Continuous long-term observation of coral reefs is essential for gathering crucial data that helps determine the state of environmental health. Coral reefs support 25% of all marine species which makes it necessary that they be monitored for good health. Traditional methods of coral reef monitoring, such as diver-based surveys and airborne observatories, are costly, time-consuming, and pose significant risks to human operators. The limited understanding of dynamics of events such as coral bleaching and their impact on marine biodiversity can be attributed to difficulty in sampling the underwater environments at high spatial and temporal resolutions. Monitoring coral reefs requires reef ecologists to undertake risky and costly diving missions. These missions often involve the use of diver propulsion vehicles, onboard lighting, and spectrometers to survey and map the reefs. There is a need for next generation collaborative robotic system for coral reef mapping and monitoring.
Researchers at Arizona State University and Zandef Deskit Inc. have developed a robotic system for coral reef mapping and monitoring. The system can use an autonomous surface vessel, an underwater autonomous drone, ExoCam-aqua imager that allows aerial synoptic imaging as well as underwater imaging, or any combination thereof. This system enables multi-spectral and spectroscopic imaging at scale and is capable of optimal retrieval of water and coral samples. Additionally, this system can assist in intervention operations such as planting of new corals, or establishment of artificial reefs. These robotic technologies enable a reduction of costs, an increase of scale and allow for high-resolution data collection and analysis across various depths and altitudes.
Potential Applications:
- Coral Reef Monitoring Programs
- Federal Agencies
- Environmental Agencies
- Universities
Benefits and Advantages:
- Low-cost integrated open-source hardware sensing and computational ecosystem that make this system accessible to the public
- Robotic boat can collect optimal water samples adaptively, using predictive models that consume real-time water quality measurements
- High-resolution data collection and analysis across various depths and altitudes