Southeastern Section - 66th Annual Meeting - 2017

Paper No. 12-2
Presentation Time: 1:00 PM-5:00 PM


GOCHENOUR, Jacob Alexander, Department of Geology & Environmental Science, James Madison University, Memorial Hall 7335, MSC 6903, Harrisonburg, VA 22807 and ADMASSU, Yonathan, Department of Geology and Environmental Science, James Madison University, Harrisonburg, VA 22807,

Sinkholes are common geomorphologic features in karst topography resulting from dissolution of soluble rocks such as limestones. High resolution LiDAR-derived DEMs allow simple visual recognition of sinkholes, but due to their large number, manual mapping can be extremely time consuming. The purpose of this research is to develop an automated extraction method for sinkholes from an airborne LiDAR-derived digital elevation model (DEM) for the Shenandoah Valley of Virginia. The DEM has to initially be pre-processed by lowering the elevation of transit routes. The core methodology to map depressions from DEMs is to use the Fill tool in ArcGIS to artificially fill depressions and subtract the filled DEM from the original. The resulting DEM will show all depressions, converted into polygons, that do not necessarily represent sinkholes. Three steps are followed to identify sinkholes based on the criteria of being circular depressions. Step one separates any conjoined sinkholes, which initially may be represented as one noncircular polygon feature, into several distinctive polygon features that retain their primary circular geometry. Step two calculates the eccentricity and circularity of each polygon to be used as criteria to identify circular depressions. Eccentricity exemplifies the manner to which the geometry generally resembles a circle, and circularity characterizes the smoothness of the curved surface of each feature. Step three combines eccentricity and circularity values since both values are robust in identifying circular features. The product of eccentricity and circularity (EC) was found to be the best identifier of sinkholes characterized by circular depressions. An EC threshold value of greater than 2.5 was used as criteria to remove false depressions and keep sinkholes represented by circular depressions. The results show a 61.82% agreement to manually mapped sinkholes. To expedite the workflow in ArcGIS, two Python script tools were created to extract depressions and identify true sinkholes.