133 publications from this institution
This study documents seafloor morphology and sediments based on multibeam, side-scan sonar and boomer surveys, as well as sediment samples taken on the inner to mid shelf of the Andaman Sea after the 2004 Indian Ocean tsunami. Preservation of submarine relief in former underwater mining areas points to limited impact of the tsunami, while channel structures parallel to the observed tsunami backwash indicate a possible higher impact. Therefore, the tsunami impact seems to be focused on some areas. The impact was probably most effective during the backwash, when stiff mud deposits containing grass, wood fragments and shells were transported by high density backwash flows. Moreover, several boulders, which might have been deposited during the tsunami backwash flow, were found in the channels in front of Pakarang Cape.
This study is part of the KOMSO project that focusses on standardised measurement methods for the long term carbon storage potentials in the Baltic Sea while the contemporaneous methane release of the seafloor works as an antagonistic player. We want to understand how pockmarks are formed and how stable these structures are. Another open question is whether vessel traffic affects the shape and degassing amount of the structures.The Baltic Sea was formed during the last Weichselian glaciation and underwent a multi-phase development. It was finally flooded during the Littorina transgression, while the basins and bays of the southern Baltic Sea contain thick glacial and post-glacial sediments. In particular, the Littorina and late Holocene deposits contain sediments rich in organic matter. Depending on the varying depth of the sulphate-methane transition zone in the different basins, which is partly modified through submarine groundwater discharge, methane is produced below this zone. This results in the accumulation of free gas within the sediments. The migration of the shallow biogenic gas forms gas fronts and leakage zones in the form of pockmarks at the seafloor.In a first step, we mapped the pockmarks in the various basins using a bathymetric grid with a general resolution of up to 10 m, in smaller areas also up to 1 m. We catalogued the mapped pockmarks according to the following criteria: (i) their location (near the coast or in the central basin, and water depth), (ii) their lateral and vertical dimensions (diameter and depth), and (iii) their form (elongate or round, single structure or clustered, depression/negative or upbending/positive). Additional parametric sediment echo sounder (SES) profiles (vertical 2D sections) allow further conclusions to be drawn, such as the stratigraphic affiliation of the gas escaping from the pockmarks and the depth of the underlying gas front.Adjacent to some existing pockmarks, the seafloor forms upward bulges above gas chimneys that may indicate the build-up of methane overpressure in the shallow subsurface. Whether these doming structures develop into new pockmarks needs to be evaluated with future differential bathymetric surveys. During the course of the projects, we will repeat SES profiles in order to determine any possible temporal or seasonal variation in the size of the pockmarks.
In marine habitat mapping, a demand exists for high-resolution maps of the seafloor both for marine spatial planning and research. One topic of interest is the detection of boulders in side scan sonar backscatter mosaics of continental shelf seas. Boulders are oftentimes numerous, but encompass few pixels in backscatter mosaics. Therefore, both their automatic and manual detection is difficult. In this study, located in the German Baltic Sea, the use of super resolution by deep learning to improve the manual and automatic detection of boulders in backscatter mosaics is explored. It is found that upscaling of mosaics by a factor of 2 to 0.25 m or 0.125 m resolution increases the performance of small boulder detection and boulder density grids. Upscaling mosaics with 1.0 m pixel resolution by a factor of 4 improved performance, but the results are not sufficient for practical application. It is suggested that mosaics of 0.5 m resolution can be used to create boulder density grids in the Baltic Sea in line with current standards following upscaling.
Advances in AI-based boulder detection using hydroacoustic data enable detailed characterization of geogenic reefs. As AI-based detection approaches the level of accuracy of human interpretation in small-scale test areas, it opens up the opportunity to efficiently analyze and characterize larger regions. Geogenic hard substrates are a key habitat for diverse benthic communities that provide crucial ecosystem services. Current classification schemes for boulder fields in the German Baltic Sea, using three categories (0 boulders, 1-5 boulders, and &gt;5 boulders) inadequately capture habitat complexity, thus limiting our understanding of these critical geogenic reefs. Convolutional neural networks were used to detect individual boulders on side scan sonar backscatter mosaics with 25 cm resolution across four study sites, covering an area of 306 km 2 in the German Baltic Sea. Region-specific AI models detected about 6.7 times more boulders than previous automated methods. A maximum of 550 boulders per 50 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"> <mml:mrow> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> 50 m grid cell were detected. A novel metric, the Boulder Field Complexity Index (BFCI), was developed to describe the complexity of boulder fields. The BFCI integrates boulder count with the transition of sharpness and the spatial distribution of individual boulders. Compared to conventional approaches, the BFCI enables the characterization of boulder field complexity on a continuous scale and reveals significant complexity differences between study sites. The coastal and shallow study site Plantagenet Ground demonstrates the highest level of complexity, whereas the offshore and deepest study site, Western Rönnebank, exhibits the lowest. The abundance of boulders is negatively correlated with water depth, with the highest densities occurring in shallow waters. The spatial variability in BFCI values reflects the heterogeneous nature of glacial till deposits and differential erosion processes that have shaped boulder field distribution. This approach provides the foundation for linking habitat heterogeneity to benthic community patterns and ecosystem functioning.
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