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

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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.

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References

  • Abbott MB, Damsgaard A, Rodenhuis GS (1973) A design system for two-dimensional nearly-horizontal flows. J Hydraul Res 11(1):1–28

    Article  Google Scholar 

  • Auynirundronkool K, Chen N, Peng C, Yang C, Gong J, Silapathong C (2012) Flood detection and mapping of the Thailand Central plain using RADARSAT and MODIS under a sensor web environment. Int J Appl Earth Obs Geoinf 14:245–255

    Article  Google Scholar 

  • Bhuiyan J, and Dutta D (2012) Analysis of flood vulnerability and assessment of the impacts in coastal zones of Bangladesh due to potential sea level rise, Nat Hazards (ISSN: 0921-030X, IF: 1.398), 61:729–743

  • BoM (2012) Australian Hydrological Geospatial Fabric (Geofabric), Product Guide, Bureau of Meteorology, Australia

  • Brakenridge R, Anderson E (2006) MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. Transbound Floods Reduc Risks Through Flood Manag 72:1–12

    Article  Google Scholar 

  • Chen Y, Huang C, Ticehurst C, Merrin L, Thew P (2013) An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping. Wetlands 33(5):823–835. doi:10.1007/s13157-013-0439-4

    Article  Google Scholar 

  • Chen Y, Wang B, Pollino C, Cuddy S, Merrin L, Huang C (2014) Estimate of flood inundation and retention on wetlands using remote sensing and GIS. Ecohydrology. doi:10.1002/eco.1467

    Google Scholar 

  • DHI (2009) MIKE 21 flow model: scientific documentation. Danish Hydraulic Institute, Denmark

    Google Scholar 

  • Doble R, Crosbie R, Peeters L, Joehnk K, Ticehurst C (2013) Modelling overbank flood recharge at a continental scale. Hydrol Earth Syst Sci Discuss 10:12573–12613. doi:10.5194/hessd-10-12573-2013

  • Drogue G, Pfister L, Leviandier T, El Idrissi A, Ifflya JF, Matgen P, Humbert J, Hoffmann L (2004) Simulating the spatio-temporal variability of streamflow response to climate change scenarios in a mesoscale basin. J Hydrol 293:255–269

    Article  Google Scholar 

  • Dutta D (2012) Flood hazard mapping using hydrodynamic modelling approach, In: Wong T (ed) Flood risk and flood management, Chapter 5 (Nanyang Technological University, Singapore). Nova Science Publishers, ISBN: 978-1-62081-220-4

  • Dutta D, Nakayama K (2009) Effects of spatial grid resolution on river flow and surface inundation simulation by physically based distributed modeling approach. Hydrol Process 23:534–545

    Article  Google Scholar 

  • Dutta D, Alam J, Umeda K, Hayashi M, Hironaka S (2007) A two dimensional hydrodynamic model for flood inundation simulation: a case study in the Lower Mekong River basin. Hydrol Process 21:1223–1237

    Article  Google Scholar 

  • Dutta D, Karim F, Ticehurst C, Marvanek S and Petheram C (2013) Floodplain inundation mapping and modelling in the Flinders and Gilbert Catchments. A technical report to the Australian Government from the CSIRO Flinders and Gilbert Agricultural Resource Assessment, part of the North Queensland Irrigated Agriculture Strategy. CSIRO Water for a Healthy Country and Sustainable Agriculture flagships, Australia, p 147

  • EXCIMAP (2007) Handbook on good practices for flood mapping in Europe, European Exchange Circle on Flood Mapping (EXCIMAP), p 57

  • FEMA (2003) Guidelines and Specifications for Flood Hazard Mapping Partners, Federal Emergency Management Agency, USA, p 49

  • Frazier PS, Page KJ (2000) Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sens 66(12):1461–1467

    Google Scholar 

  • Gallant JC, Dowling TI (2003) A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resour Res 39(12):1347. doi:10.1029/2002WR001426

    Google Scholar 

  • Gao B-C (1996) NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266

    Article  Google Scholar 

  • Geoscience Australia (2010) The national dynamic land cover dataset, Geoscience Australia, http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_71071

  • Guerschman JP, Warren G, Byrne G, Lymburner L, Mueller N, Van-Dijk A (2011) MODIS-based standing water detection for flood and large reservoir mapping: algorithm development and applications for the Australian continent. CSIRO: Water for a Healthy Country National Research Flagship Report, Canberra

  • Huang C, Chen, Wu J (2014) Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. Int J Appl Earth Obs Geoinf 26:350–362

    Article  Google Scholar 

  • Islam AS, Bala SK, Haque MA (2010) Flood inundation map of Bangladesh using MODIS time-series Images. J Flood Risk Manag 3:210–222

    Article  Google Scholar 

  • Jackson TJ, Chen D, Cosh M, Li F, Anderson M, Walthall C (2004) Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens Environ 92:475–482

