Long-Term Resilience Curve Analysis of Wenchuan Earthquake-Affected Counties Using DMSP-OLS Nighttime Light Images
This article demonstrates the potential of Defense Meteorological Satellite Program-Operational Linescan System stable nighttime light imagery for county-level earthquake resilience analysis. In this article, we firstly intercalibrated the nighttime light data and calculated the total sum of stable...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5e5a729f40714745ac2e5d1a00ea041c |
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| Sumario: | This article demonstrates the potential of Defense Meteorological Satellite Program-Operational Linescan System stable nighttime light imagery for county-level earthquake resilience analysis. In this article, we firstly intercalibrated the nighttime light data and calculated the total sum of stable lights for each of the 261 counties affected by the <italic>Mw</italic> 7.9 Wenchuan earthquake from 1992 to 2013; secondly established an earthquake resilience model based on a nighttime light index to analyze the static and dynamic resilience of different counties; and lastly discussed the possible influencing factors from geographic, disaster-related, political and socioeconomic perspectives. The results first show that the static resilience of the extremely hard-hit counties in the Wenchuan earthquake-affected area increased from south to north along the long axis of the intensity zone, and plain and hilly counties exhibited faster short-term economic recovery than plateau and mountainous counties. Moreover, all extremely hard-hit counties except Wenchuan, Dujiangyan and Pingwu recovered to the normal level before 2011, whereas 52% of the counties in the generally affected areas recovered within three years. Through linear regression analysis, we also found that the different earthquake resilience capabilities across counties are most likely related to the soil liquefaction risk, average elevation, land-use degree, and socioeconomic factors, such as the industrial structure, the population age distribution, and social welfare. |
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