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Publicly available Light Detection and Ranging (LiDAR) datasets have become widely accessible in the northeastern United States and beyond in the past 10 years. The increase in dataset availability and accessibility coupled with a number of publications detailing the types of cultural features that can be identified has made it necessary to explore and discuss positive impacts and risks to cultural features on this landscape. Access to detailed, documented locations of archaeological resources at state or federal agencies in the United States is typically limited to those with certain credentials, yet many locations of features and sites, both documented and undocumented, are now available to anyone who can access these datasets and effectively interpret them. This presents a challenge for cultural resource management professionals and the field of archaeology; for while LiDAR datasets have had many positive impacts, it is not yet obvious what the unintended impacts of feature exposure might be. Risks to sites are worth considering in the northeastern United States, where (1) region‐wide LiDAR data are publicly available and accessible, (2) many cultural features are widely accessible and not well monitored and (3) case studies have been published that provide guidance on how to identify specific types of cultural landscape features using LiDAR data. We discuss the nuances of those topics here, provide examples of how the datasets have impacted archaeology in the northeastern United States and explore possible mitigation strategies to maintain data accessibility while also protecting important cultural features in this region.

 

Centuries‐long intensive land‐use change in the north‐eastern United States provides the opportunity to study the timescale of geomorphic response to anthropogenic disturbances. In this region, forest‐clearing and agricultural practices following EuroAmerican settlement led to deposition of legacy sediment along valley bottoms, including behind mill dams. The South River in western Massachusetts experienced two generations of damming, beginning with mill dams up to 6‐m high in the eighteenth–nineteenth century, and followed by construction of the Conway Electric Dam (CED), a 17‐m‐tall hydroelectric dam near the watershed outlet in 1906. We use the mercury (Hg) concentration in upstream deposits along the South River to constrain the magnitude, source, and timing of inputs to the CED impoundment. Based on cesium‐137 (¹³⁷Cs) chronology and results from a sediment mixing model, remobilized legacy sediment comprised% of the sediment load in the South River prior to 1954; thereafter, from 1954 to 1980s, erosion from glacial deposits likely dominated (63 ± 14%), but with legacy sediments still a substantial source (37 ± 14%). We also use the CED reservoir deposits to estimate sediment yield through time, and find it decreased after 1952. These results are consistent with high rates of mobilization of legacy sediment as historic dams breached in the early twentieth century, and suggest rapid initial response to channel incision, followed by a long decay in the second half of the century, that is likely dependent on large flood events to access legacy sediment stored in banks. Identifying sources of sediment in a watershed and quantifying erosion rates can help to guide river restoration practices. Our findings suggest a short fluvial recovery time from the eighteenth–nineteenth century to perturbation during the first half of the twentieth century, with subsequent return to a dominant long‐term signal from erosion of glacial deposits, with anthropogenic sediment persisting as a secondary source. © 2020 John Wiley & Sons, Ltd.

 

This study presents a method to generate historical orthomosaics using Structure-from-Motion (SfM ) photogrammetry, historical aerial photographs, and lidar data, and then analyzes the horizontal accuracy and factors that can affect the quality of historical orthoimagery products made with these approaches. Two sets of historical aerial photographs (1934 and 1951) were analyzed, focused on the town of Woodstock in Connecticut, U.S.A. Ground control points (GCPs) for georeferencing were obtained by overlaying multiple data sets, including lidar elevation data and derivative hillshades, and recent orthoimagery. Root-Mean-Square Error values of check points (CPs ) for 1934 and 1951 orthomosaics without extreme outliers are 0.83 m and 1.37 m, respectively. Results indicate that orthomosaics can be used for standard mapping and geographic information systems (GIS ) work according to the ASPRS 1990 accuracy standard. In addition, results emphasize that three main factors can affect the horizontal accuracy of orthomosaics: (1) types of CPs, (2) the number of tied photos, and (3) terrain. 

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. 

In the northeastern United States, widespread deforestation occurred during the 17–19th centuries as a result of Euro-American agricultural activity. In the late 19th and early 20th centuries, much of this agricultural landscape was reforested as the region experienced industrialization and farmland became abandoned. Many previous studies have addressed these landscape changes, but the primary method for estimating the amount and distribution of cleared and forested land during this time period has been using archival records. This study estimates areas of cleared and forested land using historical land use features extracted from airborne LiDAR data and compares these estimates to those from 19th century archival maps and agricultural census records for several towns in Massachusetts, a state in the northeastern United States. Results expand on previous studies in adjacent areas, and demonstrate that features representative of historical deforestation identified in LiDAR data can be reliably used as a proxy to estimate the spatial extents and area of cleared and forested land in Massachusetts and elsewhere in the northeastern United States. Results also demonstrate limitations to this methodology which can be mitigated through an understanding of the surficial geology of the region as well as sources of error in archival materials. 

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. 

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.