Arctic sea ice trafficability: new strategies for a changing icescape

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Sea ice is an important part of the Arctic social-environmental system, in part because it provides a platform for human transportation and for marine flora and fauna that use the ice as a habitat. Sea ice loss projected for coming decades is expected to change ice conditions throughout the Arctic, but little is known about the nature and extent of anticipated changes and in particular potential implications for over-ice travel and ice use as a platform. This question has been addressed here through an extensive effort to link sea ice use and key geophysical properties of sea ice, drawing upon extensive field surveys around on-ice operations and local and Indigenous knowledge for the widely different ice uses and ice regimes of Utqiaġvik, Kotzebue, and Nome, Alaska. A set of nine parameters that constrain landfast sea ice use has been derived, including spatial extent, stability, and timing and persistence of landfast ice. This work lays the foundation for a framework to assess and monitor key ice-parameters relevant in the context of ice-use feasibility, safety, and efficiency, drawing on different remote-sensing techniques. The framework outlines the steps necessary to further evaluate relevant parameters in the context of user objectives and key stakeholder needs for a given ice regime and ice use scenario. I have utilized this framework in case studies for three different ice regimes, where I find uses to be constrained by ice thickness, roughness, and fracture potential and develop assessment strategies with accuracy at the relevant spatial scales. In response to the widely reported importance of high-confidence ice thickness measurements, I have developed a new strategy to estimate appropriate thickness compensation factors. Compensation factors have the potential to reduce risk of misrepresenting areas of thin ice when using point-based in-situ assessment methods along a particular route. This approach was tested on an ice road near Kotzebue, Alaska, where substantial thickness variability results in the need to raise thickness thresholds by 50%. If sea ice is thick enough for safe travel, then the efficiency of travel is relevant and is influenced by the roughness of the ice surface. Here, I develop a technique to derive trafficability measures from ice roughness using polarimetric and interferometric synthetic aperture radar (SAR). Validated using Structure-from-Motion analysis of imagery obtained from an unmanned aerial system near Utqiaġvik, Alaska, I demonstrate the ability of these SAR techniques to map both topography and roughness with potential to guide trail construction efforts towards more trafficable ice. Even when the ice is sufficiently thick to ensure safe travel, potential for fracturing can be a serious hazard through the ability of cracks to compromise load-bearing capacity. Therefore, I have created a state-of-the-art technique using interferometric SAR to assess ice stability with capability of assessing internal ice stress and potential for failure. In an analysis of ice deformation and potential hazards for the Northstar Island ice road near Prudhoe Bay on Alaska's North Slope I have identified a zone of high relative fracture intensity potential that conformed with road inspections and hazard assessments by the operator. Through this work I have investigated the intersection between ice use and geophysics, demonstrating that quantitative evaluation of a given region in the ice use assessment framework developed here can aid in tactical routing of ice trails and roads as well as help inform long-term strategic decision-making regarding the future of Arctic operations on or near sea ice

Iceberg topography and volume classification using TanDEM-X interferometry

Icebergs in polar regions affect water salinity, alter marine habitats, and impose serious hazards on maritime operations and navigation. These impacts mainly depend on the iceberg volume, which remains an elusive parameter to measure. We investigate the capability of TanDEM-X bistatic single-pass synthetic aperture radar interferometry (InSAR) to derive iceberg subaerial morphology and infer total volume. We cross-verify InSAR results with Operation IceBridge (OIB) data acquired near Wordie Bay, Antarctica, as part of the OIB/TanDEM-X Antarctic Science Campaign (OTASC). While icebergs are typically classified according to size based on length or maximum height, we develop a new volumetric classification approach for applications where iceberg volume is relevant. For icebergs with heights exceeding 5 m, we find iceberg volumes derived from TanDEM-X and OIB data match within 7 %. We also derive a range of possible iceberg keel depths relevant to grounding and potential impacts on subsea installations. These results suggest that TanDEM-X could pave the way for future single-pass interferometric systems for scientific and operational iceberg mapping and classification based on iceberg volume and keel depth

