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Article

Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015)

1
Research Center of Government Geographic Information System, Chinese Academy of Surveying & Mapping, Beijing 100830, China
2
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
3
terraPulse Inc., North Potomac, MD 20878, USA
4
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
5
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1323; https://doi.org/10.3390/rs11111323
Received: 15 March 2019 / Revised: 11 May 2019 / Accepted: 29 May 2019 / Published: 2 June 2019
(This article belongs to the Special Issue Remote Sensing in Dryland Assessment and Monitoring)
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. View Full-Text
Keywords: surface water; Landsat; time series detection; central Asia surface water; Landsat; time series detection; central Asia
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MDPI and ACS Style

Che, X.; Feng, M.; Sexton, J.; Channan, S.; Sun, Q.; Ying, Q.; Liu, J.; Wang, Y. Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015). Remote Sens. 2019, 11, 1323. https://doi.org/10.3390/rs11111323

AMA Style

Che X, Feng M, Sexton J, Channan S, Sun Q, Ying Q, Liu J, Wang Y. Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015). Remote Sensing. 2019; 11(11):1323. https://doi.org/10.3390/rs11111323

Chicago/Turabian Style

Che, Xianghong, Min Feng, Joe Sexton, Saurabh Channan, Qing Sun, Qing Ying, Jiping Liu, and Yong Wang. 2019. "Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015)" Remote Sensing 11, no. 11: 1323. https://doi.org/10.3390/rs11111323

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