indonesia Ministry of Forestry","license":"View-Only, Not for Download","data_language":"English","overview":"In May 2011, the Ministry of Forestry put into effect a two-year moratorium on the designation of new forest concessions in primary natural forests and peatlands. This moratorium is designed to allow time for the government to develop improved processes for land-use planning, strengthen information systems, and build institutions to achieve Indonesia’s low emission development goals. The two-year moratorium, renewed again in 2019, is part of Indonesia’s pledge to curtail forest clearing in a US $1 billion deal with the Norwegian government.<br><br>The first Indicative Moratorium Map (IMM) was published by the Ministry of Forestry in July 2011. The IMM is required to be revised every six months. This data set shows the Indicative Map for the Termination of the Granting of New Permits (PIPPIB) for Primary Natural Forest and Peatlands in 2020 Period I.","function":"Indicates the area of Indonesia’s moratorium against new forest concessions, designed to protect Indonesia’s peat lands and primary natural forests from future development","cautions":"","key_restrictions":"","tags":["Country data"],"why_added":"The Indonesia forest moratorium was extended in 2019 to protect environmentally sensitive lands such as peat lands from the allocation of future concessions.","learn_more":"http://www.wri.org/publication/indonesias-forest-moratorium","id":"b0158c5a-45f3-4528-b356-4d1cb91cc42f"},"versions":["v20200923"]},{"created_on":"2021-07-22T19:04:16.104270","updated_on":"2021-09-07T01:11:36.277384","dataset":"idn_land_cover_2017","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897173","updated_on":"2024-10-04T16:27:36.160173","spatial_resolution":null,"resolution_description":"1:250,000","geographic_coverage":"Indonesia","update_frequency":"None","scale":"national","citation":"Ministry of Environment and Forestry Indonesia (Kementerian Lingkungan Hidup dan Kehutanan). Land Cover 2017. Accessed through Geoportal KLHK on April 2018. [http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK_EN](http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK_EN)","title":"Land Cover Indonesia","subtitle":null,"source":"Ministry of Environment and Forestry Indonesia (Kementerian Lingkungan Hidup dan Kehutanan). Land_Cover_2017. Accessed through Geoportal KLHK in April 2018. [http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK_EN](http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK_EN)","license":"","data_language":"English","overview":"This layer shows 2017 land cover, classified by type. The data is sourced from 2017 Ministry of Environment & Forestry data (1:250,000 scale). The World Resources Institute reclassified the original land cover categories from the Ministry of Environment & Forestry dataset for use in the Suitability Mapper (2012), into the following categories:<br><br>* Primary Forest: Primary dry land forest, primary mangrove forest, primary swamp forest<br><br>* Secondary Forest: Secondary dry land forest, secondary mangrove forest, secondary swamp forest<br><br>* Plantation Forest: Plantation forest<br><br>* Grass Land: Bush/Shrub, Savannah<br><br>* Cropland: Estate crop plantation, dryland agriculture, shrub-mixed dryland farm, rice field<br><br>* Other Land: Bare land, fish pond, airport/ harbor, mining area<br><br>* Settlement: Transmigration area, settlement area<br><br>* Wetland: Swamp, swamp shrub<br><br>* Unknown: Cloud<br><br>* Bodies of Water: Bodies of water<br><br>Original data available at [http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK_EN](http://geoportal.menlhk.go.id/arcgis/rest/services/KLHK_EN) under “Land_Cover_2017.\"","function":"Official data on land cover for Indonesia.","cautions":"The original land cover categories from the Ministry of Forestry were simplified by the World Resources Institute for better display.<br><br>Exact definitions and descriptions of the methodologies used to produce this data are not available.","key_restrictions":"","tags":["Land Cover, Country data"],"why_added":"It's critical to understand nationally defined land cover to accurately pinpoint deforestation.","learn_more":"","id":"f07128e1-e797-4fc9-8f8a-b14d3f85eba4"},"versions":["v201807"]},{"created_on":"2020-07-28T02:11:57.630765","updated_on":"2020-07-28T02:11:57.630773","dataset":"idn_wood_fiber","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897400","updated_on":"2026-08-05T14:43:16.194388","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Indonesia","update_frequency":null,"scale":"global","citation":"Indonesia Ministry of Forestry, Asia Pulp and Paper, APRIL, Greenpeace, and WRI. “Indonesia wood fiber plantations.”  Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Indonesia wood fiber concessions ","subtitle":null,"source":"Ministry of Forestry Indonesia 2018. Asia Pulp and Paper, APRIL, IUPHHK_HT (wood fiber plantation concessions), provided by the Planning Department of the Ministry of Forestry, Indonesia (Direktorat Jenderal Planologi Kehutanan Kementerian Kehutanan Republik Indonesia). Google Earth Format Downloaded from: http://appgis.dephut.go.id/appgis/kml.aspx downloaded September 2010. Updated using 1) MoFor (2010) Pemanfaatan Hutan, Data dan Informasi, Tahun 2010, Ministry of Forestry Indonesia, November 2010 www.dephut.go.id/files/Buku pemanfaatan 2010.pdf and 2) MoF (2011), online WebGis Kehutanan, online interactive map http://webgis.dephut.go.id/ditplanjs/index.html accessed May 12 2011. Processed and provided by Greenpeace. Prepared by the World Resources Institute (2018).","license":"View Only, Not Downloadable.","data_language":"English","overview":"This data set was last updated in September 2018. The new version 1.4 reflects changes sent by APRIL. ","function":"Provides the boundaries of current or planned wood fiber plantations in Indonesia.","cautions":"This data set is known to be incomplete, but it is compiled from the best information currently available.","key_restrictions":"Unknown for all","tags":["Country data"],"why_added":"Wood fiber plantations are a significant land use activity in Indonesia.","learn_more":null,"id":"047f8052-95e4-4cfd-94b4-1b4dd5d0c477"},"versions":["v20200725"]},{"created_on":"2021-04-07T13:43:58.234216","updated_on":"2025-03-14T20:18:29.406111","dataset":"ifl_intact_forest_landscapes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897502","updated_on":"2026-08-05T14:43:16.578692","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":" ","scale":"global","citation":"Use the following credit when these data are displayed:  “Intact Forest Landscapes. 2000/2013/2016/2020/2025” Greenpeace, University of Maryland, World Resources Institute and Transparent World. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/) \n\nUse the following credit when these data are cited: Potapov, P., M.C. Hansen, L. Laestadius, S. Turubanova, A. Yaroshenko, C. Thies, W. Smith, et al. 2017. “The Last Frontiers of Wilderness: Tracking Loss of Intact Forest Landscapes from 2000 to 2013.” Science Advances 3 (1): e1600821. doi:10.1126/sciadv.1600821. \n","title":"Intact Forest Landscapes","subtitle":"2000/2013/2016/2020/2025, global, IFL Mapping Team","source":"Potapov, P., M.C. Hansen, L. Laestadius, S. Turubanova, A. Yaroshenko, C. Thies, W. Smith, et al. 2017. “The Last Frontiers of Wilderness: Tracking Loss of Intact Forest Landscapes from 2000 to 2013.” Science Advances 3 (1): e1600821. [doi:10.1126/sciadv.1600821](https://www.science.org/doi/10.1126/sciadv.1600821)\n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"The [Intact Forest Landscapes](http://intactforests.org/) (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat satellite imagery for the year 2000. \n\nChanges in the extent of IFLs were identified within year 2000 IFL boundary using  Landsat Analysis Ready Data (ARD), the global forest cover loss dataset (Hansen et al., 2013), Sentinel-2 imagery, and high resolution data from Google Earth and Planet. Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2025. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans. \n\nThis data can be used to assess forest intactness, alteration, and degradation at global and regional scales.\n","function":"Identifies the world’s last remaining unfragmented forest landscapes, large enough to retain all native biodiversity and showing no signs of human alteration as of the year 2020. This layer also shows the reduction in the extent of Intact Forest Landscapes from 2000 to 2025.","cautions":"The world IFL map was created through visual interpretation of Landsat satellite imagery by experts. The map may contain inaccuracies due to limitations in the spatial resolution of the imagery and lack of ancillary information about local land-use practices in some regions. In addition, the methodology assumes that fires in proximity to roads or other infrastructure may have been caused by humans, and therefore constitute a form of anthropogenic disturbance. This assumption could result in an underestimation of IFL extent in the boreal biome.\n","key_restrictions":"CC BY 4.0","tags":["Land Cover"],"why_added":"Important for distinguishing natural, undisturbed forests from other types of land use.","learn_more":"http://www.intactforests.org/","id":"7f274699-5e2d-43ca-8cdd-14efc80dca10"},"versions":["v2018","v2021","v2025"]},{"created_on":"2022-03-04T21:32:14.353812","updated_on":"2022-03-04T21:32:14.353821","dataset":"ifl_intact_forest_landscapes_2000","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897198","updated_on":"2023-05-04T13:11:58.897199","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Intact Forest Landscapes 2000","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"79ff95ac-caf1-403d-9e1c-2d3bb968eb3e"},"versions":["v2021"]},{"created_on":"2022-03-04T21:32:20.631706","updated_on":"2022-03-04T21:32:20.631713","dataset":"ifl_intact_forest_landscapes_2013","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897235","updated_on":"2023-05-04T13:11:58.897237","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Intact Forest Landscapes 2013","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"c60fa747-e19c-4f08-8a8b-cdeaeb0d6284"},"versions":["v2021"]},{"created_on":"2022-03-04T21:32:24.875502","updated_on":"2022-03-04T21:32:24.875508","dataset":"ifl_intact_forest_landscapes_2016","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897265","updated_on":"2023-05-04T13:11:58.897266","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Intact Forest Landscapes 2016","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"3bbd4f45-51cb-4606-8c2a-a5d50b753448"},"versions":["v2021","v2020"]},{"created_on":"2022-03-04T21:32:29.858095","updated_on":"2022-03-04T21:32:29.858101","dataset":"ifl_intact_forest_landscapes_2020","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897267","updated_on":"2023-05-04T13:11:58.897269","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Intact Forest Landscapes 2020","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"67dc3e98-247f-4baf-ab68-e7a7868d98bc"},"versions":["v2020","v2021"]},{"created_on":"2026-02-25T17:47:36.756819","updated_on":"2026-02-25T17:47:36.756825","dataset":"ifl_intact_forest_landscapes_2025","is_downloadable":true,"metadata":{},"versions":["v2025"]},{"created_on":"2022-05-16T19:44:15.531000","updated_on":"2022-05-16T19:44:15.531008","dataset":"incra_bra_quilombola_communities","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897286","updated_on":"2023-05-04T13:11:58.897287","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"INCRA Brazil Quilombola Communities","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"f88ecd7c-c6a0-4d44-8121-eb6998dbf45d"},"versions":["v202203"]},{"created_on":"2022-05-17T19:45:21.310625","updated_on":"2022-05-17T19:45:21.310632","dataset":"incra_bra_rural_settlements","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897289","updated_on":"2023-05-04T13:11:58.897290","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"INCRA Brazil Rural Settlements","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"2203839d-fd95-447f-85b1-3c60042b43a6"},"versions":["v202203"]},{"created_on":"2020-09-25T17:10:55.105198","updated_on":"2022-08-16T21:09:33.593732","dataset":"inpe_amazonia_prodes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897262","updated_on":"2026-08-05T14:43:17.225874","spatial_resolution":30,"resolution_description":null,"geographic_coverage":"Brazilian Legal Amazon","update_frequency":"Annual","scale":"sub_national","citation":"National Institute of Space Research (INPE). 'PRODES deforestation.' Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"PRODES deforestation","subtitle":null,"source":"[INPE](http://www.inpe.br/)","license":"[Creative Commons BY SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/deed.en)","data_language":"Portuguese","overview":"The PRODES project monitors clear cut deforestation in the Brazilian Legal Amazon, and has produced annual deforestation rates for the region since 1988. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS-2, CBERS-2B, Resourcesat-1, and UK2-DMC. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Since 2002, all PRODES data is publicly available online. Input images for each of the 220 Landsat footprints that cover the Brazilian Amazon are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2003 to 2005, analysts used image transformation to determine the components of vegetation, soil, and shadow using the program SPRING. These components were segmented and classified into the classes of forest, non-forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2005, a new methodology was implemented which makes use of the open source TerraAmazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible. The images are then masked to exclude non-forest, previous deforestation, and water using the previous year's analysis. Analysts then delineate deforested polygons in the intact forest of the previous year.  This data set shows annual deforestation between 2000 and 2015.","function":"Deforestation monitoring system for the Brazilian Amazon used by the Brazilian government to establish public policy","cautions":"PRODES only identifies forest clearings of 6.25 hectares or larger, so forest degradation or smaller clearings from fire or selective logging are not detected. Frequent cloud cover over areas of the Amazon may change the reported year of deforestation. The year reported is the first year deforestation is identified by analysts, but this does not necessarily correspond to the year of deforestation if the landscape has been covered by clouds in previous years.","key_restrictions":"CC BY SA 3.0","tags":["Forest Change"],"why_added":"Official data on deforestation for the Brazilian Amazon","learn_more":"http://www.obt.inpe.br/prodes/index.php","id":"5ae3166a-c7ce-4c80-8bea-09679aff00fd"},"versions":["v20200925","v20201201"]},{"created_on":"2022-06-07T15:47:22.197502","updated_on":"2022-06-07T15:47:22.197509","dataset":"inpe_amazon_prodes","is_downloadable":false,"metadata":{"created_on":"2023-05-04T13:11:58.897297","updated_on":"2026-08-05T14:43:16.935408","spatial_resolution":null,"resolution_description":"6.25ha","geographic_coverage":"Brazilian Legal Amazon","update_frequency":"Annually","scale":null,"citation":"National Institute of Space Research (INPE). 'PRODES deforestation.' Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"PRODES (Legal Amazon)","subtitle":"annual, 6.25ha, Legal Amazon, INPE","source":"[INPE](http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes)","license":"[Creative Commons BY SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.en)","data_language":"Portuguese","overview":"The PRODES project monitors clear cut deforestation in the Brazilian Amazon and Cerrado biomes, and has produced annual deforestation rates for the region since 1988. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS-2, CBERS-2B, Resourcesat-1, and UK2-DMC. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Since 2002, all PRODES data is publicly available online. Input images for each of the 220 Landsat footprints that cover the Brazilian Amazon and Cerrado are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2003 to 2005, analysts used image transformation to determine the components of vegetation, soil, and shadow using the program SPRING. These components were segmented and classified into the classes of forest, non-forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2005, a new methodology was implemented which makes use of the open source TerraAmazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible. The images are then masked to exclude non-forest, previous deforestation, and water using the previous year's analysis. Analysts then delineate deforested polygons in the intact forest of the previous year. This data set shows annual deforestation in 2008-2020 in the Nrazilian Legal Amazon.","function":"Deforestation monitoring system for the Brazilian Legal Amazon, used by the Brazilian government to establish public policy","cautions":"PRODES only identifies forest clearings of 6.25 hectares or larger, so forest degradation or smaller clearings from fire or selective logging are not detected. Frequent cloud cover over areas of the areas of coverage may change the reported year of deforestation. The year reported is the first year deforestation is identified by analysts, but this does not necessarily correspond to the year of deforestation if the landscape has been covered by clouds in previous years.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"8a5534e5-80d0-4805-bee6-9761a56c03d4"},"versions":["v2021","v2021.1"]},{"created_on":"2022-06-07T15:47:33.267814","updated_on":"2022-06-07T15:47:33.267819","dataset":"inpe_cerrado_prodes","is_downloadable":false,"metadata":{"created_on":"2023-05-04T13:11:58.897299","updated_on":"2023-05-04T13:11:58.897300","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"PRODES Cerrado biome","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"55c09395-d1bd-4d96-a91b-17aee06d29c8"},"versions":["v2021"]},{"created_on":"2021-06-07T20:32:25.293007","updated_on":"2022-08-16T21:10:00.717899","dataset":"inpe_prodes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897328","updated_on":"2026-08-05T14:43:17.520658","spatial_resolution":30,"resolution_description":null,"geographic_coverage":"Brazilian Amazon biome","update_frequency":"Annually","scale":"sub_national","citation":"National Institute of Space Research (INPE). 'PRODES deforestation.' Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"PRODES deforestation","subtitle":null,"source":"[INPE](http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes)","license":"[Creative Commons BY SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.en)","data_language":"English","overview":"The PRODES project monitors clear cut deforestation in the Brazilian Amazon and Cerrado biomes, and has produced annual deforestation rates for the region since 1988. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS-2, CBERS-2B, Resourcesat-1, and UK2-DMC. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Since 2002, all PRODES data is publicly available online. Input images for each of the 220 Landsat footprints that cover the Brazilian Amazon and Cerrado are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2003 to 2005, analysts used image transformation to determine the components of vegetation, soil, and shadow using the program SPRING. These components were segmented and classified into the classes of forest, non-forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2005, a new methodology was implemented which makes use of the open source TerraAmazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible. The images are then masked to exclude non-forest, previous deforestation, and water using the previous year's analysis. Analysts then delineate deforested polygons in the intact forest of the previous year. This data set shows annual deforestation in 2008-2020 in the Amazon biome, and 2000-2020 in the Cerrado biome (bi-annual from 2000-2008).","function":"Deforestation monitoring system for the Brazilian Amazon and Cerrado biomes, used by the Brazilian government to establish public policy","cautions":"PRODES only identifies forest clearings of 6.25 hectares or larger, so forest degradation or smaller clearings from fire or selective logging are not detected. Frequent cloud cover over areas of the areas of coverage may change the reported year of deforestation. The year reported is the first year deforestation is identified by analysts, but this does not necessarily correspond to the year of deforestation if the landscape has been covered by clouds in previous years.","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":null,"id":"96176848-e646-496e-ba5a-2557ba9e5199"},"versions":["v202107","v2021","v202106","v202507"]},{"created_on":"2021-05-06T12:59:55.004511","updated_on":"2021-05-06T12:59:55.004519","dataset":"intl_rivers_dam_hotspots","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897439","updated_on":"2026-08-05T14:43:17.810265","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"This data set is not global. The data is confined to the world’s 50 major river basins.","update_frequency":"As new data becomes available","scale":"global","citation":"International Rivers. \"Dam hotspots\". Available at http://tryse.net/googleearth/irivers-dev3/. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"Major dams","subtitle":null,"source":"Dams data was compiled by [International Rivers](http://www.internationalrivers.org/) from various sources, including: the [Global Reservoir and Dam (GRanD) Database](http://atlas.gwsp.org/index.php?option=com_content&task=view&id=207&Itemid=68), the Consultative Group on International Agricultural Research (CGIAR) [Challenge Program on Water and Food - Mekong](http://waterandfood.org/) (for Mekong basin dams only), the [United States National Inventory of Dams (NID)](http://nid.usace.army.mil/cm_apex/f?p=838:12), other government dam inventories, and original data collection by International Rivers.\n","license":"[CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"The [State of the World's Rivers](http://tryse.net/googleearth/irivers-dev3/) is an interactive web database that illustrates data on ecological health in the world’s 50 major river basins. Indicators of ecosystem health are grouped into the categories of river fragmentation, biodiversity, and water quality. The database was created and published by International Rivers in 2014.<br><br>The Dam Hotspots data contains over 5,000 dam locations determined by latitude and longitude coordinates. These locations were confined to the world’s 50 major river basins. The data set comes from multiple sources, and was corrected for location errors by International Rivers. The “project status”—a moving target—was determined by acquiring official government data, as well as through primary research from Berkeley and five International Rivers’ regional offices.<br>* Operational: Already existing dams.<br>* Under construction: Dams which are currently being constructed.<br>* Planned: Dams whose studies or licensing have been completed, but construction has yet to begin.<br>* Inventoried: Dams whose potential site has been selected, but neither studies nor licensing have occurred.<br>* Suspended: Dams which have been temporarily or permanently suspended, deactivated, cancelled, or revoked.<br>* Unknown: No data are currently available.","function":"Identifies dam locations for the world’s 50 major river basins","cautions":"Data results are biased towards public available data, so gaps may exist.","key_restrictions":null,"tags":null,"why_added":"Flooding from new dams can lead to forest loss (some major cases in Mekong & Brazil)","learn_more":"http://tryse.net/googleearth/irivers-dev3/","id":"7b3f71a5-b3b2-43f7-abaa-6f46a92bb2a1"},"versions":["v2014"]},{"created_on":"2021-07-22T19:30:10.190343","updated_on":"2021-07-22T19:30:10.190349","dataset":"jpl_mangrove_aboveground_biomass_stock_2000","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897487","updated_on":"2023-05-04T13:11:58.897488","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"JPL Mangrove Aboveground Biomass Stock 2000","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"baef6cfd-f3ed-4232-8557-ad5bd40b8037"},"versions":["v201902"]},{"created_on":"2024-02-26T18:32:07.092101","updated_on":"2024-02-26T18:32:07.092108","dataset":"jrc_global_forest_cover","is_downloadable":true,"metadata":{"created_on":"2024-07-11T15:09:35.474128","updated_on":"2024-10-04T16:28:01.405521","spatial_resolution":null,"resolution_description":"10 × 10m","geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":"Bourgoin, C., et al., 2024. “Mapping Global Forest Cover of the Year 2020 to Support the EU Regulation on Deforestation-free Supply Chains”.","title":"JRC Global Map of Forest Cover","subtitle":null,"source":"Joint Research Center, EU Forest Observatory","license":null,"data_language":"English","overview":"The Global Map of Forest Cover (GMFC) is a 10-m dataset developed by the Joint Research Centre (JRC) specifically to support EUDR due diligence assessments. Multiple land cover and land use datasets with global and tropics-wide cover were brought together to create a map of forest that aligns with the definition adopted in the regulation. The map has no legal value and users are encouraged to cross-reference their assessments with results obtained by analyzing other similar forest datasets such as the SBTN Natural Forest Map. <br><br>A preliminary accuracy assessment found the map has an overall accuracy of 76% with higher omission errors (ie. error caused by missing information) than commission errors (ie. error caused by incorrect information). This assessment showed that dense forests and forest edges in structured landscapes are well mapped, while complex and mixed landscapes are more prone to mapping errors. <br><br>The raw data may be accessed through the [EU Forest Observatory](https://forest-observatory.ec.europa.eu/). The full technical documentation is available through the [Publications Office of the European Union]( https://op.europa.eu/en/publication-detail/-/publication/f9baaa45-e73f-11ee-9ea8-01aa75ed71a1/language-en).","function":"Provides a map of global forest cover in 2020 to support the European Union’s Deforestation Regulation (EUDR).","cautions":"The Global Map of Forest Cover 2020 is not an authoritative map and is not legally binding. It is one of many tools that may be used to support EUDR due diligence assessments. Users are encouraged to cross-reference their results with similar maps of forest cover in 2020. <br><br>Global maps of tree crops are not comprehensive for all EUDR-relevant commodities and regions. As a result, the Global Map of Forest Cover 2020 may classify tree cover under agricultural use as forest. This is particularly true for cocoa, coffee, and rubber.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://data.jrc.ec.europa.eu/dataset/10d1b337-b7d1-4938-a048-686c8185b290","id":"f6da2175-3f58-41fe-9bc5-55710f126934"},"versions":["v2020.2","v2020.3","v2020"]},{"created_on":"2026-02-18T23:42:04.608111","updated_on":"2026-02-18T23:42:04.608116","dataset":"jrc_managed_land_can","is_downloadable":false,"metadata":{"created_on":"2026-02-18T23:42:04.613691","updated_on":"2026-02-18T23:42:04.613696","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"JRC Managed Land: Canada","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"661f59fd-c53a-4053-8c0d-91cb44c28d1a"},"versions":["v20260218"]},{"created_on":"2026-02-18T23:43:06.595249","updated_on":"2026-02-18T23:43:06.595256","dataset":"jrc_managed_land_usa","is_downloadable":false,"metadata":{"created_on":"2026-02-18T23:43:06.601106","updated_on":"2026-02-18T23:43:06.601113","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"JRC Managed Land: United States","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b6b6d621-95a1-4a43-a339-8b25bb464359"},"versions":["v20260218"]},{"created_on":"2022-01-30T21:25:07.454540","updated_on":"2022-01-30T21:25:07.454547","dataset":"jrc_surface_water_transitions_1984_2020","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897233","updated_on":"2023-05-04T13:11:58.897234","spatial_resolution":30,"resolution_description":null,"geographic_coverage":"78N-60S","update_frequency":"Varies","scale":"","citation":"Pekel, Jean-François, Andrew Cottam, Noel Gorelick, and Alan S. Belward. 2016. \"High-Resolution Mapping of Global Surface Water and Its Long-Term Changes.\" Nature 540: 418-422. doi:10.1038/nature20584. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).","title":"Global Surface Water Extent","subtitle":null,"source":"EC JRC/Google","license":"[Source open licence](https://global-surface-water.appspot.com/download)","data_language":"en","overview":"Additional Information  Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.","function":"Location and temporal distribution of global surface water from 1984 to 2020","cautions":"Any bodies of water smaller than 30 m resolution are not recognized. Similarly, bodies of water obscured by vegetation or built infrastructure are not recognized. Gaps in the image archives due to cloud cover or sensor failure can lead to instances of surface water being missed. Not all regions of the world are represented from the beginning of this data set in 1984. For example, parts of Siberia and Kolyma were not included in the image record until 1999 and 1995, respectively. The data were checked for errors of omission (not labeling a pixel as water when it was) and for errors of commission (labeling a pixel as water when it was not), and some of these errors were corrected. Some errors remain, partly due to errors of commission stemming from needing improved urban area and infrastructural information to help train the decision algorithms not to confuse built areas for water. A failure of Landsat 7's scan line corrector (SLC) caused the loss of about 22% of each scene, resulting in fragmented images. This occasionally led to potentially confusing results, which can be identified as sections of the water occurrence maps having a \"slatted appearance.\"","key_restrictions":"Source open licence","tags":["geospatial","raster","global","water_extent","surface_water","river","lake"],"why_added":"Adding to MapBuilder","learn_more":"https://global-surface-water.appspot.com","id":"4c9f9702-e212-455d-ba41-aee74379eb71"},"versions":["v20211015"]},{"created_on":"2021-06-08T01:20:59.500101","updated_on":"2021-06-08T01:20:59.500107","dataset":"khm_protected_areas","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897463","updated_on":"2023-05-04T13:11:58.897464","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"KHM Protected Areas","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"16956332-c9d3-461d-a29a-6ad3bc861253"},"versions":["v2014"]},{"created_on":"2020-12-07T16:17:40.566659","updated_on":"2020-12-07T16:17:40.566667","dataset":"landmark_icls","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897413","updated_on":"2026-08-05T14:43:18.220520","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Currently available for Afghanistan, Antigua and Barbuda, Argentina, Australia, Belize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Bolivia, Botswana, Brazil, Cambodia, Canada, Chile, Colombia, Democratic Republic of Congo, Ecuador, French Guiana, Greenland, Guyana, India, Indonesia, Iran, Ireland, Kenya, Malaysia, Mexico, Mozambique, Namibia, Nepal, New Zealand, Nicaragua, Paraguay, Peru, Philippines, South Africa, Spain, Suriname, Taiwan, Tanzania, United Kingdom, USA, Venezuela, Zambia, and Zimbabwe.   ","update_frequency":"Quarterly","scale":"global","citation":"LandMark, 2019. 'Indigenous and Community Lands.' www.landmarkmap.org. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Indigenous and community lands","subtitle":null,"source":"Please see the complete list of data providers at [LandMark](https://www.landmarkmap.org/data/). ","license":"Varies by source. Click [here]( https://www.landmarkmap.org/data/#data-6) for more detailed information on access to LandMark data.  ","data_language":"English","overview":"The LandMark Indigenous and Community Lands data set is a consolidation of numerous efforts by local, national and regional groups. Indigenous lands refer to the collectively held and governed lands of Indigenous Peoples (self-recognized). Some indigenous lands may be allocated with group consent for use by individuals and families, while others may be managed as common property. In some cases, such as in New Zealand, indigenous land is held by individuals or families. Community land refers to all lands that fall under the customary governance of the community, regardless of whether this is recognized in national law. While indigenous land is a subset of community land, for LandMark, community land refers only to collective lands held by non-Indigenous Peoples.    In addition to indigenous and community lands, this data set contains indicative areas of indigenous and community land rights. These are areas which represent where indigenous and community lands are likely to exist, but the clear delimitation, recognition and/or documentation status of these lands are not currently available. This data set exists in order to help Indigenous Peoples and communities protect their land rights and secure tenure over their lands. Additionally, providing this data in the context of additional environmental and land use data sets aids in the understanding of potential pressures on indigenous and community lands, changes in land cover and land use over time, and how Indigenous Peoples and communities are contributing to protecting the environment.  Lastly, data transparency reduces the likelihood that irregular acquisitions and expropriations go unnoticed and shines a light on the vulnerability of indigenous and community lands.    This data set is a compilation of multiple sources, including governments, individual experts, and established civil society organizations that are well-recognized and respected in the land rights community. Data is reviewed by LandMark's operation team for quality and consistency, and the attributes are formatted to fit LandMark's typology (categorization scheme) before it is posted on the platform. All data displayed on LandMark are associated with the original source and contributor to allow for traceability and verification of the information. The attributes include, when available, the methods of collection (e.g., hand-held GPS, transcribed from land title) and the scale at which data were mapped, to help convey the accuracy and quality of the spatial information (see [Community Level Data](https://www.landmarkmap.org/data/#data-5) and [Methods and Data Quality Standards](https://www.landmarkmap.org/data/#data-5)). Community level data are updated as new information is received from data providers, or approximately once per month.  The data are updated on the Global Nature Watch site quarterly.   LandMark Indigenous and community lands categories in detail:  Indigenous Lands - Acknowledged by Government: lands that are recognized in law as being held or used by peoples who self-identify as indigenous     Indigenous Lands - Not acknowledged by Government: lands that are held or used by peoples who self-identify as indigenous, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process   Community Lands - Acknowledged by Government: lands that are recognized in law as being held or used by local communities    Community Lands - Not acknowledged by Government: lands that are held or used by local communities, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process   Indicative Areas of Indigenous and Community Land Rights: areas where indigenous- and community-held lands are likely to exist, but the clear delimitation, recognition and/or documentation status of these lands are not currently available.   