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Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories
Tao, Shengli1,2; Wu, Fangfang; Guo, Qinghua3; Wang, Yongcai; Li, Wenkai4; Xue, Baolin; Hu, Xueyang1,2; Li, Peng1,2; Tian, Di1,2; Li, Chao1,2; Yao, Hui1,2; Li, Yumei; Xu, Guangcai; Fang, Jingyun1,2
2015
发表期刊ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
卷号110页码:66-76
摘要The rapid development of light detection and ranging (LiDAR) techniques is advancing ecological and forest research. During the last decade, numerous single tree segmentation techniques have been developed using airborne LiDAR data. However, accurate crown segmentation using terrestrial or mobile LiDAR data, which is an essential prerequisite for extracting branch level forest characteristics, is still challenging mainly because of the difficulties posed by tree crown intersection and irregular crown shape. In the current work, we developed a comparative shortest-path algorithm (CSP) for segmenting tree crowns scanned using terrestrial (T)-LiDAR and mobile LiDAR. The algorithm consists of two steps, namely trunk detection and subsequent crown segmentation, with the latter inspired by the well-proved metabolic ecology theory and the ecological fact that vascular plants tend to minimize the transferring distance to the root. We tested the algorithm on mobile-LiDAR-scanned roadside trees and T-LiDAR-scanned broadleaved and coniferous forests in China. Point-level quantitative assessments of the segmentation results showed that for mobile-LiDAR-scanned roadside trees, all the points were classified to their corresponding trees correctly, and for T-LiDAR-scanned broadleaved and coniferous forests, kappa coefficients ranging from 0.83 to 0.93 were obtained. We believe that our algorithm will make a contribution to solving the problem of crown segmentation in T-LiDAR scanned-forests, and might be of interest to researchers in LiDAR data processing and to forest ecologists. In addition, our research highlights the advantages of using ecological theories as guidelines for processing LiDAR data. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
关键词Terrestrial LiDAR Segmentation Shortest path Mobile LiDAR Metabolic ecology theory DBSCAN
学科领域Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI10.1016/j.isprsjprs.2015.10.007
收录类别SCI
语种英语
WOS关键词INDIVIDUAL TREES ; STEM VOLUME ; LASER ; HEIGHT ; FORM ; SEGMENTATION ; DENSITY ; CLASSIFICATION ; IDENTIFICATION ; ALLOMETRY
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号WOS:000366225400007
出版者ELSEVIER
文献子类Article
出版地AMSTERDAM
EISSN1872-8235
资助机构National Natural Science Foundation of China [41471363, 31321061, 31330012, 31270563, 41401505] ; National Science Foundation [DBI 1356077]
作者邮箱guo.qinghua@gmail.com
作品OA属性Bronze
引用统计
被引频次:136[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ibcas.ac.cn/handle/2S10CLM1/25872
专题植被与环境变化国家重点实验室
作者单位1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
2.Peking Univ, Coll Urban & Environm Sci, Dept Ecol, Beijing 100871, Peoples R China
3.Peking Univ, Minist Educ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China
4.Univ Calif Merced, Sch Engn, Sierra Nevada Res Inst, Merced, CA 95343 USA
5.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China
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Tao, Shengli,Wu, Fangfang,Guo, Qinghua,et al. Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2015,110:66-76.
APA Tao, Shengli.,Wu, Fangfang.,Guo, Qinghua.,Wang, Yongcai.,Li, Wenkai.,...&Fang, Jingyun.(2015).Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,110,66-76.
MLA Tao, Shengli,et al."Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 110(2015):66-76.
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