The Use of Interferometric Synthetic Aperture Radar (InSAR) in Archaeological Investigations and Cultural Heritage Preservation

  • Diane L. Evans
  • Tom G. Farr
Chapter
Part of the Interdisciplinary Contributions To Archaeology book series (IDCA)

Abstract

The availability of a near-global digital elevation model derived from the Shuttle Radar Topography Mission (SRTM) provides archeologists a new tool to complement other remote sensing data. SRTM data not only provide basemaps for geographic information systems and aid in geometric corrections, they also provide data for visualizations and animations that can be used in cultural resource management. SRTM data also provide a regional context in which sites can be studied. For example, slope and elevation information, combined with other factors used in predictive modeling such as availability of water and suitability for animal and crop domestication can be used to identify potential archaeological sites. The data can also help in the identification of possible migration pathways or trade routes along which settlements might occur. In addition, since topography played a role in the location of many religious sites (e.g., Borobudur in Central Java), it may be possible to recognize other topographically significant areas through a systematic analysis of the SRTM dataset. Topography has a clear influence on the location of floods and landslides as well as on soil development and erosion, and several studies have shown the value of topographic information in determining the timing and nature of wet and dry periods and the susceptibility of a region to environmental change. This information, combined with the increased recognition of the relationship between environmental change and the histories of past civilizations, can further elucidate their evolution, migration, and in some cases extinction.

Keywords

Land Subsidence Shuttle Radar Topography Mission Geophysical Research Letter North Anatolian Fault InSAR Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer 2006

Authors and Affiliations

  • Diane L. Evans
  • Tom G. Farr

There are no affiliations available

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