Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster
Introduction
Due to climate change and rapid population growth, human society is faced with increasing threats from natural disasters that can cause significant socio-economic consequences. Coastal communities around the world are particularly vulnerable to natural disasters including both large-scale rapid-moving disturbances such as hurricane and storm surges (Tebaldi, Strauss, & Zervas, 2012), and the slow-moving processes such as coastal erosion, sea level rise (Nicholls, Hoozemans, & Marchand, 1999) and reduction of ecosystem services (Spalding et al., 2014). According to the data from U.S. Census Bureau (2011), 39 % of the total population in the United States are living in counties directly on the shorelines and the population density in coastal counties is more than four times the average density of the whole United States. Since 2005 when Hurricane Katrina and Rita caused catastrophic damage in Central Gulf Coast, much attention has been paid to the resilience and long-term sustainability of coastal communities. Empirical observations suggest that, under the same strength of disasters, different communities endured different levels of disturbance and presented different recovery patterns in socio-economic (Finch, Emrich, & Cutter, 2010; Fussell, Sastry, & VanLandingham, 2010), health (Burton, 2006; Sastry & VanLandingham, 2009), and psychological conditions (Adeola, 2009). These observed disparities can be attributed to various resilience of the communities.
Resilience describes the ability of an individual or a system to adapt to and recover from external shocks or stresses (Adger, 2000). Although substantial knowledge has been gained on ecological resilience (Perz, Muñoz-Carpena, Kiker, & Holt, 2013) and engineering resilience (Yodo & Wang, 2016), there is yet a consensus on how to measure resilience of human communities due to their complexity. In general, quantitative assessment of community resilience is challenged by two issues. First, the definition of community resilience various in different domains, which will be discussed in Section 2.1. Moreover, resilience is often used interchangeably with other relevant concepts such as vulnerability and adaptive capacity. The various definitions and conceptual frameworks of community resilience influence how researchers measure resilience (Cutter et al., 2008; Lam, Reams, Li, Li, & Mata, 2016; Sherrieb, Norris, & Galea, 2010). The definition disagreement hampers the development of standard metrics to measure resilience. Second, there is lack of empirical data and approaches to quantify community resilience. Most of the existing assessments are based on an index approach, which integrates a set of presumed indicators into a composite score to measure resilience (Cutter, Burton, & Emrich, 2010; Hung, Yang, Chien, & Liu, 2016; Sempier, Swann, Emmer, Sempier, & Schneider, 2010; Sherrieb et al., 2010). The model specification, indicator selection and weighting are based on prior knowledge or expert opinions. Although these indices provide general guidance for predicting community resilience, their accuracies have not been validated against empirical observations in disasters (Bakkensen, Fox‐Lent, Read, & Linkov, 2017; Beccari, 2016).
Empirical data about human activities and states are difficult to obtain in a disaster condition when many social systems fail to function. Traditional data sources for resilience assessment (e.g. surveys and census data) have limitations in various aspects, which will be elaborated in the next section. Recently, remote sensing imageries become popular instruments to monitor human dynamics on the earth surface such as urban growth (Shahtahmassebi et al., 2016), land cover change (Joshi et al., 2016), and socio-economic conditions (Kuffer, Pfeffer, & Sliuzas, 2016). Among the various remote sensing products, night-time light (NTL) remote sensing has unique ability to capture fluctuations of human activities, which can provide empirical data for resilience assessment. This study introduces a quantitative framework to assess community resilience using the DMSP-OLS NTL annual composite images as the data source. Specifically, stable lights in the time series of DMSP-OLS annual images are used as a proxy to model recovery patterns of economic activity after Hurricane Katrina in 2005. Spatial and statistical analyses are conducted to explore the geographical disparities of the recovery patterns and their relationships with the selected resilience indicators. Specific questions answered in the case study are: 1) which communities appeared to be more or less resilient in the disaster; 2) how the observed resilience levels are associated with the environmental and socio-economic conditions? The introduced framework aims to fill the critical gap of empirical data and assessment methods for community resilience. The analysis results from the case study increase our understanding about community resilience and provide actionable information to predict and prompt community resilience.
The rest of the article is organized as follows. Section 2 briefly reviews the related work about the definitions, conceptual frameworks and assessment methods of community resilience. Section 3 introduces the data sources, assessment framework of community resilience based on NTL data and statistical analyses. Section 4 presents the analysis results in the case study of Hurricane Katrina, followed by the discussions in Section 5 and conclusions in Section 6.
Section snippets
Definition and conceptual framework
The concept of resilience was first introduced by Holling (1973), who views resilience as the ability of an ecological system to absorb change in the face of extreme perturbation and yet continue to persist. Later, Timmerman (1981) applied the concept of resilience to social systems and defined resilience as the measure of a system’s capacity to absorb and recover from disastrous events. The resilience of a social system is also known as community resilience. Extending Timmerman’s definition,
Inter-calibration of NTL images
The Stable Lights images collected by the Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) were used for this study. The images are cloud-free composites created using all the available archived DMSP-OLS smooth resolution data from the year 1992 to 2013. The DMSP/OLS images include 34 annual composites at a 30 arc second resolution collected by six different satellites (F10, F12, F14, F15, F16, and F18). Due to the absence of inter-satellite calibration and onboard
Overall economic impact
Fig. 4 demonstrates the change of NTL brightness (DN value) from 2004 to 2005 near the landfall location of Katrina, where a decline of NTL brightness can be observed in New Orleans and the surrounding coastal cities. The annual GDP estimates from the NTL data (see Fig. 5) reveal that Hurricane Katrina has fundamentally altered the economic growth in the declared disaster area. In contrast to the steady growth in the Southeast Region (despite the slight drop in 2009 due to the financial
Discussion
Despite the extensive discussions in the literature, quantitative assessment of community resilience is still a challenge due to the lack of empirical data continuously collected in disasters. In the U.S, census data are released decennially and county-level GDP data is only available in limited years, not to mention the developing world. Other data collection methods such as field surveys and interviews are costly and time-consuming. As an alternative, NTL remote sensing is an efficient means
Conclusion
This study introduces a quantitative framework for resilience assessment using DMSP-OLS Nighttime Lights images. The framework was applied to model the recovery patterns of economic activity in the affected area in Hurricane Katrina 2005. The analyses show the great economic disturbance caused by Katrina and the slow recovery in the entire affected area. The county-level analyses indicate strong spatial variation of the recovery pattern. Statistical analyses were carried out to explore the
Funding source
This article is based on work supported by two research grants from the U.S. National Science Foundation: one under the Coastlines and People (CoPe) Program (Award No. 1940091) and the other under the Methodology, Measurement & Statistics (MMS) Program (Award No. 1853866). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.
Declaration of Competing Interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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