A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations

doi:10.1109/TGRS.2019.2953654

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	A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations
	Guan, Hongcan 1; Su, Yanjun 1; Hu, Tianyu 1; Wang, Rui 1; Ma, Qin 1,2; Yang, Qiuli 1; Sun, Xiliang 1; Li, Yumei 1; Jin, Shichao 1; Zhang, Jing 1
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2020
卷号	58 期号:3 页码:2165-2177
关键词	Vegetation Laser radar Forestry Tin Three-dimensional displays Unmanned aerial vehicles Registers Forest multiplatform light detection and ranging (LiDAR) registration tree location
ISSN号	0196-2892
DOI	10.1109/TGRS.2019.2953654
文献子类	Article
英文摘要	The emerging near-surface light detection and ranging (LiDAR) platforms [e.g., terrestrial, backpack, mobile, and unmanned aerial vehicle (UAV)] have shown great potential for forest inventory. However, different LiDAR platforms have limitations either in data coverage or in capturing undercanopy information. The fusion of multiplatform LiDAR data is a potential solution to this problem. Because of the complexity and irregularity of forests and the inaccurate positioning information under forest canopies, current multiplatform data fusion still involves substantial manual efforts. In this article, we proposed an automatic multiplatform LiDAR data registration framework based on the assumption that each forest has a unique tree distribution pattern. Five steps are included in the proposed framework, i.e., individual tree segmentation, triangulated irregular network (TIN) generation, TIN matching, coarse registration, and fine registration. TIN matching, as the essential step to find the corresponding tree pairs from multiplatform LiDAR data, uses a voting strategy based on the similarity of triangles composed of individual tree locations. The proposed framework was validated by fusing backpack and UAV LiDAR data and fusing multiscan terrestrial LiDAR data in coniferous forests. The results showed that both registration experiments could reach a satisfying data registration accuracy (horizontal root-mean-square error (RMSE) < 30 cm and vertical RMSE < 20 cm). Moreover, the proposed framework was insensitive to individual tree segmentation errors, when the individual tree segmentation accuracy was higher than 80%. We believe that the proposed framework has the potential to increase the efficiency of accurately registering multiplatform LiDAR data in forest environments.
学科主题	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	1558-0644
出版地	PISCATAWAY
WOS关键词	TERRESTRIAL LASER SCANS ; POINT CLOUD REGISTRATION ; AIRBORNE LIDAR ; INDIVIDUAL TREES ; SEGMENTATION ; ALGORITHM ; BIOMASS ; SURFACE ; MODELS ; CROWNS
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000519598700051
资助机构	National Key Research and Development Program of China [2016YFC0500202] ; Key Research Program of the Chinese Academy of Science [KFZD-SW-319-06] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41871332, 0011107] ; CAS Pioneer Hundred Talents Program
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/21861]
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.Mississippi State Univ, Dept Forestry, Mississippi State, MS 39762 USA 4.Natl Forestry & Grassland Adm, China Natl Forestry Econ & Dev Res Ctr, Beijing 100714, Peoples R China 5.Natl Forestry & Grassland Adm, Acad Inventory & Planning, Beijing 100714, Peoples R China
推荐引用方式 GB/T 7714	Guan, Hongcan,Su, Yanjun,Hu, Tianyu,et al. A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2020,58(3):2165-2177.
APA	Guan, Hongcan.,Su, Yanjun.,Hu, Tianyu.,Wang, Rui.,Ma, Qin.,...&Guo, Qinghua.(2020).A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(3),2165-2177.
MLA	Guan, Hongcan,et al."A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.3(2020):2165-2177.