Alexandre Carmelo Gregory Schimel

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.

Comparison of a Self-Processed EM3000 Multibeam Echosounder Dataset with a QTC View Habitat Mapping and a Sidescan Sonar Imagery, Tamaki Strait, New Zealand

Abstract A methodology for automatically processing the data files from an EM3000 multibeam echosounder (Kongsberg Maritime, 300 kHz) is presented. Written in MatLab, it includes data extraction, bathymetry processing, computation of seafloor local slope, and a simple correction of the backscatter along-track banding effect. The success of the latter is dependent on operational restrictions, which are also detailed. This processing is applied to a dataset acquired in 2007 in the Tamaki Strait, New Zealand. The resulting maps are compared with a habitat classification obtained with the acoustic ground-discrimination software QTC View linked to a 200-kHz single-beam echosounder and to the imagery from a 100-kHz sidescan sonar survey, both performed in 2002. The multibeam backscatter map was found to be very similar to the sidescan imagery, quite correlated to the QTC View map on one site but mainly uncorrelated on another site. Hypotheses to explain these results are formulated and discussed. The maps and the comparison to prior surveys are used to draw conclusions on the quality of the code for further research on multibeam benthic habitat mapping.

Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters

Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.

Multibeam sonar backscatter data processing

Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the implementation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.

Potential influence of shells on multibeam backscatter imagery within the Te Matuku Marine Reserve, New Zealand

Backscatter imagery from a KONGSBERG EM3000 multibeam echosounder (300kHz) has been analyzed to investigate its potential for benthic habitat mapping. A MATLAB code has been developed to process both bathymetry and coregistered echo‐strength, which includes a simple correction to remove the typical multibeam echo strength swathe contrast introduced by the backscatter incident‐angle dependence. Although the correction algorithm does completely remove the effect, it is sufficient to generate maps of suitable quality for further analysis. The processing code was applied to a dataset from a survey performed on two sites in the Tamaki Strait, New Zealand, in August 2007, including a section of the Te Matuku marine reserve. Within the marine reserve, a 400m2 area displays a strong reflectivity that contrasts with its surroundings while a past ground‐truth data collection shows the full area is dominated by mud with only some differences in shell fragments distribution. Hypotheses to explain the origin of this r...