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Forest Ecosystems 2023, 10(1): 100088 https://doi.org/10.1016/j.fecs.2023.100088
Research Article | Open Access | Issue | Published: 12 January 2023

A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data

Show Author's Information Yupan Zhanga,  Yiliu Tanb,  Yuichi Ondaa( ),  Asahi Hashimotoa,  Takashi Gomic,  Chenwei Chiuc,  Shodai Inokoshic
Center for Research in Isotopes and Environmental Dynamics, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
Department of International Environmental and Agricultural Science, Tokyo University of Agriculture and Technology, 3-5-8, Saiwaie, Fuchu, Tokyo, 183-8509, Japan
Keywords:
Forest, LiDAR, Drone, Tree detection, Trunk sections
Cite this article:
Zhang Y, Tan Y, Onda Y, et al. A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data. Forest Ecosystems, 2023, 10(1): 100088. https://doi.org/10.1016/j.fecs.2023.100088
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Abstract Full text About this article

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone LiDAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone LiDAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with under-canopy sections split into heights ranging from 1 to 7 ​m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha−1 and an average tree age of 42 years. Dense point cloud data were generated from the drone LiDAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m−2, respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy (F1 – Score ​= ​0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.


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About this article

A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data

Show Author's information Yupan Zhanga, Yiliu Tanb, Yuichi Ondaa( ), Asahi Hashimotoa, Takashi Gomic, Chenwei Chiuc, Shodai Inokoshic
Center for Research in Isotopes and Environmental Dynamics, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
Department of International Environmental and Agricultural Science, Tokyo University of Agriculture and Technology, 3-5-8, Saiwaie, Fuchu, Tokyo, 183-8509, Japan

Abstract

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone LiDAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone LiDAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with under-canopy sections split into heights ranging from 1 to 7 ​m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha−1 and an average tree age of 42 years. Dense point cloud data were generated from the drone LiDAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m−2, respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy (F1 – Score ​= ​0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

Keywords: Forest, LiDAR, Drone, Tree detection, Trunk sections

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Publication history

Received: 20 July 2022
Revised: 28 November 2022
Accepted: 11 December 2022
Published: 12 January 2023
Issue date: February 2023

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© 2023 The Authors.

Acknowledgements

We gratefully acknowledge Yuichi Onda and Takashi Gomi for support of field surveys, LiDAR scanning and drone equipment for collecting LiDAR data. We thank anonymous reviewers for comments that improved the manuscript. We are also very grateful to Shodai Inokoshi and Asahi Hashimoto for the indispensable support during the field work. Further we thank Yiliu Tan for her valuable help and critical discussions during the algorithm process.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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