Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4927-2023
https://doi.org/10.5194/essd-15-4927-2023
Data description paper
 | 
02 Nov 2023
Data description paper |  | 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, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad

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Cited articles

ADEME and IGN: Contribution de l'IGN à l'établissement des bilans carbone des forêts des territoires (PCAET), 2019. 
Baldini, S., Berti, S., Cutini, A., Mannuncci, A., Mercurio, R., and Spinelli, R.: Prove sperimentali di primo diradamento in un soprassuolo di pino marittimo (Pinus pinaster Ait.) originato da incendio: aspetti silvicolturali, di utilizzazione e caratteristiche della biomassa, Ann. Ist. Sper. Selvic., 20, 385–436, 1989. 
Ball, J. E., Anderson, D. T., and Sr, C. S. C.: Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.JRS.11.042609, 2017. 
Calders, K., Verbeeck, H., Burt, A., Origo, N., Nightingale, J., Malhi, Y., Wilkes, P., Raumonen, P., Bunce, R. G. H., and Disney, M.: Laser scanning reveals potential underestimation of biomass carbon in temperate forest, Ecol. Solut. Evid., 3, e12197, https://doi.org/10.1002/2688-8319.12197, 2022. 
Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B., and Yamakura, T.: Tree allometry and improved estimation of carbon stocks and balance in tropical forests, Oecologia, 145, 87–99, https://doi.org/10.1007/s00442-005-0100-x, 2005. 
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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.
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