Search Results (120)

Tidal flats, a critical part of coastal wetlands, offer unique ecosystem services and functions. However, in China, these areas are under significant threat from industrialization, urbanization, aquaculture expansion, and coastline reconstruction. There is an urgent need for macroscopic, accurate and periodic tidal flat resource data to support the scientific management and development of coastal resources. At present, the lack of macroscopic, accurate and periodic high-resolution tidal flat maps in China greatly limits the spatio-temporal analysis of the dynamic changes of tidal flats in China, and is insufficient to support practical management efforts. In this study, we used the Google Earth Engine (GEE) platform to construct multi-source intensive time series remote sensing image collection from Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) images, and then automated the execution of improved MSIC-OA (Maximum Spectral Index Composite and Otsu Algorithm) to process the collection, and then extracted and analyzed the tidal flat data of China in 2018 and 2023. The results are as follows: (1) the overall classification accuracy of the tidal flat in 2023 is 95.19%, with an F1 score of 0.92. In 2018, these values are 92.77% and 0.88, respectively. (2) The total tidal flat area in 2018 and 2023 is 8300.34 km² and 8151.54 km², respectively, showing a decrease of 148.80 km². (3) In 2023, estuarine and bay tidal flats account for 54.88% of the total area, with most tidal flats distribute near river inlets and bays. (4) In 2023, the total length of the coastline adjacent to the tidal flat is 10,196.17 km, of which the artificial shoreline accounts for 67.06%. The development degree of the tidal flat is 2.04, indicating that the majority of tidal flats have been developed and utilized. The results can provide a valuable data reference for the protection and scientific planning of tidal flat resources in China. Full article

Fucus spp. seaweeds thrive in the cold temperate waters of the northern hemisphere, specifically in the littoral and sublittoral regions along rocky shorelines. Moreover, they are known to be a rich source of bioactive compounds. This study explored the valorization of Fucus spiralis through the extraction of bioactives and polysaccharides (PSs) for food applications and biostimulant use. The bioactives were extracted using microwave hydrodiffusion and gravity (MHG), where the condition of 300 W for 20 min resulted in the highest total phenolic content and antioxidant activity of the extract. Cellular assays confirmed that the extract, at 0.5 mg/mL, was non-cytotoxic to HaCat cells. Polysaccharides (PSs) were extracted from the remaining biomass. The residue from this second extraction contained 1.5% protein and 13.35% carbohydrates. Additionally, the free amino acids and minerals profiles of both solid residues were determined. An edible film was formulated using alginate (2%), PS-rich Fucus spiralis extract (0.5%), and F. spiralis bioactive-rich extract (0.25%). The film demonstrated significant antioxidant properties, with ABTS and DPPH values of 221.460 ± 10.389 and 186.889 ± 36.062 µM TE/mg film, respectively. It also exhibited notable physical characteristics, including high water vapor permeability (11.15 ± 1.55 g.mm.m⁻².day⁻¹.kPa⁻¹) and 100% water solubility. The residues from both extractions of Fucus spiralis exhibited biostimulant (BS) effects on seed germination and seedling growth. BSs with PSs enhanced pea germination by 48%, while BSs without PSs increased the root dry weight of rice and tomato by 53% and up to 176%, respectively, as well as the shoot dry weight by up to 38% and up to 74%, respectively. These findings underscore the potential of Fucus spiralis within the framework of a circular economy, wherein both extracted bioactives and post-extraction by-products can be used for sustainable agriculture and food applications. Full article

In scenarios such as nearshore and inland waterways, the ship spots in a marine radar are easily confused with reefs and shorelines, leading to difficulties in ship identification. In such settings, the conventional ARPA method based on fractal detection and filter tracking performs relatively poorly. To accurately identify radar targets in such scenarios, a novel algorithm, namely YOSMR, based on the deep convolutional network, is proposed. The YOSMR uses the MobileNetV3(Large) network to extract ship imaging data of diverse depths and acquire feature data of various ships. Meanwhile, taking into account the issue of feature suppression for small-scale targets in algorithms composed of deep convolutional networks, the feature fusion module known as PANet has been subject to a lightweight reconstruction leveraging depthwise separable convolutions to enhance the extraction of salient features for small-scale ships while reducing model parameters and computational complexity to mitigate overfitting problems. To enhance the scale invariance of convolutional features, the feature extraction backbone is followed by an SPP module, which employs a design of four max-pooling constructs to preserve the prominent ship features within the feature representations. In the prediction head, the Cluster-NMS method and α-DIoU function are used to optimize non-maximum suppression (NMS) and positioning loss of prediction boxes, improving the accuracy and convergence speed of the algorithm. The experiments showed that the recall, accuracy, and precision of YOSMR reached 0.9308, 0.9204, and 0.9215, respectively. The identification efficacy of this algorithm exceeds that of various YOLO algorithms and other lightweight algorithms. In addition, the parameter size and calculational consumption were controlled to only 12.4 M and 8.63 G, respectively, exhibiting an 80.18% and 86.9% decrease compared to the standard YOLO model. As a result, the YOSMR displays a substantial advantage in terms of convolutional computation. Hence, the algorithm achieves an accurate identification of ships with different trail features and various scenes in marine radar images, especially in different interference and extreme scenarios, showing good robustness and applicability. Full article

