Modelling Geoarchaeological Resources in Temperate Alluvial Environments: The Capability of Higher Resolution Satellite Remote Sensing Techniques

Crabb, Nicholas; Carey, Christopher; Howard, Andy J.; Jackson, Robin; Burnside, Niall; Brolly, Matthew

doi:10.2139/ssrn.3961261

Cited by 1 publication

(4 citation statements)

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“…Geoarchaeological deposit models provide a visual representation of the spatial and stratigraphic relationships between subsurface sediments, archaeological features, and palaeoenvironmental remains (Carey et al, 2018). Whilst they vary in their form and presentation, they fundamentally aim to improve the understanding of subsurface works are undertaken (e.g., hand augering, mechanical boreholes, or test-pitting/trial-trenching), which can be further complemented by existing Historic Environment Records (HERs), British Geological Survey (BGS) mapping, other remote sensing data sets (Crabb et al, 2022) and geophysical surveys (Bates & Bates, 2016;Verhegge et al, 2016Verhegge et al, , 2021.…”

Section: Resultsmentioning

confidence: 99%

“…The Lugg and Wye Valleys were selected for this analysis as they represent typical lowland flood plain settings within the temperate zone, and have been a recent focus for investigating the application of hyper-and multi-spectral data sets within an alluvial geoarchaeology context (Crabb et al, 2022). Both river systems have complex depositional histories, with closely related human-environmental interactions demonstrated by a palimpsest of archaeological records, which have been the focus of previous geoarchaeological investigations (Brown et al, 2005;Carey et al, 2017;Dinn & Roseff, 1992;Hemingway & Dinn, 1996;Jackson & Miller, 2011;Pears et al, 2020).…”

Section: The Lugg and Wye Valleys Herefordshire Ukmentioning

confidence: 99%

“…Although it does not enable the separability of features within multiband images, their constituent parts (individual RGB bands) can be assessed. While other separability measurements (e.g., JM distance or transformed divergence) are better suited to multiband images, it is difficult to compare the results to the assessment of single-band images provided through the M-statistic (Crabb et al, 2022;Richards, 2013). However, in contrast to these and other common statistical analyses of variance and difference (ANOVA/t tests), the M-statistic does not account for sample size.…”

Section: Evaluation Of Lidar Visualizations In Alluvial Environmentsmentioning

confidence: 99%

“…significantly in terms of their visual outputs from relatively simple twodimensional vertical cross-sections and horizontal surfaces to more computationally demanding deposit thickness maps and pseudo-threedimensional models. Some advanced deposit models also integrate a range of airborne and terrestrial remote sensing (Carey et al, 2019;Crabb et al, 2022;Schmidt et al, 2019) and geophysical survey data (Bates & Bates, 2016;Engel et al, 2022;Verhegge et al, 2016Verhegge et al, , 2021).…”

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confidence: 99%

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Lidar visualization techniques for the construction of geoarchaeological deposit models: An overview and evaluation in alluvial environments

et al. 2023

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Lidar has become an essential tool for the mapping and interpretation of natural and archaeological features within the landscape. It is also increasingly integrated and visualized within geoarchaeological deposit models, providing valuable topographic and stratigraphic control from the contemporary ground surface downwards. However, there is a wide range of methods available for the visualization of lidar elevation models and a review of existing research suggests that it remains unclear which are most appropriate for geoarchaeological applications. This paper addresses this issue by providing an overview and quantitative evaluation of these techniques with examples from archaeologically resource‐rich alluvial environments. Owing to the relatively low‐relief nature of the terrain within these temperate lowland flood plain environments, the results show that there is a small number of visualization methods that demonstrably improve the detection of geomorphological landforms that can be related to the variable distribution of archaeological resources. More specifically, a combination of Relative Elevation Models combined with Simple Local Relief Models offered an optimal approach that subsequently allows integration with deposit models. Whilst the presented examples are from a flood plain setting, deposit models are pertinent to a range of landscape contexts and the methodology applied here has wider applicability.

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