Crown Information Extraction and Annual Growth Estimation of a Chinese Fir Plantation Based on Unmanned Aerial Vehicle–Light Detection and Ranging

Forest biomass dynamics are important indicators of forest productivity and carbon sinks, which useful for evaluating ecological benefits management options. Rapid accurate methods monitoring would serve this purpose well. This study aimed at measuring aboveground (AGB) stand growth from tree crown parameters derived using unmanned aerial vehicle–light detection ranging (UAV–LiDAR). We focused on 17-year-old Chinese fir plantations in a subtropical area China monitored them UAV–LiDAR February 2019 to 2020. Two effective height (ECH) based drone discrete point clouds were evaluated ground survey data. Based the evaluation results, voxel method cloud segmentation (root-mean-squared error (RMSE) = 0.62 m, relative RMSE (rRMSE) 4.26%) was better than boundary pixel sum canopy (RMSE 1.26 rRMSE 8.63%). The (ECA) an individual extracted ECH strongly correlated with annual (coefficient determination (R2) 0.47). estimation crowns increase (ΔH) LiDAR statistically significant (R2 0.33, p < 0.01). After adding projection or ECA, model accuracy R2 increased 0.57 0.63, respectively. As scale plot level, direct ECA 1.59 Mg∙ha−1∙a−1, 15.02%) had performance indirect diameter 1.81 17.10%). mean rate AGB per middle-aged determined be 8.45 kg∙a−1 ΔH, plot-level 11.47 Mg∙ha−1∙a−1. conclude that rapid can achieved multitemporal information.