Unlocking a Fish Finder for Benthic Habitat Characterization

Autonomous underwater vehicles (AUVs) are increasingly being deployed in the study of inshore coastal marine habitats. Combined with shipboard systems, scientists are able to make in situ measurements of water column and benthic properties. In CSIRO autonomous gliders are used to collect water column data, while surface vessels are used to collect bathymetry information through the use of swath mapping, bottom grabs, and towed video systems. Although these methods have provided good data coverage for coastal and deep waters beyond 50m, there has been an increasing need for autonomous in-situ sampling in waters less than 50m. In addition, the collection of benthic and water column data has been conducted separately, and requires scientists to post-process data in lab. A new system was needed to allow for in-situ observations of both benthic habitat and water column properties in shallow waters. CSIRO has developed an AUV (Starbug X) to deliver enhanced observation capabilities of both benthic habitats and water column properties. The system is equipped with a range of built-in sensors as well as two sets of cameras allowing for the collection of multi-parameter observations. Crucially, the in-situ collection of benthic and water column data allow scientists to better relate water quality to changes in the benthic habitat. This paper discusses the development and use of a software tool that streamlines the analysis and visualization of vehicle multi-channel data stream; the STarbUg Analysis & Reporting Tool (STUART). In addition, future developments in vision based navigation, mission planning, and the integration of water column data is discussed. Finally the paper discusses how Starbug X and future STUART development fill a much needed capability gap in CSIRO's existing integrated ocean observing systems.

During the search for the missing Malaysian Airlines Flight 370 (MH370), Fugro acquired the largest extent of high-resolution bathymetry in the southern Indian Ocean to date. Integration of these recently released multibeam echosounder (MBES) backscatter, bathymetry, water column, and sub-bottom profiler (SBP) data reveal additional insights into the characteristics of the Indian Ocean seafloor and the geologic and oceanographic processes that shaped it. From June 2014 to February 2017, the M/V Fugro Equator, the M/V Fugro Supporter, and to a lesser extent, others, collected MBES and sub-bottom data over 710,000 square kilometers in the search area and along transit lines using Kongsberg EM122 MBES systems. We use Fledermaus GeoCoder to process backscatter data, and ArcGIS to create mosaics of all survey lines. Kingdom SMT is used to interpret SBP data. Water column data are interpreted in Fledermaus Midwater. Fledermaus is then used to create a 3D representation of the dataset. In this presentation, we will highlight several prominent regional seafloor features, and illustrate insights gained by integrating MBES backscatter and water column data with bathymetric analyses. Backscatter data to the north of the southern flank of Broken Ridge illustrate the complexity to which sediment has been reworked downslope where intricate patterns of low backscatter intensities are observed. Here, exposed rocks form prominent high backscatter reflectors amid the surrounding low backscatter sediments. The lateral extent of high backscatter intensity reflectors south of the Diamantina reveals the expansiveness of exposed igneous rocks that resulted from seafloor spreading. Volcanic features, including off-axis volcanoes and leaky transforms are also interpreted as high backscatter anomalies in the tectonic spreading fabric to the north of the Geelvinck fracture zone, towards the southern extent of the dataset. These examples show that by integrating the entire suite of data collected by MBES and SBP systems, more detailed interpretations of the geologic processes that shaped the seafloor may be gained than by examination of bathymetry alone.

Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors.

