Evaluating Airborne LiDAR Data-Derived Canopy Geometry for 3-D Radiative Transfer Simulations

Three-dimensional radiative transfer models (3D RTMs) are indispensable for understanding how radiation propagates within vegetation canopies and for interpreting remote sensing signals. Yet their practical deployment is hindered by the difficulty of acquiring the detailed 3D structural parameters they require. Airborne laser scanning (ALS) offers a powerful means of retrieving such information at landscape scales. Here, we quantitatively assess how ALS-derived 3-D forest reconstructions influence bidirectional reflectance factor (BRF) simulations. Using three benchmark RAMI scenes that span contrasting canopy architectures, we generated synthetic ALS point clouds and reconstructed each scene via alphashape, voxel, and ellipsoid representations. The resulting 3D scenes were then input to the LargE-Scale remote Sensing data and image simulation (LESS) model to evaluate the structural contribution to simulated canopy reflectance and to quantify the associated uncertainties. Results demonstrate that at a 10-meter resolution, all three representation methods yielded favorable results, especially in the summer birch forest, where the alphashape approach achieves the highest coefficient of determinationR2(0.94) and the lowest mean relative absolute error δ (3.5%) for NIR band. Generally, both the alphashape and voxel approaches outperformed the ellipsoid approach in representing canopies for reflectance simulation, with the alphashape method being comparable with the voxel approach. Furthermore, varying alpha values used for fitting the crowns showed thatR2and δ exhibit a consistent pattern and are minimally affected by alpha values, whereas the voxel approach shows greater sensitivity to voxel dimensions. These results demonstrate that ALS-derived canopy geometry can support radiative transfer simulations under controlled conditions and highlight the importance of selecting reconstruction strategies according to canopy structure and spatial resolution, providing practical guidance for BRF simulations and future remote sensing applications across forest types.