Publications

Pockmarks in the southern Baltic Sea

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.

Marine Habitat Mapping in Germany: Application, Progress and Challenges

<p>The increasing demand by national legislation and European marine policy amplifies the need for high resolution and area-wide habitat maps of the seafloor. The basis for a consistent and objective delineation of habitats is a consistent derivation of sediment classes. For this reason, the German Federal Maritime and Hydrographic Agency (BSH) has published a guideline for high-resolution, hydroacoustic sediment mapping, with the advice of scientific experts. Large areas within the German exclusive economic zone, in particular in the special areas of conservation (SAC) have already been mapped in agreement with this guideline.</p><p>We will introduce the mapping guideline and its sediment classification system, present the mapping progress and demonstrate the successful use of sediment maps for the creation of habitat maps. The approach by the BSH works very well for the large-scale mapping of the German North and Baltic Sea. However, more specific tasks like environmental investigations for approval procedures or scientific questions need adjustments to the mapping criteria. We will show this exemplary for hard substrate habitats. According to the guideline of the BSH (update is in progress), hard substrates presence is given as raster information with cell size of at least 100 x 100 m. For e.g. approval procedures, this raster information is not sufficient to map the protected natural habitat “reefs” in a large scale. Therefore, the German Federal Agency for Nature Conservation (BfN) has developed its own criteria where individual objects have to be detected. The object detection is still to be done manually which might be acceptable for small study sites, but not practicable for large-scale mapping. Consequently, current work is concerned with the automation of object detection to speed up the interpretation and make it objective / reproducible.</p>

Training data, model checkpoint, test areas, trawl mark density grids, and time-series slope data for automated trawl mark segmentation in the German Baltic Sea

This dataset accompanies the manuscript "Spatial extent of trawl marks in the German Baltic Sea Basins based on bathymetric grids" by Peter Feldens, Inken Schulze, Elisabeth Seidel, Svenja Papenmeier, Aicha Naumann, Daniel Oesterwind, Jacob Geersen, and Mischa Schönke (Leibniz Institute for Baltic Sea Research Warnemünde / Thünen Institut für Ostseefischerei) submitted to the Journal "Anthropocene". The data support a study that uses a U-Net convolutional neural network to segment trawl marks from multibeam echo sounder (MBES) bathymetric slope grids across approximately 1,069 km² of seafloor in the south-western German Baltic Sea. Study areas include Mecklenburg Bay, the Arkona Basin, Kiel Bay, and the Fehmarn Belt. Bathymetric data were collected between 2016 and 2025 at 1 m resolution (interpolated to 0.25 m) and provided primarily by the German Federal Maritime Agency (BSH).Download links for the BSH data are given in the paper manuscript and are available from https://marine-data.de/ . The dataset is organized into four folders, which are here available as zip files:Training_Data/Contains the image patches and masks used to train the U-Net segmentation model, together with the trained model checkpoint. Images are 256×256 pixel PNG patches of slope data covering training areas in Mecklenburg Bay, Kiel Bay, Fehmarn Belt, and the Arkona Basin (German Baltic Sea). The dataset comprises 2,318 patches, each augmented 6 times (rotation, flips, brightness/contrast changes, elastic transformations, random cropping). Masks are binary: white pixels indicate trawl marks, black pixels indicate background. The model checkpoint (checkpoint_epoch13_mbes.pth) is for a U-Net with a ResNet-34 encoder (Segmentation Models PyTorch library), pre-trained on ImageNet. Test_Areas/Contains five independent test areas (Test1–Test5) not included in the training data, used for model validation. Each test area includes a slope GeoTIFF (.tif with .tfw world file and .prj projection file), a manually annotated binary mask GeoTIFF, and a shapefile (.shp, .shx, .dbf, .prj) defining the test area boundary. Test areas are distributed across Mecklenburg Bay, the southern Arkona Basin, Kiel Bay, and the Fehmarn Belt. Grid_Densities/Contains trawl mark density grids as GeoPackage files (.gpkg) for each investigation area: Mecklenburg Bay (MB), Arkona Basin areas 1 and 2 (ARK, ARK2), Kiel Bay (KB), and the Fehmarn Belt. Fehmarn Belt split into a reference area (REF; towards west) and a Marine Protected Area (MPA; towards east). For the MPA area, densities for 2025 are included as well, establishing a 2-year time series. Density values are computed within a regular 25 m × 25 m grid and normalized to 0–1, where 0 indicates no trawl marks and 1 indicates complete coverage. Polygons smaller than 100 m² were filtered to reduce noise as described in the paper. Time_Series/Contains slope GeoTIFFs for the Fehmarn Belt Marine Protected Area (FB-MPA) from 2024 and 2025, used to assess temporal changes in trawling intensity. The data were recorded on Elisabeth Mann Borgese cruises EMB345 (cruise report: doi:10.48433/cr_emb345) and EMB369 (cruise report: doi:10.48433/cr_emb369 ) in 2024 and 2025. These time-lapse datasets document seafloor regeneration timescales and trawl mark density in the two survey years.

