Tree cover loss due to fire

- 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 data set, a collaboration between the GLAD (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). Data in this layer were generated using multispectral satellite imagery from the Landsat 7 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. 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 tree cover

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.   **Stable forest** – Area of tree cover that remained at or above 5m in height between 2000 and 2020.   **Gain** – Area of tree cover that was below 5m height in 2000 but at or above 5m height in 2020.   **Loss** – Area of tree cover that was at or above 5m height in 2000 but below 5m height in 2020.   **Disturbed** - Represents areas that experienced both loss and gain between 2000 and 2020.   Identifies net tree cover change

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.  New 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.  Alerts 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.  The GLAD-L alerts are available on \*\*Google Earth Engine\*\* with asset ID: projects/glad/alert/UpdResult  Monitor tree cover disturbance in near-real-time using Landsat imagery

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 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 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. 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. When zoomed out (50%. Identifies areas of tree cover gain

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:    - Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture.   - Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.  - 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.  - 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.   - 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).  - Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, settlements, urban areas, or built infrastructure (not associated with other classes).  - 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.    These 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.     The data is also available on Google Earth Engine.  Shows the dominant driver of tree cover loss within each 1 km grid cell and the intensity of loss over the time period