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The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel. [ABSTRACT FROM AUTHOR]

Coastlines are suffering from the effects of erosive processes, the decrease in sediment supply, the rise in mean sea level, and the construction of coastal infrastructure and drainage works, which are further exacerbated by global climate change. The area of the Parque Natural do Litoral Norte (North Coast Natural Park) reveals worsening erosion rates and the transformations directly affect the natural resources that support tourism activities, particularly beach and nature tourism. As part of the CLICTOUR project, we have selected the coastline from Restinga de Ofir to Bonança Beach as a case study. The ESRI ArcGIS software and the Digital Shoreline Analysis System (DSAS) were used to quantify coastline migration and identify the impacts on beach morphology between 2010 and 2023. Based on this information, we calculated changes in carrying capacity and scenarios for visitor usage availability to ensure the protection of fauna and flora, as well as the safety of beachgoers. The results of the linear regression rate confirm the coastline has retreated during the period analyzed (2010–2023). The outcome of these dynamics is noticeable in the beach area, promoting its reduction in area and leisure quality. Considering climate change, this study shows the importance of developing resilience strategies for coastal territories that serve as traditional summer destinations. [ABSTRACT FROM AUTHOR]

Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia's national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping projects, the full spatial extent and outlines of many Australian beaches remain unmapped. This study leverages deep learning (DL), specifically convolutional neural networks, for binary image segmentation to map beach outlines along the coast of Southeastern Australia. It focuses on Victoria and New South Wales coasts, each approximately 2000 to 2500 km in length. Our methodology includes training and evaluating the model using state-specific datasets, followed by applying the trained model to predict the beach outlines, size, shape, and morphology in both regions. The results demonstrate the model's ability to generate accurate segmentation and rapid predictions, although it faces challenges such as misclassifying cliffs and sensitivity to fine details. Overall, this research presents a significant advancement in integrating DL with coastal science, providing a scalable solution of citizen science mapping efforts for comprehensive beach mapping to support sustainable coastal management and conservation efforts across Australia. Open access datasets and models are provided to further support beach mapping efforts around Australia. [ABSTRACT FROM AUTHOR]

Small vessels contribute substantially to coastal soundscapes, presenting myriad potential impacts on marine species and ecosystems, yet are challenging to monitor using standard tracking methods (e.g. AIS). Here, we present an automated camera system developed to quantify coastal vessel traffic in a core habitat for Hector's dolphins (Cephalorhynchus hectori) within Akaroa Harbour, Aotearoa New Zealand. Analysis of 55,569 images taken during the austral summer of 2019/20 showed that traffic was highest around midday, on weekends, and in January. A mean of 41.9 vessel trips per day transited the area of core habitat. Vessel speeds estimated from photographs were assigned to 5-knot bins with 84% accuracy. Findings suggest vessel traffic in Akaroa Harbour has approximately doubled between 2006–2008 and 2020. Additionally, tours focused specifically on dolphins composed a significantly larger proportion of vessel traffic in 2020 than observed during 2006–2008. We validate the use of an automated camera system as a robust, novel, repeatable, and cost-effective tool for quantifying fluctuations in coastal vessel traffic. Such tools can provide baseline data and help assess impacts, and their use can guide management action. [ABSTRACT FROM AUTHOR]

Beaches provide a buffer zone between the sea and the land, which mitigates the impact of natural hazards on waterfront residents. In this study, the beach at the Dakenggu community in Suao Township, Yilan, was used as a long-term monitoring site for ghost crabs. The survey was conducted by researchers in collaboration with local residents, which can be considered community-based citizen science. In collaboration, researchers and residents conducted monthly surveys. The findings showed that Ocypode sinensis was a dominant species on the Dakenggu beach. Juvenile O. sinensis were more abundant in the spring (April) and the summer (July–September). Except in September, no sex predominance was observed in other months, which may be related to the breeding period of the crabs. As far as other data are concerned, O. sinensis females were significantly larger than males, and O. sinensis occurrences exhibited a significant positive correlation with temperature and a significant negative correlation with relative humidity when weather data for the survey days were analyzed. Multiple studies have shown that the ghost crab population is highly affected by offroad vehicle use on the beach. As a result, local residents can use the findings of this study to develop corresponding conservation strategies, such as permitting off-road vehicle use only in the winter (November–February) and planting coastal plants as natural barriers to prevent vehicles from destroying ghost crab habitat. Long-term environmental monitoring through community-based citizen science can document changes in the local environment, and such information can be the basis for increased community resilience to face the challenges posed by environmental change. [ABSTRACT FROM AUTHOR]

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash features. Some of these systems are constrained by their long setup time, positioning requirements and considerable hardware costs. As such, there is a need for an autonomous low-cost system (~EUR 500), such as Timex cameras, that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents an assessment of the effect of the sampling strategy (time-lapse intervals) on the precision of shoreline detection for two low-cost cameras located in a remote coastal area in western Ireland, overlooking a dissipative beach–dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for sampling intervals ranging between 1 s and 30 s (i.e., RMSDmean for Camera 1 = 1.4 m and Camera 2 = 0.9 m), and an increase in the sampling interval from 1 s to 30 s had no significant adverse effect on the precision of shoreline detection. The research shows that depending on the intended use of the detected shorelines, the current standard of 1 s image sampling interval when using Timex cameras can be increased up to 30 s without any significant loss of accuracy. This positively impacts battery life and memory storage, making the systems more autonomous; for example, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s. [ABSTRACT FROM AUTHOR]

