New AI reef conservation tool monitors, measures from space

A new coral reef conservation tool has been developed by our lab using cutting-edge artificial intelligence (AI) technology. By developing novel deep learning algorithms, we are now able to identify and measure reef halos from space. The study was published recently in Remote Sensing of Environment. Media coverage on KHON and NPR (Hawai’i Public Radio) did a great job of explaining the significance of this research! The study was also published on the NSF Discovery Files podcast.

This work stems from the goal of creating a tool that would facilitate the identification and measurement of reef halos globally. 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. 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 breakthrough is a key step in scaling up – in both space and time – our ability to monitor and quantify aspects of coral reef ecosystem health. By providing a more efficient and effective way to measure coral patch reefs and their surrounding halos, this new method paves the way for the development of a global-scale reef conservation and monitoring tool based on the phenomenon of reef halos.

In the near future, our team is aiming to develop 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.

An example of how our AI tool can identify and measure halos from a satellite image.