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

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Robert Anemone, Professor and Department Head (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: Even with the most meticulous planning, and utilizing the most experienced fossil-hunters, fossil prospecting in remote and/or extensive areas can be time-consuming, expensive, logistically challenging, and often hit or miss. While nothing can predict or guarantee with 100% assurance that fossils will be found in any particular location, any procedures or techniques that might increase the odds of success would be a major benefit to the field. Here we describe, and test, one such technique that we feel 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 that creates 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 “trained” the spatial analysis and image classification algorithms using the spectral signatures of known fossil localities discovered in the Uinta Basin prior to 2005 and then created interactive probability models highlighting other regions in the Basin having a high probability of containing fossiliferous sediments based on their spectral signatures. A fortuitous “post-hoc” validation of our model presented itself. Our model identified several paleontological “hotspots”, regions that, while not producing any fossil localities prior to 2005, 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.

Additional Information

Language: English
Date: 2012
uman evolution, fossil prospecting, remote sensing, geographic information systems, image classification, uinta basin, archaeology, anthropology

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