Abstract

Flooding is a significant hazard in Nigeria, with factors like climate change, deforestation, and inadequate urban planning increasing the risk. Accurate flood mapping across large areas is crucial for effective disaster management. This study aims to map the flood extent in Borno State triggered by the collapse of the Alou Dam in September 2024 adopting Sentinel-1 Synthetic Aperture Radar (SAR) data from the Google Earth Engine platform. The analysis employed vertical transmit/vertical receive (VV) polarization, which is highly sensitive to vertical structures, making it particularly effective for detecting flood inundation in urban areas. To ensure accurate flood mapping, the SAR data underwent processing, including speckle noise reduction through spatial smoothing using a focal mean filter. Change detection analysis was conducted by comparing SAR images before (July 5–25, 2024) and after (September 5–25, 2024) the flood event. A thresholding technique was applied to differentiate flooded from non-flooded areas, with a threshold of 5 selected after testing various values to minimize false positives, especially in non-flooded rural regions. The results indicated that approximately 356 km² of the 66,224 km² study area was inundated, affecting heavily populated regions such as Jere, Maiduguri, Mobbar, Monguno, Marte, Mafa, and Ngala. The study underscores the need for better urban planning and disaster preparedness in flood-prone areas, with SAR data playing a crucial role in monitoring and mitigating future flood events.

Key Words: Nigeria, Borno State, Flood, Sentinel-1 SAR, Alou Dam