Analysis and visualization techniques for integrating remotely sensed sea ice data with plankton observations
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 Sa...
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| Format: | Thesis |
| Language: | English |
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2008
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| Online Access: | https://eprints.utas.edu.au/19913/ https://eprints.utas.edu.au/19913/1/whole_FengJun2008_thesis.pdf |
| Summary: | 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. |
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