State of the art in remote sensing monitoring of carbon dynamics in African tropical forests

African tropical forests play a crucial role in global carbon dynamics, biodiversity conservation, and climate regulation, yet monitoring their structure, diversity, carbon stocks and changes remains challenging. Remote sensing techniques, including multi-spectral data, lidar-based canopy height and vertical structure detection, and radar interferometry, have significantly improved our ability to map forest composition, estimate height and biomass, and detect degradation and deforestation features at a finer scale. Machine learning approaches further enhance these capabilities by integrating multiple data sources to produce improved maps of forest attributes and track changes over time. Despite these advancements, uncertainties remain due to limited ground-truth validation, and the structural complexity and large spatial heterogeneity of African forests. Future developments in remote sensing should examine how multi-sensor integration of high-resolution data from instruments such as Planet, Tandem-X, SPOT and improved AI methods can refine forest composition, carbon storage and function maps, enhance large-scale monitoring of tree height and biomass dynamics, and improve forest degradation and deforestation detection down to tree level. These advancements will be essential for supporting science-based decision-making in forest conservation and climate mitigation.

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Publication Details

Journal
Frontiers in Remote Sensing
Published
2025-03-17
DOI
https://doi.org/10.3389/frsen.2025.1532280
Citations
13
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
10.39
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article

State of the art in remote sensing monitoring of carbon dynamics in African tropical forests

Liang Wan, Marijn Bauters, Nicolas Delbart, Theodomir Mugiraneza et al.
13 citations
Frontiers in Remote Sensing
Remote Sensing and LiDAR Applications
10.39
article

State of the art in remote sensing monitoring of carbon dynamics in African tropical forests

Liang Wan, Marijn Bauters, Nicolas Delbart, Theodomir Mugiraneza, Suspense A. Ifo, Thomas Bossy, Maurice Mugabowindekwe, Le Bienfaiteur Sagang, Marc Peaucelle, Jean‐Pierre Wigneron, Paul Tresson, Gabriel Jaffrain, Philippe Maisongrande, Aurélien De Truchis, Bertrand Ygorra, Philippe Ciais, Cassandra Normandin, Elhadi Adam, Eliakim Hamunyela, Camille Pinet, Frédéric Frappart, Nicolas Barbier, Martin Alexander Schwartz, Tawanda W. Gara, Conan Vassily Obame, Pierre Ploton, An Vo Quang, Bonaventure Sonké, Valentine Sollier, Solène Renaudineau
article en
13 citations

Abstract

African tropical forests play a crucial role in global carbon dynamics, biodiversity conservation, and climate regulation, yet monitoring their structure, diversity, carbon stocks and changes remains challenging. Remote sensing techniques, including multi-spectral data, lidar-based canopy height and vertical structure detection, and radar interferometry, have significantly improved our ability to map forest composition, estimate height and biomass, and detect degradation and deforestation features at a finer scale. Machine learning approaches further enhance these capabilities by integrating multiple data sources to produce improved maps of forest attributes and track changes over time. Despite these advancements, uncertainties remain due to limited ground-truth validation, and the structural complexity and large spatial heterogeneity of African forests. Future developments in remote sensing should examine how multi-sensor integration of high-resolution data from instruments such as Planet, Tandem-X, SPOT and improved AI methods can refine forest composition, carbon storage and function maps, enhance large-scale monitoring of tree height and biomass dynamics, and improve forest degradation and deforestation detection down to tree level. These advancements will be essential for supporting science-based decision-making in forest conservation and climate mitigation.

Frontiers in Remote SensingVol. 6
University of Copenhagen (DK), Institut national de l’information géographique et forestière (FR), Université de Yaoundé I (CM), University of California, Los Angeles (US), Cal Poly Humboldt (US), University of the Witwatersrand (ZA), Université de Versailles Saint-Quentin-en-Yvelines (FR), Université Paris Cité (FR), University of Namibia (NA), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Centre National d'Études Spatiales (FR), Ghent University (BE), University of Rwanda (RW), Sorbonne Paris Cité (FR), Laboratoire Interdisciplinaire des Énergies de Demain (FR), Institut Africain d'Informatique (GA), Interaction Sol Plante Atmosphère (FR), UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Marien Ngouabi University (CG), California State Polytechnic University (US)
Climate action
Openalex Percentile: Top 1%
Remote Sensing and LiDAR Applications
10.39
DOIOpen Access PDFOpenAlex
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