DIGITAL MAPPING OF AEOLIAN DUNE FORMS, WHITE RIVER BADLANDS, SOUTH DAKOTA

The goal of this study is to determine the timing of the latest episode of aeolian dune activity in the White River Badlands (WRB) south of the North Unit of the Badlands National Park (BNP), South Dakota. This presentation focuses on preliminary results of dune reconnaissance and digital mapping. This student investigator is part of the Badlands Working Group (BWG), a research collective of students and faculty from Slippery Rock and Nova Southeastern Universities. Previous BWG results indicate alluvial pediments in the BNP experienced an episode of fluvial incision initiated by approximately 1,000 AD. Investigators hypothesized that incision was related to regional drought that destabilized pediment surfaces, giving episodic fluvial events the energy to dissect the prairie surface. The BWG further hypothesized that a regional drought should have driven change in other geomorphic systems in the WRB.

The field area in the WRB is approximately 70 km north of the dune fields of the Nebraska Sand Hills. The dune fields along the White River are smaller than the Sand Hills dune fields, and occur on strath terraces along the White River. Aeolian features include aeolian clifftop deposits, parabolic dunes, and blowout depressions. The focus of this project is preliminary mapping of dunes to determine relative ages of individual dunes and to determine sampling localities on dunes with the most easily determined genesis. Due to the inaccessibility of many these dunes, geospatial analysis and remote sensing were used to help visualize geologic patterns not generally evident using a stereoscope or other traditional mapping techniques. Methodology for mapping the dunes includes field reconnaissance, aerial photographic analysis, and 3D and remote sensing analysis. USGS DEMs were converted to triangulated irregular networks (TIN) and converted into surfaces for 3D analysis. Remote sensing analysis, SPOT 5 imagery of the dune complex will be classified using Principle Component Analysis (PCA) and an Unsupervised Classification. Results of the mapping and preliminary interpretation of the analyses will be discussed.