@article{kunkel_yin_sun_champion_stevens_johnson_2022, title={Extreme Precipitation Trends and Meteorological Causes Over the Laurentian Great Lakes}, volume={4}, ISSN={["2624-9375"]}, DOI={10.3389/frwa.2022.804799}, abstractNote={Trends in extreme precipitation and their causes were analyzed for events within the Laurentian Great Lakes for several periods: 1908–2020, 1949–2020, 1980–2019, and 1980–2020. Upward trends in extreme precipitation were found for multiple metrics, including the number of exceedances of return period thresholds for several durations and average recurrence intervals (ARI), the number of extreme basin-average 4-day precipitation totals, and the annual maximum daily station precipitation. The causes of extreme events were classified into 5 meteorological categories: fronts of extratropical cyclones (ETC-FRT), extratropical cyclones but not proximate to the fronts (ETC-NFRT), mesoscale convective systems (MCS), tropical cyclones (TC), and air mass convection (AMC). For daily events exceeding the threshold for 5-yr ARI, ETC-FRTs account for 78% of all events, followed by ETC-NFRTs (12%), MCSs (6%), TCs (2%), and AMC (1%). Upward trends in the number of events by cause were found for all categories except AMC. An examination of basin-wide 4-day extreme events (40 largest events during 1980–2019) found that all events were caused by ETC-FRTs (85%) or ETC-NFRTs (15%).}, journal={FRONTIERS IN WATER}, author={Kunkel, Kenneth E. and Yin, Xungang and Sun, Liqiang and Champion, Sarah M. and Stevens, Laura E. and Johnson, Katharine M.}, year={2022}, month={May} }
 @article{johnson_ouimet_dow_haverfield_2021, title={Estimating Historically Cleared and Forested Land in Massachusetts, USA, Using Airborne LiDAR and Archival Records}, volume={13}, ISSN={["2072-4292"]}, DOI={10.3390/rs13214318}, abstractNote={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.}, number={21}, journal={REMOTE SENSING}, author={Johnson, Katharine M. and Ouimet, William B. and Dow, Samantha and Haverfield, Cheyenne}, year={2021}, month={Nov} }
 @article{johnson_ives_ouimet_sportman_2021, title={High-resolution airborne Light Detection and Ranging data, ethics and archaeology: Considerations from the northeastern United States}, volume={28}, ISSN={["1099-0763"]}, DOI={10.1002/arp.1836}, abstractNote={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.}, number={3}, journal={ARCHAEOLOGICAL PROSPECTION}, author={Johnson, Katharine M. and Ives, Timothy H. and Ouimet, William B. and Sportman, Sarah P.}, year={2021}, month={Jul}, pages={293–303} }
 @article{suh_anderson_ouimet_johnson_witharana_2021, title={Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data}, volume={13}, ISSN={["2072-4292"]}, DOI={10.3390/rs13224630}, abstractNote={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.}, number={22}, journal={REMOTE SENSING}, author={Suh, Ji Won and Anderson, Eli and Ouimet, William and Johnson, Katharine M. and Witharana, Chandi}, year={2021}, month={Nov} }
 @article{johnson_ouimet_2021, title={Reconstructing Historical Forest Cover and Land Use Dynamics in the Northeastern United States Using Geospatial Analysis and Airborne LiDAR}, volume={111}, ISSN={["2469-4460"]}, DOI={10.1080/24694452.2020.1856640}, abstractNote={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.}, number={6}, journal={ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS}, author={Johnson, Katharine M. and Ouimet, William B.}, year={2021}, month={Sep}, pages={1656–1678} }