Paper
29 October 2007 Finding archaeological cropmarks: a hyperspectral approach
Author Affiliations +
Abstract
Aerial photography has made the single most important contribution to our improved appreciation of the density, diversity and distribution of archaeological sites in Britain since WWII. This is particularly the case for areas of intensive lowland agriculture where ploughed-out sites are known only from marks in the crops growing above them. However, reconnaissance for such cropmarks is not equally effective throughout the lowlands because of the particular conditions of drier weather, well-drained soils and arable agriculture required before they become visible. In Scotland, for example, there is considerable bias in the discovery and, consequently, known distribution of archaeological sites in favour of the drier eastern side of the country, with its higher percentage of arable agriculture, as opposed to the west with its wetter climate and greater proportion of grazing land. Given that the appearance of cropmarks is linked to moisture stress in growing plants, they are potentially detectable at bandwidths outside the visible and before they become apparent therein. Using a range of imagery (CASI 2, ATM and digital vertical photographic data) from two case study sites in Lowland Scotland to facilitate comparisons, one in the east and one in the west, this paper considers the extent to which hyperspectral imagery can enhance the identification of otherwise invisible archaeological sites.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Syed Ali Aqdus, William S. Hanson, and Jane Drummond "Finding archaeological cropmarks: a hyperspectral approach", Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 674908 (29 October 2007); https://doi.org/10.1117/12.738007
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Photography

Digital photography

Vegetation

Visible radiation

Data acquisition

Hyperspectral imaging

Agriculture

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