A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds

Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint n...

Full description

Saved in:
Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 11; p. 2545
Main Authors Hui, Zhenyang, Cai, Zhaochen, Liu, Bo, Li, Dajun, Liu, Hua, Li, Zhuoxuan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 26.05.2022
Subjects
Accuracy
Branches
Computation
Connectivity analysis
Cylinders
Efficiency
Forestry
global optimization
individual tree modeling
joint neighboring growing
Lasers
Lidar
Methods
Model accuracy
object primitive
Optimization
Remote sensing
Spatial analysis
terrestrial LiDAR
Three dimensional models
Topology
Trees
Vegetation
Online AccessGet full text

Cover

More Information
Summary:Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14112545