Exploring the largest known Bronze Age earthworks in Europe through medium resolution multispectral satellite images

Αγαπίου, Άθως; Hegyi, Alexandru; Gogâltan, Florin; Stavilă, Andrei; Sava, Victor; Sarris, Apostolos; Floca, Cristian; Dorogostaisky, Leonard

doi:10.1016/j.jag.2023.103239

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…Indeed, those LBA settlements with visible ramparts were the first to attract attention through excavation in recent years. This monumentality was a key design feature for many LBA settlements, as testified by the extensive use of ditches for enclosures [106]. Nonetheless, when no / small ramparts accompanied them, the infilling of diches over time means they have very low surface visibility today.…”

Section: Plos Onementioning

confidence: 99%

Resilience, innovation and collapse of settlement networks in later Bronze Age Europe: New survey data from the southern Carpathian Basin

Molloy,

Jovanović,

Bruyere

et al. 2023

PLoS ONE

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Societies of the later Early to Middle Bronze Age (ca. 2200–1600 BC) in the Carpathian Basin exhibited complex, hierarchical and regionally influential socio-political organisation that came to an abrupt end in the 16th century BC. Considered a collapse by some, this change was characterised by abandonment of virtually all central places / nodes in settlement networks. Until recently, the complexity that characterised the period was believed to have substantially diminished alongside depopulation. This model was reinforced by a combination of the loss of established external networks and low-resolution knowledge of where and how people lived in the first stages of the Late Bronze Age (between 1600 and 1200 BC). We contest the idea of a diminished Late Bronze Age and argue that a fully opposite trajectory can be identified–increased scale, complexity and density in settlement systems and intensification of long-distance networks. We present results of a settlement survey in the southern Pannonian Plain using remote and pedestrian prospection, augmented by small-scale excavations. New absolute dates are used to define the occupational history of sites dating primarily between 1500–1200 BC. We argue that climate change played a substantial role in in the transformation of settlement networks, creating a particular ecological niche enabling societies to thrive. New and specific forms of landscape exploitation developed that were characterised by proximity to wetlands and minor watercourses. In this context, the largest monuments of Bronze Age Europe were created and inhabited. In considering the origins and demise of these megasites and related settlements, we provide a new model for Late Bronze Age societies in the Carpathian Basin and their regional relevance.

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Section: Plos Onementioning

confidence: 99%

Resilience, innovation and collapse of settlement networks in later Bronze Age Europe: New survey data from the southern Carpathian Basin

Molloy,

Jovanović,

Bruyere

et al. 2023

PLoS ONE

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“…Cropmarks, either stressed or positive, are usually created in areas where vegetation (crops) overlays near-surface archaeological remains. Buried archaeological features might retain soil moisture with a different percentage of moisture compared to non-archaeological areas and therefore impact the growth and phenological cycle of the cultivated crops on top of the surface [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…The gap is noticed since the success for detecting cropmarks using remote sensing imageries might vary depending on the area and the different environmental and soil properties. This remark is familiar to experts in the field as they recognized that the formation of cropmarks is influenced by different factors such as the soil properties, soil conditions, climatic variables, cultivation practices and, of course, the same archaeological remains themselves [21][22][23]26,29]. This research gap, i.e., that local remote sensing outcomes and methods are designed only at the local landscape level, has overshadowed all relevant research in the field, prohibiting research field progress.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Assisted Classification Algorithm for the Detection of Archaeological Proxies (Cropmarks) Based on Reflectance Signatures

Agapiou,

Gravanis

2024

Remote Sensing

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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.

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“…These methods are essential for archaeology, allowing: the identification of new sites; detailed studies of the areas where human groups once settled; and site documentation, monitoring, and interpretation [20][21][22][23][24][25][26]. Although the importance of these methods has constantly been proven in different studies concerning the prehistoric communities that inhabited latter-day Romania [27][28][29][30][31][32], when talking about the LBA or NSC cultural complex, the studies utilizing detailed RS methods and products (e.g., high-resolution DEMs or aerial photographs) are relatively few [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Inside Late Bronze Age Settlements in NE Romania: GIS-Based Surface Characterization of Ashmound Structures Using Airborne Laser Scanning and Aerial Photography Techniques

Brașoveanu,

Mihu-Pintilie,

Brunchi

2023

Remote Sensing

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The identification and delineation, through aerial photography, of the archaeological structures that present temporal resolution, as well as their characterization based on high-resolution LiDAR (Light Detection and Ranging)-derived DEMs (Digital Elevation Models) are modern techniques widely used in the archaeological prospecting of various landscapes. In this study, we present an application of Airborne Laser Scanning (ALS) and aerial photography (AP) techniques, used in order to compute geomorphometric indices specific to the ashmound structures of Late Bronze Age (LBA) archaeological sites that are visible on the soil surface. The necessity of determining the ashmounds’ geoarchaeological description stems from the fact that despite the majority of archaeologists weighing in on the subject, there is still no accepted explanation regarding their initial functionality. Thus, we believe that the GIS-based high-resolution characterization of 200 ashmound features identified in 21 Noua Culture (NC) archaeological sites will contribute to a better understanding of the ashmounds’ functionality and evolution in the heterogeneous landscape of the study area (NE Romania). Therefore, various shape indices, such as the area (A), perimeter (P), length (L), form factor (RF), circularity ratio (RC), and elongation ratio (RE) were computed for microlevel characterizations of the visible ashmounds’ structures. Additionally, LiDAR-derived DEMs with a 0.5 m resolution were used to generate more surface characteristics such as the slope (S) and hypsometric indices (HI). The outcomes indicate that the ashmounds have relatively diverse shapes (an RF range from 0.37 to 0.77; a RC range from 0.79 to 0.99; a RE range from 0.68 to 0.99), and the micro-relief slightly varies from positive to negative landforms (HI range from 0.34 to 0.61) depending on the erosion intensity (S range from 1.17° to 19.69°) and anthropogenic impact (e.g., current land use and agriculture type). Furthermore, each morphometric parameter is an indicator for surface processes, aiding in the identification of the geomorphologic and surface-erosion aspects that affect the archaeological remains, contributing to the assessment of the conservation status of the ashmound structures within the current landscape configuration. In this regard, this article presents and discusses the remote sensing (RS) techniques used, as well as the morphometric data obtained, exploring the implications of our findings for a better characterization of the NC in Romania.

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Exploring the largest known Bronze Age earthworks in Europe through medium resolution multispectral satellite images

Cited by 3 publications

References 32 publications

Resilience, innovation and collapse of settlement networks in later Bronze Age Europe: New survey data from the southern Carpathian Basin

Resilience, innovation and collapse of settlement networks in later Bronze Age Europe: New survey data from the southern Carpathian Basin

A Machine-Learning-Assisted Classification Algorithm for the Detection of Archaeological Proxies (Cropmarks) Based on Reflectance Signatures

Inside Late Bronze Age Settlements in NE Romania: GIS-Based Surface Characterization of Ashmound Structures Using Airborne Laser Scanning and Aerial Photography Techniques

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