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Application of Landsat Data and Synoptic Remote Sensing to Predictive Models for Prehistoric Archaeological Sites: An Example from the Delaware Coastal Plain

Published online by Cambridge University Press:  20 January 2017

Jay F. Custer
Affiliation:
Center for Archaeological Research, Department of Anthropology, University of Delaware, Newark, DE 19716
Timothy Eveleigh
Affiliation:
Center for Remote Sensing, University of Delaware, Newark, DE 19716.
Vytautas Klemas
Affiliation:
Center for Remote Sensing, University of Delaware, Newark, DE 19716.
Ian Wells
Affiliation:
Massachusetts Computer Associates, Wakefield, MA 01880

Abstract

In recent research using prehistoric archaeological site data from the Delaware Coastal Plain we have developed techniques to apply Landsat remote sensing data to the generation of archaeological predictive models. Rather than focusing on the specific sensing of archaeological sites, we use a synoptic approach that delineates the likely environmental settings for archaeological sites. The logistical regression statistical technique provides a quantitative link between site locations and environmental variables. By analyzing the environmental variables associated with known site locations, and known non-site locations, the logistical regression provides a probability assessment of an unsurveyed area's potential for containing archaeological sites. Supervised classification of Landsat data generates maps of environmental zones that can be related to the environmental variables used in the logistical regression. The model and the Landsat classifications have been subjected to preliminary tests and they provide accurate assessments of site potential in unsurveyed areas, as well as measures of the accuracy of the assessments. Results can be applied to cultural resource management problems and also yield useful data on prehistoric land use patterns.

Type
Reports
Copyright
Copyright © Society for American Archaeology 1986

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References

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