Coral reefs are among the most vulnerable ecosystems facing the impacts of climate change, making it necessary for robust monitoring techniques to track their health and resilience. While traditional methods of coral reef monitoring require significant manpower, remote sensing technologies offer a promising alternative. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides high-resolution data that can potentially revolutionize coral reef monitoring. This proposal aims to address critical gaps in utilizing ICESat-2 data and machine learning for coral reef monitoring.

Three interconnected chapters will explore: 1) detecting coral reefs using ICESat-2 data, 2) identifying hierarchical habitat classifications, and 3) developing a robust coral reef change-detection methodology. The proposed algorithms and methodologies seek to harness the power of machine learning and data analysis to advance remote sensing techniques for coral reef monitoring. By utilizing ICESat-2 data, this research aims to provide a reliable, cost-effective, and high-resolution alternative for monitoring coral reef changes.