Using cutting-edge AI technology, coral ecologists in the UH Mānoa School of Ocean and Earth Science and Technology (SOEST) are now able to identify and measure reef halos from space. Also known as grazing halos or sand halos, These features consist of ring-like patterns of bare sand that occur around coral patch reefs, and their presence is readily visible from satellite images.
“Reef halos may be important indicators of the health and vitality of coral reefs, but until now, their measurement and tracking has been a challenging and time-consuming process,” said Simone Franceschini, lead author of the study and postdoctoral research fellow in the Madin Lab at the Hawai’i Institute of Marine Biology (HIMB) in SOEST. “However, with this new method, we can accurately identify and measure reef halos on a global scale in a tiny fraction of the time it would take a human being to accomplish the same task.” “This work stems from our team’s understanding of the current state of AI technology and its potential applications for conservation research in coral reef ecosystems,” Madin added.
Although AI technology has shown excellent performance in the field of image analysis, the identification of halos—a complex, ecological pattern with much variation—was a challenge that required combining different deep learning algorithms.
“Reef halos are sometimes very clear in satellite imagery, with distinct edges and high contrast with background vegetation, but sometimes they are quite faint and hard to distinguish—even by a highly trained observer,” said Franceschini.”In the end, our team was able to develop a set of algorithms capable of taking into account the diversity of these patterns globally and identify and measure halos with surprising accuracy. It is hugely satisfying for us to now have built something that can accurately identify more than 90% of halos in some parts of the world.”
The team is aiming to develop, in the near future, a freely-available web app that can allow conservation practitioners, scientists, and resource managers to remotely, quickly, and inexpensively monitor aspects of reef health using satellite or drone imagery.
Image: Halos in the Red Sea. Credit: CNES/Airbus; DigitalGlobe.
The study is published in the journal Remote Sensing of Environment.
More information: Simone Franceschini et al, A deep learning model for measuring coral reef halos globally from multispectral satellite imagery, Remote Sensing of Environment (2023). DOI: 10.1016/j.rse.2023.113584