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Data are available for download at http://arcticdata.io/data/10.18739/A2KW57K57 Permafrost can be indirectly detected via remote sensing techniques through the presence of ice-wedge polygons, which are a ubiquitous ground surface feature in tundra regions. Ice-wedge polygons form through repeated annual cracking of the ground during cold winter days. In spring, the cracks fill in with snowmelt water, creating ice wedges, which are connected across the landscape in an underground network and that can grow to several meters depth and width. The growing ice wedges push the soil upwards, forming ridges that bound low-centered ice-wedge polygons. If the top of the ice wedge melts, the ground subsides and the ridges become troughs and the ice-wedge polygons become high-centered. Here, a Convolutional Neural Network is used to map the boundaries of individual ice-wedge polygons based on high-resolution commercial satellite imagery obtained from the Polar Geospatial Center. This satellite imagery used for the detection of ice-wedge polygons represent years between 2001 and 2021, so this dataset represents ice-wedge polygons mapped from different years. This dataset does not include a time series (i.e. same area mapped more than once). The shapefiles are masked, reprojected, and processed into GeoPackages with calculated attributes for each ice-wedge polygon such as circumference and width. The GeoPackages are then rasterized with new calculated attributes for ice-wedge polygon coverage such a coverage density. This release represents the region classified as “high ice” by Brown et al. 1997. The dataset is available to explore on the Permafrost Discovery Gateway (PDG), an online platform that aims to make big geospatial permafrost data accessible to enable knowledge-generation by researchers and the public. The PDG project creates various pan-Arctic data products down to the sub-meter and monthly resolution. Access the PDG Imagery Viewer here: https://arcticdata.io/catalog/portals/permafrost Data limitations in use: This data is part of an initial release of the pan-Arctic data product for ice-wedge polygons, and it is expected that there are constraints on its accuracy and completeness. Users are encouraged to provide feedback regarding how they use this data and issues they encounter during post-processing. Please reach out to the dataset contact or a member of the PDG team via support@arcticdata.io. 

Environmental impact assessments for new Arctic infrastructure do not adequately consider the likely long-term cumulative effects of climate change and infrastructure to landforms and vegetation in areas with ice-rich permafrost, due in part to lack of long-term environmental studies that monitor changes after the infrastructure is built. This case study examines long-term (1949–2020) climate- and road-related changes in a network of ice-wedge polygons, Prudhoe Bay Oilfield, Alaska. We studied four trajectories of change along a heavily traveled road and a relatively remote site. During 20 years prior to the oilfield development, the climate and landscapes changed very little. During 50 years after development, climate-related changes included increased numbers of thermokarst ponds, changes to ice-wedge-polygon morphology, snow distribution, thaw depths, dominant vegetation types, and shrub abundance. Road dust strongly affected plant-community structure and composition, particularly small forbs, mosses, and lichens. Flooding increased permafrost degradation, polygon center-trough elevation contrasts, and vegetation productivity. It was not possible to isolate infrastructure impacts from climate impacts, but the combined datasets provide unique insights into the rate and extent of ecological disturbances associated with infrastructure-affected landscapes under decades of climate warming. We conclude with recommendations for future cumulative impact assessments in areas with ice-rich permafrost. 

We studied processes of ice-wedge degradation and stabilization at three sites adjacent to road infrastructure in the Prudhoe Bay Oilfield, Alaska, USA. We examined climatic, environmental, and subsurface conditions and evaluated vulnerability of ice wedges to thermokarst in undisturbed and road-affected areas. Vulnerability of ice wedges strongly depends on the structure and thickness of soil layers above ice wedges, including the active, transient, and intermediate layers. In comparison with the undisturbed area, sites adjacent to the roads had smaller average thicknesses of the protective intermediate layer (4 cm vs. 9 cm), and this layer was absent above almost 60% of ice wedges (vs. ∼45% in undisturbed areas). Despite the strong influence of infrastructure, ice-wedge degradation is a reversible process. Deepening of troughs during ice-wedge degradation leads to a substantial increase in mean annual ground temperatures but not in thaw depths. Thus, stabilization of ice wedges in the areas of cold continuous permafrost can occur despite accumulation of snow and water in the troughs. Although thermokarst is usually more severe in flooded areas, higher plant productivity, more litter, and mineral material (including road dust) accumulating in the troughs contribute to formation of the intermediate layer, which protects ice wedges from further melting. 

