https://www.um.edu.mt/library/oar/handle/123456789/11478 2026-04-15T08:35:22Z https://www.um.edu.mt/library/oar/handle/123456789/145381 Title: Innovative methods for detecting sea turtle nests : a combination of UAV photogrammetry, GPR, and artificial intelligence for non-invasive monitoring and conservation Abstract: Sea turtle nesting represents one of the most vulnerable stages in their life cycle; therefore, protecting nesting sites is essential for the long-term survival of their populations. Traditional nest detection methods are often invasive and may disturb nesting females. This study introduces a non invasive approach for detecting and monitoring sea turtle nests through the combined use of advanced technologies. Specifically, Ground Penetrating Radar (GPR) and Artificial Intelligence (AI) are employed to automatically identify turtle tracks and assist in locating potential nesting sites. As part of this study, fieldwork was conducted at Golden Bay, Malta, where a simulated nest of loggerhead turtle (Caretta caretta) was put together to evaluate how effectively and accurately GPR can find an underground chamber containing eggs. To confirm the radar data, a 3D LiDAR model was made of the internal structure of the simulated nest, thus providing a reference dataset for the interpretation of radargrams. Meanwhile, an AI algorithm was instructed to automatically recognize turtle tracks from beach photos, thus facilitating the identification of potential nesting areas. The integrative approach of these techniques demonstrates the potential of non-invasive technologies to enhance the efficiency of sea turtle nest detection and conservation. The findings contribute to the development of modern conservation strategies, particularly within small Mediterranean rookeries such as Malta, where nesting events are rare and spatially constrained. Description: M.Sc.(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145380 Title: High–resolution 3D reconstruction of sea caves in Malta through underwater photogrammetry techniques Abstract: This thesis aims to develop a high-resolution, three-dimensional photogrammetric model of a selected sea cave in the Maltese Islands, this will allow for the monitoring of geomorphic change and the rate of coastal erosion. The resulting model will provide a spatially accurate and visually detailed baseline for scientific analysis of coastal geomorphology and long-term monitoring of erosional processes with data integrated from aerial, terrestrial, and underwater sources. This model will combine data sets from terrestrial, submerged and aerial views of the cave, something that at the time of writing has yet to be done. Data collection was accomplished via the use of two GoPro 7 Black editions for the photogrammetric model and an iPhone 15 for a LiDAR model of the terrestrial component of the cave, used by hand as a team member walked the accessible regions of the cave. A GoPro 13 black edition was carried by a second team member whilst snorkelling in grid patterns at the surface of the submerged portion. Finally, a DJI Mavic 3 multispectral drone was used for the aerial components of the site, flown from a promontory above the cave site itself. The data collected was processed through Agisoft Metashape Professional v2.2.1 (Agisoft LLC, St Petersburg, Russia) with a model being created for each component of the cave. The four models once processed were integrated to form one model with scaling accuracy confirmed by the LiDAR model. The level of accuracy in the model allowed for specific measurements to be taken such as width or height, these measurements could allow for the calculation of the mass of rock likely to fall or give bathymetric data on the current submerged section. The combination of terrestrial, underwater, UAV, and LiDAR photogrammetry proved to be a robust approach for capturing both the external and internal morphology of the cave. Each method contributed complementary datasets: UAV photogrammetry effectively mapped the promontory and entrance geometry, while underwater and terrestrial images documented the cave’s internal surfaces in high detail. The integration of LiDAR scanning from the iPhone 15 enhanced the scaling accuracy of the final model, compensating for the potential geometric distortion associated with freehand image capture. This multi-platform approach aligns with recent studies that advocate for the combination of close-range photogrammetry and LiDAR to improve the geometric precision of complex natural structures (Colica et al., 2021; Furlani et al., 2023). Description: M.Sc.(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145379 Title: Coastal glow : gauging light in Maltese coastal areas whilst exploring the impact on turtle nesting preferences Abstract: Artificial Light at Night (ALAN) is an increasingly significant anthropogenic pressure affecting coastal and marine ecosystems worldwide. Species that rely on natural darkness for key biological processes, such as the Loggerhead turtle (Caretta caretta), are particularly vulnerable. Despite Malta being among the most light-polluted countries globally, and albeit recent increases in turtle nesting activity on its beaches, the influence of ALAN on local nesting-site selection has not yet been systematically studied. This dissertation examines the extent of ALAN along key Maltese nesting beaches and evaluates how light intensity interacts with physical beach characteristics to influence nesting suitability. Using data collected by Nature Trust Malta (NTM), combined with satellite-derived radiance measurements from the VIIRS Day-Night Band, this study assesses five primary nesting beaches: Gnejna, Golden Bay, Għajn Tuffieħa (Riviera), Għadira, and Ramla. Environmental variables examined include beach elevation profiles, sand-grain texture, vegetation proximity, lunar phase, cloud cover, and long-term changes in beach depth. Radiance data from 2012–2025 were analysed to determine spatial and temporal trends in coastal illumination. Results show clear variation in beach quality and ALAN levels. Beaches with lower radiance, gradual slopes, and suitable substrate—particularly Ramla—correspond with successful nesting attempts. Conversely, beaches exposed to high artificial illumination, especially parts of Għadira, show reduced suitability and fewer nesting events. Long-term radiance trends indicate increasing light pollution across several sites, consistent with other findings in other countries. These findings are not entirely conclusive, as they require further studies in order to establish whether nesting site selection is effected by the physical beach properties, ALAN, or whether it is the combination of the physical characteristics which directly alter ALAN levels which have the greatest impact. The study highlights the urgency of implementing improved lighting management, enforcing coastal protection guidelines, and adopting ALAN-reduction measures in sensitive habitats to help ensure the long-term viability of sea turtle populations in Maltese waters. Description: M.Sc.(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145333 Title: Solving the inverse shortest path problem for earthquakes’ motion Abstract: According to Fermat’s principle, seismic waves follow paths of least travel time. Thus, shortest path algorithms such as Dijkstra’s can be used to determine these paths. Conversely, inferring the parameters of a mathematical program from an observed optimal path defines the inverse shortest path problem, an area within inverse optimisation. With its wide range of applicability, inverse optimisation has attracted considerable interest. One of the earliest topics in this field was the inverse shortest path problem, with Burton and Toint (1992) laying its foundations. Since then, this problem has been explored across several domains, with various mathematical formulations and algorithms proposed. This dissertation examines the inverse shortest path problem in depth, reviews its theoretical foundations, and applies it to a seismological case study. Three algorithms are employed to solve the problem: the column generation algorithm, a quadratic programming algorithm, and a deep inverse optimisation algorithm using a modern deep learning framework. The aim is to estimate the weight vector using these algorithms, thereby reconstructing the mathematical program that defines the shortest paths taken by seismic waves. To the best of the author’s knowledge, this is the first study to apply the inverse shortest path problem to local seismic data from the Maltese Islands and Sicily. Description: M.Sc.(Melit.) 2025-01-01T00:00:00Z