Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China

doi:10.1016/j.rse.2018.05.014

	Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China
	Zhao, Yujin 1; Zeng, Yuan ; Zheng, Zhaoju ; Dong, Wenxue ; Zhao, Dan ; Wu, Bingfang ; Zhao, Qianjun
	2018
发表期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
卷号	213 页码:104-114
摘要	Monitoring biodiversity is essential for the conservation and management of forest resources. A method called spectranomics that maps the diversity of forest species based on species-driven leaf optical traits using imaging spectroscopy has been developed for tropical forests in earlier studies. In this study we applied the spectranomics method in combination with airborne hyperspectral (PHI-3 sensor with 1 m spatial resolution) and LiDAR ( > 4 points/m(2)) data to first identify interspecies variations in biochemical and structural properties of trees and then estimate the tree species diversity within the Shennongjia Forest Nature Reserve in China. Firstly, we used the watershed algorithm based on morphological crown control to isolate individual tree crowns (ITCs) from the LiDAR data. For each ITC, we then calculated seven vegetation indices (VIs) representing key biochemical properties from the hyperspectral data and additionally derived the LiDAR-based tree height which was identified to support the discrimination of the tree species in a preceding analysis. Finally we utilized the combination of the seven selected VIs and tree height as input to a self-adaptive Fuzzy C-Means (FCM) clustering algorithm. The FCM algorithm was applied to fixed subsets of 30 m x 30 m and it was assumed that the number of clusters identified within a subset represents the number of occurring species. The species richness and Shannon-Wiener diversity index calculated from the clustering outputs correlated well with the field reference data (R-2 = 0.83, RMSE = 0.25). The results show that forest species diversity can be directly predicted using the suggested clustering method based on crown-by-crown variations in biochemical and structural properties in the examined subtropical forest without the need to distinguish the individual tree species.
关键词	Imaging spectroscopy LiDAR Leaf biochemical components Species richness Forest biodiversity Shannon-Wiener
学科领域	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.1016/j.rse.2018.05.014
收录类别	SCI
语种	英语
WOS关键词	ISOLATING INDIVIDUAL TREES ; VASCULAR PLANT RICHNESS ; IMAGING SPECTROSCOPY ; SMALL FOOTPRINT ; MEDITERRANEAN FORESTS ; FLORISTIC COMPOSITION ; TROPICAL FORESTS ; ALPHA-DIVERSITY ; CANOPY HEIGHT ; WATER-CONTENT
WOS记录号	WOS:000437383600008
出版者	ELSEVIER SCIENCE INC
文献子类	Article
出版地	NEW YORK
EISSN	1879-0704
资助机构	National Key Research and Development Program [2016YFC0500201] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41671365, 41771464] ; Major Science and Technology Program for Water Pollution Control and Treatment [2012ZX07104-001] ; Shanghai Hangyao Information Technology Cooperation
作者邮箱	zengyuan@radi.ac.cn ; zhaoyj@ibcas.ac.cn
引用统计	被引频次：43[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/2S10CLM1/20729
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 Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yujin,Zeng, Yuan,Zheng, Zhaoju,et al. Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China[J]. REMOTE SENSING OF ENVIRONMENT,2018,213:104-114.
APA	Zhao, Yujin.,Zeng, Yuan.,Zheng, Zhaoju.,Dong, Wenxue.,Zhao, Dan.,...&Zhao, Qianjun.(2018).Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China.REMOTE SENSING OF ENVIRONMENT,213,104-114.
MLA	Zhao, Yujin,et al."Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China".REMOTE SENSING OF ENVIRONMENT 213(2018):104-114.

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