The structure of benthic macrophyte habitats is known to indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats enables us to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes and describe processes that are behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. A big challenge in remote sensing mapping of benthic habitats is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study, the benthic habitat classification scheme was defined for the study areas in the relatively turbid north-eastern Baltic Sea coastal environment. Two different classification methods—image-based and the spectral library—method were used for image classification. The image-based classification method can provide benthic habitat maps from coastal areas, but requires extensive field studies. An alternative approach in image classification is to use measured and/or modelled spectral libraries. This method does not require fieldwork at the time of image collection if preliminary information about the potential benthic habitats and their spectral properties, as well as variability in optical water properties exists from earlier studies. A spectral library was generated through radiative transfer model HydroLight computations using measured reflectance spectra from representative benthic substrates and water quality measurements. Our previous results have shown that benthic habitat mapping should be done at high spatial resolution, owing to the small-scale heterogeneity of such habitats in the Estonian coastal waters. In this study, the capability of high spatial resolution hyperspectral airborne a Compact Airborne Spectrographic Imager (CASI) sensor and a high spatial resolution multispectral WorldView-2 satellite sensor were tested for mapping benthic habitats. Initial evaluations of habitat maps indicate that image-based classification provides higher quality benthic maps compared to the spectral library method.

This study aims to map three main benthic habitats (coral, seagrass, and sand) in Kapota Atoll (Wakatobi, Indonesia) using single-beam echosounder (SBES) Simrad EK15. Eight acoustic parameters are used as classification aThis study aims to map three main benthic habitats (coral, seagrass, and sand) in Kapota Atoll (Wakatobi, Indonesia) using a single-beam echosounder (SBES) Simrad EK15. The acoustic data were processed using Sonar5-Pro ​​software. Eight acoustic parameters were used as input for the classification and prediction of benthic habitats, including depth (D), five acoustic parameters of the first echo (BD, BP, AttSv1, DecSv1, and AttDecSv1), and cumulative energy of the second and third echoes (AttDecSv2 and AttDecSv3). The classification and prediction process of benthic habitats uses two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), in XLSTAT Basic+ software. The study results show that 49 combinations of acoustic parameters produce benthic habitat maps that meet the minimum accuracy standards for benthic habitat mapping (≥60%). Using eight acoustic parameters produces a more accurate benthic habitat map than using only two main SBES parameters (DecSv1 and AttDecSv2 parameters or E1 and E2 in the RoxAnn system indicating the roughness and hardness indices). The RF and SVM algorithms produce benthic habitat maps with the highest accuracy of 79.33% and 78.67%, respectively. Each acoustic parameter has a different importance for the classification of benthic habitats, where the order of importance of each acoustic parameter in the overall classification follows the following order: AttDecSv2 > D > DecSv1 > BD > AttDecSv3 > AttSv1 > AttDecSv1 > BP. Overall, using more acoustic parameters can significantly improve the accuracy of benthic habitat mapsinput, including depth (D), five acoustic parameters of the first echo (BD, BP, AttSv1, DecSv1, and AttDecSv1) and cumulative energy of the second and third echoes (AttDecSv2 and AttDecSv3). The classification and prediction process of benthic habitats uses two machine learning algorithms, namely Random Forest (RF) and Support Vector Machine (SVM). The study results show that using eight acoustic parameters produces a more accurate benthic habitat map than using only two main SBES parameters (as in the RoxAnn system: roughness and hardness indices). The RF and SVM algorithms produce benthic habitat maps with the highest accuracy of 79.33% and 78.67%, respectively. Each acoustic parameter has a different importance for the classification of benthic habitats, where five acoustic parameters have the highest importance for the overall classification, namely AttDecSv2, D, DecSv1, BD, and AttDecSv3.

With the increasing accuracy of survey equipment and the strict survey standards in modern times, the use of sophisticated equipment for hydrographic surveys has become standard practice. Multibeam echosounders (MBES) are an essential part of the inventory. This paper presents an analysis of two data sets collected using MBES and examines their applicability for wreck investigation. While the topic of MBES and wreck investigation is vast, this study is limited to two types of MBES in shallow waters. The analysis of the data sets indicates that MBES can effectively be used for wreck investigation in shallower waters when utilizing water column and backscatter data, adhering to the rigorous survey standards of today. This approach offers valuable insights for enhancing wreck investigation capabilities and meeting the demands of modern hydrographic surveys.

