Abstract

This research is the world's most biodiverse and ecologically critical ecosystems, they are being severely impacted by climate change, ocean acidification, pollution, and even overfishing. For effective conservation and management, coral reefs require timely health monitoring. This research investigates combining deep learning methods with remote sensing technologies to detect precocious coral reef degradation. Using high-resolution satellites, hyperspectral data, and convolutional neural networks (CNNs), which automatically scan for signs of stress like discoloration, algae overgrowth, and structural erosion, reefs are analyzed in depth. The model achieved over 90% accuracy on classifying diverse multi-regional reef condition assessments, surpassing traditional image processing and classification frameworks. Additionally, the system could detect patterns of reef degradation weeks before they are visually noticeable. This offers great promise for proactive intervention planning. There is no doubt that the combination of remote sensing with deep learning improves the detection accuracy and, at the same time, provides effortless, inexpensive monitoring of reefs over extensive areas. The study effectively exemplifies the AI tools designed to monitor environmental impacts in real-time to combat rising ecological pressure for coral reef conservation.