    Article  Google Scholar 

  • Karamouz M, Noori N, Moridi A, Ahmadi A (2011) Evaluation of floodplain variability considering impacts of climate change. Hydrol Process 25:90–103

    Article  Google Scholar 

  • Karim F, Dutta D, Marvanek S, Ticehurst C, Petheram C (2013) Hydrodynamic modelling of potential impacts of climate change on hydrological connectivity of floodplain wetland. In: Proceedings of the 20th international congress on modelling and simulation, Adelaide, Australia, 1–6 December 2013

  • Karim F, Dutta D, Mavanek S, Petheram C, Ticehurst C, Lerat J, Kim S, Yang A (2015) Assessing impacts of climate change and water resources development on floodplains and wetlands in the Flinders and Gilbert Catchments, Australia. J Hydrol 522:80–94

    Article  Google Scholar 

  • Lerat J, Egan C, Kim S, Gooda M, Loy A, Shao Q Petheram C (2013) Calibration of river models for the Flinders and Gilbert catchments. A technical report to the Australian Government from the CSIRO Flinders and Gilbert Agricultural Resource Assessment, part of the North Queensland Irrigated Agriculture Strategy. CSIRO Water for a Healthy Country and Sustainable Agriculture flagships, Australia

  • Muir JS and Danaher T (2008) Mapping water body extent in Queensland through time series analysis of Landsat imagery. In: Proceedings of the 14th ARSPC conference, Darwin. Australian remote sensing and photogrammetry conference

  • Petheram C, McMahon TA, Peel MC (2008) Flow characteristics of rivers in northern Australia: implications for development. J Hydrol 357:93–111

    Article  Google Scholar 

  • Rogers AS, Kearney MS (2004) Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. Int J Remote Sens 20:2317–2335

    Article  Google Scholar 

  • Ryu J-H, Won J-S, Min KD (2002) Waterline extraction from Landsat TM data in a tidal flat. A case study in Gomso Bay, Korea. Remote Sens Environ 83:442–456

    Article  Google Scholar 

  • Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009) Progress in integration of remote sensing—derived flood extent and stage data and hydraulic models, Rev Geophys Vol 47(4):RG4001

  • Sun D, Yu Y, Goldberg MD (2011) Deriving water fraction and flood maps from MODIS images using a decision tree approach. IEEE J Sel Top Appl Earth Obs Remote Sens 4(4):814–825

    Article  Google Scholar 

  • Ticehurst C, Chen Y, Karim F and Dushmanta D (2013) Using MODIS for mapping flood events for use in hydrological and hydrodynamic models: experiences so far. MODSIM2013. In: 20th international congress on modelling and simulation, Adelaide, Australia

  • Tong PHS, Auda Y, Populus J, Aizpuru M, Habshi AA, Blasco F (2004) Assessment from space of mangroves evolution in the Mekong Delta, in relation to extensive shrimp farming. Int J Remote Sens 25:4795–4812

    Article  Google Scholar 

  • Ward DP, Hamilton SK, Jardine TD, Pettit NE, Tews EK, Olley JM, Bunn SE (2013) Assessing the seasonal dynamics of inundation, turbidity, and aquatic vegetation in the Australian wet–dry tropics using optical remote sensing. Ecohydrology 6:312–323

    Article  Google Scholar 

  • Weiss DJ, Crabtree RL (2011) Percent surface water estimation for MODIS BRDF 16-day image composites. Remote Sens Environ 115:2035–2046

    Article  Google Scholar 

  • WMO (2009). Final report on flood hazard mapping project, World Meteorological Organization, p 42

  • Xiao X, Boles S, Frolking S, Li C, Bau JY, Salas W (2005) Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ 100:95–113

    Article  Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033

    Article  Google Scholar 

Download references

Acknowledgments

Major funding for the Flinders and Gilbert Agricultural Resource Assessment was provided by the Office of Northern Australia and the Australian Government’s Northern Australia Sustainable Futures program. CSIRO co-invested through its Water for a Healthy Country and Sustainable Agricultural flagships.

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Authors and Affiliations

  1. Land and Water Flagship, CSIRO, Canberra, ACT, 2601, Australia

    Catherine Ticehurst, Dushmanta Dutta, Fazlul Karim, Cuan Petheram & Juan Pablo Guerschman

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  1. Catherine Ticehurst
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  2. Dushmanta Dutta
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  3. Fazlul Karim
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  4. Cuan Petheram
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  5. Juan Pablo Guerschman
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Correspondence to Catherine Ticehurst.

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Ticehurst, C., Dutta, D., Karim, F. et al. Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling. Nat Hazards 78, 803–820 (2015). https://doi.org/10.1007/s11069-015-1743-5

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  • DOI: https://doi.org/10.1007/s11069-015-1743-5

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