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Fishery, Earth-Observation, Modelling and Prediction (FJOMP) – A pilot study

Authors: Pettersson, Lasse H.; Svendsen, Einar; Kloster, Kjell; Johannessen, Johnny A.;

Fishery, Earth-Observation, Modelling and Prediction (FJOMP) – A pilot study

Abstract

The awareness and possible applications of Earth Observatio (EO) based information products are very limited within the traditional fisheries research and management. However, systematic and integrated use of EO data with more traditional records of fisheries statistics is envisaged useful for several applications related to the fisheries. This will require that the present and future records of both satellite EO, relevant fisheries and other marine and oceanographic data are made available over periods of tens of years and aggregated to a level suitable for an efficient and integrated analysis. Long and extensive EO data records are now, by the Space Agencies or others, systematically processed or pre-processed to a higher level data products and these so called Level-3 data are binned consistently in time and space. Level-3 data products may be well suited for monthly, seasonal, inter-annual or decadal trend analysis in conjunction with other data records obtained at similar time-scales. Although the implementation of FJOMP will be at a national level it is also necessary to consider and evaluate what kind of activities that already have been initiated in other countries world-wide. In particular countries like USA, Japan, South Africa, Chile and Peru have been active in the use of EO data in support of their fisheries – both for management and commercial catch operations. Based on the Pilot Study findings and the main objectives it is suggested to implement a main FJOMP project – “Fisheries Earth Observation and Modelling Project”. It is scheduled for a 18 to 24 months duration, pending on the delay of delivery for the various external data sources used.

NERSC Technical Report no. 174. Funded by the Norwegian Space Centre, Contract no. JOP.8.3.3.08.99.2

Keywords

Earth observation, Time series, Fishery, Climate, Multiple regression, Marine environment, Model, Management, Correlation

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