Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R² = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

Tree structural information is essential for studying forest ecosystem functions, driving mechanisms, and global change response mechanisms. Although current terrestrial laser scanning (TLS) can acquire high-precision 3D structural information of forests, mutual occlusion between trees, the scanner’s field of view, and terrain changes make the point clouds captured by laser scanning sensors incomplete, further hindering downstream tasks. This study proposes a skeleton-embedded tree point cloud completion method, termed SK-TreePCN, which recovers complete individual tree point clouds from incomplete scanning data in the field. SK-TreePCN employs a transformer trained on simulated point clouds generated by a 3D radiative transfer model. Unlike existing point cloud completion algorithms designed for regular shapes and simple structures, the SK-TreePCN method addresses structurally heterogeneous trees. The 3D radiative transfer model LESS, which can simulate various TLS data over highly heterogeneous scenes, is employed to generate massive point clouds with training labels. Among the various point cloud completion methods evaluated, SK-TreePCN exhibits outstanding performance regarding the Chamfer distance (CD) and $F1$ Score. The generated point clouds display a more natural appearance and clearer branches. The accuracy of tree height and diameter at breast height extracted from the recovered point cloud achieved ${\mathit{R}}^2$ values of 0.929 and 0.904, respectively. SK-TreePCN demonstrates applicability and robustness in recovering individual tree point clouds. It demonstrated great potential for TLS-based field measurements of trees, refining point cloud 3D reconstruction and tree information extraction and reducing field data collection labor while retaining satisfactory data quality.