Testing the Improvement of Coral Reef Associated Fish Distribution Models Based on Multibeam Bathymetry by Adding Seafloor Backscatter Data

Demersal fishes constitute an essential component of the continental shelf ecosystem, and a significant element of fisheries catch around the world. However, collecting distribution and abundance data of demersal fish, necessary for their conservation and management, is usually expensive and logistically complex. The increasing availability of seafloor mapping technologies has led to the opportunity to exploit the strong relationship demersal fish exhibit with seafloor morphology to model their distribution. Multibeam echo-sounder (MBES) systems are a standard method to map seafloor morphology. The amount of acoustic energy reflected by the seafloor (backscatter) is used to estimate specific characteristics of the seafloor, including acoustic hardness and roughness. MBES data including bathymetry and depth derivatives were used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Lethrinus miniatus, Loxodon macrorhinus, Lutjanus sebae, and Scomberomorus queenslandicus. The possible improvement of model accuracy by adding the seafloor backscatter was tested in three different areas of the Ningaloo Marine Park off the west coast of Australia. For the majority of species, depth was a primary variable explaining their distribution in the three study sites. Backscatter was identified to be an important variable in the models, but did not necessarily lead to a significant improvement in the demersal fish distribution models’ accuracy. Possible reasons for this include: the depth and derivatives were capturing the significant changes in the habitat, or the acoustic data collected with a high-frequency MBES were not capturing accurately relevant seafloor characteristics associated with the species distribution. The improvement in the accuracy of the models for certain species using data already available is an encouraging result, which can have a direct impact in our ability to monitor these species.

Results from the First Phase of the Seafloor Backscatter Processing Software Inter-Comparison Project

Seafloor backscatter mosaics are now routinely produced from multibeam echosounder data and used in a wide range of marine applications. However, large differences (>5 dB) can often be observed between the mosaics produced by different software packages processing the same dataset. Without transparency of the processing pipeline and the lack of consistency between software packages raises concerns about the validity of the final results. To recognize the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain the differences become substantial. To this end, willing commercial and academic software developers were invited to generate intermediate processed backscatter results from a common dataset, for cross-comparison. The first phase of the study requested intermediate processed results consisting of two stages of the processing sequence: the one-value-per-beam level obtained after reading the raw data and the level obtained after radiometric corrections but before compensation of the angular dependence. Both of these intermediate results showed large differences between software solutions. This study explores the possible reasons for these differences and highlights the need for collaborative efforts between software developers and their users to improve the consistency and transparency of the backscatter data processing sequence.

Sources and Impacts of Bottom Slope Uncertainty on Estimation of Seafloor Backscatter from Swath Sonars

Seafloor backscatter data from multibeam echosounders are now widely used in seafloor characterization studies. Accurate and repeatable measurements are essential for advancing the success of these techniques. This paper explores the impact of uncertainty in our knowledge of the local seafloor slope on the overall accuracy of the backscatter measurement. Amongst the various sources of slope uncertainty studied, the impact of bathymetric uncertainty and scale were identified as the major sources of slope uncertainty. The bottom slope affects two important corrections needed for estimating seafloor backscatter: (1) The insonified area and; (2) the seafloor incidence angle. The impacts of these slope-related uncertainty sources were quantified for a shallow water multibeam survey. The results show that the most significant uncertainty in backscatter data arises when seafloor slope is not accounted for or when low-resolution bathymetry is used to estimate seafloor slope. This effect is enhanced in rough seafloors. A standard method of seafloor slope correction is proposed to achieve repeatable and accurate backscatter results. Additionally, a standard data package, including metadata describing the slope corrections applied, needs to accompany backscatter results and should include details of the slope estimation method and resolution of the bathymetry used.

Analysis of Seafloor Sediment Distribution using Multibeam Backscatter Data

Multibeam sonars use an acoustic wave to provide a view of seafloor topography and collect its backscatter. Seafloor backscatter image can be used as a means of quantitative classification of seafloor lithology. This allows sediment expert to examine spatial distributions of seafloor sediment types and discriminate among them. This method is expected to reduce expensive bottom-sampling programs. The research examined multibeam data collected from Indonesia Exploration 2010 Project. This project employed a Kongsberg EM302 multibeam system to collect seafloor topography and backscatter data at Sangihe Talaud Sea, North Sulawesi. The average depth of the area study is approximately 2400 m. The research applied the Angular Response Curve (ARC) method to analyze the echo strength of the backscatter as a function of the grazing angle. The range of backscatter intensity in this area is from -9dB to - 67dB. Based on the backscatter data, there are five types of sediment in this area: clay, mud, gravel, sand, and boulder.