结合背负式激光雷达和无人机机载激光雷达的云南松单木胸径和树高提取OA北大核心CSTPCD

[Objective]This study aims to explore the advantages of acquiring three-dimensional point cloud of forests based on backpack laser scanning(BLS)and unmanned aerial vehicle laser scanning(ULS)technology,and use LiDAR360 MLS and LiDAR360 software to realize accurate measurement of single tree diameter at breast height(DBH)and tree height,meanwhile,determine the optimal method for individual tree segmentation and extraction.[Method]Taking 6 circular plots of Pinus yunnanensis natural pure forest with a radius of 15.0 m in Luomian Township,Fumin County,Yunnan Province as an example,the iterative closest point algorithm(ICP)was employed to fuse BLS and ULS point cloud.LiDAR360 MLS and LiDAR360 software were used to denoise,classify,normalize,segment individual trees and extracted DBH and tree height of individual trees from the point cloud data.The correlation between measured values and estimated parameters was established by linear fitting,and the estimation effect of DBH and tree height was evaluated.[Result]Compared with LiDAR360 based on elevation information for classification,LiDAR360 MLS based on deep learning for classification was more in line with reality in the number of tree extraction.The tree extraction results from BLS and fusion point cloud were consistent,and the recall rate reached 100％.ULS performed single tree segmentation through seed points,with accuracy,recall,and F-measure of 94.59％,88.98％,and 91.70％,respectively.However,due to canopy connectivity,there existed some under-segmentation and over-segmentation.The determination coefficient(R2)and root mean square error(ERMSE)of DBH extraction based on BLS were 0.904 and 2.046 cm,respectively.R2 and ERMSE extracted by tree height were 0.791 and 1.173 m,respectively.The fusion point cloud was affected by discrete points around the trunk,and the DBH extraction effect was relatively poorer than BLS.R2 and ERMSE were 0.881 and 2.284 cm,respectively.However,the information of canopy and understory of fusion point cloud was more complete,and the estimation accuracy of tree height was higher than that of BLS,with R2 and ERMSE values of 0.933 and 0.812 m,respectively.[Conclusion]Due to differences in working principles,ULS and BLS technologies each have their own advantages in acquiring point cloud from the canopy and understory.The combination of ULS and BLS can achieve a complementary effect,which can reflect the forest spatial structure more precisely,and realize the high-precision extraction of DBH and tree height.[Ch,5 fig.3 tab.28 ref.]