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 GEDI data with a deep learning approach Article Swipe
Martin Schwartz
,
Philippe Ciais
,
Aurélien De Truchis
,
Jérôme Chave
,
Catherine Ottlé
,
Cédric Vega
,
Jean‐Pierre Wigneron
,
Manuel Nicolas
,
Sami Jouaber
,
Siyu Liu
,
Martin Brandt
,
Ibrahim Fayad
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/essd-2023-196
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/essd-2023-196
The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote sensing measurements (NASA’s GEDI LiDAR mission and ESA’s Copernicus Sentinel 1 & 2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha-1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from Airborne Laser Scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 Mg ha-1 and 19.6 Mg.ha-1 respectively, providing greater performance than other AGBD products sampled over France. These results highlight the importance of coupling remote sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023).
Related Topics
Concepts
Forest inventory
Lidar
Biomass (ecology)
Canopy
Remote sensing
Environmental science
Random forest
Tree canopy
Tree allometry
Biodiversity
Forestry
Shuttle Radar Topography Mission
Geography
Forest management
Physical geography
Digital elevation model
Ecology
Computer science
Biology
Archaeology
Biomass partitioning
Machine learning
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/essd-2023-196
- https://essd.copernicus.org/preprints/essd-2023-196/essd-2023-196.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380791683
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380791683Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/essd-2023-196Digital Object Identifier
- Title
-
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 GEDI data with a deep learning approachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-15Full publication date if available
- Authors
-
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cédric Vega, Jean‐Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, Ibrahim FayadList of authors in order
- Landing page
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https://doi.org/10.5194/essd-2023-196Publisher landing page
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https://essd.copernicus.org/preprints/essd-2023-196/essd-2023-196.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://essd.copernicus.org/preprints/essd-2023-196/essd-2023-196.pdfDirect OA link when available
- Concepts
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Forest inventory, Lidar, Biomass (ecology), Canopy, Remote sensing, Environmental science, Random forest, Tree canopy, Tree allometry, Biodiversity, Forestry, Shuttle Radar Topography Mission, Geography, Forest management, Physical geography, Digital elevation model, Ecology, Computer science, Biology, Archaeology, Biomass partitioning, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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