Abstract

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.

Graduation Date

2018

Semester

Spring

Advisor

Nam, Boo Hyun

Degree

Master of Science in Civil Engineering (M.S.C.E.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0007084

URL

http://purl.fcla.edu/fcla/etd/CFE0007084

Language

English

Release Date

May 2018

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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