A Preliminary Study on Use of LiDAR Data to Characterize Sinkholes in Central Florida

Abstract

The state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock and its susceptibility to dissolution. Numerous sinkholes, particularly Central Florida, have occurred. Florida subsidence incident reports (FSIR) contain verified sinkholes with global positioning system (GPS) information. In addition to existing detection methods such as subsurface exploration and geophysical methods, a remote sensing method can be a precise and efficient tool to detect and characterize sinkholes. By using light detection and ranging (LiDAR) data, the authors produce a GIS-based data layer of a selected area in Central Florida to identify probable sinkholes. A semi-automated model in ArcMap was then developed to detect sinkholes and also to determine geometric characteristics (e.g., depth, length, circularity, area, and volume). This remote sensing technique has a potential to detect unreported sinkholes in rural and/or inaccessible areas.