CALL FOR PROPOSALS:

ORGANIZERS

  • Harvey Thorleifson, Chair
    Minnesota Geological Survey
  • Carrie Jennings, Vice Chair
    Minnesota Geological Survey
  • David Bush, Technical Program Chair
    University of West Georgia
  • Jim Miller, Field Trip Chair
    University of Minnesota Duluth
  • Curtis M. Hudak, Sponsorship Chair
    Foth Infrastructure & Environment, LLC

 

Paper No. 9
Presentation Time: 10:35 AM

LIDAR DIGITAL ELEVATION MAPS EMPLOYED IN CAROLINA BAY SURVEY


DAVIAS, Michael, Stamford, CT 06907 and GILBRIDE, Jeanette L., North Carolina State University, Raleigh, NC 27695, michael@cintos.org

Aerial photographs of Carolina bays taken in the 1930’s sparked research into their geomorphology, but revealed only part of their unique planforms. Digital Elevation Maps (DEMs), using LiDAR-derived data, accentuate the visual presentation of these shallow basins. To support a geospatial survey of Carolina bay landforms in the continental US, 400,000 km2 of hsv-shaded DEMs were created as KML-JPEG tile sets for visualization on a virtual globe. A majority of these DEMs were generated with LiDAR data, while the remainder represents USGA 1/3 arc second data. We demonstrate the tile generation process and their integration into Google Earth for open public access over the Internet. While the generic Carolina bay planform is considered oval, we document regional variations. Using a small set of empirically derived planform shapes, we created Google Earth overlay elements to support the manual capture of individual Carolina bay shapes and orientations. The resulting overlay data element for each measured bay is extracted from Google Earth and programmatically processed to generate metrics such as geographic location, elevation, surface area and inferred orientation. When visualized in LiDAR, we document the robustness of a single planform shape across hundreds or thousands of basins within geographically large areas. We maintain that utilizing a virtual globe facility for data captures and extraction results in more reliable data sets compared to processes that reference flat map projections. This is especially true when capturing the geospatial shape and orientation of the bays, which can be skewed and distorted in the projection process. Using the process described, we have measured over 25,000 distinct Carolina bays, and have assembled their individual characteristics into a geographic information database. We examine the Google Fusion geospatial visualization facility, through which the database has been made publically accessible. Preliminary findings from the survey are briefly discussed, such as how bay surface area, eccentricity and orientation vary within and across ~700 ¼º x ¼º grid elements.
Handouts
  • Davias_GSA2011_Presentation_165-9_HQ.pdf (10.5 MB)
  • Davias_GSA2011_Notes_165-9_Small.pdf (2.7 MB)
  • Links_To_Survey_Data.pdf (47.2 kB)
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