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Nestled at the confluence of nature grandeur and human civilization, beaches command an influential presence that resonates throughout the environment, society, and culture. However, climate change and pollution overhang the beach health and need to be properly dealt with. Proactive measures involve education, responsible waste management, sustainable infrastructure, and environmental regulations, while reactive ones focus on immediate response and cleanup efforts. Nevertheless, continuous monitoring and cleaning are challenging due to various factors such as beach characteristics, hidden waste, weather conditions and, consequently, high costs. To overcome such challenges, this paper proposes an autonomous system for beach cleaning adopting an Intelligent Hierarchical Cyber-Physical System (IHCPS) approach and Information and Communication Technologies. The proposed beach waste management (BeWastMan) solution integrates an Unmanned Aerial Vehicle for the beach aerial surveillance and monitoring, a ground station for data processing, and an Unmanned Ground Vehicle to collect and sort waste autonomously. The research findings contribute to the development of innovative and fully automated approaches in beach waste management, and demonstrate the feasibility and effectiveness of the BeWastMan IHCPS by a real case study, developed in the frame of the BIOBLU project.

Beach wracks are temporary accumulations of vegetal detritus that can be found along coastlines all over the world. Although beach wracks are often perceived as a nuisance for beach users, they play a crucial ecological role in carbon and nutrient connectivity across ecosystem boundaries, especially when they reach a relevant size, as in the case of the wedge-shaped seagrass accumulations called banquette. In this study, three-dimensional mapping of a giant Posidonia oceanica banquette was carried out for the first time using high-resolution UAV photogrammetry combined with field sampling and compositional and chemical analysis. The combined approach allowed a reliable estimation of the amount and spatial distribution of both vegetal biomass and sedimentary mass, as well as of total carbon, nitrogen and phosphorus content, revealing that i) banquette act as a sediment trap and represent hot spots of seagrass biomass and carbon accumulation; ii) banquette thickness, rather than the distance from the sea, influences the spatial distribution of all variables. Moreover, high-resolution digital elevation models (DEM) revealed discontinuous patterns in detritus accumulation resulting in an unknown banquette type here termed “Multiple Mega-Ridge banquette” (MMR banquette). On the one hand, this study highlighted the high potential of the UAV approach in very accurately 3D mapping and monitoring of these structures, with relevant implications for ecosystem service estimation and coastal zone management. On the other hand, it opened new questions about the role played by temporary beach wracks and, in particular, by P. oceanica banquette in the blue carbon exchange across land-ocean boundaries.

Vertical electrical sounding (VES) as a geoelectrical method has proven its effectiveness throughout the history of groundwater geophysical investigation. In this sense, VES was carried out 47 in the study area with the aim of determining the geometry and limits of Quaternary basaltic aquifer formations and, above all, the location of electrical discontinuities in the area located in the north of Morocco, between the center of Almis Guigou and the city of Timahdite. This area is experiencing an overexploitation of the groundwater due to excessive pumping and the development of intensive agriculture activities, resulting in a continuous decrease in piezometric levels. The processing of the diagrams by WINSEV software showed the presence of an electrically resistant surface level, attributed to basaltic formations, of the Quaternary age, whose thicknesses reach at least 150 m to the SW of the area. This level is superimposed on a moderately conductive horizon which, according to local geology, corresponds to Pliocene marl and limestone alternations. The correlation of VES interpretation models allowed us to elaborate thematic maps and geoelectrical sections which illustrate the vertical and lateral extension of the basaltic reservoir as well as its thickness, which decreases in general from the south-west to the north-east; however, the main electrical discontinuities also correspond to faults and fractures, and they show a NE–SW direction sub-parallel to the major accidents of the Middle Atlas. A prospectivity map of the local aquifer was generated, coinciding with regional fault lines and confirmed by the alignment of very good flowing water boreholes. This geophysical study by electrical sounding shed light on the geometry and extension of the aquifer and opened avenues to draw further conclusions on its physical and hydrodynamic characteristics, as well as to optimize the future siting of groundwater exploitation boreholes through the elaboration of the local aquifer prospectivity map.

A didactic multiscale approach for drone modeling is proposed. Specifically, we investigate the drone structure at both macroscopic and microscopic scales, by making use of finite element and atomistic simulations, respectively. The structural analysis is performed with the aim to equip the drone with specific sensors and measuring instruments capable to detect the existence of volcanic ash, SO_2, CO_2 and other pollutants in the atmosphere after a vulcanic eruption. We show that, by modeling the tubular structure of the drone with a sandwich constituted by a a polystyrene core, carbon fiber skins and epoxy matrix, a weight saving of 7 grams for each drone arm can be obtained, in comparison to the standard commercial drones, although a slight worsening of the mechanical performances is observed. In addition, the molecular structure of the polystyrene chains has been investigated by using atomistic Molecular Dynamics simulations, providing further information on the local structure of the polymer chains. Additional improvements of the weight saving could be obtained by means of the the topological optimization techniques on the body of the drone and on the supports for landing.

