Data Fusion for Connectivity Analysis between Ocean Regions

Abstract

The ocean is an important part of the global system. Tracking the connectivity between bodies of water is crucial for understanding local, regional and global changes in the ocean dynamics that mediate the spreading of nutrients and influence the marine ecosystem and ocean productivity. We developed a Data Fusion approach that enhances and automates the existing methods for the analysis of this connectivity. This approach combines and condenses two different data sources in two stages, Data Enhancement followed by Data Reduction. The Data Enhancement stage fuses equidistantly gridded data containing physical measurements and trajectories representing movement data. The Data Reduction stage aggregates the fused data into a Markov Model representation of the transition probabilities between ocean regions. We applied this framework to an exemplary analysis for the connectivity between two oceanic areas using real ocean data stemming from marine research. We show that this method directly tackles the limitations of existing marine data analysis methods and furthermore introduces new means to answer questions that had no quantitative answers up to now.