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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. 

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. 

Commercial satellite sensors offer the luxury of mapping of individual permafrost features and their change over time. Deep learning convolutional neural nets (CNNs) demonstrate a remarkable success in automated image analysis. Inferential strengths ofCNNmodels are driven primarily by the quality and volume of hand-labeled training samples. Production of hand-annotated samples is a daunting task. This is particularly true for regional-scale mapping applications, such as permafrost feature detection across the Arctic. Image augmentation is a strategic data-space solution to synthetically inflate the size and quality of training samples by transforming the color space or geometric shape or by injecting noise. In this study, we systematically investigate the effectiveness of a spectrum of augmentation methods when applied toCNNalgorithms to recognize ice-wedge polygons from commercial satellite imagery. Our findings suggest that a list of augmentation methods (such as hue, saturation, and salt and pepper noise) can increase the model performance.

 

High-spatial-resolution satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. Knowledge discovery through artificial intelligence, big imagery, and high-performance computing (HPC) resources is just starting to be realized in Arctic permafrost science. We have developed a novel high-performance image-analysis framework—Mapping Application for Arctic Permafrost Land Environment (MAPLE)—that enables the integration of operational-scale GeoAI capabilities into Arctic permafrost modeling. Interoperability across heterogeneous HPC systems and optimal usage of computational resources are key design goals of MAPLE. We systematically compared the performances of four different MAPLE workflow designs on two HPC systems. Our experimental results on resource utilization, total time to completion, and overhead of the candidate designs suggest that the design of an optimal workflow largely depends on the HPC system architecture and underlying service-unit accounting model. 

Abstract. The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse microtopographic transitions at sub decadal scale. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. Dramatic microtopographic transformation of low-centered into high-centered IWPs can be identified using sub-meter resolution commercial satellite imagery. In this exploratory study, we have employed a Deep Learning (DL)-based object detection and semantic segmentation method named the Mask R-CNN to automatically map IWPs from commercial satellite imagery. Different tundra vegetation types have distinct spectral, spatial, textural characteristics, which in turn decide the semantics of overlying IWPs. Landscape complexity translates to the image complexity, affecting DL model performances. Scarcity of labelled training images, inadequate training samples for some types of tundra and class imbalance stand as other key challenges in this study. We implemented image augmentation methods to introduce variety in the training data and trained models separately for tundra types. Augmentation methods show promising results but the models with separate tundra types seem to suffer from the lack of annotated data.

 

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. 

Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI), big imagery, high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of petabyte-scale imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. In addition to semantic complexities, multitude factors that are inherent to sub-meter resolution satellite imagery, such as file size, dimensions, spectral channels, overlaps, spatial references, and imaging conditions challenge the direct translation of AI-based approaches from computer vision applications. Memory limitations of Graphical Processing Units necessitates the partitioning of an input satellite imagery into manageable sub-arrays, followed by parallel predictions and post-processing to reconstruct the results corresponding to input image dimensions and spatial reference. We have developed a novel high performance image analysis framework –Mapping application for Arctic Permafrost Land Environment (MAPLE) that enables the integration of operational-scale GeoAI capabilities into Arctic science applications. We have designed the MAPLE workflow to become interoperable across HPC architectures while utilizing the optimal use of computing resources.

 

We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17–0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes.