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Frontiers of Earth Science

Front. Earth Sci.
Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images
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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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.
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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
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
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
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%
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).
1 G Amarnath (2014). An algorithm for rapid flood inundation mapping from optical data using a reflectance differencing technique. J Flood Risk Manag, 7(3): 239–250
2 D Amitrano, G D Martino, A Iodice, D Riccio, G Ruello (2018). Unsupervised rapid flood mapping using Sentinel-1 GRD SAR images. IEEE Trans Geosci Remote Sens, 56(6): 3290–3299
3 E Attema, M Davidson, P Snoeij, B Rommen, N Floury (2009). Sentinel-1 mission overview. In: 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town, South Africa
4 T E Avery, G L Berlin (1992). Fundamental of Remote Sensing and Airphoto Interpretation. 5th ed. New York: Macmillan Publishing Company
5 G Boni, L Ferraris, L Pulvirenti, G Squicciarino, N Pierdicca, L Candela, A R Pisani, S Zoffoli, R Onori, C Proietti, P Pagliara (2016). A Prototype system for flood monitoring based on flood forecast combined with COSMO-SkyMed and Sentinel-1 Data. IEEE J Sel Top Appl Earth Obs Remote Sens, 9(6): 2794–2805
6 R G Congalton (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ, 37(1): 35–46
7 J Fohringer, D Dransch, H Kreibich, K Schroter (2015). Social media as an information source for rapid flood inundation mapping. Nat Hazards Earth Syst Sci, 15(12): 2725–2738
8 M Gianinetto, P Villa (2007). Rapid response flood assessment using minimum noise fraction and composted spline interpolation. IEEE Trans Geosci Remote Sens, 45(10): 3204–3211
9 M D Goldberg, S Li, S Goodman, D Lindsey, B Sjoberg, D Sun (2018). Contributions of operational satellites in monitoring the catastrophic floodwaters due to Hurricane Harvey. Remote Sens, 10(8): 1256
10 K Kaku, N Aso, F Takiguchi (2015). Space-based response to the 2011 great east Japan earthquake: lessons learnt from JAXA’s support using earth observation satellites. Int J Disaster Risk Reduct, 12: 134–153
11 Y Kwak (2017). Nationwide flood monitoring for disaster risk reduction using multiple satellite data. ISPRS Int J Geoinf, 6(7): 203
12 Y Kwak, B B Shrestha, A Yorozuya, H Sawano (2015). Rapid damage assessment of rice crop after large-scale flood in the Cambodian floodplain using temporal spatial data. IEEE J Sel Top Appl Earth Obs Remote Sens, 8(7): 3700–3709
13 S Li, D Sun, M D Goldberg, B Sjoberg, D Santek, J P Hoffman, M DeWeese, P Restrepo, S Lindsey, E Holloway (2018a). Automatic near real-time flood detection using Suomi-NPP/VIIRS data. Remote Sens Environ, 204: 672–689
14 Z Li, C Wang, C T Emrich, D Guo (2018b). A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods. Cartogr Geogr Inf Sci, 45(2): 97–110
15 K E Joyce, S E Belliss, S V Samsonov, S J McNeill, P J Glassey (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog Phys Geogr, 33(2): 183–207
16 P Manjusree, L Prasanna Kumar, C M Bhatt, G S Rao, V Bhanumurthy (2012). Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. Int J Disaster Risk Sci, 3(2): 113–122
17 S K McFeeters (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens, 17(7): 1425–1432
18 D Notti, D Giordan, F Caló, A Pepe, F Zucca, J Galve (2018). Potential and limitations of open satellite data for flood mapping. Remote Sens, 10(11): 1673
19 Y O Ouma, R Tateishi (2006). A water index for rapid mapping of shoreline changes in five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data. Int J Remote Sens, 27(15): 3153–3181
20 T Perrou, A Garioud, I Parcharidis (2018). Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin. Front Earth Sci, 12(3): 506–520
21 N Pierdicca, L Pulvirenti, M Chini (2018). Flood mapping in vegetated and urban areas and other challenges: models and methods. In: Refice A, D'Addabbo A, Capolongo D, eds. Flood Monitoring through Remote Sensing. Springer Remote Sensing/Photogrammetry. Springer, Cham:135–179
22 J F Rosser, D G Leibovici, M J Jackson (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. Nat Hazards, 87(1): 103–120
23 M Story, R Congalton (1986). Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sensing, 52(3): 397–399
24 R Shaw, T Izumi, P Shi (2016). Perspectives of science and technology in disaster risk reduction of Asia. Int J Disaster Risk Sci, 7(4): 329–342
25 R Sivanpillai, B K Jones, R M Lamb (2017). Accessing satellite imagery for disaster response through the International Charter: lessons learned from the 2011 US Midwestern Floods. Space Policy, 42: 54–61
26 R Sivanpillai, S N Miller (2010). Improvements in mapping water bodies using ASTER data. Ecol Inform, 5(1): 73–78
27 B Tomaszewski, M Judex, J Szarzynski, C Radestock, L Wirkus (2015). Geographic information systems for disaster response: a review. J Homel Secur Emerg Manag, 12(3): 571–602
28 H Xu (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
29 Y Wang, J D Colby, K A Mulcahy (2002). An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data. Int J Remote Sens, 23(18): 3681–3696
30 P F Watson, A Petrie (2010). Method agreement analysis: a review of correct methodology. Theriogenology, 73(9): 1167–1179 pmid: 20138353
31 F Zhang, X Zhu, D Liu (2014). Blending MODIS and Landsat images for urban flood mapping. Int J Remote Sens, 35(9): 3237–3253
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