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Keywords

floods, Normalized change indiex, Ratio index, infrastructure, SAR image

Document Type

Research Paper

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.

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Highlights

Flash flood maps were created using NCI and RI algorithms across the region Open-source SAR data and Snap 9.0 software addressed cloud cover and vegetation issues in flood mapping The accuracy of NCI and RI algorithms for flash flood detection was evaluated Simple, efficient algorithms provided good accuracy for flood monitoring in large areas, like the entire city of Erbil

DOI

10.30684/etj.2024.151205.1777

First Page

1378

Last Page

1386

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