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Hard-bottom habitats with complex topography and fragile epibenthic communities are still not adequately considered in benthic monitoring programs, despite their potential ecological importance. While indicators of ecosystem health are defined by major EU directives, methods commonly used to measure them are deficient in quantification of biota on hard surfaces. We address the suitability of seafloor imaging for monitoring activities. We compared the ability of high-resolution imagery and physical sampling methods (grab, dredge, SCUBA-diving) to detect taxonomic and functional components of epibenthos. Results reveal that (1) with minimal habitat disturbance on large spatial scales, imagery provides valuable, cost-effective assessment of rocky reef habitat features and community structure, (2) despite poor taxonomic resolution, image-derived data for habitat-forming taxa might be sufficient to infer richness of small sessile and mobile fauna, (3) physical collections are necessary to develop a robust record of species richness, including species-level taxonomic identifications, and to establish a baseline. [ABSTRACT FROM AUTHOR]

Given the need to describe, analyze and index large quantities of marine imagery data for exploration and monitoring activities, a range of specialized image annotation tools have been developed worldwide. Image annotation – the process of transposing objects or events represented in a video or still image to the semantic level, may involve human interactions and computer-assisted solutions. Marine image annotation software (MIAS) have enabled over 500 publications to date. We review the functioning, application trends and developments, by comparing general and advanced features of 23 different tools utilized in underwater image analysis. MIAS requiring human input are basically a graphical user interface, with a video player or image browser that recognizes a specific time code or image code, allowing to log events in a time-stamped (and/or geo-referenced) manner. MIAS differ from similar software by the capability of integrating data associated to video collection, the most simple being the position coordinates of the video recording platform. MIAS have three main characteristics: annotating events in real time, posteriorly to annotation and interact with a database. These range from simple annotation interfaces, to full onboard data management systems, with a variety of toolboxes. Advanced packages allow to input and display data from multiple sensors or multiple annotators via intranet or internet. Posterior human-mediated annotation often include tools for data display and image analysis, e.g. length, area, image segmentation, point count; and in a few cases the possibility of browsing and editing previous dive logs or to analyze the annotations. The interaction with a database allows the automatic integration of annotations from different surveys, repeated annotation and collaborative annotation of shared datasets, browsing and querying of data. Progress in the field of automated annotation is mostly in post processing, for stable platforms or still images. Integration into available MIAS is currently limited to semi-automated processes of pixel recognition through computer-vision modules that compile expert-based knowledge. Important topics aiding the choice of a specific software are outlined, the ideal software is discussed and future trends are presented. [ABSTRACT FROM AUTHOR]