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.
Highlights
Sinkholes can cause serious damage to properties and infrastructure, and sometimes human casualties occur in severe cases
The logistic regression (LR) analysis started with 17 independent variables, including digital elevation model (DEM) and 16 geomorphometric indices derived from the DEM
The regression results demonstrated that all independent variables were significant (p < 0.05)
Summary
Sinkholes can cause serious damage to properties and infrastructure, and sometimes human casualties occur in severe cases. According to the Florida Office of Insurance Regulation (FLOIR) report, the insurers’ expense has been gradually growing with increases in both frequency and severity of sinkholes. Sinkholes have been classified into two main groups [4,5]. The first group, known as solution sinkholes, involves centripetal flow to areas having the highest permeability and consequent dissolution [6,7,8]. The second group is known as subsidence sinkholes and involves downward movement of overlying soils into cavities within bedrock. Subsidence sinkholes are further classified using two descriptors: The material affected by internal erosion or deformation (cover, bedrock, and caprock) and the main subsidence mechanism (collapse, suffosion, or sagging). Details of sinkhole classification can be found in previous research by Gutiérrez et al [5]
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