干旱区生态环境知识资源中心(Arid): Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery

Arid

DOI	10.3390/rs11161909
	Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery
	Jawak, Shridhar D.1,2; Luis, Alvarinho J.2; Fretwell, Peter T.3; Convey, Peter 3; Durairajan, Udhayaraj A.4,5
通讯作者	Luis, Alvarinho J.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:16
英文摘要	Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from similar to 6.44 +/- 1.34% to similar to 11.55 +/- 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km(2) (2.85 +/- 0.10 km(2) on average) with bias values ranging from 3.49 to 31.39% (average 12.81 +/- 1.88%) and average root mean square error (RMSE) of 0.41 km(2) (14.73 +/- 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m(2)) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.
英文关键词	semi-automated classification customized NDVI Antarctic vegetation spectral processing feature extraction Worldview-2 data
类型	Article
语种	英语
国家	Norway ; India ; England
开放获取类型	gold, Green Submitted, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000484387600072
WOS关键词	LAND-COVER ; ARCTIC VEGETATION ; LARSEMANN HILLS ; TUNDRA VEGETATION ; SATELLITE DATA ; CLASSIFICATION ; RESOLUTION ; PENINSULA ; SENTINEL-2 ; NDVI
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218404
作者单位	1.Svalbard Integrated Arctic Earth Observing Syst S, SIOS Knowledge Ctr, Svalbard Sci Ctr, POB 156, N-9171 Longyearbyen, Svalbard, Norway; 2.Minist Earth Sci, Polar Remote Sensing Sect, Earth Syst Sci Org, Natl Ctr Polar & Ocean Res, Vasco Da Gama 403804, Goa, India; 3.NERC, British Antarctic Survey, Madingley Rd, Cambridge CB3 OET, England; 4.Wildlife Inst India, Dehra Dun 248001, Uttarakhand, India; 5.Univ Madras, Dept Geog, Chennai 600005, Tamil Nadu, India
推荐引用方式 GB/T 7714	Jawak, Shridhar D.,Luis, Alvarinho J.,Fretwell, Peter T.,et al. Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery[J],2019,11(16).
APA	Jawak, Shridhar D.,Luis, Alvarinho J.,Fretwell, Peter T.,Convey, Peter,&Durairajan, Udhayaraj A..(2019).Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery.REMOTE SENSING,11(16).
MLA	Jawak, Shridhar D.,et al."Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery".REMOTE SENSING 11.16(2019).