Annual aboveground carbon removal factor (Mg C/ha/yr)

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 dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Soil organic carbon stock in t/ha for 0-30 cm depth interval. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.

This map shows the rate at which forests could capture carbon from the atmosphere and store it in aboveground live biomass over the first 30 years of natural forest regrowth. It was created by combining ground-based measurements at thousands of locations around the world with 66 co-located environmental covariate layers in a machine learning model to produce a wall-to-wall map. Forest plot data used to train the model are sourced from published literature, which can be found in the Forest Carbon database (ForC, maintained by the Smithsonian Institute (https://github.com/forc-db), as well as georeferenced data from publicly available national forest inventories. Although rates were estimated over all forest and savanna biomes globally, they are filtered here by “reforestable” area, as defined in Griscom et al. 2017 (PNAS). Reforestable areas exclude areas of native grasslands and croplands to safeguard the production of food and fiber and habitat for biological diversity.  Estimates the rate at which carbon could be sequestered in aboveground live biomass during the first thirty years of natural forest regrowth in potentially reforestable areas (Mg carbon/ha/yr).

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.