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 paper
| 
02 Nov 2023
Data description paper |
| 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
Viewed
Total article views: 4,410 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jun 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,766 | 1,539 | 105 | 4,410 | 89 | 86 |
- HTML: 2,766
- PDF: 1,539
- XML: 105
- Total: 4,410
- BibTeX: 89
- EndNote: 86
Total article views: 2,454 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Nov 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,845 | 535 | 74 | 2,454 | 71 | 62 |
- HTML: 1,845
- PDF: 535
- XML: 74
- Total: 2,454
- BibTeX: 71
- EndNote: 62
Total article views: 1,956 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jun 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 921 | 1,004 | 31 | 1,956 | 18 | 24 |
- HTML: 921
- PDF: 1,004
- XML: 31
- Total: 1,956
- BibTeX: 18
- EndNote: 24
Viewed (geographical distribution)
Total article views: 4,410 (including HTML, PDF, and XML)
Thereof 4,269 with geography defined
and 141 with unknown origin.
Total article views: 2,454 (including HTML, PDF, and XML)
Thereof 2,375 with geography defined
and 79 with unknown origin.
Total article views: 1,956 (including HTML, PDF, and XML)
Thereof 1,894 with geography defined
and 62 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
9 citations as recorded by crossref.
- Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data Y. Li et al. 10.3390/rs16193650
- High-resolution sensors and deep learning models for tree resource monitoring M. Brandt et al. 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. 10.3390/fire7060204
- Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products F. Fassnacht et al. 10.1093/forestry/cpae021
- Global carbon balance of the forest: satellite-based L-VOD results over the last decade J. Wigneron et al. 10.3389/frsen.2024.1338618
- Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots N. Besic et al. 10.1109/JSTARS.2024.3425431
- Combining satellite images with national forest inventory measurements for monitoring post-disturbance forest height growth A. Pellissier-Tanon et al. 10.3389/frsen.2024.1432577
- 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 M. Schwartz et al. 10.5194/essd-15-4927-2023
- High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France L. Vallet et al. 10.5194/bg-20-3803-2023
6 citations as recorded by crossref.
- Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data Y. Li et al. 10.3390/rs16193650
- High-resolution sensors and deep learning models for tree resource monitoring M. Brandt et al. 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. 10.3390/fire7060204
- Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products F. Fassnacht et al. 10.1093/forestry/cpae021
- Global carbon balance of the forest: satellite-based L-VOD results over the last decade J. Wigneron et al. 10.3389/frsen.2024.1338618
- Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots N. Besic et al. 10.1109/JSTARS.2024.3425431
3 citations as recorded by crossref.
- Combining satellite images with national forest inventory measurements for monitoring post-disturbance forest height growth A. Pellissier-Tanon et al. 10.3389/frsen.2024.1432577
- 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 M. Schwartz et al. 10.5194/essd-15-4927-2023
- High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France L. Vallet et al. 10.5194/bg-20-3803-2023
Latest update: 13 Dec 2024
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...
Altmetrics
Final-revised paper
Preprint