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FLDSensing: Remote Sensing Flood Inundation Mapping with FLDPLN
Edwards, Jackson Robert
Edwards, Jackson Robert
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Abstract
Flood inundation mapping (FIM), which is essential for effective disaster response and management, requires rapid and accurate delineation of flood extent and depth. Remote sensing FIM, especially using satellite imagery, offers certain capabilities and advantages but also faces challenges of cloud and canopy obstructions and flood depth estimation. This study developed a hybrid approach, named FLDSensing, which combines remote sensing imagery and FLDPLN, a flood inundation model, to improve remote sensing FIM in both inundation extent and depth. The FLDSensing method first identifies clean flood edge pixels (i.e., floodwater pixels next to bare ground) which, combined with FLDPLN library, are used to estimate the water stages at certain stream pixels. Water stage is then smoothed and interpolated to the rest of the stream pixels, then a FLDPLN library is accessed to generate flood extent and depth grids. The FLDSensing method was applied over the Verdigris River in southeastern Kansas to map a flood event that occurred in late May 2019. Sentinel-2 imagery was used to generate remote sensing FIM and identify clean edge pixels. The results show a significant improvement on flood inundation extent mapping when compared to a HEC-RAS 2D benchmark, with the metrics of CSI/POD/FAR/F1-scores reaching 0.9/0.95/0.06/0.95 from 0.55/0.56/0.03/0.71 using remote sensing alone. The method also performed favorably against other existing hybrid approaches such as FLEXTH and FwDET. This study shows that integrating the FLDPLN model with remote sensing imagery provides a more complete and accurate FIM solution than remote sensing FIM alone.
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Date
2025-01-01
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University of Kansas
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This item contains archived web content.
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Edwards_ku_0099M_20124.pdf
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- Embargoed until 2176-05-31
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Keywords
Geographic information science and geodesy, Geography, Remote sensing, Disaster Managment, FIM, FLDSensing, Flood Inundation Mapping, GIS, Remote Sensing