LIDAR DIGITAL ELEVATION MAPS EMPLOYED IN CAROLINA BAY RESEARCH: NEW VIEW OF THE PIEDMONT'S SURFICIAL GEOLOGY USING GOOGLE EARTH'S VIRTUAL GLOBE

Aerial photographs of the Carolina bays have been available since the early 1930’s. Those early images sparked extensive research into their genesis, but they reveal only a small part of their unique planforms. Digital elevation maps (DEM) created with today’s Laser Imaging and Range Detection (LiDAR) systems accentuates their already-stunning visual presentation, allowing for the identification and classification of even greater quantities of these shallow basins across the Piedmont and into North America. We suspect that access to high resolution LiDAR DEMs in more regions would aid in expanding the bays’ identified range. Our research is enabled to a large part by the facilities of the Google Earth (GE) Geographic Information System (GIS). The Global Mapper GIS application was used to generate LiDAR image overlays for visualization on Google’s virtual globe, using 1/9 arc-second resolution DEM data from the United States Geological Survey (USGS). A survey was undertaken to catalogue the full extent of Carolina bays, indexed within USGS 100K Quadrants. Identifying Carolina bays on the costal plain is straight forward, given their characteristic ovoid planform, however bay planforms tend towards a circular presentation in the northern and southern areas of their geographic extent, presenting challenges. Also challenging is the rougher terrain seen when moving inland above the Piedmont. Archetypical planforms specific to each of these regions have been heuristically developed. The LiDAR maps demonstrate that the geomorphological process responsible for creating these depressions supported a rigid adherence to the identified archetype planforms across tens of thousands of instances. GE image overlays of the planforms’ geometry are used to capture each bay's location, major & minor axis, elevation, as well as the geospatial orientation of the major axis. The data is intended to support a correlation of the bays' orientations within a 3-D triangulation network.