Outline

1. The Application of Remote Sensing and Geographic Information Systems (GIS) to Prehistoric Site Location Predictive Models • Exploitation of remote sensing and GIS for real-world needs such as land use management, urban planning and many other allied fields of endeavor is well documented. However, there have been limited applications of these technologies to the unique needs of properly managing cultural resources and inadequate research in combining remote sensing and predictive models into a cohesive tool. This presentation is a brief overview on the development of such a tool within the context of the NASA Goddard Space Flight Center property in Greenbelt, MD, and the Woodland period, c. 1000 BC to 1000 AD.

2. Outline • The Study Area • Archaeological Field Work • What is a Predictive Model • The Building Blocks • Step by Step • Conclusion

3. The Study Area:NASA Goddard Space Flight Center (GSFC)Greenbelt, MD

4. Phase I Archaeological SurveyKCI Technologies, Inc (1997)

5. Phase II Archaeological SurveyJohn Milner Associates (2002)

6. What is a predictive model? • A predictive model is a planning and management tool. An archaeological predictive model, as developed using modern GIS technology and remote sensing imagery, is simply a map (or series of maps) that shows areas of probability for the location of culturally significant sites.

7. Applying New Technologies • Exploitation of geospatial information technologies, including geographic information systems (GIS) and remotely sensed data, for real-world needs such as land use management, urban planning and many other allied fields of endeavor is well documented. • However, the use of these technologies is still in its infancy when it comes to the unique needs of properly managing our collective cultural resources. • There have been a handful of projects that have used satellite-born remote sensors in archaeological applications, but, to date, what has been done in this area has been limited to purely academic research and not to real-world applications. • There are also examples, both academic and professional, of predictive models derived through geospatial analysis techniques used for the identification of potential prehistoric archaeological sites. However, there has not been enough work done in combining these two areas – remote sensing and predictive models – into a cohesive tool to meet the real-world need of cultural resource managers.

8. The Building Blocks • Geomorphological and ecological factors of the landscape. • Environmental variables of known prehistoric archaeological site locations. • Remotely sensed imagery products. • A geographic information system (GIS) software development environment. • Standard digital image processing techniques applied through COTS software.

9. Geomorphological and ecological factors of the Landscape • Derived from remotely sensed imagery products using standard image processing techniques. • Will serve as an initial classification of the project area into cover-type categories. • Primary factors: • General topography (elevation, landform, geology) • Vegetation cover (both type and density of vegetation) • Degree of modern disturbance. • Secondary factors: • Soil type • Distance to water • Slope • Aspect • Each factor, both primary and secondary, can be transformed into a “cover-type category” that explicitly defines that one particular cover type within the context of the research area.

10. Environmental variables of known prehistoric archaeological site locations. • The locations of known prehistoric sites, as taken from earlier archaeological research, can be used as a means of determining which cover-type categories are most favorable for the location of undiscovered archaeological sites within a specific geographic region and time period. • The environmental variables of known prehistoric sites are readily available within the individual site reports maintained by the Maryland Historic Trust (MHT) in Crownsville, MD.

11. Remotely sensed imagery products.

12. A geographic information system (GIS) software development environment. • ESRI’s ArcGIS suite was chosen as the software development environment as it offers many advantages. • Through its many ESRI- and third-party developed software extensions (Spatial Analyst, 3D Analyst, and many others), ArcGIS offers the largest range of GIS and image analysis tools at a competitive price. • ArcGIS is the COTS software currently being used by the Goddard Environmental Team (GET) to maintain their existing geospatial data. • NASA has been standardizing on ESRI products over the past five to six years, so its use is in line with NASA’s vision and approach to geospatial data into the future.

13. Standard digital image processing techniques applied through COTS software. • The obvious choice for image processing software has been Leica Geosystems’ ArcView Image Analysis Extension, which is designed for easy integration with ESRI’s ArcGIS suite of software.

14. Putting it All Together COTS Software Development Package

15. Step By Step • Develop basic geomorphological and environmental cover-type maps from remotely sensed data. • Catalog the environmental variables of known prehistoric sites. • Develop preliminary probability maps. • Review initial results and adjust model if necessary.

