Sediment Classification Research Articles

Small-Sample Seabed Sediment Classification Based on Deep Learning

Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming, which makes it crucial to design a well-performing classifier using small-sample seabed sediment data. To avoid data shortage, a self-attention generative adversarial network (SAGAN) was trained for data augmentation in this study. SAGAN consists of a generator, which generates data similar to the real image, and a discriminator, which distinguishes whether the image is real or generated. Furthermore, a new classifier for seabed sediment based on self-attention densely connected convolutional network (SADenseNet) is proposed to improve the classification accuracy of seabed sediment. The SADenseNet was trained using augmented images to improve the classification performance. The self-attention mechanism can scan the global image to obtain global features of the sediment image and is able to highlight key regions, improving the efficiency and accuracy of visual information processing. The proposed SADenseNet trained with the augmented dataset had the best performance, with classification accuracies of 92.31%, 95.72%, 97.85%, and 95.28% for rock, sand, mud, and overall, respectively, with a kappa coefficient of 0.934. The twelve classifiers trained with the augmented dataset improved the classification accuracy by 2.25%, 5.12%, 0.97%, and 2.64% for rock, sand, mud, and overall, respectively, and the kappa coefficient by 0.041 compared to the original dataset. In this study, SAGAN can enrich the features of the data, which makes the trained classification networks have better generalization. Compared with the state-of-the-art classifiers, the proposed SADenseNet has better classification performance.

Influence of sediment characteristics on long-term hydrology and water quality behaviour during the clogging process of a permeable asphalt

Sustainable urban drainage systems (SUDS) are common solution to address flow discharges, runoff volumes and pollutant concentrations caused by urban expansion and climate change trend. Among them, permeable pavements (PP) effectively manage and treat stormwater. The clogging process of PP has been addressed in previous studies but there is still room for improvement the knowledge about how it affects to their performance in terms of hydrological behaviour and pollutant removal and how it depends on sediment properties. The present study simulates and analyses the behaviour of a permeable asphalt through 47 sediment loading steps tested for six different sediment classes under laboratory conditions. The experiments were performed using permeable asphalt slabs in two laboratory rainfall simulators, measuring reductions in permeability, filtered and runoff flows, and water quality variations in effluents according to sediment loads and rainfall events. The results showed significant reductions of permeability (22–99 %) for sediment surface loads from 1 kg/m2 onwards, except for very fine sediments (<125 μm) (1 %). Permeability recovery after vacuum cleaning varied between 8 % and 100 % of original permeability. Sediment particle size distribution, organic matter content and rain events sequence were key variables influencing the permeability loss and its recovery, and quality of filtrated water. The hydraulic efficiency was high, filtrating almost 100 % of the applied rainfall, whenever the sediment surface load was below 4 kg/m2, above which surface runoff was produced. The efficiency of the slab for pollutant retention was 96 % for COD, 76 % for TN, 79 % for TP, and 98 % for TSS.

Satellite-Derived Bathymetry with Sediment Classification Using ICESat-2 and Multispectral Imagery: Case Studies in the South China Sea and Australia

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.

Assessing impaired benthic communities using sediment toxicity and contaminant concentrations from reference sites inside the Niagara River Area of Concern

Anthropogenically degraded benthic-macroinvertebrate communities (benthos) are one of seven beneficial use impairments (BUIs) in the Niagara River Area of Concern (AOC). Over the last 50 years, upgrades to waste-water treatment, industry closures, and sediment remediations reduced contaminant levels throughout the system. Improvements in benthic communities and sediment toxicity, however, were difficult to assess because there are no comparable reference reaches outside the AOC. A multi-phase study was initiated in 2015 to determine if data from inside the AOC could identify reference conditions, if toxicity and benthic-community data from these sites differed from other AOC sites, and if further remediation efforts were warranted in parts of the AOC. Concentrations or quotients of PAHs, PCBs, dioxins and furans, pesticides, and most metals were below their New York Sediment Class A Guidance Values at 10 sites that were subsequently designated as reference sites. Survival and growth data from Chironomus dilutus and Hyalella azteca bioassays indicated that sediments from only a few individual AOC-impact sites were toxic or significantly different from reference sites, and that mean toxicity at pooled AOC-impact and reference sites did not differ significantly. Similarly, New York Biological Assessment Profile scores and chironomid mentum deformity scores at only a few individual sites differed significantly from corresponding indices at reference sites, but neither metric differed significantly in comparisons between pooled AOC-impact and reference sites. Most analyses indicated that benthic communities were unimpaired and that removal criteria for the benthos BUI were largely met in much of the upper Niagara River AOC.

