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Abstract Recent climate change has caused declines in ice coverage which have lengthened the open water season in the Arctic and increased access to resources and shipping routes. These changes have resulted in more vessel activity in seasonally ice-covered regions. While traffic is increasing in the ice-free season, the amount of vessel activity in the marginal ice zone (ice concentration 15–80%) or in pack ice (>80% concentration) remains unclear. Understanding patterns of vessel activities in ice is important given increased safety challenges and environmental impacts. Here, we couple high-resolution ship tracking information with sea ice thickness and concentration data to quantify vessel activity in ice-covered areas of the Pacific Arctic (northern Bering, Chukchi, and western Beaufort Seas). This region is a geo-strategically critical area that contains globally important commercial fisheries and serves as a corridor for Arctic access for wildlife and vessels. We find that vessel traffic in the marginal ice zone is widely distributed across the study area while vessel traffic in pack ice is concentrated along known shipping routes and in areas of natural resource development. Of the statistically significant relationships between vessel traffic and both sea ice concentration and thickness, over 99% are negative, indicating that increasing sea ice is associated with decreasing vessel traffic on a monthly time scale. Furthermore, there is substantial vessel traffic in areas of high concentration for bowhead whales (Balaena mysticetus), and traffic in these areas increased four-fold during the study period. Fishing vessels dominate vessel traffic at low ice concentrations, but vessels categorized as Other, likely icebreakers, are the most common vessel type in pack ice. These findings indicate that vessel traffic in areas of ice coverage is influenced by distant policy and resource development decisions which should be taken into consideration when trying to predict future vessel-ice interactions in a changing climate. 

The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations ( n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments. 

Abstract The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real‐time in situ monitoring, remote sensing, and modeling. The recent progress in low‐power in situ sensing and data telemetry allows continuous tracing of under‐ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process‐based or statistical data‐driven), with the recent technological advancements and integration of machine learning and hybrid process‐based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less‐studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross‐pollination of intensive and extensive modes of study to broaden knowledge of the winter period. 

Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.