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world. Full article

Sargassum muticum (SM) poses a serious environmental issue since it is a fast-expanding invasive species occupying key areas of the European shoreline, disrupting the autochthonous algae species, and disturbing the ecosystem. This problem has concerned the general population and the scientific community. Nevertheless, as macroalgae are recognized as a source of bioactive molecules, the abundance of SM presents an opportunity as a raw material. In this work, response surface methodology (RSM) was applied as a tool for the optimization of the extraction of bioactive compounds from SM by microwave-assisted extraction (MAE). Five different parameters were used as target functions: yield, total phenolic content (TPC); and the antioxidant measurements of 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), and β-carotene bleaching (BC). After the optimal extraction conditions were determined (time = 14.00 min; pressure = 11.03 bar; ethanol = 33.31%), the chemical composition and bioactivity of the optimum extract was evaluated to appraise its antioxidant capability to scavenge reactive species and as a potential antibacterial, antidiabetic, antiproliferation, and neuroprotective agent. The results lead to the conclusion that MAE crude extract has bioactive properties, being especially active as an antiproliferation agent and as a nitric oxide and superoxide radical scavenger. Full article

Marine ecosystems serve as vital indicators of biodiversity, providing habitats for diverse flora and fauna. Canada’s extensive coastal regions encompass a rich range of marine habitats, necessitating accurate mapping techniques utilizing advanced technologies, such as remote sensing (RS). This study focused on a study area in Pistolet Bay in Newfoundland and Labrador (NL), Canada, with an area of approximately 170 km² and depths varying between 0 and −28 m. Considering the relatively large coverage and shallow depths of water of the study area, it was decided to use airborne bathymetric Light Detection and Ranging (LiDAR) data, which used green laser pulses, to map the marine habitats in this region. Along with this LiDAR data, Remotely Operated Vehicle (ROV) footage, high-resolution multispectral drone imagery, true color Google Earth (GE) imagery, and shoreline survey data were also collected. These datasets were preprocessed and categorized into five classes of Eelgrass, Rockweed, Kelp, Other vegetation, and Non-Vegetation. A marine habitat map of the study area was generated using the features extracted from LiDAR data, such as intensity, depth, slope, and canopy height, using an object-based Random Forest (RF) algorithm. Despite multiple challenges, the resulting habitat map exhibited a commendable classification accuracy of 89%. This underscores the efficacy of the developed Artificial Intelligence (AI) model for future marine habitat mapping endeavors across the country. Full article

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R² of 0.84), with FS decreasing with slope height. Full article

Fishing ports are important fishery production platforms, and the transformation of these has had a profound impact on the fishing industry. A reasonable classification solution is crucial for scientific understanding, development, and management of fishing ports. Current research on the use of spatial distributional characterization and the construction of a classification system for fishing ports to improve their management is limited. Therefore, in this study, a fishing port classification system was constructed using remote sensing images, with fishing port boundaries accurately extracted for classification. Using graphical summarization, fishing ports were classified into five types, including inland, estuarine, shoreline, gulf, and islands. A port type identification system was also constructed based on distance from the shoreline, water area, and circulation. Finally, fishing port characteristics and differences were investigated based on spatial superposition. The results demonstrate that shoreline and gulf types are the most prevalent fishing port types along the Chinese coast, accounting for 43% and 26% of the total number of fishing ports, respectively. This provides a strong foundation for China’s fishery production, processing, and trade. Through the establishment of a comprehensive scientific classification system, fishing port management can be modernized, supporting the sustainable development and utilization of coastal zones. Full article

Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification. Full article

This study presents an in-depth analysis of the dynamic beach landscapes of Hainan Island, which is located at the southernmost tip of China. Home to over a hundred natural and predominantly sandy beaches, Hainan Island confronts significant challenges posed by frequent marine natural disasters and human activities. Addressing the urgent need for long-term studies of beach dynamics, this research involved the use of CoastSat to extract and analyze shoreline data from 20 representative beaches and calculate the slopes of 119 sandy beaches around the island for the period from 2013 to 2023. The objective was to delineate the patterns of beach evolution that contribute to the prevention of sediment loss, the mitigation of coastal hazards, and the promotion of sustainable coastal zone management. By employing multi-source remote sensing imagery and the CoastSat tool, this investigation validated slope measurements across selected beaches, demonstrating consistency between the calculated and actual distances despite minor anomalies. The effective use of the finite element solution (FES) in the 2014 global tidal model for tidal corrections further aligned the coastlines with the mean shoreline, underscoring CoastSat’s utility in enabling precise coastal studies. The analysis revealed significant seasonal variations in shoreline positions, with approximately half of the monitored sites showing a seaward progression in summer and a retreat in winter, which were linked to variations in wave height. The southern beaches exhibited distinct seasonal variations, which contrasted with the general trend due to differing wave impacts. The western and southern shores showed erosion, while the northern and eastern shores displayed accretion. The calculated slopes across the island indicated that the southern beaches had steeper slopes, while the northern areas exhibited more pronounced slope variations due to wave and tidal impacts. These findings highlight the critical role of integrated coastal management and erosion control strategies in safeguarding Hainan Island’s beaches. By understanding the mechanisms driving seasonal and regional shoreline changes, effective measures can be developed to mitigate the impacts of erosion and enhance the resilience of coastal ecosystems amidst changing environmental conditions. This research provides a foundational basis for future efforts aimed at the sustainable development and utilization of coastal resources on Hainan Island. Full article

In the mining industry, one of the principal issues is the management of the waste generated during ore concentration, which represents a potential source of environmental pollution. The most acute issue originates from the mining heritage in the form of dumps formed of mining tailings that were created before the introduction of waste storage standards and may be located in urban areas. This research investigated this problem using the example of the tailings dump “Krasnaya Glinka”, located in a residential area of Pitkäranta (Karelia, Russia) in close proximity to the shoreline of Lake Ladoga. A complex approach, including the investigation of the natural water of the study area and tailings material and an experiment simulating the interaction of this material with atmospheric precipitation, allowed us to obtain the first data on the current status of the tailings dump and its surroundings and to identify environmental pollutants. This research used XRF, XRD, and EPMA analytical methods for assaying the tailings materials obtained from the dump and ion chromatography, potentiometric titration, ICP-MS, and AES for the water samples. The results show the influence of the tailings dump’s materials on the formation of the environmental impact—in the water from the area of the tailings dump, increased concentrations of chalcophilic elements are observed, for example, Zn up to 5028 µg/L. Based on this study of the tailings dump’s materials and the conducted experiment, an attempt is made to connect the chemical compositions shown in the natural water data with the specific mineral phases and processes occurring during supergene transformations in the tailings storage. As a result of the conducted research, it was found that despite more than 100 years of exposure of the tailings materials under natural factors, mostly atmospheric precipitation, equilibrium with the environment has not come. The processes of extracting toxic elements and carcinogenic mineral phases into the environment are continuing. In the process of studying the tailings materials, it was found that they are probably of economic interest as a technogenic source of W and Sn due to the contents of these components exceeding industrially significant values in the exploited fields. Full article

The accuracy and robustness of the shoreline definition from satellite imagery on different coastal types are crucial to adequately characterising beach morphology and dynamics. However, the generic and widespread application of satellite-derived shoreline algorithms is limited by the lack of robust methods and parameter assessments. This work constitutes a quantitative and comprehensive assessment of the satellite-derived waterlines from Sentinel-2 by using the novel SAET tool (Shoreline Analysis and Extraction Tool) on the exposed and mesotidal beach of La Victoria (Cádiz, SW Spain). The diverse parameters available in SAET, such as water indexes, thresholding methods, morphological filters, and kernel sizes, were combined to define water/land interface positions that were compared against coincident video-derived waterlines. Satellite-derived waterline errors are found to be affected by extraction parameters, as well as by the oceanographic and morphological conditions at the time of the image acquisition. The application of a morphological erosion filter on the water mask, which tends to shift the extracted waterline seawards and reduce bias, is the best solution at the dissipative site of La Victoria Beach. Moreover, using a 3 × 3 kernel size consistently shows higher accuracies than a larger kernel. Although there was no parameter combination showing the best skill for all dates, the employment of the Automated Water Extraction Index for images with no shadows (AWEInsh) with a threshold = 0, erosion morphological filter, and 3 × 3 kernel was, overall, the best combination of extraction parameters for this beach (average waterline RMSE of 5.96 m). The combination of the Modified Normalised Difference Water Index (MDNWI) with the Otsu thresholding also led to similar positions of the resulting waterlines and offered good accuracies. In line with other recent research efforts, our work stresses the lack of generic shoreline extraction solutions that can be applied automatically at a global level and the necessity to adapt and validate the extraction methodologies to the different types of coastlines. Full article

Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backshore environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide. Full article

Monitoring shoreline movements is essential for understanding the impact of anthropogenic activities and climate change on the coastal zone dynamics. The use of remote sensing allows for large-scale spatial and temporal studies to better comprehend current trends. This study used Landsat 5 (TM), Landsat 8 (OLI), and Sentinel-2 (MSI) remote sensing images, together with the Otsu algorithm, marching squares algorithm, and tidal correction algorithm, to extract and correct the coastline positions of the east coast of Laizhou Bay in China from 1984 to 2022. The results indicate that 89.63% of the extracted shoreline segments have an error less than 30 m compared to the manually drawn coastline. The total length of the coastline increased from 166.90 km to 364.20 km, throughout the observation period, with a length change intensity (LCI) of 3.11% due to the development of coastal protection and engineering structures for human activities. The anthropization led to a decrease in the natural coastline from 83.33% to 13.89% and a continuous increase in the diversity and human use of the coastline. In particular, the index of coastline diversity (ICTD) and the index of coastline utilization degree (ICUD) increased from 0.39 to 0.79, and from 153.30 to 390.37, respectively. Over 70% of the sandy beaches experienced erosional processes. The shoreline erosion calculated using the end point rate (EPR) and the linear regression rate (LRR) is 79.54% and 85.58%, respectively. The fractal dimension of the coastline shows an increasing trend and is positively correlated with human activities. Coastline changes are primarily attributed to interventions such as land reclamation, aquaculture development, and port construction resulting in the creation of 10,000.20 hectares of new coastal areas. Finally, the use of Kalman filtering for the first time made it possible to predict that approximately 84.58% of the sandy coastline will be eroded to varying degrees by 2032. The research results can provide valuable reference for the scientific planning and rational utilization of resources on the eastern coast of Laizhou Bay. Full article

The coastal strip, characterized by the urbanization of coastal tourist territories (CTTs), has expanded over decades through civil engineering, altering the shoreline dynamics and creating artificial beaches crucial for tourism. To examine the relationship between extensive land use in CTTs for tourism and residences and the presence of marine litter, a specific parametric study was conducted along the coast of Tenerife, the largest island in the Canary Islands. Due to Tenerife’s geographical location and exposure to the descending Gulf Stream flow, the coastal waters in the selected zone experience waste impact at both local and global scales. However, the presence of marine litter deposited by ocean currents is at a micro level and falls outside the scope of this report. This study parameterised urban reality in study areas, and the presence of macro waste has been parameterised using standardised units of measurement. This enables the establishment of source measurements that will contribute to preventative measures against this type of coastal pollution. The interdependence between tourist zoning, civil seafront engineering works along the seafront, and marine litter presence in inaccessible and visible areas for tourists requires a methodology to better understand waste origin and loading areas. This knowledge is crucial for an effective local monitoring system. A quantitative overlay reading methodology has been designed in the urban setting through calculations of urban densities, while examining the waste in these areas’ immediate infralittoral flooring through the use of visual underwater extraction. Anticipating the type and quantity of waste in each area will allow for the implementation of effective awareness, promoting action for preventative and corrective measures at the urban level. The results show a direct dependence between urban density and the presence of waste, as well as an equation that makes it possible to anticipate the amount of waste according to urban density and its relational vector. There is no discontinuity between them, as each area is affected by others to the extent that they establish the parametric continuity conditions determining each field. Therefore, it is possible to relate them beyond a one-on-one relationship. This approach fosters sustainable tourism development, reducing pressure on the sea and enhancing the utilisation of tourism revenues in measures to address waste-related challenges and promotes sustainable tourism development in Europe’s coastal regions. Full article