Comparing Shallow Landslide Risk Hazard Map Using LiDAR and 10 m Digital Elevation Model (DEM) in Mukilteo, Washington

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Western Washington is particularly vulnerable to shallow landslides due to its rainy conditions for most of the year. Moreover, the unique Puget Lowlands geology resulting from ice sheets advancement that deposited sand and gravel on top of less permeable silt and clay also contributed to this area’s susceptibility to landslide. Due to increasing concern, Washington State has ramped up its landslide mapping efforts that include assessing landslide risks in areas that have high population densities. Washington mapped its landslides using different data that range from 10-meter Digital Elevation Models (DEMs) to recent high-quality 3-meter LiDAR data. This study assesses shallow landslide risk in the Mukilteo area using the latest available LiDAR data and compares it against previously published DEM to identify any differences in morphologic features that could give a forecast to future landslide events. The landslide risk assessment of the study area will follow protocols that are published by the Washington State in April 2017 that use LiDAR derivatives such as shaded relief, slope, and topographic contour maps. The models (LiDAR and DEM) will be analyzed using ArcGIS software to look at the difference of shallow landslide population and factor of safety maps. This study hypothesizes that the mapping of shallow landslides using high-resolution LiDAR data will likely reveal a greater potential for landslide hazards in this area than DEMs because of the greater detail in LiDAR data. By understanding the difference between these two models, we can generate higher-quality landslide susceptibility maps that can serve as a preliminary cautionary step for homebuilders and the communities in general.

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  • type
    Video
  • created on
  • file format
    mp4
  • file size
    21 MB
  • creator
    Muhamad Nazhirin bin Ahmad
  • publisher
    University of Washington
  • publisher place
    Seattle, WA
  • rights
    Attribution-NonCommercial-ShareAlike 3.0 United States