Mapping Sinkholes and Closed Depressions in the Edwards Aquifer Recharge Zone: Comparison of LIDAR-Based Semi-Automated Detection Methods and Field Validation

Mapping Sinkholes and Closed Depressions in the Edwards Aquifer Recharge Zone: Comparison of LIDAR-Based Semi-Automated Detection Methods and Field Validation

Sinkholes and closed depressions play a critical role in karst hydrology by facilitating rapid recharge to aquifer systems (Ford & Williams, 2007; White, 1988), yet their spatial distribution is often poorly quantified in large recharge zones, and the automated methods used to detect them vary widely in approach and reliability. This study compares three LiDAR-based automated detection methods to determine their relative performance for mapping sinkholes and closed depressions within the Edwards Aquifer Recharge Zone, using a 0.5-meter resolution bare-earth Digital Elevation Model derived from high-density airborne LiDAR point cloud data (12.66 points/m²) obtained from OpenTopography. The three methods evaluated were a Hydrological Depression Extraction approach based on sink-filling and fill-difference raster analysis with morphometric filtering by circularity, area, and depth; a Local Depression Index analysis using a Topographic Position Index-based raster to identify relative topographic lows; and a Contour-Based Closed Depression approach employing automated sinuosity analysis to identify geometrically closed contours. The methods yielded 22, 83, and 444 candidates respectively, with no spatial overlap between the two raster-based methods, reflecting fundamental differences in input data preprocessing and detection algorithms. Field verification of 86 candidates showed that the Hydrological Depression Extraction method achieved the highest reliability (100% field confirmation), while the Local Depression Index method detected a broader but noisier population of subtle features (86.4% confirmation). The Contour-Based method independently confirmed all Hydrological Depression Extraction detections, serving as an effective geometric cross-validation framework. These results indicate that the Hydrological Depression Extraction method is best suited for high-confidence detection of well-defined depressions, whereas the Local Depression Index method offers greater sensitivity at the cost of precision. The multi-method comparative framework presented here establishes a validated basis for selecting and scaling automated depression detection across the broader Edwards Aquifer recharge zone. Keywords: Sinkholes; Karst hydrology; Recharge zone; Edwards Aquifer; LiDAR; Automated depression mapping; Morphometric filtering