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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.
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This content will become publicly available on September 1, 2025
Heterogeneity in ice-wedge permafrost degradation revealed across spatial scales
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.
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- Award ID(s):
- 2311075
- PAR ID:
- 10549989
- Publisher / Repository:
- ELSEVIER
- Date Published:
- Journal Name:
- Remote Sensing of Environment
- Volume:
- 311
- Issue:
- C
- ISSN:
- 0034-4257
- Page Range / eLocation ID:
- 114299
- Subject(s) / Keyword(s):
- Permafrost Ice-wedge Heterogeneity Degradation Mapping Climate change
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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