Pushing the sensors: how multi-sensor airborne survey can aid archaeological interpretation in non-arable landscapes.
Start date: 13 September 2011
The non-arable, upland landscapes of the UK have a long history of archaeological prospection via aerial survey. As sites in these landscapes are often inaccessible, survey in the form of monochrome or colour photography was instrumental in identifying relict landscapes such as the Dartmoor Reeves and the Iron-Age Romano-British field systems of the Salisbury Plain. However changes to traditional management practises in upland areas pose serious challenges to those who wish to observe and monitor archaeological features. With the cessation of grazing and the encroachment of scrub in many areas, features that were clearly visible only 30 years ago have now all but vanished from the aerial perspective.
This project established at Bournemouth University seeks to improve the current accepted methods for aerial prospection of non-arable landscapes by incorporating the information from digital spectral and airborne laser scanning (ALS) systems. Following a pilot study using archive Environment Agency data, the project secured a bespoke acquisition of ALS and hyperspectral data by the NERC Airborne Research and Survey Facility (ARSF) for an area of Salisbury Plain Military Training Area (SPTA), Wiltshire. Airborne data collection was supported by simultaneous ground observations including topographic and geophysical survey and soil and spectral sampling.
This paper presents the results of the analysis of these data, outlining potential methods and applications for archaeological research in environments that are not dominated by arable production. A variety of data visualisation and manipulation techniques were tested both on areas with features that had previously been identified in aerial imagery and on an area with no existing archaeological record. Results provide proof of concept for the use of both ALS and hyperspectral sensors to identify archaeological features in non-arable landscapes. It is also shown that combining the complementary data collected from different sensors is a powerful means by which archaeological features can be identified and categorised.