Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this s...

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Vydáno v:Forests Ročník 15; číslo 7; s. 1217
Hlavní autoři: Liu, Song, Deng, Yuncheng, Zhang, Jianpeng, Wang, Jinliang, Duan, Di
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2024
Témata:
Algorithms
canopy
China
Cloud point curves
Clustering
Curvature
data collection
Decision making
Environmental protection
Feasibility studies
Forest management
Forest resources
Forests
Grasses
Laser applications
Lasers
meta-analysis
Methods
Morphology
Remote sensing
Shrubs
Software
Surveys
Sustainable forestry
tree growth
China
Yunnan China
ISSN:1999-4907, 1999-4907
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Abstract Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.
AbstractList Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.
Audience Academic
Author Deng, Yuncheng
Liu, Song
Zhang, Jianpeng
Wang, Jinliang
Duan, Di
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Cites_doi 10.11834/jrs.20197383
10.3390/rs5020491
10.1080/01431160701736406
10.3390/s19010172
10.3390/drones7020065
10.3390/rs13020223
10.3390/agriculture13010130
10.4028/www.scientific.net/AMM.475-476.355
10.3390/f14081525
10.1007/s11676-020-01214-7
10.1007/s13595-011-0102-2
10.1029/2018EA000417
10.3390/rs15102644
10.3390/rs8060501
10.1016/j.isprsjprs.2016.01.006
10.1016/j.rse.2016.08.013
10.1016/j.isprsjprs.2021.01.026
10.1109/TGRS.2003.810682
10.3390/rs14225892
10.1016/j.geog.2021.10.002
10.3390/rs15010115
10.3390/rs15225317
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References Ma (ref_14) 2019; 23
Liu (ref_13) 2016; 52
Liu (ref_8) 2014; 27
Liu (ref_21) 2021; 46
Deng (ref_37) 2022; 13
Maas (ref_11) 2008; 29
ref_35
Yan (ref_12) 2018; 31
ref_34
ref_10
Shao (ref_32) 2016; 6
ref_18
Donager (ref_3) 2018; 5
ref_39
ref_16
ref_38
ref_15
Safaie (ref_17) 2021; 174
He (ref_23) 2013; 475–476
Luo (ref_6) 2024; 56
Li (ref_1) 2021; 34
Bu (ref_4) 2016; 33
Ma (ref_42) 2021; 46
Fassnacht (ref_26) 2016; 186
Stal (ref_19) 2020; 32
ref_25
Deng (ref_36) 2022; 37
ref_24
ref_22
Zhang (ref_33) 2003; 41
Xia (ref_9) 2018; 33
ref_41
ref_40
ref_2
Liu (ref_31) 2023; S1
ref_28
Zi (ref_27) 2019; 48
Raumonen (ref_20) 2013; 5
Liang (ref_29) 2016; 115
ref_7
Zhang (ref_5) 2023; 38
Dassot (ref_30) 2011; 68
References_xml – ident: ref_7
– ident: ref_28
– volume: 23
  start-page: 743
  year: 2019
  ident: ref_14
  article-title: Fine classification of near-ground point cloud based on terrestrial laser scanning and detection of forest fallen wood
  publication-title: Natl. Remote Sens. Bull.
  doi: 10.11834/jrs.20197383
– volume: 34
  start-page: 72
  year: 2021
  ident: ref_1
  article-title: Forest Management Inventory in China: History, Current Status and Trend
  publication-title: World For. Res.
– volume: 5
  start-page: 491
  year: 2013
  ident: ref_20
  article-title: Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data
  publication-title: Remote Sens.
  doi: 10.3390/rs5020491
– volume: 29
  start-page: 1579
  year: 2008
  ident: ref_11
  article-title: Automatic Forest Inventory Parameter Determination from Terrestrial Laser Scanner Data
  publication-title: Int. J. Remote Sens.
  doi: 10.1080/01431160701736406
– volume: 37
  start-page: 77
  year: 2022
  ident: ref_36
  article-title: Tree DBH Extraction Based on F-LS Algorithm
  publication-title: Remote Sens. Inf.
– ident: ref_41
  doi: 10.3390/s19010172
– ident: ref_24
  doi: 10.3390/drones7020065
– volume: 52
  start-page: 26
  year: 2016
  ident: ref_13
  article-title: Individual Tree DBH and Height Estimation Using Terrestrial Laser Scanning(TLS) in a Subtropical Forest
  publication-title: Sci. Silvae Sin.
– ident: ref_2
  doi: 10.3390/rs13020223
– ident: ref_40
  doi: 10.3390/agriculture13010130
– ident: ref_18
– ident: ref_35
– volume: 31
  start-page: 42
  year: 2018
  ident: ref_12
  article-title: Research Progress in TLS Technology in Forest Investigation
  publication-title: World For. Res.
– volume: 475–476
  start-page: 355
  year: 2013
  ident: ref_23
  article-title: The Trees Skeleton Extraction Based on Point Cloud Contraction
  publication-title: AMM
  doi: 10.4028/www.scientific.net/AMM.475-476.355
– volume: 48
  start-page: 55
  year: 2019
  ident: ref_27
  article-title: Hyperspectral characteristics analysis and discriminant of 4 typical coniferous trees species in Shangri-La
  publication-title: J. Fujian Agric. For. Univ. (Nat. Sci. Ed.)
