Optimizing Marine Ecosystem Protection: Combining SAR and Optical Remote Sensing with AI for Oil Spill and Illegal Fishing

Oil spills and illegal fishing pose significant threats to marine ecosystems and coastal communities. This study investigates sophisticated techniques for detecting and monitoring oil spills using remote sensing and data analytics. It employs satellite technologies, such as Synthetic Aperture Radar (SAR) and optical imagery, to identify oil spills by examining variations in electromagnetic waves and spectral data. The research enhances spill detection accuracy and efficiency through change detection methods and detailed analysis with tools like ArcGIS. Furthermore, the study introduces "DEEP BRINE," a unified platform that integrates data from multiple sources, including satellite imagery, environmental sensors, Flag of Convenience (FOC) registrations, and Global Fishing Watch (GFW). This platform is designed to manage oil spills and detect illegal fishing by using machine learning algorithms, such as Random Forest, for predictive analysis. DEEP BRINE aims to notify authorities of illegal fishing activities and improve oil spill response strategies, thereby safeguarding marine biodiversity and promoting sustainable fisheries. The proposed system is intended to enhance decision-making and response efforts through advanced analytics and AI, offering a comprehensive and user-friendly approach to managing environmental and regulatory challenges. DEEP BRINE aims to bolster environmental protection and support coastal communities that depend on healthy marine ecosystems.