Search Results (9)

This study investigates the subsurface remains of Jaundole Castle, a 14th-century medieval fortress located on Dole Island near Riga, Latvia. The castle, which has left no visible surface ruins, is known only from historical documents and maps. To assess whether its buried remains could be detected, a non-invasive ground penetrating radar (GPR) survey was carried out across five targeted grids. The results revealed multiple linear and circular anomalies consistent with historical records of the castle’s layout, including possible foundations of walls and towers. These findings demonstrate that GPR, when combined with historical map and image analysis, can effectively locate, and delineate lost architectural features. The integration of historical sources and geophysical data provides a replicable model for the investigation of other completely buried archaeological sites. This work contributes to the development of non-destructive prospection strategies and supports the planning of future archaeological excavations and conservation actions. Full article

Shielings are seasonal settlements found in upland pastures across Scandinavia and the North Atlantic. New investigations in the county of Møre and Romsdal, Norway, demonstrate the existence of this transhumant system by the Viking Age and Early Middle Ages. Sub-terranean features in these pastoral mountain landscapes have been identified by remote sensing technologies, but non-invasive methods still face challenges in terms of practical applicability and in confirming the presence of archaeological sites. Generally, aerial surveys, such as LiDAR and image-based modelling, excel in documenting visual landscapes and may enhance detection of low-visibility features. Thermography may also detect shallow subsurface features but is limited by solar conditions and vegetation. Magnetic methods face challenges due to the heterogeneous moraine geology. Ground-penetrating radar has yielded better results but is highly impractical and inefficient in these remote and rough landscapes. Systematic soil coring or test-pitting remain the most reliable options for detecting these faint sites, yet non-invasive methods may offer a better understanding of the archaeological contexts—between the initial survey and the final excavation. Altogether, the study highlights the dependency on landscape, soil, and vegetation, emphasising the need to consider each method’s possibilities and limitations based on site environments and conditions. Full article

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained. Full article

The detection of subsurface archaeological remains using a range of remote sensing methods poses several challenges. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work, we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near-infrared parts of the spectrum were taken in a controlled environment in Cyprus during 2011–2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, in particular in the neighborhood of 570 nm, and (2) the red edge part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis, we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors’ knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time. Full article

In intensively cultivated landscapes, many archaeological remains are buried under the ploughed soil, and detection depends on crop proxies that express subsurface features. Traditionally these proxies have been documented in visible light as contrasting areas of crop development commonly known as cropmarks. However, it is recognised that reliance on the visible electromagnetic spectrum has inherent limitations on what can be documented, and multispectral and thermal sensors offer the potential to greatly improve our ability to detect buried archaeological features in agricultural fields. The need for this is pressing, as ongoing agricultural practices place many subsurface archaeological features increasingly under threat of destruction. The effective deployment of multispectral and thermal sensors, however, requires a better understanding of when they may be most effective in documenting archaeologically induced responses. This paper presents the first known use of the FLIR Vue Pro-R thermal imager and Red Edge-M for exploring crop response to archaeological features from two UAV surveys flown in May and June 2019 over a known archaeological site. These surveys provided multispectral imagery, which was used to create vegetation index (VI) maps, and thermal maps to assess their effectiveness in detecting crop responses in the temperate Scottish climate. These were visually and statistically analysed using a Mann Whitney test to compare temperature and reflectance values. While the study was compromised by unusually damp conditions which reduced the potential for cropmarking, the VIs (e.g., Normalised Difference Vegetation Index, NDVI) did show potential to detect general crop stress across the study site when they were statistically analysed. This demonstrates the need for further research using multitemporal data collection across case study sites to better understand the interactions of crop responses and sensors, and so define appropriate conditions for large-area data collection. Such a case study-led multitemporal survey approach is an ideal application for UAV-based documentation, especially when “perfect” conditions cannot be guaranteed. Full article

Under suitable conditions, ground-penetrating radar (GPR) measurements harbour great potential for the non-invasive mapping and three-dimensional investigation of buried archaeological remains. Current GPR data visualisations almost exclusively focus on the imaging of GPR reflection amplitudes. Ideally, the resulting amplitude maps show subsurface structures of archaeological interest in plan view. However, there exist situations in which, despite the presence of buried archaeological remains, hardly any corresponding anomalies can be observed in the GPR time- or depth-slice amplitude images. Following the promising examples set by seismic attribute analysis in the field of exploration seismology, it should be possible to exploit other attributes than merely amplitude values for the enhanced imaging of subsurface structures expressed in GPR data. Coherence is the seismic attribute that is a measure for the discontinuity between adjacent traces in post-stack seismic data volumes. Seismic coherence analysis is directly transferable to common high-resolution 3D GPR data sets. We demonstrate, how under the right circumstances, trace discontinuity analysis can substantially enhance the imaging of structural information contained in GPR data. In certain cases, considerably improved data visualisations are achievable, facilitating subsequent data interpretation. We present GPR trace coherence imaging examples taken from extensive, high-resolution archaeological prospection GPR data sets. Full article

