Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster
Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster
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DOI：
10.1016/j.scs.2020.102115
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发表时间：
2020-06
影响因子：
11.7
通讯作者：
Y. Qiang;Qingxu Huang;Jinwen Xu
中科院分区：
文献类型：
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作者：
Y. Qiang;Qingxu Huang;Jinwen Xu
衡量社区复原力的一个主要挑战是缺乏对灾害的经验性观察。作为观测地球表面人类活动的有效工具，夜间光照(NTL)遥感图像可以填补衡量社区对自然灾害的恢复力的经验数据的空白。这项研究介绍了一个使用国防气象卫星计划-业务线路扫描系统(DMSP-OLS)图像来模拟自然灾害中经济活动恢复模式的量化框架。卡特里娜飓风的回顾研究显示了该框架的作用，该研究揭示了卡特里娜飓风的巨大经济影响以及经济活动的干扰和恢复模式的空间变化。统计分析探讨了可能影响经济复苏的环境和社会经济因素。该框架不是静态和全面的指数，而是将复原力作为一个动态过程进行衡量。分析结果为促进不同社区和灾难不同阶段的复原力提供了可操作的信息。除了卡特里娜飓风，复原力建模框架还适用于其他灾害类型。介绍的方法和结果增加了我们对社区复原力复杂性的理解，并为发展具有复原力和可持续发展的社区提供支持。
A major challenge for measuring community resilience is the lack of empirical observations in disasters. As an effective tool to observe human activities on the earth surface, night-time light (NTL) remote sensing images can fill the gap of empirical data for measuring community resilience in natural disasters. This study introduces a quantitative framework to model recovery patterns of economic activity in a natural disaster using the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) images. The utility of the framework is demonstrated in a retrospective study of Hurricane Katrina, which uncovered the great economic impact of Katrina and spatial variation of the disturbance and recovery pattern of economic activity. Environmental and socio-economic factors that potentially influence economic recovery were explored in statistical analyses. Instead of a static and holistic index, the framework measures resilience as a dynamic process. The analysis results provide actionable information for prompting resilience in diverse communities and in different phases of a disaster. In addition to Hurricane Katrina, the resilience modeling framework is applicable for other disaster types. The introduced approaches and findings increase our understanding about the complexity of community resilience and provide support for developing resilient and sustainable communities.