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Sediment boundaries of in H2 and the orientation of the baselines as indicated by the arrows. DSAS uses a measurement baseline method to calculate rate-ofchange statistics for a time series of shorelines/boundaries.
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Detecting changes of sediment boundaries on the seafloor is important for a better understanding of sediment dynamics and related impacts to benthic habitats. Side-scan sonars (SSS) perform more cost-effectively in shallow waters than other acoustic systems because of their larger swath widths, and the resolution of its images does not change with...
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... H2 (10.3 km 2 ) is the least complex area with only one sharp sediment boundary between high and low backscatter intensities, followed by a gradual backscatter decrease (Fig. 4). H3 is the largest (12.8 km 2 ) and most complex area (Fig. 5). The boundary between high and low backscatter intensity is strongly sinuous. Gradual changes are less present. Area H5 (2.5 km 2 ) shows similar backscatter features with H2 (sharp boundary with following gradual change), but the sharp boundary in H5 is curvier than in H2 ...
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... data for DSAS are shorelines (backscatter boundary lines in our case) from different years, baselines, and transects. In this study, boundaries were extracted from SSS mosaics collected in 2016, 2017 and 2018 over the focus areas (Figs. 4, 5, and ...
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... was proven to have the ability to distinguish between similarlooking features (Montereale Gavazzi et al., 2016). Lag deposits corresponded to higher backscatter (darker colors in our scale: grayscale values 55-255), while the surrounding finer sediments corresponded to lower backscatter (lighter colors in our scale: grayscale values (0-54)) (Figs. 4, 5, and ...
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... shoreline/ boundary line or by creating a new feature class and manually draw the baseline. The baseline can be constructed at the left, right or both sides of the boundary lines. For this study, baselines were drawn manually because it was the most efficient approach given the complex structure of the sediment boundaries in the study areas (Figs. 4, 5, and 6). DSAS requires that the proper baseline flow orientation be defined to ensure that rates of change are expressed correctly as negative and positive values ( Himmelstoss et al., 2018). In this regard, baseline segments were placed to the right of the boundaries if they showed a 'positive trend' or to the left if they showed a ...
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... analysis of change using the DSAS method was conducted to observe if changes in the position of the boundaries exist. Our results (Figs. 14, 15, and 16) showed an interesting common trend in the movement of the boundaries. In general, we noticed that the boundaries moved from northeast to southwest direction in every focus area. Boundary movements of less than 20 m were usually observed (Fig. 17). Boundary retreat was dominant in H3 and H5, while boundary advance prevailed in ...
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... The trend of movement in H2 is towards the NE-SW direction (Fig. 14). Of the 1497 transects, 66.5% have measured boundary advances and 33.5% recorded boundary retreats ( Table 2). The maximum boundary advance was 36.5 m, while the maximum boundary retreat was −35.6 m. We observed that the most common movement was boundary advance of < 10 m (Fig. ...
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... (1-184 m) in H3 was higher than in focus area H5 (1-89 m). The reason for this may be that the data for H5 were only for four months, while the data for H3 were for two years. A reliable interpretation of the boundary movements (~1 m) in area H2 is hardly possible, because the estimated uncertainty value of our boundary change analysis is ± 1 m. Fig. 14. Net boundary movement in H2 within four months. The direction of movement was observed to be from northeast to southwest. Majority of the shifts were observed perpendicular to the boundary, and were directed towards the northeast and southwest orientation. The direction of movements was opposite to the direction of tidal currents in ...
Citations
... Seafloor substrates in the western SOR are characterized by heterogenous seafloor patterns on the east and homogenous seafloor patterns on the west. The heterogenous area is composed of patches of coarse-grained materials, which are classified as lag deposits or as sorted bedforms, and surrounded by finer materials Mielck et al. 2015;Galvez et al. 2020;Papenmeier and Hass 2020). The homogenous area is characterized by finer materials, which is composed of Holocene fine and medium marine sands. ...
