Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing

Rowlands, Alun; Sarris, Apostolos

doi:10.1016/j.jas.2006.06.018

Cited by 136 publications

(81 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, as demonstrated by [21,22], not all vegetation indices are suitable for the enhancement of the satellite images and identification of these marks. Researchers, aware of this mismatch of multi-temporal datasets, are seeking additional information to confirm or revoke the image signal by applying other techniques including ground geophysical surveys.…”

Section: Introductionmentioning

confidence: 99%

Study of the Variations of Archaeological Marks at Neolithic Site of Lucera, Italy Using High-Resolution Multispectral Datasets

et al. 2016

View full text Add to dashboard Cite

Satellite images have been systematically explored by archaeologists to detect crop marks, which are considered as a proxy for the identification of buried archaeological remains. Even though several existing algorithms are frequently applied, such as histogram enhancements and vegetation indices, the detection of crop marks still remains a difficult task, while the final interpretation results can be very poor. This paper aims to present some of the current difficulties of "remote sensing archaeology" in terms of detection and interpretation of crop marks due to the crops' phenological variations. At the same time, the presented work seeks to evaluate the recently proposed linear equations for the enhancement of crop marks, initially developed for the eastern Mediterranean region. These linear equations re-project the initial n-space spectral into a new 3D orthogonal space determined by three components: a crop mark component, a vegetation component, and a soil component. For the aims of this study, the Lucera archaeological site (southern Italy), where several Neolithic trenches have been identified, was selected. QuickBird and GeoEye high-resolution satellite images were analysed, indicating that vegetation indices may mismatch some crop marks depending on the phenological stage of the vegetation cultivated in the area of the archaeological site. On the contrary, ratios from linear equations were able to spot these crop marks even in shadow areas, indicating that improvements and developments of novel methodologies and equations based on remote sensing datasets can further assist archaeological research.

show abstract

Section: Introductionmentioning

confidence: 99%

Study of the Variations of Archaeological Marks at Neolithic Site of Lucera, Italy Using High-Resolution Multispectral Datasets

et al. 2016

View full text Add to dashboard Cite

show abstract

“…These changes, related to vegetation coverage, humidity and organic material content, appear as subtle spatial discontinuities or variations in the reflectance values (i.e. tones or colours) of vegetation and soil surface (Agapiou & Hadjimitsis, 2011;Beck, 2007;Crawford, 1929;Grøn et al, 2011;Lasaponara & Masini, 2007;Rowlands & Sarris, 2007;Traviglia & Cottica, 2011;Wilson, 1982).…”

Section: Introductionmentioning

confidence: 99%

Towards an operative use of remote sensing for exploring the past using satellite data: The case study of Hierapolis (Turkey)

Lasaponara

Leucci

Masini

et al. 2016

Remote Sensing of Environment

View full text Add to dashboard Cite

This paper is focused on the presentation and discussion of an object oriented approach, applied to the Hierapolis (Turkey) site, to automatically detect the subtle features linked to buried archaeological remains. The data processing is applied twice: (i) first, globally at the whole image and, (ii) second, at the significant subsets identified by global analysis, in order to refine the previously obtained categorization. Object oriented approaches are usually based on two main steps: i) first the segmentation, ii) then the classification. Herein, we first performed the unsupervised classification step and, then, the segmentation. This choice is given by the specificity of archaeological issue, in particular: (i) the subtle features/targets to be identified are partially or totally unknown and characterized by a very small spectral separability from the background, and therefore (ii) the discrimination between archaeological class and substrates likely suffers significant confusion. To cope with these issues, the first step is based on an unsupervised classification, which provides a first 'rough' categorization of pixels; the second step, based on the segmentation, enables us to extract the geometric shape, and, in turn, to only categorize as archaeological class those pixels belonging to geometrically (rectangular and linear) shaped clusters. Outputs from this classification identify rectangular and linear features of archaeological interest whose size suggested that they may be a farm and some sectors of an aqueduct, respectively. Results from satellite based analysis were successfully evaluated by georadar and geomagnetic prospection along with field survey. From georadar and geomagnetic prospection we were able i) to confirm the presence of buried remains and ii) to detail and characterize these archaeological features at the subsoil level as well as to define the local stratigraphy. From field survey we dated the detected buried remains to a period spanning from Imperial Roman to early Byzantine historical times.

show abstract

“…This is probably due to the ability of the LiDAR technology to see through dense tropical canopy and cover large regions at one time. While high resolution satellite survey has been able to detect subsurface archaeological features in some cases using changes in soil or plant colors [4][5][6][7][8][9], in tropical foliage conditions like those in Belize, these surveys have been less successful [2].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Approach to Ground Penetrating Radar at the Maya Site of Pacbitun, Belize

et al. 2016

View full text Add to dashboard Cite

Ground penetrating radar (GPR) surveys provide distinct advantages for archaeological prospection in ancient, complex, urban Maya sites, particularly where dense foliage or modern debris may preclude other remote sensing or geophysical techniques. Unidirectional GPR surveys using a 500 MHz shielded antenna were performed at the Middle Preclassic Maya site of Pacbitun, Belize. The survey in 2012 identified numerous linear and circular anomalies between 1 m and 2 m deep. Based on these anomalies, one 1 m × 4 m unit and three smaller units were excavated in 2013. These test units revealed a curved plaster surface not previously found at Pacbitun. Post-excavation, GPR data were reprocessed to best match the true nature of excavated features. Additional GPR surveys oriented perpendicular to the original survey confirmed previously detected anomalies and identified new anomalies. The excavations provided information on the sediment layers in the survey area, which allowed better identification of weak radar reflections of the surfaces of a burnt, Middle Preclassic temple in the northern end of the survey area. Additional excavations of the area in 2014 and 2015 revealed it to be a large square structure, which was named El Quemado.

show abstract

Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing

Cited by 136 publications

References 46 publications

Study of the Variations of Archaeological Marks at Neolithic Site of Lucera, Italy Using High-Resolution Multispectral Datasets

Study of the Variations of Archaeological Marks at Neolithic Site of Lucera, Italy Using High-Resolution Multispectral Datasets

Towards an operative use of remote sensing for exploring the past using satellite data: The case study of Hierapolis (Turkey)

An Iterative Approach to Ground Penetrating Radar at the Maya Site of Pacbitun, Belize

Contact Info

Product

Resources

About