Accurate Mapping of an Extended Shell Bed Area in the North Sea with Multi-spectral Multibeam Backscatter Data

Journal Article (2025)
Author(s)

Q. Bai (TU Delft - Operations & Environment)

Sebastiaan Mestdagh (TU Delft - Operations & Environment)

A. Amiri-Simkooei (TU Delft - Operations & Environment)

Thaiënne A.G.P. van Dijk (TU Delft - Aircraft Noise and Climate Effects)

M. Snellen (TU Delft - Control & Operations)

Operations & Environment
DOI related publication
https://doi.org/10.5194/isprs-Archives-XLVIII-2-W10-2025-7-2025
More Info
expand_more
Publication Year
2025
Language
English
Operations & Environment
Issue number
2/W10-2025
Volume number
48
Pages (from-to)
7-12
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The multibeam echosounder (MBES) has been widely used in seabed mapping, considering its ability to collect continuous and broad-scale seabed measurements efficiently. The presence of shellfish or dead shell material can alter the geophysical properties of the sediment and thus affect the MBES backscatter intensity, making acoustic surveys with the MBES a potential non-invasive solution for regularly monitoring the benthic habitats of shellfish aggregations. Although there exists an increasing interest in mapping marine benthos with MBES measurements recently, the use of multi-spectral backscatter data is still limited. Thus, this research aims to enhance the acoustic mapping of benthic habitats using multi-spectral MBES data, with a focus on a shell bed region in the Dutch North Sea. With backscatter measurements from three frequencies, 90, 300, and 450 kHz, we achieved seabed classification in two steps. First, a semi-supervised backscatter completion was conducted to generate full-coverage backscatter data for each incident angle, mitigating the limited overlap between adjacent survey lines. We then classified the multi-Angle backscatter data from each individual frequency using the Gaussian Mixture Model. Our results indicate an improved seabed classification performance compared to the classical Bayesian method. Comparisons of classification maps across frequencies also show their different abilities to distinguish the shell bed region from other coarse sediments, demonstrating the value of leveraging multi-spectral backscatter data in seabed habitat mapping.