Shrub expansion has been observed across the Arctic in recent decades along with warming air temperatures, but tundra shrub expansion has been most pronounced in protected landscape positions such as floodplains, streambanks, water tracks, and gullies. Here we show through field measurements and laboratory analyses how stream hydrology, permafrost, and soil microbial communities differed between streams in late summer with and without tall shrubs. Our goal was to assess the causes and consequences of tall shrub expansion in Arctic riparian ecosystems. Our results from Toolik Alaska, show greater canopy height and density, and distinctive plant and soil microbial communities along stream sections that lose water into unfrozen ground (talik) compared to gaining sections underlain by shallow permafrost. Leaf Area Index is linearly related to the change in streamflow per unit stream length, with the densest canopies coinciding with increasingly losing stream sections. Considering climate change and the circumpolar scale of riparian shrub expansion, we suggest that permafrost thaw and the resulting talik formation and shift in streamflow regime are occurring across the Low Arctic.

 

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems. 

Abrupt thaw of ice‐rich permafrost in the Arctic Foothills yielded to the formation of hillslope erosional features. In the infrastructure corridor, we observed thermal erosion and thaw slumping that self‐healed near an embankment. To advance our understanding of processes between infrastructure and hillslope erosional features (INF‐HEF), we combined climate and remote sensing analyses to field investigations to assess an INF‐HEF system and validate our findings in a broader area along the infrastructure corridor. We identified that thaw consolidation along an embankment formed a thermokarst ditch that was ubiquitous in the broader study area, and which was extensively affected by shrubification and supported other positive feedback (e.g., snow accumulation, water impoundment, and weakened vegetation mat). The thermokarst ditch facilitated channelization of cross‐drainage water, thus increasing the terrain vulnerability to thermal erosion that evolved into thaw slumping after heavy rainfalls. The terrain resilience to thaw slumping benefited from the type of ground ice and topography prevailing at our site. The lateral discontinuity of massive ice in an ice‐wedge polygonal system (i.e., interchange soil and massive ice) compounded to a low‐slope gradient with topographic obstacles (e.g., baydzherakhs) decreased slumping activity and supported self‐stabilization.

 

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.19 and 0.20–0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels. 

On the Arctic Coastal Plain (ACP) in northern Alaska (USA), permafrost and abundant surface‐water storage define watershed hydrological processes. In the last decades, the ACP landscape experienced extreme climate events and increased lake water withdrawal (LWW) for infrastructure construction, primarily ice roads and industrial operations. However, their potential (combined) effects on streamflow are relatively underexplored. Here, we applied the process‐based, spatially distributed hydrological and thermal Water Balance Simulation Model (10 m spatial resolution) to the 30 km²Crea Creek watershed located on the ACP. The impacts of documented seasonal climate extremes and LWW were evaluated on seasonal runoff (May–August), including minimum 7‐day mean flow (MQ7), the recovery time of MQ7 to pre‐perturbation conditions, and the duration of streamflow conditions that prevents fish passage. Low‐rainfall scenarios (21% of normal, one to three summers in a row) caused a larger reduction in MQ7 (−56% to −69%) than LWW alone (−44% to −58%). Decadal‐long consecutive LWW under average climate conditions resulted in a new equilibrium in low flow and seasonal runoff after 3 years that included a disconnected stream network, a reduced watershed contributing area (54% of total watershed area), and limited fish passage of 20 days (vs. 6 days under control conditions) throughout summer. Our results highlight that, even under current average climatic conditions, LWW is not offset by same‐year snowmelt as currently assumed in land management regulations. Effective land management would therefore benefit from considering the combined impact of climate change and industrial LWWs.