Mixed Seabed Sediment Classification Based on Transferred Convolutional Neural Network: A Case Study in the Ancient River Valley

The coastal zone is connected to the hinterland of the basin and the wide sea, which is not only affected by complex natural factors, but also by human activities. The study of sediment classification in this area will help to further explain topographic evolution and dynamic mechanism. Aiming at the complex mixed sediment environment in the ancient valley area of shallow seas, an advanced deep convolutional neural network classification model based on transfer learning—selective kernel hybrid dilated ResNet-50 (SKHD-ResNet-50) is thus proposed and applied to the coastal area in the Firth of Forth, Scotland, UK. The model overcomes problems of low accuracy of sediment classification with small samples, while effectively improving the efficiency of mixed sediment classification. Five transferred convolutional neural network models including CNN, AlexNet, VGG-19, GoogleNet, and SKHD-ResNet-50, were used to conduct seabed sediment classification experiments on multibeam backscatter mosaics in the study area. An ablation study about the effect of selective kernel convolution (SKC) and hybrid dilated convolution (HDC) was also conducted to further prove the usability of the proposed method. Finally, the optimized convolutional neural network was used to build a multi-layer deep learning model for sediment classification, combining the transfer learning method with the neural network to obtain the optimal network weights through deep global optimization, thus accurately mining statistical characteristics and distribution rules of backscatter intensity data. The experimental results show that the advanced model obtains better classification accuracy than that of state-of-art methods, with overall accuracy and Kappa coefficient of 96.87% and 0.9482, respectively. Findings highlight the proposed sediment classification model, which can effectively distinguish mixed sediments, and greatly improve the accuracy and efficiency of sediment classification.

Marine Sediment Classification Spectral Ratio Technique From a Signal Decomposition View Based on Chirp Sonar Data

Sediment classification based on Chirp sonar data is very important in support of marine science and engineering. The traditional adopted spectral-ratio (SR) method is widely applied for its theory of simplicity and easy employment. However, the performance of SR method is heavily degraded by spectrum vibrations introduced by overlapping reflections. To solve this problem, a reflection signal decomposition method is proposed in this paper, which decomposes the overlapping reflection into separate reflection sub-signals to avoid the spectrum vibration. Firstly, we re-derive the overlapping reflection expression from the convolution model and give the initial model for the decomposition of the Chirp sonar signal. Then, introducing the spectrum smooth prior into consideration and incorporating it with the fidelity and the bandwidth terms, the decomposition model with constraints is proposed. After that, an iterative algorithm is introduced to solve the model optimization problem. The reflection sub-signal can be well obtained. Finally, through a least square linear fitting for the logarithm of the spectral ratio (the log-SR) of reflection sub-signals, quality factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> ) which is applied for sediment classification can finally be achieved. Both real and simulated experiments have been conducted to verify the proposed method and some meaningful discussions are also drawn.

