Data Fusion for Connectivity Analysis between Ocean Regions

Trahms, Carola; Wolker, Yannick; Handmann, Patricia; Visbeck, Martin; Renz, Matthias

doi:10.1109/escience55777.2022.00046

Cited by 2 publications

(2 citation statements)

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“…This approach aims at solving the limitations of traditional analysis methods and it follows the complete Data Fusion framework (Figure 3. Understanding the connectivity between bodies of water is important for understanding the changes in ocean dynamics that influence the spreading pathways of nutrients and thus, marine ecosystems [TWH+22]. The spreading and formation of North Atlantic Deep Water and thus, the connectivity between Labrador and Irminger Sea, are a part of the Atlantic Meridional Overturning Circulation.…”

Section: Data Fusion For Detecting Dominant Spatial Movement Patternsmentioning

confidence: 99%

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Marine Data Fusion for Analyzing Spatio-Temporal Ocean Region Connectivity

Trahms

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This thesis develops methods to automate and objectify the connectivity analysis between ocean regions. Existing methods for connectivity analysis often rely on manual integration of expert knowledge, which renders the processing of large amounts of data tedious. This thesis presents a new framework for Data Fusion that provides several approaches for automation and objectification of the entire analysis process. It identifies different complexities of connectivity analysis and shows how the Data Fusion framework can be applied and adapted to them. The framework is used in this thesis to analyze geo-referenced trajectories of fish larvae in the western Mediterranean Sea, to trace the spreading pathways of newly formed water in the subpolar North Atlantic based on their hydrographic properties, and to gauge their temporal change. These examples introduce a new, and highly relevant field of application for the established Data Science methods that were used and innovatively combined in the framework. New directions for further development of these methods are opened up which go beyond optimization of existing methods. The Marine Science, more precisely Physical Oceanography, benefits from the new possibilities to analyze large amounts of data quickly and objectively for its exact research questions. This thesis is a foray into the new field of Marine Data Science. It practically and theoretically explores the possibilities of combining Data Science and the Marine Sciences advantageously for both sides. The example of automating and objectifying connectivity analysis between marine regions in this thesis shows the added value of combining Data Science and Marine Science. This thesis also presents initial insights and ideas on how researchers from both disciplines can position themselves to thrive as Marine Data Scientists and simultaneously advance our understanding of the ocean.

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Section: Data Fusion For Detecting Dominant Spatial Movement Patternsmentioning

confidence: 99%

“…The black lines indicate a good pick for the optimal number of clusters based on these plots. The lower line at twelve clusters is the same number that was picked through an expert interview [TWH+22].…”

Section: Deciding On the Optimal Number Of Clustersmentioning

confidence: 99%