Description: With regard to current and projected sea-level rise, sandy shorelines are under increasingly strong pressure. In order to compensate the loss of material at threatened coastlines, soft coastal protection measures such as beach nourishment are considered to be environmentally-friendly and sustainable way, all over the world. This results in a higher demand of nourishing material, which is frequently extracted from the near-shore seafloor. In order to predict long- and short-term impacts of such mining on the seafloor habitats, as well as the potential for natural regeneration, we investigated the largest extraction area in the German Bight the (Westerland Dredging Area, WDA). Here, sand mining began in 1984. We conducted several measurement campaigns between the years 2008 and 2016 using a set of high-resolution hydroacoustic techniques including multibeam echo-sounder and sidescan sonar. For ground-truthing, sediment samples and underwater videos were taken. The measurements show that up to approximately 20 m deep pits with a diameter of particularly more than 1 km were dredged into the seafloor. The depressions caused by this sand mining are still detectable more than 30 years later. The formerly steep slopes at the fresh dredging pits flattened out due to slope failures and spilt sand after only a few months. Grain-size analyses revealed that mainly fine sand entered the pits in this early phase. However, after approximately one year, muddy sediments, most likely of terrigenous origin, dominate the deposition. Since the sedimentation rates of this muddy material seem to be relatively slow, a complete refill of the post-dredging pits is likely to take many decades. A natural regeneration towards the former seafloor conditions is only visible at the edges of the very oldest dredging pits. Underwater video recordings and samples show that new habitats for e.g. brittle stars were established on the muddy seafloor. These organisms are not normally found in such sand-dominated coastal areas in high abundances. The same applies to stone deposits lying on the sandy seafloor at the edges of the dredging zones. These were separated from the sand during the extraction process. Stones on the seafloor provide protection for many species and might form important hotspots for biodiversity in this area.

Description: Recent studies on seafloor mapping have presented different modelling methods to map and classify marine sediment distribution. However, are these methods classify different sediment classes the same way? And how do we choose the right model for a certain set of sediment classes? In this study, we aim to address these issues by using ensemble modelling to map the distribution of different sediment class on a dynamic, shallow continental shelf. Our data were derived from side-scan mosaics and multibeam data repeatedly collected from 2016 to 2018 in the Sylt Outer Reef (German Bight). We used a probabilistic approach for each class separately and then compared the predicted probability for each class, to see which class is more likely to be assigned to the location. Each sediment class was predicted using a combination of different classification modelling techniques, and then the result of these models was ensembled to produced one final prediction. This approach avoids selecting one single method, limits model selection bias and can provide information on the trends and variation across models. Furthermore, we also looked on the temporal changes in sediment distributions by comparing the sediment class predictions from 2016 to 2018. Our analysis suggest that combining different modelling techniques (i.e. random forest, boosting regression trees etc.) provide higher predictive accuracy than using one single modelling method. The resulting sediment distribution maps are more objective and are produced faster than manual delineated maps often considered by stakeholders. We also identify some limitations in having small sample size and we proposed that by combining certain models and choosing the proper amount of pseudo-absence or background data can address this issue.

Description: 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 varying water depth. However, as they are generally towed behind the survey vessel, they tend to have lower positioning accuracy, which makes them unreliable for change detection analyses. In this study, we present a workflow that processes SSS data in a way that makes them fit for change detection analyses. To test the capacity of SSS mosaics for change detection, we used a free software called “Digital Shoreline Analysis System”, which was developed by the United States Geological Survey for ArcGIS version 10.4 onwards. The methods were applied in three areas in the Sylt Outer Reef, German Bight, North Sea. Our results showed that with appropriate processing, SSS mosaics could be used for change detection of sharp sediment boundaries. We found a common trend in the sediment distribution patterns of coarse sediments by monitoring the movement of their boundaries. The boundaries moved in northeast-southwest direction and boundary movements of less than 20 m were typically observed. The methods presented here are semi-automated, repeatable, and replicable, which has potential for wide-scale monitoring of sediment distribution patterns.

Description: 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.

Description: A new approach on optimizing and rectifying backscatter images from side-scan sonar were applied in the seafloor images of a marine protected area in the German part of the North Sea, to monitor bedform change. Hydro-acoustic data from 2017 to 2018 of side-scan and multibeam echosounder were evaluated using the Digital Shoreline Analysis System (DSAS) to quantify the movement pattern of submarine bedforms. Results were verified with grain-size analyses and underwater video footages. The new method provides the rate of change of bedform movement, net bedform movement, and linear regression rate, which can assist in the environmental monitoring of the marine protected area.