Prototype of a multi-platform remote sensing service for fishing forecasting

The present work concerns the development of an automatic Fishing Forecasting System (FiFoS) where satellite observations, ancillary data and in situ measurements (Catch Per Unit Effort) are used to set up, calibrate and validate a fishing forecasting model. Multi-temporal and multi-sensor data fusion techniques are applied to multi-spectral data in order to detect chlorophyll and sea temperature fronts that according to physical models of the upwelling phenomena are related to areas rich of phytoplankton nutrients where a high concentration of pelagic fish is expected.