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Papers by Rebecca Bennett

in: Proceedings of the 10th International Conference on Archaeological Prospection, Vienna, Austria. May 29th - June 2nd 2013, pp 406-8, May 2013

This paper discusses the opportunities for the creation of national mapping and heritage databas... more This paper discusses the opportunities for the creation of national
mapping and heritage databases presented by semi-automatic extraction techniques applied to emerging resources like Airborne
Laser Scanning (ALS), hyperspectral and very high-resolution
satellite (VHRS) datasets. Such techniques raise many issues,
including philosophical objections and the necessity to understand
appropriate contexts in which they can be applied.

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The increasing availability of multi-dimensional remote-sensing data for large geographical areas... more The increasing availability of multi-dimensional remote-sensing data for large geographical areas is generating a new wave of landscape-scale research that promises to be every bit as revolutionary as the application of aerial photographic survey during the 20th century. Data are becoming available to historic environment professionals at higher resolution, greater frequency of acquisition and lower cost than at any previous time. However, to take advantage of this explosion of data a paradigm change is needed in the methods used to routinely evaluate aerial imagery and interpret archaeological evidence. Central to this shift is a fuller engagement with the concept of computer aided methods of feature detection as a viable way to analyse airborne and satellite data. This requires a reassessment of workflows and an understanding of the different types of information that may be generated, developing a multi-stage assessment based on explicit predicates vital to providing confidence in outputs, while improving our ability to work with vast datasets.
Automated and semi-automated image analysis is routine in fields such as environmental remote sensing, where they underpin the analysis of extensive datasets (see Lasaponara and Mansini (2012) for a review of these techniques which is not replicated here). While aspects of these developments have made their way into archaeological applications, they remain far from routinely used and are often viewed with suspicion. This paper outlines the status quo and key issues for readers unfamiliar with debates that have mainly played out at conferences and steering groups but are seldom committed to print. We summarise the difficulties surrounding the creation of geographically extensive, systematic datasets for heritage management and research from high-volume data derived from airborne laser scanning (ALS), satellite imagery and airborne digital spectral data. At the heart of this issue is the potential to incorporate methods that exponentially increase the rapidity with which initial historic environment datasets can be created, moving beyond the ‘human-timescales’ within which most archaeological survey is undertaken (i.e. ‘manual’ analysis), to fuller exploitation of computer assisted techniques. In order to do this we must recognise the fundamentally different but complimentary types of information that the two approaches produce, and assess their value and contribution as part of broader research objectives. Throughout we use the term computer vision to define all methods by which imagery can be processed, analysed and understood using computer generated algorithms (including classifications) and in particular with reference to the electronic replication of the abilities of human ocular perception (Sonka et al. 2008).

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The increasingly widespread availability of high-accuracy terrain models is revolutionizing our u... more The increasingly widespread availability of high-accuracy terrain models is revolutionizing our understanding of historic landscapes across the globe, yet much of this inherently 3D data is viewed and analyzed using 2D Geographical Information System (GIS). The ability to explore the environments in a more immersive way that takes advantage of the full data content is advantageous for professionals and researchers, but is also highly desirable for education and public outreach. This paper describes the method and outcomes of a comparison of three virtual environments; a six-sided CAVE-type immersive virtual reality system (referred to henceforth as CAVE); a 3D web application and a standard 2D desktop paradigm in the form of a GIS. Two groups of participants were used to reflect specialist and non-specialist interests.
This study showed that while the 2D GIS, the most common interface for exploring archaeological data, is well-suited to expert interpretation (based on previous familiarity with the system), it is significantly harder for non-specialists to undertake a feature identification and location task in this environment when compared with the 3D environments. Specialist users also mostly preferred the ability to view terrain data in 3D. The experience of fully-immersive CAVE-type system was valuable for a sense of place and contextualizing features in a way that was not possible in the other environments. However it was not shown that this led to improved archaeological observations during the exploration and there is some evidence that the lack of orientation made recounting features in the reflection time more difficult.
Although small-scale the experiment gave valuable insight into the use of the different environments by specialist and non-specialist groups, allowing the 3D web application to be identified as the optimal environment for pedagogical purposes.

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Interpreting archaeological topography – airborne laser scanning, aerial photographs and ground observation, 2013

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Antiquity, Mar 2013

The new generation of aerial photographers is using different wavelengths to sense archaeological... more The new generation of aerial photographers is using different wavelengths to sense archaeological features. This is effective but can be expensive. Here the authors use data already collected for environmental management purposes, and evaluate it for archaeological prospection on pasture. They explore the visibility of features in different seasons and their sensitivity to different wavelengths, using principal components analysis to seek out the best combinations. It turns out that this grassland gave up its secrets most readily in January, when nothing much was growing, and overall the method increased the number of known sites by a good margin. This study is of the greatest importance for developing the effective survey of the world's landscape, a quarter of which is under grass.

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RSPSoc ARchSIG Newsletter, Mar 2013

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Archaeological …, Jan 1, 2012

The identification of archaeological remains via the capture of localized soil and vegetation cha... more 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 ± 4.9 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. Copyright © 2012 John Wiley & Sons, Ltd.

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