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Development of a sea-ice workstation for the automated monitoring of sea ice

Published online by Cambridge University Press:  27 October 2009

Diane Boardman ,
David Darwin ,
Jolyon Martin ,
Neil Mclntyre  and
Ken Sullivan
Diane Boardman
Affiliation:
Earth Observation Sciences Ltd, Farnham Business Park, Farnham, Surrey GU9 8QL
David Darwin
Affiliation:
Earth Observation Sciences Ltd, Farnham Business Park, Farnham, Surrey GU9 8QL
Jolyon Martin
Affiliation:
Earth Observation Sciences Ltd, Farnham Business Park, Farnham, Surrey GU9 8QL
Neil Mclntyre
Affiliation:
Earth Observation Sciences Ltd, Farnham Business Park, Farnham, Surrey GU9 8QL
Ken Sullivan
Affiliation:
Earth Observation Sciences Ltd, Farnham Business Park, Farnham, Surrey GU9 8QL
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Abstract

UK-based operations that range from ship routing and resource exploration to weather forecasting and glaciology have direct and growing interests in the oceans of the polar regions. Typically, information describing sea-ice conditions in localised regions is required on short time scales. To explore this market, the UK's Defence Research Agency, as part of a programme of the British National Space Centre, has commissioned the development of a prototype sea-ice workstation by a consortium led by Earth Observation Sciences Ltd.

The sea-ice workstation (SIWS) uses data from several current earth observation sensors, thereby combining the advantages of regional survey, all-weather capability, and high-resolution imagery. The workstation has been designed to run with a minimum of operator intervention in order to optimise speed of operation and ensure consistency of results. The geophysical processing chains generate charts of the ice edge, ice type, ice concentration, ice-motion vectors, and sea-surface temperatures.

Although taking full advantage of developments made elsewhere, the project has also made significant progress in research into the automated mapping of ice types. Existing ice-motion algorithms have been significantly enhanced as well. Considerable emphasis is being placed on the validation of the results from the system in order to assess their quality, this being one of the major concerns of potential users. The sea-ice workstation was completed in July 1994 and will form the basis for a series of evaluations that are intended to assess the value of the system for mapping and monitoring sea ice.

Type
Articles
Information
Polar Record , Volume 31 , Issue 177 , April 1995 , pp. 155 - 160
Copyright
Copyright © Cambridge University Press 1995

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References

Burns, B.A., Schmidt-Grottrup, M., and Viehoff, T.. 1992. Methods for digital analysis of AVHRR sea ice images. IEEE Transactions of Geoscience and Remote Sensing 30: 589–602.CrossRefGoogle Scholar
Cavalieri, D, 1992. NASA Sea Ice Validation Program for the Defense Meteorological Satellite Program Special Sensor Microwave Imager. Washington, DC: NASA (NASA Technical Memorandum 104559).Google Scholar
Collins, M.J. 1992. Information fusion in sea ice remote sensing. In: Carsey, F. (editor). Microwave remote sensing of sea ice. Washington, DC: American Geophysical Union (Geophysical Monograph 68): 431–441.CrossRefGoogle Scholar
Drinkwater, M.R., Kwok, R., Rignot, E., Israelsson, H., Onstoytt, R., and Winebrenner, D.P.. 1992. Potential applications of polarimetry to the classification of sea ice. In: Carsey, F. (editor). Microwave remote sensing of sea ice. Washington, DC: American Geophysical Union (Geophysical Monograph 68): 419–430Google Scholar
Fily, M., and Rothrock, D.A.. 1987. Sea ice tracking by nested correlations. IEEE Transactions on Geoscience and Remote Sensing 25: 570–580.CrossRefGoogle Scholar
Kwok, R., Cummingham, G., and Holt, B.. 1992. An approach to identification of sea ice types from spaceborne SAR data. In: Carsey, F. (editor). Microwave remote sensing of sea ice. Washington, DC: Americal Geophysical Union (Geophysical Monograph 68): 355–360.Google Scholar
Maul, G.A. 1983. Zenith angle effects in multi-channel infra-red sea surface remote sensing. Remote Sensing of Environment 13: 439–451.CrossRefGoogle Scholar
Muirhead, K. 1989. Automatic classification of AVHRR images in 4th AVHRR User Meeting.Google Scholar
Steffan, K., and 11 others. 1993. Snow and ice applications of AVHRR in polar regions: report of a workshop held in Boulder, Colorado, 20 May 1992. Annals of Glaciology 17: 1–16.CrossRefGoogle Scholar
Verrall, D.J., Styles, P.J., Mclntyre, N.F., and Curtis, D.J.. 1991. Strategies for remote sensing of sea ice. Unpublished report (EOS-91/062-RP-003) for contract RAE 1B/30 for the UK Defence Research Agency.Google Scholar
Wadhams, P., Aldworth, E., Brandon, M., Crane, D., Prussen, E., Reisemann, M., Struckman, P., and Wells, S.. 1994. Sea-ice physics. In: Eicken, H., and Meincke, J. (editors). The expedition of ARKTIS-IX/1 of RV ‘Polarstern’ in 1993. Berichte zur Polarforchung 134: 71–92.Google Scholar