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Frontiers of Earth Science

Front. Earth Sci.
RESEARCH ARTICLE
Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images
Ramesh SIVANPILLAI1(), Kevin M. JACOBS2,3, Chloe M. MATTILIO4, Ela V. PISKORSKI2
1. Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY 82071, USA
2. Department of Ecosystem Science & Management, University of Wyoming, Laramie, WY 82071, USA
3. Haub School of Environment & Natural Resources, University of Wyoming, Laramie, WY 82071, USA
4. Department of Plant Sciences, University of Wyoming, Laramie, WY 82071, USA
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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     
Corresponding Author(s): Ramesh SIVANPILLAI   
Online First Date: 23 April 2020   
 Cite this article:   
Ramesh SIVANPILLAI,Kevin M. JACOBS,Chloe M. MATTILIO, et al. Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images[J]. Front. Earth Sci., 23 April 2020. [Epub ahead of print] doi: 10.1007/s11707-020-0818-0.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-020-0818-0
http://journal.hep.com.cn/fesci/EN/Y/V/I/0
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Ramesh SIVANPILLAI
Kevin M. JACOBS
Chloe M. MATTILIO
Ela V. PISKORSKI
Fig.1  Post-flood raw Landsat image (left) and classified image (right) derived from it depicts newly inundated areas (in dark blue) along with existing waterbodies such as streams, lakes and rivers resulting in overestimating the geographic extent of the impacted region.
Fig.2  Location of five study sites in the continental US.
Study area Event ID Event name
Arkansas 201603 Flood_Southern_US
Colorado 201309 Floods_CO
Indiana 201512 Flood_Midwest_US
Kentucky 201104 Floods_Central_US
Louisiana 201603 Flood_Souther_US
Tab.1  Flood events identified from the Hazards Data Distribution System website
Study area Path / Row Pre-flood Post-flood
Sensor Data Sensor Data
Arkansas 2337 OLI8 20140331 OLI8 20160320
Colorado 3432 TM5 20100925 OLI8 20130917
Indiana 2134 TM5 20110104 OLI8 20160102
Kentucky 3422 TM5 20100414 TM5 20110503
Louisiana 2338 OLI8 20140314 OLI8 20160320
Tab.2  Details of the Landsat 5 Thematic Mapper (TM5) and Landsat 8 Operational Land Imager (OLI8) images used for mapping the flooded areas
Fig.3  Flood maps generated ΔNDWI (left column) and ΔMNDWI (right column) for a) Arkansas, b) Colorado, c) Indiana, and d) Kentucky. Inundated areas in the difference images are highlighted in dark red (ΔNDWI) and bright orange (ΔMNDWI) colors
Study area DNDWI DMNDWI
TVF Processing time TVF Processing time
Arkansas 32 40 27 25
Colorado 25 20 48 35
Indiana 35 20 55 20
Kentucky 15 25 20 25
Louisiana 20 20 20 30
Tab.3  Threshold values (TVF) and time required for generating rapid flood maps from differencing Normalized (NDWI) and Modified Normalized (MNDWI) Wetness Index images
Study area Overall accuracy Kappa value
ΔNDWI ΔMNDWI ΔNDWI ΔMNDWI
Arkansas 84.6% 93.0% 0.691 0.861
Colorado 78.0% 97.4% 0.555 0.947
Indiana 89.5% 94.25% 0.782 0.881
Louisiana 88.7% 89.70% 0.771 0.794
Kentucky 93.2% 95.20% 0.858 0.903
Tab.4  Overall accuracy and kappa agreement values for the flood maps generated from differencing NDWI and MNDWI images
Study area Producer accuracy User accuracy
ΔNDWI ΔMNDWI ΔNDWI ΔMNDWI
a. Flood class
Arkansas 75.8% 90.9% 91.5% 94.7%
Colorado 74.4% 94.2% 76.2% 100.0%
Indiana 77.6% 92.1%
92.1%
91.4% 94.6%
Louisiana 83.1% 92.1% 91.4% 86.3%
Kentucky 87.1% 98.4% 96.4% 91.0%
b. Non flood class
Arkansas 93.1% 95.1% 79.8% 91.5%
Colorado 81.0% 100.0% 79.4% 95.5%
Indiana 98.9% 95.8% 84.7% 93.8%
Louisiana 93.4% 87.7% 86.8% 93.0%
Kentucky 97.6% 92.6% 91.1% 98.7%
Tab.5  Producer and user accuracy values (in percent) for the rapid flood maps generated from differencing NDWI and MNDWI images
Fig.4  Flood maps generated for the Louisiana study area from a) classifying a post-flood image using an unsupervised classification algorithm (blue color), and b) differencing a pre- and post-flood images (bright orange).
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