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The challenge

Revolutionising the "Manta Tow" technique of reef assessment

Home to a large number of marine animal species, coral reefs are unique, fragile and are in danger from global warming, pollution, overfishing and boat traffic. A comprehensive understanding of the current and future of the reef requires the collection of data using techniques such as the 'Manta Tow'. This methodology requires a snorkel diver (observer) to be towed at a constant speed behind a boat. The observer makes a visual assessment of specific variables during each two-minute session, manually recording this data on a sheet attached to the manta board.

Figure 1. The manta tow technique. ©  Australian Institute of Marine Sciences - Long term monitoring

This technique is used for a broad-scale assessment of large areas of reef and can provide an estimation of the distribution of marine organisms. However, it relies on human snorkelers, which limits its scaleability as it is relatively slow (survey rate of two knots), labour-intensive, constrained to shallow reefs (<10-15m depth) and daytime hours. Technology can greatly enhance its accuracy and efficiency.

Our response

A towed-underwater glider system

CSIRO's Scientists from Data61's Cyber Physical Systems and Oceans and Atmosphere are collaborating with BABEL-sbf and the Queensland University of Technology to develop monitoring and communication technologies for the collection of video data in coastal ecosystems, designing models and algorithms that employ the data to recognise important environmental and physiological characteristics of these ecosystems. The CSIRO research is led and sponsored through CSIRO's Active Integrated Matter Future Science Platform.

Figure 2: Autonomous glider

Currently, the team are developing a towed underwater glider platform for broad-scale coral reef surveys. The platform is equipped with computer vision, accurate underwater geo-location, near real-time communications, and marine vessel autonomy, which can improve the accuracy and latency of broad-scale surveys by orders of magnitude.

Computer vision in particular has the power to translate huge video datasets collected by the glider into higher-level actionable information, such as species population estimates and spatial coverage. Automated visual classification machine learning algorithms are applied to bypass the time-consuming process of archiving and uploading concise and large video datasets.

An autonomous underwater glider robot that is equipped with smart vision, advanced sensors and machine learning technology, here coloured green with wings and a tails for navigation, is pictured underwater with a line connecting it to SailDrone, an autonomous robot that sits on the surface of the water. Its function is to drag the glider for the capture and collection of marine visualisation capture, with it's catamaran-style design utilising wind thrust and tide strength.

An autonomous underwater glider robot equipped with smart vision.

The initial glider design must be dragged by a human-operated surface vessel, however, the process can be fully automated when deployed with a long-range autonomous surface vessel such as SailDrone.

The results

Exploration of deeper and more remote environments and mitigation of major threats

This improvement in reef survey capability will translate to major societal and environmental impacts, through better, more accurate, data-driven management of coral reef environments and timely mitigation of their major threats. In addition, our platform will enable exploration of deeper and more remote environments, which will lead to new science and better understanding of coral reef habitats.

Annual GBRMPA-funded diver-based manta operations on the Great Barrier Reef (GBR) average 1.5 hours per day and 73 days per year, covering 50 reefs in a year, or about two per cent of the GBR. A glider, surveying at 4kts and operating six hours in a day, would increase the GBR coverage 6-8 times over the 73 day-per-year-period. In addition to the dramatic coverage improvement, we estimate 10 to 100-fold improvements in target localisation accuracy and 10 to 100-fold improvements in latency of information flows, leading to a faster and more precise response.

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