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Analysis and visualization techniques for integrating remotely sensed sea ice data with plankton observations

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Feng, Jun (2008) Analysis and visualization techniques for integrating remotely sensed sea ice data with plankton observations. Research Master thesis, University of Tasmania.

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Abstract

The study of sea ice dynamics and zooplankton in the Southern Ocean has been
undertaken over a long period. Antarctic sea ice is monitored by a number of Special
Sensor Microwave Imager (SSM/I) instruments. SSM/I is a passive microwave
radiometric system and operated by the Defense Meteorological Satellite Program.
Analysed data comprising sea ice concentrations are routinely produced on a 25-km
grid and there is a complete collection covering the years from 1987 to 2007. Over
many years of zooplankton observations in Southern Ocean a large amount of
information has been collected using the Continuous Plankton Recorder. The latest
survey aims to study regional, seasonal, inter-annual and long-term variability in
zooplankton abundance, species composition, and distribution patterns in the
Southern Ocean zooplankton communities (Hosie et al. 2003).
Visualisation techniques are used to display these two important data sets. They can
facilitate the observation, analysis and the effective prediction of dynamics of the sea
ice and zooplankton. This research utilised data sets provided by the Australian
Antarctic Division (AAD) and obtained as part of their recent study of the Southern
Ocean in the region of between 50°E and 150°E, and south of 60°S.
The research has demonstrated some of the opportunities provided by the use of
scientific visualisation to present satellite images of the sea ice and associated
zooplankton information to the researchers. It will assist the researchers to analyse
the data characteristics, observing dynamic effects by manipulating user interactive
simulations. The research has also confirmed that, compared to the manual
approaches currently employed, it is a time saving process achieved by customized
computational analysis of large of datasets.

Item Type: Thesis (Research Master)
Copyright Holders: The Author
Copyright Information:

Copyright 2008 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

Available for use in the Library and copying in accordance with the Copyright Act 1968, as amended. Thesis (MComp)--University of Tasmania, 2008. Includes bibliographical references

Date Deposited: 09 Dec 2014 00:14
Last Modified: 16 Aug 2016 01:19
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