Abstract
Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour) and geometrical (i.e., plant height) components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological prospection. After the creation of orthophotographs and a canopy height model using image-based modelling, new archaeological information is extracted from this vegetation model by applying relief-enhancing visualisation techniques. Through interpretation of the resulting data, a combination of the co-registered spectral and geometrical vegetation dimensions clearly add new depth to interpretative mapping, which is typically based solely on colour differences in orthophotographs.
Highlights
Remote sensing methods are often used in landscape archaeology for the acquisition of raw data.despite an abundance of techniques that use propagated signals to observe the Earth’s surface from above, archaeological remote sensing still relies mainly on passive air- and spaceborne imaging in the optical spectrum or the active sounding technique known as Airborne Laser Scanning (ALS) for data acquisition
This paper has explored the value of extracting a vegetation elevation model from archived as remains of enclosure ditches, some of which might have been paralleled by a wall or earthwork
Aerial photographs and has shown how this new data layer can serve as a proxy to detect Apart from the main ellipsoid ditch system flanking the plateau along its long sides, which may have archaeologically-induced changes in the soil
Summary
Remote sensing methods are often used in landscape archaeology for the acquisition of raw data. As the majority of remotely sensed archaeological data still consists of aerial photographs collected during observer-biased reconnaissance flights [40], it is not surprising that the archaeological potential of these so-called Image-Based Modelling (IBM) approaches has been extensively explored from a number of perspectives during the past five years [41,42,43,44,45,46,47,48] Despite these advances in aerial image processing, it seems that nobody has really explored the new possibilities that these IBM workflows could offer for vegetation mark identification (a situation that is, not surprisingly, very similar to the lack of research on the archaeological use of ALS-based vegetation height data). We will discuss some of the current limitations and planned improvements for this workflow before this article concludes
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