Search Results (128)

Heavy metal pollution from human activities is an increasing environmental concern. This study investigates the concentrations of Cu, Pb, Zn, Cd, Hg, and As in the coastal seawater offshore of Beihai, Guangxi, in April 2021, and explores their relationships with dissolved inorganic nitrogen, phosphate, and salinity. Our results reveal higher heavy metal concentrations in the northern nearshore waters and lower levels in southern offshore areas, with surface waters generally exhibiting greater enrichment than bottom waters. Surface concentrations show a decreasing trend from the northeast to the southwest, likely influenced by prevailing northeast monsoon winds. While bottom water concentrations decline from the northwest to the southeast, which indicates the influence of riverine runoff, particularly from the Qinzhou Bay estuary. Heavy metal levels in southern Beihai waters are comparable to those in the Beibu Gulf, except for Hg and Zn, which are significantly higher in the water of the Beibu Gulf. Notably, heavy metal concentrations in both Beihai and Beibu Gulf remain considerably lower than those observed in the coastal waters of Guangdong. Overall, Beihai’s coastal seawater meets China’s Class I quality standards. Nonetheless, continued monitoring is essential, especially of the potential ecological impacts of Hg and Zn on marine life. Full article

Nearshore bathymetry is key to most oceanographic studies and coastal engineering works. This work proposes a new methodology to assess nearshore wave celerity and infer bathymetry from video images. Shoaling and breaking wave patterns were detected on the Timestacks distinctly, and wave celerity was estimated from wave trajectories. The wave type separation enabled the implementation of specific domain formulations for depth inversion: linear for shoaling and non-linear for breaking waves. The technique was validated over a rocky bottom using video acquisition of an online streaming webcam for a period of two days, with significant wave heights varying between 1.7 m and 3.5 m. The results were corroborated in comparison to ground-truth data available up to a depth of 10 m, yielding a mean bias of 0.05 m and a mean root mean square error (RMSE) of 0.43 m. In particular, RMSE was lower than 15% in the outer surf zone, where breaking processes occur. Overall, the depth-normalized RMSE was always lower than 20%, with the major inaccuracy due to some local depressions, which were not resolved. The developed technique can be readily applied to images collected by coastal monitoring stations worldwide and is applicable to drone video acquisitions. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

Coral-reef fishes in the South China Sea play a crucial role in sustaining ecosystem stability and delivering essential ecological functions. However, widespread coral degradation has led to habitat loss, intensifying environmental stress on reef-associated fish communities. To better understand their current status and guide conservation efforts, this study conducted a comprehensive, trait-based assessment of coral-reef fish diversity across 19 reef sites in the South China Sea, spanning nearshore (Sanya, Hainan) and offshore (Xisha and Nansha Islands) systems. Significant spatial differences were observed in species composition, functional trait structure, and responses to environmental disturbance. Offshore reefs, particularly in the Nansha Islands, exhibited the highest species richness, trophic complexity, and functional diversity, while nearshore reefs showed simplified community structure dominated by small, sedentary species with high microhabitat dependence. Coral cover was only weakly correlated with fish diversity and failed to reflect functional trait complexity, highlighting the limitation of relying on structural indicators alone. Using community-weighted trait metrics, PCA, and indicator species analysis, this study established a tri-principle framework for identifying priority conservation species based on ecological function, rarity, and vulnerability. Key functional species—including Chlorurus sordidus, Siganus fuscescens, and Cephalopholis urodeta—were identified, along with representative conservation sites such as Meiji Reef, Lingyang Reef, and Luhuitou. These findings underscore the need to integrate species-level and functional diversity into coral reef monitoring and management. The proposed framework provides a science-based foundation for prioritizing species and habitats, enhancing the resilience of reef ecosystems under the dual threats of climate change and anthropogenic pressure. Full article

Understanding the dynamics of phytoplankton size classes (PSCs), highly sensitive to environmental conditions in marine ecosystems, is crucial for comprehending variations in primary production and biogeochemical processes. Over the past decades, the littoral seas of Korea have undergone significant environmental shifts, yet long-term studies on PSC distribution remain limited. Employing a regionally developed deep neural network model and 20 years (2003–2022) of satellite ocean color data, we assessed spatiotemporal variability in dominant PSCs in the Yellow Sea (YS), South Sea of Korea (SS), and East/Japan Sea (EJS). Micro-size phytoplankton dominated turbid nearshore waters of the YS and western SS year-round, while nano-size phytoplankton were seasonally prevalent in the central YS and EJS. Pico-size phytoplankton exhibited strong summer dominance under warm, stratified, nutrient-depleted conditions, showing a sustained long-term expansion across all regions, particularly in the southwestern EJS. This expansion was closely linked to rising sea surface temperatures and changes in nutrient stoichiometry. The increasing dominance of smaller phytoplankton may reduce primary production, alter food web structure, and ultimately diminish fishery productivity. These findings provide new insight into climate-driven ecological shifts in marginal seas and underscore the need for integrated long-term monitoring to anticipate future ecosystem responses in a rapidly warming ocean. Full article

