Assessment of flash flood detection in Erbil city using change detection indices for SAR images

Abstract

The frequency and intensity of flash floods are expected to increase due to climate change, resulting in significant casualties and damage to infrastructure and the economy. Reliable and timely flood maps are essential for an effective disaster management plan. On December 17, 2021, a severe flash flood in Erbil City resulted in twelve fatalities and extensive damage to the affected area. The ability of synthetic aperture radar (SAR) images to penetrate clouds and heavy precipitation is vital for accurate flood imaging. This study analyzed Sentinel-1 satellite-based radar images before and after the flood to detect the inundation. Two change detection techniques, the Normalized Change Index (NCI) and the Ratio Index (RI), combined with semi-automatic thresholding were employed. Both methods successfully identified the flooded region with consistent findings. The overall accuracy of NCI and RI were 90.5% and 84.3% respectively. The accuracy assessment revealed that the NCI method outperforms the RI method in detecting the flash flood event in Erbil City. This model is viable for disaster management, enabling the evaluation of damage to critical municipal infrastructure and other assets, thus supporting effective urban governance and timely response to emergencies. At the final stage of disaster management, this model can be implemented to evaluate the extent of loss on major municipal infrastructure and other properties within the area.

Keywords

• floods
• normalized change indiex
• ratio index
• infrastructure
• sar image