Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored with the creation of a WebGIS portal that will allow local and regional stakeholders/authorities obtain an easy remote access tool to monitor the status of pocket beaches (PB) in the Maltese Archipelago and Sicily. In this paper, we provide a methodological approach for the implementation of a WebGIS necessary for very detailed dynamic mapping and visualization of geospatial coastal data; the description of the dataset necessary for the monitoring of coastal areas, especially the PBs; and a demonstration of a case study for the PBs of Sicily and Malta by using the methodology and the dataset used during the BESS project. Detailed steps involved in the creation of the WebGIS are presented. These include data preparation, data storage, and data publication and transformation into geo-services. With the help of different Open Geospatial Consortium protocols, the WebGIS displays different layers of information for 134 PBs including orthophotos, sedimentological/geomorphological beach characteristics, shoreline evolution, geometric and morphological parameters, shallow water bathymetry, and photographs of pocket beaches. The WebGIS allows not only for identifying, evaluating, and directing potential solutions to present and arising issues, but also enables public access and involvement. It reflects a platform for future local and regional coastal zone monitoring and management, by promoting public/private involvement in addressing coastal issues and providing local public administrations with an improved technology to monitor coastal changes and help better plan suitable interventions.

The amount of Earth observation images available to the public has been the main source of information, helping governments and decision-makers tackling the current world’s most pressing global challenge. However, a number of highly skilled and qualified personnel are still needed to fill the gap and help turn these data into intelligence. In addition, the accuracy of this intelligence relies on the quality of these images in times of temporal, spatial, and spectral resolution. For the purpose of contributing to the global effort aiming at monitoring natural and anthropic processes affecting coastal areas, we proposed a framework for image processing to extract the shoreline and the shallow water depth on GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV) on the coast of San Vito Lo Capo, with image preprocessing steps involving orthorectification, atmospheric correction, pan sharpening, and binary imaging for water and non-water pixels analysis. Binary imaging analysis step was followed by automatic instantaneous shoreline extraction on a digital image and satellite-derived bathymetry (SDB) mapping on GeoEye-1 water pixels. The extraction of instantaneous shoreline was conducted automatically in ENVI software using a raster to vector (R2V) algorithm, whereas the SDB was computed in ArcGIS software using a log-band ratio method applied on the satellite image and available field data for calibration and vertical referencing. The results obtained from these very high spatial resolution images demonstrated the ability of remote sensing techniques in providing information where techniques using traditional methods present some limitations, especially due to their inability to map hard-to-reach areas and very dynamic near shoreline waters. We noticed that for the period of 5 years, the shoreline of San Vito Lo Capo sand beach migrated about 15 m inland, indicating the high dynamism of this coastal area. The bathymetric information obtained on the GeoEye-1 satellite image provided water depth until 10 m deep with R² = 0.753. In this paper, we presented cost-effective and practical methods for automatic shoreline extraction and bathymetric mapping of shallow water, which can be adopted for the management and the monitoring of coastal areas.

Pocket beaches (PBs) are among the most attractive tourist sites and economic development contributors in coastal areas; however, they are negatively impacted by the combined effects of climate change and anthropogenic activities. Generally, research on PBs is conducted from the beach towards offshore. Studies on the land use/land cover (LULC) of PBs are limited and currently lacking. Such studies deserve more investigation due to the importance of LULC in PBs’ functioning. In this study, supervised classification methods were investigated for LULC mapping of the PBs located in the province of Messina. Sentinel-2B satellite images were analyzed using maximum likelihood (MaL), minimum distance (MiD), mahalanobis distance (MaD) and spectral angle mapper (SAM) classification methods. The study was conducted mainly in order to determine which classification method would be adequate for small scale Sentinel-2 imagery analysis and provide accurate results for the LULC mapping of PBs. In addition, an occurrence-based filter algorithm in conjunction with OpenStreetMap data and Google Earth imagery was used to extract linear features within 500 m of the inland buffer zone of the PBs. The results demonstrate that information on the biophysical parameters, namely surface cover fractions, of the coastal area can be obtained by conducting LULC mapping on Sentinel-2 images.

This paper presents a case study of the natural radioactivity level and radon exhalation in limestone and sandstone rocks from the archaeological park of Tindari, located in Sicily, southern Italy. These rocks were representative of natural stones utilised as building materials in the studied area. The activity concentrations of ²²⁶Ra, ²³²Th, and ⁴⁰K were assessed using high purity germanium (HPGe) gamma-ray spectrometry. Subsequently, the absorbed gamma dose rate (D), annual effective dose equivalent (AEDE), activity concentration index (ACI), and alpha index (I_α) were quantified to evaluate potential radiological health risks associated with radiation exposure from the analysed rocks. Finally, E-PERM electret ion chamber measurements were conducted to accurately quantify the radon exhalation rate from the investigated samples. The results obtained in this case study provide a foundation for further research into the background radioactivity levels in natural stones employed as building materials.

This work reports the findings of an investigation aimed at assessing, for the first time, the natural and anthropogenic radioactivity content of marine sediments collected from selected areas of Sicily, Southern Italy. In particular, it focused on evaluating the average activity concentration of detected radionuclides and the radiological hazard for humans, above all considering the use of this material for nourishing actual eroded beaches. To this aim, the quantification of the average specific activity of ²²⁶Ra, ²³²Th, and ⁴⁰K natural and ¹³⁷Cs anthropogenic radioisotopes was addressed through the employment of High-Purity Germanium (HPGe) gamma-ray spectrometry. Furthermore, the absorbed gamma dose rate (D), the annual effective dose equivalent outdoor (AEDE_out), the external hazard index (H_ex), and the excess lifetime cancer risk (ELCR) were also calculated to evaluate the radiological hazard for humans related to external exposure to ionizing radiations. Furthermore, the average specific activity of ¹³⁷Cs was found to be less than the lowest detectable activity in all cases, excluding anthropogenic radioactive contamination of the investigated samples. Finally, Pearson correlation, principal component analysis (PCA) and hierarchical cluster analysis (HCA), i.e., multivariate statistics, were carried out by analyzing detected radioactivity and radiological characteristics to evaluate their relationship with the sampling locations.