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Sinkhole Detection and Characterization Using LiDAR-Derived DEM with Logistic Regression

1
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
2
Department of Civil Engineering, Hongik University, Seoul 04066, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1592; https://doi.org/10.3390/rs11131592
Received: 2 May 2019 / Revised: 25 June 2019 / Accepted: 29 June 2019 / Published: 4 July 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Abstract

Depressions due to sinkhole formation cause significant structural damages to buildings and civil infrastructure. Traditionally, visual inspection has been used to detect sinkholes, which is a subjective way and time- and labor-consuming. Remote sensing techniques have been introduced for morphometric studies of karst landscapes. This study presents a methodology for the probabilistic detection of sinkholes using LiDAR-derived digital elevation model (DEM) data. The proposed study provides benefits associated with: (1) Detection of unreported sinkholes in rural and/or inaccessible areas, (2) automatic delineation of sinkhole boundaries, and (3) quantification of the geometric characteristics of those identified sinkholes. Among sixteen morphometric parameters, nine parameters were chosen for logistic regression, which was then employed to compute the probability of sinkhole detection; a cutoff value was back-calculated such that the sinkhole susceptibility map well predicted the reported sinkhole boundaries. According to the results of the LR model, the optimal cutoff value was calculated to be 0.13, and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.90, indicating the model is reliable for the study area. For those identified sinkholes, the geometric characteristics (e.g., depth, length, area, and volume) were computed. View Full-Text
Keywords: sinkhole; LiDAR; logistic regression; DEM sinkhole; LiDAR; logistic regression; DEM
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Kim, Y.J.; Nam, B.H.; Youn, H. Sinkhole Detection and Characterization Using LiDAR-Derived DEM with Logistic Regression. Remote Sens. 2019, 11, 1592.

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