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

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.