Guidelines for the study of the epibenthos of subtidal environments

These Guidelines for the Study of the Epibenthos of Subtidal Environments document a range of sampling gears and procedures for epibenthos studies that meet a variety of needs. The importance of adopting consistent sampling and analytical practices is highlighted. Emphasis is placed on ship‐based techniques for surveys of coastal and offshore shelf environments, but diver‐assisted surveys are also considered

On Importance of Acoustic Backscatter Corrections for Texture-based Seafloor Characterization

Seafloor segmentation and characterization based on local textural properties of acoustic backscatter has been a subject of research since 1980s due to the highly textured appearance of sonar images. The approach consists of subdivision of sonar image in a set of patches of certain size and calculation of a vector of features reflecting the patch texture. Advance of multibeam echosounders (MBES) allowed application of texture-based techniques to real geographical space, and predicted boundaries between acoustic facies became experimentally verifiable. However, acoustic return from uncalibrated MBES produces artifacts in backscatter mosaics, which in turn affects accuracy of delineation. Development of Geocoder allowed creation of more visually consistent images, and reduced the number of factors influencing mosaic creation. It is intuitively clear that more accurate backscatter mosaics lead to more reliable classification results. However, this statement has never been thoroughly verified. It has not been investigated which corrections are important for texture-based characterization and which are not essential. In this paper the authors are investigating the Stanton Banks common dataset. Raw data files from the dataset have been processed by the Geocoder at different levels of corrections. Each processing resulted in a backscatter mosaic demonstrating artifacts of different levels of severity. Mosaics then underwent textural analysis and unsupervised classification using Matlab package SonarClass. Results of seafloor characterization corresponding to varying levels of corrections were finally compared to the one generated by the best possible mosaic (the one embodying all the available corrections), providing an indicator of classification accuracy and giving guidance about which mosaic corrections are crucial for acoustic classification and which could be safely ignored

Marine Evidence-based Sensitivity Assessment (MarESA) – A Guide

The Marine Evidence-based Sensitivity Assessment (MarESA) methodology was developed by the Marine Life Information Network (MarLIN) team at the Marine Biological Association of the UK. The following guide details the approach, its assumptions, and its application to sensitivity assessment. The guide discusses: • key terms used in sensitivity assessment; • the definitions and terms used in the MarESA approach; • its assumptions; • the definition of resistance, resilience and sensitivity; • the definition of pressures and their benchmarks; • the step by step process by which the possible sensitivity of each feature (habitat, biotope or species) to each pressure is assessed; • the interpretation and application of evidence to sensitivity assessments on a pressure by pressure basis; and • limitations in the application of sensitivity assessments in management. The MarESA methodology provides a systematic process to compile and assess the best available scientific evidence to determine each sensitivity assessment. The evidence used is documented throughout the process to provide an audit trail to explain each sensitivity assessment. Unlike other expert-based approaches, this means that the MarESA assessments can be repeated and updated. The resultant 'evidence base' is the ultimate source of information for the application of the sensitivity assessments to management and planning decisions. The MarESA dataset and MarLIN website represent the largest review of the potential effects of human activities and natural events on the marine and coastal habitats of the North East Atlantic yet undertaken

Examining the Links between Multi-Frequency Multibeam Backscatter Data and Sediment Grain Size

Publication history: Accepted - 13 April 2021Acoustic methods are routinely used to provide broad scale information on the geographical distribution of benthic marine habitats and sedimentary environments. Although single-frequency multibeam echosounder surveys have dominated seabed characterisation for decades, multifrequency approaches are now gaining favour in order to capture different frequency responses from the same seabed type. The aim of this study is to develop a robust modelling framework for testing the potential application and value of multifrequency (30, 95, and 300 kHz) multibeam backscatter responses to characterize sediments’ grain size in an area with strong geomorphological gradients and benthic ecological variability. We fit a generalized linear model on a multibeam backscatter and its derivatives to examine the explanatory power of single-frequency and multifrequency models with respect to the mean sediment grain size obtained from the grab samples. A strong and statistically significant (p < 0.05) correlation between the mean backscatter and the absolute values of the mean sediment grain size for the data was noted. The root mean squared error (RMSE) values identified the 30 kHz model as the best performing model responsible for explaining the most variation (84.3%) of the mean grain size at a statistically significant output (p < 0.05) with an adjusted r2 = 0.82. Overall, the single low-frequency sources showed a marginal gain on the multifrequency model, with the 30 kHz model driving the significance of this multifrequency model, and the inclusion of the higher frequencies diminished the level of agreement. We recommend further detailed and sufficient ground-truth data to better predict sediment properties and to discriminate benthic habitats to enhance the reliability of multifrequency backscatter data for the monitoring and management of marine protected areas.This research was funded by the Marine Institute under the Marine Research Programme by the Irish Government Cruise CE19007 Backscatter and Biodiversity of Shelf Sea Habitats (BaBioSSH) survey. Staffing was supported through the Marine Protected Area Monitoring and Management (MarPAMM) project, which is supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPM) with matching funding from the Government of Ireland, the Northern Ireland Executive, and the Scottish Government, as well as the PhD studentship through a Vice Chancellor Research Scholarship of Ulster University (U.K.)

