Integrating surface reflectance from multispectral satellite imagery and GIS-enabled LiDAR-derived techniques for sinkhole hazard detection

Sinkhole hazard mapping using automated visual techniques is challenging because of the difficulty in distinguishing solution depressions from non-sinkhole depressions, such as streams, channels, or man-made circular structures in digital images. While past researchers have proposed semi-automated visual techniques for identifying solution depressions, these methods typically entail a manual visual processing step in which actual sinkhole formations are manually identified in a given geologic formation to establish a basic reference map that is subsequently applied to other areas in the specified geologic formation. This two-step process is lengthy and undermines the purpose of automated mapping. Using surface reflectance data from multispectral satellite imagery allows for identifying carbonate composition lithological units in a digital image. This study proposes integrating multispectral remote sensing with geological analysis to uncover crucial spectral patterns linked to surface mineralogy and environmental conditions associated with sinkhole formations. This integration aims to effectively identify the presence of sinkhole formations while excluding non-sinkhole artifacts from the analysis in a genuinely automated workflow. A crucial aspect of this study involved integrating high-resolution data from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) imagery to distinguish rock units in a predominantly karst terrain for identifying surface depressions. In addition, we incorporated attributes covering morphometric, geomorphic, and physical soil properties derived from LiDAR-based topographic depressions. Prior studies have utilized supervised learning methods within machine learning frameworks on datasets containing confirmed sinkholes and non-sinkholes to improve the accuracy of mapping predictions. We utilized three machine learning techniques—Linear Regression, Random Forest, and Gradient Boosting—on the features database to conduct a comparative analysis, aiming to assess the enhancement of the methodology’s effectiveness compared to other studies. We aimed to improve the classification of crucial features and minimize the need for an additional manual visual inspection step to distinguish non-sinkhole formations from potential sinkhole boundaries identified. Among these methods, Random Forest proved to be the most appropriate for recognizing features that directly indicate sinkholes. This approach yielded an impressive Receiver Operating Characteristic (ROC) curve of 92%, showcasing its effectiveness in mapping sinkholes.