Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Jeff Colby Ph.D, Professor (Creator)
Appalachian State University (ASU )
Web Site: https://library.appstate.edu/

Abstract: This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN) are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size) of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses.

Additional Information

Jeffrey D. Colby, Ashleigh B. Turner, Ryan M. Csontos, & Michael Batten (2013) "Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data" Water Volume 5 Issue 4 Version of Record Available From (www.mdpi.com)
Language: English
Date: 2013
LiDar, airborne, mobile, terrestrial, flood inundation analysis, ArcGIS

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