Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000-2015) | |
Che, XH (Che, Xianghong)1; Feng, M (Feng, Min)2,3; Sexton, J (Sexton, Joe)3; Channan, S (Channan, Saurabh)3; Sun, Q (Sun, Qing)4; Ying, Q (Ying, Qing)5; Liu, JP (Liu, Jiping)1; Wang, Y (Wang, Yong)1 | |
Source Publication | REMOTE SENSING |
2019 | |
Volume | 11Issue:11Pages:1323 |
DOI | 10.3390/rs11111323 |
Abstract | Surface water is of great importance to ecosystems and economies. Crucial to understanding hydrological variability and its relationships to human activities at large scales, open-access satellite datasets and big-data computational methods are now enabling the global mapping of the distribution and changes of inland water over time. A machine-learning algorithm, previously used only to map water at single points in time, was applied over 16 years of the USGS Landsat archive to detect and map surface water over central Asia from 2000 to 2015 at a 30-m, monthly resolution. The resulting dataset had an overall classification accuracy of 99.59% (+/- 0.32% standard error), 98.24% (+/- 1.02%) user's accuracy, and 87.12% (+/- 3.21%) producer's accuracy for water class. This study describes the temporal extension of the algorithm and the application of the dataset to present patterns of regional surface water cover and change. The findings indicate that smaller water bodies are dramatically changing in two specific ecological zones: the Kazakh Steppe and the Tian Shan Montane Steppe and Meadows. Both the maximum and minimum extent of water bodies have decreased over the 16-year period, but the rate of decrease of the maxima was double that of the minima. Coverage decreased in each month from April to October, and a significant decrease in water area was found in April and May. These results indicate that the dataset can provide insights into the behavior of surface water across central Asia through time, and that the method can be further developed for regional and global applications. |
Subject Area | Remote Sensing |
WOS ID | WOS:000472648000066 |
Language | 英语 |
Indexed By | SCI |
Keyword | Surface-water Time-series Flood Dynamics Resolution Inundation Imagery Map |
WOS Research Area | Remote Sensing |
WOS Subject | Remote Sensing |
Cooperation Status | 国际 |
Department | 大陆碰撞与高原隆升重点实验室 |
URL | 查看原文 |
Publisher | MDPI |
Subtype | Article |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.itpcas.ac.cn/handle/131C11/9284 |
Collection | 图书馆 |
Corresponding Author | Feng, M (Feng, Min) |
Affiliation | 1.Chinese Acad Surveying & Mapping, Res Ctr Govt Geog Informat Syst, Beijing 100830, Peoples R China; 2.Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China; 3.TerraPulse Inc, North Potomac, MD 20878 USA; 4.Nanjing Univ Informat Sci & Technol, CIC FEMD, Nanjing 210044, Jiangsu, Peoples R China; 5.Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA. |
Recommended Citation GB/T 7714 | Che, XH ,Feng, M ,Sexton, J ,et al. Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000-2015)[J]. REMOTE SENSING,2019,11(11):1323. |
APA | Che, XH .,Feng, M .,Sexton, J .,Channan, S .,Sun, Q .,...&Wang, Y .(2019).Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000-2015).REMOTE SENSING,11(11),1323. |
MLA | Che, XH ,et al."Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000-2015)".REMOTE SENSING 11.11(2019):1323. |
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2019257.pdf(6732KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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