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