Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images

Abstract

Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.

Keywords

Rapid Flood Mapping (RFM) / inundation maps / Satellite data / NDWI / MNDWI

Ramesh Sivanpillaiƒ received his B.Sc in physics from PSG College of Arts & Science in 1987, M.Sc in environmental studies from Cochin University of Science & Technology in 1990, M.Phil in environmental sciences from Bharathiar University, M.S. in environmental sciences from University of Wisconsin-Green Bay, and his PhD degree in forestry from Texas A&M University in 2002. He is a Senior Research Scientist at the University of Wyoming and his current research includes satellite image processing for monitoring crop fields and aquatic environments.

Kevin M. Jacobsƒ is an undergraduate student at the University of Wyoming majoring in rangeland ecology & watershed management, and environment and natural resources.

Chloe M. Mattilio ƒis a doctoral student in the Program in Ecology at the University of Wyoming. Her current research includes the use of unmanned aerial vehicles for mapping invasive plants.

Ela V. Piskorskiƒ is an undergraduate student at the University of Wyoming majoring in rangeland ecology & watershed management.

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Acknowledgments

We thank the three anonymous reviewers for their valuable comments and suggestions that improved the quality of the earlier versions of the manuscript. Ms. Abigail Gettinger (University of Wyo.) provided editorial comments on the manuscript. We thank the US Geological Survey (USGS) for providing no-cost Landsat data and supporting this work under Grant/Cooperative Agreement No. G18AP00077 to the first author. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the USGS. Mention of trade names or commercial products does not constitute their endorsement by the USGS.

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