... The seafloor features in the SOR reflect the glacial origin of the subsurface (till) deposits and represent the highly dynamic oceanographic processes on the water column and on the seafloor Heinrich et al. 2017;Feldens et al. 2018;Bartholomä et al. 2020;Galvez et al. 2020). The southern German North Sea is exposed to strong winds from the west, which results in a residual cyclonic circulation that is strongly affecting the sediment transport (Staneva et al. 2009;Port et al. 2011;Kösters and Winter 2014;Callies et al. 2017). ...
... The nadir line was cut out to 5 m in both port and starboard direction to reduce the noise in the final mosaic. To enhance the quality and spatial accuracy of the SSS data, the mosaics underwent quality control following the procedures of Galvez et al. (2020). Multibeam echosounder (MBES) data were simultaneously collected with a hull-mounted Kongsberg EM710 system (Kongsberg Maritime AS, Kongsberg, Norway). ...
Seafloor sediment mapping traditionally relies on the experience and expertise of practitioners to classify sediment classes based on acoustic backscatter data and ground-truth information. However, recent advancements in automated seafloor mapping present a major development in objective methods that offer practical application for seafloor mapping and monitoring campaigns. In this study, a class-specific approach of ensemble modelling (ensemble mapping) was used to classify the sediment classes of a large-scale seafloor area (1550 km²) in the western Sylt Outer Reef, German North Sea. A pixel-by-pixel comparison of the modelled map and manually digitized map was also conducted to assess the efficiency of the ensemble mapping approach. The resulting seafloor sediment map, with an overall accuracy of 73%, demonstrates five sediment classes that represent most of the seabed of the German North Sea. The manually classified and ensembled maps were 63% identical, but mismatches were observed in the transitional boundaries of soft sediment classes and in stony areas that were not predicted in manual classification. The inconsistencies between the two maps was attributed to the different interpretation of sediment boundaries, the simplification of the sediment classification scheme, and the ability of ensemble mapping to classify more areas than manual classification. This study found that ensemble mapping performs better in characterizing coarse materials and produces maps that are comparable to the maps produced by manual classification, while the production time and degree of subjectivity in the analysis are minimal. Hence, ensemble mapping is a viable alternative to create baseline seafloor sediment maps that can be used for environmental monitoring and resource planning.
... The thickness of the mobile sand layer around boulders, as well as the seabed morphology, give valuable insights into the prevailing sediment transport system [18,19]. Currents around objects on the seafloor generally lead to scour marks as a result of turbulent flow and subsequent sediment erosion [20,21]. ...
Rocky reefs provide complex structures in the otherwise largely sand-dominated coastal North Sea. Therefore, these reefs are highly important natural habitats for the functioning of coastal ecosystems, as they provide shelter, refuge and nursery grounds for various mobile and sessile species. In the North Sea, the spatial distribution of these habitats has been intensively investigated over recent years. However, these studies generally provide static accounts of the current state of these reef systems, but limited data exist on the temporal variations in sediment dynamics at and around natural rocky reefs. In this study, we provide observations from a multiannual time series of hydroacoustic seafloor surveys conducted at an isolated rocky reef in the North Sea. We use multibeam bathymetry and side-scan sonar backscatter data in combination with video observations, sediment sampling, and sub-bottom profiler data to assess the long-term variations of the rocky reef system. The reef is located in water depths between 11 and 17 m with an areal extent of ~0.5 km2 and is surrounded by mobile sands. The topography of the rocky reef appears to create a distinct hydrodynamic system that permits mobile sands to settle or move into bathymetrical deeper parts of the reef. Our results suggest a very dynamic system surrounding the reef with large scale scouring, sediment reworking and transport, while the shallower central part of the reef remains stable over time. We demonstrate the importance of hydrodynamics and current scouring around reefs for the local variability in seafloor properties over time. These small-scale dynamics are likewise reflected in the spatial distribution of sessile species, which are less abundant in proximity to mobile sands. The hydroacoustic mapping and monitoring of seafloor dynamics at higher spatial and temporal resolutions presents an important future direction in the study of valuable coastal habitats.
