The Archaeology and Remote Sensing of Santa Elena’s Four Millennia of Occupation
Abstract
:1. Introduction
2. The Santa Elena Landscape
2.1. Native American Histories
2.2. Spanish Colonial Period
2.3. Santa Elena’s Post-Spanish Occupation
3. The Santa Elena Landscape Project
4. Materials and Methods
4.1. Shovel Test Survey, Global Positioning Systems (GPS), and Geographic Information Systems (GIS)
4.2. LiDAR
4.3. Resistance Survey
4.4. Gradiometer Survey
4.5. GPR
5. Results
5.1. Late Archaic-Early/Middle Woodland
5.2. Late Woodland-Mississippian/Post-Spanish Native American Occupation
5.3. Spanish Colonial
5.4. The Antebellum and Postbellum Periods
6. Discussion
6.1. Four Millennia of Occupation at Santa Elena
6.2. Implications for Research Methods
- Remote sensing data and close-interval shovel testing surveys provide complimentary, but independent, datasets by which the changing nature of space and architecture can be evaluated in high-resolution.
- Even when cultural features (e.g., houses, walls) are clearly discernible in remote sensing data, shovel test data provide information on the distribution of geophysically invisible activity areas that can then be connected to more formal features.
- Shovel testing on a close (<10 m), fixed interval grid provides a different kind of “ground truthing” than what is traditionally employed. Instead of placing units directly over a given “anomaly,” shovel testing can provide a much more complete context for the interpretation of the relationships between anomalies and identifiable culture features, including spaces that contain no signatures in the remote sensing data. Thus, while traditional ground truthing only tests identifiable patterns and signals in the data, fixed interval shovel testing provides information on how these signals articulate with one another given the distribution of activities across the site, including areas that may be avoided or kept clean of refuse and other features.
- Given that the usual employment of ground-based instruments provides mostly two-dimensional datasets, shovel testing, especially ones that are excavated in levels, provide information on the distribution of artifacts and features at varying depths. For sites with long-term histories this is especially important as it allows researchers to tease apart which patterns in geophysical data are associated with specific components.
- Although researchers often use GPR on a much smaller scale than other methods, we found that a large-scale GPR survey and close-interval shovel tests, excavated in levels, provide perhaps one of the best ways to evaluate space and architecture at multi-component sites. The reason for this is that both datasets provide information not only on the horizontal distribution of features and artifacts, but also their vertical position vis-à-vis one another. Admittedly, a large-scale GPR survey and shovel testing at close intervals over an entire site, especially a large one, are perhaps some of the more labor-intensive methods of archaeological survey. Nevertheless, our estimate is that, in many cases, this is well worth the cost in time and funds.
6.3. Management Implications and Recommendations
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Thompson, V.D.; DePratter, C.B.; Lulewicz, J.; Lulewicz, I.H.; Roberts Thompson, A.D.; Cramb, J.; Ritchison, B.T.; Colvin, M.H. The Archaeology and Remote Sensing of Santa Elena’s Four Millennia of Occupation. Remote Sens. 2018, 10, 248. https://doi.org/10.3390/rs10020248
Thompson VD, DePratter CB, Lulewicz J, Lulewicz IH, Roberts Thompson AD, Cramb J, Ritchison BT, Colvin MH. The Archaeology and Remote Sensing of Santa Elena’s Four Millennia of Occupation. Remote Sensing. 2018; 10(2):248. https://doi.org/10.3390/rs10020248
Chicago/Turabian StyleThompson, Victor D., Chester B. DePratter, Jacob Lulewicz, Isabelle H. Lulewicz, Amanda D. Roberts Thompson, Justin Cramb, Brandon T. Ritchison, and Matthew H. Colvin. 2018. "The Archaeology and Remote Sensing of Santa Elena’s Four Millennia of Occupation" Remote Sensing 10, no. 2: 248. https://doi.org/10.3390/rs10020248