Літогенез верхньоюрських відкладів Зовнішньої зони Передкарпатського прогину

Geological- and biological-paleogeographical conditions of sedimentogenesis within the Carpathian segment of Tethys ocean during Tithonian were considered. The rock-forming organisms with calcic function (flora and fauna), which compose main lithological types of carbonate rocks were identified. On the base of biological-paleoceanographical and lithological investigation of different genetic types of Upper Jurassic carbonate rocks of the Outer zone of the Carpathian Foredeep and analysis of literature data on modern carbonate sediments, the structural classification of Late Jurassis epipelagic sediments of the Carpathian segment of the Meso-Tethys was made. In that classification pelitic, aleuritic, psammitic and ruditic fractions of sediments were distingnished. Geological-paleogeographical model of occurrence of the Tithonian sediments within the Carpathian segment of the Meso-Tethys (the Outer zone of the Carpathian Foredeep) was built by the author. That model presents areals of biogenic and abiogenic epipelagic sediments and depicts their facial variations. Geological-paleogeographical study of Upper Jurassic sediment complexes of epipelagic part of the Meso-Tethys has shown that they were formed within widespread interior shelf, probably, with small inclination of the sea bottom. In Upper Jurassic there was abundant growth of the benthos with calcic function and avalanchecal sedimentation of their skeletal remains took place with forming of biogenic carbonate sediments. The coral-algae biocoenosis there were biological indicators of considered parts of Tethys region. In modern basins of the World ocean analogous processes take place at the depths of about 50 m, in temperature conditions about 23–25 °C and the salinity of the sea waters about 2.7–3.8 ‰. The manifestations of diagenetic and katagenetic processes in the Upper Jurassic carbonate rocks of Outer zone of the Carpathian Foredeep have been investigated and their influence of the formation of the reservoir properties of rocks has been found out. During the diagenesis, the rocks were recrystallized, micritizated and cemented. From the mineralogical point of view, the changes consisted in the transformation of primary aragonite and magnesium-calcite skeletal remains of organisms into calcite, as well as processes of dolomitization, ferruginization and silicification. At the stage of diagenesis, Fe-containing minerals ̶ glauconite and pyrite ̶ has been formed. The dolomitization of Jurassic organogenic limestones of Outer zone of the Carpathian Foredeep has been caused by the mixing of the fresh meteoric waters with buried marine pore waters in the underground phreatic zone adjacent to the ancient coastline. Silicification is a consequence of the metasomatic substitution of the carbonate substance by silicate, which has been caused by decrease of the pH occurred after dolomitization. Katagenetic transformation of the rocks are manifested in the dissolution of the remains of organisms, grains of carbonate cement and the late cementation of pores and microfractures. Changes of carbonate rocks are associated with the bringing of certain substances in the sediment (sulphatization, celestinization) or their removal (dedolomitization, decementation). Neoplasm minerals of the katagenesis stages are represented by anhydrite and celestine. The proceses of recrystallization of carbonate rocks at different stages of katagenesis contributed to the selective leaching of limestones and dolomites and led to formation of secondary cavities and caverns, different in shape and size. The predominance of cavities of certain types determines the type of reservoir, among which are distinguished pores, pore-caverns and joint caverns. The processes of diagenesis and katagenesis are associated with the dissolution of carbonate material, which differently affects the reservoir properties of sediments. Authigenous mineral formation, with the exception of dolomitization, impairs the reservoir properties of the Upper Jurassic carbonate rocks, and recrystallization improves. Burial of carbonate rocks at depth contributes to the preservation and even improvement of their reservoir properties both due to dissolution and textural heterogeneity (jointing develops at the boundary of the elements of textural heterogeneity). Observations show that the most favourable in this respect are organogenic and detrital varieties of carbonate rocks. The presence of clay minerals in the composition of their cement increases the textural heterogeneity and, as a consequence, the ability to formation of joints.

Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud

Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud

A Sample Enhancement Method Based on Simple Linear Iterative Clustering Superpixel Segmentation Applied to Multibeam Seabed Classification

Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this paper proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a Random Forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, UK. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points, and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.

A new super resolution Faster R-CNN model based detection and classification of urine sediments

The diagnosis of urinary tract infections and kidney diseases using urine microscopy images has gained significant attention of medical community in recent years. These images are usually created by physicians’ own rule of thumb manually. However, this manual urine sediment analysis is usually labor-intensive and time-consuming. In addition, even when physicians carefully examine an image, an erroneous cell recognition may occur due to some optical illusions. In order to achieve cell recognition in low-resolution urine microscopy images with a higher level of accuracy, a new super resolution Faster Region-based Convolutional Neural Network (Faster R-CNN) method is proposed. It aims to increase resolution in low-resolution urine microscopy images using self-similarity based single image super resolution which was used during the pre-processing. De-noising based Wiener filter and Discrete Wavelet Transform (DWT) are used to de-noise high resolution images, respectively, to increase the level of accuracy for image recognition. Finally, for the feature extraction and classification stages, AlexNet, VGFG16 and VGG19 based Faster R-CNN models are used for the recognition and detection of multi-class cells. The model yielded accuracy rates are 98.6%, 96.4% and 96.2% respectively.

Evolution of marine gravel dunes on the open shelf under multi-directional currents conditions

On inner continental shelves, a variety of coarse grained bedforms, such as gravel dunes, are shaped by hydrodynamic and morphodynamic processes. The formation and evolution of bedforms reflect a balance between seabed and coastal morphology, sediment type and availability, and regional hydrodynamics. Yet, observing bedform evolution directly in the marine environment is rare, mostly due to the lack of repeat seafloor mapping surveys. In this study we use repeat bathymetry from 3 surveys over 4 years from the western Cook Strait/Te Moana-o-Raukawakawa region, New Zealand/Aotearoa. We integrate seabed morphology characterisation with sediment classification and regional hydrodynamic modelling, to investigate the evolution of gravel dunes under multi-directional current conditions. The repeat seafloor mapping reveals morphological changes to plan-view dune geometry and bifurcation of crestlines, with maximum observed vertical changes up to 3 m at water depths between 60 and 80 m. However, no dune migration was detected. Our hydrodynamic model shows that the most prominent morphological changes over the gravel dunes are spatially correlated with eddy formation, and high multi-directional near-bottom currents, reaching maximum speeds of ∼4 m s−1 and bottom stress of &amp;gt;25 N m−2 in each tidal cycle. We demonstrate that the average hydrodynamic conditions in this region are capable of mobilising coarse-grained sediment (i.e., sand to gravel), indicating that the observed morphological changes over multi-year time scales are a result of continuous remobilisation by currents, rather than extreme or storm events. Our findings demonstrate the highly dynamic nature of the seabed in Cook Strait, and the need for regular, repeat mapping surveys to ensure up-to-date seabed morphology information.

Seabed sediments classification based on side-scan sonar images using dimension-invariant residual network

Seabed sediment classification has important applications in ocean engineering and submarine pipeline design. This paper proposed the dimension-invariant residual network to improve the seabed sediments classification accuracy. The dimension of each channel of the feature extracted from the dimension-invariant residual block is the same as the original image. Compared with down-sampling, each feature vector has more feature parameters, which can improve the accuracy of seabed sediments classification. Shortcut connections were introduced in the proposed network, which can make the network easier to optimize and achieve good performance by using only a not very deep network. It also has a lower complexity compared to powerful classifiers like Transformer and has a better performance in small-scale seabed sediment classification. And to improve the accuracy of seabed sediment classification without consuming too much time for training the classifier, the network parameters optimization method was proposed in this paper. Recording the classification accuracy and training time of the network with these different parameters. Determining the parameters of the network by the proposed optimization method. The proposed classification model and optimization method were validated by using the side-scan sonar images, acquired from the area around the Shandong Peninsula of China. The layers and convolutional kernel sizes were determined to be 8 and 3, respectively. The classification accuracy of overall, rock, sand, and mud of the dimension-invariant residual network model were 97.83%, 94.04%, 97.80%, and 98.02% respectively. In comparison with state-of-the-art classifiers, the experimental results demonstrated that the proposed classification model achieved better performance, and the network parameters optimization method enables the network to achieve satisfactory classification accuracy while spending less time. The proposed classification model and network parameters optimization method show great potential in seabed sediments classification, which can also be extended to the task of underwater target recognition in future research.

