Paper
24 October 2013 Prospects and limitations of vegetation indices in archeological research: the Neolithic Thessaly case study
Author Affiliations +
Abstract
Vegetation indices have been widely used for the detection of archaeological traces, based on crop marks, during different phenological stages. Such indices can be used in order to enhance interpretation performance of multispectral and hyperspectral satellite data, for identification of buried archeological remains. Although a variety of indices exists in the literature, research is still limited and only a small group of these indices have been explored. This paper aims to highlight the prospects as well the limitations of several broadband vegetation indices for the detection of Neolithic tells in the Thessalian plain (Greece). Several multispectral Landsat 5 TM and Landsat 7 ETM+ satellite images have been used for evaluating the effectiveness of such indices. In addition, new developed algorithms and hyperspectral narrowband indices, specially designed for archaeological research, have been also used and compared using hyperspectral satellite data (EO-Hyperion). Indeed, the Normalized Archaeological Vegetation Index (NAVI) as well as a linear transformation of the Landsat 5 TM were applied to satellite data. The above were also compared to other processing algorithms such as Tasseled – Cap algorithm and Principal Component Analysis. The results have shown that several indices and new algorithms may be used for the enhancement of crop marks, while some no-widely used indices can be successfully used for archaeological purposes.
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Athos Agapiou, Dimitrios D. Alexakis, Maria Stavrou, Apostolos Sarris, Kyriakos Themistocleous, and Diofantos G. Hadjimitsis "Prospects and limitations of vegetation indices in archeological research: the Neolithic Thessaly case study", Proc. SPIE 8893, Earth Resources and Environmental Remote Sensing/GIS Applications IV, 88930D (24 October 2013); https://doi.org/10.1117/12.2028661
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KEYWORDS
Vegetation

Earth observing sensors

Satellites

Landsat

Principal component analysis

Satellite imaging

Remote sensing

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