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1. Estimating Historically Cleared and Forested Land in Massachusetts, USA, Using Airborne LiDAR and Archival Records

   https://doi.org/10.3390/rs13214318  

   Johnson, Katharine M.; Ouimet, William B.; Dow, Samantha; Haverfield, Cheyenne (November 2021, Remote Sensing)

   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. 

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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. High‐Resolution Climate Projections for the Northeastern United States Using Dynamical Downscaling at Convection‐Permitting Scales

   https://doi.org/10.1029/2018EA000426  

   Komurcu, M.; Emanuel, K. A.; Huber, M.; Acosta, R. P. (November 2018, Earth and Space Science)

   Abstract To paraphrase former Speaker of the House Tip O'Neill, “All climate change is local”—that is, society reacts most immediately to changes in local weather such as regional heat waves and heavy rainstorms. Such phenomena are not well resolved by the current generation of coupled climate models. Here it is shown that dynamical downscaling of climate reanalyses using a high‐resolution regional model can reproduce both the means and extremes of temperature and precipitation as observed in the well‐measured northeastern United States. Given this result, the downscaling is applied to climate projections for the middle and end of the 21st century under Representative Concentration Pathway (RCP) 8.5 as well as for the historical time period to help assess regional climate impacts in the northeastern United States. The resulting high‐resolution projections are intended to support regional sustainability studies for the northeastern United States and are made publicly available. 

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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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