Agriculture-Linked Deforestation

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.

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. 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. 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

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

Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. All years are available under a Creative Commons BY-4.0.

The FAOSTAT domain on Land Cover data under the Agri-Environmental Indicators section contains land cover information organized by the land cover classes of the international standard system for Environmental and Economic Accounting Central Framework (SEEA CF). The land cover information is compiled from publicly available Global Land Cover (GLC) maps: a) MODIS land cover types based on the Land Cover Classification System, LCCS; b) The European Spatial Agency (ESA) Climate Change Initiative (CCI) annual land cover maps produced by the Université catholique de Louvain (UCL)-Geomatics and now under the European Copernicus Program; c) The annual land cover maps which were produced under the European Copernicus Global Land Service (CGLS) (CGLS land cover, containing discrete land cover categorization), with spatial resolution 100m; and d) the WorldCover maps of the European Space Agency, produced at 10m resolution.

- 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