Search Results (32)

As fundamental geophysical information, the high-precision detection of shallow water bathymetry is critical data support for the utilization of island resources and coral reef protection delimitation. In recent years, the combination of active and passive remote sensing technologies has led to a revolutionary breakthrough in satellite-derived bathymetry (SDB). Optical SDB extracts bathymetry by quantifying light–water–bottom interactions. Therefore, the apparent differences in the reflectance of different bottom types in specific wavelength bands are a core component of SDB. In this study, refined classification was performed for complex seafloor sediment and geomorphic features in coral reef habitats. A multi-model synergistic SDB fusion approach constrained by coral reef habitat classification based on the deep learning framework Mamba was constructed. The dual error of the global single model was suppressed by exploiting sediment and geomorphic partitions, as well as the accuracy complementarity of different models. Based on multispectral remote sensing imagery Sentinel-2 and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) active spaceborne lidar bathymetry data, wide-range and high-accuracy coral reef habitat classification results and bathymetry information were obtained for the Yuya Shoal (0–23 m) and Niihau Island (0–40 m). The results showed that the overall Mean Absolute Errors (MAEs) in the two study areas were 0.2 m and 0.5 m and the Mean Absolute Percentage Errors (MAPEs) were 9.77% and 6.47%, respectively. And R² reached 0.98 in both areas. The estimated error of the SDB fusion strategy based on coral reef habitat classification was reduced by more than 90% compared with classical SDB models and a single machine learning method, thereby improving the capability of SDB in complex geomorphic ocean areas. Full article

Seafloor soil classification is essential for marine engineering, environmental monitoring, and geological surveys. Traditional classification methods, such as physical sampling and acoustic backscatter analysis, have inherent limitations, including spatial constraints and inconsistencies in distinguishing sediments with similar acoustic properties. This study uses frequency-dependent acoustic reflection coefficients to investigate a novel spectral-based approach to seabed soil classification. Experiments were conducted in a controlled aquatic environment to isolate the spectral characteristics of two soil types: poorly graded sand (SP) and poorly graded gravel (GP). The research employed calibrated transducers to measure reflection coefficients across the 100–400 kHz frequency range, allowing for a comparative spectral analysis between the two sediments. The results demonstrate that SP and GP exhibit distinct spectral fingerprints, with SP showing higher reflectance across all measured frequencies, while GP displays a more variable spectral response. These findings suggest that frequency-dependent reflectance provides a more sensitive and accurate classification criterion than conventional backscatter intensity analysis. By eliminating environmental variability and focusing on intrinsic soil properties, this study establishes a foundation for automated, non-invasive classification methods that could be integrated into machine learning frameworks for real-time seabed characterization. The proposed methodology enhances the precision of remote sensing techniques and presents significant advantages in offshore engineering, environmental monitoring, and hydrographic surveys. Future research should extend this approach to diverse sediment types and open marine environments to refine and validate its applicability in real-world scenarios. Full article

Accurate and efficient acquisition of the acoustic reflection properties of sediments with different grain sizes is key for sediment substrate classification and the construction of seafloor acoustic scattering models. To accurately measure surface sediments on the seafloor, an in-depth investigation of the acoustic properties of sediments with different grain sizes at different measurement distances is an indispensable prerequisite. While previous studies have extensively explored the acoustic reflection properties of sediments in mid- and low-frequency bands (e.g., 6–85 kHz), research on high-frequency reflectivity (95–125 kHz) remains limited. Existing equipment often suffers from large beam angles (e.g., >10°), leading to challenges in standardising laboratory measurements. To this end, we developed a technique using a high-frequency submersible sub-bottom profiler (HF-SSBP) to measure the high-frequency reflection intensity of homogeneous sediments screened by grain size. To ensure stable measurements of the high-frequency reflection intensity, we conducted experiments using standard acrylic plates. This demonstrates the dependability of the HF-SSBP and determines the absolute measurement error of the HF-SSBP. Variations in radiofrequency reflection intensity across different sediment types with different grain sizes in a frequency range of 95–125 kHz were investigated. The reflectance amplitude was measured and the reflectance coefficients were calculated for six uniform sediments with different grain sizes ranging from 0.1–0.3 to 2.0–2.5 mm. The scattering intensity of the six sediments with a uniform grain size distribution at the same measurement distance varies to some extent. There is variation in the intensity of acoustic wave reflections for different grain sizes, but some of the differences are not statistically significant. The dispersion coefficients of the acoustic reflection intensities for all sediments, except for those with a grain size of 1.0–1.5 mm, are less than 5% at different measurement distances. These coefficients are almost independent of the detection distance. Full article