We propose a didactic approach to use the Machine Learning protocol in order to perform weather forecast. This study is motivated by the possibility to apply this method to predict weather conditions in proximity of the Etna and Stromboli volcanic areas, located in Sicily (south Italy). Here the complex orography may significantly influence the weather conditions due to Stau and Foehn effects, with possible impact on the air traffic of the nearby Catania and Reggio Calabria airports. We first introduce a simple thermodynamic approach, suited to provide information on temperature and pressure when the Stau and Foehn effects take place. Then, in order to gain information also on the rainfall accumulation, the Machine Learning approach is presented: according to this protocol, the model is able to "learn'' from a set of input data constituted by the meteorological conditions (in our case dry, light rain, moderate rain and heavy rain) associated to the rainfall, measured in mm. We observe that, since in the input dataset provided by the Salina weather station the dry condition was the most common, the algorithm is very accurate in predicting it. Further improvements can be obtained by increasing the number of considered weather stations and the time interval.

Seismic reflection utilizes sound waves transmitted into the subsurface, reflected at rock boundaries, and recorded at the surface. Interpretation of their travel times and amplitudes are the key for reconstructing various geomorphological features across geological time (e.g., reefs, dunes, and channels). Furthermore, the integration of surface geomorphology technique mapping, such as digital elevation models, with seismic geomorphology can increase land and marine feature modelling and reduce data uncertainty, as well. This paper presents an overview of seismic and surface geomorphology techniques and proposes an integrated workflow for better geological mapping, 3D surface imaging, and reconstruction. We intend to identify which techniques are more often used and which approaches are more appropriate for better output results. We noticed that an integration of surface and subsurface geomorphology techniques could be beneficial for society in landscape mapping, reservoir characterization, and city/regional planning.

Capo Faro Promontory, located in Salina (Aeolian Islands, southern Italy), is a popular summer destination due to its volcanic morphologies, seaside, and enogastronomy. A flat area, right behind the scarp edge of a coastal cliff, hosts the Capo Faro Estate, one of the most renowned vineyards and residences on Salina Island. The promontory has been characterised in terms of geomorphological features. Remote sensing analysis, after nadir and off–nadir UAV flights, supports the field activities to explore the hazard to which the area is subjected. In particular, the coastal cliff turns out to be affected by a rapid retreat inducing landslides. Therefore, the cliff area has been investigated through a detailed stratigraphic and structural field survey. Using the generated high–resolution Digital Elevation Model, bathymetric–topographic profiles were extracted along the coastline facing the cliff. The thickness of volcanic deposits was evaluated to obtain a geological model of it. The main rock mass discontinuities have been characterised to define the structural features affecting the stability of the rock wall. The obtained results prove the contribution of such research fundamental in planning risk mitigation measures.

Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature.

The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.

The mapping of hydrothermal alteration zones associated with mineralization is of paramount importance in searching for metal deposits. For this purpose, targeting alteration zones by analyzing airborne geophysical and satellite imagery provides accurate and reliable results. In the Kelâat M’Gouna inlier, located in the Saghro Massif of the Moroccan Anti Atlas, natural gamma-ray spectrometry and ASTER satellite data were used to map hydrothermal alteration zones. Natural gamma-ray spectrometry data were processed to produce maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU. In addition, four-band ratios were computed, on ASTER data, to map the distribution of clay minerals, phyllitic minerals, propylitic minerals, and iron oxides. The combined results obtained from geophysical and satellite data were further exploited by fuzzy logic modelling using the Geographic Information System (GIS) to generate a mineral prospectivity map. Seven hydrothermal alteration zones likely to be favorable for mineralization have been identified. They show a spatial correlation with (i) known surface prospects and mineral occurrences, (ii) the granite-encasing contact zone, and (iii) the fault zones (Sidi Flah and Tagmout faults). This research therefore provides important information on the prospecting of mineral potential in the study area.

With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R2, the root mean square error and p-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 µg/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions.

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.

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.

Over the last few decades, Sicily has faced both erosion-related difficulties and limited Integrated Coastal Zone Management (ICZM). In particular, a lack of synergy between regional bodies, an absence of information exchange between scientific and administrative communities, the application of not-updated national and international best practices and the misrepresentation of environmental laws, have resulted in a system focused on risk erosion rather than on coastal area development. Following years without planning, in 2006 the Regione Siciliana launched PAI-coste (Hydro-Geological Asset Plan. It is interesting to note that in Italy, the “risk” known worldwide as geological or hydraulic or geomorphological is commonly denoted as “hydro-geological”, creating a pseudo-neologism correctly related to the groundwater circulation and not to surficial movements.), based on a diachronic comparison between cartographies and territorial qualitative information. However, it has proved to be static and obsolete and thus requires updating in order to determine the focal points for further planning and to shift it from an e-static to e-dynamic process via the GIS and WEBGIS tools. Sicilian legislation holds all the laws necessary for the creation of a continuous flow of information between local administrations and the regional government. The aim of this paper is to determine a regional management plan for the central government against coastal erosion with the inclusion of a seasonal monitoring program carried out by the local administration as a part of the Strategic Environmental Assessment (SEA), which is in turn linked to the Plan for the Administrations of Maritime State Property (PUDM; Italian acronym). The plan will be managed at the regional level and will take into account problems and particular features at a higher rather than local level. A key outcome should be the Regional Plan against Coastal Erosion, containing constant feedback from the local administration, stakeholders and citizen groups.