Rapid land-use changes have significantly changed the occurrence of heavy metals (HMs) in tropical watershed systems. However, the influence of land-use patterns on the spatial and temporal distribution of HMs in tropical river systems remains poorly understood. This study aims to explore the relationship between land-use types and HM pollution in the China’s largest tropical watershed, the Nandu River. Eight heavy metals (Cd, Pb, Cr, Cu, Zn, As, Hg, and Sb) in the surface water were monitored across river, estuary, and nearshore zones during wet and dry seasons. Our findings show a higher total concentration of eight heavy metals (ΣHMs) in the wet season (30.52 μg/L) compared to the dry season (21.53 μg/L). In the wet season, ΣHM concentrations followed the order: estuary (70.96 μg/L) > basin (31.03 μg/L) > nearshore (8.07 μg/L). In the dry season, it was basin (31.56 μg/L) > estuary (23.26 μg/L) > nearshore (7.49 μg/L). Land-use patterns had higher interpretation rates for HM distribution in the dry season (65.8–73.0%) compared to the wet season (31.0–42.4%). The 2000 m buffer zone had a greater impact on HM distribution than the 500 m and 1000 m zones. Agricultural land and construction areas were the primary contributors to HM pollution in the dry and wet seasons, respectively. Noteworthy, in the river basin, chromium (Cr) presented carcinogenic risks to both children and adults through ingestion in both seasons and arsenic (As) posed a risk to children in the dry season. This study provides valuable insights for the sustainable management of land use and improving river water quality by highlighting the relationship between land use and HM contamination in tropical river ecosystems. Full article

Understanding the evolution of the sandbar–trough system under regular wave conditions is essential for revealing the dynamic responses of coastal morphology in non-extreme environments and provides a scientific basis for long-term beach stability assessments and coastal erosion management. This study conducted a three-day field observation on Ten-Mile Silver Beach, Hailing Island, China, to investigate the coupling relationships between hydrodynamic factors and bed elevation changes during the morphological evolution of the sandbar–trough system. The results indicate that gravity wave (>0.04 Hz) energy is a key driver of bed elevation changes. During the erosion phase, gravity wave energy increases, and the peak wave energy frequency shifts toward lower frequencies, accompanied by a contraction of low-frequency energy and an expansion of high-frequency energy. In contrast, the accretion phase exhibits the opposite pattern. As the sandbar–trough system developed, the explanatory power of hydrodynamic factors on bed elevation decreased by 41% in the trough region and increased by 3.7% in the sandbar region, indicating a spatially differentiated pattern characterized by weakened forcing in the trough and enhanced response over the sandbar. During the geomorphic adjustment process, the trough area exhibited increased sensitivity, with gravity wave energy, near-infragravity wave (0.01–0.04 Hz) energy, far-infragravity wave (0.004–0.01 Hz) energy, mean wave height, and significant wave steepness reversing their influence directions on bed elevation. In contrast, the sandbar area maintained a more stable hydrodynamic control mechanism, with only the influence pattern of significant wave steepness undergoing a shift. This study enhances the understanding of geomorphology–hydrodynamics coupling within nearshore sandbar–trough systems and provides theoretical insights and technical support for monitoring and evaluating coastal erosion and accretion processes under normal wave conditions. Full article

Illegal, unreported, and unregulated (IUU) fishing significantly threatens marine ecosystems, disrupts the ecological balance of the oceans, and poses serious challenges to global fisheries management. This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. A preprocessing protocol addressing multidimensional noise in raw VMS datasets was developed, incorporating velocity normalization and gap filling to ensure data reliability. The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. Spatial analysis revealed significant policy-driven reductions in fishing intensity during the moratorium (May–August), with hotspot areas suppressed to sporadic coastal distributions. However, concentrated vessel activity in Zhejiang’s nearshore waters suggested potential illegal fishing. Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. The framework enables real-time fishing state detection and adaptive spatial management via dynamic closure policies. The findings underscore the need for strengthened surveillance during moratoriums and post-ban catch regulation to mitigate overfishing risks. Full article

