Conference Agenda

The use of Tomographic Synthetic Aperture Radar (TomoSAR) has opened new perspectives for three-dimensional forest structure retrieval, yet its operational exploitation in tropical environments remains challenged by volume decorrelation, sidelobe leakage, and signal noise. These effects often obscure the vertical separation between ground and canopy scatterers and hinder the derivation of accurate height products. Moreover, the limited availability of fully polarimetric datasets restricts the capacity to distinguish scattering mechanisms across forest layers. In this study, we present a geometry-driven methodology for the extraction of vertical forest layers directly from single-polarized TomoSAR reconstructions. The proposed framework employs a concave-hull-based volume delimitation and adaptive envelope refinement to delineate canopy and terrain surfaces from the reconstructed reflectivity profiles, while maintaining the spatial coherence of the tomographic signal. The approach is independently applied to each polarization channel of the fully polarimetric dataset acquired by ESA’s TropiSAR campaign over a dense tropical forest located in Paracou, French Guiana. This polarization-wise processing avoids decomposition artifacts and allows for a direct evaluation of single-channel structural sensitivity. Validation against LiDAR-derived reference models demonstrates that the proposed method recovers Digital Terrain Models and Canopy Height Models consistent with the vertical scattering behavior of the forest. The results highlight the robustness of the proposed strategy for noise-affected TomoSAR data and its potential for large-scale forest structure monitoring.