Search Results (45)

Collecting accurate and reliable beach morphology data is essential for informed coastal management. The beach adjacent to the seawall on North Padre Island, Texas, USA has experienced increased erosion and disrupted natural processes. City ordinance mandates the placement of bollards to restrict vehicular traffic when the beach width from the seawall toe to mean high water (MHW) is less than 45.7 m. To aid the City of Corpus Christi’s understanding of seasonal beach changes, mobile lidar scanning (MLS) surveys with a mapping-grade system were conducted in February, June, September, and November 2023, and post-nourishment in March 2024. Concurrent uncrewed aircraft system (UAS) photogrammetry surveys were performed in February and November 2023, and March 2024 to aid beach monitoring analysis and for comparative assessment to the MLS data. MLS-derived digital elevation models (DEMs) were used to evaluate seasonal geomorphology, including beach slope, width, shoreline position, and volume change. Because MHW was submerged during all surveys, highest astronomical tide (HAT) was used for shoreline analyses. HAT-based results indicated that bollards should be placed from approximately 390 to 560 m from the northern end of the seawall, varying seasonally. The March 2024 post-nourishment survey showed 102,462 m³ of sand was placed on the beach, extending the shoreline by more than 40 m in some locations. UAS photogrammetry-derived DEMs were compared to the MLS-derived DEMs, revealing mean HAT position differences of 0.02 m in February 2023 and 0.98 m in November 2023. Elevation and volume assessments showed variability between the MLS and UAS-SfM DEMs, with neither indicating consistently higher or lower values. Full article

Accurate vessel track association is a key requirement for maritime traffic monitoring and collision-avoidance systems, yet the Automatic Identification System (AIS) records commonly contain noise, missing intervals, and overlapping trajectories in congested coastal waters. We propose a Dilated Residual Connection Temporal Convolutional Network (DRC-TCN) tailored to AIS sequences; residual dilated blocks with layer normalization enable stable training while capturing long-range temporal dependencies under imperfect data. Beyond kinematic inputs, we augment AIS with buoy-based meteorological variables (wind direction and speed, gust, pressure, air temperature, and sea surface temperature) via time-aligned nearest-station fusion, allowing the model to account for environmental effects on vessel motion. Experiments on New York coastal AIS data show that DRC-TCN outperforms CNN-LSTM and vanilla TCN baselines, improving F1 score by up to 99.3% and achieving 99.7% accuracy. The results indicate that environment-aware temporal modeling strengthens the robustness of track association and supports situational awareness for next-generation intelligent navigation and ocean engineering applications. Full article

Heavy metal pollution threatens coastal ecosystems. Mangrove sediments, as transitional zones, are prone to contaminant accumulation. This study investigated eight heavy metals (Cu, Pb, Ni, As, Cr, Zn, Cd, Co) in Liusha Bay (Leizhou Peninsula, China). Field sampling, lab analysis, and multivariate statistics were used to assess pollution sources and ecological risks. The results show Al and Fe dominate sediment composition, with elevated P, Mn, and Sr. Arsenic (As) exhibiting the highest pollution severity (50% sites moderately contaminated by I_geo). Enrichment factors (EF) indicate anthropogenic contributions to As, Cu, Ni, and Co, while Cd and Pb originate mainly from natural sources. Ecological risk assessments highlight moderate risks for As and Cd at some sites. Source analysis identifies three dominant pathways: (1) lithogenic inputs (volcanic rock weathering) contributing Fe, Zn, Cr, and Ni; (2) biogenic materials (calcium carbonate-secreting organisms) influencing Cu, Mn, and Cd; and (3) anthropogenic activities (aquaculture, maritime traffic) linked to Cu and Pb. This study emphasizes localized monitoring of As and Cd in mangroves and calls for the integrated management of natural and anthropogenic drivers to mitigate pollution risks. Full article

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments. Full article

Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility. Full article

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon (eBC) and particle number size distributions (0.3–10 μm) was carried out from August 2019 to November 2020 at a coastal semi-rural site in the Basilicata region of Southern Italy. Long-term datasets were useful for aerosol characterization, helping to clearly identify traffic as a constant eBC source. For a shorter period, PM_2.5 mass concentrations were also measured, allowing the estimation of elemental and organic carbon (EC and OC), and chemical and SEM (scanning electron microscope) analysis of aerosols collected on filters. This multi-instrumental approach enabled the discrimination among different biomass burning (BB) processes, and the analysis of three case studies related to domestic heating, regional smoke plume transport, and a local smoldering process. The AAE (Ångström absorption exponent) daily pattern was characterized as having a peak late in the morning and mean hourly values that were always higher than 1.3. Full article

Wildlife-vehicle collisions (WVCs) threaten biodiversity, particularly in the Gulf of Mexico, where road expansion increases habitat fragmentation. This research analyzes WVC patterns in southeastern Mexico, estimating collision rates across road types and assessing environmental factors influencing roadkill frequency. Field monitoring in 2016 and 2023 recorded vertebrate roadkills along roads in Campeche, Chiapas, and Tabasco. Principal Component Analysis (PCA) and Generalized Additive Models (GAM) evaluated landscape influences on WVC occurrences. A total of 354 roadkill incidents involving 73 species of vertebrates were recorded, with mammals accounting for the highest mortality rate. Hotspots were identified along Federal Highway 259 and State Highways Balancán, Frontera-Jonuta, and Salto de Agua. Road type showed no significant effect. Land cover influenced WVCs, with cultivated forests, grasslands, and savannas showing the highest incidences. PCA identified temperature and elevation as key environmental drivers, while GAM suggested elevation had a weak but notable effect. These findings highlight the risks of road expansion in biodiversity-rich areas, where habitat fragmentation and increasing traffic intensify WVCs. Without targeted mitigation strategies, such as wildlife corridors, underpasses, and road signs, expanding infrastructure could further threaten wildlife populations by increasing roadkill rates and fragmenting habitats, particularly in ecologically sensitive landscapes like wetlands, forests, and coastal areas. Full article

As the first marine economic zone, the coastal zone is a complex and active ecosystem, serving as an important resource breeding area. However, during the process of economic development, coastal zone resources have been severely exploited, leading to fragile ecology and frequent natural disasters. Therefore, it is imperative to analyze coastline changes and their correlation with coastal ecological space. Utilizing long-time series high-resolution remote sensing images, Google Earth images, and key sea area unmanned aerial vehicle (UAV) remote sensing monitoring data, this study selected the coastal zone of Ningbo City as the research area. Remote sensing interpretation mark databases for coastline and typical coastal ecological space were established. Coastline extraction was completed based on the visual discrimination method. With the help of the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI) and maximum likelihood classification, a hierarchical classification discrimination process combined with a visual discrimination method was constructed to extract long-time series coastal ecological space information. The changes and the linkage relationship between the coastlines and coastal ecological spaces were analyzed. The results show that the extraction accuracy of ground objects based on the hierarchical classification process is high, and the verification effect is improved with the help of UAV remote sensing monitoring. Through long-time sequence change monitoring, it was found that the change in coastline traffic and transportation is significant. Changes in ecological spaces, such as industrial zones, urban construction, agricultural flood wetlands and irrigation land, dominated the change in artificial shorelines, while the change in Spartina alterniflora dominated the change in biological coastlines. The change in ecological space far away from the coastline on both the land and sea sides has little influence on the coastline. The research shows that the correlation analysis between coastline and coastal ecological space provides a new perspective for coastal zone research. In the future, it can provide technical support for coastal zone protection, dynamic supervision, administration, and scientific research. Full article

The need for real-time and scalable oceanographic monitoring has become crucial for coastal management, marine traffic control and environmental sustainability. This study investigates the integration of sensor technology into marine cables to enable real-time monitoring, focusing on tidal cycles and wave characteristics. A 2000 m cable demonstrator was deployed off the coast of Portugal, featuring three active repeater nodes equipped with pressure sensors at varying depths. The goal was to estimate hourly wave periods using fast Fourier transform and calculate significant wave height via a custom peak detection algorithm. The results showed strong coherence with tidal depth variations, with wave period estimates closely aligning with forecasts. The wave height estimations exhibited a clear relationship with tidal cycles, which demonstrates the system’s sensitivity to coastal hydrodynamics, a factor that numerical models designed for open waters often fail to capture. The study also highlights challenges in deep-water monitoring, such as signal attenuation and the need for high sampling rates. Overall, this research emphasises the scalability of sensor-integrated smart marine cables, offering a transformative opportunity to expand oceanographic monitoring capabilities. The findings open the door for future real-time ocean monitoring systems that can deliver valuable insights for coastal management, environmental monitoring and scientific research. Full article

Monitoring the oceans and establishing a global ocean observing system is a task of paramount importance for topics as diverse as the study of climate change, the management of marine environments, and the safety of coastal areas and marine traffic. These systems must be based on long-term observations that allow the correct modeling of the behavior of the seas and the proper environmental management of them. Despite the logical present trend toward automation, in situ measurements from oceanographic vessels are still needed at present, especially when dealing with biogeochemical variables or when seeking information from the subsurface or deep layers of the sea. Long-term measurements by oceanographic vessels can be carried out at one single fixed oceanographic station with a high sampling frequency (typically once a month) or across a grid of stations. In the latter case a larger geographical area is usually covered, but the cost is a reduction of sampling frequency. The question that arises is: what objectives can be achieved, and what questions can be answered according to the sampling frequency and the spatial coverage of the monitoring program? In this work, we analyze the influence of the sampling frequency on the capacity of observing programs to capture the temporal variability of ocean variables at different time scales and to estimate average seasonal cycles and long-term trends. This analysis is conducted through the study of sea surface chlorophyll concentrations in the Western Mediterranean. The trade-off between sampling frequency and spatial coverage is addressed. For this purpose, a monitoring program in the Spanish Mediterranean waters is used as a case study. We show that monthly and fortnightly intervals are the best sampling frequencies for describing the temporal variability of ocean variables as well as their average seasonal cycles. Quarterly sampling could also be appropriate for estimating such seasonal cycles. Surprisingly, the limitations of these low frequency samplings do not arise from the high frequency variability of ocean variables but from the shape of the seasonal cycles. Both high and low frequency sampling designs could be suitable for detecting long-linear trends, depending on the variance of the noise and that of the trend. In the case of quarterly sampling, we show that some statistics improve with the length of the time series, whereas others do not. Although some results may be related to the dynamics of this region, the results are generally applicable to any other marine monitoring system. Full article

