Search Results (1,840)

Soil salinization is a significant global ecological issue that leads to soil degradation and is recognized as one of the primary factors hindering the sustainable development of irrigated farmlands and deserts. The integration of remote sensing (RS) and machine learning algorithms is increasingly employed to deliver cost-effective, time-efficient, spatially resolved, accurately mapped, and uncertainty-quantified soil salinity information. We reviewed articles published between January 2016 and December 2023 on remote sensing-based soil salinity prediction and synthesized the latest research advancements in terms of innovation points, data, methodologies, variable importance, global soil salinity trends, current challenges, and potential future research directions. Our observations indicate that the innovations in this field focus on detection depth, iterations of data conversion methods, and the application of newly developed sensors. Statistical analysis reveals that Landsat is the most frequently utilized sensor in these studies. Furthermore, the application of deep learning algorithms remains underexplored. The ranking of soil salinity prediction accuracy across the various study areas is as follows: lake wetland (R² = 0.81) > oasis (R² = 0.76) > coastal zone (R² = 0.74) > farmland (R² = 0.71). We also examined the relationship between metadata and prediction accuracy: (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum salinity did not influence modeling accuracy. (2) Across a broad range of scales, large sample sizes may lead to error accumulation, which is associated with the geographic diversity of the study area. (3) The inclusion of additional environmental variables does not necessarily enhance modeling accuracy. (4) Modeling accuracy improves when the mean salinity of the study area exceeds 30 dS/m. Topography, vegetation, and temperature are relatively significant environmental covariates. Over the past 30 years, the global area affected by soil salinity has been increasing. To further enhance prediction accuracy, we provide several suggestions for the challenges and directions for future research. While remote sensing is not the sole solution, it provides unique advantages for soil salinity-related studies at both regional and global scales. Full article

Previous research showed that a tsunami similar to the 1755 event would inundate Caxias’ low-ground areas in Oeiras municipality, Portugal. However, the streets of downtown Caxias were not well reproduced, which is a limitation of the area’s mitigation strategies and evacuation plan. For these reasons, new Lidar data were used for the first time in Portugal. The new local topography data allowed the construction of a more accurate DEM, which was used in the tsunami numerical model to update and improve the inundation results. As a complement, a field survey was conducted in several locations to assess coastal features and protection. The numerical model results show that low-ground areas up to 6 m in height were inundated by the tsunami, including the residential area, the road, and the railway. To stop the tsunami waves from inundating these areas, it is proposed that the construction of more sea walls up to 7 m in height and a third bridge over the Barcarena Stream, only for pedestrians, ranging from 5 to 7 m in height, which will serve as a gate for the incoming tsunami waves. These coastal protections should be part of the strategy to mitigate coastal overtopping (winter storm surges and tsunamis) not only in Caxias but also in other coastal zones. Full article

Climate change is driving regime shifts across ecosystems, exposing species to novel challenges of extreme weather, altered disturbances, food web disruptions, and habitat loss. For disturbance-dependent species like the endangered piping plover (Charadrius melodus), these shifts present both opportunities and risks. While most piping plover populations show net growth following storm-driven habitat creation, similar gains have not been documented in the Eastern Canadian breeding unit. In September 2022, post-tropical cyclone Fiona caused record coastal changes in this region, prompting our study of population and nesting responses within the central subunit of Prince Edward Island (PEI). Using satellite imagery and machine learning tools, we mapped storm-induced change in open sand habitat on PEI and compared nest outcomes across habitat conditions from 2020 to 2023. Open sand areas increased by 9–12 months post-storm, primarily through landward beach expansion. However, the following breeding season showed no change in abundance, minimal use of new habitats, and mixed nest success. Across study years, backshore zones, pure sand habitats, and sandspits/sandbars had lower apparent nest success, while washover zones, sparsely vegetated areas, and wider beaches had higher success. Following PTC Fiona, nest success on terminal spits declined sharply, dropping from 45–55% of nests hatched in pre-storm years to just 5%, partly due to increased flooding. This suggests reduced suitability, possibly from storm-induced changes to beach elevation or slope. Further analyses incorporating geomorphological and ecological data are needed to determine whether the availability of suitable habitat is limiting population growth. These findings highlight the importance of conserving and replicating critical habitat features to support piping plover recovery in vulnerable areas. Full article

