Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4927-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4927-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
02 Nov 2023
Data description article |
| 02 Nov 2023
| 02 Nov 2023
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
Martin Schwartz
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Aurélien De Truchis
Kayrros SAS, 75009 Paris, France
Jérôme Chave
Laboratoire Evolution et Diversité Biologique, CNRS, UPS, IRD, Université Paul Sabatier, Toulouse, France
Catherine Ottlé
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Cedric Vega
IGN, Laboratoire d'Inventaire Forestier, 54000 Nancy, France
Jean-Pierre Wigneron
ISPA, UMR 1391, INRAE Nouvelle-Aquitaine, Bordeaux Villenave d'Ornon, France
Manuel Nicolas
Office national des forêts, département Recherche-développement-innovation, Boulevard de Constance, 77300 Fontainebleau, France
Sami Jouaber
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Siyu Liu
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Martin Brandt
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Ibrahim Fayad
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Kayrros SAS, 75009 Paris, France
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48 citations as recorded by crossref.
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- Extraction of eucalyptus age and estimation of its aboveground biomass in China with the integration of empirical model and machine learning algorithm C. Tang et al. https://doi.org/10.1016/j.fecs.2026.100440
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- Supporting ecological restoration: leveraging satellite data and machine learning to map invasive alien tree biomass L. Cogill et al. https://doi.org/10.1016/j.ecoinf.2026.103756
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- Monitoring changes of forest height in California S. Favrichon et al. https://doi.org/10.3389/frsen.2024.1459524
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- Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data Y. Li et al. https://doi.org/10.3390/rs16193650
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- Substantial contribution of trees outside forests to above-ground carbon across China Y. Su et al. https://doi.org/10.1038/s43247-025-03150-y
- Global carbon balance of the forest: satellite-based L-VOD results over the last decade J. Wigneron et al. https://doi.org/10.3389/frsen.2024.1338618
- How climate, fire types and topography drive forest biomass vulnerability to fires assessed from high resolution space-for-time analysis L. Vallet & F. Mouillot https://doi.org/10.1016/j.ecolind.2026.114850
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- Estimation of landscape water storage changes in the Qinghai-Tibet Plateau based on multi-source data J. Lei et al. https://doi.org/10.1016/j.ejrh.2025.102444
- State of the art in remote sensing monitoring of carbon dynamics in African tropical forests T. Bossy et al. https://doi.org/10.3389/frsen.2025.1532280
- Integrated carbon storage data and models for climate risk management S. Balbi et al. https://doi.org/10.1371/journal.pclm.0000584
- Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests L. Wan et al. https://doi.org/10.3389/frsen.2025.1724950
- Long-term forest-line dynamics in the French Pyrenees: an accelerating upward shift related to forest context, global warming and pastoral abandonment N. Delpouve et al. https://doi.org/10.5194/bg-22-7725-2025
- Soil smoldering in temperate forests: a neglected contributor to fire carbon emissions revealed by atmospheric mixing ratios L. Vallet et al. https://doi.org/10.5194/bg-22-213-2025
- Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products F. Fassnacht et al. https://doi.org/10.1093/forestry/cpae021
- Earth observation in national forest inventories: clear benefits yet marginal role—why? S. Francini et al. https://doi.org/10.1088/1748-9326/ae7f35
48 citations as recorded by crossref.
- High-precision airborne LiDAR remains essential for urban forestry: Revealing the limitations of recent large-scale canopy height products in urban contexts H. Dong et al. https://doi.org/10.1016/j.jag.2025.104791
- A 30 m Canopy Height Map in China Created by Fusion of Multiple Relative Height Metrics Y. Xiao et al. https://doi.org/10.1109/TGRS.2025.3572524
- Species information in multi-temporal Sentinel-2 data improves forest canopy height estimation C. Choi et al. https://doi.org/10.1016/j.agrformet.2026.111114
- Multi-Band, Multi-GNSS Polarimetric GNSS-R Observations From SCOMAG Airborne Campaign V. Dehaye et al. https://doi.org/10.1109/JSTARS.2026.3698506
- A remote sensing-based assessment of the cooling effects of urban trees in European cities Y. Su et al. https://doi.org/10.1038/s42949-026-00399-w
- Mapping of urban forest canopy height through diffusion-enhanced deep learning by using sentinel-2 and urban geospatial data N. Xu et al. https://doi.org/10.1080/17538947.2025.2553794
- An outlook on the rapid decline of carbon sequestration and perspectives for an improved monitoring of French forests P. Ciais et al. https://doi.org/10.5802/crgeos.309
- Accuracy assessment of high-resolution global canopy height maps in Patagonian Andean forests J. Lencinas et al. https://doi.org/10.2989/20702620.2025.2601032
- Super-resolved canopy height mapping from Sentinel-2 time series using airborne LiDAR HD reference data across metropolitan France E. Kalinicheva et al. https://doi.org/10.1016/j.rse.2026.115536
- DepthCanopyNet: Toward High-Precision Canopy Height Mapping via Gradient-Enhanced Learning Using Single UAV Optical Imagery Z. Song et al. https://doi.org/10.1109/TGRS.2026.3692661
- LiDAR remote sensing meets weak supervision: Concepts, methods, and perspectives Y. Gao et al. https://doi.org/10.1016/j.isprsjprs.2026.03.004
- Tracking Southern China’s Forest Growth from Space J. Chang et al. https://doi.org/10.34133/remotesensing.0810