<p>Kelp forests worldwide are under ever increasing pressure from anthropogenic impacts including kelp harvesting, pollution, and higher sea surface temperatures due to climate change. Marine spatial planning requires accurate mapping of these habitat types to inform effective policy. Key data needed for benthic habitat maps to inform policy are acquired by multibeam echosounders, which collect high resolution bathymetry and backscatter of the seafloor. An additional and previously little used product of high resolution MBES are mid-water backscatter data, termed water-column data, that have been used to identify and map kelp species that extended above the seafloor. We show that incorporating water-column data as a variable for modeling benthic marine habitat distributions can significantly improve the accuracy of benthic habitat maps, specifically where habitat categories include large species of macroalgae on shallow (2-34m) subtidal reefs. The study site has full coverage multibeam bathymetry, backscatter and water-column data, alongside comprehensive observation surveys of benthic habitats using towed video. All towed video observations were classified using a hierarchal marine biotope classification scheme. Water-column data were processed into a mosaic-like product representing the acoustic energy in a layer 0-1m above the seabed. This processing included filtering of the sidelobe artefact. The volumetric water-column mosaic along with bathymetric and backscatter derivatives combined with towed video observations were used as input variables in a supervised random forest classification algorithm to create habitat maps for the study site. Variable importance was assessed for all variables and water-column performed well as it was retained in all models. Including water-column data increased overall map accuracy up to 1.18% and improved producer class accuracies that contained macroalgae up to 2.95%. With increasing pressure on temperate macroalgal communities due to a synergy of pressures arising from warming oceans, our work provides a timely advance for mapping and monitoring changes.</p>

Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (Macrocystis pyrifera) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping.

Water column imaging of multibeam echosounder systems (MBES) are sensitive and potential devices for investigating free gas bubbles release and their ascent up the water column. These data could demonstrate previously undetected characteristics on the water’s surface and seabed which are related to Sustainable Development Goal 14 about life below water. The research utilizes a MBES to map the volume of seabed gas emission bubbles in the Adriatic Sea, Italy, using water column data. The survey covered 1.5 km2 around a four-legged gas platform at a depth of 77 meters. To achieve a 50% overlap, ten parallel transects of 1.5 km each were used, with a vessel speed range of 2-2.6 m/s and a transect spacing of 100 m. Acoustic waves from seabed seepage were visualized using water column data, with reflection intensities ranging from −63.5 dB to 29 dB, reflecting the acoustic reflectance of various suspended materials. More precise thresholds were obtained by filtering and clustering the gas bubbles using the point weight approach to separate them from the noise and water bubbles. The uneven Digital Terrain Model (DTM) indicates gas emissions through water column data. The volume of gas bubbles was determined by visualizing the points in a 3D format using XYZ coordinates. Through interpolation techniques and 3D volumetric analysis, six bubble locations were obtained with volumes of 651.12 m3, 108.30 m3, 42.00 m3, 167.20 m3, 186.00 m3, 287.81 m3, and 45.00 m3. This study is crucial because it questions the methods of Carbon Capture and Storage (CCS) by investigating the discharge of carbon into the sea. Furthermore, this study helps to identify emission sources, measure the volume of released gas, and explain the depth distribution of emissions, providing essential data for marine CCS assessment.

Seepage of gases from natural sources of subsurface hydrocarbon accumulations escaping through the seafloor to the water column is recorded in many parts of the world’s oceans. These occurrences can be acoustically and visually observed using various sensors onboard various platforms. This is particularly common when carrying out bathymetric surveys of large areas using multibeam echosounder systems with the capability of recording the whole water column acoustic backscattering. The so-called “water-column data” that these systems produce can then be inspected for acoustic anomalies that are characteristic of gas seepages (i.e. acoustic “gas flares”), and those instances and their attributes (e.g. strength, confidence, height, etc.) can be recorded in a database. The Geological Survey of Norway has been building such a database for the Norwegian offshore since 2010. To date, this database includes over 5,000 flares of varying magnitudes and sizes, detected in an area of >140,000 km2. The water-column data used for this task mainly originates from the many multibeam surveys carried out since 2005 over large areas of the Norwegian offshore for the MAREANO program, which is aimed at mapping habitats, but also from datasets acquired in associated projects and sources.We present the results from these comprehensive surveys and discuss the various challenges faced in making such a database. Our main challenges are the very large size of the datasets and our reliance on visual interpretation, which necessitate dedicated software, high-performance processing systems, storage solutions of very large capacity and fast access, considerable interpretation time, and procedures of cross-validation between different interpreters. Another challenge is the variety of the data and its quality due to various acquisition parameters, weather conditions, and water depths, but also from the use of various systems, models, generations, and frequencies. This variety impacts the visual aspect of acoustic gas flares and thus affects the ability of the interpreters to consistently estimate flare magnitude and size. However, this variety also presents research opportunities. For example, we possess several instances of acoustic gas flares that were imaged with a range of frequencies, allowing for frequency dependence analysis. Finally, we will discuss future possibilities for interpreting water-column data in more time-efficient and interpreter-independent manners.

Autonomous underwater vehicles (AUVs) are becoming commonplace in the study of inshore coastal marine habitats. Combined with shipboard systems, scientists are able to make in-situ measurements of water column and benthic properties. In CSIRO, autonomous gliders are used to collect water column data, while surface vessels are used to collect bathymetry information through the use of swath mapping, bottom grabs, and towed video systems. Although these methods have provided good data coverage for coastal and deep waters beyond 50m, there has been an increasing need for autonomous in-situ sampling in waters less than 50m deep. In addition, the collection of benthic and water column data has been conducted separately, requiring extensive post-processing to combine data streams. As such, a new AUV was developed for in-situ observations of both benthic habitat and water column properties in shallow waters. This paper provides an overview of the Starbug X AUV system, its operational characteristics including vision-based navigation and oceanographic sensor integration.

Rapid climate change is impacting Canada’s northern coastlines, including northern fish, benthic ecosystems, and ecosystem services. Ongoing environmental pressures continue to influence the social, cultural, and physiological well-being of Labrador Inuit, who are intrinsically linked with the marine environment. Collaborating closely with the Nunatsiavut Government, this research presents a detailed map of benthic faunal assemblages in an understudied northern inshore system, providing essential information on benthic habitats and incorporating community-identified fishing locations for ogak (Greenland cod). A total of 75 drop-camera transects unveiled 44,809 organisms belonging to 50 morphotaxa clustered into three distinct faunal assemblages. Fishing locations were represented in two of three assemblages, which were heterogeneous and composed mainly of pebbles, boulders, and rhodolith beds. The unrepresented assemblage was homogeneous and composed entirely of fine sediments. Numerous benthic taxa, potentially sensitive to environmental disturbances, were identified, including tube-dwelling anemones, large sea squirts, erect bryozoans, and extensive rhodolith beds. Insufficient data on benthic species and their associated habitats limit our comprehension of species distributions, abundances, and functional roles in northern waters, creating obstacles for effective self-governance. This research identifies the distribution and structure of benthic habitats in a culturally and economically important region of the Labrador coast, feeding directly into conservation and management strategies of marine habitats in Nunatsiavut that are currently coping with the pressures of climate change in Nunatsiavut.

The influence of geomorphology and sedimentary processes on shallow-water benthic habitat distribution: Esperance Bay, Western Australia

It is known that the structure of benthic macrophyte and invertebrate habitats indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats makes it possible to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes, and describe the processes behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. Our previous results clearly demonstrated that remote sensing methods can be used to map water depth and distribution of taxonomic groups of benthic algae (e.g., red, green, and brown algae) in the optically complex coastal waters of the Baltic Sea. We have as well shown that benthic habitat mapping should be done at high spatial resolution owing to the small-scale heterogeneity of such habitats in Estonian coastal waters. Here we tested the capability of high spatial resolution hyperspectral airborne image in its application for mapping benthic habitats.

Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed.