A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level

Peng Wan, Jie Shao* (Corresponding Author), Shuangna Jin, Tiejun Wang, Shengmei Yang, Guangjian Yan, Wuming Zhang* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point-wise classification strategy, which is not efficient for processing large-volume TLS datasets acquired at the forest plot level. In this study, we proposed a segment-wise classification strategy to improve the efficiency of the wood–leaf separation from large-volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment-wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state-of-the-art wood–leaf separation methods, that is, the CANUPO and LeWoS. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy. Our study demonstrates that the segment-wise classification strategy applies to the large-volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.

Original languageEnglish
Pages (from-to)2473-2486
Number of pages14
JournalMethods in ecology and evolution
Volume12
Issue number12
Early online date6 Sep 2021
DOIs
Publication statusPublished - Dec 2021

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • UT-Hybrid-D

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