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Development of SAFI Indicators

The SAFI team have overcome many challenges in achieving the aim of consolidating the mass of environmental data from Earth Observation and in situ datasources into useful information. These challenges included: to collect in situ data; to identify the most relevant data resources available from Earth Observation and models in order to address user requirements; and to take full advantage of consortium expertise in biology, Earth Observation, stakeholder engagement and data analysis, to build innovative indicators. The team also had to assess the capacity for exporting the SAFI products worldwide.

A large number of indicators were tested for the fisheries and marine aquaculture industries including, for aquaculture users:

  • Near-real-time (NRT) access to environmental parameters near existing or projected sites;
  • Wave forecasting for the management of operations; anticipation of feeding constraints and anticipation of risk-based harvesting;
  • Licencing support indicators: with access to long term historic archives of environmental data;
  • Optimal site identification for environmental criteria;
  • Productivity estimation for Atlantic mussel farms;
  • Assessment of the effects of climate change on site location.

For the fisheries sector, users can avail of:

  • NRT access to environmental parameters near existing or proposed sites;
  • Estimation and forecasting of spawning periods for small pelagic fisheries and bivalves;
  • Information on fisheries migration routes (e.g. Tuna);
  • Estimation of yearly stock abundance and recruitment at a local scale;
  • Identification of ecological niches and location of potential habitats.

When compared with in situ data from field measurements and analyses, the indicators held their own, showing good correlation between satellite and field measurements.

In relation to the exportability of the indicators, the indicators developed for the fisheries sector were found to be adaptable. It means they should not be deployed blindly on sites they were not specifically developed for, without customisation in line with local in situ data. However, the methods used to develop these indicators have proven to be exportable, showing good results when switching from one area, or one species, to another.

Some of the aquaculture indicators have shown to be transferable and capable of being applied from one area to another with very little or no adaptation. However, local knowledge is essential to adapt the thresholds and statistics used to identify potential sites. At present, site identification is based on an assessment of environmental conditions, and no social or detailed topographical factors are considered. SAFI is designed to allow the user to adapt the indicators and load additional layers of information into the system to include multiple parameters, in order to develop a comprehensive picture of their site.

Chloé Vincent (ACRI-ST)



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