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Integrated coastal radar system for Arctic waters: Monitoring of maritime coastal traffic and support of disaster response and mitigation. Hajo Eicken, Josh Jones, Hyunjin Choi Druckenmiller, Andy Mahoney Geophysical Institute, University of Alaska Fairbanks, [email protected]

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Integrated coastal radar system for Arctic waters: Monitoring of maritime coastal traffic and support of disaster response and mitigation

Hajo Eicken, Josh Jones, Hyunjin Choi Druckenmiller, Andy Mahoney

Geophysical Institute, University of Alaska Fairbanks, [email protected]

  • Arctic maritime environmental security, critical data & their acquisition

  • Integrated coastal radar system

  • Decision-support through automated motion and event tracking

  • Integrating the radar with response efforts & other systems

  • Next steps

NSIDC.org


Project status, 10 Jan 2011

  • Algorithms for automated extraction of hazard information developed & validated

  • Paper to technical journal to be submitted in Jan 2011

  • Programs for automated analysis and decision-support undergoing testing, completed by summer 2011

  • Radar design study completed, components for complete system ordered, to be installed spring/summer 2011

  • Review & framework for environmental security in ice-covered waters (strategy to tactics)

  • Paper to be submitted to MTS Special CIMES Issue

  • Dialog with USCG and other DHS-CoE continues (seminar series on Defining Risk in Offshore Resource Development, informal exchange)


  • The Arctic Ocean is opening up: Ice retreat, increased economic activity, growing maritime traffic

  • Environmental security: Response & mitigation of hazards & disasters in extreme environments (e.g., oil spill in ice)

  • Tracking & forecasting at relevant space & time scales: Address key gaps (sub-satellite scale) through integrated coastal observing system

National Snow and Ice Data Center


Arctic maritime environmental security


Arctic maritime environmental security: Strategic vs. tactical/operational perspectives


Maritime environmental security in the US Arctic

Map: A. Gaylord, Nunatech based on AK-DNR & BOEMRE & NSIDC data


Satellite coverage & ice movement


Satellite repeat rates

Coastal radar


Improving cold-regions maritime domain awareness through an integrated coastal observing system

  • Remote sensing* (km-scale): Coastal environments & infrastructure, ice hazards

  • Coastal radar (sub-km scale): Vessel & ice tracking, ice dynamics & potential disaster response

  • Aerial surveys, ice & sub-ice sensor systems*

  • Local knowledge*: Potentially important role for disaster response

  • Integration of data streams, GIS-based decision support systems

    * Leveraged through integration & assimilation of existing ocean observing system resources (AOOS.org) and partnering with Arctic Observing Network


Radar specifications and design

Current system:

  • Furuno X-band FR7112, 10kW, 1.6m open array, 22m a.s.l.

  • Xenex 2000 A/D converter/controller (4-bit dynamic range)

  • Problems: Icing & wind drag (custom-built de-icer), range 10-20 km, low effective dynamic range


Radar specifications and design

Current system:

  • Furuno X-band FR7112, 10kW, 1.6m open array, 22m a.s.l.

  • Xenex 2000 A/D converter/controller (4-bit dynamic range)

  • Problems: Icing & wind drag (custom-built de-icer), range 10-20 km, low effective dynamic range


Considerations for improved system

Ordered system:

  • Furuno X-band FAR2127, 25kW, 2.4m open array, 22m a.s.l.; heavy-duty commercial de-icing unit

  • Digital data stream: Russell Technologies Signal Processor

  • Challenges: Furuno’s migration to all-digital systems; custom-install of de-icing system (delivery now at May 2011)


Decision-support: Automated detection of ice motion, hazard events & surface vessels

Goals:

  • Analysis of radar image sequences to extract quantitative information about velocity fields and trajectories of individual features & ice pack

  • Automated detection of hazardous events (break-outs, ice shoves, etc.)

