GLAD alerts

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.  New 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.  Alerts 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.  The GLAD-S2 alerts are available on \*\*Google Earth Engine\*\* with asset ID: projects/glad/S2alert  Monitor primary forest loss in near-real time using Sentinel-2 imagery

- This data set, a collaboration between the Global Land Analysis & Discovery (GLAD) () 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.  - 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.  - 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:  and access on GEE here: .  - 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).  - 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.  Identifies areas of gross tree cover loss

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.   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.   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. 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.   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   Monitor vegetation disturbance in near-real-time using integrated alerts from multiple alerting systems

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. 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. 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. 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 The integrated deforestation alerts are available on **Google Earth Engine** with asset ID: projects/forma-250/assets/gfw_integrated_alerts/default_latest **Download** the raster tiles here: https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about Monitor forest disturbance in near-real-time using integrated alerts from three alerting systems

This data is produced by the Global Land Analysis & Discovery (GLAD) lab 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).  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 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.  The 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. You can read more about updates to the tree cover loss modeling process here.  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).  The 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.  This data is available for download from UMD here and also accessible through Google Earth Engine using the following image IDs (view the download page from UMD for more information):  - Fire certainty: users/sashatyu/2001-2025\_fire\_forest\_loss  - Date of loss: users/sashatyu/2001-2025\_fire\_forest\_loss\_annual  Identifies areas of tree cover loss due to fires compared to all other drivers of tree cover loss

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, and on Google Earth Engine (GEE) with asset ID: projects/glad/HLSDIST/current 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. 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%). While the version on the LPDAAC is updated every 2-4 days, the data is updated weekly on GNW. 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. 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. 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. Monitors global vegetation disturbance in near-real-time using harmonized Landsat-Sentinel-2 (HLS) imagery