New Feature Classes for Acoustic Habitat Mapping—A Multibeam Echosounder Point Cloud Analysis for Mapping Submerged Aquatic Vegetation (SAV)

Philipp Held,Jens Schneider von Deimling

doi:10.3390/geosciences9050235

Philipp Held, Jens Schneider von Deimling

Open Access

https://doi.org/10.3390/geosciences9050235

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Abstract

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.

Highlights

We propose that new features that are directly derived from ungridded, native Point cloud (PCL) provide additional discriminatory power for habitat mapping, as these PCLs usually hold more information than downsampled gridded data
The seabed is frequently populated with the seagrass Zostera marina as visible in the aerial photography (Figure 1) and ground-truthed by scuba seagrass
submerged aquatic vegetation (SAV) is commonly found in the photic zone; optic remote sensors struggle to detect it in deeper waters

Summary

Introduction

Multibeam echosounders (MBES) were invented in the 1960s with the first commercial devices for deep water measurements entering the market in the 1970s [1,2,3]. In addition to military purposes, multibeam’s objective was to perform accurate and efficient depth soundings, and to contribute to a better interpretation of the seabed that came along with the broader coverage, three-dimensional (3D) surface generation, and visualization that the multibeam can offer. Within the last two decades, great progress has been made in antenna array design, signal processing, and bottom detection, as well as in positioning and motion sensing, which enables highly accurate and high resolution depth measurements to be obtained today, especially at shallow water depths [4]. Multibeam systems were able to form 16 beams only across a 45-degree swath, resulting in 16 soundings or points per ping.

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