A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas

doi:10.1080/07038992.2018.1481738

	A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas
	Zhao, Xiaoqian 1; Su, Yanjun 2; Li, WenKai 3; Hu, Tianyu ; Liu, Jin ; Guo, Qinghua1,2
	2018
发表期刊	CANADIAN JOURNAL OF REMOTE SENSING
ISSN	0703-8992
卷号	44 期号:4 页码:287-298
摘要	Filtering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and environmental features such as slope, canopy cover, elevation variability, and LiDAR point density on five widely used filtering algorithms, including multi-scale curvature classification (MCC), interpolation-based filtering (IBF) algorithm, morphological filtering (MF) algorithm, progressive triangulated irregular network densification filtering (PTDF) algorithm, and slope-based filtering (SBF). The results show that the performances of these filtering algorithms are all significantly influenced by the chosen factors, but the dominant influential factor varies with algorithms. The MCC works well in steep and dense forests; IBF and MCC outperform the rest of filtering algorithms in areas with steep terrain but low vegetation coverage; and PTDF is more reliable for low-density LiDAR data. Our results can provide guidance for choosing the appropriate filtering algorithm based on the specific topographic and environmental features of a study area.
学科领域	Remote Sensing
DOI	10.1080/07038992.2018.1481738
收录类别	SCI
语种	英语
WOS关键词	AIRBORNE LIDAR ; DEM GENERATION ; CLASSIFICATION ; FOREST ; SEGMENTATION ; EXTRACTION ; ELEVATION ; MODEL
WOS记录号	WOS:000461864500003
出版者	TAYLOR & FRANCIS INC
文献子类	Article
出版地	PHILADELPHIA
EISSN	1712-7971
资助机构	National Key R&D Program of China [2017YFC0503905, 2016YFC0500202] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41471363, 31741016] ; Frontier Science Key Programs of the Chinese Academy of Sciences [QYZDY-SSW-SMC011] ; CAS Pioneer Hundred Talents Program ; US National Science FoundationNational Science Foundation (NSF) [EAR 0922307]
作者邮箱	guo.qinghua@gmail.com
引用统计	被引频次：21[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/2S10CLM1/20582
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 Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Calif Merced, Sierra Nevada Res Inst, Merced, CA 95343 USA 4.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Xiaoqian,Su, Yanjun,Li, WenKai,et al. A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas[J]. CANADIAN JOURNAL OF REMOTE SENSING,2018,44(4):287-298.
APA	Zhao, Xiaoqian,Su, Yanjun,Li, WenKai,Hu, Tianyu,Liu, Jin,&Guo, Qinghua.(2018).A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas.CANADIAN JOURNAL OF REMOTE SENSING,44(4),287-298.
MLA	Zhao, Xiaoqian,et al."A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas".CANADIAN JOURNAL OF REMOTE SENSING 44.4(2018):287-298.

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