For more information see: www.landmarkmap.org  ","function":"Depicts indigenous and community lands for select countries around the world, classified by legal recognition status.  ","cautions":"This data has been assembled from a variety of contributors and sources. Only data from governments, individual experts, and established civil society organizations in the land rights community are displayed on this platform. Even though a country may not have national or community level data included in this data set, Indigenous Peoples and communities may still hold or use land in that country. The absence of data does not indicate the absence of indigenous or community land. ","key_restrictions":"Varies by data source","tags":[""],"why_added":"To expand our land rights layer with more up to date information with a broader extent.","learn_more":"https://www.landmarkmap.org","id":"e3a61c48-3172-492c-b1bc-ecf8ec5f3d00"},"versions":["v20201215"]},{"created_on":"2023-12-22T16:07:13.373309","updated_on":"2024-05-29T20:24:38.765180","dataset":"landmark_indicative_lands","is_downloadable":true,"metadata":{"created_on":"2023-12-22T16:07:13.378357","updated_on":"2023-12-22T16:07:13.378364","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Landmark Indicative Lands","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"a8cedf28-7e70-4f80-9055-af4a53c31fe8"},"versions":["v202405","v202411","v202312","v202408","v202406"]},{"created_on":"2023-12-22T16:07:27.073109","updated_on":"2023-12-22T16:07:27.073165","dataset":"landmark_indicative_lands_points","is_downloadable":true,"metadata":{"created_on":"2023-12-22T16:07:27.079741","updated_on":"2023-12-22T16:07:27.079745","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Landmark Indicative Lands (points)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"d88fe9b2-fbaa-4a31-826f-45b25ed6aa98"},"versions":["v202406","v202405","v202312"]},{"created_on":"2021-07-02T02:09:19.646505","updated_on":"2024-05-29T20:24:19.801285","dataset":"landmark_indigenous_and_community_lands","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897468","updated_on":"2026-08-05T14:43:18.686313","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Currently available for Afghanistan, Antigua and Barbuda, Argentina, Australia, Belize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Bolivia, Botswana, Brazil, Cambodia, Canada, Chile, Colombia, Democratic Republic of Congo, Ecuador, French Guiana, Greenland, Guyana, India, Indonesia, Iran, Ireland, Kenya, Malaysia, Mexico, Mozambique, Namibia, Nepal, New Zealand, Nicaragua, Paraguay, Peru, Philippines, South Africa, Spain, Suriname, Taiwan, Tanzania, United Kingdom, USA, Venezuela, Zambia, and Zimbabwe.   ","update_frequency":"Quarterly","scale":"global","citation":"LandMark, 2019. 'Indigenous and Community Lands.' www.landmarkmap.org. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Indigenous and community lands","subtitle":null,"source":"Please see the complete list of data providers at [LandMark](https://www.landmarkmap.org/data/). ","license":"Varies by source. Click [here]( https://www.landmarkmap.org/data/#data-6) for more detailed information on access to LandMark data.  ","data_language":"English","overview":"The LandMark Indigenous and Community Lands data set is a consolidation of numerous efforts by local, national and regional groups. Indigenous lands refer to the collectively held and governed lands of Indigenous Peoples (self-recognized). Some indigenous lands may be allocated with group consent for use by individuals and families, while others may be managed as common property. In some cases, such as in New Zealand, indigenous land is held by individuals or families. Community land refers to all lands that fall under the customary governance of the community, regardless of whether this is recognized in national law. While indigenous land is a subset of community land, for LandMark, community land refers only to collective lands held by non-Indigenous Peoples.    In addition to indigenous and community lands, this data set contains indicative areas of indigenous and community land rights. These are areas which represent where indigenous and community lands are likely to exist, but the clear delimitation, recognition and/or documentation status of these lands are not currently available. This data set exists in order to help Indigenous Peoples and communities protect their land rights and secure tenure over their lands. Additionally, providing this data in the context of additional environmental and land use data sets aids in the understanding of potential pressures on indigenous and community lands, changes in land cover and land use over time, and how Indigenous Peoples and communities are contributing to protecting the environment.  Lastly, data transparency reduces the likelihood that irregular acquisitions and expropriations go unnoticed and shines a light on the vulnerability of indigenous and community lands.    This data set is a compilation of multiple sources, including governments, individual experts, and established civil society organizations that are well-recognized and respected in the land rights community. Data is reviewed by LandMark's operation team for quality and consistency, and the attributes are formatted to fit LandMark's typology (categorization scheme) before it is posted on the platform. All data displayed on LandMark are associated with the original source and contributor to allow for traceability and verification of the information. The attributes include, when available, the methods of collection (e.g., hand-held GPS, transcribed from land title) and the scale at which data were mapped, to help convey the accuracy and quality of the spatial information (see [Community Level Data](https://www.landmarkmap.org/data/#data-5) and [Methods and Data Quality Standards](https://www.landmarkmap.org/data/#data-5)). Community level data are updated as new information is received from data providers, or approximately once per month.  The data are updated on the Global Nature Watch site quarterly.   LandMark Indigenous and community lands categories in detail:  Indigenous Lands - Acknowledged by Government: lands that are recognized in law as being held or used by peoples who self-identify as indigenous     Indigenous Lands - Not acknowledged by Government: lands that are held or used by peoples who self-identify as indigenous, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process   Community Lands - Acknowledged by Government: lands that are recognized in law as being held or used by local communities    Community Lands - Not acknowledged by Government: lands that are held or used by local communities, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process   Indicative Areas of Indigenous and Community Land Rights: areas where indigenous- and community-held lands are likely to exist, but the clear delimitation, recognition and/or documentation status of these lands are not currently available.   For more information see: www.landmarkmap.org  ","function":"Depicts indigenous and community lands for select countries around the world, classified by legal recognition status.  ","cautions":"This data has been assembled from a variety of contributors and sources. Only data from governments, individual experts, and established civil society organizations in the land rights community are displayed on this platform. Even though a country may not have national or community level data included in this data set, Indigenous Peoples and communities may still hold or use land in that country. The absence of data does not indicate the absence of indigenous or community land. ","key_restrictions":"Varies by data source","tags":[""],"why_added":"To expand our land rights layer with more up to date information with a broader extent.","learn_more":"https://www.landmarkmap.org","id":"5280259c-ae74-42dc-86a8-1af06d0880db"},"versions":["v20230922","v202312","v202411","v202407","v202405","v20210909","v20201215"]},{"created_on":"2023-12-22T16:03:10.202006","updated_on":"2024-05-29T20:25:05.457691","dataset":"landmark_indigenous_and_community_lands_points","is_downloadable":true,"metadata":{"created_on":"2023-12-22T16:03:10.207176","updated_on":"2023-12-22T16:03:10.207182","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Landmark Community Level data (pts)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"4e1b8d77-6bfe-48ac-83c9-5e5093bce69b"},"versions":["v202408","v202406","v202405"]},{"created_on":"2024-01-24T19:40:50.132606","updated_on":"2024-05-29T20:24:54.960032","dataset":"landmark_indigenous_population_per_country","is_downloadable":false,"metadata":{"created_on":"2024-01-24T19:40:50.138228","updated_on":"2024-01-24T19:40:50.138234","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"","update_frequency":"","scale":null,"citation":"","title":"landmark_indigenous_population_per_country","subtitle":null,"source":"","license":"","data_language":null,"overview":"","function":"","cautions":"","key_restrictions":"","tags":null,"why_added":null,"learn_more":null,"id":"6e90a8e1-a727-4892-845d-2d14fec670c1"},"versions":["v202408","v202407","v202406","v202405","v202312"]},{"created_on":"2024-12-11T20:30:28.691984","updated_on":"2024-12-11T20:30:28.691989","dataset":"landmark_ip_lc_and_indicative_points","is_downloadable":false,"metadata":{"created_on":"2024-12-11T20:30:28.695874","updated_on":"2026-08-05T14:43:19.004560","spatial_resolution":null,"resolution_description":"Point locations","geographic_coverage":"Global, but gaps in coverage exist. Even though an area does not have coverage in this data set, Indigenous Peoples and local communities may still hold or use land. Note that the absence of data does not indicate the absence of Indigenous or local community lands. ","update_frequency":"Quarterly","scale":null,"citation":"LandMark, 2025. “Indigenous and Community Lands.” www.landmarkmap.org. Accessed from Global Nature Watch on (date). [www.globalnaturewatch.org](https://www.globalnaturewatch.org/).\n","title":"LandMark: Indigenous Peoples' and local communities' lands and territories","subtitle":"LandMark (locational points)","source":"Please see the complete list of data providers on [LandMark](<https://landmarkmap.org/data-methods/community-level-data-sources>). \n","license":"CC BY-SA 4.0. See LandMark’s [Terms of Service](LandMark_Terms_of_Service_202408.pdf).","data_language":null,"overview":"The LandMark Indigenous and Community Lands dataset is the product of an on-going initiative to map and document the collectively held and used lands and natural resources of Indigenous Peoples and local communities globally ([landmarkmap.org](https://landmarkmap.org/)). LandMark aggregates maps from numerous local, national, and regional sources into one standardized global dataset based on identity (Indigenous Peoples or local community) and legal recognition status (acknowledged or not acknowledged by government). These areas are displayed as polygons (boundaries) or locational points (when boundaries are not available). \nThe dataset is not complete – experts believe that 50% or more of the world’s land is held or used by Indigenous Peoples and local communities, while coverage of this dataset is a fraction of this; therefore, the absence of data does not mean an absence of indigenous or community land. Data contributions can be submitted to LandMark(<https://www.landmarkmap.org/data-methods/contribute-or-suggest>). \nCategories of LandMark Indigenous and community lands are defined as follows:\n- Indigenous Lands - Acknowledged by Government: lands that are recognized in law as being held or used by peoples who self-identify as indigenous\n- Indigenous Lands - Not acknowledged by Government: lands that are held or used by peoples who self-identify as indigenous, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process\n- Community Lands - Acknowledged by Government: lands that are recognized in law as being held or used by local communities\n- Community Lands - Not acknowledged by Government: lands that are held or used by local communities, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process\n- Indicative Areas of Indigenous and Community Lands: areas where indigenous- and community-held lands are likely to exist, but the clear delimitation and/or recognition status of these lands is not currently available. The purpose of this category is to promote awareness of the extent of Indigenous Peoples’ and local communities’ lands even in the absence of detailed demarcation data.\nMore information about the data and typology can be found on LandMark’s [methodology page](<https://landmarkmap.org/data-methods/methodology>).\n","function":"Depicts Indigenous Peoples’ and local communities’ lands and territories as locational points (due to lack of boundary information), classified according to a standard global typology based on identity (Indigenous or community) and legal recognition status. ","cautions":"This data has been assembled from a variety of contributors and sources via the [LandMark initiative](https://landmarkmap.org/). Even though a country or area does not have coverage in this data set, Indigenous Peoples and local communities may still hold or use land. The absence of data does not indicate the absence of indigenous or community land.\n","key_restrictions":"More detailed information is available at www.landmarkmap.org","tags":null,"why_added":null,"learn_more":" https://landmarkmap.org/  ","id":"295f56a2-896e-4c2a-9805-5afdfc35b312"},"versions":["v20260617","v202412","v20250506","v20260526"]},{"created_on":"2024-12-11T20:29:25.274346","updated_on":"2024-12-11T20:29:25.274351","dataset":"landmark_ip_lc_and_indicative_poly","is_downloadable":false,"metadata":{"created_on":"2024-12-11T20:29:25.284542","updated_on":"2026-08-05T14:43:19.334264","spatial_resolution":null,"resolution_description":"Polygon","geographic_coverage":"Global, but gaps in coverage exist. Even though an area does not have coverage in this data set, Indigenous Peoples and local communities may still hold or use land. Note that the absence of data does not indicate the absence of Indigenous or local community lands. ","update_frequency":"Quarterly","scale":null,"citation":"LandMark, 2025. “Indigenous and Community Lands.” www.landmarkmap.org. Accessed from Global Nature Watch on (date). [www.globalnaturewatch.org](https://www.globalnaturewatch.org/).\n","title":"LandMark: Indigenous Peoples' and local communities' lands and territories","subtitle":"LandMark (polygon)","source":"Please see the complete list of data providers on [LandMark](<https://landmarkmap.org/data-methods/community-level-data-sources>).\n","license":"CC BY-SA 4.0. See LandMark’s [Terms of Service](LandMark_Terms_of_Service_202408.pdf).","data_language":null,"overview":"The LandMark Indigenous and Community Lands dataset is the product of an on-going initiative to map and document the collectively held and used lands and natural resources of Indigenous Peoples and local communities globally ([landmarkmap.org](https://landmarkmap.org/)). LandMark aggregates maps from numerous local, national, and regional sources into one standardized global dataset based on identity (Indigenous Peoples or local community) and legal recognition status (acknowledged or not acknowledged by government). These areas are displayed as polygons (boundaries) or locational points (when boundaries are not available). \nThe dataset is not complete – experts believe that 50% or more of the world’s land is held or used by Indigenous Peoples and local communities, while coverage of this dataset is a fraction of this; therefore, the absence of data does not mean an absence of indigenous or community land. Data contributions can be submitted to LandMark(<https://www.landmarkmap.org/data-methods/contribute-or-suggest>). \nCategories of LandMark Indigenous and community lands are defined as follows:\n- Indigenous Lands - Acknowledged by Government: lands that are recognized in law as being held or used by peoples who self-identify as indigenous\n- Indigenous Lands - Not acknowledged by Government: lands that are held or used by peoples who self-identify as indigenous, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process\n- Community Lands - Acknowledged by Government: lands that are recognized in law as being held or used by local communities\n- Community Lands - Not acknowledged by Government: lands that are held or used by local communities, but the lands are not recognized as such in law; includes lands that are in process of obtaining recognition or customary tenure with no recognition in process\n- Indicative Areas of Indigenous and Community Lands: areas where indigenous- and community-held lands are likely to exist, but the clear delimitation and/or recognition status of these lands is not currently available. The purpose of this category is to promote awareness of the extent of Indigenous Peoples’ and local communities’ lands even in the absence of detailed demarcation data.\nMore information about the data and typology can be found on LandMark’s [methodology page](<https://landmarkmap.org/data-methods/methodology>).\n","function":"Depicts Indigenous Peoples’ and local communities’ lands and territories (polygon), classified according to a standard global typology based on identity (Indigenous or community) and legal recognition status. ","cautions":"This data has been assembled from a variety of contributors and sources via the [LandMark initiative](https://landmarkmap.org/). Even though a country or area does not have coverage in this data set, Indigenous Peoples and local communities may still hold or use land. The absence of data does not indicate the absence of indigenous or community land.\n","key_restrictions":"More detailed information is available at www.landmarkmap.org","tags":null,"why_added":null,"learn_more":"https://landmarkmap.org/","id":"e043403c-6944-4c22-9759-e60ab24d8ad3"},"versions":["v20260612","v20250625","v20250312","v20250506","v2025032","v202412","v202503","v20260526","v20260617","v20250421","v20250321","v20250909"]},{"created_on":"2025-02-07T21:32:50.765904","updated_on":"2025-02-07T21:32:50.765909","dataset":"landmark_ip_lc_and_indicative_poly_preprocessed","is_downloadable":true,"metadata":{},"versions":["v202503"]},{"created_on":"2023-12-13T16:52:30.852791","updated_on":"2024-05-29T20:23:53.178517","dataset":"landmark_natural_resource_rights","is_downloadable":false,"metadata":{"created_on":"2023-12-13T16:52:30.871335","updated_on":"2023-12-13T16:52:30.871340","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"","update_frequency":"","scale":null,"citation":"","title":"LandMark Natural Resource Rights","subtitle":null,"source":"","license":"","data_language":null,"overview":"","function":"","cautions":"","key_restrictions":"","tags":null,"why_added":null,"learn_more":null,"id":"ca0e5692-935e-41c2-9db7-58ee66b945cb"},"versions":["v202411","v202406","v202312","v202404","v202405"]},{"created_on":"2024-01-24T19:37:40.136171","updated_on":"2024-05-29T20:24:49.206400","dataset":"landmark_percent_of_land_indigenous_per_country","is_downloadable":false,"metadata":{"created_on":"2024-01-24T19:37:40.157767","updated_on":"2024-01-24T19:37:40.157773","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"","update_frequency":"","scale":null,"citation":"","title":"","subtitle":null,"source":"","license":"","data_language":null,"overview":"","function":"","cautions":"","key_restrictions":"","tags":null,"why_added":null,"learn_more":null,"id":"9c58d8a3-4682-4527-9a27-1634ecef0383"},"versions":["v202407","v1","v202408","v202312","v202405"]},{"created_on":"2025-04-24T19:51:08.127660","updated_on":"2025-04-24T19:51:08.127664","dataset":"landmark_tenure_indicators_comm","is_downloadable":true,"metadata":{"created_on":"2025-04-24T19:51:08.131069","updated_on":"2025-04-24T19:51:08.131073","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Periodically","scale":null,"citation":"Liz Alden Wily, 2025. “Indicators of the Legal Security of Indigenous and Community Lands.” Data file from LandMark. Accessible at www.landmarkmap.org.","title":"Indicators of Tenure Security in National Law: Local Communities' Land and Resource Rights","subtitle":null,"source":"LandMark: The Global Platform of Indigenous and Community Lands","license":"https://communityland.s3.amazonaws.com/LandMark_public/Data_Download/LandMark_Terms_of_Service_202408.pdf","data_language":null,"overview":"This dataset is characterized by a series of ten indicator questions that point to the security of land tenure for Indigenous Peoples or communities as established in national laws. Based upon the experience of the LandMark Operational Team and with inputs from the Steering Group, the ten indicator questions listed in the table below were identified as the most important to consider based on their indication of the strength of laws in recognizing indigenous and community land and natural resource rights. The assessment of each indicator is based on a review of relevant national laws, including the constitution, statutes, regulations, and high court cases, to the extent they are available. In countries with a federal system, such as India and Australia, the review is limited to national or federal laws, not state laws. Any international conventions signed or ratified by a country are included in the assessment only to the extent that they are incorporated into domestic law and enacted as local statute. There is no attempt to assess the implementation or enforcement of the law, or government, community or Indigenous Peoples’ perceptions of the security of their land rights. Each indicator is assigned a score of 1, 2, 3, 4, Not applicable (N/A), or No data (ND). The scoring of indicators is based exclusively on express legal provisions. Score 1 = Yes, the law addresses the issue fully. Score 2 = Partial, the law makes significant progress towards addressing the issue. Score 3 = Partial, the law makes only limited progress towards addressing the issue. Score 4 = No, the law does not address the issue. The average score for the ten indicators of the legal security of indigenous and community lands is also provided. The average score is the sum of the indicator scores of a specific collective tenure type for a country divided by the total number of indicators scored (in most cases, this number is 10). This average score provides only a snapshot of the security of indigenous and community land. It does not represent an index, in that the various indicators are not weighted based on their relative importance to secure tenure.","function":"Ten indicator questions about Indigenous Peoples’ and local communities’ rights to land and natural resources were evaluated for their strength in national laws.  The average score is presented on the map, with more information about each indicator and respective scores available by selecting a country. National laws relative to Indigenous Peoples and local communities were evaluated separately because there are often differing legal frameworks for each.","cautions":"The scoring of indicators is based exclusively on express legal provisions.There is no attempt to assess the implementation or enforcement of the law, or government, community or Indigenous Peoples’ perceptions of the security of their land rights.","key_restrictions":"More detailed information is available at www.landmarkmap.org","tags":null,"why_added":null,"learn_more":null,"id":"318d4b37-53b9-4921-95f9-106bff355701"},"versions":["v20250902","v20250529","v20250428"]},{"created_on":"2025-04-24T19:49:05.561902","updated_on":"2025-04-24T19:49:05.561906","dataset":"landmark_tenure_indicators_ip","is_downloadable":true,"metadata":{"created_on":"2025-04-24T19:49:05.570858","updated_on":"2025-04-24T19:49:05.570861","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Periodically","scale":null,"citation":"Liz Alden Wily, 2025. “Indicators of the Legal Security of Indigenous and Community Lands.” Data file from LandMark. Accessible at www.landmarkmap.org.","title":"Indicators of Tenure Seucrity in National Law: Indigenous Peoples' Land and Resource Rights","subtitle":null,"source":"LandMark: The Global Platform of Indigenous and Community Lands","license":"https://communityland.s3.amazonaws.com/LandMark_public/Data_Download/LandMark_Terms_of_Service_202408.pdf","data_language":null,"overview":"This dataset is characterized by a series of ten indicator questions that point to the security of land tenure for Indigenous Peoples or communities as established in national laws. Based upon the experience of the LandMark Operational Team and with inputs from the Steering Group, the ten indicator questions listed in the table below were identified as the most important to consider based on their indication of the strength of laws in recognizing indigenous and community land and natural resource rights. The assessment of each indicator is based on a review of relevant national laws, including the constitution, statutes, regulations, and high court cases, to the extent they are available. In countries with a federal system, such as India and Australia, the review is limited to national or federal laws, not state laws. Any international conventions signed or ratified by a country are included in the assessment only to the extent that they are incorporated into domestic law and enacted as local statute. There is no attempt to assess the implementation or enforcement of the law, or government, community or Indigenous Peoples’ perceptions of the security of their land rights. Each indicator is assigned a score of 1, 2, 3, 4, Not applicable (N/A), or No data (ND). The scoring of indicators is based exclusively on express legal provisions. Score 1 = Yes, the law addresses the issue fully. Score 2 = Partial, the law makes significant progress towards addressing the issue. Score 3 = Partial, the law makes only limited progress towards addressing the issue. Score 4 = No, the law does not address the issue. The average score for the ten indicators of the legal security of indigenous and community lands is also provided. The average score is the sum of the indicator scores of a specific collective tenure type for a country divided by the total number of indicators scored (in most cases, this number is 10). This average score provides only a snapshot of the security of indigenous and community land. It does not represent an index, in that the various indicators are not weighted based on their relative importance to secure tenure.","function":"Ten indicator questions about Indigenous Peoples’ and local communities’ rights to land and natural resources were evaluated for their strength in national laws.  The average score is presented on the map, with more information about each indicator and respective scores available by selecting a country. National laws relative to Indigenous Peoples and local communities were evaluated separately because there are often differing legal frameworks for each.","cautions":"The scoring of indicators is based exclusively on express legal provisions.There is no attempt to assess the implementation or enforcement of the law, or government, community or Indigenous Peoples’ perceptions of the security of their land rights.","key_restrictions":"More detailed information is available at www.landmarkmap.org","tags":null,"why_added":null,"learn_more":null,"id":"18a332bd-594c-43ac-8212-18bc310a836e"},"versions":["v20250902","v20250529","v20250428"]},{"created_on":"2025-06-03T17:46:21.472122","updated_on":"2025-06-03T17:46:21.472126","dataset":"lapig_degraded_pasture","is_downloadable":true,"metadata":{"created_on":"2025-06-03T17:46:21.484724","updated_on":"2025-06-03T17:46:21.484727","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Yearly","scale":null,"citation":null,"title":null,"subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":["Conservation"],"why_added":null,"learn_more":null,"id":"738e8e0f-0e2a-45aa-bcf4-4a31b8f2ef61"},"versions":["v20250530","v20250627"]},{"created_on":"2021-09-30T20:09:36.373619","updated_on":"2021-09-30T20:09:36.373625","dataset":"lbr_development_exploration_license","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897222","updated_on":"2023-05-04T13:11:58.897223","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Liberia Development Exploration License","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"e9fc2e60-97c8-4e1e-891d-4706e5ac23d7"},"versions":["v2016"]},{"created_on":"2021-09-30T20:09:20.973751","updated_on":"2021-09-30T20:09:20.973757","dataset":"lbr_mineral_development_agreement","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897179","updated_on":"2023-05-04T13:11:58.897181","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Liberia Mineral Development Agreement","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"7416405b-09d3-42c1-8ad5-8f0b6da397ab"},"versions":["v2016"]},{"created_on":"2021-09-30T20:09:49.751434","updated_on":"2021-09-30T20:09:49.751440","dataset":"lbr_mineral_exploration_license","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897182","updated_on":"2023-05-04T13:11:58.897183","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Liberia Mineral Exploration License","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"8c13c194-c318-46ca-9061-1efd42e8f0bb"},"versions":["v2016"]},{"created_on":"2020-07-22T03:15:48.491423","updated_on":"2025-02-11T16:14:10.180721","dataset":"licadho_khm_economic_land_concessions","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897358","updated_on":"2026-08-05T14:43:19.759023","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Cambodia","update_frequency":"Varies","scale":"national","citation":"LICADHO. “Cambodia Economic Concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org\n","title":"Cambodia Land Concessions","subtitle":"2024, vector, Cambodia, LICADHO","source":"[Cambodian League for the Promotion and Defense of Human Rights (LICADHO)](https://www.licadho-cambodia.org/land\\_concessions/)\n","license":"CC BY SA 4.0","data_language":"English","overview":"This data set shows land concessions granted by the Cambodian government as of 2024.\n\nSeveral different types of concessions are included in the attribute information:\n- Economic land concession: 253 concessions.\n- Special economic zone: 31 concessions.\n- Divested concession: Former state-owned rubber plantation dating back to French colony divested (i.e. privatized) in mid-2000s; 8 concessions.\n- Tourism concession: State-leased concession with tourism stated at main purpose; 5 concessions.\n- Other: State-leased concession not falling into any of the above-listed categories; 33 concessions. \n\nFor additional information, visit [LICADHO](https://www.licadho-cambodia.org/land\\_concessions/).\n\n","function":"This data shows the location of known land concessions in Cambodia, including large-scale agricultural economic land concessions, special economic zones, as well as other state-leased concessions.","cautions":"This data set contains concessions that have been documented by the Cambodian organization LICADHO and is not exhaustive.\n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_cambodia_land_concessions_v2025_public.zip","id":"e301fbea-4771-487f-99c9-00f6d9632371"},"versions":["v202106","v2025","v20200623"]},{"created_on":"2023-02-13T20:35:41.966462","updated_on":"2023-02-13T20:35:41.966467","dataset":"mapbiomas_bra_land_cover","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897484","updated_on":"2025-01-15T20:49:14.171104","spatial_resolution":null,"resolution_description":"30 × 30 meters","geographic_coverage":"Brazil","update_frequency":"Annual","scale":"national","citation":null,"title":"Brazilian land use and land cover","subtitle":"(2000-2016, MapBiomas)","source":"[MapBiomas](http://mapbiomas.org/), Collection 2.3","license":"[Terms of Use](http://mapbiomas.org/pages/terms_of_use)","data_language":null,"overview":"MapBiomas is a multi-institutional initiative to generate annual land cover and land use maps for all of Brazil using automatic classification processes applied to satellite images in Google Earth Engine. More than 20 organizations are collaborators on the project.<br><br>The displayed data comes from Collection 2.3, and shows land cover and land use annually for the period 2000 to 2016. Experts map land cover and land use for each Brazilian biome individually (Amazon, Cerrado, Caatinga, Atlantic Forest, Pampa, and Pantanal) and then combine the maps with cross-cutting mapping on agriculture, pasture, urban infrastructure and the coastal zone. All processing is done on Landsat images within the collaborative MapBiomas Workspace app created by the project to run Google Earth Engine. The process uses a Random Forest classifier to group pixels into classes, with spatial and temporal filters to remove spurious pixels and impossible land use transitions between years. More detailed methodology for each biome and theme is available [here]( http://mapbiomas.org/pages/methodology).<br><br>Maps and statistics of land cover and use change are presented for different periods (2000-2016, 2000-2001, etc.) on the MapBiomas portal. All data is available for download at the website of the project and maps are also available as assets to be used directly at Google Earth Engine platform. <br><br>Learn more: [http://mapbiomas.org](http://mapbiomas.org) or send inquiries to [contato@mapbiomas.org]( contato@mapbiomas.org)","function":"This data set shows annual land use and land cover for Brazil from 2000 to 2016","cautions":"The data has an overall accuracy of 79.5%, with different biomes and classes ranging from 63 to 88%. A complete analysis of accuracy by biome, class and year is presented on the [MapBiomas platform](http://mapbiomas.org/pages/accuracy-analysis).<br><br>MapBiomas maps have their best application up to zoom levels 12-13 (indicated in the URL). Though it is possible to view the data more zoomed in, the authors do not recommend using the data at this scale.