Advancing Antarctic Benthic Ecosystem Monitoring through Photogrammetry and Automated Recognition Technologies

Antarctic benthic ecosystems are home to a unique and diverse fauna, with high levels of endemism. Despite their ecological importance, these communities are understudied, particularly in terms of their spatial variability. The remoteness and harsh environmental conditions of Antarctica complicate efforts to understand these ecosystems. The lack of time-series data limits our comprehension of temporal variations, highlighting the need for baseline data to detect natural or anthropogenic changes. To address these challenges, recent technological advancements have introduced non-destructive methods for studying benthic dynamics and spatial patterns. This study employs improved underwater optical recording systems, optimized image sampling, and 3D mapping techniques, along with advanced software for benthic imagery analysis. Photographic and video sampling techniques are utilized to create permanent records, facilitating detailed image analyses and reducing the underwater time and expertise required for species identification. The primary goal of this research is to create a comprehensive library of Antarctic benthic organisms using AI software. This approach streamlines the identification process and reduces the need for manual classification. By integrating photogrammetry with automated organism recognition, this study aims to develop a sustainable, long-term monitoring system for Antarctic benthic ecosystems. This innovative methodology promises to enhance our understanding of temporal and spatial changes in Antarctic benthic ecosystems. The implementation of this long-term monitoring system will provide critical data to support informed conservation and management strategies in response to environmental challenges. The study's findings underscore the potential for advanced technologies to facilitate ecological research in remote and extreme environments.