Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques |
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Authors: | Dinuke Munasinghe Sagy Cohen Yu‐Fen Huang Yin‐Phan Tsang Jiaqi Zhang Zheng Fang |
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Institution: | 1. Department of Geography, University of Alabama, Tuscaloosa, Alabama;2. Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, Honolulu, Hawaii;3. Department of Civil and Environmental Engineering, University of Texas at Arlington, Arlington, Texas |
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Abstract: | The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index NDWI], modified NDWI MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography. |
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Keywords: | flooding remote sensing inundation mapping geospatial analysis image classification |
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