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Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling

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  • Catherine Ticehurst
  • Dushmanta Dutta
  • Fazlul Karim
  • Cuan Petheram
  • Juan Guerschman

Abstract

The mapping of spatial inundation patterns during flood events is important for environmental management and disaster monitoring. Remote sensing technologies provide an affordable means of capturing flood extent with reasonable spatial and temporal coverage for flood monitoring. However, it is often difficult to provide an independent validation of the mapping algorithms with other remote sensing data since they can have similar issues. Hydrodynamic (HD) modelling tools are widely used for floodplain inundation modelling to a high accuracy, but they are resource intensive, making them impractical to use for large catchments. This paper looks at improving the mapping of flood events based on the daily MODIS Open Water Likelihood (OWL) algorithm (which provides the fraction of water within a pixel) by using information from HD modelling. It compares the MODIS OWL water fraction maps (500 m pixel size) with Landsat water maps (30 m pixel size) as well as those derived from two-dimensional HD modelling (at 150 m pixel size) to determine the best water fraction threshold at which a MODIS OWL pixel is identified as flooded. Comparison with Landsat water maps shows the best MODIS OWL water fraction threshold to vary depending on the spatial distribution of water. When compared to the HD model for water fraction thresholds of 0.1 (or 10 %) and below, our results show that values of 0.05–0.1 (or 5–10 %) were best. The overall cell-to-cell match is better when the 0.1 water fraction threshold is used; however, the total number of flooded pixels better matches with the 0.05 water fraction threshold. This study has shown that the simulated floods from calibrated two-dimensional HD models can be used to reduce the uncertainty in selecting a water fraction threshold in MODIS OWL images for producing regional-scale maps of flood events. The daily MODIS water maps (of 0.1 water fraction) were used to produce catchment-wide annual flood maps showing the maximum number of days that each pixel is inundated. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Catherine Ticehurst & Dushmanta Dutta & Fazlul Karim & Cuan Petheram & Juan Guerschman, 2015. "Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 803-820, September.
    • Handle: RePEc:spr:nathaz:v:78:y:2015:i:2:p:803-820
    DOI: 10.1007/s11069-015-1743-5
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    References listed on IDEAS

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    1. Md. Bhuiyan & Dushmanta Dutta, 2012. "Analysis of flood vulnerability and assessment of the impacts in coastal zones of Bangladesh due to potential sea-level rise," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 729-743, March.
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    Cited by:

    1. G. Papaioannou & A. Loukas & L. Vasiliades & G. T. Aronica, 2016. "Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 117-132, October.
    2. P. V. Timbadiya & K. M. Krishnamraju, 2023. "A 2D hydrodynamic model for river flood prediction in a coastal floodplain," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1143-1165, January.
    3. Martin Kabenge & Joshua Elaru & Hongtao Wang & Fengting Li, 2017. "Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1369-1387, December.

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