Dual-Constraint Spatiotemporal Clustering Approach for Exploring Marine Anomaly Patterns Using Remote Sensing Products

Spatiotemporal clustering patterns of marine anomaly variations are the focus of much current global climate change research. Marine anomaly variations have multidimensional attributes and are spatiotemporally continuous; existing methods for clustering face challenges in mining effectively their spatiotemporal clustering patterns. Using long-term marine remote sensing products, we present the
dual-constraint spatiotemporal clustering approach
(DcSTCA) for exploring marine clustering patterns. The DcSTCA includes three steps. The first step constructs a spatiotemporal grid cube based on the spatial connectivity and time evolution process of marine anomaly variations, which is used to search the spatiotemporal neighborhood. The second step calculates the proximities in space, time, and thematic attribute to obtain the spatiotemporal clustering cores and spatiotemporal density of each grid cell. The final step derives spatiotemporal clustering patterns by connecting the clustering cores and their spatiotemporal neighbors according to their density connectivity. Experiments on simulated datasets are used to demonstrate the effectiveness and the advantages of the DcSTCA compared with spatial–temporal density-based spatial clustering of applications with noise (ST-DBSCAN). The applications on sea surface temperature in the Pacific Ocean show that the DcSTCA can effectively explore marine clustering patterns from remote sensing products, and these mined clustering patterns may provide new references for global change research.