Beachmeter is a Python script to automate the measurements of beaches based on a new delimitation method using digital elevation models (DEMs) of a coastal zone. It is developed to analyze multi-kilometre shorelines, especially in coastal monitoring. It can be opened as an ArcGIS toolbox. Filling out the Beachmeter form, the user inputs the raster DEMs from two measurement campaigns and information on the locally specific features of the study site. The Beachmeter delimits the beach boundaries and saves them as shapefiles. It creates beach elevation models, compares them to provide a differential map, and measures the basic morphological parameters.

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash features. Some of these systems are constrained by their long setup time, positioning requirements and considerable hardware costs. As such, there is a need for an autonomous low-cost system (~EUR 500), such as Timex cameras, that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents an assessment of the effect of the sampling strategy (time-lapse intervals) on the precision of shoreline detection for two low-cost cameras located in a remote coastal area in western Ireland, overlooking a dissipative beach–dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for sampling intervals ranging between 1 s and 30 s (i.e., RMSDmean for Camera 1 = 1.4 m and Camera 2 = 0.9 m), and an increase in the sampling interval from 1 s to 30 s had no significant adverse effect on the precision of shoreline detection. The research shows that depending on the intended use of the detected shorelines, the current standard of 1 s image sampling interval when using Timex cameras can be increased up to 30 s without any significant loss of accuracy. This positively impacts battery life and memory storage, making the systems more autonomous; for example, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s.

In the Wadden Sea, intertidal mussel beds of the blue mussel (Mytilus edulis) and the Pacific oyster (Magallana gigas) form distinct epibenthic communities and local hotspots of high biomass and biodiversity. To detect and evaluate natural and anthropogenic processes, a ground-based monitoring program started over 25 years ago in the German Wadden Sea. In this study, we describe the potential of drones and machine learning approaches for a remote sensing-based integration into an existing monitoring program of intertidal mussel beds. A fixed wing drone was used to cover an area of up to 39ha in a single flight, with an overall time saving potential of 50%. Applying machine learning approaches, a detailed extraction of the intertidal blue mussel bed coverage with an overall accuracy (OA) up to 95.6% was reached, applying a Support Vector Machine (SVM). The application of a multispectral sensor improved the classification performance. Compared to ground-based monitoring, the drone-based method provided significantly more information on the area extension, coverage, and associated algae of the mussel beds. The results show that drones can provide a non-invasive way to survey large and difficult to access areas providing detailed maps of mussel beds and their internal structures. [ABSTRACT FROM AUTHOR]

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions. [ABSTRACT FROM AUTHOR]

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines. [ABSTRACT FROM AUTHOR]

Satellite radar altimeters (SA) have been providing ocean wind and wave measurements for over 35 years. These data have been used for modelling data assimilation, improving wind and wave climatology, and determining long-term trends of the oceanic wave parameters. Fixed observational sites (in situ locations), such as buoys, have provided reliable wave observations since the early 1970s. However, their positioning is inhomogeneous, mainly in the Northern Hemisphere, and only provides point measurements. SA significant wave height (SWH) measurements have been proven as accurate as in situ observations, particularly in the open ocean. Progress in coastal altimetry sensors, upgraded data corrections, and new extraction algorithms have recently improved the quality of SA measurements closer to the coast. This study evaluates the performance of 12 SA missions from 1985 to 2020, particularly in nearshore areas. The SA SWH along-track measurements are compared with observations from 402 in situ locations, distributed worldwide within 25 km of the coastline. Results indicate a slight overestimation from the 12 SA missions, mainly for lower sea states (under 2 m high) and closer to the coast (0 to 10 km). The Sentinel-3 mission showed the highest percentages of valid measurements near the coast and presented 72.66% of collocated in situ data. This SA mission has shown the best overall performance closer to the coast, with biases, correlation coefficient, and root-mean-squared error of 0.23 m, 0.85 m, and 0.50 m, respectively. SA undersampling in coastal areas is present and can lead to underestimation during extreme wave events. The cross-validation of the wave data in two regional analyses conducted during periods of severe wave conditions is evaluated for the new altimeters' generation. [ABSTRACT FROM AUTHOR]

The eastern Mediterranean Sea is facing an unprecedented crisis due to the introduction and spread of alien species through the Suez Canal. Early detection and distribution monitoring of these species has been recently facilitated using social media and citizen science. The project "Is it Alien to you? Share it!!!" recorded over 840 observations for about 100 marine species in Cyprus (eastern Mediterranean Sea) primarily via a dedicated social network group. Through the utilization of morphological and molecular approaches on materials posted by a local fisher, this study provides the first records of Ixa monodi and Myra subgranulata in Cyprus and additional records of Macrophthalmus indicus for the island. These sightings confirm the effectiveness of citizen science in monitoring species distribution shifts. The cooperation of scientists with fishers has the potential to enhance surveillance, while concurrently increase awareness and position citizens as conservation leaders. [ABSTRACT FROM AUTHOR]