Exploring key physicochemical sediment properties influencing bioleaching of heavy metals

Bioleaching is a promising technology to remediate sediments contaminated by heavy metals. However, the complex heterogeneities of the sediments can reduce the acidification efficiency and the heavy metal removal rate, thus hindering the practical application of sediment bioleaching. This experiment conducted comparative bioleaching experiments between the inoculated group (average leaching percentages: Cu 67.64%; Zn 54.44%; Ni 29.59%) and the non-inoculated control group (Cu 37.10%; Zn 41.04%; Ni 19.89%) on 28 sediments characterized by different physicochemical properties to explore the key factors influencing bioleaching. The results indicated that the bioleaching process was predominated by the indigenous bioleaching bacteria and the bioleaching inoculum, respectively. The ACCpH=4 (acid-consuming capacity), TOC (total organic carbon), and TN (total nitrogen) of the sediments played an essential role in influencing the microbial community structure and bioleaching performance: the ACCpH=4, as the inhibitive factor, could influence the succession growth of the indigenous bioleaching bacteria and the inoculum during the bioleaching process, while the TOC and TN, as the contributing factor, could influence the metabolism of the indigenous bioleaching bacteria. Based on these results, the bioleaching process was improved with the classification and pretreatments of sediment to realize successful bioleaching of all types of the sediments examined in this research.

Volcanically-derived deposits and sequences: a unified terminological scheme for application in modern and ancient environments

Abstract The genesis of particles and their transport mechanisms are the two fundamental factors driving the accumulation of sediments associated with volcanism or derived from volcanic sources. These factors are therefore the most important criteria on which to base a useful classification of sediments in such environments. However, the relative significance of the nature of particles v. the transporting mechanism forming a deposit varies in existing terminological schemes: those schemes applied where volcanological contextual information is available tend to give precedence to the transportation process; whereas sedimentological schemes examining ancient deposits tend to focus principally on the nature of particles. Here, we provide an outline of the challenges in classifying volcanically derived sediments and put forward a scheme that aims to bridge current terminological differences and accommodate variable levels of uncertainty. This work defines three endmembers ( primary volcaniclastic , secondary volcaniclastic , volcanic epiclastic ) that correspond to (a) deposits whose particles are produced, transported and emplaced directly by volcanic mechanisms; (b) deposits whose particles are produced directly by volcanic events but transported and accumulated by non-volcanic mechanisms, either in continuum with the events or after interim storage; (c) deposits whose particles are produced by weathering/erosion of volcanic terrains and transportation of derived material by non-volcanic mechanisms. When the complex combination of genetic and transportation processes accumulating volcaniclastic sequences is not clear, but a strong relationship between an eruptive event and the studied volcaniclastic deposit can still be demonstrated, a further category ( volcanogenic ) has been introduced.

Multi-Sensor Remote Sensing of Intertidal Flat Habitats for Migratory Shorebird Conservation

Many species of shorebirds migrate long distances from their overwintering grounds in the southern hemisphere to breeding grounds in the northern hemisphere. The coastal intertidal zone, consisting of sand and mud flats exposed at low tide and covered at high tide, is heavily used as a migratory stopover or overwintering habitat. Understanding the spatial distribution of sediment types at these stopover sites is a critical step for understanding habitat use by shorebird species. Due to their importance as overwintering and stopover habitat for the imperiled western Atlantic subpopulation of the shorebird, the red knot (Calidris canutus rufa), as well as other migratory shorebirds, the northern coast of Brazil between Pará and Maranhão, and Bahía Lomas in northern Tierra del Fuego, Chile, were selected for further investigation as to the applicability of remotely sensed characterization of the intertidal flat habitats. Examination of the Landsat 8 multispectral reflectance and Sentinel-1 SAR backscatter reveals that sand and mud represent endmembers at opposite ends of a continuous gradient in feature space. While remotely sensed data can be used to discriminate between mud and sand intertidal types, the spectral relationships varied between the two very different geographic locations. The inclusion of both multispectral and radar sensing imagery can lead to important insights about the physical properties of the sediment that would be omitted by using one data source alone. Spectral unmixing techniques in Google Earth Engine were used to map the intertidal zone into general sediment classes spanning the gradient (i.e., mud, sandy mud, muddy sand, and sand). Comparison of the mapped outputs with field reference data suggests that mapping of mud- vs. sand-dominated areas can be accomplished with reasonable accuracy (overall accuracy of 75%).

