High resolution optical and acoustic remote sensing datasets of the Puck Lagoon.

The very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic's major medieval harbours, a ship graveyard, and likely other submerged features that are yet to be discovered. Prior to this project, no comprehensive high-resolution remote sensing data were available for this area. This article describes the first Digital Elevation Models (DEMs) derived from a combination of airborne bathymetric LiDAR, multibeam echosounder, airborne photogrammetry and satellite imagery. These datasets also include multibeam echosounder backscatter and LiDAR intensity, allowing determination of the character and properties of the seafloor. Combined, these datasets are a vital resource for assessing and understanding seafloor morphology, benthic habitats, cultural heritage, and submerged landscapes. Given the significance of Puck Lagoon's hydrographical, ecological, geological, and archaeological environs, the high-resolution bathymetry, acquired by our project, can provide the foundation for sustainable management and informed decision-making for this area of interest.

Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea.

Benthic habitat mapping is a rapidly growing field of underwater remote sensing studies. This study provides the first insight for high-resolution hydroacoustic surveys in the Slupsk Bank Natura 2000 site, one of the most valuable sites in the Polish Exclusive Zone of the Southern Baltic. This study developed a quick and transparent, automatic classification workflow based on multibeam echosounder and side-scan sonar surveys to classify benthic habitats in eight study sites within the Slupsk Bank. Different predictor variables, four supervised classifiers, and the generalisation approach, improving the accuracy of the developed model were evaluated. The results suggested a very high significance for the classification performance of specific geomorphometric features that were not used in benthic habitat mapping before. These include, e.g., Fuzzy Landform Element Classification, Multiresolution Index of the Valley Bottom Flatness, and Multiresolution Index of the Ridge Top Flatness. Comparison of classification results with manual maps demonstrated that Random Forest had the highest performance of four tested supervised classifiers. Because the current needs include benthic habitat mapping for the whole area of the Polish Exclusive Economic Zone, the key findings of this study may be further applied to extensive areas in the Polish waters and other vast areas worldwide.