Conference Agenda

Forest height and biomass are critical indicators for evaluating forest structure and quantifying ecosystem carbon storage. They play an essential role in forest resource monitoring and carbon cycle assessments. As most forests are located in mountainous regions, topographic effects exert a substantial influence on radar signal propagation and Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) measurements. In such regions, the conventional Random Volume over Ground (RVoG) model often yields significant systematic biases in forest height inversion results, primarily due to its omission of the coupling relationship between terrain slope and satellite observation geometry.To address this limitation, this study proposes an enhanced Sloped Random Volume over Ground (S-RVoG) model that incorporates ascending/descending orbit fusion and Digital Elevation Model (DEM) constraints to improve the accuracy and stability of forest height estimation using L-band SAOCOM PolInSAR data.Specifically, the proposed approach employs 12.5 m DEM data to accurately derive the slope and aspect of each pixel. By integrating the satellite incidence angle with terrain aspect information, slope-facing and back-slope regions are identified to correct both the local incidence angle and the vertical wavenumber (k_z). Furthermore, the DEM is utilized to directly compute and remove the terrain phase (φ₀), effectively eliminating phase distortions caused by complex topography and preventing systematic errors in the S-RVoG inversion process arising from terrain-induced effects.To further mitigate the influence of terrain-induced systematic bias, an error-constrained fusion mechanism is introduced within the improved S-RVoG framework. Forest height inversion is independently performed using both ascending and descending SAOCOM datasets. The reliability weights of each inversion result are determined according to their respective root mean square error (RMSE) values obtained through comparison with ground measurements or UAV LiDAR data. The final forest height is then derived via RMSE-inverse weighted fusion, enabling adaptive correction of systematic errors under varying observation geometries. This RMSE-constrained multi-orbit fusion strategy effectively reduces slope-induced biases and substantially enhances the accuracy and robustness of forest height estimation in mountainous areas.To validate the proposed model, a comprehensive field campaign was conducted in the western Qinling Mountains, a region characterized by highly variable topography with slopes exceeding 50° and an average slope of approximately 20°. Field measurements included tree height, species type, diameter at breast height (DBH), slope, and other relevant attributes. Additionally, UAV LiDAR data acquired over the same sampling areas were employed for validation and accuracy assessment.Experimental results demonstrate that the proposed method effectively suppresses slope-related systematic errors, improving forest height estimation accuracy by approximately 5–10% in steep terrain. Overall, the DEM-constrained, multi-orbit SAOCOM-integrated, and UAV LiDAR-validated S-RVoG model provides a physically consistent and empirically robust framework for L-band PolInSAR-based forest parameter inversion in mountainous regions with complex topography.