| The karst lands of southeastern Minnesota present ongoing challenges to environmental planners and researchers and have been the focus of a series of research projects and studies by researchers for over 30 years. As GIS, GPS, and web tools became more accessible to resource managers in the 1990s, the need for a statewide, web-accessible, and GIS-compatible karst feature inventory and database has become increasingly evident. A GIS-based database management system was developed to manage and analyze karst feature inventories at both county and statewide scales.; The conceptual model of the karst feature database includes three interactive modules: spatial operation, spatial analysis, and hydrogeological modules. All three modules manipulate data from the central database, verify and update attribute values of karst feature data, and put some of the results back to the database. A working database is developed to include many mapped karst features in Minnesota. Standardized metadata and management tools were developed for this database that will be beneficial for management and future study of karst features in Minnesota.; Nearest neighbor analysis was extended to include different orders of nearest neighbor analysis, different scales of concentrated zones of sinkholes, and directions to nearest sinkholes. The statistical results, along with the sinkhole density distribution, indicate that sinkholes tend to form in highly concentrated zones instead of as scattered individuals. The pattern changes from clustered to random to regular as the scale of the analysis decreases from 10–100 km2 to 5–30 km2 to 2–10 km2. Hypotheses that may explain this phenomenon are: (1) areas in the highly concentrated zones of sinkholes have similar geologic and topographical settings that favor sinkhole formation; (2) existing sinkholes change the hydraulic gradient in the surrounding area and increase the solution and erosional processes that eventually form more new sinkholes.; Decision tree and cartographic models were developed to create sinkhole probability maps in southeastern Minnesota. The decision tree model is implemented in GIS to create a preliminary sinkhole probability map in Goodhue, Wabasha, Olmsted, Fillmore, and Mower Counties. |
