Résumé
The thesis focuses on the contribution of spaceborne lidar to the development of Multisource Forest Inventory (MFI) methods. In France, the National Forest Inventory (NFI) method addresses the requirements of public policies at regional and national levels. However, on smaller territories, precision is often insufficient to meet the needs of management activities. MFI methods better address these needs by combining inventory data with remote sensing data. This thesis aims to improve NFI accuracy at sub-regional to local scales by integrating data from the spaceborne lidar GEDI into multisource approaches.Unfortunately, this integration is complicated due to the lack of spatial correspondence between field samples (inventory plots) and GEDI footprints. Additionally, GEDI data are poorly georeferenced, making them difficult to integrate into certain MFI approaches. This thesis focuses on these issues and is divided into three main parts.As a first step, a method for improving GEDI georeferencing, based on a high-resolution reference digital elevation model (DEM) was developed. This method compares, for a series of positions around the location indicated in the GEDI products, the ground elevations of the GEDI footprints with those of the reference DEM, generating an error map according to X and Y offsets. Using a flow accumulation algorithm on this error map, an improved position minimizing the distance from the DEM is proposed for each GEDI footprint.Next, two approaches for using GEDI data with NFI data were developed. The study sites are located in the Vosges and use ∼ 500 IFN plots and over 100,000 GEDI footprints.The first approach is a double sampling for stratification (2SS) approach, based on common variables between GEDI and NFI, without requiring spatial correspondence of the two data sources. 2SS approaches are generally based on probabilistic data samples, which is not a priori the case for GEDI's sampling pattern. Thus, a preliminary analysis was required to understand the characteristics of the spatial distribution of the GEDI sample. The relevance of the chosen common variable, i.e. the maximum tree height, was also verified. Compared with estimates based only on NFI data, the 2SS approach improved the variance of growing stock volume estimates by up to 56%.The second approach is based on a link between GEDI data and NFI data, established indirectly by using spatially exhaustive data sources, the Sentinel-2 and Sentinel-1 images. To establish the model linking the different data sources, we chose to use the k-nearest neighbor (kNN) method combined with bagging (bootstrap aggregation). The aim is to propagate information from field plots to GEDI footprints in order to "densify" NFI plots by taking advantage of GEDI forest structure measurements, which are well correlated with the forest attributes of interest (e.g. growing stock volume). First, for each NFI plot, we looked for the GEDI footprints with the characteristics of the Sentinel link variables, supplemented or not with a height link variable, that are closest to those of the NFI point. Using a kNN-bagging approach, the set of GEDI variables is therefore estimated for each NFI plot. Next, a regression model is established by kNN-bagging to estimate the volume using the best predicted GEDI variables from the previous step and the Sentinel variables. The volume is estimated at the level of all GEDI footprints. The strategy supplemented by a height link variable performed best and reached a coefficient of determination of 58%. Subsequently, using the resulting dense sample of volume plots, standard methods for small area estimation (scale of the municipality or district) or high-resolution volume mapping can be implemented.