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An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data.

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Title: An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data.
Authors: Xia, Jisheng1,2 (AUTHOR), Ma, Sunjie1,2 (AUTHOR), Luan, Guize3 (AUTHOR), Dong, Pinliang4 (AUTHOR), Geng, Rong5 (AUTHOR) xxzx@ynddj.org.cn, Zou, Fuyan6 (AUTHOR), Yin, Junzhou1,7 (AUTHOR), Zhao, Zhifang1,2 (AUTHOR)
Source: Remote Sensing. Apr2025, Vol. 17 Issue 7, p1271. 20p.
Subjects: Tree trunks, Forest surveys, Point cloud, Surface reconstruction, Statistical sampling
Abstract: Scanning forests with LiDAR is an efficient method for conducting forest resource surveys, including estimating tree diameter at breast height (DBH), canopy height, and segmenting individual trees. This study uses three-dimensional (3D) forest test data and point cloud data simulated by the Helios++ V1.3.0 software, and proposes a voxelized trunk extraction algorithm to determine the trunk location and the vertical structure of single tree trunks in forest areas. Firstly, the voxel-based shape recognition algorithm is used to extract the trunk structure of tree point clouds, then the random sample consensus (RANSAC) algorithm is used to solve the vertical structure connectivity problem of tree trunks generated by the above method, and the Alpha Shapes algorithm is selected among various point cloud surface reconstruction algorithms to reconstruct the surface of tree point clouds. Then, building on the tree surface model, a light projection scene is introduced to locate the tree trunk coordinates at different heights. Finally, the convex hull of the trunk bottom is solved by the Graham scanning method. Accuracy assessments show that the proposed single-tree extraction algorithm and the forest vertical structure recognition algorithm, when applied within the light projection scene, effectively delineate the regions where the vertical structure distribution of single tree trunks is inconsistent. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 184440749
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  Data: An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data.
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  Data: <searchLink fieldCode="AR" term="%22Xia%2C+Jisheng%22">Xia, Jisheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Sunjie%22">Ma, Sunjie</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luan%2C+Guize%22">Luan, Guize</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Pinliang%22">Dong, Pinliang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Geng%2C+Rong%22">Geng, Rong</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> xxzx@ynddj.org.cn</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Fuyan%22">Zou, Fuyan</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Junzhou%22">Yin, Junzhou</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zhifang%22">Zhao, Zhifang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2025, Vol. 17 Issue 7, p1271. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Tree+trunks%22">Tree trunks</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+surveys%22">Forest surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+reconstruction%22">Surface reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink>
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  Label: Abstract
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  Data: Scanning forests with LiDAR is an efficient method for conducting forest resource surveys, including estimating tree diameter at breast height (DBH), canopy height, and segmenting individual trees. This study uses three-dimensional (3D) forest test data and point cloud data simulated by the Helios++ V1.3.0 software, and proposes a voxelized trunk extraction algorithm to determine the trunk location and the vertical structure of single tree trunks in forest areas. Firstly, the voxel-based shape recognition algorithm is used to extract the trunk structure of tree point clouds, then the random sample consensus (RANSAC) algorithm is used to solve the vertical structure connectivity problem of tree trunks generated by the above method, and the Alpha Shapes algorithm is selected among various point cloud surface reconstruction algorithms to reconstruct the surface of tree point clouds. Then, building on the tree surface model, a light projection scene is introduced to locate the tree trunk coordinates at different heights. Finally, the convex hull of the trunk bottom is solved by the Graham scanning method. Accuracy assessments show that the proposed single-tree extraction algorithm and the forest vertical structure recognition algorithm, when applied within the light projection scene, effectively delineate the regions where the vertical structure distribution of single tree trunks is inconsistent. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17071271
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        Text: English
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        Type: general
      – SubjectFull: Forest surveys
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      – SubjectFull: Point cloud
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      – SubjectFull: Surface reconstruction
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      – SubjectFull: Statistical sampling
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      – TitleFull: An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data.
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            NameFull: Xia, Jisheng
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              Text: Apr2025
              Type: published
              Y: 2025
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