The Application of Remote Sensing and Geographic Information Systems (GIS) to Prehistoric Site Location Predictive Models

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

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The Study AreaNASA Goddard Space Flight Center
(GSFC)Greenbelt, MD
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Phase I Archaeological SurveyKCI Technologies,
Inc (1997)
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Phase II Archaeological SurveyJohn Milner
Associates (2002)
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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.

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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.

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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.

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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.

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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.

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Remotely sensed imagery products.
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A geographic information system (GIS) software
development environment.

• ESRIs 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 NASAs vision and approach to geospatial
  data into the future.

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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 ESRIs ArcGIS suite of software.

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Putting it All Together
COTS Software Development Package
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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.

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Develop basic cover-type maps from remotely
sensed data1 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.

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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 (Leicas Imagery Analyst Extension)
  to transform the remotely sensed data into
  thematic information regarding the
  geomorphological and ecological cover-types in
  the project area.

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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.

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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 users
  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.

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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.

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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.

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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.

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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.

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Bibliographic References

• JMA, Inc., Phase II Archaeological Field Survey
  of Goddard Space Flight Center. 2003.
• Wheatley, David and Gillings, Mark, Spatial
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• Demers, Michael N., Fundamentals of Geographic
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• Gillings, Mark editor, Geographical information
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• Spikins, Penny, GIS Models of Past Vegetation
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• Renfrew, Colin and Bahn, Paul, Archaeology
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• KCI Technologies, Inc., Phase I Archaeological
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• Rivett, Paul, Conceptual data modeling in an
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• Schmidt Jr., Martin F., Marylands Geology.
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• Lyons, Thomas R. and Mathien, Frances Joan
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• Aikens, C. Melvin et al, Remote Sensing A
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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,
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• Wescott, Konnie L. and Brandon, R. Joe,
  Practical Applications of GIS for
  Archaeologists A Predictive Modeling Kit. 2000.