/library/oar/handle/123456789/13767 2026-05-27T06:22:39Z 2026-05-27T06:22:39Z Alvisi, Francesca Figueiredo, Daniela Villa, Erica Bernet, Sacha Bratfanof, Edward Candeias, Rute Deidun, Alan Lheureux, Guillaume Mashkina, Olga Vesikko, Ljudmila /library/oar/handle/123456789/146712 2026-05-22T09:37:32Z 2026-01-01T00:00:00Z

Title: BlueNIGHTs : bringing a touch of blue in the EU researchers' nights for a more sustainable use of the ocean Authors: Alvisi, Francesca; Figueiredo, Daniela; Villa, Erica; Bernet, Sacha; Bratfanof, Edward; Candeias, Rute; Deidun, Alan; Lheureux, Guillaume; Mashkina, Olga; Vesikko, Ljudmila Abstract: The main objective of BlueNIGHTs was to plant 'blue' seeds across Europe to improve Ocean Literacy (OL) and grow a new network of European Researchers' Nights (ERN s) focused on Ocean issues and the achievement of the Sustainable Development Goals (SDGs). The initiative, specifically targeting SDG 14, aligns with the United Nations (UN) Decade for Ocean Science, the EU StarfishMission and major European initiatives dedicated to OL, such as EU4Ocean. Our aim was to show European citizens the different facets and faces involved in marine research by organising a series of interconnected ERNs. BlueNIGHTs brought together researchers from different European countries who collectively address Ocean challenges and solutions to demonstrate their deep understanding of the complexity of human-Ocean interaction. In this chapter, we illustrate the process of developing such a project, from conception to partnership building and work package development. We believe that sharing this experience is essential because it fosters fruitful interactions between the scientific community and society, connecting researchers with pupils and teachers and bringing Ocean issues to local communities, both near and far from the sea.

2026-01-01T00:00:00Z Deidun, Alan Prieto Gomez, Laura Gauci, Adam Zava, Bruno Corsini-Foka, Maria Marrone, Alessio /library/oar/handle/123456789/146473 2026-05-14T13:31:58Z 2026-01-01T00:00:00Z

Title: Much can happen in three months : the proliferation of the bigfin reef squid (Sepioteuthis lessoniana Lesson, 1831) within Maltese coastal waters since its first record Authors: Deidun, Alan; Prieto Gomez, Laura; Gauci, Adam; Zava, Bruno; Corsini-Foka, Maria; Marrone, Alessio Abstract: The rapid colonisation of Maltese coastal waters by the non-indigenous Sepioteuthis lessoniana Lesson, 1831 is hereby documented through the ‘Spot the Alien’ citizen science campaign, to which a considerable number of catches and sightings of the species were submitted in rapid sequence over a three-month timeframe. The study also compares sea surface temperature values for the July-December period of 2015, 2020 and 2025 for the same waters, in order to explore the putative influence that sea temperatures might have on the observed rapid expansion of this non-indigenous species (NIS). The findings of this study highlight both the notable spread of the species across local waters and the important role of citizen science in early detection and monitoring of non-indigenous species.

2026-01-01T00:00:00Z /library/oar/handle/123456789/145381 2026-04-07T13:20:32Z 2026-01-01T00:00:00Z

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 /library/oar/handle/123456789/145380 2026-04-07T13:18:31Z 2026-01-01T00:00:00Z

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