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1. Characteristics of small anthropogenic landforms resulting from historical charcoal production in western Connecticut, USA

   https://doi.org/https://doi.org/10.1016/j.catena.2020.104896  

   Bonhage, A. (December 2020, Catena)

   null (Ed.)

   Relict charcoal hearths (RCHs) are anthropogenic geomorphic features with an average diameter of 12 m found in many forests of Central Europe and in the eastern USA wherever pre-coal iron production took place or other industries demanded the production of charcoal. To expand the knowledge about their geoarchaeological significance and their legacy effect on soil properties and forest ecosystems, we propose a method for a generalized description of soil stratigraphy on RCHs. We studied 154 soil profiles at 52 RCH sites alongside two 1 km transects in Litchfield County, Connecticut, USA. The sites can be classified based on the slope inclination, with sites on <4° mostly having a single-layered stratigraphy and an elevated circular shape, while sites on slopes >4° mostly are built as levelled and multilayered platforms. The latter have two or more charcoal rich technogenic Auh-layers separated by intermediate Auh-layers mostly consisting of mineral substrate. Based on average layer thicknesses and their dependence on the sites slope inclinations, we propose a model with two idealized RCH shapes with slope controlled properties that allow for an easy computation of site diameters and elemental stocks. With ongoing advances in remote sensing of RCH sites, our proposed model can help to further understand the effects of historic land use on a landscape scale. 

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2. Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

   https://doi.org/10.3390/rs13224630  

   Suh, Ji Won; Anderson, Eli; Ouimet, William; Johnson, Katharine M.; Witharana, Chandi (November 2021, Remote Sensing)

   Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in understanding historic land use dynamics during the 17th to early 20th centuries, however previous studies have primarily used manual or semi-automated digitization methods, which are time consuming for broad-scale mapping. This study applies fully-automated deep convolutional neural networks (i.e., U-Net) with LiDAR derivatives to identify relict charcoal hearths (RCHs), a type of historical land use feature. Results show that slope, hillshade, and Visualization for Archaeological Topography (VAT) rasters work well in six localized test regions (spatial scale: <1.5 km2, best F1 score: 95.5%), but also at broader extents at the town level (spatial scale: 493 km2, best F1 score: 86%). The model performed best in areas with deciduous forest and high slope terrain (e.g., >15 degrees) (F1 score: 86.8%) compared to coniferous forest and low slope terrain (e.g., <15 degrees) (F1 score: 70.1%). Overall, our results contribute to current methodological discussions regarding automated extraction of historical cultural features using deep learning and LiDAR. 

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3. Gradients of geochemical change in relic charcoal hearth soils, Northwestern Connecticut, USA

   https://doi.org/https://doi.org/10.1016/j.catena.2020.104991  

   Donovan, S. (February 2021, Catena)

   null (Ed.)

   Relic charcoal hearths (RCHs) have produced distinct legacy effects in forest soils around the world. Recently, LiDAR imagery has revealed thousands of 18th–early 20th century RCHs in Litchfield County, Connecticut, USA; however, the effects of RCHs on a landscape-scale are not well-documented, particularly fine-scale heterogeneity within RCHs and surrounding soils. This study examines the long-term impacts of charcoal production by measuring RCH soil chemical and physical properties from three perspectives: (1) compared to adjacent reference sites (RSadj), (2) laterally at systematic distances away from the RCH center, and (3) vertically within the RCH soil profile. Mean charcoal abundance was greater in RCH sites than RSadj (p < 0.01). Soil organic carbon (SOC), total C, and extractable Ca2+, Mg2+, Na+ were greater in RCH sites as compared to RSadj (p < 0.01), and available phosphorus (p < 0.01), K+, and trace elements (Mo, Ag, Hg, and Se) were lower (p < 0.05). In vertical profiles, many RCHs had 2 charcoal-rich layers within the anthropic epipedon, demonstrating multiple episodes of charcoal production. Peaks in SOC, C:N, Ca2+, Mg2+ corresponded with charcoal-rich layers. Systematic transect sampling across the RCH boundary identified charcoal fragments in soils at distances up to 25 m beyond the RCH boundary, increasing the surface-level (0–15 cm) area of impact for an individual RCH by more than 30×, from a 5-m radius (RCH area = 78.5 m2) to a 30-m radius (total area of impact = 2826 m2). These findings capture fine-scale variations within and among RCH and reference sites and contribute to estimating the total area of forest soils impacted by historical charcoal production. 

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4. Reconstructing Historical Forest Cover and Land Use Dynamics in the Northeastern United States Using Geospatial Analysis and Airborne LiDAR

   https://doi.org/https://doi.org/10.1080/24694452.2020.1856640  

   Johnson, K.; Ouimet, W. (March 2021, Annals of the American Association of Geographers)

   null (Ed.)

   The northeastern United States experienced extensive deforestation during the seventeenth through twentieth centuries primarily for European agriculture, which peaked in the mid-nineteenth century, and followed by widespread farmstead abandonment and reforestation. Analysis of airborne light detection and ranging (LiDAR) data has revealed thousands of historical land-use features with topographic signatures across the landscape under the region’s now-dense forest canopy. This study investigates two different types of features—stone walls and relict charcoal hearths—both of which are associated with widespread deforestation in the region. Our results demonstrate that LiDAR is an effective tool in reconstructing and quantifying the distribution and magnitude of historical forest cover using these relict land use features as a reliable proxy. Furthermore, these methods allow for direct quantification of cumulative land clearing over time in each town, in addition to the extent, intensity, and spatial distribution of cleared land and forest cover. 

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5. Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

   https://doi.org/10.1002/arp.1909  

   Quintus, Seth; Davis, Dylan_S; Cochrane, Ethan_E (July 2023, Archaeological Prospection)

   Abstract Lidar datasets have been crucial for documenting the scale and nature of human ecosystem engineering and land use. Automated analysis methods, which have been rising in popularity and efficiency, allow for systematic evaluations of vast landscapes. Here, we use a Mask R‐CNN deep learning model to evaluate terracing—artificially flattened areas surrounded by steeper slopes—on islands in American Sāmoa. Mask R‐CNN is notable for its ability to simultaneously perform detection and segmentation tasks related to object recognition, thereby providing robust datasets of both geographic locations of terracing features and their spatial morphometry. Using training datasets from across American Sāmoa, we train this model to recognize terracing features and then apply it to the island of Tutuila to undertake an island‐wide survey for terrace locations, distributions and morphologies. We demonstrate that this model is effective (F1 = 0.718), but limitations are also documented that relate to the quality of the lidar data and the size of terracing features. Our data show that the islands of American Sāmoa display shared patterns of terracing, but the nature of these patterns are distinct on Tutuila compared with the Manu'a island group. These patterns speak to the different interior configurations of the islands. This study demonstrates how deep learning provides a better understanding of landscape construction and behavioural patterning on Tutuila and has the capacity to expand our understanding of these processes on other islands beyond our case study. 

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