Coral reefs are one of the most important oceanic ecosystems, yet they are under extreme environmental threats. In the recently issued IPCC report, it is projected that coral reefs will decline by 70-90% by 2100 under the current 1.5°C warming trajectory, and by >99% with a global temperature increase of 2°C. Mass coral bleaching events are the main driver of this reef degradation. Unfortunately, it is not currently possible to monitor bleaching events on a global scale, the environmental drivers are not fully understood, and reliable forecasts are not available to reef managers.
This work takes advantage of technological advances to leverage both the power of modern neural network systems, and large remote sensing datasets. Using remotely sensed data, it aims to evaluate bleaching events on coral reefs in real-time, forecast the likelihood of bleaching events in the near future using a machine-learned model, and evaluates environmental drivers of the problem.
The model's predictions may be used by ecologists to gain a better understanding of global reef health on a daily basis, reef managers and governments to better protect their local reefs, and the scientific community as we better understand the drivers of mass bleaching events.