Instantaneous sea ice drift speed from TanDEM-X interferometry

The drift of sea ice is an important geophysical process with widespread implications for the ocean energy budget and ecosystems. Drifting sea ice can also threaten marine operations and present a hazard for ocean vessels and installations. Here, we evaluate single-pass along-track synthetic aperture radar (SAR) interferometry (S-ATI) as a tool to assess ice drift while discussing possible applications and inherent limitations. Initial validation shows that TanDEM-X phase-derived drift speed corresponds well with drift products from a ground-based radar at Utqiagvik, Alaska. Joint analysis of TanDEM-X and Sentinel-1 data covering the Fram Strait demonstrates that S-ATI can help quantify the opening/closing rate of leads with possible applications for navigation. S-ATI enables an instantaneous assessment of ice drift and dynamic processes that are otherwise difficult to observe. For instance, by evaluating sea ice drift through the Vilkitsky Strait, Russia, we identified short-lived transient convergence patterns. We conclude that S-ATI enables the identification and analysis of potentially important dynamic processes (e.g., drift, rafting, and ridging). However, current limitations of S-ATI are significant (e.g., data availability and they presently only provide the cross-track vector component of the ice drift field) but may be significantly reduced with future SAR systems

Mapping Arctic Bottomfast Sea Ice Using SAR Interferometry

Bottomfast sea ice is an integral part of many near-coastal Arctic ecosystems with implications for subsea permafrost, coastal stability and morphology. Bottomfast sea ice is also of great relevance to over-ice travel by coastal communities, industrial ice roads, and marine habitats. There are currently large uncertainties around where and how much bottomfast ice is present in the Arctic due to the lack of effective approaches for detecting bottomfast sea ice on large spatial scales. Here, we suggest a robust method capable of detecting bottomfast sea ice using spaceborne synthetic aperture radar interferometry. This approach is used to discriminate between slowly deforming floating ice and completely stationary bottomfast ice based on the interferometric phase. We validate the approach over freshwater ice in the Mackenzie Delta, Canada, and over sea ice in the Colville Delta and Elson Lagoon, Alaska. For these areas, bottomfast ice, as interpreted from the interferometric phase, shows high correlation with local bathymetry and in-situ ice auger and ground penetrating radar measurements. The technique is further used to track the seasonal evolution of bottomfast ice in the Kasegaluk Lagoon, Alaska, by identifying freeze-up progression and areas of liquid water throughout winter

Ground-Based Radar Interferometry of Sea Ice

In light of recent Arctic change, there is a need to better understand sea ice dynamic processes at the floe scale to evaluate sea ice stability, deformation, and fracturing. This work investigates the use of the Gamma portable radar interferometer (GPRI) to characterize sea ice displacement and surface topography. We find that the GPRI is best suited to derive lateral surface deformation due to mm-scale horizontal accuracy. We model interferometric phase signatures from sea ice displacement and evaluate possible errors related to noise and antenna motion. We compare the analysis with observations acquired during a drifting ice camp in the Beaufort Sea. We used repeat-scan and stare-mode interferometry to identify two-dimensional shear and to track continuous uni-directional convergence. This paper demonstrates the capacity of the GPRI to derive surface strain on the order of 10−7 and identify different dynamic regions based on sub-mm changes in displacement. The GPRI is thus a promising tool for sea ice applications due to its high accuracy that can potentially resolve pre- and post-fracture deformation relevant to sea ice stability and modeling

Evaluating Landfast Sea Ice Ridging near UtqiagVik Alaska Using TanDEM-X Interferometry

Seasonal landfast sea ice stretches along most Arctic coastlines and serves as a platform for community travel and subsistence, industry operations, and as a habitat for marine mammals. Landfast ice can feature smooth ice and areas of m-scale roughness in the form of pressure ridges. Such ridges can significantly hamper trafficability, but if grounded can also serve to stabilize the shoreward ice. We investigate the use of synthetic aperture radar interferometry (InSAR) to assess the formation and movement of ridges in the landfast sea ice near Utqiagvik, Alaska. The evaluation is based on the InSAR-derived surface elevation change between two TanDEM-X bistatic image pairs acquired during January 2012. We compare the results with backscatter intensity, coastal radar data, and SAR-derived ice drift and evaluate the utility of this approach and its relevance for evaluation of ridge properties, as well as landfast sea ice evolution, dynamics, and stability.Peer reviewe

Assessing sea ice trafficability in a changing arctic

Arctic sea ice has undergone rapid changes during the last few decades, with negative implications for over-ice travel and on-ice operations, which benefit from services provided by the sea ice. A Parameter-based Trafficability Hierarchy (PATH) is presented here as a framework for developing quantitative assessment strategies that can guide planning and execution of operations on or near sea ice and quantify the impacts of recent changes on ice use. A PATH assessment has been completed for three case studies in Arctic Alaska. These cases, which correspond to a range of different icescapes and ice uses, identify and quantify different parameters linked to trafficability and safe operations. For ice road applications, PATH was used to determine an ice thickness compensation factor, a factor increasing the minimal thickness threshold for operations, to help translate sporadic auger ice thickness measurements along the Kotzebue-Kiana community ice road into an envelope for safe operations. A compensation factor as high as 1.5 was found to be necessary to ensure safety because of the high local thickness variability that is currently a concern for ice road operators. A PATH assessment of ice roughness for ice trail routing at Utqiaġvik draws on satellite remote sensing and is relevant for over-ice travel in general, including escape, evacuation, and rescue. We compared the routing of local snowmobile trails with Synthetic Aperture Radar (SAR) data products to identify specific ranges of ice conditions, roughness, and topography favored for ice trail construction. The same combination of data sources was used to identify potentially beneficial trail routes. Finally, an ice stability and safety assessment was completed for ice road construction and maintenance by industry near the Northstar Island oil production facility. We evaluated small-scale ice displacement data obtained from SAR interferometry to infer internal ice strain and stress and used these data in assessing the potential for fractures to reduce load-bearing capacity

Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry

Arctic landfast sea ice has undergone substantial changes in recent decades, affecting ice stability and including potential impacts on ice travel by coastal populations and on industry ice roads. We present a novel approach for evaluating landfast sea ice stability on a pan-Arctic scale using Synthetic Aperture Radar Interferometry (InSAR). Using Sentinel-1 images from spring 2017, we discriminate between bottomfast, stabilized, and nonstabilized landfast ice over the main marginal seas of the Arctic Ocean (Beaufort, Chukchi, East Siberian, Laptev, and Kara seas). This approach draws on the evaluation of relative changes in interferometric fringe patterns. This first comprehensive assessment of Arctic bottomfast sea ice extent has revealed that most of the bottomfast sea ice is situated around river mouths and coastal shallows. The Laptev and East Siberian seas dominate the aerial extent, covering roughly 4100 and 5100 km(2), respectively. These seas also contain the largest extent of stabilized and nonstabilized landfast ice, but are subject to the largest uncertainties surrounding the mapping scheme. Even so, we demonstrate the potential for using InSAR for assessing the stability of landfast ice in several key regions around the Arctic, providing a new understanding of how stability may vary between regions. InSAR-derived stability may serve for strategic planning and tactical decision support for different uses of coastal ice. In a case study of the Nares Strait, we demonstrate that interferograms may reveal early-warning signals for the breakup of stationary sea ice

Automated Power Lines Vegetation Monitoring using High-Resolution Satellite Imagery

Vegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional Vegetation Management operational practices have proven ineffective and are rapidly becoming obsolete due to the lack of frequent inspection of vegetation and environmental states. The rise of satellite imagery data and machine learning provides an opportunity to close the loop with continuous data-driven vegetation monitoring. This paper proposes an automated framework for monitoring vegetation along power lines using high-resolution satellite imagery and a semi-supervised machine learning algorithm. The proposed satellite-based vegetation monitoring framework aims to reduce the cost and time of power line monitoring by partially replacing ground patrols and helicopter or drone inspection with satellite data analytics. It is implemented and demonstrated for a power distribution system operator (DSO) in the west of Norway. For further assessment, the satellite-based algorithm outcomes are compared with LiDAR survey data collected by helicopters. The results show the potential of the solution for reducing the monitoring costs for electric utilities

Automated Power Lines Vegetation Monitoring using High-Resolution Satellite Imagery

Vegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional Vegetation Management operational practices have proven ineffective and are rapidly becoming obsolete due to the lack of frequent inspection of vegetation and environmental states. The rise of satellite imagery data and machine learning provides an opportunity to close the loop with continuous data-driven vegetation monitoring. This paper proposes an automated framework for monitoring vegetation along power lines using high-resolution satellite imagery and a semi-supervised machine learning algorithm. The proposed satellite-based vegetation monitoring framework aims to reduce the cost and time of power line monitoring by partially replacing ground patrols and helicopter or drone inspection with satellite data analytics. It is implemented and demonstrated for a power distribution system operator (DSO) in the west of Norway. For further assessment, the satellite-based algorithm outcomes are compared with LiDAR survey data collected by helicopters. The results show the potential of the solution for reducing the monitoring costs for electric utilities