The importance of studying coastal areas is justified by their resources, ecosystem services, and key role played in socio-economic development. Coastal landscapes are subject to increasing demands and pressures, requiring in-depth analyses for finding appropriate tools or policies for a sustainable landscape management. The present study addresses this issue globally, based on case studies from three continents: Romania (Europe), Algeria (Africa), and Vietnam (Asia), focusing on the anthropogenic pressure resulting from land use/land cover change or urban sprawl, taking into account the role of socioeconomic and political factors. The methodology consisted of producing maps and computing and analyzing indicators, correlating geospatial and socio-economic data in a synergistic manner to explore the changes of landscapes, and identify the specific driving forces. The findings show that the pressure of urbanization and tourism on coastal areas increased, while the drivers and impacts vary. Urbanization is due to derogatory planning in Romania and Algeria, and different national and local goals in Vietnam. The two drivers determine local exemptions from the national regulations, made for profit. In addition to the need for developing and enforcing policies for stopping the degradation and restoring the ecosystems, the findings underline the importance of international cooperation in policy development.

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.

The Causse of El Hajeb belongs to the Tabular Middle Atlas (TMA), in which thousands of karst landforms have been identified. Among them, collapse dolines and dissolution sinkholes have been highlighted as a source of environmental risks and geo-hazards. In particular, such sinkholes have been linked to the degradation of water quality in water springs located in the junction of the TMA and Saïss basin. Furthermore, the developments of collapse dolines in agricultural and inhabited areas enhance the risk of life loss, injury, and property damage. Here, the lack of research on newly formed cavities has exacerbated the situation. The limited studies using remote sensing or geophysical methods to determine the degree of karstification and vulnerability of this environment fail to provide the spatial extent and depth location of individual karst cavities. In order to contribute to the effort of sinkhole risk reduction in TMA, we employed remote sensing and geophysical surveys to integrate electrical resistivity tomography (ERT) and self-potential (SP) for subsurface characterization of four sinkholes identified in the Causse of El Hajeb. The results revealed the existence of sinkholes, both visible and non-accessible at the surface, in carbonate rocks. The sinkholes exhibited distinct morphologies, with depths reaching 35 m. Topography, geographic coordinates and land cover information extracted on remote sensing data demonstrated that these cavities were developed in depressions in which agricultural activities are regularly performed. The fusion of these methods benefits from remote sensing in geophysical surveys, particularly in acquisition, georeferencing, processing and interpretation of geophysical data. Furthermore, our proposed method allows identification of the protection perimeter required to minimize the risks posed by sinkholes.

The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by relying on limited in situ water depth data to extract and calibrate bathymetry from a QuickBird satellite image acquired on a highly dynamic coastal environment. The image was processed using the log-band ratio and optimal band ratio analysis (OBRA) methods. Preprocessing steps included the conversion of the raw satellite image into top of atmosphere reflectance, spatial filtering, land and water classification, the determination of the optimal OBRA spectral band pairs, and the estimation of relative SDB. Furthermore, calibration and vertical referencing were performed via in situ bathymetry acquired in November 2007. The relative bathymetry obtained from different band ratios were vertically referenced to the local datum using in situ water depth in order to obtain absolute SDB. The coefficient of determination (R2) and vertical root mean square error (RMSE) were computed for each method. A strong correlation with in situ field bathymetry was observed for both methods, with R2 = 0.8682 and RMSE = 0.518 m for the log-band ratio method and R2 = 0.8927−0.9108 and RMSE = 0.35 m for the OBRA method. This indicated a high degree of confidence of the SDB results obtained for the study area, with a high performance of the OBRA method for SDB mapping in turbid water.

The Longarini and Cuba lagoons are the largest westlands in a system of lagoons located inland of the Ambra Coast in southeastern Sicily.A reconstruction of their evolution througt a comparative study of textural, compositional and faunal data, showed facies of a lagoonal system influenced differently and in varying degrees by rivers and sea. The oldest layers are made of lagoonal pelitic sediments containing faunal assemblages (mixed biocenoses) referable to the zonal classification of the paralic environment proposed by Guelorget & Perthuisot (1983). In the more recent layers an environmental change is recorded,showing an increase in lagoonal characteristics and a decrease in "mixed" biocenoses, due to a greater input of solid materials from the adjacent hydrographic basin.