Coastal seas are under increasing pressure from extreme weather events and sea level rise, resulting in impacts such as changing hydrodynamic conditions, coastal erosion, and marine heat waves. To monitor changes in coastal marine habitats, such as reefs and macrophytes meadows, which add to the resilience of our coasts, consistent, medium- to long-term seafloor observations are needed. This project aims to deliver repeated, high-frequency sonar surveys on a stationary seabed mooring of a specific target area over a period of up to several months. A new stand-alone subsea system, the Sonarlogger, based on a battery pack, low-power logger and a high-resolution scanning sonar, was developed. It allows for long-term deployments with a customisable battery pack, WI-FI download and configurable sleep state. The system has been tested for over 130 days in dynamic coastal environments off the Belgian coast. Combined with auxiliary sensors, such as for measuring currents, waves and turbidity, this system enables comprehensive studies of morphologic changes and changing benthic ecosystems. Moreover, this system has the capacity to provide measurements of coastal environments during storms, where conventional systems may fall short, providing insights into event-based changes of the seafloor.

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

High-resolution remote sensing of turbidity in the coastal environment with unmanned aerial vehicles (UAVs) can be adversely affected by the presence of obstructions of vessels and marine objects in images, which can introduce significant errors in turbidity modeling and predictions. This study evaluates the use of two deep-learning-based inpainting methods, namely, Decoupled Spatial–Temporal Transformer (DSTT) and Deep Image Prior (DIP), to recover the obstructed information. Aerial images of turbidity plumes in the coastal environment were first acquired using a UAV system with a multispectral sensor that included obstructions on the water surface at various obstruction percentages. The performance of the two inpainting models was then assessed through both qualitative and quantitative analyses of the inpainted data, focusing on the accuracy of turbidity retrieval. The results show that the DIP model performs well across a wide range of obstruction percentages from 10 to 70%. In comparison, the DSTT model produces good accuracy only with low percentages of less than 20% and performs poorly when the obstruction percentage increases.

Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia’s national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping projects, the full spatial extent and outlines of many Australian beaches remain unmapped. This study leverages deep learning (DL), specifically convolutional neural networks, for binary image segmentation to map beach outlines along the coast of Southeastern Australia. It focuses on Victoria and New South Wales coasts, each approximately 2000 to 2500 km in length. Our methodology includes training and evaluating the model using state-specific datasets, followed by applying the trained model to predict the beach outlines, size, shape, and morphology in both regions. The results demonstrate the model’s ability to generate accurate segmentation and rapid predictions, although it faces challenges such as misclassifying cliffs and sensitivity to fine details. Overall, this research presents a significant advancement in integrating DL with coastal science, providing a scalable solution of citizen science mapping efforts for comprehensive beach mapping to support sustainable coastal management and conservation efforts across Australia. Open access datasets and models are provided to further support beach mapping efforts around Australia.

Coastal environments are vital for sustaining key industries, including fisheries, mining, real estate, and tourism, through the provisioning of essential ecosystem services. However, the intricate interplay of bio-chemical and physical processes in these areas is susceptible to disruption by humandriven coastal developments. This disruption has gradually led to adverse consequences such as altered water movement patterns, land quality degradation, habitat loss, pollutant introduction, greenhouse gas emissions, and subsequent sea-level rise. With Cape Town's 240-kilometre coastline serving as a prime example of this complex relationship between coastal processes and livelihoods, this study employs innovative remote sensing and deep learning techniques. We use Sentinel-2 satellite imagery and the Deep Learning-based CoastSat Python toolkit, specifically leveraging the Modified Normalized Difference Water Index (MNDWI) for sub-pixel level shoreline detection. Focusing on four critical Cape Town coastlines - Noordhoek-Kommetjie, Milnerton, StrandfonteinMonwabisi, and Strand, we assess and compare shoreline changes as detected by the CoastSat toolkit. Our findings reveal significant shoreline changes, with Strandfontein-Monwabisi experiencing the most substantial shift, approximately 284 metres. Noordhoek-Kommetjie exhibited a change of 110 metres, while Milnerton and Strand displayed similar changes, with 88 and 91 metres, respectively. This research not only offers valuable insights into Cape Town's dynamic coastlines, but also informs the strategic allocation of coastal management resources. Consistent data collection and analysis hold the potential to foster interactive coastal monitoring tools, enhancing the effectiveness and sustainability of coastal management practices. [ABSTRACT FROM AUTHOR]