The transition from rifting to spreading in the Red Sea: No sign of discrete spreading nodes?

No abstract is provided for this article.

Genesis and sediment dy-namics in a subaqueous dune field in Fehmarn Belt (south-western Baltic Sea)

No abstract is provided for this article.

High resolution bathymetry of the Red Sea Rift (1 arc-second) from POSEIDON cruise POS408 and PELAGIA cruises 64PE350 and 64PE351

Sub-marine Continuation of Peat Deposits From a Coastal Peatland in the Southern Baltic Sea and its Holocene Development

Coastal low-lying areas along the southern Baltic Sea provide good conditions for coastal peatland formation. During the Holocene, the transgression of the Littorina Sea has caused coastal flooding, submergence and erosion of ancient coastlines and former terrestrial material. The present Heiligensee & Hütelmoor peatland (located near Rostock in Northern Germany) was found to continue more than 90 m in front of the coastline based on on- and offshore sediment cores and geo-acoustic surveys. The seaward areal extent of the peatland is estimated with 0.16-0.2km2. The offshore limit of the former peatland roughly coincides with the offshore limit of a dynamic coast-parallel longshore bar, with peat deposits eroded seawards. While additional organic-rich layers were found further offshore below a small sand ridge system, no connection to the former peatlands can be established based on 14C age and C/N ratios. The preserved submerged peat deposits with organic carbon contents of 37 % in front of the coastal peatland Heiligensee & Hütelmoor was radiocarbon-dated to 6725 +/- 87 and 7024 +/-73 cal yr BP, respectively, indicating an earlier onset of the peatland as presently published. The formation time of the peat layers gives information about the local sea level rise. The local sea level curve derived from our 14C-dated organic-rich layers is in general agreement to nearby sea level reconstructions (North Rügen and Fischland, Northern Germany), with differences explained by local isostatic movements.

Side scan sonar backscatter mosaic offshore Thailand, Andaman Sea

The attached mosaics of side scan sonar data were recorded during 3 field campaigns in 2007, 2008 and 2010. High backscatter values are represented by darker colours. The mosaic is georeferenced in EPSG:32647 - WGS 84 / UTM zone 47N. Please refer to Feldens, P.; Schwarzer, K.; Sakuna, D.; Szczuciński, W.; Sompongchaiyakul, P. Sediment distribution on the inner continental shelf off Khao Lak (Thailand) after the 2004 Indian Ocean tsunami. <em>Earth, Planets and Space,</em> 2012, 64, 875-887; DOI:10.5047/eps.2011.09.001 and Sakuna-Schwartz, D.; Feldens, P.; Schwarzer, K.; Khokiattiwong, S.; Stattegger, K. Internal structure of event layers preserved on the Andaman Sea continental shelf, Thailand: tsunami vs. storm and flash-flood deposits. <em>Natural Hazards and Earth System Sciences,</em> 2015, 15, 1181-1199; DOI:10.5194/nhess-15-1181-201 and Feldens, P., Schwarzer K., Sakuna-Schwartz, D., Khokiattiwong, S. (in prep) Offshore geomorphological evolution in Phang Nga province (Thailand) during the Holocene: An example for a sediment starving shelf and references therein for further information on the dataset. The research was funded by Deutsche Forschungsgemeinschaft (DFG) grant No. SCHW 572/11-1 and National Research Council of Thailand (NRCT)

Living cold water corals off Morocco: Water Masses and Bathymetry

No abstract is provided for this article.

Coastal zones of the Baltic Sea in the Anthropocene: Current state and the impact of climate change

From detection to complexity: AI boulder mapping enables structural analysis of Baltic Sea reefs

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 &amp;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.