Seabed substrate mapping using MBES data

Seafloor mapping is essential for effective management and sustainable development of marine resources. Various attempts have been made to map the seafloor using single beam echo sounders, multi beam echo sounders, and side scan sonars. The purpose of this study is to map the sea floor using backscatter and bathymetry based on multi-beam echo sounders. For seafloor mapping, seafloor cover was defined as rock, gravel, sand, and mud according to the folk structure, and 135 grab data were collected for seafloor mapping and accuracy evaluation. For seafloor mapping, bathymetry depth and depth-based secondary products (aspect, curvature, slope, roughness, eastness, northness, mean, standard deviation) and backscatter intensity and secondary products that can be produced from intensity (mean, variance, roughness) was established. In addition, the output of the GLCM algorithm (angular second moment, contrast, dissimilarity, energy, entropy, homogeneity, max, mean, standard deviation) was constructed to extract various features of backscatter intensity. For seafloor cover, a random forest model, a machine learning technique that shows high performance in various fields, was selected, and the ratio of training and test datasets was selected as 8:2. To improve the performance of the random forest model, a hyperparameter was selected by applying a 5-fold cross validation and grid-search method, and the overall accuracy was 0.83.