A Novel Approach for the Detection of Standing Tree Stems from Plot-Level terrestrial Laser Scanning Data

Wuming Zhang, Peng Wan (Corresponding Author), Tiejun Wang, Shangshu Cai, Yiming Chen, Xiuliang Jin, Guangjian Yan

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
LanguageEnglish
Article number211
Pages1-19
Number of pages19
JournalRemote sensing
Volume11
Issue number2
DOIs
Publication statusPublished - 22 Jan 2019

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laser
stem
benchmarking
cloud classification
method
detection
segmentation
curvature

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Zhang, Wuming ; Wan, Peng ; Wang, Tiejun ; Cai, Shangshu ; Chen, Yiming ; Jin, Xiuliang ; Yan, Guangjian. / A Novel Approach for the Detection of Standing Tree Stems from Plot-Level terrestrial Laser Scanning Data. In: Remote sensing. 2019 ; Vol. 11, No. 2. pp. 1-19.
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abstract = "Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95{\%}). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.",
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A Novel Approach for the Detection of Standing Tree Stems from Plot-Level terrestrial Laser Scanning Data. / Zhang, Wuming; Wan, Peng (Corresponding Author); Wang, Tiejun; Cai, Shangshu; Chen, Yiming; Jin, Xiuliang; Yan, Guangjian.

In: Remote sensing, Vol. 11, No. 2, 211, 22.01.2019, p. 1-19.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Jin, Xiuliang

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