Stem Quality Estimates Using Terrestrial Laser Scanning Voxelized Data and a Voting-Based Branch Detection Algorithm

被引：1

作者：

Olofsson, Kenneth ^{[1
]}

Holmgren, Johan ^{[1
]}

机构：

[1] Swedish Univ Agr Sci, Dept Forest Resource Management, Div Forest Remote Sensing, S-90183 Umea, Sweden

来源：

REMOTE SENSING | 2023年 / 15卷 / 08期

关键词：

terrestrial laser scanning; stem quality; branch structure; TREE MODELS; LIDAR;

D O I：

10.3390/rs15082082

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

A new algorithm for detecting branch attachments on stems based on a voxel approach and line object detection by a voting procedure is introduced. This algorithm can be used to evaluate the quality of stems by giving the branch density of each standing tree. The detected branches were evaluated using field-sampled trees. The algorithm detected 63% of the total amount of branch whorls and 90% of the branch whorls attached in the height interval from 0 to 10 m above ground. The suggested method could be used to create maps of forest stand stem quality data.

引用

页数：10

共 48 条

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National Remote Sensing Bulletin, 2023, 27 (07) : 5 - 18
[2] A voting-based statistical cylinder detection framework applied to fallen tree mapping in terrestrial laser scanning point clouds
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[3] ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING
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[4] Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation
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[5] Void-Volume-Based Stem Geometric Modeling and Branch-Knot Localization in Terrestrial Laser Scanning Data
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[6] Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm
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[7] Volume Estimation of Stem Segments Based on a Tetrahedron Model Using Terrestrial Laser Scanning Data
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[8] Automatic Stem Detection in Terrestrial Laser Scanning Data With Distance-Adaptive Search Radius
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[9] Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data
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[10] A novel weighted majority voting-based ensemble approach for detection of road accidents using social media data
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