Improved Floodplain Delineation Method Using High-Density LiDAR Data

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15 Scopus citations

Abstract

With the improvements in sensor technologies over the past decade, there has been a significant decrease in the cost of acquisition and increase in the density and accuracy of Light Detection and Ranging (LiDAR) data. Due to its advantages over traditional surveying techniques, LiDAR data are widely preferred for floodplain delineation. But, processing dense LiDAR data is time-consuming and memory intense. Therefore, it is divided into manageable areas/tiles or simplified to raster DEM (Digital Elevation Model) format for feature extraction process such as floodplain delineation. This results in increase in processing time and decrease in accuracy due to loss of true elevation. Furthermore, as floodplain boundaries are unknown prior to delineation, processing time also increases as LiDAR data over larger extent is processed. Hence, there is a need of improved, automated method that will process only the LiDAR data that contribute to the floodplain. This article, describes a time-efficient floodplain delineation method that divides the LiDAR data into regular tiles and processes only the tiles that contribute to floodplain. This method is experimented using LiDAR data saved in ArcGIS "Terrain" format at 0.0, 0.1, and 0.3 m pyramid levels. These data are then preprocessed to obtain elevation information which is used to filter and process only LiDAR data tiles that truly contribute to the floodplain boundary; thus, reducing processing time. Results from two pilot hydraulic models showed that this method saved 12-34% of processing time compared to the conventional method.

Original languageEnglish (US)
Pages (from-to)68-79
Number of pages12
JournalComputer-Aided Civil and Infrastructure Engineering
Volume28
Issue number1
DOIs
StatePublished - Jan 2013

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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