干旱区生态环境知识资源中心(Arid): A New Remote Sensing Desert Vegetation Detection Index

Arid

DOI	10.3390/rs15245742
	A New Remote Sensing Desert Vegetation Detection Index
	Song, Zhenqi; Lu, Yuefeng; Ding, Ziqi; Sun, Dengkuo; Jia, Yuanxin; Sun, Weiwei
通讯作者	Lu, YF
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:24
英文摘要	Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue-saturation-value green enhancement vegetation index), based on the HSV (hue-saturation-value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red-green-blue vegetation index), MGRVI (modified green-red vegetation index), NGBDI (normalized green-red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red-green-blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.
英文关键词	HSV color space channel enhancement UAV visible imagery desert vegetation extraction land desertification monitoring HSVGVI
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001130956800001
WOS关键词	CLASSIFICATION
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/2XILL650/398353
推荐引用方式 GB/T 7714	Song, Zhenqi,Lu, Yuefeng,Ding, Ziqi,et al. A New Remote Sensing Desert Vegetation Detection Index[J],2023,15(24).
APA	Song, Zhenqi,Lu, Yuefeng,Ding, Ziqi,Sun, Dengkuo,Jia, Yuanxin,&Sun, Weiwei.(2023).A New Remote Sensing Desert Vegetation Detection Index.REMOTE SENSING,15(24).
MLA	Song, Zhenqi,et al."A New Remote Sensing Desert Vegetation Detection Index".REMOTE SENSING 15.24(2023).