Net carbon losses in Central African forests revealed by high-resolution biomass change maps

Dense humid forests in Central Africa hold vast biomass carbon stocks but are increasingly affected by small-scale disturbances that are difficult to detect with current satellite observations. Here, we generate 10 m canopy height maps for 2019–2022 using deep learning with spaceborne lidar and Sentinel-1/−2 imagery, and derive aboveground biomass changes at 30 m resolution. The maps reveal widespread small disturbance patches, 87% smaller than 1 ha. These account for 48% of biomass carbon gains in regrowing forests and 37% of carbon losses from deforestation. We estimate a net forest carbon loss of −58 ± 14 Tg C yr−1, with gross losses of −126 ± 21 Tg C yr−1 partially offset by gains of 68 ± 10 Tg C yr−1. The Democratic Republic of the Congo emerges as a net carbon source (−46 ± 11 Tg C yr−1), representing 79% of the regional loss, primarily due to degradation, despite having the largest carbon gains in young secondary forests. Our remote-sensing estimates of national carbon budgets are broadly consistent with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring. High-resolution biomass changes maps reveal net carbon losses across Central African forests, show that small disturbances drive carbon changes, and provide spatially explicit national carbon budgets broadly consistent with independent estimates.

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Publication Details

Journal
Nature Communications
Published
2026-09-17
DOI
https://doi.org/10.1038/s41467-026-77531-y
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Net carbon losses in Central African forests revealed by high-resolution biomass change maps

Jean‐François Bastin, Martin Brandt, Le Bienfaiteur Sagang, Jean‐Pierre Wigneron et al.
Nature Communications
Remote Sensing and LiDAR Applications
article

Net carbon losses in Central African forests revealed by high-resolution biomass change maps

Jean‐François Bastin, Martin Brandt, Le Bienfaiteur Sagang, Jean‐Pierre Wigneron, Aurelien De Truchis, Jérôme Chave, Youngryel Ryu, Clément Bourgoin, Yidi Xu, Philippe Ciais, Shidong Liu, Liang Wan, Pierre Ploton, Timothée Besisa, Wei Li, Arthur Vander Linden, Ibrahim Fayad, David Purnell
article en

Abstract

Dense humid forests in Central Africa hold vast biomass carbon stocks but are increasingly affected by small-scale disturbances that are difficult to detect with current satellite observations. Here, we generate 10 m canopy height maps for 2019–2022 using deep learning with spaceborne lidar and Sentinel-1/−2 imagery, and derive aboveground biomass changes at 30 m resolution. The maps reveal widespread small disturbance patches, 87% smaller than 1 ha. These account for 48% of biomass carbon gains in regrowing forests and 37% of carbon losses from deforestation. We estimate a net forest carbon loss of −58 ± 14 Tg C yr−1, with gross losses of −126 ± 21 Tg C yr−1 partially offset by gains of 68 ± 10 Tg C yr−1. The Democratic Republic of the Congo emerges as a net carbon source (−46 ± 11 Tg C yr−1), representing 79% of the regional loss, primarily due to degradation, despite having the largest carbon gains in young secondary forests. Our remote-sensing estimates of national carbon budgets are broadly consistent with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring. High-resolution biomass changes maps reveal net carbon losses across Central African forests, show that small disturbances drive carbon changes, and provide spatially explicit national carbon budgets broadly consistent with independent estimates.

Nature Communications
University of Copenhagen (DK), Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Seoul National University (KR), Université de Bordeaux (FR), University of Liège (BE), Université Fédérale de Toulouse Midi-Pyrénées (FR), Université de Versailles Saint-Quentin-en-Yvelines (FR), Chinese Academy of Sciences (CN), Université de Montpellier (FR), Institut Agro Montpellier (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Gembloux Agro-Bio Tech (BE), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Biologie du Fruit et Pathologie (FR), Joint Research Centre (IT), Territoires (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Unité mixte de recherche Œnologie (FR), Aerospace Information Research Institute (CN), Institut de Recherche pour le Développement (FR), Centre de Recherche sur la Biodiversité et l'Environnement (FR), Institute of the Environment, Tsinghua University (CN)
National Aeronautics and Space Administration, European Space Agency, European Commission, Ministère de l'Europe et des Affaires Étrangères, Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, Grand Équipement National De Calcul Intensif
Life in Land
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Remote Sensing and LiDAR Applications
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