Automatic parametrisation of beached microplastics

Four sandy beaches on the island of Malta were regularly sampled for Large MicroPlastic (LMP) particles having a diameter between 1mm and 5mm, at stations located at the waterline, and 10m inshore. The extracted LMPs were characterised (dimensions, surface roughness, colour) by microscopic analyses, as well as by a developed algorithm. Two-thirds of the isolated particles were smooth and the majority of these belonged to the grey -white colour category suggesting that these were preproduction pellets. Roughly six times as many particles were recorded within the inshore sampling stations as the particles recorded at the waterline stations. The automated image processing algorithm performed well when the dimension and colour parameter values it delivered were compared with those obtained by microscopic analyses.peer-reviewe

An integrated coastal map for the Maltese Islands

The Maltese coastal area, comprising its associated resources and services, is of substantial importance to the Maltese economy. An innovative web interface has been developed to combine information from different sources, including coastal properties, physical features, resources and amenities, into an innovative comprehensive interactive map of the Maltese coastline. It serves as a general informative tool for users in the public domain, bringing different layers of data together, and targeting a delivery over smart media like mobile phones and tablets.peer-reviewe

A spatial prioritisation exercise for marine spatial planning implementation within the North-East MPA of the Maltese Islands

The cumulative pressure/risk posed to both Posidonia oceanica seagrass meadows and maerl beds by ongoing maritime activities as well as the cumulative user-user conflict within the NE MPA were quantified and mapped to serve as a decision-support tool for MPA managers implementing MSP provisions in the area.peer-reviewe

Machine learning for galaxy morphology classification

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue.peer-reviewe

Gap filling of the CALYPSO HF radar sea surface current data through past measurements and satellite wind observations

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.peer-reviewe

Back with a bang – an unexpected massive bloom of Cassiopea andromeda (Forskaal, 1775) in the Maltese Islands, nine years after its first appearance

The upside-down jellyfish—Cassiopea andromeda (Forsskål, 1775)—is considered an established alien within the eastern Mediterranean Sea, but the species exhibits a highly sporadic occurrence further west within the basin. This study reports the second documented bloom of the species within coastal waters in the Maltese Islands and the third bloom of the species in the westernmost part of the eastern Mediterranean, a full nine years after its first appearance in this part of the Mediterranean.peer-reviewe

Machine learning for benthic sand and maerl classification and coverage estimation in coastal areas around the Maltese Islands

Analysis of the seabed composition over a large spatial scale is an interesting yet very challenging task. Apart from the field work involved, hours of video footage captured by cameras mounted on Remote Operated Vehicles (ROVs) have to be reviewed by an expert in order to classify the seabed topology and to identify potential anthropogenic impacts on sensitive benthic assemblages. Apart from being time consuming, such work is highly subjective and through visual inspection alone, a quantitative analysis is highly unlikely to be made. This study investigates the applicability of various Machine Learning techniques for the automatic classification of the seabed into maerl and sand regions from recorded ROV footage. ROV data collected from depths ranging between 50 m and 140 m and at 9.5 km from the northeast coastline of the Maltese Islands, is processed. Through the application of the presented technique, 5.23 GB of data corresponding to 2 h and 24 min of footage which was collected during June 2013, was initially cleaned and classified. An estimate for the percentage cover of the two benthic habitats (sandy seabed and maerl) was also computed by using artifacts encountered during the ROV survey and of known dimensions as a reference. Unlike other automatic seabed mapping techniques, the presented prototype processes video footage captured by a down-facing camera and not through acoustic backscatter. Image data is easier and much cheaper to capture. Promising results that indicate a very good degree of agreement between the true and predicted habitat type distribution values, were obtained.peer-reviewe

Genetic optimization for radio interferometer configurations

The large bandwidth and resolution specifications of today’s telescopes require the use of different types of collectors positioned over long baselines. Innovative feeds and detectors must be designed and introduced in the initial phases of development. The required level of resolution can only be achieved through a ground-breaking configuration of dishes and antennas. This work investigates the possibility of the genetic optimization of radio interferometer layouts given constraints on cable length, required UV density distribution and point-spread function. Owing to the large collecting area and the frequency range required for the Square Kilometre Array (SKA) to deliver the promised science, the configuration of the dishes within each station is an important issue. As a proof of concept, the Phase 1 specifications of this telescope are used to test the proposed framework.peer-reviewe

Oil spill risk assessment on the Maltese coastal areas

A significant percentage of the global oil transport goes through the Mediterranean sea. Most of the maritime traffic carrying oil and other dangerous liquid substances travels across the Malta Channel. The risk of marine spillages within the stretch of sea between Malta and Sicily is very high and beaching on the Maltese shores can cause irreversible environmental damage at the detriment of important economic resources. The aim of this work is to determine the probability and volume percentage of oil that would reach the coast in case of an accident in the proximity of the Maltese Islands. Various spill scenarios are considered to get a realistic estimate as much as possible.peer-reviewe

Automating jellyfish species recognition through faster region-based convolution neural networks

In recent years, citizen science campaigns have provided a very good platform for widespread data collection. Within the marine domain, jellyfish are among the most commonly deployed species for citizen reporting purposes. The timely validation of submitted jellyfish reports remains challenging, given the sheer volume of reports being submitted and the relative paucity of trained staff familiar with the taxonomic identification of jellyfish. In this work, hundreds of photos that were submitted to the “Spot the Jellyfish” initiative are used to train a group of region-based, convolution neural networks. The main aim is to develop models that can classify, and distinguish between, the five most commonly recorded species of jellyfish within Maltese waters. In particular, images of the Pelagia noctiluca, Cotylorhiza tuberculata, Carybdea marsupialis, Velella velella and salps were considered. The reliability of the digital architecture is quantified through the precision, recall, f1 score, and κ score metrics. Improvements gained through the applicability of data augmentation and transfer learning techniques, are also discussed. Very promising results, that support upcoming aspirations to embed automated classification methods within online services, including smart phone apps, were obtained. These can reduce, and potentially eliminate, the need for human expert intervention in validating citizen science reports for the five jellyfish species in question, thus providing prompt feedback to the citizen scientist submitting the report.peer-reviewe