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Abstract Permafrost is a sub-ground phenomenon and therefore cannot be directly observed from space. It is an Essential Climate Variable and associated with climate tipping points. Multi-annual time series of permafrost ground temperatures can be, however, derived through modelling of the heat transfer between atmosphere and ground using landsurface temperature, snow- and landcover observations from space. Results show that the northern hemisphere permafrost ground temperatures have increased on average by about one degree Celsius since 2000. This is in line with trends of permafrost proxies observable from space: surface water extent has been decreasing across the Arctic; the landsurface is subsiding continuously in some regions indicating ground ice melt; hot summers triggered increased subsidence as well as thaw slumps; rock glaciers are accelerating in some mountain regions. The applicability of satellite data for permafrost proxy monitoring has been demonstrated mostly on a local to regional scale only. There is still a lack of consistency of acquisitions and of very high spatial resolution observations. Both are needed for implementation of circumpolar monitoring of lowland permafrost. In order to quantify the impacts of permafrost thaw on the carbon cycle, advancement in wetland and atmospheric greenhouse gas concentration monitoring from space is needed. 

Abstract The presence of ground ice in Arctic soils exerts a major effect on permafrost hydrology and ecology, and factors prominently into geomorphic landform development. As most ground ice has accumulated in near-surface permafrost, it is sensitive to variations in atmospheric conditions. Typical and regionally widespread permafrost landforms such as pingos, ice-wedge polygons, and rock glaciers are closely tied to ground ice. However, under ongoing climate change, suitable environmental spaces for preserving landforms associated with ice-rich permafrost may be rapidly disappearing. We deploy a statistical ensemble approach to model, for the first time, the current and potential future environmental conditions of three typical permafrost landforms, pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere. We show that by midcentury, the landforms are projected to lose more than one-fifth of their suitable environments under a moderate climate scenario (RCP4.5) and on average around one-third under a very high baseline emission scenario (RCP8.5), even when projected new suitable areas for occurrence are considered. By 2061–2080, on average more than 50% of the recent suitable conditions can be lost (RCP8.5). In the case of pingos and ice-wedge polygons, geographical changes are mainly attributed to alterations in thawing-season precipitation and air temperatures. Rock glaciers show air temperature-induced regional changes in suitable conditions strongly constrained by topography and soil properties. The predicted losses could have important implications for Arctic hydrology, geo- and biodiversity, and to the global climate system through changes in biogeochemical cycles governed by the geomorphology of permafrost landscapes. Moreover, our projections provide insights into the circumpolar distribution of various ground ice types and help inventory permafrost landforms in unmapped regions. 

Abstract The retreat of glaciers in response to global warming has the potential to trigger landslides in glaciated regions around the globe. Landslides that enter fjords or lakes can cause tsunamis, which endanger people and infrastructure far from the landslide itself. Here we document the ongoing movement of an unstable slope (total volume of 455 × 10⁶m³) in Barry Arm, a fjord in Prince William Sound, Alaska. The slope moved rapidly between 2010 and 2017, yielding a horizontal displacement of 120 m, which is highly correlated with the rapid retreat and thinning of Barry Glacier. Should the entire unstable slope collapse at once, preliminary tsunami modeling suggests a maximum runup of 300 m near the landslide, which may have devastating impacts on local communities. Our findings highlight the need for interdisciplinary studies of recently deglaciated fjords to refine our understanding of the impact of climate change on landslides and tsunamis. 

Arctic permafrost is facing significant changes due to global climate change. As these regions are largely inaccessible, remote sensing plays a crucial rule in better understanding the underlying processes across the Arctic. In this study, we focus on the remote detection of retrogressive thaw slumps (RTSs), a permafrost disturbance comparable to slow landslides. For such remote sensing tasks, deep learning has become an indispensable tool, but limited labeled training data remains a challenge for training accurate models. We present PixelDINO, a semi-supervised learning approach, to improve model generalization across the Arctic with a limited number of labels. PixelDINO leverages unlabeled data by training the model to define its own segmentation categories (pseudoclasses), promoting consistent structural learning across strong data augmentations. This allows the model to extract structural information from unlabeled data, supplementing the learning from labeled data. PixelDINO surpasses both supervised baselines and existing semi-supervised methods, achieving average intersection-over-union (IoU) of 30.2 and 39.5 on the two evaluation sets, representing significant improvements of 13% and 21%, respectively, over the strongest existing models. This highlights the potential for training robust models that generalize well to regions that were not included in the training data. 