<br><br>This product is a working in progress. In 2018 a new collection will be launch with data from 1985 to 2017.","key_restrictions":"They would like us to use the same color scheme, link to them for downloading","tags":null,"why_added":"More comprehensive land cover data for Brazil, Norway really wants us to add it","learn_more":"http://mapbiomas.org/","id":"fd645d52-4cac-4ad3-9e2b-a2b69998a273"},"versions":["v7"]},{"created_on":"2021-07-27T14:03:48.201305","updated_on":"2021-07-27T14:03:48.201311","dataset":"mapbox_river_basins","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897526","updated_on":"2023-05-04T13:11:58.897527","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Mapbox River Basins","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b174be1a-2161-4e92-9f32-e607c1a25a57"},"versions":["v2018"]},{"created_on":"2024-12-18T17:18:53.037194","updated_on":"2024-12-18T17:18:53.037199","dataset":"mapspam_yield_coco","is_downloadable":true,"metadata":{"created_on":"2024-12-18T17:18:53.049548","updated_on":"2024-12-18T17:18:53.049552","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Global cocoa yields from MapSPAM","subtitle":null,"source":"MapSPAM","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"2c1871df-32d4-4b19-a705-9eb8bb591a10"},"versions":["v2020","v2020.2"]},{"created_on":"2024-12-21T20:53:32.495460","updated_on":"2024-12-21T20:53:32.495466","dataset":"mapspam_yield_coff","is_downloadable":true,"metadata":{"created_on":"2024-12-21T20:53:32.506058","updated_on":"2024-12-21T20:53:32.506063","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Global arabica coffee yields from MapSPAM","subtitle":null,"source":"MapSPAM","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"61aad415-3d6e-4c20-8736-3635900fe80f"},"versions":["v2020","v2020.2"]},{"created_on":"2024-12-21T21:26:28.486542","updated_on":"2024-12-21T21:26:28.486549","dataset":"mapspam_yield_oilp","is_downloadable":true,"metadata":{"created_on":"2024-12-21T21:26:28.492035","updated_on":"2024-12-21T21:26:28.492039","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Oilpalm yield from MapSPAM","subtitle":null,"source":"MapSPAM","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"2d925425-7da6-4e35-8210-5ee73a2360d2"},"versions":["v2020","v2020.2"]},{"created_on":"2024-12-21T21:32:05.612654","updated_on":"2024-12-21T21:32:05.612659","dataset":"mapspam_yield_rubb","is_downloadable":true,"metadata":{"created_on":"2024-12-21T21:32:05.617534","updated_on":"2024-12-21T21:32:05.617538","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Rubber yield from MapSPAM","subtitle":null,"source":"MapSPAM","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"bab1a128-ec8e-443e-a586-1425ea7c3db4"},"versions":["v2020","v2020.2"]},{"created_on":"2024-12-22T00:05:26.391838","updated_on":"2024-12-22T00:05:26.391843","dataset":"mapspam_yield_soyb","is_downloadable":true,"metadata":{"created_on":"2024-12-22T00:05:26.397405","updated_on":"2024-12-22T00:05:26.397409","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Soybean yield from MapSPAM","subtitle":null,"source":"MapSPAM","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"8f3dea29-77d5-4f1a-802e-f0939ceee981"},"versions":["v2020","v2020.2"]},{"created_on":"2025-01-06T17:16:18.872145","updated_on":"2025-01-06T17:16:18.872151","dataset":"mapspam_yield_sugc","is_downloadable":true,"metadata":{"created_on":"2025-01-06T17:16:18.887911","updated_on":"2025-01-06T17:16:18.887917","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":null,"title":"Global sugarcane yields from MapSPAM","subtitle":null,"source":"MapSPAM","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"ca7ffc21-9c61-4476-a409-e3e35c317695"},"versions":["v2020","v2020.2"]},{"created_on":"2022-11-11T02:14:19.718204","updated_on":"2022-11-11T02:14:19.718212","dataset":"nasa_modis_fire_alerts","is_downloadable":true,"metadata":{},"versions":["v2020","v2019"]},{"created_on":"2023-02-15T21:36:25.429499","updated_on":"2023-02-15T21:36:25.429506","dataset":"nasa_umd_forest_carbon_sequestration_potential","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897334","updated_on":"2023-05-04T13:11:58.897335","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"NASA/UMD Forest Carbon Sequestration Potential","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b0dd4168-836b-4af3-bd27-49129f4178f7"},"versions":["v202208"]},{"created_on":"2020-07-09T16:20:31.706068","updated_on":"2025-01-30T22:32:10.794802","dataset":"nasa_viirs_fire_alerts","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897162","updated_on":"2026-08-05T14:43:20.145961","spatial_resolution":375,"resolution_description":"375 × 375 meters","geographic_coverage":"Global","update_frequency":"Daily","scale":"global","citation":"Use the following credit when this data is displayed:   \n“Active fire alerts (VIIRS).” NASA. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/)\n  \nUse the following credit when this data is cited:   \nSchroeder, W., P. Oliva, L. Giglio, and I.A. Csiszar. 2014. “The New VIIRS 375 m Active Fire Detection Data Product: Algorithm Description and Initial Assessment.” Remote Sensing of Environment 143 (March): 85–96. doi:10.1016/j.rse.2013.12.008.\n","title":"VIIRS active fires","subtitle":null,"source":"Schroeder, W., P. Oliva, L. Giglio, and I.A. Csiszar. 2014. “The New VIIRS 375 m Active Fire Detection Data Product: Algorithm Description and Initial Assessment.” Remote Sensing of Environment 143 (March): 85–96. doi:10.1016/j.rse.2013.12.008.\n","license":"We acknowledge the use of data and/or imagery from NASA's [Fire Information for Resource Management System (FIRMS)](https://www.earthdata.nasa.gov/data/tools/firms), part of NASA's Earth Science Data and Information System (ESDIS).\n\n[NASA Data Use Policy](https://www.earthdata.nasa.gov/engage/open-data-services-software-policies/data-use-policy)","data_language":"English","overview":"The VIIRS active fires data ([VNP14IMGT](https://lpdaac.usgs.gov/products/vnp14imgv002/)) is the latest fire monitoring product to FIRMS (Fire Information for Resource Management System), which identifies global fire locations in near-real time. Information is collected from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor and processed with a [fire detection algorithm](http://www.sciencedirect.com/science/article/pii/S0034425713004483) to flag active fires. Each dot on the map represents the center of a 375-meter pixel that has been flagged by the algorithm. \n\nThe VIIRS data replaces the active fires data from MODIS that was previously available on Global Nature Watch. The VIIRS data has higher spatial resolution (375-meter pixels vs. 1-kilometer pixels) which improves detection of smaller fires and provides a more reliable estimate of fire perimeters. The VIIRS data is also better calibrated to detect fires at night. \n\nUsers can view up to three months of active fires data from the past two years. Fire alert data is available for download from the [NASA FIRMS](https://firms.modaps.eosdis.nasa.gov/) website. \n\nEach fire alert has a confidence value of low, nominal, or high to help users gauge the quality of individual hotspot /fire pixels. Lower confidence alerts are typically associated with areas of sun glint (reflection off clouds or metal structures) and lower relative temperature anomalies. Nominal confidence alerts are those free for potential sun glint contamination and marked by stronger temperature anomalies. High confidence alerts tend to be associated with extreme temperature anomalies. \n\nThe color of the alert is dependent on the zoom level because alert data are aggregated at larger scales. At larger scales, points range in color from orange (fewer alerts) to black (many alerts). Zoom in for greater detail. \n","function":"Displays up to three months of fire alert data for past two years","cautions":"- Not all fires are detected. There are several reasons why VIIRS may not have detected a certain fire. The fire may have started and ended between satellite overpasses. The fire may have been too small or too cool to be detected in the 375-meter pixel. Cloud cover, heavy smoke, or tree canopy may completely obscure a fire.\n- High confidence alerts are less likely to result in “false alarms” but may exclude some active fires. Users requiring maximum fire detectability should consider using alerts from all confidence levels.\n- It is not recommended to use active fire locations to estimate burned area due to spatial and temporal sampling issues.\n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail. \n","key_restrictions":"Open per NASA data policy","tags":["Forest Change","Fires"],"why_added":"Higher resolution data for fires","learn_more":"https://www.earthdata.nasa.gov/data/instruments/viirs/viirs-i-band-375-m-active-fire-data ","id":"c56d2dda-67ba-40ab-8efe-49bd695bd267"},"versions":["v20240729","v20241209","v20250127","v20250131","v20250206","v20230901","v20240118","v20240815","v20250414","v20250425","v20250408","v20250424"]},{"created_on":"2021-11-04T19:31:36.220638","updated_on":"2021-11-04T19:31:36.220643","dataset":"nexgddp_change_avg_temperature_2000_2080","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897257","updated_on":"2023-05-04T13:11:58.897258","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Unknown","scale":"","citation":"Gassert, F., E. Cornejo, and E. Nilson. 2021. “Making Climate Data Accessible: Methods for Producing NEX-GDDP and LOCA Downscaled Climate Indicators” Technical Note. Washington, DC: World Resources Institute. Available online at https://www.wri.org/research/making-climate-data-accessible. [www.resourcewatch.org](https://www.resourcewatch.org/).\n\n  \n  \nWe acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. The U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and development of software infrastructure in partnership with the Global Organization for Earth System Science Portals for CMIP.\n\n  \n  \nClimate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).\n","title":"Projected Change in Annual Average Temperature","subtitle":null,"source":"WRI/Vizzuality","license":"[Attribution Required](https://www.wri.org/publications/permissions-licensing)","data_language":"en","overview":"The Projected Change in Annual Average Temperature dataset shows the change in annual average temperature at ten year intervals between 2000 and 2080, compared to a baseline time period of 1960-1990. The data shown at each ten year interval represents a 31-year average, centered around the indicated year. For example, the average temperature in 2000 is actually an average of the temperature between the years 1985 and 2015. Average temperature projections are based on the future greenhouse gas emission rates determined by the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCP) 8.5. RCP 8.5 is a hypothetical scenario where there is no decrease in greenhouse gas emission rates within the 21st century. The dataset is shown in degrees Celsius (°C) at a spatial resolution of 0.25°.The increase in global greenhouse gas concentrations, and resulting change in climate, is set to fundamentally alter our relationship with the planet, impacting agriculture, infrastructure, disaster management, and human conflict. In order to anticipate and adapt to these changes, the Projected Change in Average Annual Temperature dataset provides projections on how temperature patterns are likely to change in the coming decades. This dataset improves the accessibility of climate data by summarizing global climate information and providing the information as an open dataset in a common geospatial format.This dataset has been processed by Vizzuality and the World Resources Institute using the National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The NEX-GDDP dataset is intended to assist the scientific community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. A previous version of this dataset was processed and produced by the [Partnership for Resilience and Preparedness (PREP)](https://prepdata.org/), aiding in PREP’s mission to build resilience to climate change by improving access to climate data.","function":"Projected change in annual average temperature","cautions":"- Some GCM perform better than others in recreating regional climate patterns, such as monsoons, in hindcasts. For specific applications, it may be appropriate to select individual models based on regional performance.\n- The downscaling approaches used to produce these indicators inherently assume that the relative spatial patterns in temperature and precipitation will remain constant under future climate change. Dramatic shifts in global weather patterns, such as the slowing or reversal of major air and ocean currents are possible, but will not be captured in these indicators.\n- The historical data used for downscaling varies in quality across the world. In particular, areas that have short records or sparse coverage of in situ weather observations may have reduced accuracy.\n- Because GCM are developed by independent research teams and incorporate different assumptions and mechanisms, it is likely that they cover a substantial range of probable futures. We provide both low and high estimates so that users can see and evaluate this likely range of outcomes.\n","key_restrictions":"Attribution Required","tags":["climate","raster","future","global","temperature","climate_change","time_period","annual","geospatial"],"why_added":"Adding to MapBuilder","learn_more":"https://www.wri.org/research/making-climate-data-accessible","id":"ea660809-bc46-4019-98bc-a7005c67aa65"},"versions":["v20211111","v20211015"]},{"created_on":"2021-11-04T19:31:54.214762","updated_on":"2021-11-04T19:31:54.214767","dataset":"nexgddp_change_cum_precipitation_2000_2080","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897310","updated_on":"2023-05-04T13:11:58.897311","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Unknown","scale":"","citation":"Gassert, F., E. Cornejo, and E. Nilson. 2021. “Making Climate Data Accessible: Methods for Producing NEX-GDDP and LOCA Downscaled Climate Indicators” Technical Note. Washington, DC: World Resources Institute. Available online at https://www.wri.org/research/making-climate-data-accessible. [www.resourcewatch.org](https://www.resourcewatch.org/).\n\n  \n  \nWe acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. The U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and development of software infrastructure in partnership with the Global Organization for Earth System Science Portals for CMIP.\n\n  \n  \nClimate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).\n","title":"Projected Change in Cumulative Precipitation","subtitle":null,"source":"WRI/Vizzuality","license":"[Attribution Required](https://www.wri.org/publications/permissions-licensing)","data_language":"en","overview":"The Projected Change in Cumulative Precipitation dataset shows the change in annual average total precipitation at ten year intervals between 2000 and 2080, compared to a baseline time period of 1960-1990. The data shown at each ten year interval represents a 31-year average, centered around the indicated year. For example, the total precipitation in 2000 is actually an average of the cumulative precipitation between the years 1985 and 2015. Precipitation projections are based on the future greenhouse gas emission rates determined by the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCP) 8.5. RCP 8.5 is a hypothetical scenario where there is no decrease in greenhouse gas emission rates within the 21st century. Cumulative average precipitation is divided by the baseline average to calculate the projected change. Values greater than 1 indicate that cumulative precipitation is increasing, while values less than one indicate that the number is decreasing. Data is presented at a spatial resolution of 0.25°.The increase in global greenhouse gas concentrations, and resulting change in climate, is set to fundamentally alter our relationship with the planet, impacting agriculture, infrastructure, disaster management, and human conflict. In order to anticipate and adapt to these changes, the Projected Change in Cumulative Precipitation dataset provides projections on how precipitation patterns are likely to change in the coming decades. Precipitation changes are projected to be more varied across the world, generally showing increases in mean precipitation in high-latitude regions and a pole-ward shift of subtropical arid regions. In addition, precipitation is projected to occur in fewer, but more intense events. Except in the few areas that are projected to see substantial drying, historically extreme precipitation events are expected to become more frequent worldwide. Even as precipitation increases, dry periods are projected to increase in length and frequency.This dataset has been processed by Vizzuality and the World Resources Institute using the National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The NEX-GDDP dataset is intended to assist the scientific community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. A previous version of this dataset was processed and produced by the [Partnership for Resilience and Preparedness (PREP)](https://prepdata.org/), aiding in PREP’s mission to build resilience to climate change by improving access to climate data.","function":"Projected change in annual cumulative precipitation","cautions":"- Some GCM perform better than others in recreating regional climate patterns, such as monsoons, in hindcasts. For specific applications, it may be appropriate to select individual models based on regional performance.\n- The downscaling approaches used to produce these indicators inherently assume that the relative spatial patterns in temperature and precipitation will remain constant under future climate change. Dramatic shifts in global weather patterns, such as the slowing or reversal of major air and ocean currents are possible, but will not be captured in these indicators.\n- The historical data used for downscaling varies in quality across the world. In particular, areas that have short records or sparse coverage of in situ weather observations may have reduced accuracy.\n- Because GCM are developed by independent research teams and incorporate different assumptions and mechanisms, it is likely that they cover a substantial range of probable futures. We provide both low and high estimates so that users can see and evaluate this likely range of outcomes.\n","key_restrictions":"Attribution Required","tags":["geospatial","global","raster","future","time_period","climate","climate_change","precipitation","annual"],"why_added":"Adding to MapBuilder","learn_more":"https://www.wri.org/research/making-climate-data-accessible","id":"03a99b85-0fe9-45c2-aa15-ddfdb03de694"},"versions":["v20211015"]},{"created_on":"2021-11-04T19:32:04.602645","updated_on":"2021-11-04T19:32:04.602650","dataset":"nexgddp_change_dry_spells_2000_2080","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897227","updated_on":"2023-05-04T13:11:58.897229","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Unknown","scale":"","citation":"Gassert, F., E. Cornejo, and E. Nilson. 2021. “Making Climate Data Accessible: Methods for Producing NEX-GDDP and LOCA Downscaled Climate Indicators” Technical Note. Washington, DC: World Resources Institute. Available online at https://www.wri.org/research/making-climate-data-accessible. [www.resourcewatch.org](https://www.resourcewatch.org/).\n\n  \n  \nWe acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. The U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and development of software infrastructure in partnership with the Global Organization for Earth System Science Portals for CMIP.\n\n  \n  \nClimate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).\n","title":"Projected Change in Dry Spells","subtitle":null,"source":"WRI/Vizzuality","license":"[Attribution Required](https://www.wri.org/publications/permissions-licensing)","data_language":"en","overview":"The Projected Change in Dry Spells dataset shows change in average annual dry spells at ten year intervals between 2000 and 2080, compared to a baseline time period of 1960-1990. A dry spell is a five day period with less than 1 millimeter (mm) of precipitation. The data shown at each ten year interval represents a 31-year average, centered around the indicated year. For example, the number of dry spells in 2000 is actually an average of the annual number of dry spells between the years 1985 and 2015. Precipitation projections are based on the future greenhouse gas emission rates determined by the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCP) 8.5. RCP 8.5 is a hypothetical scenario where there is no decrease in greenhouse gas emission rates within the 21st century. Change in dry spells is shown as the multiplicative difference between annual average dry spells during the projected decade and the baseline time period. Values greater than 1 indicate that the number of dry spells is increasing, while values less than one indicate that the number is decreasing. The dataset is shown at a spatial resolution of 0.25°.The increase in global greenhouse gas concentrations, and the resulting change in climate, is set to fundamentally alter our relationship with the planet, impacting agriculture, infrastructure, disaster management, and human conflict. In order to anticipate and adapt to these changes, the Projected Change in Dry Spells dataset provides projections on how precipitation patterns are likely to change in the coming decades. This dataset improves the accessibility of climate data by summarizing global climate information and providing the information as an open dataset in a common geospatial format.This dataset has been processed by Vizzuality and the World Resources Institute using the National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The NEX-GDDP dataset is intended to assist the scientific community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. A previous version of this dataset was processed and produced by the [Partnership for Resilience and Preparedness (PREP)](https://prepdata.org/), aiding in PREP’s mission to build resilience to climate change by improving access to climate data.","function":"Projected change in average annual dry spells","cautions":"- Some GCM perform better than others in recreating regional climate patterns, such as monsoons, in hindcasts. For specific applications, it may be appropriate to select individual models based on regional performance.\n- The downscaling approaches used to produce these indicators inherently assume that the relative spatial patterns in temperature and precipitation will remain constant under future climate change. Dramatic shifts in global weather patterns, such as the slowing or reversal of major air and ocean currents are possible, but will not be captured in these indicators.\n- The historical data used for downscaling varies in quality across the world. In particular, areas that have short records or sparse coverage of in situ weather observations may have reduced accuracy.\n- Because GCM are developed by independent research teams and incorporate different assumptions and mechanisms, it is likely that they cover a substantial range of probable futures. We provide both low and high estimates so that users can see and evaluate this likely range of outcomes.\n","key_restrictions":"Attribution Required","tags":["geospatial","global","future","time_period","annual","climate","climate_change","drought","raster"],"why_added":"Adding to MapBuilder","learn_more":"https://www.wri.org/research/making-climate-data-accessible","id":"dd93a5f5-9b90-4c9f-9133-36831f644cf6"},"versions":["v20211015"]},{"created_on":"2021-09-08T14:21:02.692973","updated_on":"2025-02-13T22:56:25.116173","dataset":"osinfor_per_forest_concessions","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897151","updated_on":"2026-08-05T14:43:20.530082","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Peru","update_frequency":"Varies","scale":"national","citation":"Supervisory Body for Forest and Wildlife Resources (OSINFOR). “Peru Forest Concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org\n","title":"Peru forest concessions","subtitle":"2025, vector, Peru, OSINFOR","source":"[Supervisory Body for Forest and Wildlife Resources (OSINFOR)](https://www.gob.pe/osinfor)\n","license":"Contact [OSINFOR](https://www.gob.pe/osinfor) for more information.","data_language":"English","overview":"Displays boundaries of areas allocated by the forest authority and supervised by the Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, conservation, ecotourism, wildlife, afforestation/reforestation, medicinal plants, and other commodity crops. The concession grants the licensee the exclusive right to the sustainable use of natural resources granted under the conditions and limitations established by the respective title. The concession grants the holder the right to use and enjoy the natural resource granted and, consequently, ownership of the fruits and products extracted.\n\nTypes of concessions are indicated in the attribute data of each boundary, and include:\n- Timber Concessions - Areas granted for timber operations in production forests permanently established in primary or secondary forests. Granted in two categories: between 5,000-10,000 hectares or areas greater than 10,000 hectares, both renewable for up to forty years.\n- Non-Timber Forest Products - Areas granted for harvesting of non-timber products, such as fruits, nuts, buds, latex, resins, gums, flowers, fibers, medicinal plants, and whose extraction does not lead to the removal of forest cover. Maximum area of 10,000 hectares, renewable up to forty years.\n- Management areas for the production of non-timber forest products including aguaje (Mauritia flexuosa), shiringa (Hevea brasiliensis), and ungurahui (Oenocarpus bataua) are also included.\n- Conservation Concessions - Concessions aimed at directly contributing to conservation of plant and wildlife through protection and compatible uses such as research, education, and ecological restoration. Logging for timber is not allowed.\n- Ecotourism Concessions - Concession for the development of activities related to recreation and ecotourism, contributing to the conservation of nature, animals and cultural values of this site, and allowing for beneficial social and economic participation of the local communities. Commercial logging is not permitted. Areas valid for forty years for a maximum area of 10,000 hectares.\n- Wildlife Concessions - Public lands granted for wildlife management and aimed at sustainable enjoyment of authorized species. Renewable for up to twenty-five years.\n- Afforestation/ Reforestation - Areas designated for reforestation and afforestation activities.\n- Forest management concessions for the extraction of Aguaje (Moriche palm), Shiringa (rubber), and Ungurahui (Amazonian palm) are also included.\n\n\nOnly active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source.\n\nWMS access is available [here](https://sisfor.osinfor.gob.pe/osinfor/services/capas\\_osinfor/CONCESION\\_FORESTAL\\_v2/MapServer/WMSServer).\n","function":"Displays boundaries of areas allocated by the forest authority and supervised by the Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber forest products, conservation, ecotourism, wildlife, afforestation/reforestation, medicinal plants, and other commodity crops.","cautions":"Coverage includes information on the concessions granted by the forest authority and may be updated by transfer, resizing, and compensation of areas, among others.\n","key_restrictions":"","tags":["Country data"],"why_added":"OSINFOR is a partner - helpful to them for monitoring their concessions","learn_more":"https://gfw2-data.s3.amazonaws.com/concessions/2025/final_layers/public/gfw_peru_forest_concessions_v2025_public.zip","id":"7429dc03-5e0c-41c2-8ea2-e8d4101f1948"},"versions":["v2015","v2025","v2023"]},{"created_on":"2021-04-29T20:28:22.519015","updated_on":"2025-02-13T22:56:28.385142","dataset":"osinfor_peru_permanent_production_forests","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897455","updated_on":"2026-08-05T14:43:20.864519","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Peru","update_frequency":" ","scale":"national","citation":"Supervisory Body for Forest and Wildlife Resources (OSINFOR). “Peru permanent production forests.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"Peru permanent production forests","subtitle":"2015, vector, Peru, OSINFOR","source":"\\[Supervisory Body for Forest and Wildlife Resources (OSINFOR)]\\(https://www.gob.pe/osinfor)\n","license":null,"data_language":"Espanol","overview":"Permanent production forests are areas of natural primary forest that, under a ministerial resolution of the Ministry of Agriculture, are available to private interests for the preferential use of wood and other forest resources as well as wildlife as proposed by the forest and wildlife authority.\nIn these areas, use rights for different products of wood and wildlife may be granted, as long as they don’t affect the long-term potential of said resources.\n","function":"Displays official forest zoning in Peru.","cautions":"Boundaries are subject to change from rezoning","key_restrictions":null,"tags":null,"why_added":"Useful for OSINFOR monitoring","learn_more":null,"id":"667ef26b-a3dc-4bd5-b4dd-e8167bebf868"},"versions":["v2015"]},{"created_on":"2025-11-18T22:05:48.023785","updated_on":"2025-11-18T22:05:48.023789","dataset":"osm_congo_basin_logging_roads","is_downloadable":true,"metadata":{},"versions":["v2014","v2019"]},{"created_on":"2023-08-23T22:52:55.836155","updated_on":"2023-08-23T22:52:55.836166","dataset":"pangaea_global_mining","is_downloadable":true,"metadata":{},"versions":["v2"]},{"created_on":"2021-07-23T17:49:56.441549","updated_on":"2025-02-13T22:56:33.023308","dataset":"per_forest_concessions","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897513","updated_on":"2023-05-04T13:11:58.897514","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Peru Forest Concessions","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"c6c5b632-c3bb-4a29-b982-e0ac99c781ef"},"versions":["v2016","v201610"]},{"created_on":"2021-07-23T19:09:55.357432","updated_on":"2025-01-27T20:53:09.011751","dataset":"per_protected_areas","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897516","updated_on":"2026-08-05T14:43:21.275013","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Peru","update_frequency":"June 2016","scale":"national","citation":"“Peru Protected Areas.” Supervisory Body for Forest and Wildlife Resources (OSINFOR), accessed through Global Nature Watch on [date] www.globalnaturewatch.org.\n","title":"Peru protected areas","subtitle":"2016, vector, Peru, SERNANP","source":"[Servicio Nacional de Áreas Naturales Protegidas (SERNANP)](http://www.sernanp.gob.pe/home/)","license":null,"data_language":"Espanol","overview":"This layer shows three different levels of protected areas as well as their buffer zones. <br><br>**National protected areas** are managed by the national government, under the jurisdiction of the National Service of Natural Protected Areas (SERNANP). These areas are divided in three categories of use: indirect use, direct use, and reserved zone. In indirect areas, the extraction of natural resources or other types of environmental modification are not allowed. These areas only permit non-manipulative scientific investigation and tourist, recreational, educational, and cultural activities under regulated conditions. In direct use areas, the use of natural resources is allowed, primarily by local people, under the guidelines of a Management Plan approved and supervised by the relevant national authority. Reserved Zones are established as a transitory category in areas that have the conditions necessary to be considered as a natural protected area, but that require additional study to determine their extension and category.<br><br>**Regional protected areas** are bound by the same laws established for national protected areas, but are managed by regional governments. Regional protected areas are not divided into categories, but this does not mean that their conservation objectives are all the same.<br><br>**Private protected areas** are conservation areas that are created partially or totally on private property. The environmental, biological, or scenic properties of this land are complementary to the coverage of national PAs, supporting biodiversity conservation and increasing the opportunities for scientific investigation, education, and tourism. The recognition of private PAs is based in an agreement between the State and the owner of the land with the objective of conserving biodiversity for a renewable 10+ year period. <br><br>**Buffer zones** are areas adjacent to natural protected areas that, because of their nature and location, require special treatment to guarantee the conservation of the protected area. The activities in the buffer zone should not put the objectives of the protected area at risk. All use of natural resources in the buffer zone requires prior approval by SERNANP.","function":"Shows the location of national, regional, and private protected areas in Peru and buffer zones","cautions":null,"key_restrictions":"We are not allowed to make changes to the data (I have only been cleaning/ translating attributes - but this will not be available for download)","tags":null,"why_added":"OSINFOR requested protected area boundaries for Peru","learn_more":"http://www.sernanp.gob.pe/ques-es-un-anp","id":"7c4b9145-cf1b-4643-bfdf-0e314d130501"},"versions":["v2016"]},{"created_on":"2020-07-23T03:43:42.405817","updated_on":"2025-01-29T19:15:35.716761","dataset":"rspo_oil_palm","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897360","updated_on":"2026-08-05T14:43:21.657536","spatial_resolution":null,"resolution_description":"Manually delineated","geographic_coverage":"Global coverage for wherever RSPO member concessions exist. Currently, certified areas exist in: Brazil, Cambodia, Cameroon, Colombia, Costa Rica, Cote d'Ivoire, Democratic Republic of the Congo, Dominican Republic, Ecuador, Gabon, Ghana, Guatemala, Honduras, Liberia, Madagascar, Malaysia, Mexico, Nicaragua, Nigeria, Panama, Papua New Guinea, Peru, Sao Tome and Principe, Sierra Leone, Solomon Islands, Sri Lanka, and Thailand","update_frequency":"Varies","scale":"global","citation":"Roundtable on Sustainable Palm Oil (RSPO) Member Companies. Compiled by RSPO Secretariat. “RSPO Oil Palm Concessions.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org\n","title":"RSPO Member Concessions","subtitle":"(2025, vector, select countries, RSPO)","source":"[Roundtable on Sustainable Palm Oil (RSPO) Member Companies](https://rspo.org/as-an-organisation/tools/georspo/)\n","license":"[Disclaimer for Map Publication](http://d1ooe4m5rq52vq.cloudfront.net/downloads/DisclaimerforRSPOMapPublication_FINAL.pdf)","data_language":"English","overview":"This data layer displays the concession boundaries of Roundtable on Sustainable Palm Oil (RSPO) through November 2025. Concession boundaries are submitted by RSPO members as well as processor and trader members.\n","function":"Displays the concession boundaries of RSPO member companies, including both certified and non-certified concessions.","cautions":"RSPO concession boundaries were accessed from GeoRSPO. Plantation boundaries published on GeoRSPO cover all RSPO member concessions globally, except for Indonesia due to data sharing restrictions. \nNote that some polygons overlap or are duplicated in the dataset.\n","key_restrictions":"No external sharing","tags":["Land Use"],"why_added":"To improve forest monitoring for sustainably certified concessions.","learn_more":"https://rspo.org/resources/?category=georspo","id":"f646e860-165a-400e-ad32-eccff31ad2d4"},"versions":["v20230120","v2025","v20200114","v20200401"]},{"created_on":"2021-02-09T20:45:44.790906","updated_on":"2021-02-09T20:45:44.790913","dataset":"rspo_southeast_asia_land_cover_2010","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897428","updated_on":"2026-08-05T14:43:22.083173","spatial_resolution":30,"resolution_description":null,"geographic_coverage":"Indonesia and Malaysia ","update_frequency":null,"scale":"regional","citation":"Gunarso, et al. (2013) 'SE Asia Landcover' Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"RSPO Southeast Asia Landcover 2010","subtitle":null,"source":"Gunarso, et al. (2013)","license":"Creative Commons Attribution 4.0 International (CC BY 4.0) license.","data_language":"English","overview":"This data set represents the land cover for parts of Southeast Asia. Data was generated from Landsat 4, 5, and 7 images. There are 22 land cover classifications which are based on adaptations from classifications from the Ministry of Agriculture of Indonesia and the Ministry of Forestry of Indonesia. Land cover units were digitized using visual interpretation with supplemental geospatial data. For peatlands, and swamp lands, additional landscape context was also used. ","function":"This data represents land cover categories in Southeast Asia.","cautions":"The primary objective of this dataset, including the classification categories, was for use in the palm oil sector.","key_restrictions":null,"tags":["Land Cover","Production Suitability","Country data"],"why_added":"To provide information on land cover classes in Indonesia and Malaysia.","learn_more":null,"id":"18e8bf5b-28e9-4a28-baba-9b149f44f58e"},"versions":["v2013"]},{"created_on":"2023-07-21T15:22:56.655736","updated_on":"2026-01-23T17:14:52.143103","dataset":"sbtn_natural_forests_map","is_downloadable":true,"metadata":{"created_on":"2023-07-21T15:22:56.669431","updated_on":"2026-08-05T14:36:03.627909","spatial_resolution":null,"resolution_description":"30 × 30 meters","geographic_coverage":"Global","update_frequency":"Sporadic","scale":null,"citation":"Use the following credit when these data are displayed: \n“Natural Forests”. SBTN. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](www.globalnaturewatch.org). \n\nUse the following credit when these data are cited: \nMazur, E., M. Sims, E. Goldman, M. Schneider, M.D. Pirri, C.R. Beatty, F. Stolle, Stevenson, M. 2025. “SBTN Natural Lands Map v1.1”. Science Based Targets Network. [Technical documentation](https://sciencebasedtargetsnetwork.org/wp-content/uploads/2025/02/Technical-Guidance-2025-Step3-Land-v1\\_1-Natural-Lands-Map.pdf).\n","title":"Natural Forest","subtitle":"2020, 30 m, Global, Science Based Targets Network (SBTN), version 1.1","source":"Mazur, E., M. Sims, E. Goldman, M. Schneider, M.D. Pirri, C.R. Beatty, F. Stolle, Stevenson, M. 2025. “SBTN Natural Lands Map v1.1”. Science Based Targets Network. [Technical documentation](https://sciencebasedtargetsnetwork.org/wp-content/uploads/2025/02/Technical-Guidance-2025-Step3-Land-v1_1-Natural-Lands-Map.pdf). \n","license":null,"data_language":null,"overview":"This dataset displays the extent of natural forest in 2020, drawing from the relevant classes delineated in the Science Based Targets Network’s (STBN) Natural Lands Map. These data draw from multiple sources to develop a global map of natural and non-natural land cover with a globally consistent classification scheme. \n\nDefinitions of natural ecosystems and forests published by the Accountability Framework Initiative (AFi) and Food and Agriculture Organization (FAO) were referenced to develop the map. AFi and FAO define forests as land spanning more than 0.5 hectares that is dominated by trees greater than 5-m in height and a canopy cover of more than 10 percent or areas able to reach these thresholds in-situ. AFi defines a natural forest as one that resembles – in terms of species composition, structure, and ecological function – what would be found in a given area absent major human impacts. Because species composition and ecological function cannot be directly mapped with earth observation data, AFi and FAO definitions were operationalized using proxy data to exclude tree cover that would not meet the natural forest definition. AFi guidance also states that thresholds established by national and sub-national forest definitions may take precedence, so regional maps of land cover were given priority over global data in select regions. \n\nNon-natural tree cover may include tree crops, planted forests, patches of trees under 0.5 hectares, or trees within areas predominantly under agricultural or urban land use. \n\nUsers should refer to the [technical documentation](https://sciencebasedtargetsnetwork.org/wp-content/uploads/2025/02/Technical-Guidance-2025-Step3-Land-v1\\_1-Natural-Lands-Map.pdf) for more details on the input data used to develop the map as well as its limitations. \n","function":"Identifies areas of natural forest in 2020","cautions":"This dataset is based on the relevant forest classes delineated in the Science Based Targets Networks (SBTN) Natural Lands Map, which itself was developed by bringing together multiple datasets which identify the extent of natural and non-natural lands. Users are encouraged to cross-check their analyses using additional data, including high resolution imagery, and follow up with in-person field visits. Full details on the limitations of these can be found in the [technical documentation](https://sciencebasedtargetsnetwork.org/wp-content/uploads/2025/02/Technical-Guidance-2025-Step3-Land-v1\\_1-Natural-Lands-Map.pdf). \n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://github.com/wri/natural-lands-map?tab=readme-ov-file","id":"9955bd1c-128f-49d8-89b2-7f374a1e359d"},"versions":["v202310","v202504","v202410"]},{"created_on":"2025-10-06T18:56:10.222045","updated_on":"2025-10-06T18:56:10.222054","dataset":"sbtn_natural_lands","is_downloadable":true,"metadata":{"created_on":"2025-10-06T18:56:10.236441","updated_on":"2025-10-06T18:56:10.236450","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Yearly","scale":null,"citation":null,"title":null,"subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":["Conservation"],"why_added":null,"learn_more":null,"id":"7bf8baae-2f6f-4d17-8a05-05ecd2720a58"},"versions":null},{"created_on":"2025-10-06T19:27:52.002079","updated_on":"2025-10-06T19:27:52.002084","dataset":"sbtn_natural_lands_classification","is_downloadable":true,"metadata":{"created_on":"2025-10-06T19:27:52.007769","updated_on":"2025-10-06T19:27:52.007773","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Yearly","scale":null,"citation":null,"title":null,"subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":["Conservation"],"why_added":null,"learn_more":null,"id":"f5a199c9-67c1-4e0b-b1c3-954ca4fd93c9"},"versions":["v1.1"]},{"created_on":"2025-01-02T03:57:11.158399","updated_on":"2025-01-02T03:57:11.158405","dataset":"sfb_bra_sicar","is_downloadable":true,"metadata":{"created_on":"2025-01-02T03:57:11.164167","updated_on":"2025-01-02T03:57:11.164171","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Brazil","update_frequency":null,"scale":null,"citation":null,"title":null,"subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":["Conservation"],"why_added":null,"learn_more":null,"id":"a8d3075a-8e2f-4fbc-8caf-a2c6b149fd6e"},"versions":["v202401","v202508"]},{"created_on":"2025-02-14T13:58:31.761644","updated_on":"2025-02-25T13:15:52.174162","dataset":"test_001_lcb_test","is_downloadable":true,"metadata":{"created_on":"2025-02-14T13:58:32.582733","updated_on":"2025-02-25T13:15:52.675777","spatial_resolution":null,"resolution_description":"Vector geospatial point data.","geographic_coverage":"Global","update_frequency":"Not planned","scale":null,"citation":"asdfasdf asdfas doi:12152.12515","title":"Test dataset 001","subtitle":"this _is_ a test","source":"internal WRI / datalab","license":"ALL RIGHTS RESERVED","data_language":null,"overview":"This is a test dataset that is used internally. It should not be utilized for any trusted purpose and may disappear without notice.","function":"API testing","cautions":"This data is without meaning.","key_restrictions":null,"tags":["test","delete-me-if-you-want","now with spaces"],"why_added":"API testing","learn_more":"https://gotta.be/a/url/i/bet","id":"ab2e647c-9660-4ee0-b37e-89a4fc1aa411"},"versions":["v001","v003","v002"]},{"created_on":"2025-02-13T18:11:21.957027","updated_on":"2025-02-13T18:11:21.957034","dataset":"test-001-lcb-test","is_downloadable":true,"metadata":{"created_on":"2025-02-13T18:11:21.963189","updated_on":"2025-02-13T18:11:21.963193","spatial_resolution":null,"resolution_description":"Vector geospatial point data.","geographic_coverage":"Global","update_frequency":"Not planned","scale":null,"citation":"asdfasdf asdfas doi:12152.12515","title":"Test dataset 001","subtitle":"this _is_ a test","source":"internal WRI / datalab","license":"ALL RIGHTS RESERVED","data_language":null,"overview":"This is a test dataset that is used internally. It should not be utilized for any trusted purpose and may disappear without notice.","function":"API testing","cautions":"This data is without meaning.","key_restrictions":null,"tags":["test","delete-me-if-you-want","now with spaces"],"why_added":"API testing","learn_more":"https://gotta.be/a/url/i/bet","id":"e066d8e9-1630-42f7-ac3b-74affb2087b2"},"versions":null},{"created_on":"2025-03-05T20:35:53.116012","updated_on":"2025-03-05T20:35:53.116016","dataset":"test_chaco_y_chiquitano_field_boundaries","is_downloadable":true,"metadata":{"created_on":"2025-03-05T20:35:53.130776","updated_on":"2025-03-05T20:35:53.130780","spatial_resolution":null,"resolution_description":"Vector geospatial polygon data.","geographic_coverage":"NONE","update_frequency":"Not planned","scale":null,"citation":"NOT PUBLISHED","title":"Field Boundaries for Chaco y Chiquitano","subtitle":"","source":"internal WRI / datalab","license":"ALL RIGHTS RESERVED","data_language":null,"overview":"This is a test dataset that is used internally. It should not be utilized for any trusted purpose and may disappear without notice.","function":"API testing","cautions":"This is not final or approved data. Do not use.","key_restrictions":null,"tags":["test"],"why_added":"API testing","learn_more":"https://wri.org","id":"0a7b8699-04e8-40a4-9f48-f3a2b186cd69"},"versions":["v001"]},{"created_on":"2024-11-20T17:43:35.520204","updated_on":"2024-11-20T17:43:35.520209","dataset":"test_wat_006_projected_water_stress","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2023-12-06T00:50:45.163866","updated_on":"2023-12-06T00:50:45.163876","dataset":"to_delete","is_downloadable":true,"metadata":{},"versions":["v2","v3","v1","v4"]},{"created_on":"2021-11-04T19:32:17.205925","updated_on":"2021-11-04T19:32:17.205931","dataset":"tropomi_avg_nitrogen_dioxide_last_month","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897313","updated_on":"2023-05-04T13:11:58.897314","spatial_resolution":3500,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"Daily","scale":"","citation":"European Space Agency. 2018. ESA Sentinel-5P TROPOMI L3 products. Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).","title":"Air Quality: Nitrogen Dioxide (NO₂) Satellite Measurements","subtitle":null,"source":"TROPOMI/ESA/KNMI/DLR/SRON/BIRA-IASB/STFC/MPIC/S[&]T/Uni-Bremen","license":"[Attribution required](https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice)","data_language":"en","overview":"The Air Quality: NO₂ Satellite Measurements dataset provides global monthly averages of nitrogen dioxide (NO₂) density in the troposphere. Each value shown in the dataset represents the density of NO₂ between Earth’s surface and the top of the troposphere. The NO₂ density is reported with units of moles of NO₂ per square meter of air (mol/m²).Nitrogen dioxide (NO₂) is one of the most common compounds in the nitrogen oxides (NOx) group. Other nitrogen oxides include nitric acid (HNO₃) and nitric oxide (NO). NO₂ is used as the indicator for the larger group of nitrogen oxides, meaning if NO₂ is present it is likely other nitrogen oxides are as well. NO₂ is primarily created by the burning of fuel, which can be from cars, trucks and buses, power plants, and off-road equipment. Breathing air with a high concentration of NO₂ can irritate airways in the human respiratory system. Acute exposures can aggravate respiratory diseases, particularly asthma, leading to respiratory symptoms, like coughing, wheezing or difficulty breathing. Longer chronic exposures to elevated concentrations of NO₂ may contribute to the development of asthma and potentially increase susceptibility to respiratory infections. People with asthma, as well as children and the elderly are generally at greater risk for the health effects of NO₂.The dataset is made up of data collected from the Sentinel-5 Precursor (S5p) mission, a low Earth orbit polar satellite system. The S5p mission is part of the Global Monitoring of the Environment and Security (GMES/COPERNICUS) space component program headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Its goal is to  provide information and services on air quality, climate, and the ozone layer. The S5p mission includes the TROPOspheric Monitoring Instrument (TROPOMI), which takes daily global observations of key atmospheric components, such as NO₂, at a 5.5 x 3.5 kilometer (km) resolution.Resource Watch also shows TROPOMI data for [carbon monoxide (CO)](https://resourcewatch.org/data/explore/Air-Quality-Measurements-TROPOMI-CO), [ozone (O₃)](https://resourcewatch.org/data/explore/Air-Quality-Measurements-TROPOMI-O), and [absorbing aerosol index (AAI)](https://resourcewatch.org/data/explore/Air-Quality-Measurements-TROPOMI-AER-AI).","function":"Average monthly density of nitrogen dioxide (NO₂) in the troposphere","cautions":"- The current surface albedo climatology used has a spatial resolution of 0.5° x 0.5° (approximately 55 x 55 km), which is coarse compared to the much higher spatial resolution used by S5p TROPOMI of 3.5 x 5.5 km. As a consequence, the albedo grid affects the NO₂ column products quality, especially in coastal areas.\n\n  \n  \n- In general, TROPOMI underestimates the tropospheric NO₂ densities at polluted sites. The median negative biases of the daily comparisons are generally less than 50% (the product requirement for tropospheric NO₂), but can be quite variable depending on station and NO₂ level. \n\n  \n  \n- Good coherence is found between TROPOMI and Multi Axis Differential Absorption Spectroscopy (MAX-DOAS) NO₂ datasets with a correlation coefficient of 0.84.\n  \n  \n- For more information on the data quality, please see the [product readme file](https://sentinel.esa.int/documents/247904/3541451/Sentinel-5P-Nitrogen-Dioxide-Level-2-Product-Readme-File).\n","key_restrictions":"Attribution required","tags":["geospatial","global","air_quality","raster","historical","pollution","health","near_real_time","SDG_11_Sustainable_Cities_and_Communities","SDG_target_11.6"],"why_added":"Adding to MapBuilder","learn_more":"https://sentinel.esa.int/web/sentinel/missions/sentinel-5p","id":"980999bf-9fce-4b51-a2f4-215e88ed5795"},"versions":["v20220715","v20220614","v20211015","v20220123"]},{"created_on":"2021-04-09T19:37:22.853769","updated_on":"2021-04-09T19:37:22.853775","dataset":"tsc_drivers","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897505","updated_on":"2026-08-05T14:43:22.535257","spatial_resolution":10000,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"","scale":"global","citation":"Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen. 2018. 'Classifying Drivers of Global Forest Loss.' *Science.* Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Tree Cover Loss by Dominant Driver","subtitle":null,"source":"Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen. 2018. 'Classifying Drivers of Global Forest Loss.' *Science.* https://science.sciencemag.org/content/361/6407/1108","license":"","data_language":"English","overview":"This data set shows the dominant driver of [tree cover loss](https://earthenginepartners.appspot.com/science-2013-global-forest) from 2001-2018, using the following five categories:* **Commodity-driven deforestation:** Large-scale deforestation linked primarily to commercial agricultural expansion.* **Shifting agriculture:** Temporary loss or permanent deforestation due to small- and medium-scale agriculture.* **Forestry:** Temporary loss from plantation and natural forest harvesting, with some deforestation of primary forests.* **Wildfire:** Temporary loss, does not include fire clearing for agriculture.* **Urbanization:** Deforestation for expansion of urban centers. The commodity-driven deforestation and urbanization categories represent permanent deforestation, while tree cover usually regrows in the other categories. The data were generated using decision tree models to separate each 10 km grid cell into one of the five categories. The decision trees were created using 4,699 sample grid cells, and use metrics derived from the [Hansen tree cover, tree cover gain, and tree cover loss](https://earthenginepartners.appspot.com/science-2013-global-forest), [NASA fires](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data), [global land cover](http://www.earthenv.org/landcover.html), and [population count](http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev10). Separate decision trees were created for each driver and each region (North America, South America, Europe, Africa, Eurasia, Southeast Asia, Oceania), for a total of 35 decision trees. The final outputs were combined into a global map, which is then overlaid with tree cover loss data to indicate the intensity of loss associated with each driver around the world. All model code, reference samples, decision trees, and the final model are available in the Supplementary Materials of the paper.","function":"Shows the dominant driver of tree cover loss within each 10 km grid cell and the intensity of that loss for the time period 2001-2018. ","cautions":"This data set is intended for use at the global or regional scale, not for individual pixels. Individual grid cells may have more than one driver of tree cover loss, with variation over space and time.  Aside from the commodity-driven deforestation and urbanization classes, which are assumed to represent permanent conversion from a forest to non-forest state, this data set does not indicate the stability or changing condition of the forest land use after the tree cover loss occurs. The data set also does not distinguish between natural or anthropogenic wildfires. The accuracy of the data was assessed using a validation sample of 1,565 randomly selected grid cells. The overall accuracy of the model was 89%, with individual class accuracies ranging from 55% (urbanization) to 94% (commodity-driven deforestation).","key_restrictions":"","tags":["Forest Change"],"why_added":"Global picture of the drivers of tree cover loss - allows us to better separate out and understand drivers spatially","learn_more":"","id":"9fe4eae8-8a9a-43ab-91e9-d74e101b790c"},"versions":["v2020"]},{"created_on":"2021-07-09T17:54:36.118578","updated_on":"2025-01-27T20:44:37.617273","dataset":"tsc_tree_cover_loss_drivers","is_downloadable":true,"metadata":{"created_on":"2024-07-11T15:09:24.048314","updated_on":"2026-08-05T14:43:22.892840","spatial_resolution":null,"resolution_description":"10 × 10 km","geographic_coverage":"Global","update_frequency":"Annual","scale":"global","citation":"Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen. 2018. “Classifying Drivers of Global Forest Loss.” *Science.* Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Tree cover loss by dominant driver","subtitle":null,"source":"Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen. 2018. “Classifying Drivers of Global Forest Loss.” Science. [https://science.sciencemag.org/content/361/6407/1108](https://science.sciencemag.org/content/361/6407/1108)","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This data set shows the dominant driver of [tree cover loss](https://earthenginepartners.appspot.com/science-2013-global-forest) from 2001-2023 using the following five categories:<br><br>* **Commodity-driven deforestation:** Large-scale deforestation linked primarily to commercial agricultural expansion.<br>* **Shifting agriculture:** Temporary loss or permanent deforestation due to small- and medium-scale agriculture.<br>* **Forestry:** Temporary loss from plantation and natural forest harvesting, with some deforestation of primary forests.<br>* **Wildfire:** Temporary loss, does not include fire clearing for agriculture.<br>* **Urbanization:** Deforestation for expansion of urban centers.<br><br>The commodity-driven deforestation and urbanization categories represent permanent deforestation, while tree cover usually regrows in the other categories. <br><br>The data were generated using decision tree models to separate each 10 km grid cell into one of the five categories. The decision trees were created using 4,699 sample grid cells, and use metrics derived from the [Hansen tree cover, tree cover gain, and tree cover loss](https://earthenginepartners.appspot.com/science-2013-global-forest), [NASA fires](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data), [global land cover](http://www.earthenv.org/landcover.html), and [population count](http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev10). Separate decision trees were created for each driver and each region (North America, South America, Europe, Africa, Eurasia, Southeast Asia, Oceania), for a total of 35 decision trees. The final outputs were combined into a global map, which is then overlaid with tree cover loss data to indicate the intensity of loss associated with each driver around the world.<br><br>All model code, reference samples, decision trees, and the final model are available in the Supplementary Materials of the paper.","function":"Shows the dominant driver of tree cover loss within each 10 km grid cell and the intensity of that loss for the time period 2001-2023.","cautions":"This data set is intended for use at the global or regional scale, not for individual pixels. Individual grid cells may have more than one driver of tree cover loss, with variation over space and time. <br><br>Aside from the commodity-driven deforestation and urbanization classes, which are assumed to represent permanent conversion from a forest to non-forest state, this data set does not indicate the stability or changing condition of the forest land use after the tree cover loss occurs. The data set also does not distinguish between natural or anthropogenic wildfires. <br><br>The accuracy of the data was assessed using a validation sample of 1,565 randomly selected grid cells. The overall accuracy of the model was 89%, with individual class accuracies ranging from 55% (urbanization) to 94% (commodity-driven deforestation).","key_restrictions":null,"tags":null,"why_added":"Global picture of the drivers of tree cover loss - allows us to better separate out and understand drivers spatially","learn_more":null,"id":"0cc54ec8-ca98-4290-a395-17bae8d75e03"},"versions":["v2022","v2023","v2021","v2021.1","v2020"]},{"created_on":"2022-06-23T15:44:59.079222","updated_on":"2025-02-11T16:11:10.285133","dataset":"umd_adm0_net_tree_cover_change_from_height","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897275","updated_on":"2026-08-05T14:43:23.216424","spatial_resolution":null,"resolution_description":"Administrative area","geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":"Use the following credit when this data is displayed: \nAccessed through Global Nature Watch on 04/10/2022. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/). <br><br> \nUse the following credit when this data is cited: \nPotapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. <https://doi.org/10.3389/frsen.2022.856903>; summarized by administrative area at WRI.\n","title":"UMD Net Tree Cover Change (from height, adm0)","subtitle":"2000-2020, global, UMD/NASA GEDI","source":"Potapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. [https://doi.org/10.3389/frsen.2022.85690](https://doi.org/10.3389/frsen.2022.856903); summarized by administrative area at WRI \n","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":null,"overview":"This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of net tree cover change across all global land (except Antarctica and other Arctic islands) at the administrative area. The data shows how much more or less tree cover a given area (country, state/province, or county/municipality) had in 2020 compared to 2000. This is not an annual data set and is displayed as a 20-year cumulative layer with a baseline year of 2000 and end year of 2020. Tree cover change was determined using tree cover extent for the years 2000 and 2020, which are based on tree height information. Tree height was determined by the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height between 52°N and 52°S globally. The tree cover extent maps for 2000 and 2000 were produced by attributing pixels with ≥5 m height as the tree cover extent. The tree cover gain or loss was derived from a comparison between the year 2000 and 2020 maps, with steps taken to eliminate small changes in height which are “noise” in the data. The net change corresponds to the area of gross gain minus the area of gross loss to show the overall change. The net change percent per administrative region was then derived by calculating tree cover gain or loss as a percentage of the baseline 2000 tree cover area. <br><br> \n**Stable forest** – Area of tree cover that remained at or above 5m in height between 2000 and 2020. <br><br> \n**Gain** – Area of tree cover that was below 5m height in 2000 but at or above 5m height in 2020. <br><br> \n**Loss** – Area of tree cover that was at or above 5m height in 2000 but below 5m height in 2020. <br><br> \n**Disturbed** - Represents areas that experienced both loss and gain between 2000 and 2020. <br><br> \n","function":"Identifies net tree cover change","cautions":"In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities.<br><br> \nA direct comparison of the years 2000 and 2020 forest height products, without the application of thresholds which define forest height increase and decrease in this product, will not provide consistent and meaningful results and will not be comparable to this product. Integrated use of other products such as canopy cover density data also available on GNW should be performed with caution. <br><br> \nAccuracy metrics were calculated for forest extent 2000, forest extent 2020 and for the stable forest area within that time period. Overall accuracy, User's Accuracy (based on the false positives, and calculated as 100%-Commission Error) and Producer’s error (based on the false negatives, and calculated as 100%-Omission error) were all >93%. <br><br> \n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"4a2e8cdb-3298-4fdd-976c-7788b10c0230"},"versions":["v202209","v202205","v202208","v202503","v202501"]},{"created_on":"2022-06-23T15:45:04.694775","updated_on":"2025-02-11T16:11:13.631514","dataset":"umd_adm1_net_tree_cover_change_from_height","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897281","updated_on":"2023-05-04T13:11:58.897282","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"UMD Net Tree Cover Change (from height, adm1)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"6aa2b90a-f3f6-4227-ba11-d70562b39b25"},"versions":["v202205","v202209","v202208","v202501","v202503"]},{"created_on":"2022-06-23T15:45:11.347212","updated_on":"2025-02-11T16:11:15.718971","dataset":"umd_adm2_net_tree_cover_change_from_height","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897326","updated_on":"2023-05-04T13:11:58.897327","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"UMD Net Tree Cover Change (from height, adm2)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"52dc1b08-6346-4b3d-88ae-071fb74312e4"},"versions":["v202501","v202205","v202209","v202208","v202503"]},{"created_on":"2021-03-02T22:06:25.702172","updated_on":"2021-03-02T22:06:25.702179","dataset":"umd_area_2013","is_downloadable":true,"metadata":{},"versions":["v2013","v1.10","v20231024"]},{"created_on":"2021-04-02T18:53:25.899447","updated_on":"2021-04-02T18:53:25.899453","dataset":"umd_drivers","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897497","updated_on":"2026-08-05T14:43:23.610269","spatial_resolution":10000,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"","scale":"global","citation":"Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen. 2018. 'Classifying Drivers of Global Forest Loss.' *Science.* Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Tree Cover Loss by Dominant Driver","subtitle":null,"source":"Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen. 2018. 'Classifying Drivers of Global Forest Loss.' *Science.* https://science.sciencemag.org/content/361/6407/1108","license":"","data_language":"English","overview":"This data set shows the dominant driver of [tree cover loss](https://earthenginepartners.appspot.com/science-2013-global-forest) from 2001-2018, using the following five categories:* **Commodity-driven deforestation:** Large-scale deforestation linked primarily to commercial agricultural expansion.* **Shifting agriculture:** Temporary loss or permanent deforestation due to small- and medium-scale agriculture.* **Forestry:** Temporary loss from plantation and natural forest harvesting, with some deforestation of primary forests.* **Wildfire:** Temporary loss, does not include fire clearing for agriculture.* **Urbanization:** Deforestation for expansion of urban centers. The commodity-driven deforestation and urbanization categories represent permanent deforestation, while tree cover usually regrows in the other categories. The data were generated using decision tree models to separate each 10 km grid cell into one of the five categories. The decision trees were created using 4,699 sample grid cells, and use metrics derived from the [Hansen tree cover, tree cover gain, and tree cover loss](https://earthenginepartners.appspot.com/science-2013-global-forest), [NASA fires](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data), [global land cover](http://www.earthenv.org/landcover.html), and [population count](http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev10). Separate decision trees were created for each driver and each region (North America, South America, Europe, Africa, Eurasia, Southeast Asia, Oceania), for a total of 35 decision trees. The final outputs were combined into a global map, which is then overlaid with tree cover loss data to indicate the intensity of loss associated with each driver around the world. All model code, reference samples, decision trees, and the final model are available in the Supplementary Materials of the paper.","function":"Shows the dominant driver of tree cover loss within each 10 km grid cell and the intensity of that loss for the time period 2001-2018. ","cautions":"This data set is intended for use at the global or regional scale, not for individual pixels. Individual grid cells may have more than one driver of tree cover loss, with variation over space and time.  Aside from the commodity-driven deforestation and urbanization classes, which are assumed to represent permanent conversion from a forest to non-forest state, this data set does not indicate the stability or changing condition of the forest land use after the tree cover loss occurs. The data set also does not distinguish between natural or anthropogenic wildfires. The accuracy of the data was assessed using a validation sample of 1,565 randomly selected grid cells. The overall accuracy of the model was 89%, with individual class accuracies ranging from 55% (urbanization) to 94% (commodity-driven deforestation).","key_restrictions":"","tags":["Forest Change"],"why_added":"Global picture of the drivers of tree cover loss - allows us to better separate out and understand drivers spatially","learn_more":"","id":"96fb8243-6cbd-4001-8d57-2fc78b78d237"},"versions":["v2020"]},{"created_on":"2024-11-25T23:28:23.117286","updated_on":"2025-03-19T15:33:37.631280","dataset":"umd_glad_dist_alerts","is_downloadable":true,"metadata":{"created_on":"2024-11-25T23:28:27.277853","updated_on":"2026-08-05T14:43:23.890493","spatial_resolution":null,"resolution_description":"30 × 30 m","geographic_coverage":"Global","update_frequency":"Underlying product updated daily, with image revisit time every 2-4 days. Product on GNW updated weekly. ","scale":null,"citation":"Source: \"DIST-ALERT\". UMD/GLAD and NASA, accessed through Global Nature Watch on [date]\n","title":"Global all ecosystem disturbance alerts (DIST-ALERT)","subtitle":"weekly, 30 m, global, UMD/GLAD and NASA","source":"Hansen, M.. OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1). 2024, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001 \n\n Pickens, A.H., Hansen, M.C., Song, Z. et al. Rapid monitoring of global land change. Nat Commun 16, 8948 (2025). https://doi.org/10.1038/s41467-025-64014-9 ","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":null,"overview":"This dataset is a derivative of the OPERA’s DIST-ALERT product (OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product), which is derived through a time-series analysis of harmonized data from the NASA/USGS Landsat and ESA Sentinel-2 satellites (known as the HLS dataset). The product identifies and continuously monitors vegetation cover change in 30-m pixels across the globe. It can be accessed through the LPDAAC website here: [https://search.earthdata.nasa.gov/search?q=C2746980408-LPCLOUD](https://search.earthdata.nasa.gov/search?q=C2746980408-LPCLOUD), and on Google Earth Engine (GEE) with asset ID: projects/glad/HLSDIST/current \n\n The DIST-ALERT on GNW is a derivative of this, and additional data layers not used in the GNW product are available through the LPDAAC and GEE such as initial vegetation fraction, and disturbance duration. \n\n Specifically, three layers are used to create the GNW DIST-ALERT. The VEG-DIST-DATE provides the date of the alert, the VEG-DIST-COUNT is used to determine the confidence (2-3 consecutive observations for low confidence and 4+ observations for high confidence) and VEG-ANOM-MAX is used as a threshold for determining loss (>30%). \n\n While the version on the LPDAAC is updated every 2-4 days, the data is updated weekly on GNW. \n\n The product detects notable reductions in vegetation cover (measured as “vegetation fraction” or the percent of the ground that is covered by vegetation) for every pixel every time the new satellite data is acquired and the ground is not obscured by clouds or snow. \n\n The current vegetation fraction estimate is compared to the minimum fraction for the same time period (within 15 days before and after) in the previous 3 years, and if there is a reduction, then the system identifies an alert in that pixel. Anomalies of at least a 10% reduction from the minimum become alerts in the original product, and on GNW, a higher threshold of 30% is used, to reduce noise, and false alerts in the dataset. Because the product compares each pixel to the minimum for the same time period in previous years, it takes into account regular seasonal variation in vegetation cover. \n\n As the product is global and detects vegetation anomalies, much of the data may not be applicable to GNW users monitoring forests. Therefore, we mask the alerts with UMD’s tree cover map, allowing users to view only alerts within 30% canopy cover. ","function":"Monitors global vegetation disturbance in near-real-time using harmonized Landsat-Sentinel-2 (HLS) imagery","cautions":" \n- These alerts detect vegetation cover loss or disturbance. This product does not distinguish between human-caused and other disturbance types. For example, where alerts are detected within plantation forests, alerts may indicate timber harvesting operations, without a conversion to a non-forest land use, and when alerts are detected within crop land, alerts may represent crop harvesting  \n-  We do not recommend using the alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. For global land cover changes, [20 year data](https://glad.umd.edu/dataset/GLCLUC2020) may be more useful for certain purposes and should be explored. Additionally, updates to the methodologies and variation in cloud cover between months and years pose additional risks to using alerts for inter/intra-annual comparison.  \n \n- The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations within tree cover are provided in the Places to Watch data layer.  \n \n- We provide a masked version of the product within “tree cover” which is defined as all vegetation greater than 5 meters in height (2020) with greater than 30% canopy cover (2010), and may take the form of natural forests or plantations. Annual tree cover loss from 2021 is masked out.   \n \n- In contrast to other alert systems available on GNW, DIST-ALERT continues to monitor pixels where it has identified an alert in the past. The DIST-ALERT retains the date of the most recent detection of disturbance, keeping users informed of the most up-to-date changes within tree cover. \n- Two confidence levels are provided. The approach determines confidence level by the number of anomalous observations, with more observations meaning a higher confidence level. That is, two to three anomalies detected result in a low confidence alert, whereas four or more mean a high confidence alert. UMD’s Google Earth Engine app [(<https://glad.earthengine.app/view/dist-alert>)](<https://glad.earthengine.app/view/dist-alert>) displays alerts, with a different approach used to define confidence, and a different threshold for the vegetation reduction which triggers alerts.   \n \n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.   \n\n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001","id":"b52a60ec-4419-4018-9157-8c175f4d58e3"},"versions":["v20251125","v20250405","v20250104","v20260912","v20260908","v20260704","v20250927","v20260815","v20260808","v20250118","v20260801","v20260406","v20250920","v20251018","v20260510","v20260822","v20250510","v20251204","v20241207","v20260329","v20250208","v20250419","v20241123","v20251101","v20251227","v20241130","v20250503","v20250211","v20260404","v20250426","v20260712","v20250830","v20260319","v20250621","v20250708","v20260516","v20260502","v20250816","v20260301","v20260627","v20260321","v20260221","v20250524","v20250726","v20251220","v20250614","v20260207","v20251011","v20250315","v20250531","v20251209","v20250222","v20250517","v20250802","v20251025","v20250129","v20250823","v20251201","v20250329","v20260103","v20251108","v20260613","v20260725","v20260419","v20250215","v20260607","v20250906","v20260412","v20251004","v20250308","v20260718","v20250412","v20260530","v20241228","v20260124","v20241214","v20250125","v20250322","v20260110","v20250301","v20251206","v20250201","v20251115","v20260620","v20251122","v20241221","v20260426","v20260117","v20250809","v20250607","v20250628","v20251213","v20250112","v20251111","v20260523","v20250712","v20260131","v20250723","v20260328","v20250913"]},{"created_on":"2024-12-05T22:00:09.108428","updated_on":"2024-12-05T22:00:09.108432","dataset":"umd_glad_dist_alerts_coverage","is_downloadable":true,"metadata":{"created_on":"2024-12-05T22:00:09.125125","updated_on":"2024-12-05T22:00:09.125129","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"UMD GLAD Land Disturbance Alerts geographic coverage layer","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b78fd55d-828b-4e66-97f4-50b902b27213"},"versions":["v202412"]},{"created_on":"2021-05-27T12:23:39.881855","updated_on":"2025-02-20T18:18:33.883412","dataset":"umd_glad_landsat_alerts","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897140","updated_on":"2026-08-05T14:43:24.211894","spatial_resolution":30,"resolution_description":"30 × 30 m ","geographic_coverage":"30°N to 30°S (includes the southern end of Brazil to provide complete coverage for Brazil) ","update_frequency":"Updated daily, image revisit time every 8 days ","scale":"regional","citation":"Use the following credit when these data are displayed: \n\nSource: “GLAD-L alerts”. GLAD/UMD, accessed through Global Nature Watch on [date] \nUse the following credit when these data are cited: \n\nHansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. _Environmental Research Letters_, 11 (3). Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org \n","title":"GLAD alerts ","subtitle":"daily, 30 m, tropics, UMD/GLAD","source":"Hansen, M.C., A. Krylov, A. Tyukavina, P.V. Potapov, S. Turubanova, B. Zutta, S. Ifo, B. Margono, F. Stolle, and R. Moore. 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11 (3). [https://dx.doi.org/10.1088/1748-9326/11/3/034008](https://dx.doi.org/10.1088/1748-9326/11/3/034008)\n","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"This dataset, created by the [[GLAD]](http://glad.geog.umd.edu/) (https://glad.geog.umd.edu/) (Global Land Analysis & Discovery) lab at the University of Maryland and supported by Global Nature Watch, is the first Landsat-based alert system for tree cover loss. While most existing loss alert products use 250-meter resolution MODIS imagery, these alerts have a 30-meter resolution and thus can detect loss at a much finer spatial scale. These alerts are operational for land areas between 30 degrees north and south. \n\nNew Landsat 8 and 9 images are downloaded as they are posted online, assessed for cloud cover or poor data quality, and compared to the three previous years of Landsat-derived metrics (including ranks, means, and regressions of red, infrared and shortwave bands, and ranks of NDVI, NBR, and NDWI). The metrics and the latest Landsat image are run through seven decision trees to calculate a median probability of forest disturbance. Pixels with probability >50% are reported as tree cover loss alerts. The entire process is run in Google Earth Engine to ensure reliable updates and scalability. For more information on methodology, see the [paper in Environmental Research Letters](https://iopscience.iop.org/article/10.1088/1748-9326/11/3/034008). \nAlerts are not classified as high confidence until two or more out of four consecutive observations are labelled as tree cover loss. Alerts are removed from the dataset after four consecutive observations or more than 180 days if they are not classified as high confidence. You can choose to view only high confidence alerts in the menu, though keep in mind that using only high confidence alerts misses the newest detections of tree cover loss. \n\nThe GLAD-L alerts are available on \\*\\*Google Earth Engine\\*\\* with asset ID: projects/glad/alert/UpdResult \n","function":"Monitor tree cover disturbance in near-real-time using Landsat imagery","cautions":" \n- Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests, alerts may indicate timber harvesting operations, without a conversion to a non-forest land use.  \n- The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this.  \n \n- We do not recommend using deforestation alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. Additionally, updates to the methodologies and variation in cloud cover between months and years pose additional risks to using deforestation alerts for inter/intra-annual comparison.  \n \n- The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations are provided in the Places to Watch data layer.  \n \n-   While Landsat 8 and 9 satellites (formerly Landsat 7 and 8) together have a revisit period of 8 days, cloud cover can limit the availability of imagery, particularly in the wet season. Alert dates represent the instance of detection, though tree cover loss could have taken place earlier, possibly weeks earlier, due to persistent cloud cover. Note that the GLAD-L alerts were formerly sourced from Landsat 7 imagery which had a known scan line issue that sometimes resulted in false positive alerts, until April 2023 when the input was switched to Landsat 9 instead. \n \n- GLAD-L alerts are within “tree cover” which is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations. “Tree cover loss” indicates the canopy removal of at least half a pixel and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.  \n \n- The confidence level may change retroactively as source data is updated; alerts that have not become high confidence within 180 days are removed from the dataset.  For GLAD-L alerts, every new alert starts out as \"low confidence\" when loss is first detected (e.g. one anomalous result is detected). Alerts are then classified as high confidence when forest loss has also been identified at that location in a second satellite image within four additional (5 total) cloud-free observations.   \n \n- Once an alert pixel reaches high confidence, forest loss will not be detected by the GLAD-L system at that location again.  \n \n- In Peru, where the alert system was first developed, the authors evaluated the data to have 13.5% false positives (loss detected where none occurred), though the majority of those false positives (9.5%) occur on the edges of clearings. On edges, the 30 m Landsat pixels show a mix of forest and other land cover, which makes them prone to error in the system. The rate of false positives drops to 1% when only considering high confidence alerts. The data has 33% false negatives (undetected loss where it has occurred), though most of these occur in secondary forests—likely because the algorithm was created to capture primary forest loss. The higher rate of false negatives compared to false positives also indicates that the alerts are a conservative estimate of the tree cover loss that is actually occurring. \n \n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.   \n\n","key_restrictions":"","tags":["Forest Change"],"why_added":"Getting even nearer to real-time, first data set of Landsat alerts!","learn_more":"https://glad-forest-alert.appspot.com/","id":"f272d653-5675-4971-966f-1446a33e4896"},"versions":["v20260409","v20220601","v20260911","v20220711","v20250701","v202005","v20230401","v20210714","v20250101","v20230725","v20260828","v20250401","v20260912","v20260905","v20211001","v20260914","v20260917","v20221002","v20220101","v20260820","v20260821","v20240401","v20260824","v20260913","v20260909","v20230112","v20260831","v20260910","v20260916","v20260818","v20260902","v20260904","v20260826","v20260907","v20260823","v20240101","v20250927","v20260819","v20260830","v20260906","v20260827","v20260915","v20260829","v20260701","v20260908","v20260825","v20260901","v20260903","v20260822","v20231001","v20241001","v20240701"]},{"created_on":"2022-03-10T20:24:08.066302","updated_on":"2022-03-10T20:24:08.066308","dataset":"umd_glad_landsat_alerts_coverage","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897238","updated_on":"2023-05-04T13:11:58.897240","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Coverage Layer for GLAD-L","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"df024564-90e0-4c7a-984e-40529f0b920c"},"versions":["v2024","v2014"]},{"created_on":"2021-04-06T20:36:10.462163","updated_on":"2025-02-20T18:18:38.835793","dataset":"umd_glad_sentinel2_alerts","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897510","updated_on":"2026-08-05T14:43:24.633584","spatial_resolution":10,"resolution_description":"10 × 10 m","geographic_coverage":"Amazon basin","update_frequency":"Updated daily, image revisit time every 5 days ","scale":"regional","citation":"Use the following credit when this data is displayed: \nSource: “GLAD-S2 alerts”. GLAD/UMD, accessed through Global Nature Watch on [date] \nUse the following credit when this data is cited: \nPickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. Accessed through Global Nature Watch. [www.globalnaturewatch.org](http://www.globalnaturewatch.org/) on [date] \n","title":"Deforestation alerts (GLAD-S2)","subtitle":"daily, 10 m, Amazon, UMD/GLAD ","source":"Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. \\[https://glad.earthengine.app/view/s2-forest-alerts]\\(https://glad.earthengine.app/view/s2-forest-alerts)\n","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"This dataset is a forest loss alert product developed by the GLAD (Global Land Analysis and Discovery) lab at the University of Maryland. GLAD-S2 alerts utilize data from the European Space Agency’s Sentinel-2 mission, which provides optical imagery at a 10 m spatial resolution with a 5-day revisit time. The shorter revisit time, when compared to GLAD Landsat alerts, reduces the time to detect forest loss and between the initial detection of forest loss and classification as high confidence. This is particularly advantageous in wet and tropical regions, where persistent cloud cover may delay detections for weeks to months. GLAD-S2 alerts are available for primary forests in the Amazon basin from January 1st, 2019, to present, updated daily. \n\nNew Sentinel-2 images are analyzed as soon as they are acquired. Cloud, shadow, and water are filtered out of each new image, and a forest loss algorithm is applied to all remaining clear land observations. The algorithm relies on the spectral data in each new image in combination with spectral metrics from a baseline period of the previous two years. \nAlerts become high confidence when at least two of four subsequent observations are flagged as forest loss (this corresponds to “high,” “medium,” and “low” confidence loss on the GLAD app linked below). The alert date represents the date of forest loss detection. Users can choose to display only high confidence alerts on the map, but keep in mind this will filter out the most recent detections of forest loss. Additionally, forest loss will not be detected again on pixels with high confidence alerts. Alerts that have not become high confidence within 180 days are removed from the dataset. \n\nThe GLAD-S2 alerts are available on \\*\\*Google Earth Engine\\*\\* with asset ID: projects/glad/S2alert \n","function":"Monitor primary forest loss in near-real time using Sentinel-2 imagery ","cautions":" \n- Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests (more likely to happen in the GLAD-L system), alerts may indicate timber harvesting operations, without a conversion to a non-forest land use.  \n  \n- The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this.  \n \n- We do not recommend using deforestation alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. Additionally, updates to the methodologies and variation in cloud cover between months and years pose additional risks to using deforestation alerts for inter/intra-annual comparison.  \n \n- The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations are provided in the Places to Watch data layer.  \n \n- GLAD-S2 alerts are within the primary forest mask of [[Turubanova et al (2018)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta) in the Amazon river basin, with 2001-present forest loss from \\[Hansen et al. (2013)]\\(https://www.science.org/doi/10.1126/science.1244693) removed.   \n \n- The confidence level may change retroactively as source data is updated; alerts that have not become high confidence within 180 days are removed from the dataset.  For GLAD-S2 alerts, every new alert starts out as \"low confidence\" when loss is first detected (e.g. one anomalous result is detected). Alerts are then classified as high confidence when forest loss has also been identified at that location in a second satellite image within three additional (4 total) cloud-free observations. Once an alert pixel reaches high confidence, forest loss will not be detected by GLAD-S2 at that location again.  \n \n- The accuracy of this product has not been assessed. \n \n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.   \n\n","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":"https://glad.earthengine.app/view/s2-forest-alerts#lon=-64.47;lat=-9.98;zoom=11;","id":"66d24a0a-e9ce-40d4-bdde-d9cd510c5087"},"versions":["v20260825","v20211001","v20260912","v20260905","v20240401","v20260914","v20260917","v20230401","v20220412","v20260821","v20251001","v20260824","v20260913","v20260916","v20260909","v20260910","v20260823","v20260818","v20260904","v20260826","v20260911","v20260907","v20250408","v20260829","v20260819","v20260830","v20260915","v20250101","v20260827","v20260906","v20260820","v20240102","v20260701","v20260401","v20221001","v20260901","v20260903","v20260822","v20250701","v20220701","v20231001","v20230613","v20260101","v20230101","v20210406","v20241001","v20240701","v20220110","v20210707"]},{"created_on":"2021-03-23T19:16:51.654618","updated_on":"2021-03-23T19:16:51.654623","dataset":"umd_glad_sentinel2_alerts_coverage","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897492","updated_on":"2023-05-04T13:11:58.897493","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Amazonia","update_frequency":null,"scale":null,"citation":null,"title":"UMD GLAD+ Alerts Coverage","subtitle":null,"source":"University of Maryland","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b64121fb-92e8-4fbe-aaca-e2006acbd64d"},"versions":["v20210413","v20210323"]},{"created_on":"2022-11-02T23:25:02.953924","updated_on":"2022-11-02T23:25:02.953930","dataset":"umd_land_cover","is_downloadable":true,"metadata":{},"versions":["v2020"]},{"created_on":"2022-06-22T18:14:27.513830","updated_on":"2022-06-22T18:14:27.513837","dataset":"umd_land_cover_2000_2020","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897259","updated_on":"2024-10-04T16:28:02.014516","spatial_resolution":30,"resolution_description":"30","geographic_coverage":"Global","update_frequency":"Unknown","scale":"","citation":"Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing. [https://doi.org/10.3389/frsen.2022.856903](https://doi.org/10.3389/frsen.2022.856903). Accessed through Resource Watch, (date). [www.resourcewatch.org](https://www.resourcewatch.org).","title":"Land Cover 2000-2020","subtitle":null,"source":"University of Maryland","license":"Creative Commons Attribution License (https://glad.umd.edu/dataset/GLCLUC2020)","data_language":"en","overview":"The global land use and land cover maps were created by the Global Land Analysis and Discovery Lab (GLAD) laboratory, and the data is available at 30 m spatial resolution for 2000 and 2020. The GLAD laboratory used the spatiotemporally consistent [Landsat Analysis Ready Data (GLAD ARD)](https://glad.umd.edu/ard) to quantify changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent over the twenty year period. Each thematic product was independently derived using state-of-the-art, locally and regionally calibrated machine learning tools. The dataset was validated using a statistical sampling which confirms its high accuracy. The Global Land Analysis and Discovery Lab (GLAD) laboratory in the Department of Geographical Sciences at UMD investigates methods, causes, and impacts of global land surface change. Earth observation imagery is the primary data source, and the land cover extent and change is the primary topic of interest. GLAD aspires to generate new science insights concerning land resources, educate the next generation of remote sensing-based land change scientists, and disseminate land monitoring capabilities to operational settings nationally and internationally.<br>","function":"Global land use and land cover map for 2000 and 2020","cautions":"- Land cover mapping was limited by the Landsat clear-sky data availability. The incompleteness of the Landsat observation time series decreases the map accuracy in regions with persistent cloud cover.\r\n  \n  \n- Discrete land cover classes mapping in heterogeneous landscapes was constrained by the high proportion of mixed pixels at the Landsat spatial resolution. Most LULC classes have higher map accuracy over large homogeneous areas compared to fragmented landscapes and class patch edges.\r\n  \n  \n- The spectral similarity between different LCLU classes may preclude class discrimination.\r\n  \n  \n- The forest height product has issues related to GEDI data quality and Landsat data availability. Small changes in forest height between the years 2000 and 2020 may not indicate the actual forest structure change but represent the noise in the model outputs.\r\n  \n  \n- Dynamic classes (LCLU class loss and gain) have lower accuracies compared to static maps.\r\n  \n  \n- Map-based estimates are not adequate for national and international reporting due to unknown spatial and temporal variability of map uncertainty.\r","key_restrictions":"Creative Commons Attribution License (CC BY)","tags":["geospatial","global","historical","raster","climate_change","climate","time_period","land_use","land_cover","land","wetland","forest","urban","urbanization","cropland","water","snow_and _ice","glacier","SDG_13_Climate_Action","carbon"],"why_added":"Adding to MapBuilder","learn_more":"https://doi.org/10.3389/frsen.2022.856903","id":"838b3f87-139b-4454-bd8e-c0b629733382"},"versions":null},{"created_on":"2021-06-17T20:08:10.801682","updated_on":"2021-08-23T20:43:42.232516","dataset":"umd_modis_burned_areas","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897347","updated_on":"2026-08-05T14:43:25.031615","spatial_resolution":500,"resolution_description":"500m x 500m","geographic_coverage":"Global","update_frequency":"Monthly","scale":"global","citation":"Giglio, L. et al. (2018). “Monthly MODIS Burned Area Product (MCD64A1 v006).” Accessed on [date] from Global Nature Watch.","title":"Global Burned Areas","subtitle":null,"source":"Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. *Remote sensing of environment*, 217, 72-85.","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"The MODIS Burned Area data product uses a multi-stage algorithm to classify individual 500-m grid cells as either burned or unburned over a single calendar month. For a single MODIS tile, the change-detection algorithm 1) produces composite imagery that summarizes persistent changes in the time series of a burn-sensitive vegetation index, 2) uses spatial and temporal active-fire information to guide the statistical characterization of burn-related and non-burn-related change, and 3) estimates a probabilistic threshold to identify grid cells containing burned areas. One month of daily observations before and after the mapping period are required to accommodate the moving windows employed in the change-detection process, so three consecutive months of observations are required to map one calendar month of burning. In addition to detecting the burned area extent, the data product also approximates the day of burning in each month for each 500-m grid cell based on active fire detections.","function":"Displays monthly burned area extent and date of burn based on differences in a burn-sensitive Vegetation Index derived from MODIS shortwave infrared surface reflectance bands","cautions":"- Burned areas in cropland should generally be treated as low confidence and may be under-reported due to the inherent difficulty in mapping agricultural burning reliably [see Hall et al. 2016 for more information](https://doi.org/10.1016/j.rse.2016.07.022)<br>- The monthly products for August 2000 and June 2001 are heavily degraded due to extended Terra MODIS outages<br>- The Aqua MODIS satellite experienced a failure of about two weeks starting August 16, 2020; loss of data in Africa, eastern Asia, Indonesia, and Ocean a are not expected to significantly degrade the MCD64A1 burned are product in these regions since the Terra MODIS continued to function normally<br>- A global accuracy assessment for burned areas found an overall accuracy of 97%, commission error (i.e., false positive) of 24%, omission error (i.e., false negative) rate of 37%, producer’s accuracy of 63%, and user’s accuracy of 76%; temporal accuracy was 44% for same-day fire detection, and 68% within two days","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":null,"id":"bb8c246c-5911-444c-b1da-c980098a13e0"},"versions":["v20221104","v202207","v2021060","v20220129"]},{"created_on":"2021-05-17T20:39:05.757905","updated_on":"2025-02-11T16:11:25.794919","dataset":"umd_regional_primary_forest_2001","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897350","updated_on":"2026-08-05T14:43:25.383823","spatial_resolution":30,"resolution_description":"30 × 30 meters","geographic_coverage":"Pan-Tropical","update_frequency":" ","scale":"regional","citation":"Turubanova, S., Potapov, P.V., Tyukavina, A. and Hansen, M.C., 2018. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters, 13(7), p.074028. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Primary Forests","subtitle":"30m, tropics, UMD/GLAD","source":"Turubanova, S., Potapov, P.V., Tyukavina, A. and Hansen, M.C., 2018. [Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia](http://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta). Environmental Research Letters, 13(7), p.074028.","license":"","data_language":"English","overview":"Primary forests are among the most biodiverse forests, providing a multitude of ecosystem services, making them crucial to monitor for national land use planning and carbon accounting. This data set defines primary forests as \"mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history.\" Researchers classified Landsat images into primary forest data, using a separate algorithm for each region.","function":"This data set maps the extent of primary forests in the global pan-tropical regions in 2001.","cautions":"","key_restrictions":"","tags":["Land Cover, Country data"],"why_added":"This data is the most up to date primary forest layer published by UMD, and covers more regions than previous primary forest data sets do.","learn_more":"https://glad.umd.edu/dataset/primary-forest-humid-tropics","id":"c2ca1553-730d-45d6-948e-843398bbc22e"},"versions":["v201901"]},{"created_on":"2021-01-22T20:00:58.189411","updated_on":"2021-01-22T20:00:58.189418","dataset":"umd_soy_planted_area","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897426","updated_on":"2026-08-05T14:43:25.673013","spatial_resolution":null,"resolution_description":"30 x 30m","geographic_coverage":"South America","update_frequency":"Annual","scale":"regional","citation":"Source: Song et. al. 2021. “Soy Planted Area”. Accessed through Global Nature Watch on [date].","title":"Soy Planted Area","subtitle":"(2000-2021, S. America, Song et al. 2021)","source":"Song, X.P., M.C. Hansen, P. Potopov, B. Adusei, J. Pickering, M. Adami, A. Lima, V. Zalles, S.V. Stehman, D.M. Di Bella, C.M. Cecilia, E.J. Copati, L.B. Fernandes, A. Hernandez-Serna, S.M. Jantz, A.H. Pickens, S. Turubanova, and A. Tyukavina. 2021. Massive soybean expansion in South America since 2000 and implications for conservation.","license":null,"data_language":"English","overview":"This data set provides a spatially-explicit overview of the annual planted area of soybean cultivation across South America’s agricultural frontiers, as described in Song et. al. (2021). Data is available annually from 2000 to 2019 at a 30m spatial resolution for the following biomes: Amazonia, Atlantic Forest, Caatinga, Cerrado, Chaco, Chiquitania, Pampas, and Pantanal.<br><br>Song et. al. (2021) integrates long-term satellite imagery with three years of continent-wide field observations to produce annual maps of soybean cultivation. Researchers collected Landsat and MODIS imagery for November 1 through April 30 of each growing season and created 16-day time-series with phenological metrics. These metrics were combined with data on elevation, slope, and aspect and training data from 225 sampling areas to develop soybean probability maps for each year. The field observations were then used to assess the accuracy of the resulting maps, which are constrained to match the areas of field observations. <br><br>This dataset addresses the absence of long-term, consistent government statistics by expanding the spatial and temporal scope of previous maps of soybean cultivation. The maps and workflow described in Song et. al. (2021) provides key historical baseline information for tracking commodity-driven deforestation and highlights emerging hotspots of soybean expansion.","function":"Provides a long-term, continental perspective of soybean expansion at a high spatial resolution","cautions":"- Each yearly threshold refers to harvest year. For example, 2001 refers to the 2000-2001 agricultural season.<br>- The accuracy of the 2017, 2018, and 2019 maps were assessed as 96%, 94%, and 96%, respectively. The soy class had 8-14% rate of false positives, and a 14-17% rate of false negatives. The accuracy of the maps from previous years could not be assessed, but was found to generally match total annual harvest area statistics reported by the US Department of Agriculture.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"886f6ab7-6c8b-4228-a37f-dac817f81c6b"},"versions":["v2","v3","v1","v4"]},{"created_on":"2023-10-16T22:06:31.937429","updated_on":"2023-10-16T22:06:31.937434","dataset":"umd_soy_planted_area_buffered_10km","is_downloadable":true,"metadata":{},"versions":["v2022"]},{"created_on":"2021-03-03T17:09:14.125914","updated_on":"2021-08-25T17:44:47.545096","dataset":"umd_tree_cover_density_2000","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897143","updated_on":"2026-08-05T14:43:25.992653","spatial_resolution":30,"resolution_description":"30 × 30 meters","geographic_coverage":"Global land (excluding Antarctica and Arctic islands)","update_frequency":"","scale":"global","citation":"Use the following credit when these data are displayed:\nSource: Hansen/UMD/Google/USGS/NASA, accessed through Global Nature Watch\n\nUse the following credit when these data are cited:\nHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from:https://glad.umd.edu/dataset/global-2010-tree-cover-30-m. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org \n","title":"Tree cover","subtitle":"(2000/2010, Hansen/UMD/Google/USGS/NASA)","source":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” _Science_ 342 (15 November): 850–53. [doi: 10.1126/science.1244693](https://doi.org/10.1126/science.1244693). Data available from: [https://glad.earthengine.app/view/global-forest-change](https://glad.earthengine.app/view/global-forest-change).\n","license":"[CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This data set, a collaboration between the [GLAD](http://glad.geog.umd.edu/) (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the years 2000 and 2010 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).<br><br>Data in this layer were generated using multispectral satellite imagery from the [Landsat 7](http://landsat.usgs.gov/) thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.<br><br>The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.","function":"Identifies areas of tree cover","cautions":"For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities.","key_restrictions":"CC BY 4.0","tags":["Land Cover"],"why_added":"Important baseline for the Hansen change layers","learn_more":"https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html","id":"ca602916-e706-4c2f-b0f1-23dc3976a47f"},"versions":["v1.6","v1.8"]},{"created_on":"2021-03-02T21:41:57.300919","updated_on":"2021-03-02T21:41:57.300929","dataset":"umd_tree_cover_density_2010","is_downloadable":true,"metadata":{"created_on":"2025-04-02T17:40:45.410950","updated_on":"2025-04-02T17:40:45.410954","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":null,"subtitle":null,"source":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” _Science_ 342 (15 November): 850–53. [doi: 10.1126/science.1244693](https://doi.org/10.1126/science.1244693). Data available from: [https://glad.earthengine.app/view/global-forest-change](https://glad.earthengine.app/view/global-forest-change).\n","license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html","id":"d58e284b-aac2-46a7-a2bd-c2b628adf784"},"versions":["v1.6"]},{"created_on":"2021-06-14T20:33:41.036741","updated_on":"2021-06-14T20:33:41.036745","dataset":"umd_tree_cover_gain","is_downloadable":true,"metadata":{"created_on":"2024-07-11T15:09:23.802886","updated_on":"2026-08-05T14:43:26.340466","spatial_resolution":null,"resolution_description":"30 × 30 meters","geographic_coverage":"Global land area (excluding Antarctica and other Arctic islands)","update_frequency":"Every three years","scale":"global","citation":"Use the following credit when these data are displayed:\nSource: Hansen/UMD/Google/USGS/NASA, accessed through Global Nature Watch\n\nUse the following credit when these data are cited:\nHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” *Science* 342 (15 November): 850–53. Data available on-line from:http://earthenginepartners.appspot.com/science-2013-global-forest. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org \n","title":"Tree cover gain","subtitle":"(12 years, 30m, global, Hansen/UMD/Google/USGS/NASA)","source":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” *Science* 342 (15 November): 850–53. Data available from: [earthenginepartners.appspot.com/science-2013-global-forest](http://earthenginepartners.appspot.com/science-2013-global-forest).","license":"[CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This data set, a collaboration between the [GLAD](http://glad.geog.umd.edu/) (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover gain across all global land (except Antarctica and other Arctic islands) at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were generated using multispectral satellite imagery from the [Landsat 7](http://landsat.usgs.gov/) thematic mapper plus (ETM+) sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30 meter pixels) in the satellite images were assembled and a supervised learning algorithm was then applied to identify per pixel tree cover gain.<br><br>Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations.<br><br>When zoomed out (< zoom level 13), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).<br><br>The tree cover canopy density of the displayed data is >50%.<br>","function":"Identifies areas of tree cover gain","cautions":"In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. “Gain” is defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations. <br><br>Due to variation in research methodology and date of content, tree cover, loss, and gain data sets cannot be compared accurately against each other. Accordingly, “net” loss cannot be calculated by subtracting figures for tree cover gain from tree cover loss, and current (post-2000) tree cover cannot be determined by subtracting figures for annual tree cover loss from year 2000 tree cover. <br><br>The authors evaluated the overall prevalence of false positives (commission errors) in this data at 24%, and the prevalence of false negatives (omission errors) at 26%, though the accuracy varies by biome and thus may be higher or lower in any particular location. Read our [blog series](http://blog.globalforestwatch.org/data/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1.html) on the accuracy of this data for more information.<br>","key_restrictions":"CC BY 4.0","tags":null,"why_added":"Best available global data of tree cover gain","learn_more":"http://science.sciencemag.org/content/342/6160/850","id":"d797dc72-1353-4a46-9467-fd226b8d3d88"},"versions":["v202206","v1.2"]},{"created_on":"2022-09-15T19:00:23.555769","updated_on":"2025-04-02T17:45:45.513650","dataset":"umd_tree_cover_gain_from_height","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897294","updated_on":"2026-08-05T14:43:26.654962","spatial_resolution":null,"resolution_description":"30 × 30 meters","geographic_coverage":"Global","update_frequency":null,"scale":null,"citation":"Use the following credit when this data is displayed:\nAccessed through Global Nature Watch on [date]. www.globalnaturewatch.org. \n\nUse the following credit when this data is cited:\nPotapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. [https://doi.org/10.3389/frsen.2022.856903](https://doi.org/10.3389/frsen.2022.856903)","title":"Tree Cover Gain","subtitle":"(20 years, 30 m, global, UMD/NASA GEDI)","source":"Potapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. https://doi.org/10.3389/frsen.2022.856903","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":null,"overview":"This data set from the [GLAD](https://glad.umd.edu/) (Global Land Analysis & Discovery) lab at the University of Maryland measures areas of tree cover gain from the year 2000 to 2020 across the globe at 30 × 30 meter resolution, displayed as a 20-year cumulative layer. Tree cover gain was determined using tree height information from the years 2000 and 2020. Tree height was modeled by the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. Gain was identified where pixels had tree height ≥5 m in 2020 and tree height <5 m in 2000. <br><br>Tree cover gain may indicate a number of potential activities, including natural forest growth, the tree crop rotation cycle, or tree plantation management. <br><br>When zoomed out (< zoom level 12), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 12). ","function":"Identifies areas of tree cover gain","cautions":"In this data set, “tree cover” is defined as woody vegetation with the height of 5 m and taller, and may take the form of natural woodlands, forests, or tree plantations across a range of canopy densities. Tree cover gain does not equate directly to restoration, afforestation or reforestation. <br><br>Due to variation in research methodology and date of content, tree cover, gain, and annual loss data sets cannot be compared accurately against each other. Accordingly, “net” cannot be calculated by subtracting figures for tree cover gain from the annual tree cover loss data set. Instead, the net tree cover change layer should be used, which was calculated exclusively from tree height data.  <br><br>Integrated use of other products such as canopy cover density data also available on GNW should be performed with caution. <br><br>The authors evaluated the accuracy of the product, and the overall accuracy was found to be 99.3%, commission error (false positives) of 28.6%, and omission error (false negatives) of 42.2%. The accuracy does vary by biome and thus may be higher or lower in any particular location. As the omission error is higher than the commission error, this indicates that the product provides conservative estimates of forest dynamics.  <br><br>There was confusion between forest enhancement (existing forest height increase) and forest gain (establishment of forests within the year 2000 non-forest land), and this was more prominent in boreal forest areas where forest height in the year 2000 was difficult to determine. ","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://glad.umd.edu/dataset/GLCLUC2020","id":"97df4638-eef3-4532-bc94-57952751e941"},"versions":["v20240126","v202206"]},{"created_on":"2022-03-29T21:07:30.712427","updated_on":"2022-03-29T21:07:30.712435","dataset":"umd_tree_cover_height_2000","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897211","updated_on":"2023-05-04T13:11:58.897213","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"UMD Tree Cover Height 2000","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"8ee17e0c-3584-45df-93b7-3547da576a1b"},"versions":["v2022"]},{"created_on":"2020-10-13T15:36:54.524977","updated_on":"2025-02-11T16:11:36.457030","dataset":"umd_tree_cover_height_2019","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897481","updated_on":"2026-08-05T14:43:27.082439","spatial_resolution":30,"resolution_description":null,"geographic_coverage":"Global, with prototype data above 52°N","update_frequency":"","scale":"global","citation":"P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, and M. Hofton. (2020). Mapping and monitoring global forest canopy height through the integration of GEDI and Landsat data. https://doi.org/10.5281/zenodo.4008406. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Tree cover height","subtitle":null,"source":"P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton. (2020). Mapping and monitoring global forest canopy height through the integration of GEDI and Landsat data. https://doi.org/10.5281/zenodo.4008406.","license":"","data_language":"English","overview":"A new, 30-m spatial resolution global forest canopy height map was developed through the integration of the [Global Ecosystem Dynamics Investigation] (https://gedi.umd.edu/) (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height between 52°N and 52°S globally. The Global Land Analysis and Discover team at the University of Maryland ([UMD GLAD] (https://glad.umd.edu/)) integrated the GEDI data available to date (April-October 2019) with the year 2019 Landsat analysis-ready time-series data ([Landsat ARD] (https://glad.umd.edu/ard/home)). The GEDI RH95 (relative height at 95%) metric was used to calibrate the model. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling. The 'moving window' locally calibrated and applied regression tree ensemble model was implemented to ensure high quality of forest height prediction and global map consistency. The model was extrapolated in the boreal regions (beyond the GLAD data range) to create the global forest height prototype map.","function":"Show the height of global forest canopy in the year 2019.","cautions":"The global forest height map is a prototype product that has known issues related to GEDI data quality and Landsat data availability. GEDI data overestimate forest height on slopes within temperate and subtropical mountain grasslands, e.g. in New Zealand and Lesotho. The tree height over cities and suburbs may be confounded with the building height, as GEDI data do not discriminate between the height of vegetation and man-made objects. The GEDI calibration uncertainties (specifically, geolocation precision and land surface height estimation) may be responsible for some of the map errors. The tree height model saturated above 30m and may not adequately represent the height of the tallest trees. The global product will be updated in the future to address most of the issues.","key_restrictions":"","tags":["Land Cover"],"why_added":"","learn_more":"https://glad.umd.edu/dataset/gedi/","id":"0a7f29fb-3faf-4972-be2e-c541f3f604bb"},"versions":null},{"created_on":"2022-03-29T21:07:36.621848","updated_on":"2025-02-11T16:11:53.569558","dataset":"umd_tree_cover_height_2020","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897273","updated_on":"2026-08-05T14:43:27.363780","spatial_resolution":null,"resolution_description":"30 meters","geographic_coverage":"Global, with prototype data above 52°N","update_frequency":null,"scale":"global","citation":"Potapov et al. 2021. Mapping global forest canopy height through integration of GEDI and Landsat data.\nAccessed through Global Nature Watch on [date]. \\[www.globalnaturewatch.org]\\(www.globalnaturewatch.org).\n","title":"Tree cover height","subtitle":"2000/2020, 30m, global, UMD/NASA GEDI","source":"Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M.C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C.E. and Armston, J., 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, p.112165. [https://doi.org/10.1016/j.rse.2020.112165](https://doi.org/10.1016/j.rse.2020.112165)","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"A new, 30-m spatial resolution global forest canopy height map was developed through the integration of the [Global Ecosystem Dynamics Investigation](https://gedi.umd.edu/) (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height between 52°N and 52°S globally. The Global Land Analysis and Discover team at the University of Maryland ([UMD GLAD](https://glad.umd.edu/)) integrated the GEDI data available to date (April-October 2019) with the year 2019 Landsat analysis-ready time-series data ([Landsat ARD](https://glad.umd.edu/ard/home)). The GEDI RH95 (relative height at 95%) metric was used to calibrate the model. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling. The “moving window” locally calibrated and applied regression tree ensemble model was implemented to ensure high quality of forest height prediction and global map consistency. The model was extrapolated in the boreal regions (beyond the GEDI data range) to create the global forest height prototype map.","function":"Show the height of global forest canopy in the years 2000 and 2020.","cautions":"The global forest height map is a prototype product that has known issues related to GEDI data quality and Landsat data availability. GEDI data overestimate forest height on slopes within temperate and subtropical mountain grasslands, e.g. in New Zealand and Lesotho. The tree height over cities and suburbs may be confounded with the building height, as GEDI data do not discriminate between the height of vegetation and man-made objects. The GEDI calibration uncertainties (specifically, geolocation precision and land surface height estimation) may be responsible for some of the map errors. The tree height model saturated above 30m and may not adequately represent the height of the tallest trees. The global product will be updated in the future to address most of the issues.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"[https://glad.umd.edu/dataset/GLCLUC2020](https://glad.umd.edu/dataset/GLCLUC2020) ","id":"a99a1f67-16ed-4367-b0aa-31178fda8c17"},"versions":["v2022","v2022.1"]},{"created_on":"2021-02-15T23:51:03.128862","updated_on":"2025-02-11T16:10:48.659268","dataset":"umd_tree_cover_loss","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897434","updated_on":"2026-08-05T14:43:27.691193","spatial_resolution":30,"resolution_description":"30 m","geographic_coverage":"Global land area (excluding Antarctica and other Arctic islands).","update_frequency":"Annual","scale":null,"citation":"Hansen et al., 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.”. Accessed through Global Nature Watch on 21/11/2024[date]. [www.globalnaturewatch.org](www.globalnaturewatch.org) \n\n","title":"Tree cover loss","subtitle":"annual, 30m, global, UMD GLAD","source":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” _Science_ 342 (15 November): 850–53. [doi: 10.1126/science.1244693](https://doi.org/10.1126/science.1244693). Data available from: [https://glad.earthengine.app/view/global-forest-change](https://glad.earthengine.app/view/global-forest-change).\n","license":"[CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)","data_language":"en","overview":"- This data set, a collaboration between the Global Land Analysis & Discovery (GLAD) (<https://glad.geog.umd.edu/>) lab at the University of Maryland (UMD), Google, USGS, and NASA, measures areas of tree cover loss across all global land (except - Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval, and additional images used for 2023, 2024 and 2025. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. \n- In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time, and is biome-dependent. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced. \n- This data set has been updated five times since its creation, and now includes loss up to 2025 (Version 1.13). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2025, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Since the entire historical timeseries was not reprocessed with the updated methodology, time-series assessments should be performed with caution. Read more about the Version 1.13 update here: <https://storage.googleapis.com/earthenginepartners-hansen/GFC-2025-v1.13/download.html> and access on GEE here: <https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2025_v1_13>. \n- When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). \n- The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold. \n","function":"Identifies areas of gross tree cover loss","cautions":"- In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.  \n- Due to variation in research methodology and date of content, tree cover, loss, and gain data sets on GNW cannot be compared accurately against each other. Accordingly, “net” loss cannot be calculated by subtracting figures for tree cover gain from tree cover loss, and current (post-2000) tree cover cannot be determined by subtracting figures for annual tree cover loss from year 2000 tree cover.  \n- The 2011-2025 data was produced using an updated methodology: <https://storage.googleapis.com/earthenginepartners-hansen/GFC-2025-v1.13/download.html>. Comparisons between the original 2001-2010 data and the 2011-2025 update should be performed with caution.  \n- In the original publication, the authors evaluated the overall prevalence of false positives (commission errors) in this data at 13%, and the prevalence of false negatives (omission errors) at 12%, though the accuracy varies by biome and thus may be higher or lower in any particular location. The model often misses disturbances in smallholder landscapes, resulting in lower accuracy of the data in sub-Saharan Africa, where this type of disturbance is more common. Largely because of a delay between an actual disturbance and the event being observed by the satellite imagery, the authors are 75% confident that the loss occurred within the stated year, and 97% confident that it occurred within a year before or after. Users of the data can smooth out such uncertainty by examining the average over multiple years. Read our blog series: [globalnaturewatch.org/blog/data/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1.html](http://globalnaturewatch.org/blog/data/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1.html) on the accuracy of this data for more information. \n","key_restrictions":"CC BY 4.0","tags":["Forest Change"],"why_added":"Best available global data on forest change","learn_more":"https://storage.googleapis.com/earthenginepartners-hansen/GFC-2025-v1.13/download.html","id":"340b0b76-8b86-4939-bcbf-dd6c682bc3de"},"versions":["v1.9.1","v1.10","v1.8","v1.12","v1.11","v1.13","v1.9"]},{"created_on":"2022-03-10T16:08:42.292446","updated_on":"2025-01-31T18:54:39.892436","dataset":"umd_tree_cover_loss_from_fires","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897201","updated_on":"2026-08-05T14:43:28.095351","spatial_resolution":null,"resolution_description":"30 meters","geographic_coverage":"Global land area (excluding Antarctica and other Arctic islands)","update_frequency":"Annual","scale":null,"citation":"Use the following credit when this data is displayed:  \n“Tree cover loss due to fire.” UMD/GLAD. Accessed from Global Nature Watch on [date]. [www.globalnaturewatch.org](https://www.globalnaturewatch.org/)  \n  \nUse the following credit when this data is cited:  \nTyukavina, A., P. Potapov, M.C. Hansen, A.H. Pickens, S.V. Stehman, S. Turubanova, D. Parker, et al. 2022. “Global Trends of Forest Loss Due to Fire From 2001 to 2019.” Frontiers in Remote Sensing 3. [https://www.frontiersin.org/article/10.3389/frsen.2022.825190](https://www.frontiersin.org/article/10.3389/frsen.2022.825190). \n","title":"Tree cover loss due to fire","subtitle":"(annual, 30m, global, UMD/GLAD)","source":"Tyukavina, A., P. Potapov, M.C. Hansen, A.H. Pickens, S.V. Stehman, S. Turubanova, D. Parker, et al. 2022. “Global Trends of Forest Loss Due to Fire From 2001 to 2019.” Frontiers in Remote Sensing 3. [https://www.frontiersin.org/article/10.3389/frsen.2022.825190](https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.825190/).  \n  \nData available at [https://glad.umd.edu/dataset/Fire\\_GFL/](https://glad.umd.edu/dataset/Fire\\_GFL/).\n","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This data is produced by the [Global Land Analysis & Discovery (GLAD) lab](https://glad.geog.umd.edu/) at the University of Maryland (UMD) and measures areas of tree cover loss due to fire compared to all other drivers across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30-meter resolution. The data were generated using global Landsat-based annual change detection metrics for 2001-2025 as input data to a set of regionally calibrated classification tree ensemble models. The result of the mapping process can be viewed as a set of binary maps (tree cover loss due to fire vs. tree cover loss due to all other drivers). \n\nIn this data set, “tree cover” is defined as all vegetation greater than 5 meters in height and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time and is biome-dependent. Tree cover loss due to fire may be caused by natural or human-induced fire activity. \nThe analysis method for the base tree cover loss map on GNW that is used as input for this dataset has been modified in numerous ways to improve detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations for data covering the 2011-2025 period. Due to these changes, comparing trends across the 2000-2010 and 2011-2025 periods should be [performed with caution](https://www.globalnaturewatch.org/blog/data/20-years-global-tree-cover-loss-data-trends/). You can read more about updates to the tree cover loss modeling process [here](https://storage.googleapis.com/earthenginepartners-hansen/GFC-2023-v1.11/download.html). \n\nWhen zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30-meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). \nThe tree cover density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold. \nThis data is available for download from UMD [here](https://glad.umd.edu/dataset/Fire_GFL/) and also accessible through Google Earth Engine using the following image IDs (view the download page from UMD for more information): \n- Fire certainty: users/sashatyu/2001-2025\\_fire\\_forest\\_loss \n- Date of loss: users/sashatyu/2001-2025\\_fire\\_forest\\_loss\\_annual \n\n","function":"Identifies areas of tree cover loss due to fires compared to all other drivers of tree cover loss","cautions":"-  Tree cover is defined as all vegetation greater than 5 meters in height and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time and is biome-dependent. This may not necessarily equate to deforestation. \n- This dataset does not include low-intensity and understory forest fires that do not result in substantial tree canopy loss at the scale of a 30 m pixel. Fires within recent forest loss due to other drivers are also excluded. Therefore, this data does not include the burning of felled logs following mechanical canopy removal, which is common in slash and burn agriculture and large-scale deforestation. \n- Consistent with the global tree cover loss map on GNW, this data only maps the first stand replacing forest disturbance for each pixel between 2001 and 2025. Areas of tree cover loss due to fire that occurred when forest regrowth followed an initial disturbance early in the study period are not detected in this data. \n- Comparing trends across the 2000-2010 and 2011-2025 periods should be [performed with caution](https://www.globalnaturewatch.org/blog/data/20-years-global-tree-cover-loss-data-trends/). \n\n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"c4417eb1-2874-4087-a765-7c8a675c42d3"},"versions":["v20220309","v20230424","v20230315","v20240301","v20220424","v20220324","v1.12","v1.13"]},{"created_on":"2021-05-06T13:31:36.141953","updated_on":"2021-05-06T13:31:36.141962","dataset":"usa_conservation_easements","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897376","updated_on":"2023-05-04T13:11:58.897377","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"USA Conservation Easements","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"3d8a476a-66fb-4961-bdce-46293bef2fe1"},"versions":["v2014"]},{"created_on":"2021-09-08T14:31:37.378532","updated_on":"2021-09-08T14:31:37.378538","dataset":"usgs_usa_land_cover","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897246","updated_on":"2026-08-05T14:43:28.374350","spatial_resolution":30,"resolution_description":null,"geographic_coverage":"Contiguous United States (excluding Alaska and Hawaii)","update_frequency":"Every 5 years","scale":"national","citation":"Multi-Resolution Land Characteristics Consortium. \"2016 USA land cover.\"  National Land Cover Database. Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org ","title":"USA land cover","subtitle":null,"source":"Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. “A comprehensive change detection method for updating the National Land Cover Database to circa 2016.” Remote Sensing of Environment, 132: 159 – 175.","license":"U.S. Federal data is offered for free and without restriction. [https://project-open-data.cio.gov/policy-memo/](https://project-open-data.cio.gov/policy-memo/)","data_language":"","overview":"The National Land Cover Database 2016 (NLCD 2016) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2016 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2016. As with two previous NLCD land cover products NLCD 2016 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2016 is based primarily on a decision-tree classification of circa 2016 Landsat satellite data.\n\nLand cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies.","function":"Identifies land cover for the United States, utilizing the National Land Cover Database (NLCD) for 2016","cautions":"NLCD land cover products have been published for 2001, 2006, 2011 and 2016. A formal accuracy assessment has not been conducted for NLCD 2016 Land Cover.\n\nAn assessment of accuracy for the NLCD land cover product found overall accuracies for the 2001 and 2006 products were 79% and 78%, respectively, with accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates.","key_restrictions":"","tags":["Country data"],"why_added":"","learn_more":"http://www.mrlc.gov/index.php","id":"fdb3126e-48bd-4931-a5bb-9b3585ba7223"},"versions":["v2016"]},{"created_on":"2024-11-20T17:43:02.037839","updated_on":"2024-11-20T17:43:02.037843","dataset":"wat_006_projected_water_stress","is_downloadable":true,"metadata":{},"versions":null},{"created_on":"2021-01-20T21:26:04.885095","updated_on":"2025-01-31T23:41:40.655242","dataset":"wcs_forest_landscape_integrity_index","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897423","updated_on":"2026-08-05T14:43:28.685052","spatial_resolution":300,"resolution_description":"300 x 300m","geographic_coverage":"Global","update_frequency":"As new data becomes available","scale":"global","citation":"Use the following credit when these data are displayed:  \n“Forest Landscape Integrity Index”. WCS. Accessed from Global Nature Watch on [date]. www.globalnaturewatch.org.   \n\nUse the following credit when these data are cited:  \nGrantham, H.S., A. Duncan, T.D. Evans, K.R. Jones, H.L. Beyer, R. Schuster, J. Walston, et al. 2020. “Anthropogenic Modification of Forests Means Only 40% of Remaining Forests Have High Ecosystem Integrity.” Nature Communications 11 (1): 5978. doi:10.1038/s41467-020-19493-3. \n","title":"Forest Landscape Integrity Index","subtitle":"(2019, 300 m, global, WCS)","source":"Grantham, H.S., A. Duncan, T.D. Evans, K.R. Jones, H.L. Beyer, R. Schuster, J. Walston, et al. 2020. “Anthropogenic Modification of Forests Means Only 40% of Remaining Forests Have High Ecosystem Integrity.” Nature Communications 11 (1): 5978. [doi:10.1038/s41467-020-19493-3]( https://www.nature.com/articles/s41467-020-19493-3)\n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"To produce the Forest Landscape Integrity Index (FLII), four data sets were combined representing: (i) forest extent; (ii) ‘observed’ pressure from high impact, localized human activities for which spatial datasets exist, specifically: infrastructure, agriculture, and recent deforestation; (iii) ‘inferred’ pressure associated with edge effects, and other diffuse processes, (e.g. activities such as hunting and selective logging) modelled using proximity to observed pressures; and iv) anthropogenic changes in forest connectivity due to forest loss. These datasets were combined to produce an index score for each forest pixel (300m), with the highest scores reflecting the highest forest integrity, and applied to forest extent for the start of 2019. Globally consistent parameters were used for all elements (i.e. parameters do not vary geographically). All calculations were conducted in Google Earth Engine.","function":"Displays forest condition as a continuous index determined by degree of anthropogenic modification","cautions":"The forest base map follows the Global Forest Cover product [(Hansen et. al. 2013)](https://science.sciencemag.org/content/342/6160/850) and as such includes both ‘natural’ forests and planted trees. It uses 20% canopy cover threshold and is resampled to 300 m. Cover losses 2000-2019 classed as temporary by [Curtis et al. (2018)](https://science.sciencemag.org/content/361/6407/1108), i.e. rotational forestry and swidden, continue to be treated as forest, with a due penalty for the modification experienced. Levels of human modification mapped are conservative; for example, no fires in are treated as anthropogenic (since in some systems many are natural), and the effects of climate change are not captured. Not all effects from geographical variations in levels of governance are captured. Online tools are being developed to enable users to tailor the global assumptions, weights, and criteria (e.g. forest definitions, treatment of fire) to more local contexts. Further caveats can be found in the paper. The paper categorizes the continuous variable into illustrative classes of high, medium and low forest integrity, benchmarked against known locations: high integrity scores are those ≥9.6, medium integrity scores >6.0 but <9.6, and low integrity scores ≤6.0.","key_restrictions":"","tags":["Land Cover"],"why_added":"Jan 2025","learn_more":"https://www.forestintegrity.com/","id":"c39c30d8-d403-420f-b712-081b5746ed61"},"versions":["v20190824"]},{"created_on":"2023-10-13T18:42:13.822619","updated_on":"2023-10-13T18:42:13.822625","dataset":"wdpa_licensed_protected_areas","is_downloadable":false,"metadata":{"created_on":"2023-10-13T18:42:13.839088","updated_on":"2024-10-04T16:27:54.881368","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global","update_frequency":"Quarterly","scale":"global","citation":"Protected Area, Key Biodiversity Area, and Species data reproduced and incorporated under licence from the [Integrated Biodiversity Assessment Tool (IBAT)](https://www.ibat-alliance.org/). IBAT is provided by BirdLife International, Conservation International, IUCN and UNEP-WCMC. Contact ibat@ibat-alliance.org for further information.","title":"Protected areas","subtitle":null,"source":"The World Database on Protected Areas, which compiles protected area data from governments, NGOs, and international secretariats","license":"[Terms of Use](https://www.protectedplanet.net/en/legal)","data_language":"English","overview":"The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via [Protected Planet](https://protectedplanet.net), the online interface for the World Database on Protected Areas (WDPA). The WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced information about protected areas.<br><br>**[IUCN Management Categories](https://www.iucn.org/theme/protected-areas/about/categories)**<br><br>Not all protected areas receive the same degree of protection. While some have strict guidelines designed to preserve intact ecosystems, others allow for sustainable land use, often including limited resource extraction. In addition, not all countries use the same terminology when designating a protected area. Accordingly, the [International Union for Conservation of Nature](https://www.iucn.org) defined universal management categories that stipulate the level of protection for most protected areas.<br><br>As you click through protected areas in this layer, note the “legal designation” and the explanations below to better understand the degree to which an area is protected.<br><br>- **Ia. Strict Nature Reserves.** Protected areas designed to preserve biodiversity and all geological features. Limited human use (e.g., scientific study, education) is allowed and carefully monitored. Strict Nature Reserves are often used to understand the impact of indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of preservation. Other common designations: Biological Reserve, Botanical Reserve<br><br>- **Ib. Wilderness Areas.** Protected areas managed to preserve ecosystem processes with limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s center), but they allow for local indigenous groups to maintain subsistence lifestyles. These areas are often established to restore disturbed environments. Other common designations: Wilderness Reserve, Wildlife Area<br><br>- **II. National Parks.** Protected areas designed to preserve large-scale ecosystems and support human visitation. With conservation as a priority, these areas allow infrastructure and contribute to the local economy by providing opportunities for environmental educational and recreation. Other common designations: State Park, Class A Park, Park Reserve, Provincial Park<br><br>- **III. National Monuments or Features.** Areas established to protect a specific natural feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or environmental importance and the immediate surroundings. Accordingly, Natural Monuments or Features are typically smaller in area and have high human impact resulting from visitor traffic. Other common designations: Natural Features Reserve, Nature Monument, Botanical Garden<br><br>- **IV. Habitat and Species Management Areas.** Areas designed to conserve specific wildlife populations and/or habitats. Habitat and Species Management Areas often exist within a larger ecosystem or protected area and are carefully managed (e.g., through hunting abatement or habitat restoration) to conserve a target species or habitat. Other common designations: National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve, Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary<br><br>- **V. Protected Landscapes and Seascapes.** Protected areas with ecological, biological, or cultural importance that have been shaped by human use of the landscape. Protected landscapes and seascapes typically cover entire bodies of land or ocean and allow for a number of for-profit activities (e.g., ecotourism) in accordance with the region’s management plan. Other common designations: National Forest, State Natural Area, Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature Reserve, State Natural Area<br><br>- **VI. Protected Areas with Sustainable Use of Natural Resources.** Areas designed to manage natural resources and uphold the livelihoods of surrounding communities. These regions have a low level of human occupation, small-scale developments (i.e., not industrial), and part of the landscape in its natural condition. Other common designations: Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest, Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve<br><br>- [UNESCO-MAB Biosphere Reserves](https://www.unesco.org/en/mab/wnbr/about?hub=66369): areas under UNESCO’s Man and the Biosphere Programme designated to “promote sustainable development based on local community efforts and sound science.”<br><br>- [World Heritage Sites](https://whc.unesco.org): areas considered to have “outstanding universal value” and meet at least one of ten criteria, as described [here](https://whc.unesco.org/en/criteria).<br><br>- [Ramsar Sites—Wetlands of International Importance](\"https://www.ramsar.org): wetlands that hold significant value designated under the Ramsar Convention on Wetlands.<br>","function":"Displays areas that are legally protected according to various designations (e.g., national parks, state reserves, and wildlife reserves) and managed to achieve conservation objectives","cautions":"Boundaries come from a variety of sources, with varying accuracy, up-to-dateness, and resolution. Data for some countries may be imprecise, miss some protected areas, or include boundaries that have since been cancelled.<br><br>Protected area designations, such as “National Park,” can be applied differently in different countries and may be associated with different IUCN categories. Only areas that meet the IUCN [definition of protected areas](https://www.iucn.org/our-work/protected-areas-and-land-use) are collected by WDPA, and thus the data may differ from national-level data on protected areas.<br><br>Protected areas with no boundary data are displayed as boxes which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.<br>","key_restrictions":"No commercial use without permission - defined as ”use for profit, or any use by an individual or entity operating within or on behalf of or to the benefit of or to assist the activities of any entity other than a not-for-profit organisation”. Email business-support@unep-wcmc.org for permissions.\nNo redistribution, including WMS.","tags":["Conservation"],"why_added":null,"learn_more":null,"id":"9c44b20e-2006-4b6f-bb0f-3ac8c812fa1f"},"versions":["v202511","v202508","v202602","v202608","v202605","v202505"]},{"created_on":"2020-07-22T04:04:07.178539","updated_on":"2025-02-11T16:12:15.460631","dataset":"wdpa_protected_areas","is_downloadable":false,"metadata":{"created_on":"2023-05-04T13:11:58.897389","updated_on":"2026-08-05T14:43:28.983410","spatial_resolution":null,"resolution_description":"nan","geographic_coverage":"Global","update_frequency":"Sub-annually","scale":"global","citation":"IUCN and UNEP-WCMC (2022), The World Database on Protected Areas (WDPA) [On-line], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net. Accessed through Global Nature Watch in (date). www.globalnaturewatch.org","title":"Protected areas","subtitle":"2024, vector, global, IUCN/UNEP-WCMC","source":"The World Database on Protected Areas, which compiles protected area data from governments, NGOs, and international secretariats","license":"[Terms of Use](https://www.protectedplanet.net/en/legal)","data_language":"English","overview":"The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via [Protected Planet](http://protectedplanet.net), the online interface for the World Database on Protected Areas (WDPA). The WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced information about protected areas.<br><br>**[IUCN Management Categories](http://www.iucn.org/theme/protected-areas/about/categories)**<br><br>Not all protected areas receive the same degree of protection. While some have strict guidelines designed to preserve intact ecosystems, others allow for sustainable land use, often including limited resource extraction. In addition, not all countries use the same terminology when designating a protected area. Accordingly, the [International Union for Conservation of Nature](http://www.iucn.org/) defined universal management categories that stipulate the level of protection for most protected areas.<br><br>As you click through protected areas in this layer, note the “legal designation” and the explanations below to better understand the degree to which an area is protected.<br><br>- **Ia. Strict Nature Reserves.** Protected areas designed to preserve biodiversity and all geological features. Limited human use (e.g., scientific study, education) is allowed and carefully monitored. Strict Nature Reserves are often used to understand the impact of indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of preservation. Other common designations: Biological Reserve, Botanical Reserve<br><br>- **Ib. Wilderness Areas.** Protected areas managed to preserve ecosystem processes with limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s center), but they allow for local indigenous groups to maintain subsistence lifestyles. These areas are often established to restore disturbed environments. Other common designations: Wilderness Reserve, Wildlife Area<br><br>- **II. National Parks.** Protected areas designed to preserve large-scale ecosystems and support human visitation. With conservation as a priority, these areas allow infrastructure and contribute to the local economy by providing opportunities for environmental educational and recreation. Other common designations: State Park, Class A Park, Park Reserve, Provincial Park<br><br>- **III. National Monuments or Features.** Areas established to protect a specific natural feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or environmental importance and the immediate surroundings. Accordingly, Natural Monuments or Features are typically smaller in area and have high human impact resulting from visitor traffic. Other common designations: Natural Features Reserve, Nature Monument, Botanical Garden<br><br>- **IV. Habitat and Species Management Areas.** Areas designed to conserve specific wildlife populations and/or habitats. Habitat and Species Management Areas often exist within a larger ecosystem or protected area and are carefully managed (e.g., through hunting abatement or habitat restoration) to conserve a target species or habitat. Other common designations: National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve, Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary<br><br>- **V. Protected Landscapes and Seascapes.** Protected areas with ecological, biological, or cultural importance that have been shaped by human use of the landscape. Protected landscapes and seascapes typically cover entire bodies of land or ocean and allow for a number of for-profit activities (e.g., ecotourism) in accordance with the region’s management plan. Other common designations: National Forest, State Natural Area, Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature Reserve, State Natural Area<br><br>- **VI. Protected Areas with Sustainable Use of Natural Resources.