HF radar systems form part of many operational coastal monitoring systems providing near-real-time surface currents for many useful applications. Although wave measurements have been possible with these systems for many years, they have not yet been adopted widely for operational monitoring because they have not been thought to be sufficiently accurate or reliable. However, the value of such data is beginning to be appreciated, and this is motivating more work on wave measurement with HF radar systems with many more papers on accuracy assessment and data availability appearing in the literature. In this paper, the wave measurement capability, limitations, and differences between different radar types are reviewed, and methods to assess accuracy are discussed and applied to phased array HF radar data obtained from the University of Plymouth WERA radars using the Seaview Software inversion method during April and November 2012 compared with directional buoy data. Good accuracy over a range of different wave parameters will be demonstrated. Newly available single-radar inversions are shown to be less accurate than dual-radar inversions, although they still provide useful data, and ways to improve performance are discussed. Swell and wind–sea components in the directional spectra are identified, and qualitative agreement with buoy peak parameters is demonstrated. Recommendations are given on statistical methods for the validation of wave parameters. [ABSTRACT FROM AUTHOR]

Coastal compartments provide a hierarchical framework to manage beaches and coastal ecosystems in Australia. This study examines the individual behaviour of three adjacent beaches within Shoalhaven Bight, a secondary compartment on the south coast of New South Wales (NSW). The Shoalhaven River discharges intermittently into the northernmost of the beaches, and a fluvial component is detectable within beach and foredune sands. The distribution and orientation of headlands constrains dispersal of fluvial sediments, and results in lower wave-energy conditions further south, implying limited sand transport from one beach, or tertiary compartment, to another. Two years of monthly subaerial surveys across ten different profiles and modelled storm-wave conditions during the major storms, were used to compare the response of beach volume and shoreline position. The greatest changes in volume were observed near Shoalhaven Heads and the northern ends of Culburra and Warrain-Currarong beaches. Results indicate the distinct individual response of neighbouring beaches over the two-year period, in contrast to general trends in beach behavior that might be inferred from long-term regional monitoring programs. However, they also demonstrate the limitations of short-term observations in comparison to those longer-term studies. The NSW beach systems of are some of the best understood in the world in terms of regional behaviour, as a consequence of several decades of surveys on key beaches. However, more focused local monitoring programs may be needed to establish detailed coastal sand transport, and an understanding of intermittent delivery of fluvial sand and longshore inputs and outputs, within and between different compartments. [ABSTRACT FROM AUTHOR]

Strand monitoring is valuable for identifying human-caused threats to sea turtles but has limitations in fully understanding their overall impact. To address this gap, we conducted a survey to examine sea turtle interactions with longline fisheries, focusing on the types of hooks used for different target fish species. The study aimed to determine the prevalence of sea turtles entangled with hooks in these fisheries and to investigate seasonal and spatial variations in strandings. From August 2015 to December 2020, regular surveys were carried out along 2,013 km, on the South and Southeast coasts of Brazil to monitor sea turtle strandings. Data were collected on the presence and types of hooks, as well as the developmental stages of the affected turtles. The study analyzed spatial and seasonal variations and conducted necropsies to determine causes of death and assess internal damage from fishing gear. A total of 4,631 stranded sea turtles were documented: Caretta caretta, n=3,678, w/hooks=96; Lepidochelys olivacea, n=753, w/hooks=20, and Dermochelys coriacea, n=200, w/hooks= 01. Four types of hooks were identified, with a predominance of “J” hooks typically used in the region to capture dolphinfish, Coryphaena hippurus (72/122 hooks). The most affected developmental stages were large juveniles of C. caretta and L. olivacea. The distribution of hooks differed among the states; “J” hooks targeting C. hippurus on the continental shelf were predominantly found in São Paulo state. In contrast, circular hooks for swordfish (Xiphias gladius) and tuna hooks for tuna (Thunnus spp.), used in offshore fisheries, were more common in southern states. The abundance of “J”, hooks increased during the spring months, aligning with the C. hippurus fishing season. “J” hooks were primarily found in the esophagus of stranded turtles (88/122), indicating that their size and shape facilitate ingestion. Necropsies revealed that 43.1% of deaths (50/116) were due to intestinal lesions caused by hooks, fishing lines, and/or cables. Despite the small relative frequency of individuals found with hooks (117/4631), pelagic longline fisheries, especially those targeting C. hippurus, are a serious threat to sea turtle populations and, therefore, require specific monitoring and mitigating measures. Furthermore, the number of affected individuals may be underestimated since stranding does not represent total mortality at sea.

Abstract Sound speed profiles in the ocean are determined by seawater properties, where horizontal variability of thermal stratification modulates sonar detection distance. This work assesses the impacts of upwelling dynamics on sound speed profile in enclosed coastal areas by means of temperature observations acquired with a low-cost platform in Anjos Cove, Cabo Frio, Brazil. The Integrated Acquisition System for Research in Acoustics (IARA) consists of a customized 10-meter-long chain of smart temperature sensors (±0.1°C accuracy) mounted in an anchored vertical cable and an echo sounder for tide level measurements. From 2021 to 2023 a number of intrusion events of the cold South Atlantic Central Water (SACW) took place along the Cabo Frio continental shelf, some of which were recorded in the inner region of the Anjos Cove by IARA. Sound speed profiles computed by applying TEOS-10 standards indicate that the sound propagation within Anjos Cove varies often, i.e., several orders of magnitude, under the influence of SACW intrusion events. The phenomenon reflects information from deeper waters on a coastal scale, even at small depths.

