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Abstract: In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions. [Copyright &y& Elsevier]

Recent Arctic warming has led to reduced sea-ice thickness and a more dynamic landfast ice cover with potential widespread consequences for ice users. Here, we develop an approach to assess the small-scale deformation of landfast ice critical to on-ice operations using synthetic aperture radar interferometry (InSAR). InSAR has previously proven successful in determining long-term qualitative climatology of ice deformation around on-ice operations, but is now used to explore its potential for providing quantitative guidance for ice road planning, construction, and maintenance. A validation effort using X-band SAR and high-precision GPS data over Elson Lagoon, Alaska, confirms the ability of InSAR to accurately estimate 3-dimensional sea ice strain values accumulated between SAR image acquisitions, using an inverse model. The inverse model was further applied to L-band InSAR data over the Northstar Island ice road near Prudhoe Bay, Alaska. Assuming an elasto-brittle rheology, the derived strain values yielded a spatial distribution of internal stress consistent with preexisting ice defects and morphology. In several localized regions of the study area, stress values exceeded expected yield stress. Resulting relative fracture intensity potential was shown to conform with local knowledge based on road inspections by engineers, and may be used to guide ice road planning, construction and maintenance efforts. The results presented here demonstrate that InSAR is an accurate tool for estimating landfast ice deformation and stability in support of ice use. The findings may also provide substantial new insights into the mechanics of landfast ice. [ABSTRACT FROM AUTHOR]

Rapid environmental change and increases in use of shorefast ice by industry and coastal communities highlight the need for an approach to accurately assess landfast sea-ice stability on seasonal timescales. While stability can sometimes be inferred from field measurements, current methods are lacking robustness and the ability to be automated and applied over large areas and long time scales to ensure safety and document change in the context of transportation, indigenous ice uses and industrial development. This paper introduces an inverse model capable of reconstructing three-dimensional deformation fields from one-dimensional interferometric L-band Synthetic Aperture Radar (SAR) phase patterns. We apply this method at three landfast ice locations on the Alaska Beaufort Sea coast near Barrow and Prudhoe Bay. We find the small-scale displacements estimated from the model consistent with regional patterns of ice motion. Our study suggests that interferometry can provide planning and decision-support information for ice road development and structures operating within ice. Moreover, InSAR can potentially increase our understanding of sea ice on a fundamental level in terms of large-scale stability and long-term changes in ice dynamics. [ABSTRACT FROM AUTHOR]

Landfast ice along the Arctic coasts plays an important role in supporting ecosystem services, local communities and offshore activities by industry. Seasonal predictions of landfast ice conditions are generally lacking in current seasonal forecasting products but are needed especially for planning of on-ice activities such as the construction of ice roads. This study focuses on the planned offshore development of Liberty Island where there is a need to generate seasonal forecasts for the construction of ice roads in Foggy Island Bay along the Beaufort Sea coast in northern Alaska. Due to the lack of prior in-situ observations of ice thickness in the region, a combination of newly obtained field measurements, remote sensing data analysis and modeling were employed to produce prototype seasonal forecasts of ice thickness in the landfast ice around Liberty Island. Seasonal forecasts initialized in September and March were built using Climate Forecast System seasonal forecast model data coupled with a single column ice model to forecast ice thickness that capture the start and end of the operational season, respectively. The results showed that the model forecasts had modest skill in capturing the timing of key ice thickness thresholds needed to support vehicle traffic during the start of the season in November–December but very limited skill with end of season and ice breakup forecasts. Much of the forecast skill was improved through bias correction but such improvement was hampered by the lack of long-term observational data in the region. Integration of the observational data and modeling is necessary to begin development of seasonal forecasts in this data sparse region. • Seasonal sea ice thickness forecasts developed for Foggy Island Bay, Alaska. • Forecasts needed to support ice road operations for Liberty Island development. • Required integration of field observations, forecast model data, and an ice model. • Forecast skill was improved through bias correction. [ABSTRACT FROM AUTHOR]