Tropical Tree Cover

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

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product (GFCC30SR), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).

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.  “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.  The 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.  For more detailed information, please refer to the Technical Note. Data is available for download in all countries except Papua New Guinea. If you are aware of any additional planted trees data, please let us know by filling out this form.  Identifies planted forests and tree crops on a near-global scale.

- 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 shows the dominant driver of tree cover loss from 2001-2023 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, NASA fires, global land cover, and population count. 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. 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.

A new, 30-m spatial resolution global forest canopy height map was developed through 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 Global Land Analysis and Discover team at the University of Maryland (UMD GLAD) integrated the GEDI data available to date (April-October 2019) with the year 2019 Landsat analysis-ready time-series data (Landsat ARD). 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. Show the height of global forest canopy in the years 2000 and 2020.