Resumen
A new method for multibeam echosounder (MBES) data analysis is presented with the aim of improving habitat mapping, especially when considering submerged aquatic vegetation (SAV). MBES data were acquired with 400 kHz in 1?8 m water depth with a spatial resolution in the decimeter scale. The survey area was known to be populated with the seagrass Zostera marina and the bathymetric soundings were highly influenced by this habitat. The depth values often coincide with the canopy of the seagrass. Instead of classifying the data with a digital terrain model and the given derivatives, we derive predictive features from the native point cloud of the MBES soundings in a similar way to terrestrial LiDAR data analysis. We calculated the eigenvalues to derive nine characteristic features, which include linearity, planarity, and sphericity. The features were calculated for each sounding within a cylindrical neighborhood of 0.5 m radius and holding 88 neighboring soundings, on average, during our survey. The occurrence of seagrass was ground-truthed by divers and aerial photography. A data model was constructed and we applied a random forest machine learning supervised classification to predict between the two cases of ?seafloor? and ?vegetation?. Prediction by linearity, planarity, and sphericity resulted in 88.5% prediction accuracy. After constructing the higher-order eigenvalue derivatives and having the nine features available, the model resulted in 96% prediction accuracy. This study outlines for the first time that valuable feature classes can be derived from MBES point clouds?an approach that could substantially improve bathymetric measurements and habitat mapping.