An official website of the United States government
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.
more »
« less
Ice Wedge Network Centerline and Ice-Wedge Polygon Coverage in the Bernard River Watershed, Banks Island Canada; 2010-2020
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.
more »
« less
- PAR ID:
- 10554721
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Permafrost Hydrology
- Format(s):
- Medium: X Other: text/xml
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. In this paper, we combine a detailed multi-generational, interdisciplinary, and international literature review along with our own field experiences to explore the development of low-centered ice-wedge polygons and their orthogonal networks. Low-centered polygons, a type of ice-wedge polygonal ground characterized by elevated rims and lowered wet central basins, are critical indicators of permafrost conditions. The formation of these features has been subject to numerous inconsistencies and debates since their initial description in the 1800s. The development of elevated rims is attributed to different processes, such as soil bulging due to ice-wedge growth, differential frost heave, and the accumulation of vegetation and peat. The transition of low-centered polygons to flat-centered, driven by processes like peat accumulation, aggradational ice formation, and frost heave in polygon centers, has been generally overlooked. Low-centered polygons occur in deltas, on floodplains, and in drained-lake basins. There, they are often arranged in orthogonal networks that comprise a complex system. The prevailing explanation of their formation does not match with several field studies that practically remain unnoticed or ignored. By analyzing controversial subjects, such as the degradational or aggradational nature of low-centered polygons and the formation of orthogonal ice-wedge networks, this paper aims to clarify misconceptions and present a cohesive overview of lowland terrain ice-wedge dynamics. The findings emphasize the critical role of ice wedges in shaping Arctic permafrost landscapes and their vulnerability to ongoing climatic and landscape changes.more » « less
-
The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.more » « less
-
Climate change pressure on the Arctic permafrost is rising alarmingly, creating a decisive need to produce Pan-Arctic scale permafrost landform and thaw disturbance information from remote sensing (RS) data. Very high spatial resolution (VHSR) satellite images can be utilized to detect ice-wedge polygons (IWPs) – the most important and widespread landform in the Arctic tundra region - across the Arctic without compromising spatial details. Automated analysis of peta-byte scale VHSR imagery covering millions of square kilometers is a computationally challenging task. Traditional semantic segmentation requires the use of task specific feature extraction with conventional classification techniques. Semantic complexity of VHSR images coupled with landscape heterogeneity makes it difficult to use conventional classification approaches to produce Pan-Arctic scale geospatial products. This leads to adapting deep convolutional neural network (DLCNN) approaches that have excelled in computer vision (CV) applications. Transitioning domains from everyday image understanding to remote sensing image analysis is challenging. This study aims to systematically investigate two main obstacles confronted when adapting DLCNNs in large-scale RS image analysis tasks; 1) the limited availability labeled data sets and 2) the prohibitive nature of hyperparameter tunning when designing DLCNNs that can capture the rich characteristics embedded in remotely-sensed images. With a case study on the production of the first pan-Arctic ice-wedge polygon map using thousands of VHSR images, we demonstrate the use of transfer learning and the impact of hyperparameter tuning with a 16% improvement of the Mean Average Precision (mAP50).more » « less
-
Permafrost thaw exhibits an array of spatially heterogenous patterns. As the Arctic continues to warm, those spatial patterns of permafrost thaw, or degradation, are becoming increasingly intricate and dynamic. In particular, ice-wedge permafrost degradation contains a high degree of spatial heterogeneity as ice wedges transition through undegraded, degraded, and stabilized stages. Developing accurate remote sensing methods for characterizing degradation will better allow us to monitor and forecast Arctic landscape evolution and associated land-atmosphere carbon-climate interactions. In this study, we (i) characterized ice-wedge degradation stages across a regional scale using a novel hydrogeomorphic approach. Then, we (ii) assessed the heterogeneity of degradation from meter- to kilometer-scales, and (iii) identified landscape properties associated with degradation patterns. We leveraged the unique spectral and geometric properties of ice-wedge degradation stages to map those stages across 366 km2 of the Arctic Coastal Plain near Prudhoe Bay, Alaska in sub-meter resolution Worldview-2 satellite imagery. Then, we validated the maps with in-situ observations, airborne LIDAR, and drone multispectral surveys. We evaluated spatial heterogeneity in ice-wedge degradation through a clustering approach. Specifically, we grouped regions into hydrogeomorphic clusters defined by similarities in trough widths and flooding, which reflect distinct degradation stages. This analysis revealed that ice-wedge degradation is heterogeneous across both meter and kilometer scales. At the meter scale, a single ice-wedge polygon is generally bounded by varied degradation stages. In addition, the most advanced stages of degradation occur in areas of low trough relative elevation and at the junctions between troughs. At the kilometer-scale, distinct clustering of degradation stages was identified across the region and linked to spatial patterns in topography: regional clusters of advanced degradation occurred in higher elevation areas. The millennial-scale evolution of the landscape has resulted in heterogeneous topographic, hydrologic, and cryogenic characteristics; these varied features exhibit diverse responses to warming events, which reflect the dynamic interplay that occurs between permafrost landscapes and climate change.more » « less