  • Automated delineation of stable/unstable zones

Collaboration with University of Delaware, Dept. of Computer & Information Sciences

Dr. Chandra Kambhamettu, Director – Video/Image Modeling & Synthesis Lab ([email protected])

Rohith MV, Ph.D. candidate ([email protected])


Decision-support: Automated detection of ice motion, hazard events & surface vessels

Challenges:

  • Complex occlusions

  • Low signal-to-noise ratio

  • Signal strength highly sensitive to position & orientation of reflectors

  • Inhomogeneous distribution of features

  • Non-rigid body motion


Motion Field: Feature tracking

  • Sparse motion fields: Feature tracking (Lagrangian velocity vectors)

  • Lucas-Kanade tracker (edge/point detection based on eigenvalues of time-shifted radar return signal)

  • Movement

  • of points

  • linearized

  • Least-sq.

  • solution


Motion Field: Feature tracking

  • Sparse motion fields: Feature tracking (Lagrangian velocity vectors)

  • Lucas-Kanade tracker (edge/point detection based on eigenvalues of time-shifted radar return signal)

  • Movement

  • of points

  • linearized

  • Least-sq.

  • solution


Motion Field: Feature tracking

  • Sparse motion fields: Feature tracking (Lagrangian velocity vectors)

  • Lucas-Kanade tracker (edge/point detection based on eigenvalues of time-shifted radar return signal)

  • Movement

  • of points

  • linearized

  • Least-sq.

  • solution


Motion Analysis: Stable Regions

  • Velocity potential field (hourly-daily mean) defines contours between stationary and moving ice

  • Contour refined from smoothness & potential constraints

  • Compares well with manual & SAR data, more accurate due to higher sampling rate


Motion Analysis: Break-out detection

  • Early, automated identification of break-outs (hazard mitigation)

  • Hidden Markov Model approach: Statistics of radar backscatter used to estimate state & trajectory of system (velocity & backscatter variations associated w/ break-out)


Motion Analysis: Break-out detection

  • Early, automated identification of break-outs (hazard mitigation)

  • Hidden Markov Model approach: Statistics of radar backscatter used to estimate state & trajectory of system (velocity & backscatter variations associated w/ break-out)


Motion Analysis: Break-out detection

  • Early, automated identification of break-outs (hazard mitigation)

  • Hidden Markov Model approach: Statistics of radar backscatter used to estimate state & trajectory of system (velocity & backscatter variations associated w/ break-out)


Motion Analysis: Detecting & Tracking Anomalous Motion

  • Automated tracking of individual ice floes

  • Detection of anomalous motion (non-linear acceleration/deceleration), e.g., grounding ice


Linkage to ice based wire-less sealevel & temperature sensors

Integration with remote sensing data & local trail information

Use as decision-support tool

Integration with observatory components

2008 Barrow Ice Trails -

Map produced by

Matthew Druckenmiller and collaborators


Jacob Adams Crew Trail, 2008

2008 Barrow Ice Trails -

Map produced by

Matthew Druckenmiller

Photo: Craig George


Safety on the ice: Iñupiaq knowledge, environmental observing systems & safety engineering

  • Local expertise: specific role of local, indigenous knowledge (LIK) in regulation and planning still being discussed

  • Value & primacy of LIK with respect to safety mostly undisputed

  • Example of Escape, Evacuation, Rescue (EER)

Based on ISO 19906 - DRAFT


Integration into IR: Needs & next steps

  • Meet needs of USCG & response teams

  • Integration with remote power module & ocean radar

  • Integration with local expertise

  • Arctic maritime environmental security: Training & discourse w/ USCG, industry & stake-holders – Nx2020 Risk Seminar Series, further continuing education offerings?

Based on Alaska Clean Seas, Tech. Manual & FEMA Handbook

  • Building capacity: Link with DHS Disasters, Coastal Infrastructure & Emergency Management (DIEM) Center at UNC


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