- BiomSHARP: Biomass Super-Resolution for High Accuracy Prediction L. Albors et al. https://doi.org/10.1109/TGRS.2025.3636434
- Nonlinear climate–ecosystem functional interactions shape aboveground forest biomass dynamics in the Qilian Mountains: Insights from remote sensing and explainable machine learning X. Yang et al. https://doi.org/10.1016/j.ecoinf.2025.103546
- Integrating stepwise residual refinement and explainable AI for interpretable forest volume modeling in Hokkaido, Japan K. Iizuka et al. https://doi.org/10.1016/j.rsase.2025.101740
- Bridging spatio-temporal gaps in ALS data using Landsat time series and forest disturbance-recovery metrics via multi-task neural networks S. Francini et al. https://doi.org/10.1016/j.srs.2025.100318
- Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots N. Besic et al. https://doi.org/10.1109/JSTARS.2024.3425431
- Extraction of eucalyptus age and estimation of its aboveground biomass in China with the integration of empirical model and machine learning algorithm C. Tang et al. https://doi.org/10.1016/j.fecs.2026.100440
- The utility of dynamic forest structure from GEDI lidar fusion in tropical mammal species distribution models P. Burns et al. https://doi.org/10.3389/frsen.2025.1563430
- Supporting ecological restoration: leveraging satellite data and machine learning to map invasive alien tree biomass L. Cogill et al. https://doi.org/10.1016/j.ecoinf.2026.103756
- GEDI and Sentinel data integration for quantifying agroforestry tree height and stocks G. D'Amico et al. https://doi.org/10.1016/j.jenvman.2025.127197
- Development of mobile application for tree height measurement using geometric principle: Establishing global database of tree height and data M. Mahmud et al. https://doi.org/10.1016/j.atech.2025.100846
- Automatic Mapping of 10 m Tropical Evergreen Forest Cover in Central African Republic with Sentinel-2 Dynamic World Dataset W. Zhao et al. https://doi.org/10.3390/rs17040722
- Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France H. Cuny et al. https://doi.org/10.5194/bg-23-2365-2026
- Monitoring changes of forest height in California S. Favrichon et al. https://doi.org/10.3389/frsen.2024.1459524
- Canopy height and biomass distribution across the forests of Iberian Peninsula Y. Su et al. https://doi.org/10.1038/s41597-025-05021-9
- Refined big data on carbon sequestration for urban trees: 3D information and spatial carbon stock K. Cui et al. https://doi.org/10.1016/j.scs.2025.106901
- Accounting for 10 m Resolution Mapping for Above-Ground Biomass of Urban Trees in C40 Cities Across Eurasia Continent G. Yan et al. https://doi.org/10.3390/rs17233898
- kNN - Bagging NFI, GEDI, Sentinel-2 and Sentinel-1 data to produce estimates of forest volumes A. Schleich et al. https://doi.org/10.1016/j.foreco.2025.122964
- Ecosystem service provisioning in the Grand Est, France D. Shanafelt https://doi.org/10.1016/j.indic.2026.101409
- Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data Y. Li et al. https://doi.org/10.3390/rs16193650
- High-resolution sensors and deep learning models for tree resource monitoring M. Brandt et al. https://doi.org/10.1038/s44287-024-00116-8
- Assessment of Pinus halepensis Forests’ Vulnerability Using the Temporal Dynamics of Carbon Stocks and Fire Traits in Tunisia F. Rezgui et al. https://doi.org/10.3390/fire7060204
- Substantial contribution of trees outside forests to above-ground carbon across China Y. Su et al. https://doi.org/10.1038/s43247-025-03150-y
- Global carbon balance of the forest: satellite-based L-VOD results over the last decade J. Wigneron et al. https://doi.org/10.3389/frsen.2024.1338618
- How climate, fire types and topography drive forest biomass vulnerability to fires assessed from high resolution space-for-time analysis L. Vallet & F. Mouillot https://doi.org/10.1016/j.ecolind.2026.114850
- Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis Y. Wang et al. https://doi.org/10.3390/rs17162898
- Securing the forest carbon sink for the European Union’s climate ambition M. Migliavacca et al. https://doi.org/10.1038/s41586-025-08967-3
- Validating recent global canopy height maps over China's forests based on UAV lidar data A. Chen et al. https://doi.org/10.1016/j.rse.2025.114957
- Retrieving yearly forest growth from satellite data: A deep learning based approach M. Schwartz et al. https://doi.org/10.1016/j.rse.2025.114959
- Estimation of landscape water storage changes in the Qinghai-Tibet Plateau based on multi-source data J. Lei et al. https://doi.org/10.1016/j.ejrh.2025.102444
- State of the art in remote sensing monitoring of carbon dynamics in African tropical forests T. Bossy et al. https://doi.org/10.3389/frsen.2025.1532280
- Integrated carbon storage data and models for climate risk management S. Balbi et al. https://doi.org/10.1371/journal.pclm.0000584
- Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests L. Wan et al. https://doi.org/10.3389/frsen.2025.1724950
- Long-term forest-line dynamics in the French Pyrenees: an accelerating upward shift related to forest context, global warming and pastoral abandonment N. Delpouve et al. https://doi.org/10.5194/bg-22-7725-2025
- Soil smoldering in temperate forests: a neglected contributor to fire carbon emissions revealed by atmospheric mixing ratios L. Vallet et al. https://doi.org/10.5194/bg-22-213-2025
- Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products F. Fassnacht et al. https://doi.org/10.1093/forestry/cpae021
- Earth observation in national forest inventories: clear benefits yet marginal role—why? S. Francini et al. https://doi.org/10.1088/1748-9326/ae7f35
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Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
As forests play a key role in climate-related issues, their accurate monitoring is critical to...
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