This study investigates land-cover changes along riparian zones in South Carolina, focusing on intermittent and perennial streams to assess the impact of urbanization, forest loss, and impervious surface expansion on sensitive ecosystems. South Carolina’s diverse geography, ranging from coastal marshes to the Blue Ridge Mountains, and subtropical humid climate, offers a rich context for understanding environmental changes. The research utilizes various geospatial datasets, including the National Land Cover Database (NLCD), National Hydrography Dataset (NHD), and National Agricultural Imagery Program (NAIP) imagery, to evaluate changes in forest cover, urbanization, and impervious surfaces from 2011 to 2021 as a decade of transition. The study areas were divided into buffer zones around intermittent and perennial streams, following South Carolina’s riparian management guidelines. The results indicate significant land-cover transitions, including a total of 3184.56 hectares of non-urban areas converting to forest within the 100 m buffer around intermittent streams. In contrast, 137.43 hectares of forest transitioned to urban land in the same buffer zones, with Spartanburg and Greenville leading the change. Intermittent stream buffers exhibited higher imperviousness (4.6–5.5%) compared to perennial stream buffers (3.3–4.5%), highlighting the increased urban pressure on these sensitive areas. Furthermore, tree canopy loss was significant, with counties such as Greenwood and Chesterfield experiencing substantial reductions in canopy cover. The use of high-resolution NAIP imagery validated the land-cover classifications, ensuring accuracy in the results. The findings emphasize the need for effective land-use management, particularly in the riparian zones, to mitigate the adverse impacts of urban expansion and to safeguard water quality and biodiversity in South Carolina’s streams. Full article

This study focuses on typical regions of strong ageostrophic processes in the Kuroshio using high-resolution remote sensing satellite reanalysis data and Argo float data. By analyzing the relationship between the Rossby number and chlorophyll concentration from June to August in the summer of 2020, the spatial characteristics of ageostrophic processes and their impact on the phytoplankton community distribution are explored. The results indicate that ageostrophic processes, driven by coastal topography, are stably generated in the regions of the Bashi Channel, northeastern Taiwan waters, southwestern Kyushu Island, and southern Shikoku Island. Furthermore, the intensity of these ageostrophic processes shows an overall positive correlation with chlorophyll concentration. The local mixing and subfront circulations induced by ageostrophic processes pump deep nutrients into the euphotic zone, supporting the growth and reproduction of phytoplankton, which leads to the formation of significant chlorophyll hotspots in regions controlled by ageostrophic processes. Full article

Tourism still represents a means of generating revenues in the coastal areas in the Mexican Caribbean, despite the growing concern about the social and environmental impacts. The Nichupte Lagoon System (NLS), the most representative lagoon of Quintana Roo State for being in the middle of Cancun’s hotel development, has experienced a continuous drop-off in its water quality due to several factors, including dredging and wastewater discharges from different anthropogenic activities, which modify the flux of nutrients, increase the number of pathogenic microorganisms, and promote physicochemical changes in this ecosystem. Three sampling campaigns (2018, 2019, and 2020) were carried out in the NLS in August, which is the month of greatest tourist occupancy. To evidence the presence of anthropogenic wastewater in the NLS, the caffeine tracer was used, and to determine the water quality, 43 sampling stations were monitored for “in situ” physicochemical parameters (salinity and dissolved oxygen), and water samples were collected for the quantification of nutrients (NO₂⁻ + NO₃⁻, NH₄⁺, SRP and SRSi) and chlorophyll-a (Chl-a). For data analysis, the lagoon was subdivided into five zones (ZI, ZII, ZIII, ZIV, and ZV). Caffeine spatial and time variation evidence (1) the presence of anthropogenic wastewater in all areas of the NLS probably resulting from the tourist activity, and (2) wastewater presence is directly influenced by the coupling of the hydrological changes driven by anomalous rain events and the number of tourists. This same tendency was observed for nutrients that increased from 2018 to 2019 and the trophic state changed from oligotrophic to hypertrophic in all areas, as a result of previous anomalous precipitations in 2018, followed by normal precipitations in 2019. From 2019 to 2020, the nutrients decreased due to the drop in tourism due to COVID-19, promoting fewer nutrients in the lagoon, but, also coupled with an anomalous precipitation event (Cristobal storm), resulted in a dilution phenomenon and an oligotrophic state. The cluster analysis indicated that the least similar zones in the lagoon were the ZI and ZV due to their geomorphology that restricts the connection with the rest of the system. Principal component analysis revealed that wastewater presence evidenced by the caffeine tracer had a positive association with dissolved oxygen and chlorophyll-a, indicating that the arrival of nutrients from wastewater amongst other sources promotes algal growth, but this could develop into an eutrophic or hypertrophic state under normal precipitation conditions as seen in 2019. This study shows the relevance of monitoring in time of vulnerable karstic systems that could be affected by anthropogenic contamination from wastewater inputs, stressing the urgent need for efficient wastewater treatment in the area. The tourist industry in coastal karstic lagoons such as the NLS must have a Wastewater Treatment Program as a compensation measure for the anthropic pressure that is negatively changing the water quality of this highly relevant socio-environmental system. Full article

