GIS DATA, TECHNIQUES, AND MODELS FOR KARST MAPPING IN ALABAMA

Thousands of mapped epigenic and hypogenic karst features such as sinkholes, springs, sinking streams, and caves can be found across Alabama, with approximately 30% of the state’s area underlain by carbonate rocks at or near the surface. Over the last ten years, the Geological Survey of Alabama (GSA) has applied 2D and 3D GIS techniques to help create karst data and maps in the state. GSA’s karst products have been used in a wide variety of applications, including state and county hazard mitigation plans for emergency management, land use planning and development, environmental investigations, landowner information, and regional karst research.

An array of geospatial data, including topographic map DRGs, Radar, LiDAR, National Hydrography Dataset, bedrock and structural geology, water well logs, and field-collected data, have allowed greater capability of karst mapping and analyses than were previously possible with hard copy maps alone. Limitations of older topographic data such as DRGs, NED, and SRTM, include decades-old publication or acquisition dates and low spatial resolution (30-meter). Alternatively, LiDAR data has a greater advantage with more current acquisition dates and higher vertical and horizontal detail. And, while statewide LiDAR coverage is not available today, increasing LiDAR data availability through the USGS 3D Elevation Program (3DEP), has greatly contributed to advancing karst mapping capabilities and efficiency.

Foci of karst GIS projects at GSA have included creation of statewide sinkhole density maps; studies of karst in areas of environmental concern; integrative mapping of sinkholes, disappearing streams, and springs as part of the larger surface-to-groundwater picture; and modeling and correlations of related epigenic and hypogenic karst. Combining LiDAR, surface data (sinkhole locations, bedrock lithology, karst pavement joints, and area lineaments), and subsurface data (cave geometry, lithology, and structure) in a GIS allows 3D analyses of the variables. Data are then correlated spatially and statistically within ArcGIS software, helping identify structural and stratigraphic preferences of karst horizons, thus quantitatively contributing to sinkhole hazard mapping in ongoing GSA projects.