Gradient-based edge detection and feature classification of sea-surface images of the Southern California Bight

An edge detection algorithm was developed that is capable of objectively detecting significant edges in remotely sensed images of the surface ocean. The algorithm utilizes a gradient-based edge detector that is less sensitive to noise in the input image than previously used detectors and has the ability to detect edges at different length scales. The algorithm was used to provide a statistical view of surface front occurrences in the Southern California Bight using six years of satellite observations of sea-surface temperature (AVHRR) and chlorophyll (SeaWiFS). Regions of high front occurrence probability were identified near capes, headlands, and islands. In onshore direction, chlorophyll concentration increased and temperature decreased, indicative of coastal upwelling. The algorithm was further applied to coincident time series of temperature and chlorophyll to investigate the event-scale dynamics of mesoscale features and the spatial relationships between physical and biological processes. A simple scheme for identifying and classifying eddies delineated by the edge detection algorithm was developed to yield a census of eddy occurrence in the Southern California Bight.