16. Develop basic cover-type maps from remotely sensed data[1] Acquire the Data • A “cover-type map” is vector data created within GIS software that shows the distribution of a single geomorphological or environmental factor of prehistoric site location. • The primary multispectral data source should meet the following criteria: • High ground resolution so that environmental variables of prehistoric site location can be properly identified. • Availability of panchromatic, visible light and near-infrared (NIR) imagery products. • Proper coverage of the proposed project area both geographically and temporally. • Reasonable cost.

18. Develop basic cover-type maps from remotely sensed data.[2] Process the Data • The Quickbird imagery products would then be brought into the image processing software environment (Leica’s Imagery Analyst Extension) to transform the remotely sensed data into thematic information regarding the geomorphological and ecological cover-types in the project area.

19. Catalog the environmental variables of known prehistoric sites. • Acquire Data • Detailed archaeological site data for the local region can be acquired from the Maryland Historic Trust (MHT). • MHT is the same source of archaeological site data used in similar predictive model projects, such as a predictive model developed for the Aberdeen Proving Grounds (APG) in 1996 (Wescott and Brandon, 2000). • Process Data • Categorize the known prehistoric site locations with respect to their site-types (i.e., shell midden, lithic scatter, and others) and environmental attributes associated with individual site location (such as soil type, distance to water, wetland vs. dry land, slope, aspect, etc.). • The result of this step will be tabular data (Excel tables) relating site-type to environmental attribute, providing a basis for the extrapolation of potential archaeological site locations from the cover-type maps derived from the remotely sensed data.

20. Develop preliminary probability maps.[1] Distribution and frequency of known prehistoric sites • Distribution and Frequency Maps • Associate tabular site-type data (derived from the individual archaeological site reports) with the vector cover-type data (derived from the remotely sensed imagery) within the GIS software environment (ArcGIS). • The process for creating such thematic maps from existing tabular and vector data is a standard function of the GIS toolkit and can be found in any textbook (Demers, 2000) or software user’s manual. • Importance • It is an accepted approach, found in many recent archaeological projects using predictive models (Wescott and Brandon, 2000), that what is true for the larger region can be assumed to be true for a representative subset of that region as far as settlement patterns go. • As an example of this reasoning, it could be said that if the frequency of prehistoric lithic scatter sites within moderately dense, undisturbed woodland throughout the Maryland Coastal Plain Province (of which GSFC is a subset) is roughly 1 per square mile (a purely hypothetical number), then one would expect somewhere around 19 prehistoric lithic scatters within the 19 square miles within the GSFC property line that is also moderately dense, undisturbed woodland.

21. Develop preliminary probability maps.[2] Probability of UnknownPrehistoric Sites • The environmental parameters of known prehistoric site locations along with the level of modern disturbance will serve to derive the first cut probability maps by assigning weighted values to each cover-type. • The result of this weighting will be a series of maps that visually display the probability of locating a specific prehistoric site-type within a cover-type category.

22. Develop preliminary probability maps.[3] Visibility Maps • Visibility maps can show how easy or hard it is to identify unknown prehistoric sites both by remote sensing survey and archaeological field survey. Visibility is determined from the primary factors of vegetation density, land cover type, topography, elevation, geology, and landform, and so can be derived solely from remote sensing imagery.

23. Review initial results and adjust model if necessary. • It is necessary to provide statistical validity to the resulting probability maps by comparing them to the known locations of prehistoric sites within the GSFC property line. • There are two archaeological field surveys at GSFC that will provide the necessary information to perform this check – a 1997 Phase I survey (KCI Technologies, 1998) and a 2003 Phase II survey (John Milner Associates, 2004). • This will also help further refine the probability maps in that anything about the existing site locations that does not mesh well with our model will serve as an indication of something in the model that needs to be adjusted. Earlier stages can be revisited to adjust the probability model and resulting maps.

24. Conclusion • The process outlined in this presentation is easily performed within the scope of the technology available today. Many other disciplines have been successfully using this general approach to improve their management of resources through the use of remotely sensed imagery and GIS techniques – and there is no reason why cultural resource managers can not benefit in the same way. • The ground resolution of the remotely sensed data has traditionally been the limiting factor with using remote sensing imagery for archaeological purposes. This is no longer absolutely true with the new breed of high resolution platforms currently offering imagery products commercially, with ground resolutions quickly approaching the sub-meter level. • The day will come when the techniques shown in this presentation are part of the standard toolkit of cultural resource managers and field archaeologists – it is simply a matter of how and when these tools are adopted.