This study investigates the impact of artificial light at night (ALAN) along the Sicilian coasts, using satellite data from 2016 to 2023, focusing on three distinct spatial domains: terrestrial areas within 1 km from the coastline, marine areas extending up to 1 km offshore, and marine areas up to 1 nautical mile from the coast. In coastal zones, ALAN is a significant anthropogenic pressure with potentially detrimental effects on ecosystems. By integrating satellite data with geographic datasets such as Corine Land Cover (CLC), Natura 2000 protected areas, and Posidonia oceanica meadows, this study aims to characterize and analyse the temporal and spatial variations in ALAN across these domains. The findings reveal substantial differences in light pollution between domains and over time, with coastal terrestrial areas exhibiting the highest levels of ALAN. In contrast, marine areas further offshore experience reduced light pollution, particularly within the 1-nautical-mile domain. This study also indicates that protected areas, especially those within the Natura 2000 network, show significantly lower ALAN levels than non-protected areas, highlighting the effectiveness of conservation efforts. Statistical analyses, including ANOVAs, demonstrate that factors such as geographic domain, year, province, and CLC classes significantly influence ALAN distribution. This study advocates for considering ALAN as a critical factor in environmental impact assessments, such as those under the Maritime Spatial Planning Directive (MSP) and Marine Strategy Framework Directive (MSFD), providing valuable insights to support policies aimed at mitigating the environmental impact of light pollution on coastal and marine ecosystems. Full article

Coastal areas are the most biologically productive and undoubtedly among the most complex ecosystems. Algae are responsible for most of the gross primary production in these coastal regions. However, despite the critical importance of algae for the global ecosystem, the biodiversity of many algal groups is understudied, partially due to the high complexity of morphologically identifying algal species. The current study aimed to take advantage of the recently developed technology for biotic community assessment through the high-throughput sequencing (HTS) of environmental DNA (eDNA), known as the “eDNA metabarcoding”, to characterize littoral algal communities in the Northern Gulf of Mexico (NGoM). This study demonstrated that eDNA metabarcoding, based on the universal plastid amplicon (UPA) and part of the large nuclear ribosomal subunit (LSU) molecular markers, could successfully differentiate coastal biotic communities among littoral zones and geographical locations along the shoreline of the NGoM. The statistical significance of separation between biotic communities was partially dependent on the dissimilarity calculation metric; thus, the differentiation of algal community structure according to littoral zones was more distinct when phylogenetic distances were incorporated into the diversity analysis. Current work demonstrated that the relative abundance of algal species obtained with eDNA metabarcoding matches previously established zonation patterns for these species. In addition, the present study detected molecular signals of 44 algal species without previous reports for the Gulf of Mexico, thus providing an important, molecular-validated baseline of species richness for this region. Full article

