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The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application.

Coastal areas are dynamic multidimensional systems challenged by the complex interactions between natural, environmental, and human-induced pressures, as well as the ever-changing climate. A comprehensive evaluation of their spatial and temporal features enables the development of effective practices required to apply integrated coastal zone management (ICZM) policies. ICZM seeks to address the vulnerability of coastal areas in an attempt to mitigate their weaknesses and increase their resilience. Hence, coastal vulnerability assessment is a prerequisite to proceed with optimal adaptation or upgrading actions. Currently, assessments are performed by considering different approaches related to dividing coastal areas into segments to observe the spatial variations of vulnerability. The present research seeks to investigate the impact of the spatial segmentation of coastal areas on the assessment of their vulnerability. To achieve this, a case study of the coastal zone of the Municipality of Thebes, located in the Northeastern Corinthian Gulf, Greece, is examined. Five segmentation approaches are applied in terms of a physical-based vulnerability assessment for two different time horizons, (a) the present and (b) the future, by incorporating the climate change impacts. This study allows for optimizing practices to estimate vulnerability parameters and obtain reliable results for practical applications while reducing time-consuming analyses.

Coastal erosion and its impacts on the involved communities is a topic of great scientific interest that also reflects the need for modern as well as cost and time-effective methodologies to be integrated into or even to substitute traditional investigation methods. The present study is based on an integrated approach that involves the use of data derived from UAV (Unmanned Aerial Vehicle) surveys. The study illustrates the long- to short-term shoreline evolution of the Molise coast (southern Italy) and then focuses on two selected beach stretches (Petacciato and Campomarino beaches), for which annual UAV surveys were performed from 2019 to 2021, to assess their most recent shoreline and morpho-topographical changes and related effects on their coastal vulnerability. UAV data were processed using the Structure from Motion (SfM) image processing tool. Along the beach profiles derived from the produced DEMs, the coastal vulnerability of the selected beach stretches was evaluated by using the Coastal Vulnerability Assessment (CVA) approach. The results obtained highlight some significant worsening of CVA indexes from 2019 to 2021, especially for the Campomarino beach, confirming the importance of the periodic updating of previous data. In conclusion, the easy use of the UAV technology and the good quality of the derived data make it an excellent approach for integration into traditional methodologies for the assessment of short-term shoreline and beach changes as well as for monitoring coastal vulnerability.

The present study focuses on the quantification of coastal risks associated with erosion and inundation accelerated by sea level rise and extreme storms events in the specific conditions of micro-tidal semi-enclosed seas. The main objective is to develop a measure that characterises climate-related external hazards, the exposure (of people and assets at risk of being damaged) and vulnerability of human and natural systems. This is accomplished by means of adaption of the concept of nondimensional coastal risk (or resilience) index (CRI), as a function of coastal vulnerability and exposure indices, to the conditions of sedimentary shores of the eastern Baltic Sea and testing its suitability for low-lying coastal zones considering their environmental and socioeconomic characteristics. The study area is an about 45 km long coastal section of Lithuania in the south-eastern Baltic Sea. We introduce a set of locally relevant coastal vulnerability and exposure variables, apply an Analytical Hierarchy Process to calculate the criteria weights and GIS multi-criteria evaluation approach to calculate the CRI values. The coastal segments with high vulnerability often have low values of the exposure index. About 11% of the study area is under very high risk. The largest CM values occur at a certain distance from the touristic or industrial spots near Klaipeda, around the Palanga pier and to the north of áventoji. These coastal sectors are highly populated areas that suffer from sediments deficit due to coastal engineering structures.

