Visual Methods for Monitoring Mesophotic-to-Deep Reefs and Animal Forests: Finding a Compromise Between Analytical Effort and Result Quality

Visual methods based on remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are increasingly used to study and monitor mesophotic-to-deep benthic marine ecosystems. To date, these techniques are frequently used to meet the requirements for benthic habitat mapping of most national and international directives and marine ecosystem management programs (e.g., Marine Strategy Framework Directive (MSFD) and OSPAR Convention), by supporting the exploration of taxonomical composition of biological communities, the identification of ecologically relevant habitat, and the identification of areas of priority for conservation. However, the processing of visual data is challenging in terms of analytical time, with automatic and semi-automatic methods that require ad hoc sampling strategies and/or instrumentation. Therefore, video survey analysis of benthic marine habitat is largely restricted to a limited subset of photograms, often extracted manually. By comparing video frame extractions performed at regular time and distance intervals, this chapter explores how ROV video subset methods may affect the estimation of the substrate cover extent and the taxonomical composition of the biological communities, with the aim to identify an efficient compromise between analytical effort and quality of results.