MAPPING AND CHARACTERIZATION OF PALEOLAKE SHORELINES IN GRAND PORTAGE NATIONAL MONUMENT, MINNESOTA

At the end of the Wisconsin Glaciation, meltwater from the retreating Laurentide Ice Sheet filled the Lake Superior basin to levels higher than the present. Over time, water levels fluctuated due to the opening of drainage outlets and the isostatic rebound of land after the removal of ice sheet weight. Shoreline features such as beaches, terraces, and wave-cut cliffs formed during periods of stability, and remained as strandlines on the landscape as levels dropped. At Grand Portage National Monument, in Northeastern Minnesota, earlier researchers used ground surveys and topographic maps to catalog strandlines and associate them with named paleolakes. However, advances in remote sensing and mapping technologies in recent years have allowed for increasingly thorough surveys of these features.

For this project we used Geographic Information Systems (GIS), high resolution digital elevation models, Light Detection and Ranging (LiDAR) imagery, and ground truthing to map and characterize paleolake shorelines in Grand Portage National Monument. Visible strandlines are identified using LiDAR, and these features are correlated to paleolakes based on previously modeled elevations. Sediments in the vicinity of these shorelines are cored and described to characterize their environment of deposition: either shoreline-related or not shoreline-related. GIS is used to create shapefiles of the shoreline locations based on their approximated elevations and the results of sediment characterization. Mapping of sediment sample locations in association with paleolake shorelines allow us to correct and confirm the shoreline positions as estimated by isostatic rebound models.

This project is part of a broader study that seeks to develop a predictive model for archaeological sites along the north shore of Lake Superior in Minnesota. Additional sediment and GIS analyses will be completed to determine the nature of the shorelines with respect to exposure to wave action, and the type of landform or paleoenvironment present in a particular area. We plan to include these more specific aspects of the shoreline environment as factors to improve the accuracy of a predictive model.