Do small landforms have large effects? A review on the legacies of pre-industrial charcoal burning

Raab, Thomas; Raab, Alexandra; Bonhage, Alexander; Schneider, Anna; Hirsch, Florian; Birkhofer, K.; Drohan, Patrick J.; Wilmking, Martin; Kreyling, J.; Malik, Ireneusz; Wistuba, Małgorzata; Maaten, Ernst van der; Maaten‐Theunissen, Marieke van der; Urich, Tim

doi:10.1016/j.geomorph.2022.108332

Cited by 12 publications

(3 citation statements)

References 94 publications

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“…As a result of high amounts of charcoal remaining in the soils, these sites (categorized as Spolic Technosols; IUSS Working Group World Reference Base, WRB, 2014) have a distinct enrichment of total organic and pyrogenic carbon (e.g., Borchard et al, 2014; Hirsch et al, 2017), resulting in significant changes of the soil physical and chemical properties (e.g., Donovan et al, 2020; Schneider et al, 2019). This enrichment is also affecting vegetation patterns and dynamics as well as microbial growth and abundance (e.g., Raab et al, 2022). Efforts have been made to quantify the changes caused by historical charcoal burning on today's soil landscapes, referred to as legacy effects, on a larger‐than‐site specific scale.…”

Section: Introductionmentioning

confidence: 99%

Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning YOLOv4 framework

Vaart

Bonhage

Schneider

et al. 2022

Archaeological Prospection

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In the past decade, numerous studies have successfully mapped thousands of former charcoal production sites (also called relict charcoal hearths) manually using digital elevation model (DEM) data from various forested areas in Europe and the north‐eastern USA. The presence of these sites causes significant changes in the soil physical and chemical properties, referred to as legacy effects, due to high amounts of charcoal that remain in the soils. The overwhelming amount of charcoal hearths found in landscapes necessitates the use of automated methods to map and analyse these landforms. We present a novel approach based on open source data and software, to automatically detect relict charcoal hearths in large‐scale LiDAR datasets (visualized with Simple Local Relief Model). In addition, the approach simultaneously provides both general as well as domain‐specific information, which can be used to further study legacy effects. Different versions of the methodology were fine‐tuned on data from north‐western Connecticut and subsequently tested on two different areas in Connecticut. The results show that these perform adequate, with F1‐scores ranging between 0.21 and 0.76, although additional post‐processing was needed to deal with variations in LiDAR quality. After testing, the best performing version of the prediction model (with an average F1‐score of 0.56) was applied on the entire state of Connecticut. The results show a clear overlap with the known distribution of charcoal hearths in the state, while new concentrations were found as well. This shows the usability of the approach on large‐scale datasets, even when the terrain and LiDAR quality varies.

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Section: Introductionmentioning

confidence: 99%

Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning YOLOv4 framework

Vaart

Bonhage

Schneider

et al. 2022

Archaeological Prospection

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“…In the Erzgebirge, as in all other low mountain ranges and in the lowlands of central Europe (e.g. Ludemann, 2010;Schmidt et al, 2016;Rutkiewicz et al, 2019;Kaiser et al, 2020b;Raab et al, 2022), there are countless remains of historical charcoal production. Remains of so-called charcoal kilns or charcoal hearths are detectable as circular and thin accumulations of charcoal several metres in diameter.…”

Section: Further Palaeobotanical Data Sources From the Regionmentioning

confidence: 99%

Holocene forest and land-use history of the Erzgebirge, central Europe: a review of palynological data

Kaiser

Theuerkauf

Hieke

2023

E&G Quaternary Sci. J.

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Abstract. The ongoing ecological conversion of mountain forests in central Europe from widespread Picea monocultures to mixed stands conceptually also requires a historical perspective on the very long-term, i.e. Holocene, vegetation and land-use dynamics. Detailed sources of information for this are palynological data. The Erzgebirge in focus here, with a maximum height of 1244 m a.s.l., represents an extreme case of extensive historical deforestation since the Middle Ages due to mining, metallurgy, and other industrial activities, as well as rural and urban colonisation. For this regional review we collected and evaluated 121 pollen diagrams of different stratigraphic, taxonomic, and chronological resolution. This number makes this region an upland area in central Europe with an exceptionally high density of palynological data. Using well-dated diagrams going back to the early Holocene, main regional vegetation phases were derived: the Betula–Pinus phase (ca. 11 600–10 200 cal yr BP), the Corylus phase (ca. 10 200–9000 cal yr BP), the Picea phase (ca. 9000–6000 cal yr BP), the Fagus–Picea phase (ca. 6000–4500 cal yr BP), the Abies–Fagus–Picea phase (ca. 4000–1000 cal yr BP), and the anthropogenic vegetation phase (ca. 1000–0 cal yr BP). Some diagrams show the presence or even continuous curves of potential pasture and meadow indicators from around 2000 cal BCE at the earliest. Even cereal pollen grains occur sporadically already before the High Medieval. These palynological indications of a local prehistoric human impact also in the higher altitudes find parallels in the (geo-)archaeologically proven Bronze Age tin placer mining and in the geochemically proven Iron Age metallurgy in the Erzgebirge. The pollen data show that immediately before the medieval clearing, i.e. beginning at the end of the 12th century CE, forests were mainly dominated by Fagus and Abies and complemented by Picea with increasing share towards the highest altitudes. According to historical data, the minimum of the regional forest cover was reached during the 17th–18th centuries CE. The dominance of Picea in modern pollen spectra is caused by anthropogenic afforestation in the form of monocultures since that time. Future palynological investigations, preferably within the framework of altitudinal transect studies, should aim for chronologically and taxonomically high-resolution and radiometrically well-dated pollen diagrams from the larger peatlands.

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“…They explored the following input feature spaces: slope, VAT (Visualization for Archaeological Topography tool), slope and an HS, and all available terrain derivatives. They documented variability in model performance and differences in the best set of features depending on the landscape being predicted (Raab et al., 2022; Suh et al., 2021). Given this observed variability, we argue that there is a need to further investigate the impact of feature space and selected LSPs on resulting model performance for mapping of geomorphic features.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data

Maxwell

Odom

Shobe

et al. 2023

Earth and Space Science

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Do small landforms have large effects? A review on the legacies of pre-industrial charcoal burning

Cited by 12 publications

References 94 publications

Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning YOLOv4 framework

Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning YOLOv4 framework

Holocene forest and land-use history of the Erzgebirge, central Europe: a review of palynological data

Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data

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