– volume: S1
  start-page: 32
  year: 2023
  ident: ref_31
  article-title: DEM construction of airborne LiDAR data in complex mountainous areas at different densities
  publication-title: Bull. Surv. Mapp.
– ident: ref_22
  doi: 10.3390/f14081525
– volume: 32
  start-page: 1503
  year: 2020
  ident: ref_19
  article-title: Assessment of Handheld Mobile Terrestrial Laser Scanning for Estimating Tree Parameters
  publication-title: J. For. Res.
  doi: 10.1007/s11676-020-01214-7
– volume: 46
  start-page: 105
  year: 2021
  ident: ref_21
  article-title: A fine extraction method of forest point cloud in complex background
  publication-title: Sci. Surv. Mapp.
– volume: 68
  start-page: 959
  year: 2011
  ident: ref_30
  article-title: The Use of Terrestrial LiDAR Technology in Forest Science: Application Fields, Benefits and Challenges
  publication-title: Ann. For. Sci.
  doi: 10.1007/s13595-011-0102-2
– volume: 5
  start-page: 753
  year: 2018
  ident: ref_3
  article-title: Examining Forest Structure with Terrestrial Lidar: Suggestions and Novel Techniques Based on Comparisons Between Scanners and Forest Treatments
  publication-title: Earth Space Sci.
  doi: 10.1029/2018EA000417
– ident: ref_10
  doi: 10.3390/rs15102644
– volume: 33
  start-page: 238
  year: 2018
  ident: ref_9
  article-title: Application Status and Prospect of TLS in Forest Resources Inventory
  publication-title: J. Northwest For. Univ.
– ident: ref_34
  doi: 10.3390/rs8060501
– ident: ref_25
– volume: 115
  start-page: 63
  year: 2016
  ident: ref_29
  article-title: Terrestrial Laser Scanning in Forest Inventories
  publication-title: ISPRS J. Photogramm. Remote Sens.
  doi: 10.1016/j.isprsjprs.2016.01.006
– volume: 33
  start-page: 68
  year: 2016
  ident: ref_4
  article-title: Designing 3D Trees Point Cloud Data Processing Software System
  publication-title: Comput. Appl. Softw.
– volume: 56
  start-page: 44
  year: 2024
  ident: ref_6
  article-title: Airborne LiDAR in Forest Resources Investigation and Monitoring
  publication-title: For. Sci. Technol. Inf.
– volume: 38
  start-page: 405
  year: 2023
  ident: ref_5
  article-title: Automatic Filtering of Understory Vegetation based on Point Cloud Main Direction of Terrestrial Laser Scanning
  publication-title: Remote Sens. Technol. Appl.
– volume: 186
  start-page: 64
  year: 2016
  ident: ref_26
  article-title: Review of Studies on Tree Species Classification from Remotely Sensed Data
  publication-title: Remote Sens. Environ.
  doi: 10.1016/j.rse.2016.08.013
– volume: 174
  start-page: 19
  year: 2021
  ident: ref_17
  article-title: Automated Street Tree Inventory Using Mobile LiDAR Point Clouds Based on Hough Transform and Active Contours
  publication-title: ISPRS J. Photogramm. Remote Sens.
  doi: 10.1016/j.isprsjprs.2021.01.026
– ident: ref_15
– volume: 41
  start-page: 872
  year: 2003
  ident: ref_33
  article-title: A Progressive Morphological Filter for Removing Nonground Measurements from Airborne LIDAR Data
  publication-title: IEEE Trans. Geosci. Remote Sens.
  doi: 10.1109/TGRS.2003.810682
– ident: ref_38
  doi: 10.3390/rs14225892
– volume: 6
  start-page: 17
  year: 2016
  ident: ref_32
  article-title: The Research of Improved Progressive Triangulated Irregular Network Densification Filtering Algorithm
  publication-title: Beijing Surv. Mapp.
– volume: 13
  start-page: 38
  year: 2022
  ident: ref_37
  article-title: A Novel Fast Classification Filtering Algorithm for LiDAR Point Clouds Based on Small Grid Density Clustering
  publication-title: Geod. Geodyn.
  doi: 10.1016/j.geog.2021.10.002
– volume: 46
  start-page: 122
  year: 2021
  ident: ref_42
  article-title: An improved K-means clustering method for DBH extraction from point cloud
  publication-title: Sci. Surv. Mapp.
– volume: 27
  start-page: 49
  year: 2014
  ident: ref_8
  article-title: Applications of Airborne Laser Scanning and Terrestrial Laser Scanning to Forestry
  publication-title: World For. Res.
– ident: ref_16
  doi: 10.3390/rs15010115
– ident: ref_39
  doi: 10.3390/rs15225317
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Snippet Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing...
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SubjectTerms Algorithms
canopy
China
Cloud point curves
Clustering
Curvature
data collection
Decision making
Environmental protection
Feasibility studies
Forest management
Forest resources
Forests
Grasses
Laser applications
Lasers
meta-analysis
Methods
Morphology
Remote sensing
Shrubs
Software
Surveys
Sustainable forestry
tree growth
Title Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting
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