The transport of pollutants with flowing waters is one of the most common processes in the natural environment. In general, this process is described by a system of differential equations, including the continuity equation, dynamic equations, pollutant transport equations and equations of state. For the analyzed problem of pollutant migration in wide rivers and the coastal zone, a two-dimensional model is particularly useful because the velocity and mass concentration profile is vertically averaged. In this model, taking into account the dispersion flux leads to appropriate equations, and the dispersion process is described by the dispersion tensor. Due to the transverse isotropy of the dispersion process, the coordinates of this tensor are expressed in terms of local dispersion coefficients along the direction of the velocity and in the direction perpendicular to it. Commonly used methods for determining mass dispersion coefficients refer to a gradient velocity profile, typical for rivers. However, in the coastal zone, the velocity profile changes from gradient to drift when shear stresses on the surface caused by the wind begin to dominate. The drift profile also occurs in estuaries, where there is a difference in the density of fresh and salt water. This paper analyzes the numerical solution of the two-dimensional dispersion equations in the coastal zone for the dispersion coefficients adopted for the gradient and drift velocity profiles and then assesses how this affects the final result. Four typical scenarios of pollutant migration in the coastal zone of the Bay of Puck are presented. The calculated dispersion coefficients differ significantly depending on the adopted velocity profile: for the gradient, D_LG = 0.17 [m²/s], and for the drift, D_LD = 89.94 [m²/s]. Full article

Coastal zones, where land meets ocean, are home to a large portion of the global population and play a crucial role in human survival and development. These regions are shaped by complex geological processes and influenced by both natural and anthropogenic factors, making effective management essential for addressing population growth, environmental degradation, and resource sustainability. However, the inherent complexity of coastal zones complicates their study, and traditional in situ methods are often inefficient. Remote sensing technologies have significantly advanced coastal zone research, with different sensors providing diverse perspectives. These sensors are typically used for classification tasks (e.g., coastline extraction, coastal classification) and retrieval tasks (e.g., aquatic color, wetland monitoring). Recent improvements in resolution and the advent of deep learning have led to notable progress in classification, while platforms like Google Earth Engine (GEE) have enabled the development of high-quality, global-scale products. This paper provides a comprehensive overview of coastal zone interpretation, discussing platforms, sensors, spectral characteristics, and key challenges while proposing potential solutions for future research and management. Full article

This study integrates macroscopic dynamic triaxial tests with microscopic discrete element simulations to comprehensively examine the dynamic deformation characteristics of marine soft soils under cyclic loading. Unlike previous research that typically focuses solely on experimental or numerical methods, this approach combines both techniques to enable a holistic analysis of soil behavior. The dynamic triaxial tests assessed macroscopic responses, including strain evolution and energy dissipation, under varying dynamic stress ratios, confining pressures, and water contents. Concurrently, discrete element simulations uncovered the microscopic mechanisms driving these behaviors, such as particle rearrangement, porosity variations, and shear zone development. The results show that (1) The strain range of marine soft soils increases significantly with higher dynamic stress ratios, confining pressures, and water contents; (2) Cumulative dynamic strain and particle displacement intensify at water contents of 50% and 55%. However, at a water content of 60%, the samples exhibit significant damage characterized by the formation of shear bands throughout the entire specimen; (3) As water content increases, energy dissipation in marine soft soils accelerates under lower confining pressures but increases more gradually under higher confining pressures. This behavior is attributed to enhanced particle packing and reduced pore space at elevated confining pressures. This integrated methodology not only enhances analytical capabilities but also provides valuable engineering insights into the dynamic response of marine soft soils. The findings offer essential guidance for the design and stabilization of marine soft soil infrastructure in coastal urban areas. Full article

The aim of this study is to explore how marine ranching construction enhances the market-oriented potential for energy conservation and emission reduction in China’s coastal cities, and its motivation is to assess the role of marine ranching in promoting sustainable development and environmental protection in these urban areas. With a sample of 53 coastal cities, including experimental-group cities designated as national marine-ranching demonstration zones and a control group of other coastal cities, this research employs theoretical pathway analysis and a quasi-natural experiment design. The findings reveal that marine ranching notably improves both the green innovation capability and industrial upgrading in coastal cities, ultimately stimulating their market-oriented emission-reduction potential. Importantly, extreme weather conditions are found to disrupt the positive impact of marine ranching on the emission-reduction potential in coastal cities, while financial stability ensures its sustained beneficial effects. This study underscores the crucial role of marine ranching in promoting sustainable development and emission reduction in China’s coastal urban areas, emphasizing the importance of addressing climate challenges and maintaining financial stability. Full article