Accurate, rapid, and automatic seafloor sediment classification represents a crucial challenge in marine sediment research. To address this, our study proposes a seafloor sediment classification method integrating convolutional neural networks (CNNs) with small-sample multi-beam backscatter data. We implemented four CNN architectures for classification—LeNet, AlexNet, GoogLeNet, and VGG—all achieving an overall accuracy exceeding 92%. To overcome the scarcity of seafloor sediment acoustic image data, we applied a deep convolutional generative adversarial network (DCGAN) for data augmentation, incorporating a de-normalization and anti-normalization module into the original DCGAN framework. Through comparative analysis of the generated versus original datasets using visual inspection and grayscale co-occurrence matrix methods, we substantially enhanced the similarity between synthetic and authentic images. Subsequent model training using the augmented dataset demonstrated improved classification performance across all architectures: LeNet showed a 1.88% accuracy increase, AlexNet an increase of 1.06%, GoogLeNet an increase of 2.59%, and VGG16 achieved a 2.97% improvement. Full article

Bottom-grab samplers have long been the standard to describe nearshore marine habitats both qualitatively and quantitively. However, sediment samplers are designed to collect specific grain sizes and therefore have biases toward those sediments. Here, we discuss seafloor characterizations based on grain size analysis alone vs. grain size analysis augmented with quantitative benthic imagery. We also use both datasets to inform a prevalent benthic habitat classification system. The Coastal and Marine Ecological Classification Standard (CMECS) was used to test this hypothesis. CMECS was adopted by the federal government to standardize habitat classification in coastal U.S. waters. CMECS provides a hierarchal framework to define and interpret benthic habitats but does not prescribe specific sampling methods. Photography has been utilized for many decades in benthic ecology but has rarely been employed in habitat classification using CMECS. No study to date has quantitatively examined the benefit of incorporating benthic imagery into the classification of biotopes using CMECS. The objective of this study is to classify a roughly 1 km² subtidal area within Herring Cove in Provincetown, MA with CMECS and quantify the benefit of augmenting classification with low-cost imagery. A benthic habitat survey of the study area included grab sampling for grain-size analysis and invertebrate taxonomy, benthic imagery, water quality sampling at 24 sampling stations, and acoustic mapping of the study area. Multivariate statistical analyses were employed to classify biotic communities and link environmental and biological data to classify biotopes. The results showed that benthic imagery improved the classification and mapping of CMECS components. Furthermore, the classification of habitats and biotopes was improved using benthic imagery data. These findings imply that the incorporation of low-cost benthic imagery is warranted in coastal benthic biotope classification and mapping studies and should be regularly adopted. This study has implications for coastal benthic ecologists classifying benthic habitats within the CMECS framework. Full article

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data. Full article

Given the rapid expansion of offshore wind development in the United States (US), the accurate mapping of benthic habitats, specifically surficial sediments, is essential for mitigating potential impacts on these valuable ecosystems. However, offshore wind development has outpaced results from environmental monitoring efforts, compelling stakeholders to rely on a limited set of public geospatial data for conducting impact assessments. The present study therefore sought to develop and evaluate a systematic workflow for generating regional-scale sediment maps using public geospatial data that may pose integration and modeling challenges. To demonstrate this approach, sediment distributions were characterized on the northeastern US continental shelf where offshore wind development has occurred since 2016. Publicly available sediment and bathymetric data in the region were processed using national classification standards and spatial tools, respectively, and integrated using a machine learning algorithm to predict sediment occurrence. Overall, this approach and the generated sediment composite effectively predicted sediment distributions in coastal areas but underperformed in offshore areas where data were either scarce or of poor quality. Despite these shortcomings, this study builds on benthic habitat mapping efforts and highlights the need for regional collaboration to standardize seafloor data collection and sharing activities for supporting offshore wind energy decisions. Full article

The classification of marine sediment based on acoustic data is crucial for various applications such as marine resource exploitation, marine engineering construction, and marine ecological environment maintenance. It serves as a valuable alternative to limited geological sampling. However, the accuracy of sediment classification is limited due to constraints in acoustic data detection methods, data quality, and classification techniques. To address this issue, this study proposes an automatic classification method for marine sediment using an improved U-convolutional neural network and K-means clustering algorithm. In the coding part, a spatial pyramid pool layer is introduced to fuse low-dimensional feature data of different scales with the features of each level of the corresponding coding layer. This fusion method enhances the accuracy of the constructed relationship between the physical property parameters of the seabed bottom. The K-means clustering algorithm is optimized through selecting the point at the density center as the initial clustering center during the initial clustering center selection stage. This approach solves the sensitivity problem of the initial clustering center of K-means, improves the edge extraction effect of sediment types, and enhances the classification accuracy of sediment types. To validate the proposed method, an application test is conducted in the Northern Slope area of the South China Sea. The mean grain size of sediments in the study area is predicted using the improved U-Net neural network and the seafloor reflection intensity of the sub-bottom profile. Compared to the standard U-Net network results, the mean grain size prediction results show an increase of 4.9% and 2.8%, respectively. The sediment with the predicted mean grain size is then classified using the K-means clustering algorithm, resulting in the division of five sediment types: gravelly sand, sand, silty sand, sandy silt, and clayey silt. These classifications align well with the South China Sea sediment type map. The findings of this study not only provide an important supplement to existing marine sediment classification methods but also contribute significantly to understanding the sedimentary environment and processes in the Northern Slope of the South China Sea. Full article

The upper part of the Krka River estuary and Prokljan Lake are a specific example of a well-stratified estuarine environment in a submerged river canyon. Here, we reconstructed the geomorphological evolution of the area and classified the data gathered in the study, integrating multibeam echosounder data, backscatter echosounder data, side-scan sonar morpho-bathymetric surveys, and acoustic sub-bottom profiling, with the addition of ground-truthing and sediment analyses. This led to the successful classification of the bottom sediments using the object-based image analysis method. Additional inputs to the multibeam echosounder data improved the segmentation of the seafloor classification, geology, and morphology of the surveyed area. This study uncovered and precisely defined distinct geomorphological features, specifically submerged tufa barriers and carbonate mounds active during the Holocene warm periods, analogous to recent tufa barriers that still exist and grow in the upstream part of the Krka River. Fine-grained sediments, classified as estuarine sediments, hold more organic carbon than coarse-grained sediments sampled on barriers. A good correlation of organic carbon with silt sediments allowed the construction of a prediction map for marine sedimentary carbon in this estuarine/lake environment using multibeam echosounder data. Our findings highlight the importance of additional inputs to multibeam echosounder data to achieve the most accurate results. Full article

Seabed soil layer composed of soft sediments, which has a high water content, low bulk density and low shear strength, has great influence on deep-sea engineering devices. Therefore, accurate measurement of the mechanical properties of seabed sediments is a prerequisite for the construction and safe operation of deep-sea projects. In this study, a deep-sea engineering geology in situ test system was developed to measure cone resistance, sleeve friction, pore pressure and shear resistance in seafloor sediments. The system was tested on land, and the feasibility of the system was verified. We conducted sea trials in the South China Sea and acquired datasets from five stations. The data were analyzed using the Eslami–Fellenius soil classification map, and the soil classification of the site was obtained. The obtained values of cone resistance, sleeve friction, pore pressure and shear resistance can provide mechanical data support for deep-sea engineering in this area. Full article

