High-resolution geospatial analysis for disaster risk management
Keywords: population synthesis
flood detection
built environment
population modelling
disaster risk
geospatial analysis

Rapid advancements in Earth observation, localised data collection, and modelling techniques are revolutionising global understanding of disaster risk. If properly validated and appropriately used, these emerging techniques for high-resolution geospatial analysis can help developing countries overcome challenges pertaining to the first priority of the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR): understanding disaster risk. This thesis focuses on three potential breakthroughs in risk information for developing countries: (i) mapping population exposure; (ii) demonstrating more granular insights into population vulnerability; and, (iii) measuring the extent of flood events.
For mapping population exposure, a simplified equation is presented that separates masking variables and density variables. The equation focuses on two distinct objectives: (i) increasing the precision and accuracy of a strong binary mask to limit the spatial extent of settled populations; and, (ii) improving modelling techniques of ancillary variables to account for variations in density within an identified spatial extent. A critical assessment of population modelling techniques that heavily rely on derived characteristics of the built environment as a masking variable is also presented. The assessment confirms that density variables directly relating to the binary masking variable are effective; however, their application requires careful consideration due to its propensity to amplify both positive results and errors. The research provides evidence in support of hybrid techniques towards global population modelling.
For demonstrating more granular insights into population vulnerability, a novel methodology is presented for generating spatially referenced synthetic households by combining population synthesis techniques for small area estimation with dasymetric population modelling. The methodology results in an output vector layer of households represented as points with unique structural and socioeconomic attributes which can be applied towards a better understanding of population characteristics, particularly those which are geographically influenced and may not conform to administrative boundaries linked to aggregated census statistics. A case study of Dumuria Upazila, Bangladesh, validates the methodology and provides an example of how georeferenced surveys can be applied towards error assessments in data-scarce regions, which remains a challenge in global population modelling.
Lastly, for measuring the extent of flood events, a novel methodology applies freely available synthetic aperture radar (SAR) data for flood detection over large geographic areas immediately following a cyclone hazard event. The methodology results in enhanced precision, demonstrated in coastal districts of Bangladesh heavily affected by 2019 Cyclone Fani. The case study also demonstrates how high-resolution geospatial analysis can supplement locally reported estimates of damages by providing quicker estimates and a means for identifying potential bias from self-reporting. Furthermore, insights into the socioeconomic characteristics of potentially affected households provide a layer of information not covered by post-disaster needs assessments.