... ANÁLISE MULTITEMPORAL DA LINHA COSTEIRA NOS ÚLTIMOS 48 ANOS (1972-2020) E PROJEÇÃO EVOLUTIVA PARA OS ANOS DE 2030 E 2040 NA COSTA OESTE DA FOZ DO RIO PARÁ (AMAZÔNIA OCIDENTAL BRASIL)França and Souza Filho, 2003;Mahapatra et al., 2014;Mentaschi et al., 2018;Muskananfola et al., 2020;Orlando et al., 2019). Para a avaliação e quantificação das taxas de erosão e acresção o Digital Shoreline Analysis (DSAS) do software ArcGis, auxilia para determinar as variações ocorridas na LC(Conti and Rodrigues, 2011;Farias and Maia, 2010;Galvez et al., 2020;Mahapatra et al., 2014;Ranieri and El- Robrini, 2015;. A partir dessa vertente teórica exposta, esta composição ...
... Side-scan sonar imagery is frequently used to detect debris and other obstructions on the seafloor that may be hazardous to navigation or to seafloor installations by the oil and gas industry. A high-precision, dual-frequency side-scan sonar system can obtain seabed information along the routes for example, anchor/trawl board scours, large boulders, debris, bottom sediment changes, and any item on the seabed having a horizontal dimension in excess of 0.5 m [5,6]. Side-scan data are frequently acquired along with bathymetric soundings and sub-bottom profiler data, thus providing a glimpse of the shallow structure of the seabed. ...
A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm improving contour extraction is presented. Contour extraction is based on automatically estimating the optimal threshold for converting the original gray scale images into binary images. The proposed algorithm clears the shadows and masks most of the impairments in side-scan sonar images. The idea is to select a proper threshold towards the rightmost local minimum of the histogram, i.e., closest to the white values. For this purpose, the histogram envelope is approximated by two alternative contour extraction methods: polynomial curve fitting and data smoothing. Experimental results indicate that the proposed algorithm produces superior results than popular thresholding methods and common edge detection filters, even after corrosion expansion. The algorithm is simple, robust and adaptive and can be used in automatic target recognition, classification and storage in large-scale multimedia databases.
... Mapping of the SOR was given importance because of the complexity of the seafloor habitats (i.e., boulder reefs, gravel patches, and sands) in the area, which stands out in the relatively sand-dominated German North Sea. Semi-and fully-automated procedures for the detection of stones have been tested in area H3 [38] and sediment dynamics have been studied in both areas [39,40]. ...
... The depth in H5 is slightly deeper than H3, with water depths ranging between 36 and 42 m. High backscatter areas were observed in deeper areas, while low backscatter regions dominated at shallow water depths [40]. ...
... The default maximum reliable swath width was 90 • . Side-scan data were processed using the QPS Fledermaus Geocoder Toolbox v.7.8.8 software (Quality Positioning Services BV, Zeist, the Netherlands) to reduce the artefacts in the raw data and produce SSS mosaics that were compatible for change analyses (see [40] for details on the procedure). The process applied backscatter, beam pattern, and angle-varying gain corrections, and improved the spatial accuracy of the SSS mosaics (spatial accuracy: ±0.25 m). ...
Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate numbers of ground-truth samples and without consideration of the imbalances in the ground-truth datasets. In this study, we aim to address these issues by conducting class-specific predictions using ensemble modelling to map seafloor sediment distributions with minimal ground-truth data combined with hydroacoustic datasets. The resulting class-specific maps were then assembled into a sediment classification map, in which the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. The methods presented can also be used for other underwater exploration studies with minimal ground-truth data. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the marine conservation area during two different short timescales: 2016–2018 (17 months) and 2018–2019 (4 months). The analyses of the sediment shifts showed that the western area of the Sylt Outer Reef experienced sediment fluctuations but the morphology of the bedform features was relatively stable. The results provided information on the seafloor dynamics, which can assist in the management of the marine conservation area.
... Side-scan sonars (SSS) perform more effectively in shallow waters than other acoustic systems because of their larger swath widths [2], and the resolution of received data does not change with varying water depth, so they are able to provide near-photographic high-resolution images of underwater areas [11]. Sonar applications are wide, that is the mapping of the main types of sediments [16], sediment distribution patterns study, as a result of natural processes [2] and bank engineering reconstruction [9], differentiation and precise location of various types of natural and or manmade objects by different textural responses [4]. ...
