UAV-LiDAR and Machine Learning for Estimating Individual Tree Diameter at Breast Height in Chinese Fir Plantations Approaching Maturity

Chinese fir (Cunninghamia lanceolata) is a major plantation species in southern China, and diameter at breast height (DBH) is an essential variable for forest inventory, biomass estimation, and carbon accounting. However, direct DBH extraction from UAV-LiDAR is limited in near-mature stands because lower-stem points are often occluded by dense canopies. This study developed a UAV-LiDAR-based workflow for individual tree segmentation, structural parameter extraction, and multivariate DBH estimation in near-mature Chinese fir plantations. Field measurements were conducted in 16 sample plots. Segmentation was evaluated against a common reference set of 428 field trees across the three CHM configurations, and 412 trees with unambiguous one-to-one field-to-crown matches were retained for DBH modeling. Canopy height models (CHMs) with spatial resolutions of 0.5, 0.25, and 0.1 m were compared using watershed segmentation. For the selected 0.25 m CHM, maximum crown-polygon height (CHM_H) and equivalent-area crown diameter (CD) were extracted. CD was calculated from crown area (CA), and CA was therefore excluded from all final multivariate models to avoid deterministic redundancy. Among the tested resolution-smoothing configurations, the 0.25 m CHM yielded the highest recall, precision, and F-score values of 94.86%, 92.69%, and 93.76%, respectively. In the height-metric comparison, CHM_H achieved R2 = 0.802 and RMSE = 0.928 m, slightly outperforming H90 under the prespecified RMSE criterion. On the same holdout, classical height–crown allometry yielded R2 = 0.605, RMSE = 1.701 cm, and MAE = 1.297 cm. BPNN retained the highest reported validation R2 (0.662) and the lowest reported RMSE (1.581 cm) and was selected as the best-performing model under the study design. The workflow provides a practical basis for non-destructive DBH estimation and precision inventory in near-mature Chinese fir plantations.