{"data":{"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 [()]() 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":["v20241123","v20241130","v20241207","v20241214","v20241221","v20241228","v20250104","v20250112","v20250118","v20250125","v20250129","v20250201","v20250208","v20250211","v20250215","v20250222","v20250301","v20250308","v20250315","v20250322","v20250329","v20250405","v20250412","v20250419","v20250426","v20250503","v20250510","v20250517","v20250524","v20250531","v20250607","v20250614","v20250621","v20250628","v20250708","v20250712","v20250723","v20250726","v20250802","v20250809","v20250816","v20250823","v20250830","v20250906","v20250913","v20250920","v20250927","v20251004","v20251011","v20251018","v20251025","v20251101","v20251108","v20251111","v20251115","v20251122","v20251125","v20251201","v20251204","v20251206","v20251209","v20251213","v20251220","v20251227","v20260103","v20260110","v20260117","v20260124","v20260131","v20260207","v20260221","v20260301","v20260319","v20260321","v20260328","v20260329","v20260404","v20260406","v20260412","v20260419","v20260426","v20260502","v20260510","v20260516","v20260523","v20260530","v20260607","v20260613","v20260620","v20260627","v20260704","v20260712","v20260718","v20260725","v20260801","v20260808","v20260815","v20260822","v20260908","v20260912","v20260919","v20260926","v20261003","v20261017","v20261024"]},"status":"success"}