Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars

Lamarche, Geoffroy; Lurton, Xavier

doi:10.1007/s11001-017-9315-6

Cited by 85 publications

(75 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each survey the system was calibrated against a reference seabed to make sure the backscatter level consistent across surveys. The reference seabed for each survey was chosen at a uniform, sandy and flat location, consistent with the recommendation of Lamarche and Lurton (). Backscatter processing included correction for transmission loss and ensonification area, and removal of the system implemented model and the angular dependence (Siwabessy et al, ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

et al. 2018

View full text Add to dashboard Cite

Sediment properties are known to influence acoustic backscatter intensity. This sediment-acoustic relationship has been investigated previously through using physical geoacoustic models and empirical methods and found to be complex and nonlinear. Here we employ a robust machine-learning statistical model (random forest decision tree) to investigate the most likely nonlinear sediment-backscatter relationships. The analysis uses colocated sediment and acoustic backscatter data (collected from a 300-kHz multibeam sonar system) for 564 locations in four different areas on the Australian margin. Seven sediment grain size properties (%gravel, %sand, %mud, mean grain size, sorting, skewness, and kurtosis) were used to predict the acoustic backscatter responses at individual incidence angles. The modeling results demonstrate the effectiveness of this multivariate predictive approach for the investigation of sediment-acoustic relationship. Thus, we find that for incidence angles between 1°and 41°, the sediment variables explain around 70% of variance in the backscatter intensity. Sediment mud content was found to be the most important sediment variable in the model and has a significant negative relationship with backscatter intensity. Mean grain size was the second ranked sediment variable and found to have a positive relationship with backscatter intensity. The results also show that sediment mud content plays a key role in sorting-backscatter and sand-backscatter relationships. Using only two sediment properties, mud content and mean grain size, together it was possible to largely explain the sediment-acoustic relationship. The strongest backscatter return occurred with medium sediment mud content and large mean grain sizes (or muddy coarse sand).

show abstract

Section: Methodsmentioning

confidence: 99%

“…It is important that we maintain backscatter level consistent across surveys (Lamarche & Lurton, ). For every survey in this study, the backscatter level was monitored using the build‐in self‐test procedure as part of mobilization and calibration procedure at the beginning of a survey.…”

Section: Methodsmentioning

confidence: 99%

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Therefore, despite having over 100 min of video recordings, we decided to generalize the obtained material and identify one class of ground-truth sample per specific point location. A deep investigation of the video recordings in conjunction with sediment analysis and hydroacoustic data distinguished six classes of habitats [6], which are presented in Table 1. The characteristic examples of the backscatter images shown in Table 1 occupy a spatial area of 9 m 2 and were presented using a false composite with R and G bands that corresponded to the backscatter intensity at 400 kHz and 150 kHz, respectively.…”

Section: Ground-truth Sampling and Analysismentioning

confidence: 99%

“…Figure 3A,B present the distribution of the mean backscatter intensity versus the specified habitat class for both of the frequencies used: 150 and 400 kHz. The values of the backscatter intensity were expressed as relative intensity values in the logarithmic scale in dB [6]. In general, the diagrams depicting the discrimination of the habitat classes showed a clear separation between the two groups of habitat classes.…”

Section: Discrimination Of Ground-truth Samplesmentioning

confidence: 99%

Nearshore Benthic Habitat Mapping Based on Multi-Frequency, Multibeam Echosounder Data Using a Combined Object-Based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea

et al. 2018

View full text Add to dashboard Cite

Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. Based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. Object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.

show abstract

“…Three types of calibration for backscatter data or systems can be implemented (Lamarche and Lurton 2017): (1) in-factory (Brown et al 2015); (2) absolute in-field, and (3) relative in-field (Rice et al 2015) calibrations. Factory calibration, usually conducted in tanks, is undertaken at manufacturing time and is therefore the responsibility of the manufacturers.…”

Section: Introductionmentioning

confidence: 99%

Seafloor multibeam backscatter calibration experiment: comparing 45°-tilted 38-kHz split-beam echosounder and 30-kHz multibeam data

2017

Self Cite

View full text Add to dashboard Cite

Obtaining absolute seafloor backscatter measurements from hydrographic multibeam echosounders is yet to be achieved. We propose a low-cost experiment to calibrate the various acquisition modes of a 30-kHz Kongsberg EM 302 multibeam echosounder in a range of water depths. We use a 38-kHz Simrad EK60 calibrated fisheries split-beam echosounder mounted at 45° angle on the vessel's hull as a reference for the calibration. The processing to extract seafloor backscatter from the EK60 requires bottom detection, ray tracing and motion compensation to obtain acceptable geo-referenced backscatter measurements from this non-hydrographic system. Our experiment was run in Cook Strait, New Zealand, on well-known seafloor patches in shallow, mid, and deep-water depths. Despite acquisition issues due to weather, our results demonstrate the strong potential of such an approach to obtain system's absolute calibration which is required for quantitative use of backscatter strength data.

show abstract

Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars

Cited by 85 publications

References 60 publications

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

Nearshore Benthic Habitat Mapping Based on Multi-Frequency, Multibeam Echosounder Data Using a Combined Object-Based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea

Seafloor multibeam backscatter calibration experiment: comparing 45°-tilted 38-kHz split-beam echosounder and 30-kHz multibeam data

Contact Info

Product

Resources

About