This technical note aims to present a method for developing a Digital Terrain Model (DTM) of the coastal zone based on topobathymetric data from remote sensors. This research was conducted in the waterbody adjacent to the Vistula Śmiała River mouth in Gdańsk, which is characterised by dynamic changes in its seabed topography. Bathymetric and topographic measurements were conducted using an Unmanned Aerial Vehicle (UAV) and two hydrographic methods (a Single-Beam Echo Sounder (SBES) and a manual survey using a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) receiver). The result of this research was the development of a topobathymetric chart based on data recorded by the above-mentioned sensors. It should be emphasised that bathymetric data for the shallow waterbody (less than 1 m deep) were obtained based on high-resolution photos taken by a UAV. They were processed using the “Depth Prediction” plug-in based on the Support Vector Regression (SVR) algorithm, which was implemented in the QGIS software as part of the INNOBAT project. This plug-in allowed us to generate a dense cloud of depth points for a shallow waterbody. Research has shown that the developed DTM of the coastal zone based on topobathymetric data from remote sensors is characterised by high accuracy of 0.248 m (p = 0.95) and high coverage of the seabed with measurements. Based on the research conducted, it should be concluded that the proposed method for developing a DTM of the coastal zone based on topobathymetric data from remote sensors allows the accuracy requirements provided in the International Hydrographic Organization (IHO) Special Order (depth error ≤ 0.25 m (p = 0.95)) to be met in shallow waterbodies. Full article

Caged mussels enable biomonitoring investigations due to their tendency to absorb contaminants in industrial zones. This study aimed to investigate the levels of seven indicator polychlorinated biphenyls (i₇PCB) congeners in the biomonitoring organism Mytillus galloprovincialis over two years (2016–2018) after transplantation from three stations in the Eastern Aegean Sea industrial zone. Additionally, i₇PCBs were found in Nemrut Bay’s surface sediments. The highest PCB level was found at Site 5 located near a petroleum refinery of the Petkim Port. According to sediment quality criteria, PCB levels at Sites 1, 4, 5, and 6 may have an adverse biological impact. PCB concentrations varied among samples; congeners 28, 153, 101, and 118 were most prevalent in sediments, whereas congeners 138, 153, 101, and 118 were most prevalent in mussels. i₇PCB concentrations were below the maximum residual levels permitted in fishery products. In addition, calculations of the hazard ratio and estimated daily intake show no potential negative impacts from PCB exposure. Significantly positive correlations appeared between PCB (28, 52, 101, 118) levels and the condition index of the mussels. The highest amounts of ∑i₇PCB in mussels were identified in September 2016 at Site 2 and in October 2017 at Site 1. Industrialization around the Port of Nemrut had a harmful impact on Sites 1 and 2. Preventing marine pollution plays a key role in ensuring the sustainability of marine living resources and sustainable coastal management. Full article

This study aimed to develop a methodological framework for predicting shoreline dynamics using machine learning techniques, focusing on analyzing generalized data without distinguishing areas with higher or lower retreat rates. Three sites along the southwestern Kara Sea coast were selected for this investigation. The study analyzed key coastal features, including lithology, permafrost, and geomorphology, using a combination of field studies and remote sensing data. Essential datasets were compiled and formatted for computer-based analysis. These datasets included information on permafrost and the geomorphological characteristics of the coastal zone, climatic factors influencing the shoreline, and measurements of bluff top positions and retreat rates over defined time periods. The positions of the bluff tops were determined through a combination of imagery with varying resolutions and field measurements. A novel aspect of the study involved employing geostatistical methods to analyze erosion rates, providing new insights into the shoreline dynamics. The data analysis allowed us to identify coastal areas experiencing the most significant changes. By continually refining neural network models with these datasets, we can improve our understanding of the complex interactions between natural factors and shoreline evolution, ultimately aiding in developing effective coastal management strategies. Full article