Next Article in Journal
Insight into Heterogeneous Calcite Cementation of Turbidite Channel-Fills from UAV Photogrammetry
Next Article in Special Issue
How Do Continuous High-Resolution Models of Patchy Seabed Habitats Enhance Classification Schemes?
Previous Article in Journal
Potential Instability of Gas Hydrates along the Chilean Margin Due to Ocean Warming
Previous Article in Special Issue
Optimizing an Inner-Continental Shelf Geologic Framework Investigation through Data Repurposing and Machine Learning
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

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

Institute of Geosciences at Kiel University, Marine Geophysics and Hydroacoustics, Otto-Hahn-Platz 1, 24148 Kiel, Germany
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(5), 235; https://doi.org/10.3390/geosciences9050235
Received: 29 March 2019 / Revised: 15 May 2019 / Accepted: 16 May 2019 / Published: 23 May 2019
(This article belongs to the Special Issue Geological Seafloor Mapping)
  |  
PDF [7023 KB, uploaded 27 May 2019]
  |  

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. View Full-Text
Keywords: habitat mapping; submerged aquatic vegetation; multibeam echosounder; point cloud habitat mapping; submerged aquatic vegetation; multibeam echosounder; point cloud
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Held, P.; Schneider von Deimling, J. New Feature Classes for Acoustic Habitat Mapping—A Multibeam Echosounder Point Cloud Analysis for Mapping Submerged Aquatic Vegetation (SAV). Geosciences 2019, 9, 235.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top