Abstract
Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem.
Highlights
According to the Sustainable Development Goal #14 (SDG 14) identified by the United Nations (UN) [1], overfishing is a global challenge which “reduces food production, impairs the functioning of ecosystems and reduces biodiversity”
We show that the variation in fishing-vessel count obtained with our technique agrees with Global Fishing Watch data on fishing-vessel presence
We presented a method for the classification of Synthetic Aperture Radar (SAR) vessel detections into fishing and non-fishing-vessel classes
Summary
According to the Sustainable Development Goal #14 (SDG 14) identified by the United Nations (UN) [1], overfishing is a global challenge which “reduces food production, impairs the functioning of ecosystems and reduces biodiversity”. Improving the sustainable use of marine resources requires monitoring tools which can provide long-term observations of (i) the world’s fish stocks and (ii) fishing fleets’ activity. The latter can be monitored by several systems. AIS and VMS are cooperative systems that rely on vessels to report their information. These systems are compulsory for vessels of the European Union (EU) which are larger than 15 m [2], and vessel data can be collected from shore-based or satellite receivers
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