In the Wadden Sea, intertidal mussel beds of the blue mussel (Mytilus edulis) and the Pacific oyster (Magallana gigas) form distinct epibenthic communities and local hotspots of high biomass and biodiversity. To detect and evaluate natural and anthropogenic processes, a ground-based monitoring program started over 25 years ago in the German Wadden Sea. In this study, we describe the potential of drones and machine learning approaches for a remote sensing-based integration into an existing monitoring program of intertidal mussel beds. A fixed wing drone was used to cover an area of up to 39ha in a single flight, with an overall time saving potential of 50%. Applying machine learning approaches, a detailed extraction of the intertidal blue mussel bed coverage with an overall accuracy (OA) up to 95.6% was reached, applying a Support Vector Machine (SVM). The application of a multispectral sensor improved the classification performance. Compared to ground-based monitoring, the drone-based method provided significantly more information on the area extension, coverage, and associated algae of the mussel beds. The results show that drones can provide a non-invasive way to survey large and difficult to access areas providing detailed maps of mussel beds and their internal structures.

Despite their ecological, economic, and social importance, mangrove ecosystems suffer from high levels of degradation caused by a combination of anthropogenic stressors and the effects of climate change. Their degradation inevitably reduces the provision of ecosystem services and ultimately impacts human well-being, especially in coastal communities of Small Islands Developing States (SIDS). To timely identify and manage stressors causing local mangrove degradation, in situ monitoring is required. However, the financial means and human capacity to do so are often limited in SIDS, hampering adequate management of their mangrove forests. In search of a cost-effective alternative, we evaluated the use of Sentinel-2 satellites to monitor mangrove extent and species distribution in Lac Bay, a bay located on the small tropical island of Bonaire (Caribbean Netherlands). We also evaluated the mangrove’s ecological condition through two biophysical variables 1) Effective Leaf Area Index (LAIe) and 2) Net Primary Productivity (NPP). Our results showed that Sentinel-2 data are a valuable tool for mapping the extent of mangrove forests in Bonaire and species composition (mean overall accuracy > 95 %). Using five Sentinel-2 images from 2021 and 2022, the extent of mangrove forests in Lac Bay was estimated to be on average 222.3 ha, of which 136.0 ha were classified as Rhizophora mangle (red mangrove) and 77.1 ha as Avicennia germinans (black mangrove). Mean values for predicted LAIe ranged from 3.37 to 3.85 for Lac Bay, with significantly higher values in the wet season (3.82 ± 0.57) compared to the dry season (3.40 ± 0.56). The generic Simplified Level-2 Prototype Processor (SL2P) underestimated the LAIe values in Lac Bay, with moderate differences between SL2P values and in situ data (BDE = 0.41, RMSE = 1.09). Mean NPP values were estimated to be 8.82 ± 1.46 (g Cm−2 d -1). LAIe and NPP maps showed a zonal distribution, with highest values in the mid-West and East on the seaward side, and lowest values in the northern landward part of Lac Bay. The method developed in this study provides a cost-effective way to monitor the extent, composition, and ecological condition of mangrove forests, which can be used by small island states to make informed decisions about the management and protection of mangrove ecosystems.

The submarine outfall of Mar del Plata city at Camet was projected considering the mean and maximum of forecasted sewage discharges, the inner-shelf depth, coliform concentration and its decay (T90) mainly induced by sunlight effect and costal salinity. In 2016 the outfall was operating with a length of 3,810 m and diffusers in the last 526 m. An economical method to monitor its performance in relation to the surroundings, is remote-sensing techniques, applying either visible or radar images. Tidal currents parallel to the coast are responsible for the transport of the sedimentary plume in the far field, after a primary dilution from a depth of 11 m. Visible images (1.5 to 6 m spatial resolution) are effective in monitoring the plume entrained in the upper portion of the water column. These analyses led to study the interaction between waves and coastal currents. Radar images (30 m resolution X and C bands) permit to survey the slick-alike plume that differs from the environment water by the surface roughness. Comparing both techniques visible images can distinguish the different colours of the plume; instead, the radar images are showing the surface roughness from the slick-alike plume. The main advantage of active sensors is that they can map the plume during a cloudy weather and even during night time.

The eastern coastline of the Gulf of Trieste (north-eastern Adriatic Sea, Italy) is characterized by the occurrence of coastal and submarine freshwater springs of karstic origin. In one of these areas, we performed a survey with a drone with a thermal camera installed, in tandem with in situ oceanographic sampling with a CTD. Drone images revealed a small time-space scale (i.e., up to a few meters) phenomenon of freshwater plumes floating over seawater. Comparing sea surface temperature data with those acquired in situ revealed that the phenomenon was not clearly detectable by the classical oceanographic monitoring, this surface spring freshwater layer being too thin. Instead, the drone's thermal camera detected these dynamics with great accuracy, indicating that aerial drones can be efficiently used for studying fine-scale events involving surface waters (e.g., spills/pollution). The experience gained allowed us to discuss some of the advantages and disadvantages of using drone thermal imaging for monitoring alongshore areas. [ABSTRACT FROM AUTHOR]

Abstract Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring.

