| The skeleton model of trees can abstractly reflect the characteristic information such as geometric shape and topological structure of trees.Therefore,it has been widely used in three-dimensional model of trees,digital forestry,landscape design and other fields.LiDAR scanning technology has the characteristics of all-weather operation,high efficiency and strong penetration.Its application in forestry investigation is developing rapidly.LiDAR scanners can obtain accurate 3D point cloud data on the surface of trees.By processing the point cloud data,the growth parameters of trees are obtained.This has become one of the hotspots of forestry research in recent years.The object of this research is poplar.The data used in this research is the point cloud data of airborne LiDAR and knapsack LiDAR.In this study,a registration algorithm based on single tree segmentation,a branch and leaf classification algorithm based on principal component analysis and region growth,and a tree skeleton extraction method based on clustering were proposed.The focus of this paper is to build a single tree skeleton based on multi-source point cloud data.The main research works and conclusions are as follows:(1)Automatic registration algorithms for multi-source point cloud dataIn this paper,a new registration algorithm based on single tree segmentation was proposed,which combined the scanning data of knapsack LiDAR and airborne LiDAR.This can improve the quality of point cloud data.This paper chose to compare it with the iterative nearest point method and the improved normal distribution transformation method.Experiments were carried out on three sample plots,and the performance of algorithms was tested by root mean square error.The root mean square errors of the proposed method for three sample plots were 2.27 cm,2.94 cm and 1.98 cm respectively.Compared with the registration results of the other two algorithms,the proposed method had higher accuracy.(2)Tree growth parameters and branch point cloud extractionThe tree height,DBH and crown width were extracted by geometric calculation and least square fitting circle algorithm.In this paper,a point cloud branch and leaf classification algorithm based on principal component analysis and region growth was proposed.Then the proposed algorithm was tested on the data of elm and poplar,and the results showed that the proposed algorithm had high accuracy.(3)Skeleton point extraction and linkingIn this paper,an improved tree skeleton extraction method based on clustering was proposed.The trunk and twigs were treated separately.The trunk adopted the random sampling consistent cylindrical fitting method,while the twig used the least square straight line fitting method.All the extracted skeleton points were linked,and finally a more real skeleton model of the tree was obtained.The skeleton model can effectively and simply describe the geometric information and topological connection information of trees.It is of great significance in building an accurate three-dimensional model of trees and studying the carbon cycle,leaf area index and biomass estimation of forests. |
