Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.

International Press of Boston - publishers of scholarly mathematical and scientific journals and books

Understanding the marine environment dynamics to accordingly design computational predictive tools, represents a factor of paramount relevance to implement suitable policy plans. In this framework, mesoscale marine events are important to study and understand since human related activities, such as commercial fishery, strongly depend on this type of phenomena. Indeed, the dynamics of water masses affect the local habitats due to nutrient and organic substance transport, interfering with the fauna and flora development processes. Mesoscale events can be classified based on the presence of specific hydrodynamics features, such as water filaments, counter-currents or meanders originating from upwelling wind action stress. In this paper, a novel method to study these phenomena is proposed, based on the analysis of Sea Surface Temperature imagery captured by satellite missions (METOP, MODIS Terra/Aqua). Dedicated algorithms are presented, with the goal to detect and identify different observed scenarios based on the extraction and analysis of discriminating quantitative features. Promising results returned by the application of the proposed method to data captured within the maritime region in front of the southwestern Iberian coasts are presented.

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT’s Metop and NASA’s Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.