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Abstract

Areal changes of high-altitude inland lakes on the Qaidam Basin (QB) of the Tibetan Plateau are reliable indicators of climate change and anthropogenic disturbance. Due to the physical difficulties to access, our knowledge of the spatial patterns and processes of climatic and human impacts on the Basin has been limited. Focusing on lake area changes, this study used long-term Landsat images to map the patterns of lakes and glaciers in 1977, 1990, 2000, and 2015, and to monitor the spatially explicit changes of lakes between 1977 and 2015. Results revealed that the total number of lakes (area > 0.5 km2 ) increased by 18, while their total area expanded by 29.8%, from 1761.5 ± 88.1 km2 to 2285.9 ± 91.4 km2 . Meanwhile, glaciers have decreased in area by 259.16 km2 in the past four decades. The structural equation model (SEM) was applied to examine the integrative effects of natural and anthropogenic factors on lake area. Precipitation change exhibited the most significant influence on lake area in the QB from 1977 to 2000, while human activities also played an important role in the expansion of lakes in the QB in the period 2000–2015. In particular, extensive exploitation of salt lakes as mining resources resulted in severe changes in lake area and landscape. The continuously expanding salt lakes inundated the road infrastructure nearby, posing great threats to road safety. This study shed new light on the impacts of recent environmental changes and human interventions on lakes in the Qaidam Basin, which could assist policy-making for protecting the lakes and for strengthening the ecological improvement of this vast, arid basin.

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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Li, H., Mao, D., Li, X., Wang, Z., & Wang, C. (2019). Monitoring 40-Year Lake Area Changes of the Qaidam Basin, Tibetan Plateau, Using Landsat Time Series. Remote Sensing, 11(3), 343. doi:10.3390/rs11030343

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