How old are the towns and villages in Central Europe? Archaeological data reveal the size of bias in dating obtained from traditional historical sources

Fanta, Václav; Zouhar, Jan; Beneš, Jaromír; Bumerl, Jiří; Sklenička, Petr

doi:10.1016/j.jas.2019.105044

Cited by 9 publications

(9 citation statements)

References 12 publications

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“…500 years earlier, Chalupská slať 550 years, Stará Jímka 350 years, Rokytecká slať 600 years and Šmauzy 200 years; Figure 8). This is consistent with the results of Fanta et al (2020), who concluded that Medieval written sources systematically postdate the establishment of many villages in Bohemia by around 250 years. Pollen records therefore strongly suggest a much earlier history of settlement in central Šumava.…”

Section: Primary Anthropogenic Indicators: the Question Of Local Farmingsupporting

confidence: 92%

“…The clear spatial relationship between the archaeological and palaeoecological records enabled a detailed assessment of the nature and intensity of prehistoric settlement in the mid-mountains. The beginning of the permanent colonisation of Šumava is associated with the establishment of the glass industry and logging in the High Middle Ages (Fanta et al, 2020; Zavřel and Anděra, 2003) although earlier activities were connected mainly with the access routes passing through the mountains in several directions, presumably since prehistory. Three branches of the ‘Golden Trail’ (Goldener Steig) and the north-trending Bohemian route were functioning at least from the eleventh century (Figure 1; Zavřel and Kubů, 2007a, 2007b).…”

Section: The Archaeological Contextmentioning

confidence: 99%

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The prehistory and early history of the Šumava Mountains (Czech Republic) as seen through anthropogenic pollen indicators and charcoal data

et al. 2020

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In the lower forested mountain ranges of Europe, human impact on nature is usually confined to the written history of the Middle Ages. Our research in the Šumava mountains aims to specify the nature and intensity of human impact on vegetation, especially during agricultural prehistory. We use results from a multidisciplinary study of the unique La Tène archaeological site in the Sklářské Valley (elevation 802 m a.s.l.) and from a pollen and charcoal record 60 m away. With knowledge of this reference site we focus on the meaning of anthropogenic pollen indicators in 13 other pollen sites from central Šumava. From ca 3300 cal yr BP we detect an increase in NAP, Betula, Pinus and secondary anthropogenic indicators in pollen records – vegetation changes of anthropogenic origin. Charcoal records show a significant peak around 3200 cal yr BP. We found weak anthropogenic influence on the La Tène archaeological site in the Sklářské Valley, and much stronger anthropogenic pollen signals at other pollen sites dated to the Late Bronze and Iron Ages. Some of these sites are situated on trade routes which have been known since Medieval times but which most likely have much older origins. During prehistory, pollen data reveal no specific human activity such as pasturing or arable farming but reflect small-scale disturbances that supported growth of Betula and Pinus and an abundance of herbs. Such human impact could be connected primarily to activities along trade routes and to hunting, but other factors cannot be excluded.

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Section: Primary Anthropogenic Indicators: the Question Of Local Farmingsupporting

confidence: 92%

Section: The Archaeological Contextmentioning

confidence: 99%

The prehistory and early history of the Šumava Mountains (Czech Republic) as seen through anthropogenic pollen indicators and charcoal data

et al. 2020

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“…The main aim of those missions was to create a global (or near-global in the case of SRTM) digital elevation model (DEM) of the Earth's surface. The resulting models (i.e., SRTM DEM, NASA DEM, TanDEM-X DEM and Copernicus DEM) were made freely available and have become an essential source of Earth's surface information, widely used, among others, in forestry [5], ecology [6,7], archaeology [8], and hydrology [9,10].…”

Section: Introductionmentioning

confidence: 99%

Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

et al. 2021

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The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

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“…These studies were either focused on a single factor or used a qualitative approach. Several studies have evaluated the influencing factors and laws that govern the distribution of ancient settlements from an archaeological perspective (PARSONS, 1972;DEMJÁN & DRESLEROVÁ, 2016;FANTA et al, 2020). These studies have emphasized the relationship between early complex rural settlements and specific environments (GREEN & PETRIE, 2018), often focusing on a fixed historical period, with an emphasis on the protection of relics.…”

Section: Introductionmentioning

confidence: 99%

“…These studies have emphasized the relationship between early complex rural settlements and specific environments (GREEN & PETRIE, 2018), often focusing on a fixed historical period, with an emphasis on the protection of relics. With the development of computer and remote sensing technologies, spatial analysis technology based on geographic information systems (GIS) and mathematical modelling have been used to quantitatively analyze the spatial patterns of TVs (SEVENANT & ANTROP, 2007;BRAGA et al, 2016;YANG et al, 2018;FANTA et al, 2020). These studies often adopt the methods of the case study and lack ofquantitative analysis of large samples of TVs.…”

Section: Introductionmentioning

confidence: 99%

Distribution of Chinese traditional villages and influencing factors for regionalization

Wang

et al. 2021

Cienc. Rural

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Traditional Villages (TVs) are typical and representative of the agricultural civilization in millions of Chinese villages. The distribution of TVs shows spatial heterogeneity, based on the complexity and diversity of several influencing factors. In this study, 6,819 Chinese TVs were identified and the influencing factors that affect their distribution were screened in terms of three indicator groups: climatic, geographic, and humanity-related factors. Additionally, the K-means clustering algorithm clustered the TVs into different distribution regions. The quantitative relationships between the dominant influencing factors of different distribution regions were revealed to ensure a lucid understanding of the regional distribution of TVs. The results indicated that 1) climatic factors have the greatest impact on the spatial distribution of TVs, followed by geographic factors, particularly the elevation, and then by human factors, of which ethnic distribution played a relatively important role. 2) Twenty-one TV clustering distributions were obtained, which were classified into eight regions of TV distribution with different dominant influencing factors. Management and protective strategies were formulated based on the attribute analysis of influencing factors in each region. The obtained results delineated homogeneous TV distribution regions via the clustering method to achieve an accurate statistical analysis of the influencing factors. This study proposes a new perspective and reference for managing and protecting the diversity, continuity, and integrity of TVs across administrative regions.

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How old are the towns and villages in Central Europe? Archaeological data reveal the size of bias in dating obtained from traditional historical sources

Cited by 9 publications

References 12 publications

The prehistory and early history of the Šumava Mountains (Czech Republic) as seen through anthropogenic pollen indicators and charcoal data

The prehistory and early history of the Šumava Mountains (Czech Republic) as seen through anthropogenic pollen indicators and charcoal data

Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

Distribution of Chinese traditional villages and influencing factors for regionalization

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