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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. 

Stable isotope paleoaltimetry is one of the most commonly used approaches for quantifying the paleoelevation history of an orogen yet this methodology is often limited to arid to semi-arid climates, mountain systems with a clear orographic rainshadow and terrestrial basins. We present a new approach to reconstructing past topography and relief that uses the catchment-integrated signature of organic molecular biomarkers to quantify the hypsometry of fluvially-exported biomass. Because terrestrially-produced biomolecules are synthesized over the full range of global climate conditions and can be preserved in both terrestrial and marine sediments, the geochemistry of fluvially-transported sedimentary biomarkers can provide a means of interrogating the evolution of topography for a range of environments and depositional settings, including those not well suited for a traditional isotope paleoaltimetry approach. We show an example from Taiwan, a rapidly eroding tropical mountain system that is characterized by high rates of biomass production and short organic residence time and discuss key factors that can influence molecular isotope signal production, transport and integration. Data show that in high relief catchments of Taiwan, river sediments can record integration of biomass produced throughout the catchment. Sedimentary biomarker δ 2 H n C29 in low elevation river deposition sites is generally offset from the δ 2 H n C29 value observed in local soils and consistent with an isotope composition of organics produced at the catchment mean elevation. We test the effect of distinct molecular production and erosion functions on the expected δ 2 H n C29 in river sediments and show that elevation-dependent differences in the production and erosion of biomarkers/sediment may yield only modest differences in the catchment-integrated isotopic signal. Relating fluvial biomarker isotope records to quantitative estimates of organic source elevations in other global orogens will likely pose numerous challenges, with a number of variables that influence molecular production and integration in a river system. We provide a discussion of important parameters that influence molecular biomarker isotope signatures in a mountain system and a framework for employing a molecular paleohypsometry approach to quantifying the evolution of other orogenic systems. 

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. 

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.