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A platform for research: civil engineering, architecture and urbanism
Mapping flood extent of Cyclone Freddy using Sentinel-1 SAR data in Google Earth Engine in Southern Malawi
Abstract Tropical cyclones are one of the most devastating natural hazards that impact humans as they often result in extensive flooding and loss of life. Accurate flood information is critical for emergency response during the period of natural hazards, as well as post hazard management and relief efforts. However, there is a knowledge gap on the common use of Sentinel-1 data in sub-tropical environment, such as Malawi, especially on employing different polarization combinations of Synthetic Aperture Radar (SAR) imagery. Here, we focused on the impacts of cyclone Freddy on Southern Malawi by mapping the flood extent and exploring the factors that increased the risks of flooding. We used the threshold method by testing the suitability of two individual polarizations (Vertical–Vertical (VV), Vertical–Horizontal (VH)) and two combinations (VV + VH, VV x VH). We then assessed the association of different factors with flooding using the principal component approach (PCI). The results from this study showed that the best polarization combination was VV + VH (accuracy = 89%) which predicted the flood extent of over 1000 km2 across the Malawian southern region. The environmental factors that had major influence on flooding include distance from the coast, elevation, land cover types and slope. Thus, low elevated areas with a gentle slope, close to the southern coast of Lake Malawi and near to water bodies, such as rivers and lakes were more negatively impacted by cyclone Freddy, especially if they were in disturbed areas, such as cropland and settlements. The insights from this study are important in developing early warning systems, managing risks, post hazard response and informing policies on flood risk management.
Mapping flood extent of Cyclone Freddy using Sentinel-1 SAR data in Google Earth Engine in Southern Malawi
Abstract Tropical cyclones are one of the most devastating natural hazards that impact humans as they often result in extensive flooding and loss of life. Accurate flood information is critical for emergency response during the period of natural hazards, as well as post hazard management and relief efforts. However, there is a knowledge gap on the common use of Sentinel-1 data in sub-tropical environment, such as Malawi, especially on employing different polarization combinations of Synthetic Aperture Radar (SAR) imagery. Here, we focused on the impacts of cyclone Freddy on Southern Malawi by mapping the flood extent and exploring the factors that increased the risks of flooding. We used the threshold method by testing the suitability of two individual polarizations (Vertical–Vertical (VV), Vertical–Horizontal (VH)) and two combinations (VV + VH, VV x VH). We then assessed the association of different factors with flooding using the principal component approach (PCI). The results from this study showed that the best polarization combination was VV + VH (accuracy = 89%) which predicted the flood extent of over 1000 km2 across the Malawian southern region. The environmental factors that had major influence on flooding include distance from the coast, elevation, land cover types and slope. Thus, low elevated areas with a gentle slope, close to the southern coast of Lake Malawi and near to water bodies, such as rivers and lakes were more negatively impacted by cyclone Freddy, especially if they were in disturbed areas, such as cropland and settlements. The insights from this study are important in developing early warning systems, managing risks, post hazard response and informing policies on flood risk management.
Mapping flood extent of Cyclone Freddy using Sentinel-1 SAR data in Google Earth Engine in Southern Malawi
Darius Phiri (author) / Charles Mulenga (author) / Vincent R. Nyirenda (author)
2025
Article (Journal)
Electronic Resource
Unknown
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| Springer Verlag | 2025
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