The Coastal Vulnerability and Exposure Degree (CVED) of the Gran Canaria Island and the coastal risk of Playa de Las Canteras (Las Palmas de G.C.) have been assessed by means of a GIS analysis. The evaluation of coastal vulnerability (H) has been performed by using two different levels of analysis: the first one regarding the entire Island (Coastal Vulnerability Index method - CVI), the second one regarding specifically Las Canteras Beach, selected for its socio-economic importance (Coastal Vulnerability Assessment method - VA). The application of the CVI method, based on geologic-geomorphologic and meteomarine data easily available, has allowed the regional scale assessment of coastal vulnerability (H) along the Gran Canaria coastline and, therefore, the individuation of more critical coast stretches deserving further analysis. The application of the VA method, based on more specific morphologic-sedimentary beach features that allow to consider both the beach retreat due to storm surge and the coastal inundation due to run-up on the beach, has provided a large scale detail on the coastal vulnerability of Las Canteras Beach. Socio-economic and damage indexes have been determined for the 15 coastal municipalities included in the study area and, by means of the resulting matrix product, the degree of exposure (Ex) along the Gran Canaria coast has been assessed. Finally, the combination of coastal vulnerability and exposure levels has allowed to obtain the Coastal Vulnerability and Exposure Degree (CVED) for the entire Island and the risk levels that characterize specifically Las Canteras Beach. Major results in terms of CVED highlight prevailing low vulnerability and exposure levels along the western coast, and overall high vulnerability and exposure levels along the eastern coast, from Las Palmas de G.C. to San Bartolomé de Tirajana. The focus made on Playa de Las Canteras has allowed to identify the areas characterized by medium and high risk levels that represent approximately one third (940 m) of the entire beach and are located in its central part. The used two-level analysis approach has proved its efficiency by highlighting the degree of coastal vulnerability along the study coast and, especially, more critical coast stretches (CVI method) such as the Playa de Las Canteras that can be successfully analysed with the CVA approach allowing for a large-scale assessment of risk aspects, essential for a correct definition of priority management strategies. Results obtained with the present study, along with the awareness of the increasing phenomena of coastal erosion and marine flooding arising on the mid-long term due to the effects of global climate change, highlight the need for the competent public administrations of Gran Canaria to develop a strategic approach to coastal management and sustainable development that considers as a whole socioeconomic values and natural resources, coastal vulnerability and exposure degree, and risk aspects.

Abstract The identification of morphological changes in coastal areas plays a fundamental role in assessment of their spatio-temporal evolution. The focus of this research is to analyze the morphodynamic evolution of shoreline, built-up areas, and vulnerability assessment of Gwadar coastal zone. Using Landsat data, from 1987 to 2021, shorelines were extracted and both long and short-term shoreline changes were assessed in a GIS environment. The results indicate high accretion and sediment entrainment due to anthropogenic developments in Eastern and Western zones. Moderate to high erosion was observed in Eastern zone, whereas, only moderate erosion was observed in the Western zone. The values of greatest retreat and advance along the shoreline of -65.67 m and 827.9 m respectively were recorded in the Eastern zone. The dominant factor of coastal evolution was anthropogenic, augmented by an increase of 13.86% in built-up area. Short-term analysis revealed that erosion and accretion dominated intermittently, with greatest erosion observed during 1994–2001, while, highest accretion occurred from 2001–2007. Furthermore, results of coastal vulnerability assessment indicate that about 15.22% of shoreline consisting of sandy formations is highly vulnerable to hazards. In conclusion, integrated approaches using remote sensing and spatial analysis represent a significant framework for long-term synoptic monitoring of coastal areas.

Small Island Developing States (SIDS) face significant challenges due to coastal hazards, climate change impacts, and data limitations that hinder effective coastal management. The Guyana Coastal Analysis System (G-CAS) was developed as a web-based geospatial tool to address these challenges by integrating remote sensing, machine learning, and cloud computing technologies. This study presents G-CAS as a replicable framework that enhances coastal monitoring and decision-making processes in Guyana and similar SIDS. The system consists of four core analytical modules: Shoreline Analysis, Coastal Squeeze Assessment, Bathymetric Change Detection, and Flood Detection and Modelling. These modules provide near real-time, data-driven insights into shoreline erosion, wetland compression, underwater depth variations, and flood risk exposure. Results from the application of the Shoreline Analysis module indicate spatially variable shoreline retreat rates, with critically eroded sections requiring urgent intervention. The Coastal Squeeze Assessment highlights areas where infrastructure restricts landward migration, increasing vulnerability to habitat loss. Bathymetric mapping reveals dynamic sediment transport patterns, essential for understanding coastal stability and marine ecosystem health. The Flood Detection and Modelling module assists in identifying high-risk zones, particularly in low-lying coastal settlements, supporting early warning systems and disaster mitigation planning. The offering of a cost-effective, scalable, and accessible coastal monitoring tool like G-CAS provides a data-driven foundation for coastal adaptation strategies in Guyana and beyond. The findings show the importance of integrating geospatial technologies into national coastal management frameworks to support climate resilience, disaster risk reduction, and sustainable development. This study highlights the potential for similar coastal analysis systems to be adopted across SIDS, ensuring evidence-based decision-making and enhanced environmental stewardship in response to climate change.