Mapping anthropic activities in aquatic environments is crucial to support their sustainable management. Aquatic traffic is one of the human-related activities gaining relevance nowadays, and remote sensing can support the description of the distribution of vessels, particularly small boats or other vessels not tracked with other tools. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we performed a systematic review of the literature to describe current trends, key methodologies, and gaps, with special regard to the challenges of monitoring small boats that are not equipped with Global Positioning System (GPS) transponders. A total of 133 studies published between 1992 and 2024 were included. The research effort is mainly dedicated to developing new methods or upgrading existing ones, with only a few studies focusing on applications in a contest of environmental studies and, among these, only a few focusing on small boats. To promote the use of remote sensing by environmental scientists, coastal, and fishery managers, explicative case studies are delineated, showing how boat identification through satellites can support environmental studies. Moreover, a guideline section for using remote sensing to integrate monitoring of small boats is given to promote newcomers to this field. Full article

The common bottlenose dolphin (Tursiops truncatus) exhibits significant intraspecific diversity globally, with distinct ecotypes identified in various regions. In the Guadeloupe archipelago, the citizen science NGO OMMAG has been monitoring these dolphins for over a decade, documenting two distinct morphotypes. This study investigates whether these morphotypes represent coastal and oceanic ecotypes, which have not been previously identified in the region. We characterized morphological differences between the two morphotypes, analyzed genetic variation in individuals stranded around Guadeloupe over the past ten years, and modeled their habitats. Results revealed that these morphotypes align with the ecotypes described in the Atlantic Ocean, forming two distinct genetic groups corresponding to Caribbean ecotypes. Habitat modeling showed differences in habitat preferences between the morphotypes. These findings provide strong evidence for the existence of two distinct ecotypes of T. truncatus in Guadeloupe. Considering the varied risks of exposure to maritime traffic, our study suggests that these ecotypes should be managed as separate units within the species to better inform conservation strategies in the region. Full article

The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively. Full article

This research addresses a gap in localized air quality assessments by measuring pollution levels in Klaipeda, a Baltic port city, using passive solid particle collectors and nitrogen dioxide (NO₂) diffusion tubes. Passive sampling techniques were employed due to their cost-effectiveness and ease of deployment, allowing for practical monitoring over short-term periods. By targeting diverse functional zones, this study aims to provide a comprehensive analysis of air pollution patterns and seasonal variations in the region. Air pollution, primarily from NO₂ and particulate matter (PM), poses significant risks to public health, especially in densely populated urban areas. Air quality was assessed by measuring total suspended particulates (TSP) and NO₂ concentrations across 19 strategically chosen sites, covering key functional zones, such as industrial areas, green spaces, residential neighborhoods, transport hubs, and the port. Results show elevated pollution levels near major roads and the port area, likely driven by heavy traffic, industrial emissions, and port activities. These patterns correlate with areas of higher population density, highlighting the intersection of air quality challenges with human health risks in urbanized zones. Seasonal data reveal a notable peak in NO₂ concentrations during winter, likely due to increased heating demand and reduced atmospheric dispersion. These findings suggest that air quality management strategies should be adaptive to seasonal fluctuations, particularly by addressing emissions from heating sources in colder months. The study underscores the necessity of integrating sustainable urban planning with targeted air quality interventions. Expanding green spaces, enhancing traffic regulation, and establishing protective zones near industrial areas are critical strategies for mitigating pollution. These insights are essential for guiding both urban development and public health policies in Klaipeda and other coastal cities facing similar environmental challenges. Full article

In recent years, the increasing volume and complexity of ship traffic has raised the probability of collision accidents in ports, waterways, and coastal waters. Due to the relative rarity of collision accidents, near misses have been used in the research to study the collision risk in the relevant water areas. However, the factor of near miss identification is usually limited to the relative distance between ships, and the instantaneous quantification and geographical distribution of collision risk is not paid enough attention. Therefore, this article proposed a domain-based regional collision risk model that can quantify the collision risk by detecting near miss scenarios. The proposed model is capable of quantifying the collision risk in the water area instantaneously and periodically and can be used to depict the geographical distribution of collision risks in combination with a grid method and the spatial interpolation technique. To validate the proposed model, some experimental case studies were carried out using automatic identification system (AIS) data from the Bohai Strait. The results show the capability and advantage of the proposed model in regional collision risk identification and visualization, which is helpful for maritime surveillance when monitoring and organizing ship traffic and may therefore contribute to the improvement of maritime safety. Full article