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Article

Tree Trunk Curvature Extraction Based on Terrestrial Laser Scanning Point Clouds

School of Earth and Space Sciences, Peking University, Beijing 100871, China
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Author to whom correspondence should be addressed.
Forests 2025, 16(5), 797; https://doi.org/10.3390/f16050797
Submission received: 25 March 2025 / Revised: 7 May 2025 / Accepted: 8 May 2025 / Published: 9 May 2025
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

Abstract

The degree of tree curvature exerts a significant influence on the utilization of forestry resources. This study proposes an enhanced quantitative structural modeling (QSM) method, founded upon terrestrial laser scanning (TLS) point cloud data, for the precise extraction of 3D curvature characteristics of tree trunks. The conventional approach operates under the assumption that the tree trunk constitutes an upright rotating body, thereby disregarding the tree trunk’s true curvature morphology. The proposed method is founded on the classical QSM algorithm and introduces two zoom factors that can dynamically adjust the fitting parameters. This improvement leads to enhanced accuracy in the representation of tree trunk curvature and reduced computational complexity. The study utilized 146 sample trees from 13 plots in Jixi, Anhui Province, which were collected and pre-processed by TLS. The study combines point cloud segmentation, manual labeling of actual curvature and dual-factor experiments, and uses quadratic polynomials and simulated annealing algorithms to determine the optimal model factors. The validation results demonstrate that the enhanced method exhibits a greater degree of concordance between the predicted and actual curvature values within the validation set. In the regression equation, the coefficient of the two-factor method for fitting a straight line is 0.95, which is substantially higher than the 0.75 of the one-factor method. Furthermore, the two-factor model has an R2 of 0.21, indicating that the two-factor optimization method generates a significantly smaller error compared to the one-factor model (with an R2 of 0.12). In addition, this study discusses the possible reasons for the error in the results, as well as the shortcomings and outlook. The experimental results demonstrate the augmented method’s capacity to accurately reconstruct the 3D curvature of tree trunks in most cases. This study provides an efficient and accurate method for conducting fine-grained forest resource measurements and tree bending studies.
Keywords: 3D point cloud data; light detection and ranging (LiDAR); trunk bending; terrestrial laser scanning (TLS); quantitative structure models (QSM); individual tree modeling 3D point cloud data; light detection and ranging (LiDAR); trunk bending; terrestrial laser scanning (TLS); quantitative structure models (QSM); individual tree modeling

Share and Cite

MDPI and ACS Style

Fan, C.; Lan, Y.; Zhang, F. Tree Trunk Curvature Extraction Based on Terrestrial Laser Scanning Point Clouds. Forests 2025, 16, 797. https://doi.org/10.3390/f16050797

AMA Style

Fan C, Lan Y, Zhang F. Tree Trunk Curvature Extraction Based on Terrestrial Laser Scanning Point Clouds. Forests. 2025; 16(5):797. https://doi.org/10.3390/f16050797

Chicago/Turabian Style

Fan, Chenxin, Yizhou Lan, and Feizhou Zhang. 2025. "Tree Trunk Curvature Extraction Based on Terrestrial Laser Scanning Point Clouds" Forests 16, no. 5: 797. https://doi.org/10.3390/f16050797

APA Style

Fan, C., Lan, Y., & Zhang, F. (2025). Tree Trunk Curvature Extraction Based on Terrestrial Laser Scanning Point Clouds. Forests, 16(5), 797. https://doi.org/10.3390/f16050797

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