Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests

Accurate maps of canopy height (CH) and aboveground biomass (AGB) are needed for monitoring forests over large regions. Producing such data is particularly challenging over the complex, diverse and dense humid tropical forests of Africa where signal saturation observed from optical and radar satellites and complex responses in LiDAR data require advanced mapping techniques to capture high biomass and tall height values. Here, we trained a deep learning (U-Net) model to generate the first annual maps (2019–2022) of top CH at 10 m resolution over the African dense forest region, using Sentinel-1/-2 images trained on LiDAR-derived height data from the Global Ecosystem Dynamics Investigation mission (GEDI). To predict AGB from CH on a 30-m grid, we calibrated allometric models combining AGB data from field inventories, CH from our map, and wood density from a new high-resolution (1 km) map. The CH map has a mean absolute error (MAE) of 4.54 m and an underestimation bias of 1.54 m compared to independent airborne LiDAR data (5.93 m and 1.40 m compared to independent GEDI data). Evaluation of the AGB map against independent measurements from field sites suggests an improved accuracy (MAE = 79.65 Mg/ha, bias = 6.47 Mg/ha) compared to recent datasets such as ESA-CCI, NCEO, and GEDI L4B. Our map also captures the large-scale spatial gradients of AGB across African dense forests, as observed in a comprehensive dataset of forest concession measurements aggregated at a 1-km scale. Interpretable machine learning was used to assess the contribution of ancillary variables (e.g., climate, soil, forest type) to biomass prediction. While some variables were relevant, their inclusion failed to improve AGB estimates in high and low biomass extremes and introduced spatial artifacts, limiting their utility for consistent annual mapping. Together, our annual CH and AGB maps offer an open, scalable tool for monitoring forest disturbances and interannual biomass dynamics. Future work will focus on refining biomass–height relationships to further improve AGB estimation.

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Journal
Frontiers in Remote Sensing
Published
2025-12-19
DOI
https://doi.org/10.3389/frsen.2025.1724950
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
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article

Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests

Liang Wan, Dieu-Merci Assumani, Jean‐François Bastin, Pascal Boeckx et al.
Frontiers in Remote Sensing
Remote Sensing and LiDAR Applications
article

Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests

Liang Wan, Dieu-Merci Assumani, Jean‐François Bastin, Pascal Boeckx, Corneille E. N. Ewango, Aida Cuní‐Sanchez, Thomas Bossy, Bonaventure Sonké, Le Bienfaiteur Sagang, Martin Schwartz, Elizabeth Kearsley, Marc Peaucelle, Jean‐Pierre Wigneron, Paul Tresson, David Purnell, Jérôme Chave, Youngryel Ryu, Yang Su, Thalès de Haulleville, Fabian Jörg Fischer, Yidi Xu, Philippe Ciais, Bhély Angoboy Ilondea, Jan Bogaert, Frédéric Frappart, Maxime Réjou-Méchain, Aurélien de Truchis, Laurent Durieux, Nicolas Barbier, Jean‐Remy Makana, Hans Verbeeck, Ibrahim Fayad, Wannes Hubau, Conan Vassily Obame, Pierre Ploton, Ewan Sean, Antoine Plumacker, Moses B. Libalah, Arthur Vander Linden, Tackang Yang, Gabriel Belouze
article en

Abstract

Accurate maps of canopy height (CH) and aboveground biomass (AGB) are needed for monitoring forests over large regions. Producing such data is particularly challenging over the complex, diverse and dense humid tropical forests of Africa where signal saturation observed from optical and radar satellites and complex responses in LiDAR data require advanced mapping techniques to capture high biomass and tall height values. Here, we trained a deep learning (U-Net) model to generate the first annual maps (2019–2022) of top CH at 10 m resolution over the African dense forest region, using Sentinel-1/-2 images trained on LiDAR-derived height data from the Global Ecosystem Dynamics Investigation mission (GEDI). To predict AGB from CH on a 30-m grid, we calibrated allometric models combining AGB data from field inventories, CH from our map, and wood density from a new high-resolution (1 km) map. The CH map has a mean absolute error (MAE) of 4.54 m and an underestimation bias of 1.54 m compared to independent airborne LiDAR data (5.93 m and 1.40 m compared to independent GEDI data). Evaluation of the AGB map against independent measurements from field sites suggests an improved accuracy (MAE = 79.65 Mg/ha, bias = 6.47 Mg/ha) compared to recent datasets such as ESA-CCI, NCEO, and GEDI L4B. Our map also captures the large-scale spatial gradients of AGB across African dense forests, as observed in a comprehensive dataset of forest concession measurements aggregated at a 1-km scale. Interpretable machine learning was used to assess the contribution of ancillary variables (e.g., climate, soil, forest type) to biomass prediction. While some variables were relevant, their inclusion failed to improve AGB estimates in high and low biomass extremes and introduced spatial artifacts, limiting their utility for consistent annual mapping. Together, our annual CH and AGB maps offer an open, scalable tool for monitoring forest disturbances and interannual biomass dynamics. Future work will focus on refining biomass–height relationships to further improve AGB estimation.

Frontiers in Remote SensingVol. 6
Royal Museum for Central Africa (BE), Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Université de Yaoundé I (CM), Seoul National University (KR), University of Liège (BE), Université Fédérale de Toulouse Midi-Pyrénées (FR), Université de Versailles Saint-Quentin-en-Yvelines (FR), Université de Montpellier (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Gembloux Agro-Bio Tech (BE), Ghent University (BE), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Biologie du Fruit et Pathologie (FR), Sustainability Institute (ZA), Santé et Agroécologie du Vignoble (FR), Institut Africain d'Informatique (GA), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Université Pédagogique Nationale (CD), Unité mixte de recherche Œnologie (FR), National Institute of Biomedical Research (CD), Institut Agro Montpelier (FR), Institut de Recherche pour le Développement (FR), BlueGreen Labs (bv) (Belgium), University of York (GB), Norwegian University of Life Sciences (NO), Technical University of Munich (DE), Université de Kisangani (CD)
National Aeronautics and Space Administration, European Space Agency, European Commission, Fonds De La Recherche Scientifique - FNRS, Centre National d’Etudes Spatiales, Fonds Wetenschappelijk Onderzoek, Ministère de l'Europe et des Affaires Étrangères, Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche
Openalex Percentile: Top 60%
Remote Sensing and LiDAR Applications
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