Evaluating UAV and Handheld LiDAR Point Clouds for Radiative Transfer Modeling Using a Voxel-Based Point Density Proxy

forest. Structural analysis-computed coverage gap ratio (CGR) revealed distinct behaviors. UAV-LiDARs effectively captured canopy structures (10-45% CGR), whereas HLS provided superior understory coverage, but exhibited a high upper-canopy CGR (>40%). Integrating datasets reduced the CGR to below 10%, demonstrating strong complementarity. Radiative transfer simulations correlated well with Sentinel-2 NIR reflectance, with the UAV-LiDAR (r = 0.73-0.75) outperforming the HLS (r = 0.64-0.69). These results highlight the critical importance of upper-canopy modeling for nadir-viewing sensors. Although integrating HLS data did not improve correlation due to the dominance of upper-canopy signals, structural analysis confirmed that fusion is essential for achieving volumetric completeness. A voxel size range of 50-100 cm was identified as effective for balancing structural detail and radiative stability. These findings provide practical guidelines for selecting and integrating LiDAR platforms in forest monitoring, emphasizing that while aerial sensors suffice for top-of-canopy reflectance, multi-platform fusion is requisite for full 3D structural characterization.