This study examines the surf and swash zone dynamics of a microtidal, low-energy, dissipative beach in Kos Island, Greece, using high-frequency optical monitoring with a Beach Optical Monitoring System (BOMS) and in situ wave measurements during the winter period. Increased wave heights induced the offshore migration of the wave-breaking zone with significant alongshore variability; however, no triggering of NOM (Net Offshore Movement) behavior was verified, while occasional rhythmic patterns were observed in the breaking location under moderate wave conditions. Shoreline dynamics showed transient erosional episodes coupled with elevated run-up excursions, yet the shoreline showed signs of recovery, suggesting a quasi-equilibrium state. Run-up energy spectra were consistently dominated by lower frequencies than those of incoming waves under both low- and high-energy conditions. This behavior is attributed to the nearshore sandbars acting as low-pass filters, dissipating high-frequency wave energy and allowing for lower-frequency motions to dominate run-up processes. A widely used empirical wave run-up predictor corresponded well with the video observations, confirming its applicability to low-energy dissipative beaches. These results underscore the role of submerged sandbars in regulating wave energy dissipation and stabilizing beach morphology under low-to-moderate wave conditions. Full article

Nearshore ship detection is an important task in marine monitoring, playing a significant role in navigation safety and controlling illegal smuggling. The continuous research and development of Synthetic Aperture Radar (SAR) technology is not only of great importance in military and maritime security fields but also has great potential in civilian fields, such as disaster emergency response, marine resource monitoring, and environmental protection. Due to the limited sample size of nearshore ship datasets, it is difficult to meet the demand for the large quantity of training data required by existing deep learning algorithms, which limits the recognition accuracy. At the same time, artificial environmental features such as buildings can cause significant interference to SAR imaging, making it more difficult to distinguish ships from the background. Ship target images are greatly affected by speckle noise, posing additional challenges to data-driven recognition methods. Therefore, we utilized a Concurrent Single-Image GAN (ConSinGAN) to generate high-quality synthetic samples for re-labeling and fused them with the dataset extracted from the SAR-Ship dataset for nearshore image extraction and dataset division. Experimental analysis showed that the ship recognition model trained with augmented images had an accuracy increase of 4.66%, a recall rate increase of 3.68%, and an average precision (AP) with Intersection over Union (IoU) at 0.5 increased by 3.24%. Subsequently, an enhanced YOLOv7 algorithm (YOLOv7 + ESE) incorporating channel-wise information fusion was developed based on the YOLOv7 architecture integrated with the Squeeze-and-Excitation (SE) channel attention mechanism. Through comparative experiments, the analytical results demonstrated that the proposed algorithm achieved performance improvements of 0.36% in precision, 0.52% in recall, and 0.65% in average precision (AP@0.5) compared to the baseline model. This optimized architecture enables accurate detection of nearshore ship targets in SAR imagery. Full article

Accurate mapping of nearshore bathymetry is essential for coastal management, navigation, and environmental monitoring. Traditional bathymetric mapping methods such as sonar surveys and LiDAR are often time-consuming and costly. This paper introduces BathyFormer, a novel vision transformer- and encoder-based deep learning model designed to estimate nearshore bathymetry from high-resolution multispectral satellite imagery. This methodology involves training the BathyFormer model on a dataset comprising satellite images and corresponding bathymetric data obtained from the Continuously Updated Digital Elevation Model (CUDEM). The model learns to predict water depths by analyzing the spectral signatures and spatial patterns present in the multispectral imagery. Validation of the estimated bathymetry maps using independent hydrographic survey data produces a root mean squared error (RMSE) ranging from 0.55 to 0.73 m at depths of 2 to 5 m across three different locations within the Chesapeake Bay, which were independent of the training set. This approach shows significant promise for large-scale, cost-effective shallow water nearshore bathymetric mapping, providing a valuable tool for coastal scientists, marine planners, and environmental managers. Full article

Coastal erosion hotspots frequently emerge in the downdrift zones of ports situated along open littoral drift seashores, often necessitating coastal protection measures. This study aims to evaluate the effectiveness of nearshore nourishment in mitigating coastal erosion using the downdrift zone of the Klaipėda Port (Baltic Sea) as a case study. In 2022, 79,390 m³ of sand was discharged at 2.0–3.5 depths at this site, forming an artificial sandbar parallel to the shoreline. The dynamics of the nourishment deposits were monitored for two years through beach and nearshore morphometric measurements and beach sand lithological composition sampling. Monitoring data indicated that the majority of the sand from the artificial sandbar migrated towards the subaerial coast, with minor depth variations also observed at depths of 4.0–5.6 m. Minor accretion in the nearshore was observed in regions beyond the designated nourishment area. The nearshore nourishment has successfully stabilised the subaerial coast at the discharge site for over two years, with 21.1% of the nourished sand accumulating on the subaerial coast and the shoreline position advancing seaward by an average of 10 metres. About 69.4% of the nourished sand remained at the nourishment site between the shoreline and the offshore boundary of the artificial sandbar, while approximately 9.5% was transported to the adjacent coast beyond the nourishment area. Full article

