Automatic mapping of aquaculture activity in the Atlantic Ocean

Abstract

The production of wild fish has remained relatively stable in the last two decades, whereas aquaculture organism production has increased to the point where it has exceeded wild catches. In this context, accurate and up-to-date information about the current usage of marine areas for aquaculture is crucial for the planning of marine activities. However, this data is often limited to national authorities, and discrepancies between planned and real practices can arise in available data. In this study, a novel methodology to automatically map and verify the current activity of aquaculture crops across European regions based on freely available satellite data is proposed. The European Space Agency’s (ESA) Sentinel-1 mission provides Synthetic Aperture Radar (SAR) images, which serve as the basis for the analysis. Multiple SAR images of the same locations are processed using ESA Sentinel Application Platform (SNAP) software and merged to remove temporal noise-like artifacts caused by factors such as ships and waves. Next, the iDPolRAD algorithm is employed to detect potential aquaculture sites, which initially include noise from coastal zones and unwanted human and natural structures that pass through the filter. The aquaculture sites are classified using a ResNet18 model with 93% of the sites correctly classified. This implies that it is feasible to monitor marine areas using satellite radar data to track aquaculture areas. However, generalization power across regions is poor likely due to the diversity of types of structures used and species cultivated. Further studies are needed to investigate factors influencing the detectability of different aquaculture sites such as cage geometry or SAR image resolution in order to enhance the accuracy and comprehensiveness of the mapping process. This study highlights the potential of SAR data, coupled with image processing and classification techniques, as a viable means to map large marine areas dedicated to aquaculture.

Keywords

• Environmental impacts
• Remote sensing
• Big data
• Water quality
• Sentinel-1
• Aquaculture mapping