Precise upwelling and filaments automatic extraction from multisensorial imagery

Abstract

The upward movement of cool and nutrient-rich waters towards the surface leads to horizontal alterations in the distribution of physical, chemical and biological properties. Remote sensing is being extensively applied to detect such coastal upwellings; however, the enormous amount of data daily generated obliges to develop automatic detection and prediction tools. The problem of identifying oceanographic mesoscale structures has been studied using a variety of image processing techniques, however, the outstanding difficulties encountered in the traditional approaches are the presence of noise, mainly due to clouds and other atmospheric phenomena; the fact that gradients are weak and provide excess of information; the strong morphological variation that impedes an accurate geometric representation and the absence of a valid analytical model for the structures. In this context, the proposed automatic upwelling extraction methodology overcomes the preceding detection inconveniences and achieves a highly accurate structure detection and identification. This automatic technique has been applied to the detection and feature extraction of coastal upwellings and filaments in the northwest African coast, the Alboran Sea and Cape Ghir using imagery from the AVHRR/2&3, SeaWiFS and MODIS sensors. The system has proven to be very effective and robust in a wide variety of climate conditions.