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Article

Vulnerability Analysis to Drought Based on Remote Sensing Indexes

by 1,2, 2,3,*, 4 and 3
1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing 100875, China
2
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
3
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
4
Department of Geography, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(20), 7660; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17207660
Received: 21 September 2020 / Revised: 10 October 2020 / Accepted: 16 October 2020 / Published: 20 October 2020
(This article belongs to the Special Issue Managing Disaster Risk in a Changing World)
A vulnerability curve is an important tool for the rapid assessment of drought losses, and it can provide a scientific basis for drought risk prevention and post-disaster relief. Those populations with difficulty in accessing drinking water because of drought (hereon “drought at risk populations”, abbreviated as DRP) were selected as the target of the analysis, which examined factors contributing to their risk status. Here, after the standardization of disaster data from the middle and lower reaches of the Yangtze River in 2013, the parameter estimation method was used to determine the probability distribution of drought perturbations data. The results showed that, at the significant level of α = 0.05, the DRP followed the Weibull distribution, whose parameters were optimal. According to the statistical characteristics of the probability density function and cumulative distribution function, the bulk of the standardized DRP is concentrated in the range of 0 to 0.2, with a cumulative probability of about 75%, of which 17% is the cumulative probability from 0.2 to 0.4, and that greater than 0.4 amounts to only 8%. From the perspective of the vulnerability curve, when the variance ratio of the normalized vegetation index (NDVI) is between 0.65 and 0.85, the DRP will increase at a faster rate; when it is greater than 0.85, the growth rate of DRP will be relatively slow, and the disaster losses will stabilize. When the variance ratio of the enhanced vegetation index (EVI) is between 0.5 and 0.85, the growth rate of DRP accelerates, but when it is greater than 0.85, the disaster losses tend to stabilize. By comparing the coefficient of determination (R2) values fitted for the vulnerability curve, in the same situation, EVI is more suitable to indicate drought vulnerability than NDVI for estimating the DRP. View Full-Text
Keywords: remote sensing index for drought; vulnerability; drought at risk populations; rapid assessment; the middle and lower reaches of the Yangtze River; China remote sensing index for drought; vulnerability; drought at risk populations; rapid assessment; the middle and lower reaches of the Yangtze River; China
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MDPI and ACS Style

Jia, H.; Chen, F.; Zhang, J.; Du, E. Vulnerability Analysis to Drought Based on Remote Sensing Indexes. Int. J. Environ. Res. Public Health 2020, 17, 7660. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17207660

AMA Style

Jia H, Chen F, Zhang J, Du E. Vulnerability Analysis to Drought Based on Remote Sensing Indexes. International Journal of Environmental Research and Public Health. 2020; 17(20):7660. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17207660

Chicago/Turabian Style

Jia, Huicong, Fang Chen, Jing Zhang, and Enyu Du. 2020. "Vulnerability Analysis to Drought Based on Remote Sensing Indexes" International Journal of Environmental Research and Public Health 17, no. 20: 7660. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17207660

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