A Process-Oriented Exploration of the Evolutionary Structures of Ocean Dynamics with Time Series of a Remote Sensing Dataset.

Advanced Earth observation technologies provide a tool for the study of ocean dynamics either in basins or in oceans. In a comparison of when and where, how ocean dynamics evolves in space and time is still a challenge. In view of an evolutionary scale, this paper proposes a novel approach to explore the evolutionary structures of ocean dynamics with time series of a raster dataset. This method, called PoEXES, includes three key steps. Firstly, a cluster-based algorithm is enhanced by process semantics to obtain marine snapshot objects. Secondly, the discriminant rule is formulated on the basis of successive marine snapshot objects' spatiotemporal topologies to identify marine sequence objects and marine linked objects. Thirdly, a sequence-linked object-based algorithm (SLOA) is used for marine sequence objects and linked objects to obtain their evolutionary structures and to achieve four evolutionary relationships, i.e., development, merging, splitting, and a splitting-merging relationship. Using the evolutionary relationships and their occurring orders in a lifespan of ocean dynamics, this paper reformulates five types of evolutionary structures, which consist of origination nodes, linked nodes, sequence nodes and dissipation nodes. The evolutionary-scale-based dynamic structure ensures the optimum evolutionary relationships of ocean dynamics as much as possible, which provides a new way to design a spatiotemporal analysis model for dealing with geographical dynamics. To demonstrate the effectiveness and the advantages of PoEXES, a real 40-year dataset of satellite-derived sea surface temperatures is used to explore the evolutionary structure in global oceans; the new findings may help to better understand global climate change.