25. Bill Dickinson Jr. • Dickinson Jr., William B., “A Remotely-Sensed Decision-Support Tool • For Facilities Planning” (NASA SBIR proposal), 2005. • Dickinson Jr., William B., “Proposed use of Vegetation Indices at Goddard Space Flight Center” (NASA internal document, NNG04AZ01C), 2004. • Dickinson Jr., William B., “Cultural Resource Management: An Application of Remotely Sensed Data and Advanced Image Processing Technologies” (NASA SBIR proposal), 2004. • Dickinson Jr., William B., “Archaeological Predictive Models: A Look Into Commercial Potential” (white paper), 2003. • Dickinson Jr., William B., “Safety and Environmental Branch GIS Planning Document” (NASA internal document, NAS5-99001), 2001.

26. Bibliographic References • JMA, Inc., “Phase II Archaeological Field Survey of Goddard Space Flight Center.” 2003. • Wheatley, David and Gillings, Mark, “Spatial technology and archaeology: The archaeological applications of GIS.” 2002. • Demers, Michael N., “Fundamentals of Geographic Information Systems.” 2000. • Gillings, Mark editor, “Geographical information systems and landscape archaeology.” 1999. • Spikins, Penny, “GIS Models of Past Vegetation: An Example from Northern England, 10,000-5000 BP.” 1999. • Kickert, R.N., Tonella, G., Simonov, A., and Krupa, S.V., “Predictive modeling of effects under global change.” 1999. • Renfrew, Colin and Bahn, Paul, “Archaeology Theories, Methods and Practice.” 1998. • KCI Technologies, Inc., “Phase I Archaeological Field Survey of Goddard Space Flight Center (GSFC).” 1997. • Rivett, Paul, “Conceptual data modeling in an archaeological GIS.” 1997. • Schmidt Jr., Martin F., “Maryland’s Geology.” 1997. • Conyers, Lawrence B. and Goodman, Dean, “Ground-Penetrating Radar: An Introduction for Archaeologists.” 1997. • Jensen, John R., “Introductory Digital Image Processing: A Remote Sensing Perspective.” 1996. • Lyons, Thomas R. and Mathien, Frances Joan editors, “Cultural Resources Remote Sensing.” 1980. • Aikens, C. Melvin et al, “Remote Sensing: A Handbook for Archaeologists and Cultural Resource Managers, Basic Manual Supplement: Oregon.” 1980.

27. Some other Research on Archaeological Predictive Models • Madry, Scott, “GIS and Remote Sensing for Archaeology: Burgundy, France.” 2004. • Craig, Nathan and Aldenderfer, Mark, “Preliminary Stages in the Development of a Real-Time Digital Data Recording System for Archaeological Excavation Using ArcView GIS 3.1.” ESRI Journal of GIS in Archaeology, Volume 1, April 2003. • Johnson, Ian and Wilson, Andres, “The TimeMap Project: Developing Time-Based GIS Display for Cultural Data.” ESRI Journal of GIS in Archaeology, Volume 1, April 2003. • Comer, Douglas C., “Environmental History at an Early Prehistoric Village: An Application of Cultural Site Analysis at Beidha, in Southern Jordan.” ESRI Journal of GIS in Archaeology, Volume 1, April 2003. • Clement, Christopher O., De, Sahadeb, and Wilson Kloot, Robin, “Using GIS to Model and Predict Likely Archaeological Sites.” 2002. • Sherbinin, Alex de et al., “A CIESIN Thematic Guide to Social Science Applications of Remote Sensing.” 2002. • Burson, Elizabeth, “Geospatial Data Content, Analysis, and Procedural Standards for Cultural Resources Site Monitoring.” U.S. Army Corps of Engineers, 2001. • Wescott, Konnie L. and Brandon, R. Joe, “Practical Applications of GIS for Archaeologists: A Predictive Modeling Kit.” 2000.