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Increasingly diverse pathogen occurrence in coastal and mariculture areas demands improved monitoring platforms to prevent economic and public health implications. Accessible databases with up-to-date knowledge and taxonomy are critical for detecting and screening environmental pathogens. Condensed from over 3000 relevant reports in peer reviewed articles, we constructed an aquaculture bacterial pathogen database that provides specialized curation of over 210 bacterial pathogenic species impacting aquaculture. Application of the aquaculture bacterial pathogen database to environmental DNA metabarcoding monitoring data in Hong Kong coastal and mariculture waters effectively characterized regional pathogen profiles over a one-year period and improved identification of new potential pathogen targets. The results highlighted the increase in potential pathogen abundance related to aquaculture activity and the associated inorganic nitrogen load, which was chiefly due to the enrichment of Vibrio during the atypical dry winter season. The value of the aquaculture bacterial pathogen database for empowering environmental DNA-based approaches in coastal marine pathogen surveillance benefits water resource management and aquaculture development on a global scale.

The Taranto basin, a complex marine ecosystem with typical lagoon features, has been declared Site of National Interest (SIN) because of serious contamination of marine sediment. Since February 2014, The Coastal Engineering Laboratory (LIC) of DICATECh of the Polytechnic University of Bari (Italy) maintains a place-based research programme in the Mar Grande and Mar Piccolo of Taranto, providing records of hydrodynamic and water-quality measurements. Taranto, like the rest of Italy, had a lockdown since March 2020 which has reduced the industrial activity which is the main source of human stress on the ecosystem of the Mar Piccolo and Mar Grande. Here, the impact of COVID-19 lockdown on sea surface temperature SST is investigated using the satellite and in situ measurements. Rising sea surface temperatures related to global warming are increasingly affecting marine habitats and people who depend on them. [ABSTRACT FROM AUTHOR]

The advantages derived from the use of Uncrewed Aerial Vehicles (UAVs) are well-established: they are cost-effective and easy to use. There are numerous environmental applications, particularly when monitoring contexts characterized by rapid morphological changes and high rates of sediment transport, such as coastal areas. In this paper, three different case studies of survey and monitoring with high resolution and accuracy obtained through the use of UAVs are presented; these concern transgressive coastal sites. Results allow for the definition and quantification of coastal landforms and processes, including: (i) The anatomy of a parabolic dune and the rate of landward migration that could interfere with a tourist settlement; (ii) The mode and timing of morphological recovery and realignment of a barrier island overwashed by storm surge episodes; and (iii) The potential flood risk of a progradational spit that is a nesting site of a species of migratory breeding birds of conservation concern. The results demonstrate and confirm that, through a good coupling of drone-sensed quality data and accurate topographic control, quantitative estimates that are useful in assessing the impacts of natural processes involving both human and natural assets can be obtained. [ABSTRACT FROM AUTHOR]

Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring. Earth observation (EO) data, derived from remote and proximal sensing (e.g. via Unmanned Aerial Vehicles – UAVs), have been recently demonstrated to be essential tools for ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. Research based on EO data analyses often tend to focus on images processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a local scale, using a supervised machine learning model, through the combination of a vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g. LiDAR), Object‐Based Image Analysis and landscape metrics analysis. Our findings demonstrate that UAV‐VHR data are a valid tool to produce high spatial resolution information in sandy beach ecosystems, giving ecology research a new way of responsive, timely, and cost‐effective ecosystem monitoring. [ABSTRACT FROM AUTHOR]

Ocean color is the result of absorption and scattering, as light interacts with the water and the optically active constituents. The measurement of ocean color changes enables monitoring of these constituents (dissolved or particulate materials). The main objective of this research is to use digital images to estimate the light attenuation coefficient ( K d ), the Secchi disk depth ( Z S D ), and the chlorophyll a ( C h l a) concentration and to optically classify plots of seawater using the criteria proposed by Jerlov and Forel using digital images captured at the ocean surface. The database used in this study was obtained from seven oceanographic cruises performed in oceanic and coastal areas. Three approaches were developed for each parameter: a general approach that can be applied under any optical condition, one for oceanic conditions, and another for coastal conditions. The results of the coastal approach showed higher correlations between the modeled and validation data, with r p values of 0.80 for K d , 0.90 for Z S D , 0.85 for Chla, 0.73 for Jerlov, and 0.95 for Forel–Ule. The oceanic approach failed to detect significant changes in a digital photograph. The most precise results were obtained when images were captured at 45° (n = 22; F r   c a l = 11.02 > F r   c r i t = 5.99 ). Therefore, to ensure precise results, the angle of photography is key. This methodology can be used in citizen science programs to estimate ZSD, Kd, and the Jerlov scale. [ABSTRACT FROM AUTHOR]

