Research on a Method for Individual Tree Segmentation for Mountainous Coniferous Forests Using UAV-LiDAR Technology
The Chinese fir(Cunninghamia lanceolata)was taken as the research object.Unmanned aerial vehicle LiDAR(UAV-LiDAR)point cloud data was combined with field survey data which was used to explore and innovate the existing forest resource survey model,improve the efficiency of forest resource field survey,update the resource survey data,ensure the current status of the data.Considering that the study area is mountainous with undulating terrain,an improved triangulated irregular network densification filtering algorithm was selected for filtering and classification.The methods for individual tree segmentation including watershed segmentation,point cloud segmentation,and layer stacking seed point segmentation algorithm were compared,and the extraction of tree parameters including individual tree position,tree height,and crown width in the study area was completed,and the technical process of UAV-LiDAR on the inversion of forest parameters was optimized.Ten plots were selected from 30 plots,The measured value of sample wood was compared with the estimated value of individual tree segmentation of these plots.The results were as follows:The layer stacking seed point algorithm had the best segmentation effect;the F-score of the individual tree segmentation algorithm ranged from 64.61％to 85.29％,the point cloud segmentation algorithm was moderate,its F-score ranged from 56.00％to 80.60％,and the watershed segmentation algorithm which F-score ranged from 45.57％to 69.45％had a slightly poorer segmentation effect.Within the same method,there were differences in segmentation effects in different plots,which might be related to plot terrain and tree structure morphology.When there were tree occlusions or irregular tree distribution structures in the plot,the individual tree segmentation accuracy would be reduced to a certain extent.Therefore,establishing a forest parameter inversion model suitable for different forest conditions based on UAV-LiDAR data is the direction for future efforts.
LiDARindividual tree segmentationfilterChinese fir(Cunninghamia lanceolata)forest structure parameters
NETL
NSTL
万方数据
