How much of the greenhouse gas methane is transported from the seafloor to the atmosphere is unclear. Here, we present data describing an extensive ebullition event that occurred in Eckernförde Bay, a shallow gas-hosting coastal inlet in the Baltic Sea, in the fall of 2014. A weak storm induced hydrostatic pressure fluctuations that in turn stimulated gas ebullition from the seabed. In a finely tuned sonar survey of the bay, we obtained a hydroacoustic dataset with exceptionally high sensitivity for bubble detection. This allowed us to identify 2849 bubble seeps rising within 28 h from the seafloor across the 90 km² study site. Based on our calculations, the estimated bubble-driven episodic methane flux from the seafloor across the bay is 1,900 μMol m⁻² d⁻¹. Our study demonstrates that storm-associated fluctuations of hydrostatic pressure induce bulk gas-driven ebullitions. Given the extensive occurrence of shallow gas-hosting sediments in coastal seas, similar ebullition events probably take place in many parts of the Western Baltic Sea. However, these are likely to be missed during field investigations, due to the lack of high-quality data acquisition during storms, such that atmospheric inputs of marine-derived methane will be highly underestimated.

Submarine groundwater discharge into coastal areas is a common global phenomenon and is rapidly gaining scientific interest due to its influence on marine ecology, the coastal sedimentary environment, and its potential as a future freshwater resource. We conducted an integrated study of hydroacoustic surveys combined with geochemical pore water and water column investigations at a well‐known groundwater seep site in Eckernförde Bay (Germany). We aim to better constrain the effects of shallow gas and submarine groundwater discharge on high‐frequency multibeam backscatter data and to present acoustic indications for submarine groundwater discharge. Our high‐quality hydroacoustic data reveal hitherto unknown internal structures within the pockmarks in Eckernförde Bay. Using precisely positioned sediment core samples, our hydroacoustic‐geochemical approach can differentiate intrapockmark regimes that were formerly assigned to pockmarks of a different nature. We demonstrate that high‐frequency multibeam data, in particular the backscatter signals, can be used to detect shallow free gas in areas of enhanced groundwater advection in muddy sediments. Intriguingly, our data reveal relatively small (typically <15 m across) pockmarks within the much larger, previously mapped pockmarks. The small pockmarks, which we refer to as “intrapockmarks,” have formed due to the localized ascent of gas and groundwater; they manifest themselves as a new type of “eyed” pockmarks, revealed by their acoustic backscatter pattern. Our data suggest that, in organic‐rich muddy sediments, morphological lows combined with a strong multibeam backscatter signal can be indicative of free shallow gas and subsequent advective groundwater flow.

A new method for multibeam echosounder (MBES) data analysis is presented with the aim of improving habitat mapping, especially when considering submerged aquatic vegetation (SAV). MBES data were acquired with 400 kHz in 1–8 m water depth with a spatial resolution in the decimeter scale. The survey area was known to be populated with the seagrass Zostera marina and the bathymetric soundings were highly influenced by this habitat. The depth values often coincide with the canopy of the seagrass. Instead of classifying the data with a digital terrain model and the given derivatives, we derive predictive features from the native point cloud of the MBES soundings in a similar way to terrestrial LiDAR data analysis. We calculated the eigenvalues to derive nine characteristic features, which include linearity, planarity, and sphericity. The features were calculated for each sounding within a cylindrical neighborhood of 0.5 m radius and holding 88 neighboring soundings, on average, during our survey. The occurrence of seagrass was ground-truthed by divers and aerial photography. A data model was constructed and we applied a random forest machine learning supervised classification to predict between the two cases of “seafloor” and “vegetation”. Prediction by linearity, planarity, and sphericity resulted in 88.5% prediction accuracy. After constructing the higher-order eigenvalue derivatives and having the nine features available, the model resulted in 96% prediction accuracy. This study outlines for the first time that valuable feature classes can be derived from MBES point clouds—an approach that could substantially improve bathymetric measurements and habitat mapping.

Seagrass meadows are crucial habitats since they serve as fish nurseries and food sources for many marine species. They prevent nearshore erosion and are an important CO2 sink. As the plants are bound to the photic zone, seagrass meadows normally populate the shallow coastal zones. Unmanned aerial vehicles (UAV) are gaining popularity within the earth sciences community. Most surveys are of terrestrial nature and carried out by using the camera of the UAV to obtain orthophotos and three-dimensional surface models of a survey area. In comparison to space-borne systems, UAVs are capable of higher resolution image quality and time independent measurements, which enables an event-based surveying approach. We here present a submarine habitat mapping study, obtained by using an UAV flying 75 m above the water surface. Within the frame of the BONUS ECOMAP project, we aim to conduct repeated UAV surveys over the seasonal cycle to observe changes within coastal seagrass bed habitats. The key study area is located in the Baltic Sea offshore Heidkate (near Kiel, Germany). For data acquisition, we are using a commercial DJI Inspire 2 UAV with a gimbal mounted 20.8 megapixel Zenmuse X5S camera with a 15 mm/ 1.7 ASPH lens. For less reflection and distortion at the air-water interface, we are using a B&W circular polarized filter. Ground control points are measured and leveled with a Leica RTK system, which has a lateral resolution of ~2 cm. We process the data with the commercial software Pix4D™ and Agisoft PhotoScan™ to compute orthomosaic images and digital elevation/surface models. Since February 2018, we were able to conduct repeated surveys offshore Heidkate and Wendtorf (Germany). The average resolution of the orthomosaic data is better than 5 cm/px. First results show that we can obtain high-resolution images of habitats within water depths less than ~4 m in the Baltic Sea. Penetration is limited to factors like wave action, suspended sediment load and angle of the solar radiation. We perform supervised classification and pattern detection for habitat identification and discrimination. The data show the presence of seagrass, algae but also rocks, which are exposed at the seafloor. All scenes show a seasonal variability of the extent of seagrass meadows which are affected by migrating sand bars and major storm events. These data are the basis for a long-term monitoring framework, we are currently establishing in the working area.