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Emissions • Net Flux • GHG. Green House Gas • Carbon • Forest • Carbon Sink • Tree Cover Loss
The Gross GHG Emissions (forest) dataset quantifies carbon dioxide emissions from forest loss, including both aboveground and belowground biomass, across the tropics. It reflects gross emissions without accounting for forest regrowth, supporting climate impact assessments and mitigation planning.
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This GHG emissions dataset is part of the forest carbon flux model presented by Harris et al. (2021), which provides a global framework for monitoring forest greenhouse gas (GHG) emissions.
It estimates gross carbon emissions (2001–2023) resulting from stand-replacing disturbances using tree cover loss data from Hansen et al. (2013). Emissions include CO₂, CH₄, and N₂O from all major ecosystem carbon pools and are calculated per pixel based on 2000 carbon stocks, adjusted for regrowth prior to disturbance.
Emissions are assigned to the year of disturbance, with no accounting for post-disturbance regrowth, which is treated separately in a companion removals layer. Emission estimates are influenced by factors such as fire presence, peatland disturbance, and disturbance drivers.
Annual updates to tree cover loss and drivers, as well as methodological revisions—including improved belowground biomass ratios, updated peat and fire data, and revised global warming potentials (GWPs)—were incorporated in 2023–2024.
The dataset offers three downloadable raster formats for emissions per hectare or per pixel, with or without tree cover density (TCD) thresholds, supporting both visual mapping and quantitative area-specific calculations.
All data are harmonized to a spatial resolution of 0.00025°.
Spatial Resolution
30m x 30m
The World Resources Institute (WRI) is a global research organization focused on sustainable development and environmental protection. WRI works on issues like climate, energy, food, and forests helping governments and businesses make informed, sustainable choices.
Global Fishing Watch is an international nonprofit organization that uses satellite technology and machine learning to map and monitor global fishing activity in near real-time. By promoting transparency in ocean governance, it helps governments, researchers, and the public combat illegal fishing and support sustainable fisheries management.