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Theses

Estimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR)

Abstract : The estimation and monitoring of forest resources and carbon stocks are major issues for wood industry and public bodies. Forests play an important role in national and international plans for climate change mitigation (carbon storage, climate regulation, biodiversity, renewable energy). In temperate forests, monitoring is done at two different levels: on one hand, at local level, in small areas by the acquisition of many measures of forest structure parameters, and, on the other hand, by statistics at national level or in large administrative areas that are provided annually by public bodies. Temperate forests are highly anthropogenic (high spatial variability and fragmentation of stands), so there is currently a strong need for a more refined and regular maps of forest resources in these regions. Optical and radar satellite images provide information on the state of vegetation, tree structure and spatial organization of forests. In an exceptional context of free global availability, diversity, and quality of images with high spatial and temporal resolution, the aim of this PhD work is to set up the methodological bases for a generic and semi-automatic production of forest parameters mapping (biomass, diameter, height, etc.). We have assessed the potential of Sentinel-1 (C-band radar), Sentinel-2 (optical) time series, and ALOS2-PALSAR2 (radar, L-band) annual mosaics to estimate forest structure parameters. These satellite data are combined, using supervised learning algorithms and field measurements, to construct models for estimating aboveground biomass (AGB), mean tree diameter (DBH), height, basal area and tree density. These models can then be spatially applied over the entire territory by using satellite images, providing thus continuous information on the spatial resolution of the images used (10 to 20 meters). This approach has been conceived and tested on four study sites with different forest species and structural and environmental properties: the inner and the dune zone of the Landes forest (maritime pines), the Orléans forest (oak and Scots pines), and the forest of Saint-Gobain (oaks, hornbeams and beeches). The investigated issues are the satellite data to be used, the selection of explanatory variables, the choice of regression algorithms and their parameterization, the differentiation of forest types and the spatialization of forest parameter estimates. The primitives derived from satellite data provide information on the optical properties of soil and vegetation, the spatial organization of trees, the structure and volume of live wood of crowns and trunks. The use of nonlinear multivariate regression algorithms allows to obtain forest parameter estimates with relative error performance in the order of 15 to 35 % for the basal area (~ 2.8 to 5.9 m2/ha) depending on forest types, 5 to 20 % for height (~ 1.3 to 3 m), and 5 to 25 % for DBH (~ 1.5 to 8 cm). The results highlight the improvement by combining several types of satellite data (optical, multi-frequency radar and spatial texture indexes), as well as the importance of differentiating forest types for the construction of models. This high-resolution, regular mapping of the forest resource is very promising to help improving the monitoring and policy of territorial and national strategies for the timber sector, biodiversity and carbon storage.
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David Morin. Estimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR). Milieux et Changements globaux. Université Paul Sabatier - Toulouse III, 2020. Français. ⟨NNT : 2020TOU30079⟩. ⟨tel-03098487⟩

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