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- W4385781377 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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<sup>-1</sup>) 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<sup>-1</sup> and 19.6 Mg.ha<sup>-1</sup> 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 <a href=https://doi.org/10.5281/zenodo.7840108 target=_blank rel=noopener>https://doi.org/10.5281/zenodo.7840108</a> (Schwartz et al., 2023)." @default.
- W4385781377 created "2023-08-13" @default.
- W4385781377 date "2023-08-12" @default.
- W4385781377 modified "2023-10-18" @default.
- W4385781377 title "Comment on essd-2023-196" @default.
- W4385781377 doi "https://doi.org/10.5194/essd-2023-196-rc2" @default.
- W4385781377 hasPublicationYear "2023" @default.
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