With the planning and construction of marine ranching in China, water quality has become one of the critical limiting factors for the development of marine ranching. Due to geographical differences, marine ranches exhibit varying water quality conditions under the influence of the continental shelf. To the best of our knowledge, there is limited research on satellite-based water quality monitoring for marine ranching and the spatiotemporal variations in marine ranches in different geographical locations. Chlorophyll-a (Chl-a) is a key indicator of the ecological health and disaster prevention capacity of marine ranching, as it reflects the conditions of eutrophication and is crucial for the high-quality, sustainable operation of marine ranching. Using a physically based model, this study focuses on the retrieval of Chl-a concentration in Daya Bay. The coefficient of determination (R²) between the model retrieval values and the in situ Chl-a data is 0.69, with a root mean square error (RMSE) of 1.52 μg/L and a mean absolute percentage error (MAPE) of 44.25%. Seasonal variations in Chl-a concentration are observed in Daya Bay and are higher in spring–summer and lower in autumn–winter. In the YangMeikeng waters, Chl-a concentration shows a declining trend with the development of marine ranching. A comparison between the YangMeikeng (nearshore) and XiaoXingshan (offshore) marine ranches suggests that offshore ranching may be less impacted by terrestrial pollutants. The primary sources of Chl-a input in Daya Bay are the Dan’ao River and the aquaculture areas in the northeastern part of the bay. This study can provide valuable information for the protection and management of marine ranching. Full article

The precise acquisition of water depth data in nearshore shallow waters bears considerable strategic significance for marine environmental monitoring, resource stewardship, navigational infrastructure development, and military security. Conventional bathymetric survey methodologies are constrained by their spatial and temporal limitations, thus failing to satisfy the requirements of large-scale, real-time surveillance. While satellite remote sensing technologies present a novel approach to water depth inversion in shallow waters, attaining high-precision inversion in nearshore areas characterized by elevated levels of suspended sediments and diminished transparency remains a formidable challenge. To tackle this issue, this study introduces an enhanced XGBoost model grounded in the Newton–Raphson optimizer (NRBO–XGBoost) and successfully applies it to water depth inversion investigations in the nearshore shallow waters of the Beibu Gulf. The research amalgamates Sentinel-2B multispectral imagery, nautical chart data, and in situ water depth measurements. By ingeniously integrating the Newton–Raphson optimizer with the XGBoost framework, the study realizes the automatic configuration of model training parameters, markedly elevating inversion accuracy. The findings reveal that the NRBO–XGBoost model attains a coefficient of determination (R²) of 0.85 when compared to nautical chart water depth data, alongside a scatter index (SI) of 21%, substantially surpassing conventional models. Additional validation analyses indicate that the model achieves a coefficient of determination (R²) of 0.86 with field-measured data, a mean absolute error (MAE) of 1.60 m, a root mean square error (RMSE) of 2.13 m, and a scatter index (SI) of 13%. Moreover, the model exhibits exceptional performance in extended applications within the waters of Zhanjiang Port (R² = 0.90), unequivocally affirming its dependability and practicality in intricate nearshore water environments. This study not only provides a fresh solution for remotely sensing water depth in complex nearshore water settings but also imparts valuable technical insights into the associated underwater surveys and marine resource exploitation. Full article

Since 2011, recurring Sargassum Brown Tides (SBTs), caused by periodic massive influxes of holopelagic Sargassum spp., have impacted seagrass meadows in the 50–200 m wide nearshore fringes of Mexican Caribbean reef lagoons. The present study aimed to assess the cumulative effects of SBTs in 2015 and 2018–2019 through a spatial–temporal analysis of seagrass meadows in the Puerto Morelos reef lagoon. We hypothesized that the impacts of the SBTs likely extended beyond the near-shore fringe and were detectable across the seagrass landscape throughout the entire reef lagoon. Through time, the spatial configuration of the seagrass meadows presented a new self-organized configuration linked to spatial fragmentation, an increase in the number of patches but a decrease in size, and changes in vegetation communities, indicating a shift in ecosystem state. This shift may serve as an early warning signal of reef system deterioration. Monitoring seagrass meadow status using this approach provides a deeper understanding of their dynamics, shifts and resilience, and will facilitate the development of timely management strategies. Full article