Abstract:
The coastal ecosystems of the Northern Red Sea are globally significant yet remain poorly understood due to limited field access and scarce field observations. This thesis presents a comprehensive framework for ecosystem monitoring in this remote and rapidly developing region, combining high-resolution satellite and UAV imagery, targeted in situ observations, numerical hydrodynamic modeling, and machine learning. Key outcomes include detailed benthic habitat maps, new understanding of physical-biological interactions in reef-bound lagoons, and a regional probabilistic ocean colour model for monitoring chlorophyll-a. These contributions address critical data gaps and offer scalable, non-invasive methods for near real-time monitoring and data-driven management of sensitive marine environments.

Bio:
Nikolaos Papagiannopoulos is working under the supervision of Professor Ibrahim Hoteit. His research focuses on the integration of remote sensing, field data, and numerical modeling to monitor and understand ecosystem dynamics in coastal and data-sparse marine environments. He has contributed to regional advancements in habitat mapping, phytoplankton monitoring, and machine learning applications for ocean colour analysis. His work is particularly centered on the NEOM region, where he aims to support sustainable coastal management through innovative, scalable approaches to environmental observation.