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This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system's capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. [ABSTRACT FROM AUTHOR]

Northern high-latitude river ice provides critical natural infrastructure for winter travel, commerce, hunting, fishing, and recreation in rural areas with little or no road access. Open water zones (OWZs) in river ice are dangerous for such travel and are most common during early winter. Changes in the occurrence and duration of OWZs may also indicate more widespread shifts in ice regimes across Alaska and other northern regions. To aid in detecting open water hazards and broader changes in winter conditions, we developed a supervised classification with a principal component analysis (PCA) using both polarizations of Sentinel-1 C-band synthetic aperture radar (SAR) dual-polarized data for rivers in early winter to discriminate between ice cover and open water. Previous SAR river ice classifications have focused on one or two river reaches often with an emphasis on moving ice during spring break-up, hampering generalization of these results to other rivers and seasons. To address this limitation, we used 12 reaches from eight rivers for training and validation with the aim to combine data from different river types to create an ice classification that could be applied to northern high-latitude rivers from October through January. The classification was trained using shore-based time-lapse photos, aerial photos, and on-ice observations, and validated with shore-based time-lapse photos and independent citizen scientists' photo observations. Overall accuracy for the classification ranged from 65 to 93% with a corresponding range of 0.31–0.84 Cohen's Kappa statistic (K ̂). We report some ambiguity between open water and smooth ice, especially in slower-flowing parts of rivers. We conclude that VV and VH thresholds can therefore be customized to increase accuracy, depending on specific river attributes such as river morphology, silt/sediment load, and channel flow velocity. This classification, which allows for mapping long river reaches in low-light winter conditions, can be performed on historical Sentinel-1 imagery to determine areas that display open water year after year. Once customized to a particular river, it can be automated to provide current open water zone maps to Alaskan and other rural northern communities worldwide to aid safer travel on ice. [Display omitted] • River ice classification developed using first principal component of C-band SAR. • Classification trained with shore cameras, field work, and citizen scientist photos. • Validation showed best accuracy in multi-channeled, large rivers carrying some silt. • Classification identified dangerous open water zones for early winter travelers. [ABSTRACT FROM AUTHOR]

The formation, growth, and decay of freshwater ice on lakes and rivers are fundamental processes of northern regions with wide-ranging implications for socio-ecological systems. Ice thickness at the end of winter is perhaps the best integration of cold-season weather and climate, while the duration of thick and growing ice cover is a useful indicator for the winter travel and recreation season. Both maximum ice thickness (MIT) and ice travel duration (ITD) can be estimated from temperature-driven ice growth curves fit to ice thickness observations. We simulated and analyzed ice growth curves based on ice thickness data collected from a range of observation programs throughout Alaska spanning the past 20–60 years to understand patterns and trends in lake and river ice. Results suggest reductions in MIT (thinning) in several northern, interior, and coastal regions of Alaska and overall greater interannual variability in rivers compared to lakes. Interior regions generally showed less variability in MIT and even slightly increasing trends in at least one river site. Average ITD ranged from 214 d in the northernmost lakes to 114 d across southernmost lakes, with significant decreases in duration for half of sites. River ITD showed low regional variability but high interannual variability, underscoring the challenges with predicting seasonally consistent river travel. Standardization and analysis of these ice observation data provide a comprehensive summary for understanding changes in winter climate and its impact on freshwater ice services. [ABSTRACT FROM AUTHOR]

Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In this study, we document the occurrence, reconstruct the timing, and highlight the effects of beaver activity on a small creek valley confined by ice-rich permafrost on the Seward Peninsula, Alaska using multi-dimensional remote sensing analysis of satellite (Landsat-8, Sentinel-2, Planet CubeSat, and DigitalGlobe Inc./MAXAR) and unmanned aircraft systems (UAS) imagery. Beaver activity along the study reach of Swan Lake Creek appeared between 2006 and 2011 with the construction of three dams. Between 2011 and 2017, beaver dam numbers increased, with the peak occurring in 2017 (n = 9). Between 2017 and 2019, the number of dams decreased (n = 6), while the average length of the dams increased from 20 to 33 m. Between 4 and 20 August 2019, following a nine-day period of record rainfall (>125 mm), the well-established dam system failed, triggering the formation of a beaver-induced permafrost degradation feature. During the decade of beaver occupation between 2011 and 2021, the creek valley widened from 33 to 180 m (~450% increase) and the length of the stream channel network increased from ~0.6 km to more than 1.9 km (220% increase) as a result of beaver engineering and beaver-induced permafrost degradation. Comparing vegetation (NDVI) and snow (NDSI) derived indices from Sentinel-2 time-series data acquired between 2017 and 2021 for the beaver-induced permafrost degradation feature and a nearby unaffected control site, showed that peak growing season NDVI was lowered by 23% and that it extended the length of the snow-cover period by 19 days following the permafrost disturbance. Our analysis of multi-dimensional remote sensing data highlights several unique aspects of beaver engineering impacts on ice-rich permafrost landscapes. Our detailed reconstruction of the beaver-induced permafrost degradation event may also prove useful for identifying degradation of ice-rich permafrost in optical time-series datasets across regional scales. Future field- and remote sensing-based observations of this site, and others like it, will provide valuable information for the NSF-funded Arctic Beaver Observation Network (A-BON) and the third phase of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Field Campaign. [ABSTRACT FROM AUTHOR]