The uptake of airborne laser scanned (ALS) data (commonly known as airborne lidar) for heritage landscape assessment has grown rapidly in the past decade as data have become increasingly available. Likewise there has been a recent upsurge in published techniques for modelling the ground surface from ALS data to highlight archaeological features. However, many end-users of the data are not trained in remote sensing and visualization techniques and the lack of comparative assessment of techniques has increased the complexity of interpretation of the ALS-derived models. This study quantitatively compares five visualization techniques ranging from the commonly used shaded relief model to newer local relief and sky view factor modelling for a study area in the UK. Outputs are compared with the baseline data of the English Heritage National Mapping Programme aerial photographic archive transcription and assessed with respect to percentage visibility of feature length. Ancillary aspects of the outputs are discussed, such as geospatial shift of features, suitability for profile mapping, ease of interpretation and ability to combine with other data sources. It is concluded that although the overall performance of the models in terms of feature recognition is relatively even, consideration of all factors enables more transparent modelling choices to be made and facilitates critical interpretation of the features recorded.

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 AE 4.9 nm 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.

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Abstract

The Stonehenge Riverside Project is a collaborative enterprise directed by six academics from five UK universities, investigating the place of Stonehenge within its contemporary landscape. In this contribution, a series of novel approaches being employed on the project are outlined, before the results of investigations at the Greater Stonehenge Cursus, Woodhenge, the Cuckoo Stone and Durrington Walls are discussed.