Integrated Flood, Transport, and Economic Exposure Assessment Using Sentinel-1 SAR: A Case Study of Cyclone Ditwah

Abstract

Flooding remains one of the most destructive natural hazards worldwide, causing substantial infrastructure damage, economic disruption, and social vulnerability, particularly in rapidly urbanising tropical regions. In November 2025, Cyclone Ditwah brought intense rainfall to western Sri Lanka, leading to widespread inundation in the Gampaha Divisional Secretariat Division. This study develops and evaluates an integrated geospatial framework combining flood detection, transport network disruption analysis, and economic exposure assessment using Sentinel-1 Synthetic Aperture Radar (SAR) data. Pre-flood (1–15 November 2025) and post-flood (28 November–4 December 2025) C-band SAR imagery were processed through radiometric calibration, speckle filtering, terrain correction, and logarithmic backscatter differencing to derive a binary flood mask. The inundation layer was subsequently integrated with a connectivity-classified road network and a geocoded economic establishment dataset to evaluate transport disruption and spatial-economic exposure using an Economic Activity Exposure Index (EAEI) and Local Moran’s I clustering. The proposed workflow was evaluated using 540 stratified validation points derived from independent high-resolution reference data. Validation results yielded an overall accuracy of 81.5%, precision of 79.3%, recall of 85.2%, F1-score of 82.1%, and a Cohen’s Kappa coefficient of 0.63, indicating substantial agreement and suitability for rapid post-flood assessment. Results reveal that flooding disproportionately affected highly connected transport corridors and economically clustered zones, suggesting localised but structurally significant disruption. The study demonstrates the operational usefulness of Sentinel-1 SAR for rapid flood assessment and highlights the importance of integrating hazard mapping with infrastructure topology and economic clustering to support geospatial decision-making for disaster risk management.