Estimating Growing Stock Volume at Tree and Stand Levels for Chinese Fir (Cunninghamia lanceolata) in Southern China Using UAV Laser Scanning

UAV laser scanning (UAV-LS) combines extensive scanning coverage with high point cloud density, enabling efficient and precise acquisition of key forest attributes. Based on field-measured data and UAV-LS data from 138 Chinese fir (Cunninghamia lanceolata) plantation plots in southern China, this study systematically developed growing stock volume (GSV) estimation models at both tree and stand levels. The models included base models (allometric models), linear models, dummy variable models incorporating age groups, and nonlinear mixed-effects models incorporating random effects (plot and area levels for the tree level, and only the area level for the stand level). The results showed the following: (1) Stand-level GSV prediction relied primarily on height metrics, achieving optimal performance through a combination of the 10th cumulative height percentile (AIH10) and canopy cover (CC), both of which showed near-linear relationships with GSV; tree-level GSV was predicted by LiDAR-derived tree height (LH) and crown width (LCW), with LH explaining most variation. (2) Tree-level models achieved R2 = 0.639–0.725 and RMSE = 0.050–0.058 m3, exhibiting larger individual prediction errors (mean percentage standard error, MPSE > 30%) with smaller aggregate prediction errors (mean prediction error, MPE < 1%); stand-level models reached R2 = 0.785–0.879 and RMSE = 46.052–61.314 m3 ha−1 while maintaining controlled errors across scales (MPE < 5%, MPSE < 20%). (3) At both the tree and stand levels, the nonlinear mixed-effects model outperformed the others, followed by the dummy variable model and the base model, with the linear model exhibiting the worst performance; area-level random effects primarily influenced the baseline value of tree-level GSV and the allometric relationship between stand-level GSV and AIH10, whereas plot-level random effects affected the allometric relationships of tree-level GSV with LH and LCW. This study confirms the effectiveness of UAV-LS for large-scale forest resource monitoring, while underscoring the necessity of incorporating spatial heterogeneity in GSV estimation.