The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration. Full article

Augusta Bay is an embayment of the Hyblean sector in south-eastern Sicily (Southern Italy) that faces the Ionian Sea and includes the Rada di Augusta, a wide littoral sector sheltered by breakwaters, which hosts intense harbor activities. Rada di Augusta and the adjacent Priolo embayment were listed in the National Remediation Plan (NRP) by the Italian Ministry of Environment, as they have suffered major anthropic impacts over the last seventy years. Indeed, extensive petrochemical and industrial activities, military and commercial maritime traffic, as well as agriculture and fishery activities, have resulted in a highly complex combination of impacts on the marine environment and seafloor. In this paper, we investigate the extent of human-driven physical impacts on the continental shelf, offshore of Rada di Augusta, by means of Multibeam echosounder, Side-Scan Sonar and Chirp Sonar profilers, as well as direct seabed samplings. At least seven categories of anthropogenic footprints, i.e., anchor grooves and scars, excavations, trawl marks, targets, dumping trails, isolated dumping and dumping cumuli, mark the recent human activities at the seafloor. The practice of dredge spoil disposal, possibly protracted for decades during the last century, has altered the seafloor morphology of the central continental shelf, by forming an up-to-9 m-thick hummocky deposit, with acoustic features noticeably different from those of any other shelf lithosome originated by natural processes. All available data were reported in an original thematic map of the seafloor features, offering an unprecedented opportunity to unravel sediment facies distribution and localization of anthropogenic disturbance. Finally, the shelf area was ranked, based on the coexistence of multiple stressors from human-driven physical harm, thus providing a semi-quantitative analysis of environmental damage classification in the area. Full article

Seabed sedimentary bedforms (SSBs) are strong indicators of current flow (direction and velocity) and can be mapped in high resolution using multibeam echosounders. Many approaches have been designed to automate the classification of such SSBs imaged in multibeam echosounder data. However, these classification systems only apply a geomorphological contextualisation to the data without making direct assertions on the velocities of benthic currents that form these SSBs. Here, we apply an object-based image analysis (OBIA) workflow to derive a geomorphological classification of SSBs in the Moira Mounds area of the Belgica Mound Province, NE Atlantic through k-means clustering. Cold-water coral reefs as sessile filter-feeders benefit from strong currents are often found in close association with sediment wave fields. This OBIA provided the framework to derive SSB wavelength and wave height, these SSB attributes were used as predictor variables for a multiple linear regression to estimate current velocities. Results show a bimodal distribution of current flow directions and current speed. Furthermore, a 5 k-means classification of the SSB geomorphology exhibited an imprinting of current flow consistency which altered throughout the study site due to the interaction of regional, local, and micro scale topographic steering forces. This study is proof-of-concept for an assessment tool applied to vulnerable marine ecosystems but has wider applications for applied seabed appraisals and can inform management and monitoring practice across a variety of spatial and temporal scales. Deriving spatial patterns of hydrodynamic processes from widely available multibeam echosounder maps is pertinent to many avenues of research including scour predictions for offshore structures such as wind turbines, sediment transport modelling, benthic fisheries, e.g., scallops, cable route and pipeline risk assessment and habitat mapping. Full article

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. Full article

Bottom trawling is one of the most significant anthropogenic pressures on physical seafloor integrity. The objective classification of physical impact is important to monitor ongoing fishing activities and to assess the regeneration of seafloor integrity in Marine Protected Areas. We use high-resolution bathymetric data recorded by multibeam echo sounders to parameterize the morphology of trawl mark incisions and associated mounds in the Fehmarn Belt, SW Baltic Sea. Trawl marks are recognized by continuous incisions or isolated depressions with depths up to about 25 cm. Elevated mounds fringe a subset of the trawl marks incisions. A net resuspension of sediment takes place based on the volumetric difference between trawl mark incisions and mounds. While not universally applicable, the volume of the trawl mark incisions is suggested as an indicator for the future monitoring of the physical impact of bottom trawling in the Baltic Sea basins. Full article