UAV-LiDAR Single Tree Segmentation Methods in Different Forest Types Performance Evaluation
In order to evaluate different single tree segmentation methods and determine the optimal method and param-eter settings for high-precision single tree segmentation using unmanned aerial vehicle LiDAR in multiple forest types,this study compared four different methods using canopy height model(CHM)and normalized point cloud(NPC)data.Wa-tershed,variable window search,point cloud segmentation,and K-means clustering were used for single tree segmenta-tion,and tree height parameters were extracted from nine plots of three forest types.Performance evaluation of segmen-tation results was conducted through actual measurement of plot data,and the influence of different segmentation param-eters on the results was explored.Among all the plots,the overall F-score of single tree segmentation ranges from 0.63 to 0.9,with significant differences between different single tree segmentation methods.The versatility of variable window search is good,while PCS performs better in complex single tree segmentation.The segmentation accuracy of the two ap-proaches is complementary in plots of different levels of complexity.Adopting differentiated segmentation strategies can help improve the efficiency and accuracy of segmentation tasks.Analyzed and summarized the segmentation performance and parameter setting schemes of various single tree segmentation methods under different forest stand conditions,provi-ding important reference for the accurate acquisition of forest structural parameters by unmanned aerial vehicle LiDAR technology.
万方数据
维普
