Extraction of tree branch skeletons from terrestrial LiDAR point clouds
Three-dimensional (3D) branch structures provide vital information for understanding tree phenotypic characteristics and for ecological studies related to carbon sequestration. Light detection and ranging (LiDAR) has been widely applied to capture the 3D structural information of individual trees. W...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124005028 |
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| Summary: | Three-dimensional (3D) branch structures provide vital information for understanding tree phenotypic characteristics and for ecological studies related to carbon sequestration. Light detection and ranging (LiDAR) has been widely applied to capture the 3D structural information of individual trees. Wood–leaf separation and tree skeleton extraction are essential prerequisites for accurately estimating tree attributes (e.g., stem volume, aboveground biomass, and crown characteristics) and representing the tree branch network. Owing to the complex internal branch morphology and intercanopy component occlusion, precise extraction of the tree skeleton from point clouds remains a challenging issue. In this study, we propose an improved approach for extracting tree skeletons on the basis of the geometric features of point clouds. The approach consists of two steps: separation of the wood and leaves, followed by extraction of the tree skeleton. In the first step, the point clouds of the trees are sliced horizontally. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to cluster each layer of the point clouds and detect the main trunk. Subsequently, random sample consensus (RANSAC) circle feature detection and linear feature constraints are applied to achieve wood–leaf separation. In the second step, the wood point clouds are used to extract the initial tree skeleton via a minimum spanning tree (MST), and the initial tree skeleton is further optimized. Various comparative experiments are conducted on terrestrial-LiDAR-scanned data from nine trees across six species. The results show that the proposed method performs effectively, with overall wood–leaf separation accuracies ranging from 86% to 93%. Additionally, the extracted branch skeleton accurately reflects the natural geometric structure of the trees. The wood points and tree skeletons are further used to estimate tree attributes, demonstrating the potential of our method for the quantitative representation of trees and their ecological characteristics (e.g., carbon sequestration). |
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| ISSN: | 1574-9541 |