Detection Of Crop Marks Research Articles

Detection of buried archaeological remains with the combined use of satellite multispectral data and UAV data

Active and passive remote sensing sensors have been applied successfully in the detection of crop marks (vegetation with a different spectral reflectance compared to its surroundings) related with buried archaeological remains. However, the detection of such crop marks depends on the sensor used, the status of the cover and the algorithm applied on the data. Moreover, buried archaeological remains generally produce microrelief marks, which can be very difficult to detect. The purpose of this work is to demonstrate that the combined use of data from the multispectral orbital sensor WorldView-2 and RGB and near infrared cameras mounted on an Unmanned Aerial Vehicle (UAV) equipped with a Global Navigation Satellite System (GNSS) can be successfully applied to the detection of buried archaeological remains. Principal Component Analysis, the Normalized Difference Vegetation Index (NDVI) and a purposely proposed band combination were obtained from WorldView-2 data to detect crop marks. The cameras carried by the UAV provide a Real Color composite, the NDVI and a high precision Digital Surface Model. The methodology developed in this work consists of searching for locations that exhibit both crop and microrelief marks with a similar shape. The WorldView-2 NDVI and the normalized Digital Surface Model of the UAV are filtered. An Archaeological Binary Map is constructed, in which pixels with both NDVI and normalized elevation above corresponding threshold values are interpreted as susceptible of containing buried archaeological remains and are given the value of one, otherwise zero. One of the locations of the Archaeological Binary Map, with a very regular pattern, is subsequently surveyed with Ground Penetrating Radar to find a buried structure, the location and shape of which match perfectly those of the Archeological Binary Map.

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

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.

Orthogonal equations for the detection of hidden archaeological remains de-mystified

Spectral variations of vegetation, known as crop marks, have been widely used for archaeological research as a proxy to detect buried archaeological remains. Such marks can be recognized using space-borne data and image analysis techniques supported by the existing archaeological knowledge of the area under study. Orthogonal equations for the enhancement and detection of crop marks using multispectral satellite images have been recently proposed in the literature. The proposed equations are linear transformations of the initial spectral bands of multispectral datasets aiming to the improvement of the satellite images. For the calculation of the n-space coefficients of this linear transformation a four-step methodology was followed, separately for each sensor. This paper aims to provide the fundamental concept of the development of these equations as well as some aspects related with the application and accuracy assessment. Spectral characteristics of the sensor, atmospheric effects, and spectral calibration of the datasets as well as the selection of the appropriate period for applying these equations for the enhancements of crop marks are also discussed. Such orthogonal equations may be further developed and applied for any kind of sensor either hyperspectral or multispectral for the detection of buried archaeological remains. An example of the applicability of the orthogonal equations at Stonehenge archaeological site is also demonstrated.