Boulders on the seabed in coastal marine environments provide key geo- and ecosystem functions and services. They serve as natural coastal protection by dissipating wave energy, and they form an important hard substrate for macroalgae, and hence for coastal marine reefs that serve as important habitats for fish. The aim of this study was to investigate the possibility of developing an automated method to classify boulders from topo-bathymetric LiDAR data in coastal marine environments. The Rødsand lagoon in Denmark was used as study area. A 100 m × 100 m test site was divided into a training and a test set. The classification was performed using the random forest machine learning algorithm. Different tuning parameters were tested. The study resulted in the development of a nearly automated method to classify boulders from topo-bathymetric LiDAR data. Different measure scores were used to evaluate the performance. For the best parameter combination, the recall of the boulders was 57%, precision was 27%, and F-score 37%, while the accuracy of the points was 99%. The most important tuning parameters for boulder classification were the subsampling level, the choice of the neighborhood radius, and the features. Automatic boulder detection will enable transparent, reproducible, and fast detection and mapping of boulders.

The Baltic Sea is one of the largest brackish water bodies in the world (Segerstråle 1957) with a number of basins varying from almost fresh water in the northern part of the Bothnian Bay via the more brackish conditions in the southern part to the saline waters of the Kattegat. The Baltic Sea is subject to severe environmental degradation caused by commercial and leisure activities, including fisheries, dredging, tourism, coastal development and land-based pollution sources. This causes severe pressures on vulnerable marine habitats and natural resources, and a tool for aiding marine management is therefore strongly needed. The marine landscape concept presented by Roff &Taylor (2000) is based on the use of available broad-scale geological, physical and hydrographical data to prepare ecologically meaningful maps for areas with little or no biological information. The concept, which was elaborated by Day & Roff (2000) was applied in UK waters (Connor et al. 2006) before it was adopted by the BALANCE project described here. The aim of developing marine landscape maps is to characterise the marine environment of the Baltic Sea region (the Baltic Sea together with the Kattegat) using geophysical and hydrographical parameters. Such maps can be applied, for example, to an assessment of the Baltic-wide network of marine protected areas, and thus provide a sustainable ecosystem-based approach to the protection of the marine environment from human activities, and contribute to the conservation of marine biodiversity. The BALANCE project is based on transnational and cross-sectoral co-operation with participants from nine countries surrounding the Baltic Sea as well as Norway (Fig. 1), and is partially financed by the European Union through the BSR INTERREG IIIB programme.

Using the Mesolithic site of Tudse Hage in the Great Belt of Denmark, this paper proposes a generic stepwise process to create geoarchaeological models that output seamless morphology maps in a GIS. This was achieved using remote sensing databases and the collection of marine geophysical data, above and below the seabed. On the basis of these data, key areas, with sediment sequences representative of the postglacial transgression surfaces, were identified. Core samples were taken for palaeoenvironmental analysis and dating that enabled a reconstruction of the relative sea-level changes. Using this information, palaeogeographic coastline maps of the Kongemose, late Kongemose, Ertebølle, and Neolithic periods in the Tudse Hage area were prepared, and potential hotspots for archaeological sites were proposed. Since their inundation, submerged prehistoric archaeological sites have been, and are, dynamic, with anthropogenic and natural processes affecting their stability and preservation. With the advocation of in situ preservation as a means of managing underwater cultural heritage, predicting where sites have survived these processes, and where they can be found, in advance of subsea development or other anthropogenic exploitation, is essential. Future natural threats to sites preserved in situ were determined through the modelling of seabed currents and sediment erosion.

We report the outcomes of a comprehensive study of the potential consequences of the implementation of the EU Maritime Spatial Planning Directive (MSPD) in Danish waters. The analyses are anchored in a framework developed in support of data-driven Ecosystem-Based Maritime Spatial Planning. The data for the models include not only human stressors but also information on the distribution of ecosystem components ranging from planktonic communities over benthic communities to fish, seabirds and marine mammals. We have established a baseline, based on state-of-the-art data sets, with respect to combined effects upon ecosystem components. Future scenarios for the developments in human stressors were estimated for 2030 and 2050 based on information on existing policies, strategies and plans and were compared to the baseline. In addition, we developed a scenario for implementation of the Marine Strategy Framework Directive (MSFD), i.e. working towards meeting the objectives of Good Environmental Status. Our results indicate that (1) combined human stressors will possibly increase in 2030 and 2050 compared to the baseline, (2) increased combined human stressors are likely to lead to a worsening of the environmental and ecological status sensu the Marine Strategy Framework Directive and the Water Framework Directive (WFD), and (3) the MSPD implementation process appears to conflict with the MSFD and WFD objectives. Accordingly, we are sceptical of claims of an untapped potential for Blue Growth in Danish marine waters.

Detailed maps of the seabed and knowledge of its habitats are critical for a wide range of tasks, such as sustainable development, and environmental protection. Boulders on the seabed form an important environment for ecosystems, but the detection of them is challenging. In this study, we aim to improve the understanding of boulder predictors and to determine connections between predictors and boulder environments on different spatial scales. The Relief-F filter feature selection algorithm was used on four 30 m × 30 m areas in Rødsand lagoon, containing one boulder each, to determine the most relevant predictors. The predictors could be divided into four groups detecting different boulder characteristics: colour contrast, height, boulder boundaries, and spherical geometry. Twelve different types of boulder environments were evaluated. Bare, spherical boulders on sandy seabeds can be predicted from all four predictor groups. It is not possible to detect non-spherical boulders on seabed covered by vegetation. The best predictors for boulder detection depend on the shape and size of the boulder and the surrounding sediment and vegetation. The predictors were evaluated on a larger 400 × 2500 m area. When up-scaling the boulder detection area, larger seabed structures may affect the results. Therefore, knowledge about these structures can be used to remove errors and uncertainties from machine learning input data.