Optimizing the use of open and sub-meter (VHR) resolution satellite data to generate an aquaculture atlas

Aquaculture, and in particular marine aquaculture, is expected to grow significantly over the following decades (source FAO, OECD). This growth should be sustainable and the impact of aquaculture on the environment should then be carefully monitored.  A prerequisite for this monitoring is the identification and location of aquaculture sites, particularly in coastal areas where conflict of spatial usage is frequent. BlueBRIDGE has implemented a tool to generate an Aquaculture Atlas and has tested it in three countries: Greece, Malta and Indonesia (See https://bluebridge.d4science.org/web/aquacultureatlasgeneration).

For Greece and Malta use cases, the aim was to create an inventory of all fish farms using in input existing information available at FAO (NASO database), and very high resolution Earth Observation data required to detect relatively small aquaculture structures (e.g. cages with a diameter of 10 metres).

For the Indonesian use case, the aim was to provide a preliminary regional mapping of the aquaculture areas (shrimp ponds) in South Sulawesi and to discriminate them from other wet coastal areas (rice paddies).

Remote sensing was the key tool for production of an aquaculture atlas, and the best practice implemented in BlueBRIDGE is related to the optimization of the use of open and VHR satellite data.

The BlueBRIDGE best practice


To optimize the use of open and VHR satellite data the following approaches were implemented:

  • Exploiting Copernicus and other relevant Earth Observation (EO) data: Copernicus data can be used for identification and discrimination of large aquaculture features in remote coastal areas (for example in BlueBRIDGE, Copernicus data have been used to discriminate rice paddies and shrimp ponds in Indonesia). Copernicus data can be displayed as ancillary information when the resolution is not sufficient to detect systematically small aquaculture features (this data has been used to generate the Greek Atlas).
  • Avoiding duplication of facilities and data storage: The separation of the process between two infrastructures (1 - at CLS for image processing and data store and 2- BlueBRIDGE infrastructure for aquaculture product storage and further exploitation by users or other VREs) avoids duplication of image storage and processing facilities;
  • Using existing, proven, validated and standard features: Both CLS and BlueBRIDGE VRE rely on existing infrastructure services that serve multiple communities. All elements aim to provide re-usable and adaptable services that can also serve other purposes. Examples include a single webviewer for very different products, the use of infrastructure features for users and GUI management, and the use of interoperable services between CLS and BlueBRIDGE infrastructures.

The above mentioned best practice is at the heart of the Aquaculture Atlas VRE. This VRE can support both institutional stakeholders with a mission of aquaculture status assessment (at global, regional or national level) and scientific users.



Why this is considered a best practice

Best Practice Analysis


The best practice was validated by analysing the results of the Atlas generated by the BlueBRIDGE VRE.  The analysis of the results relied on internal validation procedures (at CLS): once approved by the CLS analysts, the results were accessed in superuser by experts designated by FAO. Once validated by the superuser, the features were made accessible to the standard user.


It is expected that the resulting Aquaculture Atlas VRE will contribute to updating existing databases in use (NASO at FAO), and support the development of this capacity in emerging economies who can operate the VRE.


The practice contributed to an innovative way to use VHR Data (Bing or Google Earth, subject to license conditions) or Sentinel data (time series of Sentinel 1 and 2). It also equipped the service with a data management and review service, which allows the community to collaborate across the EA and GIS boundary. The images produced can be reviewed interactively.

Success Factors

Agreement to exploit VHR data in this context at an affordable cost, where required (e.g. Greek use case.), support for capacity building actions (e.g. for Indonesian use case) to extend the services portfolio of the VRE to align with local needs (such as advanced GIS features with post-process the results)


For future operations some consideration is needed to guide the process from the research context and maturity level (proof of concept through three use cases) achieved in BlueBRIDGE, to operational provision of HR EO data to real communities. This is being refined (See D2.5) with FAO leading the development of a sustainable business model. This model will cover the MoU/agreements with commercial data providers/national space agencies or through FAO.

Replicability and/or up-scaling

The Greek (or Malta) use case is easy replicable in any other country of a similar size. It only requires around 8 man-days + VHR license cost.

The Indonesian use case instead requires around 30 man-days as there are less input data available, and it needs more time for algorithm tuning and validation (Sentinel use). Estimation and production model (need for other partners to be considered) to be refined for fine resolution.

Lessons Learnt

The Atlas Generation VRE, resulting from the implementation of the above described practice, meets all the community requirements, and can be used. However, for a complete analysis of the imagery, more contextual information is required such as in situ measurements, hydrographic data, and the recommendation is therefore to embed the Atlas Generation VRE in a wider spatial planning or country-atlas