Flood inundation and damage assessment of the degraded Semliki River plains using SAR data, Google Earth Engine, and GIS techniques

The Semliki River valley in Ntoroko district has experienced devastating annual floods since 2019. Recurrent floods in Ntoroko District have displaced thousands and devastated pasturelands, disrupting livelihoods. Therefore, rapid assessment of flooded areas is crucial for developing effective mitigation strategies, disaster preparedness plans, and proactive policies to enhance resilience and mitigate the impact of future flood events. This study introduced a combined approach using Synthetic Aperture Radar (SAR) imagery and a digital elevation model (DEM) to map flood extent, depth, and building exposure in the Semliki Valley. Using Sentinel-1 SAR images taken both before and during the flood, combined with the ALOS PALSAR DEM, inundated areas and flood depths were determined, based on thresholding the SAR backscatter of the VH polarisation images. The flood extent maps were generated using Google Earth Engine and GIS techniques to create depth maps by subtracting the surface elevation from the height/surface of the flood waters. Building exposure and impact analysis for two flood events was ascertained through spatial join and overlay. The results showed that the 2023 flood event inundated approximately 1,968 hectares, including 1,553 hectares of pastureland and 74 buildings, while the 2024 event covered 1,139 hectares, equally inundating 1,050 hectares of pastureland and 54 buildings. Further analysis revealed that despite the smaller extent, the 2024 flood event caused a severe impact on the buildings compared to the 2023 flood disaster.

Authors

Publication Details

Journal
Journal of Degraded and Mining Lands Management
Published
2025-06-30
DOI
https://doi.org/10.15243/jdmlm.2025.124.8379
Citations
1
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
1.11
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article

Flood inundation and damage assessment of the degraded Semliki River plains using SAR data, Google Earth Engine, and GIS techniques

Gertrude Akello, Andrew Mulabbi, Remigio Turyahabwe, John Calvin Esagu
1 citations
Journal of Degraded and Mining Lands Management
Flood Risk Assessment and Management
1.11
article

Flood inundation and damage assessment of the degraded Semliki River plains using SAR data, Google Earth Engine, and GIS techniques

Gertrude Akello, Andrew Mulabbi, Remigio Turyahabwe, John Calvin Esagu
article en
1 citations

Abstract

The Semliki River valley in Ntoroko district has experienced devastating annual floods since 2019. Recurrent floods in Ntoroko District have displaced thousands and devastated pasturelands, disrupting livelihoods. Therefore, rapid assessment of flooded areas is crucial for developing effective mitigation strategies, disaster preparedness plans, and proactive policies to enhance resilience and mitigate the impact of future flood events. This study introduced a combined approach using Synthetic Aperture Radar (SAR) imagery and a digital elevation model (DEM) to map flood extent, depth, and building exposure in the Semliki Valley. Using Sentinel-1 SAR images taken both before and during the flood, combined with the ALOS PALSAR DEM, inundated areas and flood depths were determined, based on thresholding the SAR backscatter of the VH polarisation images. The flood extent maps were generated using Google Earth Engine and GIS techniques to create depth maps by subtracting the surface elevation from the height/surface of the flood waters. Building exposure and impact analysis for two flood events was ascertained through spatial join and overlay. The results showed that the 2023 flood event inundated approximately 1,968 hectares, including 1,553 hectares of pastureland and 74 buildings, while the 2024 event covered 1,139 hectares, equally inundating 1,050 hectares of pastureland and 54 buildings. Further analysis revealed that despite the smaller extent, the 2024 flood event caused a severe impact on the buildings compared to the 2023 flood disaster.

Journal of Degraded and Mining Lands ManagementVol. 12(4)
Openalex Percentile: Top 23%
Flood Risk Assessment and Management
1.11
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