气候变化领域集成服务门户(CCPortal): Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China

CCPortal

DOI	10.1016/j.rcar.2023.08.002
	Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China
	Du, QingSong; Li, GuoYu; Chen, Dun; Qi, ShunShun; Zhou, Yu; Wang, Fei; Cao, YaPeng
发表日期	2023
ISSN	2097-1583
EISSN	2949-7302
起始页码	202
结束页码	209
卷号	15 期号:4
英文摘要	There are a large number of lakes, rivers, and other natural water bodies distributed in the permafrost area of the Qinghai-Tibet Plateau (QTP). The changes in water bodies will affect the distribution of water resources in surrounding areas and downstream areas, resulting in environmental impact and bringing potential flood disasters, which will induce more serious issues and problems in alpine and high-altitude areas with a fragile habitat (such as the QTP in China). Generally, effective, reasonable, and scientific monitoring of large-scale water bodies can not only document the changes in water bodies intuitively, but also provide important theoretical reference for subsequent environmental impact prediction, and disaster prevention and mitigation in due course of time. The large-scale water extraction technology derived from the optical remote sensing (RS) image is seriously affected by clouds, bringing about large differences among the extracted water result products. Synthetic aperture radar (SAR) RS technology has the unique advantage characteristics of all-weather, all-day, strong penetration, and not being affected by clouds, which is hopeful in extracting water body data, especially for days with cloudy weather. The data extraction of large-scale water bodies based on SAR images can effectively avoid the errors caused by clouds that become prevalent at present. In this paper, the Hoh Xil Salt Lake on the QTP and its surrounding five lakes are taken as the research objects. The 2-scene Sentinel-1 SAR image data covering the whole area on 22 August 2022 was used to verify the feasibility of extracting water body data in permafrost zones. Furthermore, on 22 August 2022, the wealth here was cloudy, which made the optical RS images, e.g., Sentinel-2 images full of clouds. The results show that: using the Sentinel-1 image and threshold segmentation method to extract water body data is efficient and effective with excellent results in permafrost areas. Concretely, the Sentinel-1 dualpolarized water index (SDWI), calculated by combining dual vertical-vertical (VV) polarized and vertical-horizontal (VH) polarized data is a useful index for water extraction and the result is better than each of the VV or VH polarized images.
关键词	Permafrost region Water body extraction Salt Lake in Hoh Xil Qinghai-Tibet Plateau Sentinel-1 Ecological environment impact Disaster prevention and mitigation
WOS研究方向	Geography, Physical
WOS记录号	WOS:001150352600001
来源期刊	RESEARCH IN COLD AND ARID REGIONS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283085
作者单位	Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Fujian University of Technology; Jiangsu University; Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Du, QingSong,Li, GuoYu,Chen, Dun,et al. Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China[J],2023,15(4).
APA	Du, QingSong.,Li, GuoYu.,Chen, Dun.,Qi, ShunShun.,Zhou, Yu.,...&Cao, YaPeng.(2023).Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China.RESEARCH IN COLD AND ARID REGIONS,15(4).
MLA	Du, QingSong,et al."Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China".RESEARCH IN COLD AND ARID REGIONS 15.4(2023).