Vegetation Abundance and Health Mapping Over Southwestern Antarctica Based on WorldView-2 Data and a Modified Spectral Mixture Analysis

doi:10.3390/rs13020166

	Vegetation Abundance and Health Mapping Over Southwestern Antarctica Based on WorldView-2 Data and a Modified Spectral Mixture Analysis
	Sun, Xiaohui 1; Wu, Wenjin ; Li, Xinwu ; Xu, Xiyan 2; Li, Jinfeng 3
	2021
发表期刊	REMOTE SENSING
卷号	13 期号:2
摘要	In polar regions, vegetation is especially sensitive to climate dynamics and thus can be used as an indicator of the global and regional environmental change. However, in Antarctica, there is very little information on vegetation distribution and growth status. To fill this gap, we evaluated the ability of both linear and nonlinear spectral mixture analysis (SMA) models, including a group of newly developed modified Nascimento's models for Antarctic vegetated areas (MNM-AVs), in estimating the abundance of major Antarctic vegetation types, i.e., mosses and lichens. The study was conducted using WorldView-2 satellite data and field measurements over the Fildes Peninsula and its surroundings, which are representative vegetated areas in Antarctica. In MNM-AVs, we introduced secondary scattering components for vegetation and its background to account for the sparsity of vegetation cover and reassigned their coefficients. The new models achieved improved performances, among which MNM-AV3 achieved the lowest error for mosses (lichens) abundance estimation with RMSE = 0.202 (0.213). Compared with MNM-AVs, the linear model performed particularly poor for lichens (RMSE = 0.322), which is in contrast to the case of mosses (RMSE = 0.212), demonstrating that spectral signals of lichens are more prone to mix with their backgrounds. Abundance maps of mosses and lichens, as well as a map of moss health status for the entire study area, were then obtained based on MNM-AV3 with around 80% overall accuracy. Moss areas account for 0.7695 km(2) in Fildes and 0.3259 km(2) in Ardley Island; unhealthy mosses amounted to 40% (49%) of the area in the summer of 2018 (2019), indicating considerable environmental stress.
关键词	Antarctica vegetation abundance WorldView-2 spectral mixture analysis moss health evaluation
学科领域	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.3390/rs13020166
收录类别	SCI
语种	英语
WOS关键词	KING-GEORGE-ISLAND ; TERRESTRIAL VEGETATION ; BARTON PENINSULA ; LICHEN FLORA ; MOSS ; COVER ; STATION ; GROWTH ; SOILS ; INDEX
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000611556600001
出版者	MDPI
文献子类	Article
出版地	BASEL
EISSN	2072-4292
资助机构	Strategic Priority Research Program of Chinese Academy of Sciences [XDA19070203] ; International Partnership Program of the Chinese Academy of Sciences [183611KYSB20200059, 131C11KYSB20160061]
作者邮箱	sunxh01@aircas.ac.cn ; wuwj@radi.ac.cn ; lixw@aircas.ac.cn ; xiyan.xu@tea.ac.cn ; lijinfeng@ibcas.ac.cn
作品OA属性	Green Published
引用统计	被引频次：9[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/2S10CLM1/26589
专题	系统与进化植物学国家重点实验室
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 2.Chinese Acad Sci, Qilu Res Inst, Aerosp Informat Res Inst, Jinan 250101, Peoples R China 3.Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China 4.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, 20 Nanxincun, Beijing 100093, Peoples R China
推荐引用方式 GB/T 7714	Sun, Xiaohui,Wu, Wenjin,Li, Xinwu,et al. Vegetation Abundance and Health Mapping Over Southwestern Antarctica Based on WorldView-2 Data and a Modified Spectral Mixture Analysis[J]. REMOTE SENSING,2021,13(2).
APA	Sun, Xiaohui,Wu, Wenjin,Li, Xinwu,Xu, Xiyan,&Li, Jinfeng.(2021).Vegetation Abundance and Health Mapping Over Southwestern Antarctica Based on WorldView-2 Data and a Modified Spectral Mixture Analysis.REMOTE SENSING,13(2).
MLA	Sun, Xiaohui,et al."Vegetation Abundance and Health Mapping Over Southwestern Antarctica Based on WorldView-2 Data and a Modified Spectral Mixture Analysis".REMOTE SENSING 13.2(2021).

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