Open Access Thesis
Sinkholes--Iowa--Data processing; Optical radar--Iowa--Data processing;
Accurate and detailed mapping of sinkholes is necessary to ensure sinkhole monitoring and management. Historically, sinkholes were found and digitized manually by a visual examination of aerial photos or through field surveying. This paper develops a new, multicriteria LiDAR-based sinkhole extraction method and automated processes to detect sinkholes and their boundaries. This technique of extraction is unique as it identifies sinkhole boundaries automatically using remotely sensed data, compared to traditional methods of manually tracing the perimeter. A sinkhole detection module was developed within a GIS environment to determine location and boundaries of the sinkholes. Several small study areas were selected to test different extraction methods. Three tested methods included the fill, slope and object-oriented methods. A combination of the fill and slope methods demonstrated the most reliable extraction results. A geoprocessing model and Python scripting was then implemented to automate the procedure. This automated sinkhole extraction method was applied to the entire study area in northeast Iowa. The primary data for the study were one meter Light Detection and Ranging (LiDAR) dataset. aerial photos, GPS, and existing sinkhole data were used for method calibration and accuracy assessment. The second part of the study focused on the sinkhole quantitative characteristics derived from the LiDAR based sinkhole map. The characteristics include perimeter, area, shape, maximum depth, lineation, and orientation. Statistical analysis was then preformed in order to determine geometric patterns, morphological and generic groupings, and possible correlations with geomorphic and environmental parameters.
Year of Submission
Master of Arts
Department of Geography
1 PDF file (viii, 93 p. : illustrations (some color), maps (some color))
2013 - Jonathon Launspach
Launspach, Jonathon, "Automated sinkhole extraction and morphological analysis in northeast Iowa using high-resolution LiDAR data" (2013). Theses and Dissertations @ UNI. 33.