Erosion is a major threat for coasts worldwide, beaches in particular, which constitute one of the most valuable coastal landforms. Vulnerability assessments related to beach erosion may contribute to planning measures to counteract erosion by identifying, quantifying and ranking vulnerability. Herein, we present a new index, the Beach Vulnerability Index (BVI), which combines simplicity in calculations, easily obtainable data and low processing capacity. This approach provides results not only for different beaches, but also for different sectors of the same beach and enables the identification of the relative significance of the processes involved. It functions through the numerical approximation of indicators that correspond to the mechanisms related to the processes that control beach evolution, such as sediment availability, wave climate, beach morhodynamics and sea level change. The BVI is also intended to be used as a managerial tool for beach sustainability, including resilience to climate change impact on beach erosion.

Responsible tourism practices promote the idea that responsibility refers to both visitors and local communities, ensuring the protection and conservation of the natural environment and cultural heritage and contributing to a better quality of life. The role of “sustainability” in a tourist development strategy is to protect and reveal what already exists (“reuse, recycle, renovate”) rather than consume. In these terms, cultural routes can serve as valuable tourism assets that can attract visitors and generate economic activity, while protecting the environmental and cultural heritage. This study is focused on Symi, a small and remote Greek island better known as a religious tourism destination, examining how cultural routes could be at the core of an economic development and social cohesion strategic plan. Symi served as a case study due to the island’s rich cultural and natural heritage, including historic buildings, natural attractions, monuments, and archaeological sites. The revealing of those assets is crucial for the advancement of an integrated local strategy for cultural tourism in terms of responsible tourism. The primary objective of this study is to analyze how the development of a cultural routes network, integrating monuments from diverse historical periods, significant religious sites, and areas of significant architectural and environmental value, can contribute to a responsible tourism approach and further a sustainable development that generates multiple benefits for the local community in an insular region.

This study evaluates the accuracy of bathymetric maps generated from multispectral satellite datasets acquired from different multispectral sensors, namely the Worldview 2, PlanetScope, and the Sentinel 2, in the bay of Elounda in Crete. Image pre-processing steps were implemented before the use of the three empirical methods for estimating bathymetry. A dedicated correction and median filter have been applied to minimize noise from the sun glint and the sea waves. Due to the spectral complexity of the selected study area, statistical correlation with different numbers of bands was applied. The analysis indicated that blue and green bands obtained the best results with higher accuracy. Then, three empirical models, namely the Single Band Linear Algorithm, the Multiband Linear Algorithm, and the Ratio Transform Algorithm, were applied to the three multispectral images. Bathymetric and error distribution maps were created and used for the error assessment of results. The accuracy of the bathymetric maps estimated from different empirical models is compared with on-site Single beam Echo Sounder measurements. The most accurate bathymetric maps were obtained using the WorldView 2 and the empirical model of the Ratio Transform algorithm, with the RMSE reaching 1.01 m.

The demand for a new concept of heritage, in which monuments and landscapes are considered active factors in creating a sense of history, is esteemed not only from a scientific and academic perspective, but as well as part of a more sensitive and efficient strategy to link cultural heritage and tourism, by bringing an integrative perspective to the forefront. Implementing such strategies is strictly correlated with the ability to support decision-makers and to increase people’s awareness towards a more comprehensive approach to heritage preservation. In the present work, a robust socioeconomic impact model is presented. Moreover, this work attempts to create an initial link between the economic impacts and natural hazards induced by the changes in the climatic conditions that cultural heritage sites face. The model’s novel socioeconomic impact analysis is the direct and indirect revenues related to the tourism use of a site, on which local economies are strongly correlated. The analysis indicated that cultural heritage sites provide a range of both market and non-market benefits to society. These benefits provide opportunities for policy interventions for the conservation of the cultural heritage sites and their promotion, but also to their protection against the impacts of climate change and natural disasters.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions.