Classification of underwater photogrammetry data for temperate benthic rocky reef mapping. (5th June 2022) - Ternon, Q.; Danet, V.; Thiriet, P.; Ysnel, F.; Feunteun, E.; Collin, A.
Abstract: The fine characterization of the substrate is a baseline to thoroughly investigate the relations between organisms and their biotopes. Cutting edge spatial technologies now provide access to accurate information on biotopes and biocenoses both in terrestrial and in marine environments. Photogrammetry is one of them and has recently been applied in submarine environments especially in shallow clear water. In this study, we investigated the potential of photogrammetry to characterise benthic habitats in turbid environments. Although more challenging, turbid environments are more frequent in temperate marine coastal areas. We selected two rocky sites in the bay of Saint-Malo (Brittany, France), differentiated by their level of turbidity, one being a marine site exposed to natural tides (Buharats), while the other (Bizeux) is subjected to both natural tides and artificial currents created by the functioning of a hydroelectric dam. The different substrates observed were classified into eight classes at a centimetre resolution using photogrammetry-based spatial and multispectral predictors. The spatial benthic terrain predictors were derived from a digital surface model (DSM) at various spatial scales, and the multispectral predictors were retrieved from the red-green-blue (RGB, natural colours) orthomosaic imagery. An overall classification was computed for Buharats and Bizeux, with accuracies of 84.76% and 79.54% respectively, revealing a good quality of the substrate classification. The combination of RGB, DSM, and several spatial benthic terrain variables, with a pixel resolution of 5 and 10 mm, and a kernel size of 30, 60 and 90 pixels leads to the best benthic substrate classification (highest overall accuracy). At the class scale, producer's (PA) and user's (UA) accuracy showed that big boulders and field material were correctly distinguished. Small boulders and cobbles, having similar sizes, showed the lowest classification performances. This classification methodology provides new perspectives for mesoscale (100 m 2 to 1 km 2 ) semi-automatic mapping of the fine resolution (1 cm) relationship between benthic organisms and their substrate. Highlights: Photogrammetry is applicable for high resolution (∼1 cm) mesoscale (∼100 m 2 ) reef characterization in turbid coastal waters. Supervised classification provides good performance for benthic substrate mapping at a centimetre resolution. Resulting maps enable detection of landscape variability within and between sites.