A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data

doi:10.1016/j.isprsjprs.2014.03.014

	A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data
	Lu, Xingcheng 1; Guo, Qinghua1 ; Li, Wenkai 1; Flanagan, Jacob 1
	2014
发表期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN	0924-2716
卷号	94 页码:1-12
摘要	Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
关键词	Lidar Deciduous forest Tree segmentation Intensity 3-D structure Bottom-up
学科领域	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.1016/j.isprsjprs.2014.03.014
收录类别	SCI
语种	英语
WOS关键词	WAVE-FORM LIDAR ; SMALL-FOOTPRINT ; STEM VOLUME ; SAMPLING DENSITY ; FOREST STRUCTURE ; F-SCORE ; BIOMASS ; HEIGHT ; CROWNS ; AREA
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000339134000001
出版者	ELSEVIER
文献子类	Article
出版地	AMSTERDAM
EISSN	1872-8235
资助机构	Directorate For Geosciences [1043051, 1339015] Funding Source: National Science Foundation
作者邮箱	qguo@ucmerced.edu
引用统计	被引频次：144[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/2S10CLM1/27027
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.Univ Calif Merced, Sch Engn, Merced, CA 95343 USA
推荐引用方式 GB/T 7714	Lu, Xingcheng,Guo, Qinghua,Li, Wenkai,et al. A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2014,94:1-12.
APA	Lu, Xingcheng,Guo, Qinghua,Li, Wenkai,&Flanagan, Jacob.(2014).A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,94,1-12.
MLA	Lu, Xingcheng,et al."A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 94(2014):1-12.

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