Carbon accumulation potential from natural forest regrowth in reforestable areas

This carbon removals layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2025. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. (2022). Atmospheric carbon removed in each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2025) and must be divided by 25 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). We have made several updates to the model since its original release. For documentation through the current version, please refer to this blog. For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to Gibbs et al. (2025). Removals are available for download in two different area units over the model duration: 1) megagrams of CO2 removed/ha, and 2) megagrams of CO2 removed/pixel. The first is appropriate for visualizing (mapping) removals because it represents the density of removals per hectare. The second is appropriate for calculating the removals in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total removals for that area. The values in the latter were calculated by adjusting the removals per hectare by the size of each pixel, which varies by latitude. When estimating removals occurring over a defined number of years between 2001 and 2025 to compare to emissions, divide total carbon removals by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Download data from here. Access data on GEE here Displays forest carbon removals by forest sinks

- This is a global, wall-to-wall map of aboveground biomass (AGB) at approximately 30-meter resolution. This data product expands on the methodology presented in Baccini et al. (2012) to generate a global map of aboveground live woody biomass (AGB) density (megagrams biomass per ha) at 0.00025-degree (approximately 30-meter) resolution for the year 2000. Aboveground biomass was estimated using a multi-step process of calculating AGB at more than seven hundred thousand points with LiDAR with regional allometric equations, then using those to train a wall-to-wall model based on Landsat imagery. Pixels without tree canopy were assigned a biomass density of 0 Mg/ha. Additional information on the creation of this map can be found in Harris et al. 2021.  - Aboveground biomass is available for download in two different units: 1) megagrams AGB per hectare, and 2) megagrams AGB per pixel. The first is appropriate for visualizing (mapping) AGB and estimating average AGB density in an area of interest because it represents the density of AGB per hectare. The second is appropriate for calculating the total aboveground biomass stock in an area of interest because the values of the pixels in the AOI can be summed to obtain the total AGB stock in that area. The values in the latter were calculated by adjusting the AGB per hectare by the size of each pixel, which varies by latitude.  Shows aboveground live woody biomass density in the year 2000

Annual aboveground carbon removal factor measured in megagrams of carbon per hectare per year, published with Harris et al. 2021 in Nature Climate Change.

This net flux layer is part of the forest greenhouse gas flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest fluxes which can assist a variety of actors and organizations with tracking greenhouse gas (GHG) fluxes from forests and in decreasing emissions or increasing removals by forests. Net forest flux represents the exchange of GHGs between forests and the atmosphere, calculated as the difference between GHGs emitted by forests and carbon removed by (or sequestered by) forests during the model period. Net carbon flux is calculated by subtracting average gross removals from annual gross emissions in each forested pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of GHGs between 2001 and 2025. Net fluxes are calculated following IPCC Guidelines for national greenhouse gas inventories in each pixel where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. 2022. This layer reflects the cumulative net flux during the model period (2001-2025) and must be divided by 25 to obtain average annual net flux; net flux values cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). We have made several updates to the model since its original release. For documentation through the current version, please refer to this blog. For a more detailed description of the changes included through the 2023 tree cover loss launch (released spring 2024) and a comparison of the model's fluxes with those from the Global Carbon Budget and national greenhouse gas inventories, please refer to Gibbs et al. (2025). Net flux is available for download in two different area units over the model duration: 1) megagrams of CO2e emissions/ha, and 2) megagrams of CO2e emissions/pixel. The first is appropriate for visualizing (mapping) net flux because it represents the density of GHG fluxes per hectare. The second is appropriate for calculating the net flux in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total net flux for that area. The values in the latter were calculated by adjusting the net flux per hectare by the size of each pixel, which varies by latitude. When estimating net flux occurring over a defined number of years between 2001 and 2025, divide the values by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Download data from here. Access data on GEE here Displays the net flux of greenhouse gases from forests, calculated as the difference between gross forest greenhouse gas emissions from stand-replacing forest disturbances and gross carbon removals from forest growth

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 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