Over the last few centuries, mapping the ocean seabed has been a major challenge for marine geoscientists. Knowledge of seabed bathymetry and morphology has significantly impacted our understanding of our planet dynamics. The history and scientific trends of seabed mapping can be assessed by data mining prior studies. Here, we have mined the scientific literature using the keyword “seabed mapping” to investigate and provide the evolution of mapping methods and emphasize the main trends and challenges over the last 90 years. An increase in related scientific production was observed in the beginning of the 1970s, together with an increased interest in new mapping technologies. The last two decades have revealed major shift in ocean mapping. Besides the range of applications for seabed mapping, terms like habitat mapping and concepts of seabed classification and backscatter began to appear. This follows the trend of investments in research, science, and technology but is mainly related to national and international demands regarding defining that country’s exclusive economic zone, the interest in marine mineral and renewable energy resources, the need for spatial planning, and the scientific challenge of understanding climate variability. The future of seabed mapping brings high expectations, considering that this is one of the main research and development themes for the United Nations Decade of the Oceans. We may expect a new higher resolution ocean seafloor map that might be as influential as The Floor of the Oceans map.

Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse.

Improvements to acoustic seafloor mapping systems have motivated novel marine geological and benthic biological research. Multibeam echosounders (MBES) have become a mainstream tool for acoustic remote sensing of the seabed. Recently, “multispectral” MBES backscatter, which is acquired at multiple operating frequencies, has been developed to characterize the seabed in greater detail, yet methods for the use of these data are still being explored. Here, we evaluate the potential for seabed discrimination using multispectral backscatter data within a multi-method framework. We present a novel MBES dataset acquired using four operating frequencies (170, 280, 400, and 700 kHz) near the Doce River mouth, situated on the eastern Brazilian continental shelf. Image-based and angular range analysis methods were applied to characterize the multifrequency response of the seabed. The large amount of information resulting from these methods complicates a manual seabed segmentation solution. The data were therefore summarized using a combination of dimensionality reduction and density-based clustering, enabling hierarchical spatial classification of the seabed with sparse ground-truth. This approach provided an effective solution to synthesizing these data spatially to identify two distinct acoustic seabed classes, with four subclasses within one of the broader classes, which corresponded closely with seafloor sediment samples collected at the site. The multispectral backscatter data also provided information in likely, unknown, sub-surface substrate differences at this site. The study demonstrates that the adoption of a multi-method framework combining image-based and angular range analysis methods with multispectral MBES data can offer significant advantages for seafloor characterization and mapping.

There is a great demand to develop new acoustic techniques to efficiently map the seabed and automate the interpretation of acoustic, sedimentological, and imaging data sets, eliminating subjectivity. Here, we evaluate the potential, limitations and complementariety of distinct supervised and automatic classification techniques in the mapping of reefs by comparing these results with a reference map. The study was carried out in the Abrolhos Continental Shelf (Eastern Brazilian Continental Margin) using a multibeam echosounder and side scan sonar (SSS) dataset. Two automatic supervised techniques were applied. A reference map was derived by detailed manual interpretation carried out by three experts. The two supervised classification techniques were: benthic terrain modeler (BTM), a morphometric classification with focus on spatial analyses of the bathymetric grid derivatives, and object-based image analysis (OBIA), a segmentation applied to the backscatter data from the SSS mosaic. Both automatic techniques obtained similar values of reef coverage area, but overestimated the reef area when compared with the reference map. The agreement between BTM and OBIA results and the reference map was 69% and 67%, respectively. Disagreement was mainly due to quantity of reef (both methods over-estimated reef), while the disagreement in spatial allocation was relatively low, it indicates that both methods are reasonable representation of the spatial patterns of reef. Efficient mapping of reef in the wider area of the Abrolhos Continental Shelf will be best achieved by a further development of automatic methods tested against reference maps obained from representative areas of the seabed. By combining the results of the two automatic methods, it was possible to create an ensemble map, which achieved better agreement with the reference dataset.

Macroalgae provide key ecosystem services, forming habitats such as kelp and fucoid forests, rhodolith beds, and coralline reefs that sustain high biodiversity. However, multiple stressors, including climate change, harmful fishery practices, and pollution and coastal urbanization are driving macroalgal diversity loss and changing species composition and abundance. This study aims to assess macroalgal representativeness, richness and endemism in 16 Marine Protected Areas (MPAs), including four oceanic islands, and test if macroalgal assemblages in MPAs are distributed along a latitudinal gradient (0–29° S) in the Southwest Atlantic. To investigate the processes underlying community patterns, β-diversity was decomposition turnover and nestedness components. The complexity of taxonomic structure was measured by taxonomic distinctness. Overall, the studied MPAs comprised 695 macroalgal taxa, about 69% of Brazilian taxa, and 36% of the endemics. Rhodophyta were dominant (449 species) in most studied areas, except at Trindade and Martim Vaz Archipelago MPA, followed by Chlorophyta (158 species) and Ochrophyta (88 species). Macroalgal species composition in MPAs varied with latitude, and not with area size. They were continuously distributed across northeastern, eastern and southeastern Brazil and oceanic island ecoregions, between Fernando de Noronha (3° S) and São Paulo Litoral Norte (23° S) MPAs. Macroalgal diversity dissimilarity among MPAs was 92%, dominated by the turnover component (88%) and nestedness as a minor component (4%), indicating that dissimilarities are mainly driven by the replacement of species, rather than a gradual loss or gain of species. Effective protection of these MPAs should be prioritized as they harbor highly diverse, unique, and heterogeneous macroalgal assemblages along the SWA, surrounded by heavily impacted areas. Assessment of human impacts on macroalgal habitats in MPAs would contribute to defining effective management actions. Mid- and offshore shelf macroalgal habitats, particularly rhodolith beds, which harbor rare kelp species and mesophotic reefs, remain underprotected, and should be integrated into marine spatial planning for biodiversity conservation.

Anais Hidrográficos da Sociedade Brasileira de Hidrografia (3086-0563) - Backscatter Acústico Do Fundo Marinho: Sintetizando Potenciais Aplicações No Contexto Da Hidrografia Brasileira