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Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia

Authors

  • Sergii Skakun,

    Corresponding author
    1. Space Research Institute NASU-SSAU, Kyiv, Ukraine
    2. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
    • Address correspondence to Sergii Skakun, Space Research Institute NASU-SSAU, Glushkov Ave., 40, build. 4/1, Kyiv, 03680, Ukraine; tel: +38-044-5262553; fax: +38-044-5264124; serhiy.skakun@ikd.kiev.ua.

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  • Nataliia Kussul,

    1. Space Research Institute NASU-SSAU, Kyiv, Ukraine
    2. National Technical University of Ukraine “Kyiv Polytechnic Institute,” Kyiv, Ukraine
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  • Andrii Shelestov,

    1. Space Research Institute NASU-SSAU, Kyiv, Ukraine
    2. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
    3. National Technical University of Ukraine “Kyiv Polytechnic Institute,” Kyiv, Ukraine
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  • Olga Kussul

    1. National Technical University of Ukraine “Kyiv Polytechnic Institute,” Kyiv, Ukraine
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Abstract

In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood-prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov-Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat-5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.

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