The 82nd Annual Meeting of the American Association of Physical Anthropologists (2013)


Let your fingers do the walking: A simple spectral signature model for “remote” fossil prospecting

GLENN C. CONROY1,2, CHARLES W. EMERSON3, ROBERT L. ANEMONE4 and BETH TOWNSEND5.

1Anatomy & Neurobiology, Washington University Medical School, 2Anthropology, Washington University, 3Geography, Western Michigan University, 4Anthropology, Western Michigan University, 5Anatomy, Midwestern University

Thursday 8:15-8:30, Ballroom C Add to calendar

Paleoanthropological explorations are time-consuming, expensive, logistically challenging, and often hit or miss. Success can be serendipitous. Therefore, any technique that might increase the odds of locating fossil localities, particularly those in remote and extensive badland areas, would be a major contribution to the field. Here we describe, and test, a technique that has great potential for increasing the probability of finding fossiliferous sediments - a relatively simple spectral signature model using the spatial analysis and image classification functions of ArcGIS®10. We demonstrate how these tools can create interactive thematic land cover maps that can be used for “remote” fossil prospecting. Our test case is the extensive Eocene sediments of the Uinta Basin, Utah, a fossil prospecting area encompassing ~1200 square kilometers. Using Landsat 7 ETM+ satellite imagery, we first “trained” the spatial analysis and image classification algorithms using the spectral signatures of known fossil localities discovered in the Uinta Basin prior to 2005. We then created interactive probability models of the Uinta Basin which highlighted other regions in the Basin predicted to be fossiliferous based on the similarities of their spectral signatures to the fossiliferous “training” sites. A fortuitous “post-hoc” validation of our model presented itself. Our model identified several potential paleontological “hotspots”, regions that had not produced any fossil localities prior to 2005, but had high probabilities of being fossiliferous based on the similarities of their spectral signatures to those of previously known fossil localities. Subsequent fieldwork found fossils in all the regions predicted by the model.

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