The state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains 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 an alternative and efficient means to detect and characterize sinkholes with a wide coverage. the first part of this study is aimed at developing a method to detect sinkholes in Missouri by using Light Detection and Ranging (LiDAR) data. Morphometrical parameters such as TPI (Topographic Position Index), CI (Convergence Index), SI (Slope Index), and DEM (Digital Elevation Model) have a high potential to help detect sinkholes, based on local ground conditions and study area. The GLM (General Linear Model) built in R software is used to obtain morphometrical indices of the study terrain to be trained and build a logistic regression model to detect sinkholes. In the second part of the study, a semi-automated model in ArcMap is then developed to detect sinkholes and also to estimate geometric characteristics of sinkholes (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.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu
Nam, Boo Hyun
Master of Science in Civil Engineering (M.S.C.E.)
College of Engineering and Computer Science
Civil, Environmental, and Construction Engineering
Length of Campus-only Access
Masters Thesis (Open Access)
Rajabi, Amirarsalan, "Sinkhole Detection and Quantification Using LiDAR Data" (2018). Electronic Theses and Dissertations. 5776.