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Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks. [ABSTRACT FROM AUTHOR]

Scientific management of coral reefs is a global research topic due to their latent economic value and ecological significance. This research benefits from optical and acoustic remote sensing technology with high spatial resolution, sufficient coverage, and stable repeatability. However, each sensor with its own set of limitations, such as insufficient penetration ability, limited sensor resolution and range, which often hinder their applications in the precise classification of coral reef habitats. To address these issues, a novel scheme integrating airborne laser bathymetry (ALB), multibeam echo sounder (MBES) and the multispectral image is designed to serve large-scale and high-precision coral reef habitat sediments research. This scheme focuses on valuable attribute mining, inconsistent coverage, feature default, and redundancy according to multi-source data used in engineering demands. After per-system pretreating, numerous properties, including band, waveform, intensity, terrain, and derivative characteristics, are extracted and quantitatively analyzed to be responsible for habitat description. Subsequently, a complete seabed terrain is generated using the LightGBM regression model to establish the conversion relationship between satellite images and depth. Eventually, a new benthic habitat sediments classification method called RF-SAPSO-LightGBM is introduced with a powerful generalization ability. The unique functions of automatic default feature completion and optimal parameter search improve the discrimination ability of complex sediments. The results indicate that the new method outperforms five other approaches in sediment classification accuracy, and the combination of multi-source data provides more extensive coverage and higher accuracy with an overall of 87. 9%. In comparison, the accuracy values for multispectral image and terrain data alone are 74.5% and 54.7% respectively. • Optical-acoustic sensors were more effective when used together rather than separately for mapping coral reef habitat. • The SAPSO algorithm for LightGBM parameter optimization improved the sediment classification accuracy by 7.4%. • Feature completion effectively improved the accuracy of coral reef habitat mapping. • The RMSE of the derived depth by the LightGMB regression model reached 0.89 m. [ABSTRACT FROM AUTHOR]