Regular monitoring is essential for vulnerable coastal locations such as areas of landward retreat. However, for coastal practitioners, surveying is limited by budget, specialist personnel/equipment and weather. In combination structure-from-motion and multi-view stereo (SfM-MVS) has helped to improve accessibility to topographic data acquisition. Pole-mounted cameras with SfM-MVS have gained traction but to guarantee coverage and reconstruction quality, greater understanding of camera position and interaction is required. This study uses a multi-camera array for image acquisition and reviews processing procedures in Agisoft Photoscan (Metashape). The camera rig was deployed at three sites and results were verified against a terrestrial laser scanner (TLS) and independent precision estimates. The multi-camera approach provided effective image acquisition ~11 times faster than the TLS. Reconstruction quality equalled (>92% similarity) the TLS, subject to processing parameters. A change in the image alignment parameter demonstrated a significant influence on deformation, reducing reprojection error by~94%. A lower densification parameter ('High') offered results ~4.39% dissimilar from the TLS at 1/8th of the processing time of other parameters. Independent precision estimates were <8.2 mm for x, y and z dimensions. These findings illustrate the potential of multi-camera systems and the influence of processing on point cloud quality and computation time. [ABSTRACT FROM AUTHOR]

The intertidal zone is a harsh environment for marine life as conditions are often both extreme and variable. A wide range of sessile organisms are partially or fully emersed (exposed above the water line) during low tide. In the tropics, when corals are emersed, high light and temperature can be detrimental to their survival. To date, there is no commercially-available logger that can detect periods of emersion, information that is useful for marine research and for coastal resource management. Here, we present a low-cost Arduino-based multi-parameter logger called ‘EmerSense’ which can detect instances of emersion while simultaneously recording light and temperature profiles. We describe the different steps involved in fabricating EmerSense, including hardware construction and software design, and discuss the results of our field testing at an intertidal coral reef in Singapore.

Sandy beaches are subject to changes due to multiple factors, that are both natural (e.g. storms) and anthropogenic. Great efforts are being made to monitor these ecosystems and understand their dynamics in order to assure their conservation. The identification of anthropogenic changes and its differentiation from natural ones is an important task for coastal monitoring. In this study, we present a methodology for the detection of anthropogenic changes in a coastal ecosystem by automatically detecting active bulldozers in continuous beach video data. PCA is used to highlight changes in consecutive images due to moving objects. Next, the YOLO object detection algorithm is used to identify the bulldozers in the change images. YOLO was specifically trained for the task, obtaining a precision of 0.94 and a recall of 0.81. An automatic tool was developed, and the process was carried out on two months of video data, consisting of approximately 19 000 images. The resulting information was compared with changes derived from 3D data obtained from a permanent laser scanner. The correlation among the results of the two methodologies was computed. For a validation area and daily time frame a correlation of 0.88 was obtained between the number of detected bulldozers and the area affected by changes in height larger than 0.3 m.

The main objective of the present paper is to achieve the simulation of the long-term (e.g., annual) morphological evolution (bathymetry and shoreline) of a coastal area (Mastichari beach, Greece) due to wind-generated waves in an accurate and cost-effective way. For this purpose, wave climate schematization techniques are adopted in order to reduce the computational effort without losing or distorting the crucial information. Referring to the current practice, the wave chronology is not considered in detail in these acceleration techniques, and therefore, sea bottom and shoreline evolution are not estimated as a function of time. To fill this gap, a novel and easy-to-use chronology-based wave input reduction method is developed to produce representative wave time series of different intensities and durations. The performance of all the applied techniques in the present study is evaluated, and a comparative analysis is conducted. It is shown that the new chronology-based wave input reduction method can achieve a model run-time reduction of about 70%, while at the same time, it provides satisfactorily accurate results. In general, this research could be useful for coastal engineering studies and coastal zone monitoring, and thus, it would be a valuable tool for coastal engineers and marine scientists. [ABSTRACT FROM AUTHOR]

Boulder-sized clasts on rocky coasts are considered as erosional signatures of extreme wave events and boulder attributes are often used in numerical models to estimate wave characteristics. The use of unmanned aerial vehicle (UAV) technology and related software has facilitated the monitoring of coastal areas, by generating models from which 2D and 3D measurements can be derived. However, the reliability and preciseness of such measurements is still to be determined. This study seeks to analyse the accuracy of boulder measurements by comparing the dimension data obtained through in-situ measurements with ex situ data generated from digital models, based on UAV images. The study area is a bouldered sloping coast located on the southeast coast of Malta (Central Mediterranean) that has developed into multiple limestone dipped strata with a fractured and heavily jointed morphology. The dimensions of c. 200 boulders in different morphological settings, such as clusters or ridges, have been statistically compared. The results show a very strong correlation between the two datasets, both in 2D and 3D; however some notable differences were observed at the individual boulder level. For the majority of boulders analysed, the A and B axes dimensions varied by ±10% to 20%. The C axis proved to be harder to measure accurately and showed a wider range of difference. Boulder volume results in the majority of cases varied from 0% to ±40%. Some tested methods of volume calculation may be more accurate and realistic than others depending on the boulder position in relation to other clasts and shore morphology. An automated digital analysis of the terrain surface to identify the boulder extents may offer possibilities for a more accurate estimation of boulder attributes. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is transforming coastal science from a "data-poor" field into a "datarich" field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the bestmonitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earthobserving satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and everexpanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earthobserving satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems. [ABSTRACT FROM AUTHOR]