... Side-scan sonars (SSS) perform more effectively in shallow waters than other acoustic systems because of their larger swath widths [2], and the resolution of received data does not change with varying water depth, so they are able to provide near-photographic high-resolution images of underwater areas [11]. Sonar applications are wide, that is the mapping of the main types of sediments [16], sediment distribution patterns study, as a result of natural processes [2] and bank engineering reconstruction [9], differentiation and precise location of various types of natural and or manmade objects by different textural responses [4]. Co-use of side-scan location and ground scanning radar or magnetometer proved to be the most effective for assessment section of loose deposits and distribution of the surface sediments and identification of technogenic objects [14]. ...
The generalized results of the Kuybyshev Reservoir bed relief survey using modern methods are presented on the example of two sites. Sakony site area is located on the low left bank of the Kuybyshev Reservoir in the place of sand and gravel mix extraction, Sviyazhsk site – around the cultural heritage object Sviyazhsk island-town. The change in the bottom topography was monitored using the Interferometric Side-Scan Sonar and the HyScan software. Bathymetric maps and transverse profiles for study sites were built. A description of the bottom relief in the studied areas is given. As a result, reservoir bottom survey technique was developed to update its morphometry, possibility of obtained data application is discussed.
... The areas have been the subject of the national seafloor mapping program SedAWZ, which is coordinated by the Federal Maritime Hydrographic Agency (BSH) [32,33]. Semi-and fully-automated procedures for the detection of stones have been tested in area H3 [34] and sediment dynamics have been studied in both areas [35,36]. ...
... The depth in H5 is slightly deeper than H3, with water depth ranging between 36 and 42 m. High backscatter areas were observed in deeper areas, while low backscatter regions dominate at shallow water depths [36]. ...
... However, the survey track distances were too wide to achieve a swath overlap of the MBES data. Side-scan data were processed using QPS Fledermaus Geocoder Toolbox v.7.8.8 software (see [36] for details on the procedure). The process applied backscatter, beam pattern, and angle-varying gain corrections; and improved the spatial accuracy of the SSS mosaics (spatial accuracy: ±0.25 m). ...
Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate number of ground-truth samples, which raises the question whether these methods are applicable to studies with smaller numbers of ground-truth data. In this study, we aim to address this issue by conducting sediment class-specific predictions using ensemble modelling to map areas with limited or without ground-truth data and combined with hydro-acoustic datasets. The resulting class-specific maps were then assembled into one map, where the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the area. The analyses of sediment shifts showed that the western area of the Sylt Outer Reef is highly active, and the results of the analyses assisted in providing recommendations on future seafloor monitoring activities.
... Then, the sonar image constructed using these backscatter strengths can reflect the important information on the seabed, which enables the wide applications for side-scan sonars. In benthic habitat mapping, side-scan sonars can provide the backscatter information from the seabed to construct benthic habitat maps to help protect coastal ocean ecosystems [6][7][8][9][10]. In marine engineering, side-scan sonars are commonly used to detect and track engineering ...
As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars.
The Paleo Elbe Valley is the most prominent subsurface structure in the southern North Sea. During the Weichselian (marine isotope stage (MIS) 2), the valley traversed the exposed sea floor and drained the southern margin of the Scandinavian ice sheet. Today the valley is filled with up to 16 m thick sediments, but the responsible processes and drivers remain unknown. To unravel these processes and describe the valley’s evolution with Holocene transgression, we use shallow seismic data and vertical high-resolution grain-size core data. At the base of the western shore, supralittoral fine sands are overlain by a thin layer of clay dated to 9.8 cal. ka BP. The major sediment package consists of marine silt with internal seismic reflectors inclined in a northeastern direction, indicating a sediment transport from the southwest. The valley infill started when the western shore was flooded around 9.6 cal. ka BP and can be divided into two phases. During the first one (9.6–8.1 cal. ka BP) the sedimentation rate was highly driven by wind and waves. The second phase (8.1–5.0 cal. ka BP) was mainly tidal dominated but shows also storm event deposits in the north. Around 5.0 cal. ka BP the valley was almost filled.