Developing a Lagrangian sediment transport model for open channel flows

A three-dimensional stochastic Lagrangian particle tracking sediment transport model is developed to solve the discrete advection–dispersion equation using a combination of empirical dispersion equations. The performance of three widely-used longitudinal dispersion coefficient equations was examined to select one of them as the primary dispersion equation term in the developed model. Also, a conditional empirical equation was used to consider the effect of vertical dispersion term in top layers near the water surface. The performance of particle tracking model (PTM) to calculate the sediment concentration was evaluated for various sediment classes (very fine sand, fine sand, and medium sand) using available laboratory dataset. Multiple statistical measures were calculated using the Taylor diagram for each dispersion equation. Based on the result, the developed particle tracking model estimated the suspended sediment concentration in a rectangular open channel with a correlation coefficient (R) of 0.96, standard deviation (STD) of 0.262, and root mean square deviation (RMSD) of 0.06 for three different sediment classes proving the acceptable accuracy of the developed model for different range of sediment gradations. In addition, the accuracy of PTM was compared to recently developed models. Comparison between the particle tracking model and the analytical solution of the advection–dispersion equation showed that the developed model predicted the maximum concentration of suspended sediment 6% and 9.4% lower than the analytical solution result for 30 and 50 s simulation time in the straight channel, respectively. However, there was a good agreement between the longitudinal and transverse sediment concentration distribution from the model and the analytical solution approach. The result of the sensitivity analysis and validation process showed that the model could be used to simulate sediment transport in open channel flows.

Mapping of the loose sediments of glacial and periglacial formations in areas with boreal vegetation using remote sensing

The southeastern Fennoscandian Shield is a specific region dominated by glacial and periglacial originated Quaternary deposits. In dense boreal vegetation zones, direct remote decoding of Quaternary sediments is impossible. Our aim is to assess the efficiency of machine learning algorithms for identification of types of Quaternary deposits based on spectral data of vegetation cover and digital elevation model data. A comparative analysis of two classification methods, discriminant analysis (DA) and classification and regression trees (CARTs), was performed. Two models, DA and CART, were constructed based on a set of spectral variables. They included principal components and spectral indices, such as the normalized difference vegetation index, the normalized difference moisture index, and the clay minerals ratio. When the absolute height and the topographic position index (DA+ and CART+ models) are added to a set of independent variables, the classification of Quaternary sediments becomes more accurate. The nonparametric CART method was shown to be more accurate in differentiating loose deposits. The correctness of the CART+ model for the reference sample of observation points was as high as 90.5%. According to the trained data, mapping of Quaternary deposits was carried out. Kappa analysis showed that the agreement with the map of Quaternary deposits for the CART+ and DA+ models was 0.5 and 0.44, respectively. For the DA and CART models, the agreement was much lower. It is noted that the sets of spectral data for the vegetation cover more clearly show the grain-size composition of loose sediments. To make the classification of Quaternary deposits more correct, morphometric indicators were added. Furthermore, the CART+ method was used to estimate the maximum height of glaciolacustrine sediments (117.5 m), which corresponds to the maximum absolute mark of the periglacial reservoir.

Benthic alkalinity fluxes from coastal sediments of the Baltic and North seas: comparing approaches and identifying knowledge gaps

Abstract. Benthic alkalinity production is often suggested as a major driver of net carbon sequestration in continental shelf ecosystems. However, information on and direct measurements of benthic alkalinity fluxes are limited and are especially challenging when biological and dynamic physical forcing causes surficial sediments to be vigorously irrigated. To address this shortcoming, we quantified net sediment–water exchange of alkalinity using a suite of complementary methods, including (1) 224Ra budgeting, (2) incubations with 224Ra and Br− as tracers, and (3) numerical modeling of porewater profiles. We choose a set of sites in the shallow southern North Sea and western Baltic Sea, allowing us to incorporate frequently occurring sediment classes ranging from coarse sands to muds and sediment–water interfaces ranging from biologically irrigated and advective to diffusive into the investigations. Sediment–water irrigation rates in the southern North Sea were approximately twice as high as previously estimated for the region, in part due to measured porewater 224Ra activities higher than previously assumed. Net alkalinity fluxes in the Baltic Sea were relatively low, ranging from an uptake of −35 to a release of 53 µmolm-2h-1, and in the North Sea they were from 1 to 34 µmolm-2h-1. Lower-than-expected apparent nitrate consumption (potential denitrification), across all sites, is one explanation for our small net alkalinity fluxes measured. Carbonate mineral dissolution and potentially precipitation, as well as sulfide re-oxidation, also appear to play important roles in shaping net sediment–water fluxes at locations in the North Sea and Baltic Sea.

Microbial community metabolic alterations and resistance to metals and antibiotics driven by chronic exposition to multiple pollutants in a highly impacted tropical coastal bay

Microbial communities from Sepetiba Bay (SB, Rio de Janeiro, Brazil), characterized by 16S rRNA gene (V4–V5 region) sequencing analysis, were found to be correlated with the metallic contamination factor and the Quality Ratio (QR) index. Consistently, the predicted function of microbial communities, obtained with Tax4Fun2, showed that the functional patterns in SB internal sector under the highest anthropogenic pressure were different from that observed in the external sector with the lowest contamination level. Signal transduction, cellular community, membrane transport, and energy metabolism were among the KEGG pathways favored by metallic contamination in the SB internal sector, while lipid metabolism, transcription, and translation were among the pathways favored in the SB external sector. Noteworthy, the relative proportions of KEGG pathways and genes associated with metallic homeostasis showed significant differences according to the SB sectors, consistently with the ecological risk classification (QR index) of sediments. The functional prediction approach is an economically viable alternative and presents an overview of the main pathways/genes favored in the SB microbiota exposed to long-term pollution. In contrast, the microgAMBI, ecological status index based on bacterial community composition, was not consistent with the metallic contamination of SB, suggesting that this index requires improvements to be applied in tropical areas. Our study also revealed a strong correlation between metal resistance genes (MRG) and antibiotic resistance genes (ARG), indicating that MRG and ARG are co-selected by the metallic contamination prevailing in SB.

MBES Seabed Sediment Classification Based on a Decision Fusion Method Using Deep Learning Model

High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic resource assessment. Multibeam echo-sounding systems (MBES) have become the most popular tool in terms of acoustic equipment for seabed sediment classification. However, sonar images tend to consist of obvious noise and stripe interference. Furthermore, the low efficiency and high cost of seafloor field sampling leads to limited field samples. The factors above restrict high accuracy classification by a single classifier. To further investigate the classification techniques for seabed sediments, we developed a decision fusion algorithm based on voting strategies and fuzzy membership rules to integrate the merits of deep learning and shallow learning methods. First, in order to overcome the influence of obvious noise and the lack of training samples, we employed an effective deep learning framework, namely random patches network (RPNet), for classification. Then, to alleviate the over-smoothness and misclassifications of RPNet, the misclassified pixels with a lower fuzzy membership degree were rectified by other shallow learning classifiers, using the proposed decision fusion algorithm. The effectiveness of the proposed method was tested in two areas of Europe. The results show that RPNet outperforms other traditional classification methods, and the decision fusion framework further improves the accuracy compared with the results of a single classifier. Our experiments predict a promising prospect for efficiently mapping seafloor habitats through deep learning and multi-classifier combinations, even with few field samples.