Construction of Suaeda Salsa Vegetation Index Based on GF-1 WFV Images

被引：0
|
作者：
Li Y. [1 ,2 ]
Chen Y. [1 ]
Chen H. [2 ]
Wang C. [2 ]
机构：
[1] State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing
[2] Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2019年 / 44卷 / 12期
基金：
中国国家自然科学基金;
关键词：
GF-1; WFV; Intertidal area; Red vegetation; SSVI; Suaeda salsa; Vegetation index;
D O I：
10.13203/j.whugis20180105
中图分类号：
学科分类号：
摘要：
At present, most of the vegetation indices are mainly constructed for the green vegetation while the vegetation indices for red vegetation are less. In addition, the vegetation indices for the identification and extraction of wetland or intertidal vegetation are relatively few. Therefore, in order to expand the research on the construction of red vegetation index, we constructed the Suaeda salsa vegetation index (SSVI) based on GF-1 WFV (wide field of view) image by comparing the spectral reflectance characteristics of various land covers in the GF-1 WFV image and considering the red characteristics. Then, for the sake of evaluating the extraction precision of the SSVI, we took the Shuangtaizi Estuary Wetland Nature Reserve in Liaoning Province as the study area, used SSVI and other indexs to extract the Suaeda salsa from five GF-1 WFV images of different years. Then, we compared their average extraction accuracy and average misclassified pixels account for the proportion of the study area. The results show that the average extraction accuracy of SSVI was 88.6%, and the average misclassification pixels accounts for 5.1% of the study area, this indicated that extraction ability of SSVI is better than other vegetation indices. The SSVI showed the highest precision and the best effect among the five vegetation indices. Besides, the large time span between the five images also proved that SSVI was affected less by the temporal factor, it was robust and had good applicability. In summary, the SSVI can be effectively used for the identification and extraction of Suaeda salsa, and to monitor its temporal and spatial changes. © 2019, Research and Development Office of Wuhan University. All right reserved.
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页码：1823 / 1831
页数：8
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