Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. Based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. Object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.

Glacial landforms are a significant element of landscape in many regions of Earth. The increasing availability of high-resolution digital elevation models (DEMs) provides an opportunity to develop automated methods of glacial landscape exploration and classification. In this study, we aimed to: 1) identify glacial landforms based on high-resolution DEM datasets; 2) determine relevant geomorphometric and spectral parameters and object-based features for the mapping of glacial landforms; and 3) develop an accurate workflow for glacial landform classification based on DEM. The developed methodology included the extraction of secondary features from DEM, feature selection with the Boruta algorithm, object-based image analysis, and random forest supervised classification. We applied the workflow for three study sites: one in Svalbard and two in Poland. It allowed the identification of six categories of glacial landforms: till plains, end moraines, hummocky moraines, outwash/glaciolacustrine plains, valleys, and kettle holes. The majority of relevant secondary features represented DEM spectral parameters calculated from 2-D Fourier analysis. The supervised classification models with the highest performance exhibited up to 96% overall accuracy with regard to a groundtruth dataset. This study showed that glacial landforms can be identified using novel image-processing methodology and spectral parameters of high-resolution DEM. The complete classification workflow developed herein provides a solution for the transparent generation of thematic maps of glacial landforms that may be reproducible and transferrable to various glacial regions worldwide.

The directionality of ambient noise in an Arctic tidewater glacier bay was measured using two horizontally spaced, broadband hydrophones. Segments of noise were divided into two frequency bands and analyzed for arrival angle. These data show that different classes of source radiate noise in distinct spectral bands and are spatially diverse. A previously unidentified source, the interaction of surface gravity waves with underside of ice ledges at the periphery of icebergs, is described. The generation of noise by ice-wave interaction suggests that surface waves should be measured if ambient noise is to be used to monitor ice dynamics in glacial fjords.

Scientific exploration of seabed substrata has significantly progressed in the last few years. Hydroacoustic methods of seafloor investigation, including multibeam echosounder measurements, allow us to map large areas of the seabed with unprecedented precision. Through time-series of hydroacoustic measurements, it was possible to determine areas with distinct characteristics in the inlets of the Lagoon of Venice, Italy. Their temporal variability was investigated. Monitoring the changes was particularly relevant, considering the presence at the channel inlets of mobile barriers of the Experimental Electromechanical Module (MoSE) project installed to protect the historical city of Venice from flooding. The detection of temporal and spatial changes was performed by comparing seafloor maps created using object-based image analysis and supervised classifiers. The analysis included extraction of 25 multibeam echosounder bathymetry and backscatter features. Their importance was estimated using an objective approach with two feature selection methods. Moreover, the study investigated how the accuracy of classification could be affected by the scale of object-based segmentation. The application of the classification method at the proper scale allowed us to observe habitat changes in the tidal inlet of the Venice Lagoon, showing that the sediment substrates located in the Chioggia inlet were subjected to very dynamic changes. In general, during the study period, the area was enriched in mixed and muddy sediments and was depleted in sandy deposits. This study presents a unique methodological approach to predictive seabed sediment composition mapping and change detection in a very shallow marine environment. A consistent, repeatable, logical site-specific workflow was designed, whose main assumptions could be applied to other seabed mapping case studies in both shallow and deep marine environments, all over the world.

Determination of High Arctic regions bathymetry is strictly dependent from weather and ice mass quantity. Due to safety, it is often necessary to use a small boat to study fjords area, especially close to glaciers with unknown bathymetry. This precludes the use of modern multi−beam echosounders, and so traditional single−beam echosounders have been used for bathymetry profiling. Adequate interpolation techniques were determined for the most probable morphological formations in−between bathymetric profiles. Choosing the most accurate interpolation method allows for the determination of geographical regionalisation of submarine elevations of the Brepollen area (inner part of Hornsund, Spitsbergen). It has also been found that bathymetric interpolations should be performed on averaged grid values, rather than individual records. The Ordinary Kriging Method was identified as the most adequate for interpolations and was compared with multi beam scan− ning, which was possible to make due to a previously modelled single beam interpolation map. In total, eight geographical units were separated in Brepollen, based on the bathy− metry, slope and aspect maps. Presented results provide a truly new image of the area, which allow for further understanding of past and present processes in the High Arctic.

High-resolution images of the seabed obtained with the use of hydroacoustic measurements allow a detailed identification of inaccessible seabed areas such as the Hans Glacier foreland in the Hornsund Fjord on Spitsbergen. Analyses presented in the paper were carried out on a Digital Elevation Model (DEM) of the bay’s seafloor exposed in the process of deglaciation, obtained from bathymetric data recorded by a multibeam echosounder. The main objective of this study was to show the relevance of the autocorrelation length parameter used to describe the roughness of the bottom surface based on the example of seafloor postglacial forms in the Hans Glacier foreland. The resulting parameter reflects the scale of the terrain roughness, which varies between geomorphologic forms. Maps of the autocorrelation length were derived from successive tiles of the data, overlapping by 90%. Based on this, the two-dimensional Fourier transform (2D FFT) was successively conducted, and the power spectral density and autocorrelation were calculated following the Wiener–Khinchin theorem. The thus obtained parameter describes the scale of the glacial bay seafloor roughness, which was assigned to the geomorphological features observed.