Beach carrying capacity management under Covid-19 era on the Basque Coast by means of automated coastal videometry

A new occupancy index model based on artificial vision for enhancing beach management

This study proposes a new method to more effectively plan the use of beaches by combining indices and artificial vision systems. The Overcrowding Index (Iocr) measures the number of people on the beach in relation to its surface area, while the Distancing Index (Idis) evaluates the spatial distribution and distance between beachgoers. Both indices are combined to generate an overall index called the Occupancy Index (Iocu). The proposed methodology uses cameras and computer vision algorithms such as YOLOX and ByteTrack to automate the counting of people and measure distances. This allows for continuous monitoring of the quantity (carrying capacity and density) and distribution of beachgoers (degree of social distancing), as well as a functional prototype in which the indices are calculated in real time. It was observed that as density increased, Iocr showed an inverse trend, being close to 0 when approaching maximum density. The calculation of the distance between groups validated that, even with medium densities, close to the shoreline, the reference distance of 2 m was not accomplished, obtaining a very low Idis (0.18). The resulting Iocu was 0.31, validating the appropriate integration of both indices. Overall, the system's effectiveness for accurately monitoring the number of users and their distribution, and calculating the defined indices for beach management, is demonstrated. The proposed approach provides a valuable tool, allowing a more efficient management of beaches according to their actual occupancy and user distribution.

Comparison of wave overtopping estimation models for urban beaches. Towards an early warning system on the Basque coast

This study compares the performance of different wave overtopping estimation models at urban beaches. The models selected for comparison are the Mase et al. (2013) and EurOtop parametric models and the XBeach process-based model in surfbeat and non-hydrostatic mode. Seven energetic storms are selected between 2015 and 2022 with offshore significant wave height ranging between 3 m and 8 m and peak period between 12 s and 20 s to perform the model comparison. The information required to run and validate the models (beach slope, shoreface shape, absence/presence of overtopping) was collected for each storm from coastal videometry. To account for the uncertainties derived from the incident waves randomness and the bathymetry shape when using the process-based model, a series of simulations with random seed boundary conditions were run over two different realistic profile shapes for each storm. The present study is a pilot study on the beach of Zarautz; however, it can be extended to other beaches of the Basque coast. Results indicate that while Mase et al. (2013) and EurOtop tend to reasonably predict the absence or presence of overtopping events, they tend to underestimate the hazard level at the beach of Zarautz. Additionally, the beach underwater profile shape can affect the process-based model performance at intermediate intensity storms and to a lesser extend during moderate storms. Finally, the hazard level at the beach of Zarautz varies significantly alongshore due to the configuration of the seawall, highlighting the need for local adaptation measures. Considering that there is no model that systematically performs better than others, it might be reasonable to use model assemble techniques to draw conclusions from a probabilistic perspective.

An overview on metal pollution on touristic sandy beaches: Is the COVID-19 pandemic an opportunity to improve coastal management?

The worldwide spread of the SARS-CoV-2 caused an unprecedented lockdown measures in most countries with consequences on the world society, economy, and sanitary systems. This situation provided an opportunity to identify the effects of human confinement on natural environments, like touristic sandy beaches, which are stressed due to anthropogenic pressures. Based on previous articles about heavy metals sources and levels in these ecosystems, this paper discusses the dynamic of these pollutants and a regulatory scenario associated with COVID-19 sanitation policies. The main findings suggest that 39% of the studies were on Asian sandy beaches, 16% from Europe, while America and Africa with 23% each. Also Co, Cd, Cu, Cr, Zn, Pb, Ni, Fe and Mn were the most frequently analyzed metals in sediments and in several cases their concentrations exceed international guidelines assessment. Finally, even though beaches are under several metals inputs, tourism plays a key role in these ecosystems quality. After analyzing the potential indirect effect of COVID-19 measures on metals dynamics, we propose some key recommendations and management strategies to mitigate heavy metal pollution on sandy tourist beaches. These proposals are useful for decision-makers and stakeholders to improve sandy beach management, mainly those beaches not addressed from a management perspective; and their implementation should be adapted according to the regulations and legislation of each country.

