Advanced Detection and Classification of Kelp Habitats Using Multibeam Echosounder Water Column Point Cloud Data

Kelps are important habitat-forming species in shallow marine environments, providing critical habitat, structure, and productivity for temperate reef ecosystems worldwide. Many kelp species are currently endangered by myriad pressures, including changing water temperatures, invasive species, and anthropogenic threats. This situation necessitates advanced methods to detect kelp density, which would allow tracking density changes, understanding ecosystem dynamics, and informing evidence-based management strategies. This study introduces an innovative approach to detect kelp density with multibeam echosounder water column data. First, these data are filtered into a point cloud. Then, a range of variables are derived from these point cloud data, including average acoustic energy, volume, and point density. Finally, these variables are used as input to a Random Forest model in combination with bathymetric variables to classify sand, bare rock, sparse kelp, and dense kelp habitats. At 5 m resolution, we achieved an overall accuracy of 72.5% with an overall Area Under the Curve of 0.874. Notably, our method achieved high accuracy across the entire multibeam swath, with only a 1 percent point decrease in model accuracy for data falling within the part of the multibeam water column data impacted by sidelobe artefact noise, which significantly expands the potential of this data type for wide-scale monitoring of threatened kelp ecosystems.