Predicted Mapping of Seabed Sediments Based on MBES Backscatter and Bathymetric Data: A Case Study in Joseph Bonaparte Gulf, Australia, Using Random Forest Decision Tree

Wei Xu,Hao Hu,Shuwei Zheng,Heqin Cheng

doi:10.3390/jmse9090947

Abstract

Predictive mapping of seabed sediments based on multibeam bathymetric (BM), and backscatter (BS) data is effective for mapping the spatial distribution of the substrate. A robust modeling technique, the random forest decision tree (RFDT), was used to predict the seabed sediments in an area of the Joseph Bonaparte Gulf, Northern Australia, using the multibeam data and seabed sediment samples collected simultaneously. The results showed that: (1) Using multibeam bathymetry data in addition to multibeam backscatter data improves the prediction performance of the RFDT. In comparison to only multibeam backscatter data, the prediction performance achieved a ~10% improvement in sediment properties; it achieved a ~44.45% improvement of overall accuracy in sediment types, and a ~0.55 improvement in Kappa. (2) The underlying relationships between sediment properties and multibeam data show that there is an opposite non-linear correlation between sediment property-BS and sediment property-BM. For example, there is an obvious negative relationship between %mud-BS at incidence angles of 13° and 21°, but the relationship between %mud-BM is positive. As such, the RFDT is a useful and well-performing method in predicting the relationship between sediment properties and multibeam data and in predicting the distribution of sediment properties and types. However, the sediment prediction method in deep-water areas with high gravel content needs to be further evaluated.

Highlights

Seabed sediments are an important seabed interface as accurate seabed characteristics over a large area are significant in mapping benthic habitats [1,2,3,4,5], identifying seabed geological environment [6,7,8,9,10], and managing marine protected areas [11]
We found that the second experiment had good performance in overall accuracy (Figure 6a); we chose the exploratory variables including BM, backscatter at the incidence angle (BIA) at 13, 34and 37◦, which were chosen as the best combination of exploratory variables to predict the sediment types, to obtain the probability density functions (PDFs)
We found that if sediment property had a positive correlation with BS, it was negatively correlated with BM, and if sediment properties had a negative correlation with BS, it was positively correlated with BM (Figure 3)

Summary

Introduction

Seabed sediments are an important seabed interface as accurate seabed characteristics over a large area are significant in mapping benthic habitats [1,2,3,4,5], identifying seabed geological environment [6,7,8,9,10], and managing marine protected areas [11]. Various methods have been used for automatic classification and mapping, including decision trees (DT) [30,33,34,35,36], random forest decision tree (RFDT) [37,38,39,40,41], support vector machines (SVM) [42,43], artificial neural networks (ANN) [44], QTC Multiview [26,27], clustering [45] and maximum likelihood classifier (MLC) [35,43] Of these methods, the RFTD, which uses the bagging process for performance evaluation [37], is used to directly obtain the overall prediction accuracy. Whether the acoustic type of the supervised classification is consistent with the true substrate type of the sample area remains to be studied

Methods

Results

Conclusion