The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

doi:10.3390/rs11232880

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data
	Yang, Qiuli 7; Su, Yanjun 7; Jin, Shichao 7; Kelly, Maggi 4,5; Hu, Tianyu 7; Ma, Qin 8; Li, Yumei 7; Song, Shilin 7; Zhang, Jing 7; Xu, Guangcai 7
刊名	REMOTE SENSING
	2019
卷号	11 期号:23
关键词	individual segmentation method leaf area index canopy cover tree density coefficient of variation of tree height
DOI	10.3390/rs11232880
文献子类	Article
英文摘要	This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.
学科主题	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	2072-4292
出版地	BASEL
WOS关键词	LASER SCANNER DATA ; FOREST CANOPY ; DELINEATION ALGORITHM ; FILTERING ALGORITHMS ; HEIGHT MODELS ; F-SCORE ; AREA ; IMAGERY ; CROWNS ; VARIABLES
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000508382100161
资助机构	Key Deployment Project of the Chinese Academy of Sciences [KFZD-SW-319-06] ; National Key R&D Program of China [2017YFC0503905] ; CAS Pioneer Hundred Talents Program ; Provincial Key Technology Research and Development Program of Sichuan Ministry of Natural Resources for Ecological Geohazard Prevention and Mitigation in the 8.8 Jiuzhaigou Earthquake Area [KJ-2018-21] ; Provincial Key R&D Program of the Sichuan Ministry of Science and Technology [2019YFS0074]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/19847]
专题	植被与环境变化国家重点实验室
作者单位	1.Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830002, Peoples R China 2.Xinjiang Lidar Appl Engn Technol Res Ctr, Urumqi 830002, Peoples R China 3.Xinjiang Land & Resources Informat Ctr, Urumqi 830002, Peoples R China 4.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 5.Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA 6.Mississippi State Univ, Dept Forestry, Mississippi State, MS 39762 USA 7.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 8.Univ Calif Berkeley, Div Agr & Nat Resources, Berkeley, CA 94720 USA
推荐引用方式 GB/T 7714	Yang, Qiuli,Su, Yanjun,Jin, Shichao,et al. The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data[J]. REMOTE SENSING,2019,11(23).
APA	Yang, Qiuli.,Su, Yanjun.,Jin, Shichao.,Kelly, Maggi.,Hu, Tianyu.,...&Guo, Qinghua.(2019).The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data.REMOTE SENSING,11(23).
MLA	Yang, Qiuli,et al."The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data".REMOTE SENSING 11.23(2019).