** Areas designed to manage natural resources and uphold the livelihoods of surrounding communities. These regions have a low level of human occupation, small-scale developments (i.e., not industrial), and part of the landscape in its natural condition. Other common designations: Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest, Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve<br><br>- [UNESCO-MAP Biosphere Reserves](http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/): areas under UNESCO’s Man and the Biosphere Programme designated to “promote sustainable development based on local community efforts and sound science.”<br><br>- [World Heritage Sites](http://whc.unesco.org/): areas considered to have “outstanding universal value” and meet at least one of ten criteria, as described [here](http://whc.unesco.org/en/criteria/).<br><br>- [Ramsar Sites—Wetlands of International Importance](http://www.ramsar.org/): wetlands that hold significant value designated under the Ramsar Convention on Wetlands.","function":"Displays areas that are legally protected according to various designations (e.g., national parks, state reserves, and wildlife reserves) and managed to achieve conservation objectives","cautions":"Boundaries come from a variety of sources, with varying accuracy, up-to-dateness, and resolution. Data for some countries may be imprecise, miss some protected areas, or include boundaries that have since been cancelled. <br><br>Protected area designations, such as “National Park,” can be applied differently in different countries and may be associated with different IUCN categories. Only areas that meet the IUCN [definition of protected areas](http://www.iucn.org/theme/protected-areas/about) are collected by WDPA, and thus the data may differ from national-level data on protected areas.<br><br>Protected areas with no boundary data are displayed as boxes which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.","key_restrictions":"No commercial use without permission - defined as \"use for profit, or any use by an individual or entity operating within or on behalf of or to the benefit of or to assist the activities of any entity other than a not-for-profit organisation\". Email business-support@unep-wcmc.org for permissions.\n\nNo redistribution, including WMS.","tags":["Conservation"],"why_added":"Best available data on protected areas at a global scale","learn_more":null,"id":"7bc5154f-7755-47d1-919d-a2706d9515bc"},"versions":["v202407","v202106","v202512","v202510.2","v202010","v202407.1","v202407.2","v202208","v202204","v202012","v202008","v202108","v202302","v202102","v202405","v202007","v202402","v202510.1","v202510","v202202","v202308"]},{"created_on":"2021-06-21T15:47:29.597059","updated_on":"2021-06-21T15:47:29.597064","dataset":"wdpa_protected_areas__burned_areas__daily_alerts","is_downloadable":true,"metadata":{},"versions":["v20210621","v20210622","v20220713","v20221104","v20220127"]},{"created_on":"2021-06-21T15:47:32.924289","updated_on":"2021-06-21T15:47:32.924295","dataset":"wdpa_protected_areas__burned_areas__weekly_alerts","is_downloadable":true,"metadata":{},"versions":["v20210621","v20210622","v20220713","v20221104","v20220127"]},{"created_on":"2021-06-21T15:47:36.008248","updated_on":"2021-06-21T15:47:36.008253","dataset":"wdpa_protected_areas__burned_areas__whitelist","is_downloadable":true,"metadata":{},"versions":["v20210621","v20210622","v20220713","v20221104","v20220127"]},{"created_on":"2021-04-13T13:41:52.571361","updated_on":"2021-04-13T13:41:52.571367","dataset":"wdpa_protected_areas__glad__daily_alerts","is_downloadable":true,"metadata":{},"versions":["v20260918","v20260916"]},{"created_on":"2021-04-13T13:41:54.821877","updated_on":"2021-04-13T13:41:54.821884","dataset":"wdpa_protected_areas__glad__summary","is_downloadable":true,"metadata":{},"versions":["v20210927.2","v202206","v202104","v202209.1","v202209","v202408"]},{"created_on":"2021-04-13T13:41:57.024108","updated_on":"2021-04-13T13:41:57.024114","dataset":"wdpa_protected_areas__glad__weekly_alerts","is_downloadable":true,"metadata":{},"versions":["v20211231","v20220101"]},{"created_on":"2021-04-13T13:41:58.939090","updated_on":"2021-04-13T13:41:58.939096","dataset":"wdpa_protected_areas__glad__whitelist","is_downloadable":true,"metadata":{},"versions":["v20210927.2","v202206","v202104","v202209","v202209.1","v202408"]},{"created_on":"2021-10-15T18:31:50.840816","updated_on":"2021-10-15T18:31:50.840823","dataset":"wdpa_protected_areas__integrated_alerts__daily_alerts","is_downloadable":true,"metadata":{},"versions":["v20260918","v20260917"]},{"created_on":"2021-04-13T20:13:50.189088","updated_on":"2021-04-13T20:13:50.189095","dataset":"wdpa_protected_areas__modis__daily_alerts","is_downloadable":true,"metadata":{},"versions":["v20220713","v20251203","v202206.1","v202104","v20221104","v202206","v202207","v20221109","v20240122","v20240815","v202104.1"]},{"created_on":"2021-04-13T20:13:53.051659","updated_on":"2021-04-13T20:13:53.051665","dataset":"wdpa_protected_areas__modis__weekly_alerts","is_downloadable":true,"metadata":{},"versions":["v20220713","v202104","v202207","v20221104","v20221109","v20240122","v20240815","v20251203","v202104.1"]},{"created_on":"2021-04-13T20:13:55.527323","updated_on":"2021-04-13T20:13:55.527329","dataset":"wdpa_protected_areas__modis__whitelist","is_downloadable":true,"metadata":{},"versions":["v202207","v202104","v20220713","v20221104","v20240122","v20221109","v202104.1","v20240815","v20251203"]},{"created_on":"2021-04-13T16:41:38.161395","updated_on":"2021-04-13T16:41:38.161401","dataset":"wdpa_protected_areas__tcl__change","is_downloadable":true,"metadata":{},"versions":["v20240404","v20240325","v20251202","v20230312","v20251209","v20221012","v20250515","v20240122","v20260407","v20240813","v202204","v20230502","v20220721","v202104","v20221011","v20221104","v20251203"]},{"created_on":"2021-04-13T16:41:41.925999","updated_on":"2021-04-13T16:41:41.926005","dataset":"wdpa_protected_areas__tcl__summary","is_downloadable":true,"metadata":{},"versions":["v202204","v20251202","v20230502","v20240325","v20240404","v20230312","v20221011","v20251209","v20250515","v20240122","v20260407","v20240813","v202104","v20220721","v20221104","v20221012","v20251203"]},{"created_on":"2021-04-13T16:41:45.124849","updated_on":"2021-04-13T16:41:45.124855","dataset":"wdpa_protected_areas__tcl__whitelist","is_downloadable":true,"metadata":{},"versions":["v202204","v20230502","v20251202","v20250515","v20240325","v20240404","v20230312","v20221011","v20251209","v20221012","v20240122","v20260407","v20240813","v20220721","v202104","v20221104","v20251203"]},{"created_on":"2021-04-13T20:38:04.676684","updated_on":"2021-04-13T20:38:04.676690","dataset":"wdpa_protected_areas__viirs__daily_alerts","is_downloadable":true,"metadata":{},"versions":["v20230113","v202104","v20230109","v20221109","v20221104","v20240122","v20240815","v20251203","v202104.1","v20220713","v20230901"]},{"created_on":"2021-04-13T20:38:07.817006","updated_on":"2021-04-13T20:38:07.817012","dataset":"wdpa_protected_areas__viirs__weekly_alerts","is_downloadable":true,"metadata":{},"versions":["v202104","v20230113","v20221104","v20230109","v20221109","v20240122","v20240815","v20251203","v202104.1","v20230901","v20220713"]},{"created_on":"2021-04-13T20:38:10.682383","updated_on":"2021-04-13T20:38:10.6823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m","geographic_coverage":"Global ","update_frequency":"Not updated. Represents the year 2000.","scale":null,"citation":"Harris et al. (2021). Global maps of 21st century forest carbon fluxes. Accessed on [date] from Global Nature Watch. \n","title":"Aboveground live woody biomass density","subtitle":"(2000, 30m, global, Harris et al. 2021)","source":"Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. [https://doi.org/10.1038/s41558-020-00976-6](https://doi.org/10.1038/s41558-020-00976-6)\n","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":null,"overview":"- This is a global, wall-to-wall map of aboveground biomass (AGB) at approximately 30-meter resolution. This data product expands on the methodology presented in Baccini et al. (2012) to generate a global map of aboveground live woody biomass (AGB) density (megagrams biomass per ha) at 0.00025-degree (approximately 30-meter) resolution for the year 2000. Aboveground biomass was estimated using a multi-step process of calculating AGB at more than seven hundred thousand points with LiDAR with regional allometric equations, then using those to train a wall-to-wall model based on Landsat imagery. Pixels without tree canopy were assigned a biomass density of 0 Mg/ha. Additional information on the creation of this map can be found in Harris et al. 2021. \n- Aboveground biomass is available for download in two different units: 1) megagrams AGB per hectare, and 2) megagrams AGB per pixel. The first is appropriate for visualizing (mapping) AGB and estimating average AGB density in an area of interest because it represents the density of AGB per hectare. The second is appropriate for calculating the total aboveground biomass stock in an area of interest because the values of the pixels in the AOI can be summed to obtain the total AGB stock in that area. The values in the latter were calculated by adjusting the AGB per hectare by the size of each pixel, which varies by latitude. \n","function":"Shows aboveground live woody biomass density in the year 2000","cautions":"- Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. \n- The biomass density value individual pixels have large uncertainty. Values in individual pixels are expected to differ from biomass estimates in field-measured plots. \n- Aboveground biomass may be overestimated at low values and underestimated at high values. \n","key_restrictions":null,"tags":null,"why_added":"Core data set for GNW Climate","learn_more":null,"id":"2bd671ee-50b5-465c-8012-21905edf6202"},"versions":["v4"]},{"created_on":"2021-09-23T13:15:42.586471","updated_on":"2021-09-23T13:15:42.586476","dataset":"whrc_aboveground_woody_biomass_stock_2000","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897159","updated_on":"2023-05-04T13:11:58.897161","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WHRC Aboveground Woody Biomass Stock 2000","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"50b2c203-5a44-480e-8e25-4f649c39fe5e"},"versions":["v1.4"]},{"created_on":"2022-07-27T18:32:22.529438","updated_on":"2022-07-27T18:32:22.529446","dataset":"wri_agriculture_linked_deforestation","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897283","updated_on":"2023-05-04T13:11:58.897285","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Global","update_frequency":"This data will be updated as new/improved data becomes available","scale":null,"citation":"Citation: Goldman, E., M.J. Weisse, N. Harris, and M. Schneider. 2020. “Estimating the Role of Seven Commodities in Agriculture-Linked Deforestation: Oil Palm, Soy, Cattle, Wood Fiber, Cocoa, Coffee, and Rubber.” Technical Note. Washington, DC: World Resources Institute. Available online at: wri.org/publication/estimating-the-role-of-sevencommodities-in-agriculture-linked-deforestation.","title":"Agriculture-Linked Deforestation","subtitle":null,"source":"WRI","license":"Creative Commons Attribution 4.0 International License (CC-BY)","data_language":null,"overview":"This data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions.","function":"Provides annual agriculture-linked deforestation estimates for oil palm, soy, cattle, cocoa and coffee for the years 2001-2015 by administrative boundary","cautions":"This analysis is limited by various data and attribution issues and methodological assumptions, including the following: \n Commodity data sets have limited coverage and quality. Only oil palm has recent, detailed maps of extent at a global level. The analysis also uses detailed data on South American soy. Outside of these regions and commodities, the analysis relies on global 10-kilometer resolution data on crop and pasture extent. These data are from 2010 (2000 for pasture), so the amount of forest replaced by a specific commodity is assumed to be proportional to its area during that year and may be misrepresented if significant expansion or contraction of that commodity has occurred since then. While Goldman et al. (2020) presents results using detailed pasture data for Brazil, this data set includes pasture results for the coarse method only. \n The data cannot capture complex land-use change transitions. The analysis does not consider other possible land uses between the deforestation event and the establishment of the commodity. The analysis also does not consider any forms of indirect land-use change (e.g., the target commodity displacing other activities that may, in turn, expand into forested areas).  \n The data measure tree cover loss rather than deforestation directly. All tree cover loss in an area later used for one of the target commodities is assumed to be deforestation because forest replaced with a crop or pasture represents a permanent land-use change. Historical data from Indonesia and Malaysia were used to filter out older oil palm plantations from the analysis to avoid counting old, unproductive oil palm trees being felled as tree cover loss.  \n The data may miss some forms of tree cover loss. The Hansen et al. (2013) tree cover loss data may not detect all changes related to commodity production. Much of the production of cocoa and coffee occurs on very small farms (less than one hectare) that may not be captured by the tree cover loss data. The analysis may also underestimate the conversion of dry forest and woody savanna areas, which are not well represented in the tree cover loss data. For the detailed soy analysis, we define tree cover as any woody vegetation with a minimum of 10 percent canopy cover (analyses for other commodities use 30 percent) to minimize underestimations in South American biomes such as the Cerrado and the Chaco. \n Further discussion about the methods, assumptions, and limitations of this analysis is available in Goldman et al. (2020).","key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"60e389bf-baaf-4e05-ba07-897ceef6f95d"},"versions":["v202010"]},{"created_on":"2021-11-08T20:37:21.467818","updated_on":"2021-11-08T20:37:21.467824","dataset":"wri_cmr_agro_industrial_zones","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897230","updated_on":"2026-08-05T14:43:29.641603","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Cameroon","update_frequency":" ","scale":"national","citation":"World Resources Institute.\"Cameroon agro-industrial zones.” Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org.","title":"Cameroon agro-industrial zones","subtitle":null,"source":"World Resources Institute.","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"This data layer shows the boundaries of agro-industrial zones, where oil palm and rubber tree plantations, as well as other crops, may be established. In Cameroon, industrial agriculture falls outside of the National Forest Estate. Agricultural plantations are allocated by the Ministry of Economy and Planning to private entities under long-term, renewable contracts, which are then monitored by the Ministry of Agriculture. The agro-industrial data set was mapped using satellite imagery, with ground-truthing to determine the crop type and operating company.","function":"This data layer shows the boundaries of agro-industrial zones, where oil palm and rubber tree plantations, as well as other crops, may be established. ","cautions":"Official documentation was often lacking, so boundaries should be considered approximate and non-exhaustive.","key_restrictions":"","tags":["Country data"],"why_added":"To expand on the Central Africa oil palm data ","learn_more":"","id":"273b7661-0fc1-4c44-9937-c8589fac9ea7"},"versions":["v2019","v20190403"]},{"created_on":"2025-01-07T22:34:04.215417","updated_on":"2025-01-07T22:34:04.215423","dataset":"wri_globalpasturewatch_grasslands","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:35:28.669995","updated_on":"2025-01-30T00:35:28.670002","dataset":"wri_globalpasturewatch_grasslands_2000","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:48:58.199375","updated_on":"2025-01-30T00:48:58.199381","dataset":"wri_globalpasturewatch_grasslands_2001","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:46.337444","updated_on":"2025-01-30T00:49:46.337451","dataset":"wri_globalpasturewatch_grasslands_2002","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:47.964665","updated_on":"2025-01-30T00:49:47.964675","dataset":"wri_globalpasturewatch_grasslands_2003","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:49.353753","updated_on":"2025-01-30T00:49:49.353760","dataset":"wri_globalpasturewatch_grasslands_2004","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:50.917805","updated_on":"2025-01-30T00:49:50.917811","dataset":"wri_globalpasturewatch_grasslands_2005","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:52.366527","updated_on":"2025-01-30T00:49:52.366532","dataset":"wri_globalpasturewatch_grasslands_2006","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:54.247883","updated_on":"2025-01-30T00:49:54.247888","dataset":"wri_globalpasturewatch_grasslands_2007","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:55.664080","updated_on":"2025-01-30T00:49:55.664087","dataset":"wri_globalpasturewatch_grasslands_2008","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:57.338527","updated_on":"2025-01-30T00:49:57.338548","dataset":"wri_globalpasturewatch_grasslands_2009","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:49:59.058763","updated_on":"2025-01-30T00:49:59.058770","dataset":"wri_globalpasturewatch_grasslands_2010","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:01.001081","updated_on":"2025-01-30T00:50:01.001086","dataset":"wri_globalpasturewatch_grasslands_2011","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:02.400622","updated_on":"2025-01-30T00:50:02.400629","dataset":"wri_globalpasturewatch_grasslands_2012","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:03.900944","updated_on":"2025-01-30T00:50:03.900950","dataset":"wri_globalpasturewatch_grasslands_2013","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:05.349836","updated_on":"2025-01-30T00:50:05.349846","dataset":"wri_globalpasturewatch_grasslands_2014","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:06.539765","updated_on":"2025-01-30T00:50:06.539770","dataset":"wri_globalpasturewatch_grasslands_2015","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:08.738663","updated_on":"2025-01-30T00:50:08.738670","dataset":"wri_globalpasturewatch_grasslands_2016","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:10.543943","updated_on":"2025-01-30T00:50:10.543947","dataset":"wri_globalpasturewatch_grasslands_2017","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:11.862662","updated_on":"2025-01-30T00:50:11.862674","dataset":"wri_globalpasturewatch_grasslands_2018","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:14.694690","updated_on":"2025-01-30T00:50:14.694695","dataset":"wri_globalpasturewatch_grasslands_2019","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:16.313589","updated_on":"2025-01-30T00:50:16.313595","dataset":"wri_globalpasturewatch_grasslands_2020","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:18.066589","updated_on":"2025-01-30T00:50:18.066596","dataset":"wri_globalpasturewatch_grasslands_2021","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:19.696332","updated_on":"2025-01-30T00:50:19.696341","dataset":"wri_globalpasturewatch_grasslands_2022","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2025-01-30T00:50:21.027641","updated_on":"2025-01-30T00:50:21.027651","dataset":"wri_globalpasturewatch_grasslands_2023","is_downloadable":true,"metadata":{},"versions":["v1"]},{"created_on":"2024-11-20T16:56:21.923650","updated_on":"2024-11-20T16:56:21.923656","dataset":"wri_global_power_plant_database","is_downloadable":true,"metadata":{"created_on":"2024-11-20T16:56:21.941577","updated_on":"2024-11-20T16:56:21.941581","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":"TBD","title":"Global Power Plant Database","subtitle":null,"source":"TBD","license":"TBD","data_language":"en","overview":"TBD","function":"TBD","cautions":"TBD","key_restrictions":null,"tags":[],"why_added":null,"learn_more":"TBD","id":"094cdf14-3477-4a1d-954c-90393420cd0c"},"versions":["v1.3"]},{"created_on":"2024-11-21T19:11:48.693033","updated_on":"2025-01-27T20:44:44.781276","dataset":"wri_google_tree_cover_loss_drivers","is_downloadable":true,"metadata":{"created_on":"2024-11-21T19:11:48.702378","updated_on":"2026-08-05T14:43:29.950177","spatial_resolution":null,"resolution_description":"0.01 x 0.01 degree (approximately 1 km at the equator)","geographic_coverage":"Global","update_frequency":"Annual","scale":null,"citation":"Use the following credit when these data are displayed: “Tree cover loss by dominant driver”. WRI/Google DeepMind. Accessed from Global Nature Watch on [Date]. [www.globalnaturewatch.org](http://www.globalnaturewatch.org)\n\nUse the following credit when these data are cited: Sims, M.J., R. Stanimirova, A. Raichuk, M. Neumann, J. Richter, F. Follett, J. MacCarthy, K. Lister, C. Randle, L. Sloat, E. Esipova, J. Jupiter, C. Stanton, D. Morris, C. M. Slay, D. Purves, and N. Harris. 2025. “Global Drivers of Forest Loss at 1 Km Resolution.” _Environmental Research Letters_ 20 (7): 074027. \n[doi:10.1088/1748-9326/add606](https://doi.org/10.1088/1748-9326/add606).\n","title":"WRI Google Drivers of Tree Cover Loss (1km)","subtitle":"2001-2025, 1 km, global, WRI/Google DeepMind","source":"Sims, M.J., R. Stanimirova, A. Raichuk, M. Neumann, J. Richter, F. Follett, J. MacCarthy, K. Lister, C. Randle, L. Sloat, E. Esipova, J. Jupiter, C. Stanton, D. Morris, C. M. Slay, D. Purves, and N. Harris. 2025. “Global Drivers of Forest Loss at 1 Km Resolution.” _Environmental Research Letters_ 20 (7): 074027. [doi:10.1088/1748-9326/add606](https://doi.org/10.1088/1748-9326/add606).\n","license":"CC by 4.0","data_language":null,"overview":"This product shows the dominant driver of tree cover loss from 2001-2025. A driver is defined as the direct cause of tree cover loss, and can include both temporary disturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to a non-forest land use (e.g., deforestation). The dominant driver is defined as the direct driver that caused the majority of tree cover loss within each 1 km cell over the time period. Classes are defined as follows: \n \n- Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture.  \n- Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure. \n- Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary cultivation that is later abandoned and followed by subsequent regrowth of secondary forest or vegetation. \n- Logging: Forest management and logging activities occurring within managed, natural or semi-natural forests and plantations, often with evidence of forest regrowth or planting in subsequent years.  \n- Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity afterward. Fires may be started by natural causes (e.g. lightning) or may be related to human activities (accidental or deliberate). \n- Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, settlements, urban areas, or built infrastructure (not associated with other classes). \n- Other natural disturbances: Tree cover loss due to other non-fire natural disturbances (e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed by salvage or sanitation logging, it is classified as logging. \n \n\nThese data were produced in a collaboration between the World Resources Institute and Google DeepMind. The data were developed using a global neural network model (ResNet) trained on a set of samples collected through visual interpretation of very high-resolution satellite imagery. The model used satellite imagery (Landsat 7 & 8, Sentinel-2) and ancillary data to classify the seven driver categories. Overall accuracy of the model is 90.5%, with regional accuracies varying from 82.8% in Southeast Asia to 94.1% in Asia. Global per class producer’s and user’s accuracy are highest for the permanent agriculture, logging, and wildfire classes (over 90%), and generally lower for rarer classes, such as hard commodities, settlements and infrastructure, and other natural disturbances. A full description of the methods and accuracy statistics are available in the publication.  \n \n\nThe data is also available on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/projects\\_landandcarbon\\_assets\\_wri\\_gdm\\_drivers\\_forest\\_loss\\_1km\\_v1\\_3\\_2001\\_2025). \n","function":"Shows the dominant driver of tree cover loss within each 1 km grid cell and the intensity of loss over the time period","cautions":"The grouping of drivers into deforestation and temporary disturbances (i.e., likely followed by regrowth) is indicative of how these classes are defined in the data and should be considered an approximation. This product does not monitor regrowth or permanence of loss following each loss event. \n\nThis product does not distinguish between the loss of natural forest and planted trees (e.g., plantations, tree crops, or agroforestry systems). While tree cover loss associated with the permanent agriculture, hard commodities, and settlements & infrastructure classes represent a close approximation of deforestation, they do not always represent the conversion of natural forests to other land uses and in some cases may represent loss of planted trees. Similarly, replacement of natural forest with wood fiber plantations is not distinguished from routine harvesting within existing plantations established before 2000, as these are both included in the logging class. \n\nThese data are limited in scope to attributing drivers to tree cover loss as mapped by the [Hansen et al. 2013](https://www.science.org/doi/10.1126/science.1244693) tree cover loss product, and therefore the detection of loss is subject to the accuracy of that product. This product shows the dominant driver in each 1 km cell over the entire period. It does not show multiple drivers if they occur in the same cell at smaller scales, nor does it detail the sequence of drivers if multiple occurred at different times within the period. A full description of limitations is included in the publication. \n\n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km","id":"0a19820d-7970-4a21-b1a6-5a9a75c200b3"},"versions":["v20241224","v1.13","v20241121","v1.12"]},{"created_on":"2026-08-07T17:07:33.611257","updated_on":"2026-08-07T17:07:33.611262","dataset":"wri_land_ghg_monitoring_system","is_downloadable":true,"metadata":{},"versions":["v1.0.1","v1.0.2","v1.0.3"]},{"created_on":"2022-08-05T21:49:30.584919","updated_on":"2022-08-05T21:49:30.584925","dataset":"wri_mexico_ageb_socio_economic_vulnerability","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897249","updated_on":"2023-05-04T13:11:58.897250","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WRI Cities Socio-economic Vulnerability by ageb (Mexico)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"eb1bfb8e-f279-4c3b-b339-9bee0df48b5b"},"versions":["v2020.2","v2020"]},{"created_on":"2022-08-09T14:55:23.156994","updated_on":"2022-08-09T14:55:23.157001","dataset":"wri_mexico_block_socio_economic_vulnerability","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897539","updated_on":"2023-05-04T13:11:58.897540","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WRI Cities Socio-economic Vulnerability by block (Mexico)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"6483cc63-672b-4346-b495-885d651eb015"},"versions":["v2020.2","v2020.1","v2020"]},{"created_on":"2022-08-05T20:40:13.629271","updated_on":"2022-08-05T20:40:13.629279","dataset":"wri_mexico_locality_socio_economic_vulnerability","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897466","updated_on":"2023-05-04T13:11:58.897467","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WRI Cities Socio-economic vulnerability by locality (Mexico)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"18360b6b-afe8-4817-81f1-478edb470075"},"versions":["v2020.2","v2020"]},{"created_on":"2022-08-05T20:40:05.254119","updated_on":"2022-08-05T20:40:05.254125","dataset":"wri_mexico_municipality_socio_economic_vulnerability","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897371","updated_on":"2023-05-04T13:11:58.897372","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WRI Cities Socio-economic vulnerability by municipality (Mexico)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"2cb050dd-ba52-425b-a897-c7171958e6f8"},"versions":["v2020.2","v2020"]},{"created_on":"2022-08-05T20:40:18.785756","updated_on":"2022-08-05T20:40:18.785761","dataset":"wri_mexico_state_socio_economic_vulnerability","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897479","updated_on":"2023-05-04T13:11:58.897480","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WRI Cities Socio-economic vulnerability by state (Mexico)","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"cc39efe3-1e3d-46d1-8167-2549d394e90b"},"versions":["v2020"]},{"created_on":"2021-09-15T18:35:30.160003","updated_on":"2021-09-15T18:35:30.160011","dataset":"wri_trees_in_complex_landscapes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897156","updated_on":"2023-05-04T13:11:58.897158","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Trees in Complex Landscapes","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"f148b2d0-9dc0-4c40-8c3b-17e2fefa973b"},"versions":["v20210915"]},{"created_on":"2021-10-05T19:40:06.320666","updated_on":"2021-10-05T19:40:06.320672","dataset":"wri_trees_in_mosaic_landscapes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897254","updated_on":"2023-05-04T13:11:58.897255","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Trees in Mosaic Landscapes","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"b703a13f-feca-42ad-99cd-c1558261de1d"},"versions":["v20220603"]},{"created_on":"2021-10-13T20:49:30.863419","updated_on":"2021-10-13T20:49:30.863425","dataset":"wri_trees_in_mosaic_landscapes_coverage","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897185","updated_on":"2023-05-04T13:11:58.897186","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Coverage Layer for WRI Trees in Mosaic Landscapes","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"70d19264-3b8a-4952-9f2b-8bb9ef4e116e"},"versions":["v20220228","v20211013"]},{"created_on":"2023-01-20T19:25:00.656830","updated_on":"2023-01-20T19:25:00.656836","dataset":"wri_tropical_tree_cover","is_downloadable":true,"metadata":{"created_on":"2024-07-11T15:09:28.370430","updated_on":"2026-08-05T14:43:30.267142","spatial_resolution":null,"resolution_description":"10 x 10 meters, half hectare","geographic_coverage":"4.3 billion hectares of the tropics (-23.44 to 23.44 latitude)","update_frequency":"Yearly change detection maps starting in 2017 are planned for 2024 release.","scale":null,"citation":"Use the following credit when this data is displayed: Source: [date], accessed through Global Nature Watch on [date] \n\nUse the following credit when this data is cited: Brandt,\nBrandt, J., Ertel, J., Spore, J., & Stolle, F. (2023). WALL-to-wall \nmapping of tree extent in the tropics with sentinel-1 and sentinel-2. Remote Sensing of Environment, 292, 113574. https://doi.org/10.1016/j.rse.2023.11357","title":"Tropical Tree Cover","subtitle":"(2020, 10m / half hecatre, Tropics)","source":"World Resources Institute","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"The tropical tree cover data maps tree extent at the ten-meter scale and tree cover at the half hectare scale to enable accurate monitoring of trees in urban areas, agricultural lands, and in open canopy and dry forest ecosystems. The data extends over 4.3 billion hectares of the global tropics. <br><br>The data is derived from multi-temporal convolutional neural network models applied to Sentinel optical and radar imagery. The 10-meter dataset is a binary tree extent layer that is similar to a land cover map, while the tree cover data represents fractional cover at a half-hectare scale. More details on the methodology and analyses can be found on [the GitHub page](https://github.com/wri/sentinel-tree-cover/wiki/Product-Specifications).","function":"Displays tree extent at the ten-meter scale and tree cover at the half hectare scale to enable accurate monitoring of trees in urban areas, agricultural lands, and in open canopy and dry forest ecosystems","cautions":"This dataset uses a different definition of a tree and a different definition of tree cover than does Hansen et al. (2013). This dataset defines a tree according to both the height and crown diameter. Woody vegetation higher than 5 meters regardless of crown diameter, or between 3 and 5 meters with a minimum crown diameter of 5 meters is considered a tree. This definition is different from Hansen et al. (2013) which defines a tree as any vegetation at least 5 meters in height. The tropical tree cover dataset does not disambiguate plantation trees from non-plantation trees. <br><br>Analyses or statistics derived for shapefiles smaller than 0.5 ha may not be accurate.","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://data.globalforestwatch.org/datasets/gfw::tropical-tree-cover/explore","id":"69c02d74-1857-4dfd-8d2a-486b3561320c"},"versions":["v2020.1","v2020.2","v2020"]},{"created_on":"2023-06-21T19:54:46.588072","updated_on":"2023-06-21T19:54:46.588078","dataset":"wri_tropical_tree_cover_extent","is_downloadable":true,"metadata":{"created_on":"2023-06-21T19:54:46.606659","updated_on":"2023-06-21T19:54:46.606667","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WRI Tropical Tree Cover Extent (10m) ","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"8b7dd802-bce1-4dff-a21f-13930541509a"},"versions":["v20220922"]},{"created_on":"2021-01-20T15:00:17.653833","updated_on":"2021-01-20T15:00:17.653841","dataset":"wur_africa_radd_coverage","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897421","updated_on":"2026-08-05T14:43:30.654925","spatial_resolution":10,"resolution_description":null,"geographic_coverage":"Humid tropical forests in: Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, Republic of the Congo","update_frequency":"Every 6-12 days","scale":"regional","citation":"Reiche et al. 'RADD alerts'. Accessed through Global Nature Watch on [DATE]. www.globalnaturewatch.org.","title":"Deforestation alerts (RADD) Coverage","subtitle":null,"source":"Congo Basin: Wageningen University and Research, as described in Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. [YEAR]. Forest disturbance alerts for the Congo Basin using Sentinel-1. [PUBLICATION].","license":"","data_language":"English","overview":"This dataset is a deforestation alert product that uses data from the European Space Agency's Sentinel-1 satellites to detect forest disturbances in near real-time and to confirm alerts within weeks. The RADD (RAdar for Detecting Deforestation) alerts use a detection methodology produced by Wageningen University and Research (WUR), with support from the World Resources Institute, and are operationalized by Satelligence for Indonesia and Malaysia through Google Compute Engine (for commercial and public use) and through Google Earth Engine (for scientific and public use) for the Congo Basin. These alerts are particularly advantageous in monitoring tropical forests, as Sentinel-1's cloud-penetrating radar and frequent revisit times (6-12 days) allow for more consistent monitoring than alert products based on optical satellite images. Alerts are available for the primary humid tropical forest areas of Indonesia, Malaysia, and six Congo Basin countries at a 10m spatial resolution, with coverage from January 2019 to the present (starting January 2018 for Indonesia and Malaysia). The alerts presented here were implemented in two different processing environments, resulting in slight methodological differences as described below.  **Indonesia and Malaysia**: The alerts for Indonesia and Malaysia were developed thanks to the support of a coalition of [ten major palm oil producers and buyers](https://www.wri.org/news/2019/10/release-palm-oil-industry-jointly-develop-radar-monitoring-technology-detect). As such, the alerts were particularly tuned to capture forest changes potentially related to palm oil production. In particular, the alerts only detect changes in areas with slope less than 5 degrees and less than 1500 meters of elevation, as oil palm plantations typically occur in low, flat areas. Sentinel-1 images are pre-processed using the SNAP toolbox, after which they are normalized. Forest disturbance alerts are then detected using a probabilistic algorithm. Each disturbance alert is detected from a single observation in the latest image, and then confirmed with subsequent imagery within a maximum 90-day period if the forest disturbance probability is above 97.5%. The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. For more information on methodology and validation, please refer to [Reiche et. al. (YEAR)](LINK TO PAPER).  **Congo Basin**: Pre-processed Sentinel-1 images are collected from Google Earth Engine, then quality controlled and normalized using historical time-series metrics. Forest disturbance alerts are then detected using a probabilistic algorithm. Each disturbance alert is detected from a single observation in the latest image, and then confirmed with subsequent imagery within a maximum 90-day period if the forest disturbance probability is above 97.5%. Unconfirmed alerts are provided for forest disturbance probabilities above 85%. The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. Alerts are detected within areas of primary humid tropical forest, defined by [Turubanova et al. (2018)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta) and with 2001-2018 forest loss ([Hansen et al. 2013](https://science.sciencemag.org/content/342/6160/850)) and mangrove ([Bunting et al. 2018](https://www.mdpi.com/2072-4292/10/10/1669)) removed. For more information on methodology and validation, please refer to [Reiche et. al. (YEAR)](LINK TO PAPER). The version presented here (v1) has been updated from that described in the paper (v0), with changes to the forest mask and a reduction of the minimum mapping unit.","function":"Near real-time forest disturbance alerts in primary humid tropical forests using Sentinel-1's cloud-penetrating radar sensors","cautions":"- This product does not separate human-caused deforestation from other forest disturbances - False detections may occur in swamp forests due to the high sensitivity of C-band (~5.6 cm) radar to moisture variations - Areas with a slope greater than 5 degrees and elevation greater than 1500 meters are excluded from the Indonesia and Malaysia alerts - A validation of confirmed alerts in the Congo Basin indicated a high level of accuracy (2% false positives, 5% false negatives) for disturbances greater than 0.2 ha. ","key_restrictions":"","tags":["Forest Change"],"why_added":"","learn_more":null,"id":"5fd1bbe6-f021-47b7-9b0b-a1470ae3ed54"},"versions":["v20210120"]},{"created_on":"2025-09-16T17:53:01.072149","updated_on":"2025-09-16T17:53:01.072152","dataset":"wur_alert_drivers","is_downloadable":true,"metadata":{"created_on":"2025-09-16T17:53:01.081839","updated_on":"2026-09-08T23:20:33.204780","spatial_resolution":null,"resolution_description":"10 × 10 m","geographic_coverage":"Three major forest basins of the tropics in the Amazon, Congo, Insular Southeast Asia (and mainland Malaysia)","update_frequency":"Drivers are attributed to newly detected alerts monthly, and the updates are delivered around the second week of each month. Driver attributions can be updated up to 3 months following an alert’s detection.","scale":null,"citation":" Use the following credit when these data are cited: \n\n Bart Slagter, Laura Cue La Rosa, Anika Berger et al. Rapid satellite monitoring reveals complexity of tropical tree cover loss drivers at fine scale, 02 March 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-7424252/v1](https://doi.org/10.21203/rs.3.rs-7424252/v1) \n\n Use the following credit when these data are displayed: \n\n Source: \"Drivers of Disturbance Alerts\". WUR and GNW, accessed through Global Nature Watch on [date] \n","title":"Drivers of disturbance alerts","subtitle":"2022-2026, 10 m, tropics, WUR","source":"Bart Slagter, Laura Cue La Rosa, Anika Berger et al. Rapid satellite monitoring reveals complexity of tropical tree cover loss drivers at fine scale, 02 March 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-7424252/v1](https://doi.org/10.21203/rs.3.rs-7424252/v1)","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"This dataset enhances pantropical near-real-time forest disturbance monitoring systems by using a deep learning model to classify direct drivers of forest disturbances on a monthly basis. Produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing, with support from Global Nature Watch (GNW), the dataset is built upon GNW's integrated disturbance alerts, which contains WUR's RADD (Radar for Detecting Deforestation) alerts and University of Maryland's GLAD (Global Land Analysis & Discovery) alerts, harnessing post-disturbance imagery from Sentinel-1 and Sentinel-2 satellites. Only high and highest confidence alerts are classified. This approach enables the identification of these direct causes driving deforestation: small-scale agriculture, large-scale agriculture, road development, selective logging, mining, wildfire, flooding, and other natural disturbances.  \n The alert drivers offer more targeted enforcement of laws and regulations, improved estimations of ecological impacts, and better understanding of carbon emissions related to forest disturbances. \n\n Read the paper here: [Rapid satellite monitoring reveals complexity of tropical tree cover loss drivers at fine scale | Research Square](https://www.researchsquare.com/article/rs-7424252/v1) \n\n **Alert Driver class definitions** \n\n **Non-natural drivers (human-caused)** \n\n **Small-scale agriculture**: Clearings smaller than 2 ha, commonly related to shifting cultivation (temporary clearing for cultivation with later regrowth) or smallholder farming. In smallholder landscapes with mixed disturbances, this may include artisanal logging and fuelwood collection (typically burned for cooking). \n\n **Small-scale agriculture with fire**: Clearings for small-scale agriculture (see above) where fire was likely used for the clearing, observed by a coinciding VIIRS fire alert and low post-disturbance Sentinel-2 Normalized Burn Ratio \n\n **Large-scale agriculture**: Clearings larger than 2 ha for the establishment of crops or pastures, commonly related to industrial agriculture (e.g. production of soy, palm oil, beef, etc.), clearcuts, and large-scale clearings for land speculation. \n\n **Large-scale agriculture with fire**: Clearings for large-scale agriculture (see above) where fire was likely used for the clearing, observed by a coinciding VIIRS fire alert and low post-disturbance Sentinel-2 Normalized Burn Ratio \n\n **Road development**: Clearings for the establishment of roads, commonly related to facilitating industrial timber harvests, but can include roads for any purpose. \n\n **Selective logging**: Small-scale disturbances caused by selective tree felling and skidding (paths where felled logs are dragged or transported), commonly related to industrial timber harvests. \n\n **Mining**: Forest clearing to facilitate artisanal and industrial mineral extraction. \n\n **Natural drivers** \n\n **Flooding**: Disturbances or clearings caused by floodings and meandering rivers. This includes both natural and human-induced flooding. \n\n **Other natural disturbances**: Disturbances or clearings without visible human-induced cause. This includes windthrows, droughts, landslides and naturally dying trees.  \n\n  **Other drivers** \n\n **Wildfire**: Large-scale disturbances due to fire, without immediate land clearing for agricultural activity. This includes both human-induced and naturally induced fires. This class excludes controlled fires used for agricultural clearing, but includes escaped wildfires caused by controlled fires. \n\n **Unlabeled**: Pixels where a confidence threshold for a prediction is not reached. \n\n \n The dataset covers the primary humid tropical forest (Turubanova et al. 2018) of the Amazon (Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname and Venezuela, restricted to the biogeographic outlines of the Amazon biome as defined by RAISG), Congo Basin (Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Republic of the Congo) and Southeast Asia (Brunei, East- Timor, Indonesia, Malaysia and Papua New Guinea). \n\n The model was trained on a dataset of labeled disturbance alerts, interpreted with high-resolution Planet imagery across the Amazon, Congo Basin, and Southeast Asia. The model integrates the complementary strengths of radar (Sentinel-1) and optical (Sentinel-2) imagery, leveraging convolutional neural networks (CNNs) to classify disturbances. Even though the dataset initially displayed historical alert data (2022-2025), the classifications were applied in a simulated near-real-time monitoring scenario to classify monthly disturbance alerts into one of the specified classes, achieving an overall accuracy of 0.89 Macro-F1. Accuracy varies by region, with the Congo Basin performing best. After the addition of the DIST-ALERT product, there are more alerts and therefore, areas of misclassification may be larger. Most misclassifications stem from confusion between agriculture and mining, which often co-occur, and between flooding and wildfires. \n\n Historical drivers (2022-2025) were classified up to three times within the three months following each alert, with the most confident result retained. These 2022-2025 classifications were postprocessed by selecting per disturbance patch the most occurring driver class, leading to some initial classifications being overwritten by another class in postprocessing. The current method, running from January 2026 onwards, selects the most occurring driver class only for disturbance patches within the three-month windows,  As a result, an alert can be updated up to 3 times, and the latest classification is retained. Classifications may therefore change for up to 4 months after the initial alert and do not change after that. . A class ‘unconfident’ is built from pixels that do not exceed a 0.5 class probability from the classification output. When the most occurring class for a disturbance patch is ‘unconfident’, this alerts remains unlabeled. \n\n Post-processing steps identified where fire coincided with the clearing of agricultural land, based on the presence of VIIRS fire alerts (within a 500 m buffer around the alert and a 6-month window) and a low post-disturbance normalized-burn ratio (lower than 0.0) in the following month’s Sentinen-2 composite. \n\n Smoothing was applied to all classes except road development and selective logging, which occur at finer scales. For patches smaller than 5 km, the dominant driver was assigned. For larger patches in the 2022-2025 data, pixel-level classifications were retained but smoothed with a 5 × 5-pixel majority filter. This step was applied to the whole time series; therefore, the historical product has an advantage in differentiation between small- and large-scale agriculture compared to the operational product. In a near-real-time setting, large clearings may initially be labeled as small-scale until they exceed the 2 ha threshold. \n\n In the future, monthly driver attributions for disturbance alerts will expand to new regions. The driver attributions will be continuously mapped and openly distributed via Global Nature Watch, as an extension to the integrated disturbance alerts. We also provide an annual version of this dataset, which incorporates some post processing – specifically the differentiation between large-scale and small-scale agriculture is made at the end of the year, meaning that agricultural clearings which are identified incrementally are identified based on their size at the end of the year. \n\n See the integrated disturbance alert layer [metadata](https://globalnaturewatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6ImludGVncmF0ZWQtZGVmb3Jlc3RhdGlvbi1hbGVydHMtOGJpdCIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWUsImxheWVycyI6WyJpbnRlZ3JhdGVkLWRlZm9yZXN0YXRpb24tYWxlcnRzLThiaXQiXX0seyJkYXRhc2V0IjoicG9saXRpY2FsLWJvdW5kYXJpZXMiLCJsYXllcnMiOlsiZGlzcHV0ZWQtcG9saXRpY2FsLWJvdW5kYXJpZXMiLCJwb2xpdGljYWwtYm91bmRhcmllcyJdLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlfV19&mapMenu=eyJtZW51U2VjdGlvbiI6ImRhdGFzZXRzIiwiZGF0YXNldENhdGVnb3J5IjoiZm9yZXN0Q2hhbmdlIn0%3D&modalMeta=gfw_integrated_dist_alerts) for additional details about the disturbance alerts. \n \n The alert drivers are available on Google Earth Engine with asset IDs: \n\n **Latest data** (ee.ImageCollection containing data from January 1, 2026 – end of last month): \n - projects/wurnrt-drivers/assets/operational_postprocessed_sa_collection \n - projects/wurnrt-drivers/assets/operational_postprocessed_afr_collection \n - projects/wurnrt-drivers/assets/operational_postprocessed_sea_collection \n\n *Note: this collection updates monthly, so new images will be added over time, but the collection path itself will stay the same.*  \n\n**Historical archive** (ee.Image containing data from 2022–2025): \n - projects/wurnrt-drivers/assets/distribution/driverclassification_afr_202201_202512 \n - projects/wurnrt-drivers/assets/distribution/driverclassification_sa_202201_202512 \n - projects/wurnrt-drivers/assets/distribution/driverclassification_sea_202201_202512","function":"Assigns probable drivers to the integrated disturbance alerts using machine learning and Sentinel imagery","cautions":" \n- This dataset focuses on classifying the key drivers of forest disturbances and may not include all potential causes of deforestation. \n  \n- While the input disturbance alerts are updated daily, the drivers model is run monthly. Each month, the model reclassifies all alerts detected in the past 3 months. As a result, an alert can be updated up to 3 times, and the latest classification will be shown. Classifications may therefore change for up to 4 months after the initial alert. \n- Classifications are only reported with a minimum mapping unit of 5 connected pixels. \n-  Alert pixels remain unclassified when the model predicts a class probability lower than 0.5. This threshold was selected to ensure a small proportion of alerts remain unclassified. In the historical dataset (2022–2025), before the DIST alerts were integrated, this threshold was set to 0.75 and only 4.3% of alerts were left unclassified.  \n- Because large agricultural clearings often develop over time, an alert may first be labeled as small-scale agriculture until the cleared area exceeds the 2-hectare threshold.  \n- As the model was trained using a previous version of integrated alerts, the addition of the DIST-ALERT system may lead to areas of misclassification where new types of disturbances are now captured, especially in South America.  \n- The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this.   \n- We do not recommend using disturbance alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. Additionally, updates to the methodologies and variation in cloud cover between months and years pose additional risks to using disturbance alerts for inter/intra-annual comparison. \n  \n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.   \n\n","key_restrictions":null,"tags":null,"why_added":"3","learn_more":"https://wurnrt-drivers.projects.earthengine.app/view/forest-disturbance-alert-drivers","id":"dfd11402-e552-431f-b25d-17819e3e98d9"},"versions":["v20260630","v20260828","v20250916","v20260506"]},{"created_on":"2025-10-07T18:45:00.973837","updated_on":"2025-10-07T18:45:00.973840","dataset":"wur_alert_drivers_coverage","is_downloadable":true,"metadata":{"created_on":"2025-10-07T18:45:00.985321","updated_on":"2025-10-07T18:45:00.985323","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"Coverage of drivers of deforestation alerts","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"8a0cce0f-3249-4b7c-b0e8-03c9e23c51b0"},"versions":["v20251007","v20251007.1"]},{"created_on":"2025-03-11T21:30:58.433144","updated_on":"2025-11-26T19:35:11.418882","dataset":"wur_forest_roads","is_downloadable":true,"metadata":{"created_on":"2025-03-11T21:30:58.442414","updated_on":"2026-08-05T14:43:31.382504","spatial_resolution":null,"resolution_description":"","geographic_coverage":"Congo Basin","update_frequency":"Yearly","scale":null,"citation":"To cite the data: \n Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment. [https://doi.org/10.1016/j.rse.2024.114380](https://doi.org/10.1016/j.rse.2024.114380) \n\n Kleinschroth, F., Laporte, N., Laurance, W.F. et al. Road expansion and persistence in forests of the Congo Basin. Nat Sustain 2, 628–634 (2019). [https://doi.org/10.1038/s41893-019-0310-6](https://doi.org/10.1038/s41893-019-0310-6) \n\n Use the following credit when this data is displayed: Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org  \n","title":"Congo Basin forest roads","subtitle":"2024, vector, Congo Basin, various sources","source":"2019-2024: \n\n Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment. [https://doi.org/10.1016/j.rse.2024.114380](https://doi.org/10.1016/j.rse.2024.114380) \n\n 2003 -2018: \n\n Kleinschroth, F., Laporte, N., Laurance, W.F. et al. Road expansion and persistence in forests of the Congo Basin. Nat Sustain 2, 628–634 (2019). [https://doi.org/10.1038/s41893-019-0310-6](https://doi.org/10.1038/s41893-019-0310-6) \n\n <2003: \n\n Laporte, N. T., Stabach, J. A., Grosch, R., Lin, T. S. & Goetz, S. J. Expansion of industrial logging in Central Africa. Science 316, 1451 (2007). [https://doi.org/10.1126/science.1141057](https://doi.org/10.1126/science.1141057)","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) The data may be used by anyone, anywhere, anytime without permission or royalty payment. Attribution using the appropriate citation is requested.","data_language":"English","overview":"This layer combines multiple forest roads datasets covering six Congo Basin countries: Cameroon, Central African Republic, Democratic Republic of Congo, Equatorial Guinea, Gabon, and Republic of Congo. \n\n The pre-2019 roads data are from Kleinschroth et al (2019)  which combines crowd-sourced data from Open Street Map (OSM), manually digitized roads from the Logging roads initiative led by Moabi, the Joint Research Center of the European Commission and Global Nature Watch, data from Laporte et al. (2007), who manually digitized Landsat images from 1970-2003, and earlier efforts from Kleinschroth et al. (2017) for certain regions. \n\n Roads after 2019 are mapped by Slagter et al. 2024, with an advanced detection method based on Sentinel-1 and Sentinel-2 satellite imagery and a deep learning model. This integration serves to offer a more comprehensive forest roads dataset for the Congo Basin. While the machine learning techniques enhance our ability to detect and map roads going forward, the older data holds value as it captures signals from roads that may no longer be visible or detectable today. This information can still provide context for users and insight into forest intactness, past logging activities, and the human footprint. \n\n The automated road detection algorithm from 2019 onwards implements both the Sentinel-2 optical sensors, which provide detailed imagery during clear weather conditions, as well as the Sentinel-1 radar sensors which can ‘see through’ clouds during persistent rainy seasons in the tropics. Therefore, the opening of commonly narrow and transient road segments can be precisely located on a monthly basis. \n\n Some old, overgrown roads—often 20–30 years old and present in the Laporte/Kleinschroth dataset—are detected again by WUR when they are reopened for a new logging cycle. This accounts for about one-third of WUR’s road detections. For some users, this distinction is valuable, as reopening old logging roads generally has less environmental impact than constructing entirely new ones. In addition, it provides an indication where new harvest cycles are occurring in previously logged forests.  Intersections between new and old roads in the data layer on GNW may indicate such reopenings. However, due to differences in detection/collection methodology, these roads may not perfectly align spatially, even when they represent the same underlying feature. \n\n\n To download recent forest roads from WUR (>2019): [Forest roads (Congo Basin) (zenodo.org)](https://zenodo.org/records/15563307) \n\n To download historical forest roads (<2019): Data from: [Road expansion and persistence in forests of the Congo Basin - Research Collection (ethz.ch)](https://www.research-collection.ethz.ch/entities/researchdata/a3a53255-d47d-42e0-aae2-7d66ebf8637c) \n\n\n Read the blog here: [https://www.globalnaturewatch.org/blog/data-and-tools/congo-basin-forest-road-mapping/](https://www.globalnaturewatch.org/blog/data-and-tools/congo-basin-forest-road-mapping/)","function":"Shows the development of forest roads in the Congo Basin","cautions":" \n- This layer combines crowdsourced data (digitized logging roads <2001-2018) and machine learning detected forest roads (2019-2024). \n \n- The majority of mapped roads relate to selective logging, but not all. Overlaying the roads with boundaries of logging concessions can help to identify this. \n \n- This data layer does not provide information regarding the legality, usage, or specific characteristics of the roads detected. Additionally, the width of the roads as visualized in the dataset is not to scale and does not reflect their actual physical dimensions. \n \n- Old roads may have been abandoned and are not easily visible using recent satellite imagery. Roads prior to 2019 are only mapped in forests >75% canopy cover in Cameroon, Gabon, Equatorial Guinea, Central African Republic, Republic of Congo and the DRC. The 2019-onwards road dataset uses a forest mask based on primary humid tropical forest [(Turubanova et al., 2018)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c) and areas with >75% tree cover [(Hansen et al., 2013)](https://www.science.org/doi/10.1126/science.1244693), excluding forest loss until 2018 and fragments <10 ha. As a result, some unconnected gaps may appear in fragmented forests.","key_restrictions":null,"tags":null,"why_added":"3","learn_more":"https://www.wur.nl/en/research-results/chair-groups/environmental-sciences/laboratory-of-geo-information-science-and-remote-sensing/research/sensing-measuring/congo-basin-forest-roads.htm","id":"13e699b8-324b-42c3-a096-403077aabe61"},"versions":["v2025","v2014"]},{"created_on":"2025-09-19T15:55:50.023557","updated_on":"2025-09-19T15:55:50.023561","dataset":"wur_integration_alert_drivers_class","is_downloadable":true,"metadata":{},"versions":["v20250925","v20260630","v20260828.1","v20260828","v20260916"]},{"created_on":"2025-09-19T15:55:40.715258","updated_on":"2025-09-19T15:55:40.715262","dataset":"wur_integration_alert_drivers_date","is_downloadable":true,"metadata":{},"versions":["v20250925"]},{"created_on":"2021-06-29T15:25:15.947078","updated_on":"2025-02-20T18:18:42.234000","dataset":"wur_radd_alerts","is_downloadable":true,"metadata":{"created_on":"2024-07-11T15:09:26.087954","updated_on":"2026-08-05T14:43:31.779786","spatial_resolution":null,"resolution_description":"10 × 10 m ","geographic_coverage":"Humid tropical forest in South America, Central America, sub-Saharan Africa and Southeast Asia ","update_frequency":"Updated weekly, image revisit time every 6-12 days ","scale":"regional","citation":"Use the following credit when this data is displayed: \nSource: \"RADD alerts\". WUR, accessed through Global Nature Watch on [date] \n\nUse the following credit when this data is cited: \nReiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. [https://doi.org/10.1088/1748-9326/abd0a8](https://doi.org/10.1088/1748-9326/abd0a8) \n","title":"Deforestation alerts (RADD)","subtitle":"weekly, 10 m, tropics, WUR ","source":"Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. [https://doi.org/10.1088/1748-9326/abd0a8](https://doi.org/10.1088/1748-9326/abd0a8) \n","license":"[CC by 4.0](https://creativecommons.org/licenses/by/4.0/) ","data_language":"English","overview":"RAdar for Detecting Deforestation (RADD) is a deforestation alert product that uses data from the European Space Agency’s Sentinel-1 satellites to detect forest disturbances in near-real-time and to confirm alerts within weeks. The RADD alerts use a detection methodology produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing. These alerts are particularly advantageous in monitoring tropical forests, as Sentinel-1’s cloud-penetrating radar and frequent revisit times (6-12 days) allow for more consistent monitoring than alert products based on optical satellite images. Alerts are available for the primary humid tropical forest areas of South America, sub-Saharan Africa and Southeast Asia at 10 m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South America, Central America, and Southeast Asia. Pre-processed Sentinel-1 images are collected from Google Earth Engine, then quality controlled and normalized using historical time-series metrics. Forest disturbance alerts are then detected using a probabilistic algorithm. Each disturbance alert is detected from a single observation in the latest image, and then marked as high confidence with subsequent imagery within a maximum 90-day period if the forest disturbance probability is above 97.5%. Unconfirmed alerts are provided for forest disturbance probabilities above 85%. The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. Alerts are detected within areas of primary humid tropical forest, defined by  [Turubanova et al (2018)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta) and with 2001-2018 forest loss [(Hansen et al. (2013))](https://www.science.org/doi/10.1126/science.1244693) and mangrove [(Bunting et al. 2018) ](https://www.mdpi.com/2072-4292/10/10/1669) removed. For more information on methodology and validation, please refer to [Reiche et. al. (2021)](https://iopscience.iop.org/article/10.1088/1748-9326/abd0a8). The version presented here (v1) has been updated from that described in the paper (v0), with changes to the forest mask and a reduction of the minimum mapping unit.  \n\nThe RADD alerts were made possible thanks to the support of a coalition of [ten major palm oil producers and buyers](https://www.wri.org/news/release-palm-oil-industry-jointly-develop-radar-monitoring-technology-detect-deforestation). Under the project, Wageningen University and Research (WUR) developed the detection method and Satelligence first scaled the system in Indonesia and Malaysia and provided additional prioritization of alerts for on-the-ground follow up. Additional support was provided by the US Forest Service and Norway’s International Climate and Forest Initiative. The alerts are currently operated by WUR using Google Earth Engine.  \nThe RADD alerts are available on \\*\\*Google Earth Engine\\*\\* with asset ID: projects/radar-wur/raddalert/v1 \n","function":"Monitor primary forest disturbance in near-real-time using Sentinel-1’s cloud-penetrating radar sensors ","cautions":" \n- Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests (more likely to happen in the GLAD-L system), alerts may indicate timber harvesting operations, without a conversion to a non-forest land use.  \n \n- The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this.  \n \n- We do not recommend using deforestation alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. Additionally, updates to the methodologies, differing number of systems (in the case of the integrated alerts), and variation in cloud cover between months and years pose additional risks to using deforestation alerts for inter/intra-annual comparison.  \n \n- The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations are provided in the Places to Watch data layer.  \n \n- False detections may occur in swamp forests due to the high sensitivity of short wavelength C-band radar to moisture variations \n \n- Small-scale changes (e.g., logging roads, small-scale agriculture) are typically detected in a timely manner as forest edges are relatively straightforward to detect using short wavelength C-band radar. Large-scale patches (e.g., plantation expansion) may take longer to reach a high enough probability to be flagged as alerts. Those large patches may appear similar to undisturbed forest in the radar image due to conditions like wet soil or remaining woody debris. \n \n- The product is constrained by the global forest baseline used, which may result in inconsistencies at the local level. In areas that are incorrectly labelled as primary forest in the baseline, there may be some commission errors in the alerts. In areas where forest loss occurred prior to the start of the RADD alerts but was missed by the baseline input data (and thus not removed from the forest baseline), alerts may be detected well after the disturbance occurred. This will only affect alerts from early 2019 (Africa) and early 2020 (other geographies). \n \n- RADD alerts are within primary humid forests. Forest loss is defined as complete or partial removal of tree cover within a pixel, and a minimum-mapping unit of 0.1 ha is used.  \n \n- The confidence level may change retroactively as source data is updated; alerts that have not become high confidence within 90 days are removed from the dataset. For RADD, researchers use 2 years of data to create historical image metrics showing previous forest condition, preprocess every new Sentinel-1 image, and apply a forest disturbance detection algorithm which calculates the probability that a pixel is disturbed. If the probability of disturbance is greater than 0.85, it becomes a low confidence alert. Subsequent observations within the next 90 days are used to update the probability that the forest was disturbed. When the probability reaches above 0.975, the alert becomes classified as high confidence. \n \n- Once an alert pixel reaches high confidence, forest loss will not be detected by the RADD alert system at that location again.  \n \n- A validation of confirmed alerts in the Congo Basin indicated a high level of accuracy (2% false positives, 5% false negatives) for disturbances greater than 0.2 ha.  \n \n- When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.   \n\n","key_restrictions":null,"tags":null,"why_added":null,"learn_more":"https://www.wur.nl/en/Research-Results/Chair-groups/Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/Research/Sensing-measuring/RADD-Forest-Disturbance-Alert.htm","id":"674a0512-4657-4201-a9b4-ac686093ef44"},"versions":["v20250330","v20251005","v20230101","v20240108","v20230402","v20241006","v20231001","v20240407","v20220109","v20240714","v20250105","v20220626","v20250706","v20221002","v20220403","v20210704","v20260412","v20260913","v20260906","v20260823","v20260830","v20260705","v20230702","v20211017"]},{"created_on":"2021-03-01T15:24:05.544789","updated_on":"2021-03-01T15:24:05.544796","dataset":"wur_radd_coverage","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897436","updated_on":"2023-05-04T13:11:58.897438","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"RADD Alerts Coverage","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"4527ee6f-4cbf-4c9d-977e-7d6cb16eebe8"},"versions":["v20221216","v20240704","v20210221","v20211016"]},{"created_on":"2021-05-06T13:26:12.227333","updated_on":"2021-05-06T13:26:12.227339","dataset":"wwf_terrestrial_ecoregions","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897374","updated_on":"2023-05-04T13:11:58.897375","spatial_resolution":null,"resolution_description":null,"geographic_coverage":null,"update_frequency":null,"scale":null,"citation":null,"title":"WWF Terrestrial Ecoregions of the World","subtitle":null,"source":null,"license":null,"data_language":null,"overview":null,"function":null,"cautions":null,"key_restrictions":null,"tags":null,"why_added":null,"learn_more":null,"id":"bee4f609-d24a-4c68-8d10-e7a39cd91c72"},"versions":["v2020"]},{"created_on":"2020-12-07T16:17:37.545571","updated_on":"2020-12-07T16:17:37.545578","dataset":"wwf_tiger_conservation_landscapes","is_downloadable":true,"metadata":{"created_on":"2023-05-04T13:11:58.897410","updated_on":"2026-08-05T14:43:32.193753","spatial_resolution":null,"resolution_description":null,"geographic_coverage":"Bangladesh, Bhutan, Cambodia, China, India, Indonesia, Laos, Malaysia, Myanmar, Nepal, Russia, Thailand and Vietnam","update_frequency":"Updated annually","scale":"regional","citation":"WWF and RESOLVE. 'Tiger Conservation Landscapes.' Accessed through Global Nature Watch on [date]. www.globalnaturewatch.org","title":"Tiger Conservation Landscapes","subtitle":null,"source":"*Tiger Conservation Landscapes* Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007. 'The fate of wild tigers.' BioScience 57 (June 2007): 508-14.  *Tx2 Tiger Conservation Landscapes* Wikramanayake, E., Dinerstein, E., Seidensticker, J., Lumpkin, S., Pandav, B., Shrestha, M., Mishra, H., Ballou, J., Johnsingh, A.J.T., Chestin, I., Sunarto, S., Thinley, P., Thapa, K., Jiang, G., Elagupillay, S., Kafley, H., Pradhan, N.M.B., Jigme, K., Teak, S., Cutter, P., Aziz, Md. A., Than, U. 2011. A landscape-based conservation strategy to double the wild tiger population. Conservation Letters, 4 (3):219-227.  *Terai Arc Landscape corridors* Wikramanayake, E., M. McNight, E. Dinerstein, A. Joshi, B. Gurung, D. Smith. 2004. Designing a Conservation Landscape for Tigers in Human-Dominated Environments. Conservation Biology (18):839-844.","license":"[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)","data_language":"English","overview":"These three data sets, produced by WWF and RESOLVE, show the location of current tiger habitat and priority areas for habitat conservation.  *Tiger Conservation Landscapes: * Tiger Conservation Landscapes (TCLs) are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat.  *Tx2 Tiger Conservation Landscapes: * This data set displays 29 Tx2 Tiger Conservation Landscapes (Tx2 TCLs), defined areas that could double the wild tiger population through proper conservation and management by 2020.  *Terai Arc Landscape corridors: * This data set displays 9 forest corridors on the Nepalese side of the Terai Arc Landscape (TAL). Corridors are defined as existing forests connecting current Royal Bengal tiger meta-populations in Nepal and India.","function":"These three layers show the location of current tiger habitats, areas of habitat expansion, and critical tiger corridors.","cautions":"Tiger Conservation Landscapes were created under the assumption that suitable habitat depends on quality and size of land cover and prey base.  Land cover data was problematic in certain geographies due to the presence of tree plantations. In some cases, forest cover was overestimated or underestimated.  The tiger location database, on which this data set was built, is incomplete for some regions, and the data comes from a variety of sources and research methods.","key_restrictions":"","tags":["Conservation"],"why_added":"","learn_more":"","id":"357475a2-5d15-4243-b0ba-3a03c40ce104"},"versions":["v20201207"]}],"status":"success"}