Date Available


Year of Publication


Degree Name

Master of Science (MS)

Document Type

Master's Thesis


Arts and Sciences



First Advisor

Dr. James Dinger


This study utilizes a digital elevation model of the surface derived from high-resolution LiDAR (Light Detection and Ranging) and aerial-image technologies to map sinkholes in the Royal Spring groundwater basin. Shade-relief maps, with vertical exaggeration, were very helpful in the initial characterization of depressions. Then, aerial-photography sets were likewise helpful in identifying man-made structures such as retention basins, swimming pools, and parking lots, and to identify new sinkholes.

Field checking was necessary to further define depressions into two categories: 1.) potential sinkholes and 2.) probable sinkholes. This study had a lower success rate (50 percent) for identifying sinkholes via LiDAR when compared to a study in Floyds Fork watershed,Kentucky(88 percent). This difference in success rate is most likely due to the differences in land uses between the two areas. The Royal Spring groundwater basin has a larger percentage of urban area and twice as much pasture-cropland as Floyds Fork.

This method could be improved by modifying the parameters in which polygons are identified as sinkholes. False positives compose a large quantity of polygons that are initially identified as possible sinkholes. If these discrepancies could be removed or reduced, it would significantly reduce time spent in the field. The newly identified sinkholes in the study area should be used for future land-use planning in order to decrease or avoid personal injuries and property damage.

Digital Object Identifier (DOI)

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Geology Commons