The coast is the most dynamic part of the Earth’s surface due to its strategic position at the interface of the land and the sea. It is, therefore, exposed to hazards and specific risks because of the geography as well as the geological and environmental characteristics of different countries. The coastal environment is essentially dynamic and evolving in time and space, marked by waves, tides, and seasons; moreover, it is subjected to many marine and continental processes (forcing). This succession of events significantly influences the frequency and severity of coastal hazards. The present paper aims at describing and characterizing the hazards and vulnerabilities on the Cameroonian coast. Cameroon possesses 400 km of coastline, which is exposed to various hazards. It is important to determine the probabilities of these hazards, the associated effects, and the related vulnerabilities. In this study, in this stable intraplate setting, the methodology used was diverse and combined techniques for the study of the shore and methods for the treatment of climatic data. Also, historical data were collected during field observations and from the CRED website for all the natural hazards recorded in Cameroon. In addition, documents on climate change were consulted. Remotely sensed data, combined with GIS tools, helped to determine and assess the associated risks. A critical grid combining a severity and frequency analysis was used to better understand these hazards and the coastal vulnerabilities of Cameroon. The results show that Cameroon’s coastal margins are subject to natural processes that cause shoreline changes, including inundation, erosion, and accretion. This study identified seven primary hazard types (earthquakes, volcanism, landslides, floods, erosion, sea level rise, and black tides) affecting the Cameroonian coastline, with the erosion rate exceeding 1.15 m/year at Cape Cameroon. Coastal populations are continuously threatened by these natural or man-induced hazards, and they are periodically subjected to catastrophic disasters such as floods and landslides, as experienced in Cameroon. In addition, despite the existence of the National Contingency Plan devised by the Directorate of Civil Protection, National Risk, and Climate Change Observatories, the implementation of disaster risk reduction and mitigation strategies is suboptimal.

Integration of impacts of sea-level rise to coastal zone management practices are performed through coastal vulnerability assessments. Out of the types of vulnerability assessments, a proposed model demonstrated that relative vulnerability of different coastal environments to sealevel rise may be quantified using basic information that includes coastal geomorphology, rate of sea-level rise, and past shoreline evolution for the National Assessment of Coastal Vulnerability to Sea-Level Rise for U.S. Coasts. The proposed methodology focuses on identifying those regions where the various effects of sea-level rise may be the greatest. However, the vulnerability cannot be directly equated with particular physical effects. Thus, using this concept as a starting point, a coastal vulnerability matrix and a coastal vulnerability index that use indicators of impacts of sea-level rise are developed. The developed model compares and ranks different regions according to their vulnerabilities while prioritizing particular impacts of sea-level rise of the region. In addition, the developed model determines most vulnerable parameters for adaptation measures within the integrated coastal zone management concept. Using available regional data, each parameter is assigned a vulnerability rank of very low to very high (1–5) within the developed coastal vulnerability matrix to calculate impact sub-indices and the overall vulnerability index. The developed methodology and Thieler and Hammar-Klose the proposed methodology were applied to the Göksu Delta, Turkey. It is seen that the Göksu Delta shows moderate to high vulnerability to sea-level rise. The outputs of the two models indicate that although both models assign similar levels of vulnerability for the overall region, which is in agreement with common the literature, the results differ significantly when in various parts of the region is concerned. Overall, the proposed Thieler and Hammar-Klose method assigns higher vulnerability ranges than does the developed coastal vulnerability index sea-level rise (CVI-SLR) model. A histogram of physical parameters and human influence parameters enables enable decision makers to determine the controllable values using the developed model.

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems.