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Roads constructed on permafrost can have a significant impact on the surrounding environment, potentially inducing permafrost degradation. These impacts arise from factors such as snow accumulation near the road, which affects the soil's thermal and hydrological regime, and road dust that decreases the snow's albedo, altering the timing of snowmelt. However, our current understanding of the magnitude and the spatial extent of these effects is limited. In this study we addressed this gap by using remote sensing techniques to assess the spatial effect of the Inuvik to Tuktoyaktuk Highway (ITH) in Northwest Territories, Canada, on snow accumulation, snow albedo and snowmelt patterns. With a new, high resolution snow depth raster from airborne laser scanning, we quantified the snow accumulation at road segments in the Trail Valley Creek area using digital elevation model differencing. We found increased snow accumulation up to 36m from the road center. The magnitude of this snow accumulation was influenced by the prevailing wind direction and the embankment height. Furthermore, by analyzing 43 Sentinel-2 satellite images between February and May 2020, we observed reduced snow albedo values within 500m of the road, resulting in a 12-days-earlier onset of snowmelt within 100m from the road. We examined snowmelt patterns before, during and after the road construction using the normalized difference snow index from Landsat-7 and Landsat-8 imagery. Our analysis revealed that the road affected the snowmelt pattern up to 600m from the road, even in areas which appeared undisturbed. In summary, our study improves our understanding of the spatial impact of gravel roads on permafrost due to enhanced snow accumulation, reduced snow albedo and earlier snowmelt. Our study underscores the important contribution that remote sensing can provide to improve our understanding of the effects of infrastructure development on permafrost environments. [ABSTRACT FROM AUTHOR]

In the warming Arctic, retrogressive thaw slumping (RTS) has emerged as the primary thermokarst modifier of icerich permafrost slopes, raising urgency to investigate the distribution and intensification of disturbances and to determine trajectories of landscape evolution and the cascade of effects. Tracking RTS is challenging due to constraints of remote sensing products and a narrow understanding of thaw-driven landforms, however, high-resolution elevation models provide new insights into geomorphic change. Structural traits, such as RTS depth-of-thaw or volume, can be obtained through allometric scaling. To address fundamental knowledge gaps related to area-volume scaling of RTS, a suitable surface interpolation technique was first needed to model pre-disturbance topography upon which volume estimates could be based. Among 8 methods with 32 parameterizations, Natural Neighbour surface interpolation achieved the best precision in reconstructing pre-disturbed slope topography (90th percentile Root Mean Square Difference ± 1.0 m). An inverse association between RTS volume and relative volumetric error was observed, with uncertainties <10% for large slumps and <20% for small-to-medium slumps. Second, a Multisource Slump Inventory (MSI) for two study areas in the Beaufort Delta (Canada) was required to characterize the diverse range of disturbance morphologies and activity levels, which provided temporally consistent information on thaw slump affected slopes and attributes. The MSI delineation of three high-resolution hillshade DEMs (airborne stereo-imagery, LiDAR, ArcticDEM) revealed temporal and spatial trends in these multi-year, chronic mass-wasting features. For example, in the Tuktoyaktuk Coastal Plains, a +38% increase in active RTS and +69% increase in total active surface area were observed between 2004 and 2016. However, the total area of RTS did not change considerably (+3.5%) because the vast majority of active thaw slumping processes have occurred in association with past disturbances. Interpretation of thaw-driven change is thus dependent on how active RTS are defined to support disturbance inventories. Third, the pre-disturbance topographies, MSI digitizations, and DEMs were integrated to explore allometric scaling relationships between RTS area and eroded volume. The power-law model indicated non-linearity in the rates of RTS expansion and intensification across scale (adj-R2 of 0.85, n=1,522), but also revealed that elongated, shoreline RTS reflects outliers poorly represented by the modelling. This study highlights the importance of linking field-based knowledge to feature identification and the utility of high-resolution DEMs in quantifying rates of RTS erosion beyond tracking change in the planimetric area. Observations further suggested variation in depth-scaling of RTS populations is based on morphometry, terrain position, and complexity of the disturbance area, as well as the method and ontology by which slumps are inventoried. [ABSTRACT FROM AUTHOR]

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