This communication emanates from the lack of a European-scale study for investigating the potential threats that subsurface archaeological remains face today due to soil loss by water. This research analyses the impact of soil loss on potential subsurface archaeological evidence by integrating open geospatial datasets deriving from two pertinent European studies. The first study’s dataset is related to soil erosion (soil loss provoked by water activity), which was reclassified into three groups alluding the level of threat on potential subsurface archaeological contexts, as follows: (1) areas presenting soil loss from 0 until 5 t/h per year, which are characterised as low threat areas; (2) areas presenting soil loss from 5 until 10 t/h per year, which are characterised as moderated threat; and (3) areas presenting soil loss beyond 10 t/h per year, which are considered as high-risk areas. The second study’s dataset refers to the capacity of soils to preserve specific archaeological materials, classified in four categories based on the properties of the archaeological material (bones, teeth, and shells (bones); organic materials (organics); metals (Cu, bronze, and Fe) (metals); and stratigraphic evidence (strati). Both datasets were imported into a Geographical Information System (GIS) for further synthesis and analysis, while the average threat of soil loss per year was evaluated in a country level (nomenclature of territorial units for statistics (NUTS) level 0). The overall results show that approximately 10% of European soils that potentially preserve archaeological remains are in high threat due to soil loss, while similar patterns—on a European level—are found for areas characterised with moderate to high risk from the soil loss. This study is the first attempt to present a proxy map for subsurface cultural material under threat due to soil loss, covering the entire European continent. Full article

This paper presents the results of a research study where ground penetrating radar (GPR) was successfully used to reveal the remains of the Württemberg-Stambol Gate in the subsurface of Republic Square, in Belgrade, Serbia. GPR investigations were carried out in the context of renovation works in the square, which involved rearranging traffic control, expanding the pedestrian zone, renewing the surface layer, and valorising existing archaeological structures. The presence of the gate remains was suggested by historical documents and information from previous restoration works. A pulsed radar unit was used for the survey, with antennas having 200- and 400-MHz central frequencies. Data were recorded over a grid and two three-dimensional models were built, one for each set of antennas. The grid was the same for both sets of antennas, therefore the two models could be compared. Several horizontal cross sections of the models were plotted, corresponding to different depths; these images were carefully examined and interpreted, paying particular attention to signatures that could originate from the sought archaeological structures. Reflections coming from the gate remains were identified in both models, in the same region of the survey area and at the same depth; the geometry, size, and layout of the gate columns, as well as of other construction elements belonging to the gate, were determined with very good accuracy. Based on the GPR findings, archaeological excavation works were carried out in the region where the foundation remains were estimated to be. The presence of the remains was confirmed, with various columns and side walls. This case study demonstrates and further corroborates the effectiveness and reliability of GPR for the non-invasive prospection of archaeological structures hidden in the heterogeneous subsurface of urban environments. In the opinion of the authors, GPR should be incorporated as a routine field procedure in construction and renovation projects involving historical cities. Full article

The integration of different remote sensing datasets acquired from optical and radar sensors can improve the overall performance and detection rate for mapping sub-surface archaeological remains. However, data fusion remains a challenge for archaeological prospection studies, since remotely sensed sensors have different instrument principles, operating in different wavelengths. Recent studies have demonstrated that some fusion modelling can be achieved under ideal measurement conditions (e.g., simultaneously measurements in no hazy days) using advance regression models, like those of the nonlinear Bayesian Neural Networks. This paper aims to go a step further and investigate the impact of noise in regression models, between datasets obtained from ground-penetrating radar (GPR) and portable field spectroradiometers. Initially, the GPR measurements provided three depth slices of 20 cm thickness, starting from 0.00 m up to 0.60 m below the ground surface while ground spectral signatures acquired from the spectroradiometer were processed to calculate 13 multispectral and 53 hyperspectral indices. Then, various levels of Gaussian random noise ranging from 0.1 to 0.5 of a normal distribution, with mean 0 and variance 1, were added at both GPR and spectral signatures datasets. Afterward, Bayesian Neural Network regression fitting was applied between the radar (GPR) versus the optical (spectral signatures) datasets. Different regression model strategies were implemented and presented in the paper. The overall results show that fusion with a noise level of up to 0.2 of the normal distribution does not dramatically drop the regression model between the radar and optical datasets (compared to the non-noisy data). Finally, anomalies appearing as strong reflectors in the GPR measurements, continue to provide an obvious contrast even with noisy regression modelling. Full article