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

Liang Wan^1*Philippe Ciais¹Aurélien de Truchis²Ewan Sean²Fabian Jörg Fischer³David Purnell¹Gabriel Belouze¹Ibrahim Fayad¹Martin Schwartz¹Yidi Xu¹Yang Su¹Maxime Réjou Méchain⁴Nicolas Barbier⁴Paul Tresson⁴Jean-François Bastin⁵Jan Bogaert⁵Arthur Vander Linden⁵Antoine Plumacker⁵Bhely ANGOBOY⁶Dieumerci Assumani⁷Thales de Haulleville⁸Le Bienfaiteur Sagang⁹Laurent Durieux¹⁰Youngryel Ryu¹¹Tackang Yang¹¹Conan Vassily Obame¹²Thomas Bossy¹Frédéric Frappart¹³Marc Peaucelle¹³Jean-Pierre Wigneron¹³Jerome Chave¹⁴Aida Cuni-Sanchez¹⁵Wannes Hubau⁸Hans Verbeeck⁸Pascal Boeckx⁸Jean-Remy Makana¹⁶Corneille Ewango¹⁷Elizabeth Kearsley¹⁸Pierre Ploton⁴

• ¹Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
• ²kayrros SAS, PARIS, France
• ³TUM School of Life Sciences, Freising, Germany
• ⁴AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France
• ⁵TERRA Teaching and Research Centre, Gembloux Agro Bio-Tech, University of Liege, Liege, Belgium
• ⁶Institut National pour l’Etude et la Recherche Agronomiques, Kinshasa-Gombe, Democratic Republic of Congo
• ⁷Faculté de Gestion des Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of Congo
• ⁸Universiteit Gent, Ghent, Belgium
• ⁹University of California Los Angeles Institute of the Environment & Sustainability, Los Angeles, United States
• ¹⁰IRD, UAR Data Terra (263 IRD, 2013 CNRS, UMS 1511 INRAE), Montpellier, France
• ¹¹Seoul National University, Gwanak-gu, Republic of Korea
• ¹²Agence Gabonaise d’Etudes et d’Observations Spatiales (AGEOS), Libreville, Gabon
• ¹³ISPA, UMR 1391 INRAE/Bordeaux Science Agro, Villenave d’Ornon, France
• ¹⁴CRBE, Université de Toulouse, CNRS, IRD, Toulouse INP, Toulouse, France
• ¹⁵Department of International Environmental and Development Studies (NORAGRIC), Norwegian University of Life Sciences, 1433Ås, Norway
• ¹⁶Université de Kisangani, Faculté des Sciences, Laboratoire d’écologie et aménagement forestier, Kisangani, Democratic Republic of Congo
• ¹⁷Universite de Kisangani, Kisangani, Democratic Republic of Congo
• ¹⁸BlueGreen Labs, Melsele, Belgium

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.