Much of the Arctic tundra is underlain by a network of ice wedges that formed during millennia of repeated frost cracking on cold winter days and later infilling of snowmelt water. Growing ice wedges push the soil upwards, forming connected ridges on the ground surface and the ubiquitous ice-wedge polygon tundra. Melting of the top of the ice wedge causes the ground surface to collapse with the rims transforming into snow- and water-collecting troughs — a phenomenon observed at multiple sites across the Arctic tundra in a decade or less. Continued melt establishes a new drainage network only a metre or two wide and less than a half-metre deep, where a doubling of runoff and reduced surface water storage is possible without changes in precipitation. Across the Arctic, lakes are disappearing, while precipitation and river runoff are increasing. So far, the sub-metre microtopographical changes have not entered the scientific analyses encompassing regional and pan-Arctic hydrology. The data and technology are now here to quantify the network of ice wedges across large regions and, though individually small, the ice wedges add up to large numbers. What at first may appear as contradicting hydrological change (for example, shrinking lakes despite increasing precipitation) could be explained by a sudden evolution of the stream network where the new channels are narrow but bountiful: the capillaries of the Arctic tundra hydrological system. 

Ice-wedge polygon (IWP) is a landform found in landscapes underlain by permafrost. IWPs form due to the development of ice wedges, where each IWP is bounded by ice wedges. Ice wedges form due to repeated cracking of the soil during winter and by snowmelt water infiltrating into the cracks and freezing. Repeated over thousands of years, the process results in ice wedges several 10s of feet deep. The melting of the top of the ice wedge results in ground subsidence and depending how extensive the thaw is across the landscape, new ponds or lateral drainage channels form. This data collection supported an assessment of the length of the ice wedge network in the Barnard River watershed (10,540 km2), Banks Island, Canada. The data collection is derived from the pan-Arctic map of ice-wedge polygons (Witharana et al. 2023, Ice-wedge polygon detection in satellite imagery from pan-Arctic regions, Permafrost Discovery Gateway, 2001-2021. Arctic Data Center. doi:10.18739/A2KW57K57), which used Maxar satellite imagery from 2010-2020 for Banks Island. Two types of datasets are included: (1) Polyline shapefile of mapped ice wedge centerlines. This dataset was produced with an approach adopted from Ulrich, Mathias, et al. "Quantifying wedge‐ice volumes in Yedoma and thermokarst basin deposits." Permafrost and Periglacial Processes 25.3 (2014): 151-161. A buffer that represents widths at the top of ice wedges is created around each IWP. A buffer width of 5 meters was chosen, since this allowed buffers of adjacent polygons to overlap. These buffers are then skeletonized in order to trace their centerlines, which ultimately represents the network of ice-wedges that form the IWPs in a landscape. (2) Polygon shapefile of IWP coverage (as percentage of land cover within 1 kilometer (km) x 1 km rectangular grid cells) across the 10,540 km2 Bernard River Watershed, Banks Island, Canada. Code for ice-wedge centerline extraction can be found at https://github.com/PermafrostDiscoveryGateway/IW-Network-Extraction. This data collection accompanies the manuscript published in Nature Water (Liljedahl, A.K., Witharana, C., and Manos, E., 2024. The Capillaries of the Arctic Tundra. Nature Water, doi:10.1038/s44221-024-00276-9) and the geospatial data is available to view in the Permafrost Discovery Gateway. 

This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) and active layer detachments slides (ALD) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of 3 meters (m), complemented by ArcticDEM (Digital Elevation Models) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1, is the first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. 

Risk assessment of infrastructure exposed to ice-rich permafrost hazards is essential for climate change adaptation in the Arctic. As this process requires up-to-date, comprehensive, high-resolution maps of human-built infrastructure, gaps in such geospatial information and knowledge of the applications required to produce it must be addressed. Therefore, this study highlights the ongoing development of a deep learning approach to efficiently map the Arctic built environment by detecting nine different types of structures (detached houses, row houses, multi-story blocks, non-residential buildings, roads, runways, gravel pads, pipelines, and storage tanks) from recently-acquired Maxar commercial satellite imagery (<1 m resolution). We conducted a multi-objective comparison, focusing on generalization performance and computational cost, of nine different semantic segmentation architectures. K-fold cross validation was used to estimate the average F1-score of each architecture and the Friedman Aligned Ranks test with the Bergmann-Hommel posthoc procedure was applied to test for significant differences in generalization performance. ResNet-50-UNet++ performs significantly better than five out of the other eight candidate architectures; no significant difference was found in the pairwise comparisons of ResNet-50-UNet++ to ResNet-50-MANet, ResNet-101-MANet, and ResNet-101-UNet++. We then conducted a high-performance computing scaling experiment to compare the number of service units and runtime required for model inferencing on a hypothetical pan- Arctic scale dataset. We found that the ResNet-50-UNet++ model could save up to ~ 54% on service unit expenditure, or ~ 18% on runtime, when considering operational deployment of our mapping approach. Our results suggest that ResNet-50-UNet++ could be the most suitable architecture (out of the nine that were examined) for deep learning-enabled Arctic infrastructure mapping efforts. Overall, our findings regarding the differences between the examined CNN architectures and our methodological framework for multi-objective architecture comparison can provide a foundation that may propel future pan-Arctic GeoAI mapping efforts of infrastructure.