TY - GEN
T1 - A Preliminary Study on Use of LiDAR Data to Characterize Sinkholes in Central Florida
AU - Rajabi, Amirarsalan
AU - Kim, Yong Je
AU - Kim, Sung Hee
AU - Kim, Yong Seong
AU - Kim, Bum Joo
AU - Nam, Boo Hyun
N1 - Publisher Copyright:
© ASCE.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85048824343&partnerID=8YFLogxK
U2 - 10.1061/9780784481585.003
DO - 10.1061/9780784481585.003
M3 - Conference contribution
AN - SCOPUS:85048824343
T3 - Geotechnical Special Publication
SP - 23
EP - 31
BT - Geotechnical Special Publication
A2 - Stuedlein, Armin W.
A2 - Suleiman, Muhannad T.
A2 - Lemnitzer, Anne
PB - American Society of Civil Engineers (ASCE)
T2 - 3rd International Foundation Congress and Equipment Expo 2018: Advances in Geomaterial Modeling and Site Characterization, IFCEE 2018
Y2 - 5 March 2018 through 10 March 2018
ER -