An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data

doi:10.3390/rs17071271

	An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data
	Xia, Jisheng 1,2; Ma, Sunjie 1,2; Luan, Guize 3; Dong, Pinliang 4; Geng, Rong 5; Zou, Fuyan 6; Yin, Junzhou 1,7; Zhao, Zhifang 1,2
	2025-04-03
发表期刊	REMOTE SENSING
卷号	17 期号:7 页码:20
摘要	Scanning forests with LiDAR is an efficient method for conducting forest resource surveys, including estimating tree diameter at breast height (DBH), canopy height, and segmenting individual trees. This study uses three-dimensional (3D) forest test data and point cloud data simulated by the Helios++ V1.3.0 software, and proposes a voxelized trunk extraction algorithm to determine the trunk location and the vertical structure of single tree trunks in forest areas. Firstly, the voxel-based shape recognition algorithm is used to extract the trunk structure of tree point clouds, then the random sample consensus (RANSAC) algorithm is used to solve the vertical structure connectivity problem of tree trunks generated by the above method, and the Alpha Shapes algorithm is selected among various point cloud surface reconstruction algorithms to reconstruct the surface of tree point clouds. Then, building on the tree surface model, a light projection scene is introduced to locate the tree trunk coordinates at different heights. Finally, the convex hull of the trunk bottom is solved by the Graham scanning method. Accuracy assessments show that the proposed single-tree extraction algorithm and the forest vertical structure recognition algorithm, when applied within the light projection scene, effectively delineate the regions where the vertical structure distribution of single tree trunks is inconsistent.
关键词	LiDAR vertical structure of forest random sample consensus light projection scene
DOI	10.3390/rs17071271
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China ; Open Project of Technology Innovation Center for Natural Ecosystem Carbon Sink[CS2023D01] ; Open Project of Technology Innovation Center for Natural Ecosystem Carbon Sink[KC-24248928] ; [42061038]
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001464298500001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40579
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Geng, Rong
作者单位	1.Yunnan Univ, Sch Earth Sci, Kunming 650500, Peoples R China 2.Yunnan Int Joint Lab China Laos Bangladesh Myanmar, Kunming 650500, Peoples R China 3.Yunnan Univ, Inst Int Rivers & Ecosecur, Kunming 650500, Peoples R China 4.Univ North Texas, Dept Geog & Environm, Denton, TX 76201 USA 5.Yunnan Ctr Geol Informat, Kunming 650051, Peoples R China 6.Minist Nat Resources, Technol Innovat Ctr Nat Ecosyst Carbon Sink, Kunming 650111, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Xia, Jisheng,Ma, Sunjie,Luan, Guize,et al. An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data[J]. REMOTE SENSING,2025,17(7):20.
APA	Xia, Jisheng.,Ma, Sunjie.,Luan, Guize.,Dong, Pinliang.,Geng, Rong.,...&Zhao, Zhifang.(2025).An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data.REMOTE SENSING,17(7),20.
MLA	Xia, Jisheng,et al."An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data".REMOTE SENSING 17.7(2025):20.