| Title: |
Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests |
| Authors: |
Wan, Liang; Ciais, Philippe; de Truchis, Aurélien; Sean, Ewan; Fischer, Fabian, Jörg; Purnell, David; Belouze, Gabriel; Fayad, Ibrahim; Schwartz, Martin; Xu, Yidi; Su, Yang; Réjou-Méchain, Maxime; Barbier, Nicolas; Tresson, Paul; Bastin, Jean-François; Bogaert, Jan; Vander Linden, Arthur; Plumacker, Antoine; Angoboy Ilondea, Bhely; Assumani, Dieu-Merci; de Haulleville, Thales; Sagang, Le, Bienfaiteur; Durieux, Laurent; Ryu, Youngryel; Yang, Tackang; Obame, Conan, Vassily; Bossy, Thomas; Frappart, Frédéric; Peaucelle, Marc; Wigneron, Jean-Pierre; Chave, Jerome; Cuni-Sanchez, Aida; Hubau, Wannes; Verbeeck, Hans; Boeckx, Pascal; Makana, Jean-Remy; Ewango, Corneille; Kearsley, Elizabeth; Sonké, Bonaventure; Libalah, Moses; Ploton, Pierre |
| Contributors: |
Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE); Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA); Contrôles des cycles biogéochimiques terrestres (BIOGEO); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)); Technische Universität Munchen = Technical University Munich = Université Technique de Munich (TUM); Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP); Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD Occitanie )-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM); Gembloux Agro-Bio Tech Faculté universitaire des sciences agronomiques de Gembloux ( FUSAGx ); Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège); Interactions Sol Plante Atmosphère (UMR ISPA); Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE); University of Yaoundé 1 = Université de Yaoundé I |
| Source: |
EISSN: 2673-6187 ; Frontiers in Remote Sensing ; https://hal.inrae.fr/hal-05441369 ; Frontiers in Remote Sensing, 2025, 6, ⟨10.3389/frsen.2025.1724950⟩ |
| Publisher Information: |
CCSD; Frontiers Media |
| Publication Year: |
2025 |
| Collection: |
Université de Versailles Saint-Quentin-en-Yvelines: HAL-UVSQ |
| Subject Terms: |
allometric relations; deep learning; Sentinel-1; Sentinel-2; GEDI; African dense forests; aboveground biomass; Forest height; [SDE.BE]Environmental Sciences/Biodiversity and Ecology |
| Description: |
International audience ; 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 ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
IRD: fdi:010095998 |
| DOI: |
10.3389/frsen.2025.1724950 |
| Availability: |
https://hal.inrae.fr/hal-05441369; https://hal.inrae.fr/hal-05441369v1/document; https://hal.inrae.fr/hal-05441369v1/file/Wan_etal_Frontiers_in_Remote_Sensing_2025.pdf; https://doi.org/10.3389/frsen.2025.1724950 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.F2E09324 |
| Database: |
BASE |