Publication Date
9-1-2022
Document Type
Article
Publication Title
Remote Sensing
Volume
14
Issue
18
DOI
10.3390/rs14184491
Abstract
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.
Keywords
Google Earth Engine, Iran, Landsat, remote sensing, surface water area, surface water dynamics, surface water variations, water scarcity
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Civil and Environmental Engineering
Recommended Citation
Alireza Taheri Dehkordi, Mohammad Javad Valadan Zoej, Hani Ghasemi, Mohsen Jafari, and Ali Mehran. "Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery" Remote Sensing (2022). https://doi.org/10.3390/rs14184491
