A novel geometric feature-based wood-leaf separation method for large and crown-heavy tropical trees using handheld laser scanning point cloud

点云 牙冠(牙科) 稳健性(进化) 热带森林 热带气候 数学 计算机科学 遥感 人工智能 材料科学 生态学 生物 地质学 复合材料 生物化学 基因
作者
Meilian Wang,Man Sing Wong
出处
期刊:International Journal of Remote Sensing [Informa]
卷期号:44 (10): 3227-3258 被引量:1
标识
DOI:10.1080/01431161.2023.2217981
摘要

As the prerequisite of non-destructively measuring structural parameters and leaf distribution, wood-leaf separation plays an essential role in forest inventories. However, fewer studies focus on the separation and measurement of large tropical trees with heavy crowns and complex branch structures. This study proposed a novel method to automatically separate the leaf and wood points of large tropical trees based on geometric features. Instead of identifying all wood points using the same rules, we used different methods to separate small and large wood components, respectively. The identification of small wood components was implemented mainly by the differences in point density and linear distribution pattern between leaf and wood points, while the identification of large wood components was implemented through the comprehensive analysis of verticality, linearity, anisotropy, as well as point density. To improve the separation accuracy and implementation effectiveness, the segment-wise and point-wise methods were combined in this study. The robustness and generalization of the proposed method were tested using two datasets, i.e. twenty large tropical trees with heavy crowns and twenty-four general tropical trees without heavy crowns. The separation results verified that the proposed method could achieve good separation of wood and leaf points of large crown-heavy tropical trees with the accuracy of up to 91.5%. The highest separation accuracy of general tropical trees was about 95.03%. The examination of the tropical trees without heavy crowns demonstrated that the proposed method has promising robustness and generalization ability. In addition, to fill the gap in the large tropical tree point clouds, an open-source dataset library was built for the wood-leaf separation research, including manually labelled 20 large crown-heavy tropical trees with different types of branch structures and basic structural parameters of each tree (tree height, crown spread, and diameter at the breast height (DBH)).

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