Towards Better Mapping of Seagrass Meadows using UAV Multispectral and Topographic Data

James, D.; Collin, A.; Houet, T.; Mury, A.; Gloria, H., and Le Poulain, N., 2020. Towards better mapping of seagrass meadows using UAV multispectral and topographic data. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1117-1121. Coconut Creek (Florida), ISSN 0749-0208Seagrasses are exposed to global changing such as ocean warming and acidification, sedimentation, eutrophication and direct degradation. Seagrass meadows are traditionally monitored with visible imagery in red-green-blue (RGB). To date, the acquisition methodology dedicated to coastal habitats ranges from satellite to SoNAR through LiDAR sensors. This research proposes to evaluate the contribution of unmanned airborne vehicle (UAV) multispectral bands (red edge - RE -, and near-infrared - NIR) and digital surface model (DSM) to the habitat classification based on the traditional red-green-blue (RGB) dataset. Surveyed by a fixed-wing UAV (eBee+ provided with the Sequoia and S.O.D.A. sensors), five classes were studied: water, Zostera marina L. eelgrass, boat, sediment, and algae. Supported by the maximum likelihood algorithm, the classifications showed a very satisfactory performance for the RGB benchmark (93.64%), and gains when the spectral bands and DSM were added separately (RE+NIR contributions: +4.46%, DSM contribution: +4.78%) and jointly (RE+NIR+DSM contributions: +5.84%). The contributions of the spectral and topographic data were further discussed at class level, with an emphasis on the boat-driven seagrass fragmentation (15% of the meadow).