Résumé
This research presents a comprehensive canopy height map of French Guiana at 10 m spatial resolution, employing a data fusion approach integrating optical (Sentinel-2), radar (Sentinel-1 and ALOS), and ancillary data sources. The primary objective is to leverage a U-Net neural network model, trained and validated using Global Ecosystem Dynamics Investigation (GEDI) data as reference canopy height. We aim at understanding how canopy height prediction models can be improved through the integration of relevant remote sensing and environmental descriptors related to canopy structure. The accuracies of the generated canopy height maps are assessed against high-resolution airborne LiDAR (ALS) acquisitions conducted by the French National Forest Office. We observe that enriching input data with height above nearest drainage (HAND) as well as forest landscape information yielded improved accuracies for the prediction models. Moreover, accounting for GEDI database uncertainties, through filtering of usable waveforms and correction of geolocation errors, also resulted in a performance gain for canopy height estimation using a U-Net model.