Detection of archaeological crop marks by using satellite QuickBird multispectral imagery

Lasaponara, Rosa; Masini, Nicola

doi:10.1016/j.jas.2006.04.014

Cited by 191 publications

(111 citation statements)

References 11 publications

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“…These relate to the perimeter of another farm (see Fig. 7, upper box), dating from the early Imperial Roman and the proto-Byzantine age, already known because of their discovery in 2008 by satellite remote sensing and field survey (Lasaponara & Masini, 2007). The extraction of this feature confirms the effectiveness of the approach proposed in this paper.…”

Section: Results From Quickbird-2 Image Processing and Analysissupporting

confidence: 72%

“…The different marks associated with the presence of archaeological remains can be detected using satellite images by exploiting specific spectral channels or their numerical combinations. In particular, the VHR satellite red channel has been found to be capable of revealing soil marks according to soil spectral signatures (Masini & Lasaponara, 2006a), while the near-infrared (NIR) channel and vegetation indices are capable of enhancing crop-marks (Lasaponara & Masini, 2007). Moreover, the capability of VHR satellite data to capture the presence of archaeological marks is strongly dependent on seasonal and data acquisition time.…”

Section: Rational Basis Of the Methodologymentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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

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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.

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Section: Results From Quickbird-2 Image Processing and Analysissupporting

confidence: 72%

Section: Rational Basis Of the Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

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“…The appearance of the crop anomalies and crop marks is accounted for by a combination of different factors and parameters (crop, soil depth, etc.). A difference in soil moisture of the features is considered a key factor as it may enhance or reduce the growth of crops overlying archeological features [11]. Some research focusing on the appearance time of crop marks shows that archeological crop marks in England or the Mediterranean region are more visible at certain seasons every year [12,13].…”

Section: Introductionmentioning

confidence: 99%

Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series

et al. 2017

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Abstract:Buried archeological features show up as crop marks that are mostly visible using high-resolution image data. Such data are costly and restricted to small regions and time domains. However, a time series of freely available medium resolution imagery can be employed to detect crop growth changes to reveal subtle surface marks in large areas. This paper aims to study the classical Chinese settlements of Taosi and Erlitou over large areas using Landsat NDVI time series crop phenology to determine the optimum periods for detection and monitoring of crop anomalies. Burial areas (such as the palace area and the sacrificial area) were selected as the research area while the surrounding empty fields with a low density of ancient features were used as reference regions. Landsat NDVI covering two years' growth periods of wheat and maize were computed and analyzed using Pearson's correlation coefficient and Euclidean distance. Similarities or disparities between the burial areas and their empty areas were computed using the Hausdorff distance. Based on the phenology of crop growth, the time series NDVI images of winter wheat and summer maize were generated to analyze crop anomalies in the archeological sites. Results show that the Hausdorff distance was high during the critical stages of water for both crops and that the images of high Hausdorff distance can provide more obvious subsurface archeological information.

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“…Different types of remote sensing imagery have been used including aerial photographs [1,2], spaceborne optical images [3][4][5][6], spaceborne and airborne RADAR images [7], airborne LIDAR images [8,9], airborne imaging spectroscopy and hyperspectral imagery [10,11]. In the literature, much work has been done to develop automatic algorithms to support the digitalization of man-made structures such as buildings and roads [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

et al. 2015

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This paper considers the problem of detecting archaeological traces in digital aerial images by analyzing the pixel variance over regions around selected points. In order to decide if a point belongs to an archaeological trace or not, its surrounding regions are considered. The one-way ANalysis Of VAriance (ANOVA) is applied several times to detect the differences among these regions; in particular the expected shape of the mark to be detected is used in each region. Furthermore, an effect size parameter is defined by comparing the statistics of these regions with the statistics of the entire population in order to measure how strongly the trace is appreciable. Experiments on synthetic and real images demonstrate the effectiveness of the proposed approach with respect to some state-of-the-art methodologies.

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Detection of archaeological crop marks by using satellite QuickBird multispectral imagery

Cited by 191 publications

References 11 publications

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

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

Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

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