The application of vegetation indices for the prospection of archaeological features in grass-dominated environments

This source preferred by Andrew Ford, Kate Welham and Ross Hill

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Authors: Bennett, R., Welham, K., Hill, R.A. and Ford, A.L.J.

Journal: Archaeological Prospection

Volume: 19

Issue: 3

Pages: 209-218

eISSN: 1099-0763

ISSN: 1075-2196

DOI: 10.1002/arp.1429

The identification of archaeological remains via the capture of localized soil and vegetation change in aerial imagery is a widely used technique for the prospection of new features. The near infrared (NIR) region has been shown by environmental applications to exhibit the signs of vigour and stress better than reflectance in the visible region, and this has led to interest in the application of digital spectral data for archaeological prospection. In this study we assess quantitatively the application of 12 common vegetation indices to archive Compact Airborne Spectrographic Imager digital spectral data acquisitions from January and May 2001 in a grassland environment. The indices are compared with the true colour composite (TCC), best performing spectral band (711.2±4.9nm NIR) and the transcription of the aerial photographic archive. The results of the study illustrate that the calculation of a number of vegetation indices can assist with the identification of archaeological features in spectral data. However, the performance of the indices varies by season and although the features detected are shown to be complementary to those detected by the TCC, few indices out-perform the TCC in terms of feature numbers identified. It was also shown that the Normalised Difference Vegetation Index (NDVI), the most commonly applied index in archaeological prospection to date, performed poorly in comparison to indices such as the Modified Red Edge Simple Ratio Index, Simple Ratio Index and Modified Red Edge Normalized Difference Vegetation Index. It is therefore recommended that the application of appropriate vegetation indices can enhance archaeological feature detection when combined with the TCC but that the calculation of the NDVI alone is insufficient to detect additional features. © 2012 John Wiley & Sons, Ltd.

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