Seafloor Mapping Using Underwater Remote Sensing Approaches

The investigation of the marine environment is often limited due to poor visibility of targets (like the seafloor, fish species, etc.) at various depths and spatial scales. Such challenges have been difficult to overcome due to the inherent dissipative nature of light waves in seawater (absorptive and scattering nature of seawater). Mitigating these effects has been the focus of the underwater remote sensing community for decades, but recent concurrent advances in hardware, software, and methodologies are leading to significant improvements in various domains (e.g., biology, geology, archaeology, offshore engineering, seafloor, and benthic habitat mapping). Importantly, the exploration (exploitation), documentation, and recording of underwater environments remain challenging tasks that stimulate the research, design, and development of new sensors, devices, techniques, and methods for recording underwater environments.

Seafloor mapping, exploration, and monitoring have evolved considerably over the past few decades following drastic technological developments of hydroacoustic and optical remote sensing technologies (e.g., multibeam echosounders (MBES); Synthetic Aperture Sonar (SAS), satellite-derived bathymetry (SDB), underwater photogrammetry, bathymetric LiDAR). This has resulted in major advances in the qualitative and quantitative depiction of the seafloor attributes, material properties, and benthic habitats, benefitting a wide range of scientific disciplines, from geology to biology and hydrodynamics, and enhancing our understanding of theoretical aspects, such as seafloor mechanistic processes, as well as applied science, towards environmental, restoration, conservation, and management.

In this Research Topic we welcome researchers and authors to submit original research and review articles focused on underwater remote sensing approaches, to better understand the advances & challenges of seafloor exploration. Submissions on the following topics are invited but are not limited to:
• Machine learning integrating underwater imagery and hydroacoustics to perform quantitative seafloor mapping;
• Development of imaging/ground truthing devices for the interpretation of the seafloor properties;
• Advanced acoustic seafloor classification techniques;
• Comparative analyses of seafloor predictive performance using different remote sensing approaches;
• Remote sensing of challenging environments, like subtidal natural hard substrate habitats;
• Automatic detection and spatial modeling of environmental and anthropogenic targets, like stones, piles, nodules, etc.