INTEGRATING TOPOGRAPHIC AND UNDERWATER MEASUREMENTS FOR COMPREHENSIVE COASTAL AREA INSPECTION: AN INTERDISCIPLINARY APPROACH

The Ph.D. research project focuses on the definition, implementation and validation of a methodology to execute integrated aerial and underwater surveys of coastal areas and harbor infrastructures to develop new services for marinas, public authorities, and blue economic operators. The present thesis work is carried out with an interdisciplinary approach mainly involving geomatics, robotics, ecology and other disciplines to suggest a methodology that may be realistically adopted by scientists. The integration of robotic technologies, including Unmanned Aerial and Surface Vehicles (UAVs and ASVs), with various remote sensing techniques, such as underwater acoustic sensors (single and/or multibeam echosounders), arial and underwater photogrammetry, enables the acquisition of reliable and high-resolution data. This facilitates a holistic understanding of coastal environments by generating 3D models that seamlessly integrate the seabed and landscape into a unique reference frame. A common strategy is applied across the three survey scenarios, each employing different techniques, to produce a unified 3D model that merges both submerged and emerged survey data. A key challenge addressed in working with these different environments is underwater positioning, solved through an innovative image-based techniques. The methodology involves synchronized surveys, where UAVs capture images of a snorkeler or ASV performing underwater measurements. These images are used to estimate the position of the snorkeler or ASV, enabling the georeferencing of underwater features. The approach relies on accurately determining the underwater camera’s position by synchronizing its data with UAV imagery in both time and space. The resulting georeferenced 3D model provides a comprehensive spatial representation from seabed to land. In contrast, for harbor environment, where safety rules, navigation protocols, and airspace restrictions make things trickier. Here, the approach to merging datasets has to work within these limits, to obtain a complete and accurate 3D model. This cost-effective and robust solution enhances the understanding of underwater habitats and their spatial relationships, supporting coastal management and resilience efforts. The methodology has been developed and validated across various scenarios, demonstrating its practicality and effectiveness.