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Open AccessArticle

Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations

1
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
2
School of Geographic Sciences, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
3
Department of Geographic Information Science, Nanjing University, Nanjing 210023, China
4
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
5
Department of Geography, Kansas State University, Manhattan, KS 66506, USA
6
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
7
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 25; https://doi.org/10.3390/rs11010025
Received: 16 October 2018 / Revised: 8 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)
The number of reservoirs is rapidly increasing owing to the growth of the world’s economy and related energy and water needs. Yet, for the vast majority of reservoirs around the world, their locations and related information, especially for newly dammed reservoirs, are not readily available due to financial, political, or legal considerations. This study proposes an automated method of identifying newly dammed reservoirs from time series of MODIS-derived NDWI (normalized difference water index) images. Its main idea lies in the detection of abrupt changes in the NDWI time series that are associated with land-to-water conversion due to the reservoir impoundment. The proposed method is tested in the upper reach of the Yellow River that is severely regulated by constructed reservoirs. Our results show that five newly dammed reservoirs were identified in the test area during 2000–2018. Validated against high-resolution Google Earth imagery, our method is effective to determine both locations of the emerging medium-size reservoirs and the timing of their initial water impoundments. Such information then allows for a refined calculation of the reservoir inundation extents and storage capacities through the combination of higher-resolution Landsat imagery and SRTM DEM. The comparison of our estimated reservoir areas and capacities against documented information further indicates that the integration of multi-mission remote sensing data may provide useful information for understanding reservoir operations and impacts on river discharges. Our method also demonstrates a potential for regional or global inventory of emerging reservoirs, which is crucial to assessing human impacts on river systems and the global water cycle. View Full-Text
Keywords: remote sensing; reservoir; NDWI; Yellow River; BFAST; time series remote sensing; reservoir; NDWI; Yellow River; BFAST; time series
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Zhang, W.; Pan, H.; Song, C.; Ke, L.; Wang, J.; Ma, R.; Deng, X.; Liu, K.; Zhu, J.; Wu, Q. Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations. Remote Sens. 2019, 11, 25.

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