Two aerial surveys were processed with Structure from Motion - Multiview-Stereo with a Co-Alignment approach in two sectors of a wetland: El Picaflor (EP) and El Sarandí (ES). Shoreline changes were analysed with Digital Shoreline Analysis System (DSAS) using high-resolution orthophotos and change detection was calculated with M3C2. In EP the shoreline changes rate averages -0.71 m/yr and the M3C2 comparison shows that the coast is mainly erosional. In ES the shoreline rates of change averages 0.59 m/yr and the M3C2 comparison shows both erosion and deposition. When EP and ES were compared, the main differences are land use, orientation of the coast, and the presence of intertidal vegetation. The use of the Co-Alignment approach together with DSAS proves to be a valuable technique for monitoring shoreline changes at a high spatial and temporal resolution in this wetland and can be applied to study other coastal wetlands worldwide. [ABSTRACT FROM AUTHOR]

BioBlitz surveys are important events aiming to record all the living species in an area within a limited timeframe and an important tool to keep track of alien and invasive species. During a BioBlitz held in the Burlamacca Canal (Viareggio Marina Area, northern Tyrrhenian Sea, Italy), 17 taxa were found, among which an "odd" crab species. Its taxonomic identification was confirmed through an integrative taxonomic approach as the invasive Harris mud crab Rhithropanopeus harrisii (A. A. Gould, 1841). Subsequently, a total of 33 specimens were collected along a 5 x 1 m transect, with a density of 6.6 individuals/m2. As the sampled material included adults and ovigerous females, we suggest that the species has already established in the area. The pathway of arrival is uncertain, although secondary spreading amidst fouling communities may have played a major role, with specimens arriving from the nearby Livorno area. Future studies may explore relationships between the alien crab and the resident biota as well as whether the species has already colonised Lake Massaciuccoli and its wetlands. [ABSTRACT FROM AUTHOR]

In this study, we investigated the levels of heavy metals in sediment, shrimps, silver catfish and their potential bioaccumulation, trophic transfer and the associated risk to human health from the Escravos estuary in southern Nigeria. The values of heavy metals in shrimps and silver catfish were disproportionately higher than the allowable limits for seafood, with values in the gills and the rest of the fish higher than those of the muscle, liver and gonads. Furthermore, the increased BSAF for Cd, Cr and Cu in the study, underscore the uptake of metals from sediments into shellfish. High EDI and HR values from silver catfish for adults and children indicate immediate health risks, with the values for children considerably high, emphasizing the urgent need for regulatory measures and continuous monitoring of seafood from the estuary to protect the health of coastal population within the subregion. [Display omitted] • Cd, Cr and Cu in shrimp were higher than the limits for heavy metals in crustaceans. • Cd, Cr and Pb in silver catfish were above the limits for heavy metals in fish. • Metals in fish are in the decreasing order of RoF > gills > muscle > liver > gonads. • Increased BSAF was reported for Cd, Cr and Cu from sediment to pink shrimp. • EDI and HR values in fish indicate substantial health risks for adults and children. [ABSTRACT FROM AUTHOR]

Citizen science-based monitoring projects have emerged as powerful tools for understanding morphodynamic changes in beaches and fostering the democratization of coastal research and management. The CoastSnap initiative exemplifies this approach. This study provides a comprehensive overview of the background and evolution of the CoastSnap initiative in Spain, analyzing its performance during its first five operational years in the country (2018–2022) and its success in engaging diverse participants and stakeholders. The Spanish CoastSnap network effectively harnessed community involvement leading to significant data collection to enhance scientific understanding of shoreline dynamics. The analysis of community participation statistics demonstrates the influence of local customs on CoastSnap use, with variable engagement trends related to geographic factors and different beach users' preferences. Challenges in maintaining project momentum and ensuring data accuracy underscore the need for continuous support and resources to sustain citizen science initiatives, while also encouraging the integration of local knowledge and expertise. This study provides insights for similar citizen science projects, highlighting the potential of collaborative efforts in addressing the complex monitoring of coastal ecosystems. The organization, achievements, impacts, and challenges of the Spanish CoastSnap network offer valuable guidance for further applications of the CoastSnap approach, future research directions, and enhance knowledge exchange to establish international best practices. [Display omitted] • Transparent scientific outcomes legitimate citizen science for coastal monitoring. • The CoastSnap approach democratizes coastal research and management. • CoastSnap Spain has strong community uptake influenced by location specific factors. • Dedicated resources boosted success, highlighting the need for sustained funding. • Spanish experiences aid in advancing international CoastSnap best practices. [ABSTRACT FROM AUTHOR]