Beach tourists behavior and beach management strategy under the ongoing prevention and control of the COVID-19 pandemic: A case study of Qingdao, China

The sudden outbreak of COVID-19 has led beach tourism to a complete halt in January 2020, disrupting millions of livelihoods and businesses. Due to the economic importance of beach tourism, many governments reopened tourist beaches after the number of confirmed cases decreased. It is essential to open beaches orderly to meet the needs of tourists, maintain beach's health and restore coastal economy under the new reality. This paper selected Qingdao in China as a case study, drew on a questionnaire survey among beach tourists, summarized the effects of the COVID-19 on beach tourism industry and tourism enterprise, analyzed beach tourists' psychology and behavior, and developed beach management strategy under the ongoing prevention and control of COVID-19. The results showed that the COVID-19 pandemic caused severe damage to beach tourism which bases on travel and mobility, and this industry was temporarily suspended. With the changing epidemic situation, beach tourism witnessed a gradual recovery from stagnation to local tourism. Meanwhile, tourism enterprises were hit by the devastating impact of the COVID-19, causing problems such as business reduction, tense cash flow, high operating cost and unclear market prospect. Under the normalization of pandemic prevention, tourists did not have severe fear and anxiety about the pandemic, and placed great importance on the prevention and control measures, emergency measures and pandemic risk level of the beach destination. The pandemic also reshaped the perception and mode of beach tourism. Ecological tourism, travelling with family, and local tourism became the primary choices for tourists. Beach congestion, health status, and the quality of tourism services were the biggest concerns for tourists. Additionally, social media and short video APP became the new marketing channels. Finally, beach management strategies were proposed from the aspects of pandemic prevention and control, emergency management, information communication, tourist management, service management, and environmental management.

Deep Learning and Internet of Things for Beach Monitoring: An Experimental Study of Beach Attendance Prediction at Castelldefels Beach

Smart seaside cities can fully exploit the capabilities brought by Internet of Things (IoT) and artificial intelligence to improve the efficiency of city services in traditional smart city applications: smart home, smart healthcare, smart transportation, smart surveillance, smart environment, cyber security, etc. However, smart coastal cities are characterized by their specific application domain, namely, beach monitoring. Beach attendance prediction is a beach monitoring application of particular importance for coastal managers to successfully plan beach services in terms of security, rescue, health and environmental assistance. In this paper, an experimental study that uses IoT data and deep learning to predict the number of beach visitors at Castelldefels beach (Barcelona, Spain) was developed. Images of Castelldefels beach were captured by a video monitoring system. An image recognition software was used to estimate beach attendance. A deep learning algorithm (deep neural network) to predict beach attendance was developed. The experimental results prove the feasibility of Deep Neural Networks (DNNs) for beach attendance prediction. For each beach, a classification of occupancy was estimated, depending on the number of beach visitors. The proposed model outperforms other machine learning models (decision tree, k-nearest neighbors, and random forest) and can successfully classify seven beach occupancy levels with the Mean Absolute Error (MAE), accuracy, precision, recall and F1-score of 0.03, 92.7%, 92.9%, 92.7%, and 92.7%, respectively.

Integrated coastal zone management in the context of COVID-19

With the increasing need for and emergence of research on ocean and coastal issues in the context of the COVID-19 pandemic, the Ocean & Coastal Management journal presents this Special Issue with relevant articles within the scope of Coastal Management in times of COVID-19. This Special Issue received 43 tentative abstracts, 29 manuscripts were submitted, and finally, 12 articles were accepted. We provide a wide panorama of those twelve articles that integrate the special issue, covering a diverse range of topics regarding coastal management in the COVID-19 pandemic. Seven papers are studies that discuss environmental and social problems during this time in coastal zones, while the other five explore the use of technology to face COVID-19 on beaches. These twelve articles give some insights to improve coastal management, focused on tourist beaches, natural disasters, and fisheries. In sum, this special issue offers an organized compendium of high-level articles, as a contribution to evolve towards the better ocean and coastal management within the rapid emerging of publications about COVID-19.

New Beach Landscapes to Promote Social Distancing and Coastal Conservation during and after the COVID-19 Pandemic

The Coronavirus disease 2019 (COVID-19) is a pandemic that has altered practically all human activities worldwide. Since the pandemic started at the beginning of 2020, infections have fluctuated drastically over time. It is difficult to predict how this situation will evolve in the coming months/years or when a return to some semblance of 'normal' activity might occur. Because of global lock-up and distancing measures, the beaches, otherwise filled with tourists, first emptied and then had a reduced density of visitors owing to a wide variety of social-distancing measures. Therefore, new safety protocols need to include a wide range of aspects, such as epidemiological conditions, socioeconomic realities, and ecological contexts in which the pandemic occurs. Here, we propose new nature-based landscapes for sandy beaches to help maintain the social distancing of beach visitors while beaches and dunes are restored. When sufficient sediment is available, the maintenance and restoration of healthy beaches with incipient dunes and vegetation will help reduce contagion, promote human health, and recover natural ecosystems.