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Journal of Arid Land  2016, Vol. 8 Issue (4): 533-545    DOI: 10.1007/s40333-016-0048-1
Research Articles     
Assessing ecological vulnerability in western China based on Time-Integrated NDVI data
JIN Jia1,2, WANG Quan1,3*
1 Joint Research Center of Drylands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
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Abstract  Arid and semi-arid areas are the most vulnerable regions to climate change. Clear understanding of the effects of climate change on ecosystems in arid and semi-arid regions and the ecosystem vulnerability is important for ecosystem management under the background of climate change. In this study, we conducted a vulnerability assessment on various ecosystems from 1982 to 2013 in western China with large areas of arid and semi-arid lands based on the Time-Integrated Normalized Difference Vegetation Index (TINDVI) data and climate data. The results indicated that grasslands were the most vulnerable ecosystem to climate change in western China, especially for those in Tibetan Plateau. Croplands in oases were not vulnerable to climate change compared to rain-fed croplands in semi-arid regions (e.g. Gansu and Inner Mongolia), which was attributed to the well-developed drip irrigation technology in oases. Desert and Gobi ecosystems were slightly vulnerable to climate change during the past several decades. The assessment results, as revealed in this study, can provide a reference for taking appropriate actions to protect the ecosystems in western China.

Key wordssediment yield      particle-size distribution      fractal dimension      wind and water erosion     
Received: 31 July 2015      Published: 10 August 2016

The National Basic Research Program of China (2012CB956204) and the National Natural Science Foundation of China (41101249).

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Cite this article:

JIN Jia, WANG Quan. Assessing ecological vulnerability in western China based on Time-Integrated NDVI data. Journal of Arid Land, 2016, 8(4): 533-545.

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