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.

Tidal channels are crucial for the functioning of wetlands, though their morphological properties, which are relevant for seafloor habitats and flow, have been understudied so far. Here, we release a dataset composed of Digital Terrain Models (DTMs) extracted from a total of 2,500 linear kilometres of high-resolution multibeam echosounder (MBES) data collected in 2013 covering the entire network of tidal channels and inlets of the Venice Lagoon, Italy. The dataset comprises also the backscatter (BS) data, which reflect the acoustic properties of the seafloor, and the tidal current fields simulated by means of a high-resolution three-dimensional unstructured hydrodynamic model. The DTMs and the current fields help define how morphological and benthic properties of tidal channels are affected by the action of currents. These data are of potential broad interest not only to geomorphologists, oceanographers and ecologists studying the morphology, hydrodynamics, sediment transport and benthic habitats of tidal environments, but also to coastal engineers and stakeholders for cost-effective monitoring and sustainable management of this peculiar shallow coastal system.

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.