Salhab, Melda; (2024) Can data from space address critical data gaps on earth? Investigating the extent to which holistic disaster risk can be estimated using remote sensing data. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Disasters, caused by natural or human related hazards, claim tens of thousands of lives and cause over $300 billion in direct asset losses annually. While only 22% of disaster events occur in low-income countries, those events are disproportionately responsible for 67% of deaths, even when controlling for magnitude and scale. The discrepancy is largely due to differences in exposure and vulnerability. Despite growing recognition of these factors, disaster risk assessments often suffer from critical gaps in high-quality, location-based hazard, exposure, and vulnerability data. This thesis addresses these gaps by developing novel methods that integrate remote sensing and socio-economic data for multi-dimensional disaster risk assessment. First, a methodological framework is developed and implemented that combines satellite-derived flood hazard data (fluvial, pluvial, and coastal) with population and poverty data, resulting in global and subnational flood disaster risk data. The coarse survey-derived poverty data is the most significant limitation to the spatial resolution of the results, so the second part of the thesis explores whether satellite data, including nightlights, land cover, and NO2 emissions, can be used to estimate highly granular economic activity. The approach developed is novel in several ways, including its use of geographic weighted regression, which enables spatial heterogeneity to be captured and evaluated. The results find that 1.81 billion people are exposed to high flood risk globally, with 89% located in low- and middle-income countries. Additionally, the thesis demonstrates that high resolution economic activity can be estimated using satellite data, with the model achieving an overall predictive performance of 0.95 (R2) with variables displaying spatially heterogeneous relationships across the study area. The implications of this research are far-reaching: improving the quality and availability of disaster risk data enable policy-makers to target interventions to the most vulnerable populations. The methodologies developed extend beyond flood risk, offering replicable approaches for other disaster risk assessments. Ultimately, this thesis contributes to a more informed, data-driven approach to disaster risk reduction, with the potential to save lives and reduce economic losses globally.

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