Seafloor Habitat Mapping by Combining Multiple Features From Optic and Acoustic Data: A Case Study From Ganquan Island, South China Sea

Abstract

Seafloor habitat mapping plays an important role in marine environment monitoring and marine geological research. Optic and acoustic remote sensing are becoming common survey tools in seafloor habitat mapping. However, a single acoustic or optic technique may have a limited detection range and be more susceptible to the impact of image quality. Additionally, it is challenging to satisfy the requirements for accurate detection since single-source data cannot fully reflect the substrate distribution characteristics. This article developed a method for detecting coastal seafloor habitats through the fusion of multiscale optics and acoustics data. First, the original feature set was composed of multispectral satellite data and bathymetric data by multibeam echo sounder and airborne light detection and ranging at different scales, which improved the capacity to represent feature information. Then, a ReliefF-mRMR method was implemented to select optimal features with appropriate scales and remove redundant features. Finally, the optimal features were employed in model training and classification of several supervised classifiers to verify the effectiveness of the strategy. The developed method was applied to the Ganquan Island survey in the South China Sea. The results demonstrated that, after integrating multisource data, the accuracies were up to 3.31% and 17.28% higher than those obtained using multispectral data or bathymetric data alone, respectively. ReliefF-mRMR exhibited better performance than other feature selection methods. The average coral coverage in the study area was estimated to range from 70.85% to 80.33%. This research highlights the greater potential of multisource data for precisely detecting seafloor habitats.