GRADHIST — A method for detection and analysis of oceanic fronts from remote sensing data

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Abstract Oceanic shelf sea fronts have significant effects on local dynamics, ecology and climate. An assessment of the impact of climate change on frontal positions and frontal gradients requires reliable reference data and the possibility to monitor oceanic fronts. Therefore, the development of algorithms which automatically detect frontal positions from Earth Observation (EO) data is an important tool to analyse long EO time series, i.e. to process big data volumes. The development of GRADHIST was driven by the need to generate a climatology for North Sea fronts. GRADHIST is a new algorithm for the detection and mapping of oceanic fronts, which is based on a combination and refinement of the gradient algorithm of Canny (1986) and the histogram algorithm of Cayula and Cornillon (1992). GRADHIST preserves the main principles of both algorithms and can be applied to various ocean parameters as well as to different sensors with very little effort. GRADHIST was validated and tested using both synthetic and real data and applied to sea surface temperature and ocean colour parameters retrieved from satellite data; i.e. data from MODIS (Moderate Resolution Imaging Spectroradiometer), MERIS (MEdium Resolution Imaging Spectrometer), AVHRR (Advanced Very High Resolution Radiometer) and AATSR (Advanced Along-Track Scanning Radiometer). Selected results and statistical analysis of a new North Sea climatology for oceanic fronts are presented and discussed.