干旱区生态环境知识资源中心(Arid): Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery

Arid

DOI	10.3390/rs14184491
	Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery
	Dehkordi, Alireza Taheri; Zoej, Mohammad Javad Valadan; Ghasemi, Hani; Jafari, Mohsen; Mehran, Ali
通讯作者	Mehran, A
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:18
英文摘要	Within water resources management, surface water area (SWA) variation plays a vital role in hydrological processes as well as in agriculture, environmental ecosystems, and ecological processes. The monitoring of long-term spatiotemporal SWA changes is even more critical within highly populated regions that have an arid or semi-arid climate, such as Iran. This paper examined variations in SWA in Iran from 1990 to 2021 using about 18,000 Landsat 5, 7, and 8 satellite images through the Google Earth Engine (GEE) cloud processing platform. To this end, the performance of twelve water mapping rules (WMRs) within remotely-sensed imagery was also evaluated. Our findings revealed that (1) methods which provide a higher separation (derived from transformed divergence (TD) and Jefferies-Matusita (JM) distances) between the two target classes (water and non-water) result in higher classification accuracy (overall accuracy (OA) and user accuracy (UA) of each class). (2) Near-infrared (NIR)-based WMRs are more accurate than short-wave infrared (SWIR)-based methods for arid regions. (3) The SWA in Iran has an overall downward trend (observed by linear regression (LR) and sequential Mann-Kendall (SQMK) tests). (4) Of the five major water basins, only the Persian Gulf Basin had an upward trend. (5) While temperature has trended upward, the precipitation and normalized difference vegetation index (NDVI), a measure of the country's greenness, have experienced a downward trend. (6) Precipitation showed the highest correlation with changes in SWA (r = 0.69). (7) Long-term changes in SWA were highly correlated (r = 0.98) with variations in the JRC world water map.
英文关键词	remote sensing Google Earth Engine surface water area surface water dynamics surface water variations water scarcity Iran Landsat
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:000858870900001
WOS关键词	CAP TRANSFORMATION ; INDEX NDWI ; DIFFERENCE ; DYNAMICS ; LAKE ; TM ; SENTINEL-2 ; BODY ; DERIVATION ; MANAGEMENT
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/2XILL650/394199
推荐引用方式 GB/T 7714	Dehkordi, Alireza Taheri,Zoej, Mohammad Javad Valadan,Ghasemi, Hani,et al. Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery[J],2022,14(18).
APA	Dehkordi, Alireza Taheri,Zoej, Mohammad Javad Valadan,Ghasemi, Hani,Jafari, Mohsen,&Mehran, Ali.(2022).Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery.REMOTE SENSING,14(18).
MLA	Dehkordi, Alireza Taheri,et al."Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery".REMOTE SENSING 14.18(2022).