Classifying seabed sediments using local auto-correlation features

Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. We propose in this paper a method that applies a higher-order local auto-correlation feature and a subspace method to the acoustic image provided by the side-scan sonar to classify seabed sediment automatically. In texture classification, the proposed method outperformed other methods such as a gray level co-occurrence matrix and a Local Binary Pattern operator. Experimental results show that the proposed method produces consistent maps of a seafloor

Geomorphometric characterization of pockmarks by using a GIS-based semi-automated toolbox

Pockmarks are seabed depressions developed by fluid flow processes that can be found in vast numbers in many marine and lacustrine environments. Manual mapping of these features based on geophysical data is, however, extremely time-consuming and subjective. Here, we present results from a semi-automated mapping toolbox developed to allow more efficient and objective mapping of pockmarks. This ArcGIS-based toolbox recognizes, spatially delineates, and morphometrically describes pockmarks. Since it was first developed, the toolbox has helped to map and characterize several thousands of pockmarks on the UK continental shelf, especially within the central North Sea. This paper presents the latest developments in the functionality of the toolbox and its adaptability for application to other geographic areas (Barents Sea, Norway, and Malin Deep, Ireland) with varied pockmark and seabed morphologies, and in different geological settings. The morphometric characterization of vast numbers of pockmarks allows an unprecedented statistical analysis of their morphology. The outputs from the toolbox provide an objective, quantitative baseline for combining this information with the geological and oceanographical knowledge of individual areas, which can provide further insights into the processes responsible for their development and their influence on local seabed conditions and habitat

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Understanding the marine environment : seabed habitat investigations of the Dogger Bank offshore draft SAC

This report details work carried out by the Centre for Environment, Fisheries and Aquaculture Science (Cefas), British Geological Surveys (BGS) and Envision Ltd. for the Joint Nature Conservation Committee (JNCC). It has been produced to provide the JNCC with evidence on the distribution and extent of Annex I habitat (including variations of these features) on the Dogger Bank in advance of its possible designation as a Special Area of Conservation (SAC). The report contains information required under Regulation 7 of the Conservation (Natural Habitats, &c.) Regulations 2007 and will enable the JNCC to advise the Department for Environment, Food and Rural Affairs (Defra) as to whether the site is deemed eligible as a SAC. The report provides detailed information about the Dogger Bank and evaluates its features of interest according to the Habitats Directive selection criteria and guiding principles. This assessment has been made following a thorough analysis of existing information combined with newly acquired field survey data collected using ‘state of the art’ equipment. In support of this process acoustic (sidescan sonar and multibeam echosounder) and groundtruthing data (Hamon grabs, trawls and underwater video) were collected during a 19-day cruise on RV Cefas Endeavour, which took place between 2-20 April 2008. Existing information and newly acquired data were combined to investigate the sub-surface geology, surface sediments and bedforms, epifaunal and infaunal communities of the Dogger Bank. Results were integrated into a habitat map employing the EUNIS classification. Key results are as follows: • The upper Pleistocene Dogger Bank Formation dictates the shape of the Dogger Bank. • The Dogger Bank is morphologically distinguishable from the surrounding seafloor following the application of a technique, which differentiates the degree of slope. • A sheet of Holocene sediments of variable thickness overlies the Dogger Bank Formation. At the seabed surface, these Holocene sediments can be broadly delineated into fine sands and coarse sediments. • Epifaunal and infaunal communities were distinguished based on multivariate analysis of data derived from video and stills analysis and Hamon grab samples. Sediment properties and depth were the main factors controlling the distribution of infauna and epifauna across the Bank. • Epifaunal and infaunal community links were explored. Most stations could be categorised according to one of four combined infaunal/epifaunal community types (i.e. sandy sediment bank community, shallow sandy sediment bank community, coarse sediment bank community or deep community north of the bank). • Biological zones were identified using modelling techniques based on light climate and wave base data. Three biological zones, namely infralittoral, circalittoral and deep circalittoral are present in the study site. • EUNIS level 4 habitats were mapped by integrating acoustic, biological, physical and optical data. Eight different habitats are present on the Dogger Bank. This report also provides some of the necessary information and data to help the JNCC ultimately reach a judgement as to whether the Dogger Bank is suitable as an SAC. In support of this process the encountered habitats and the ecology of the Dogger Bank are compared with other SACs known to contain sandbank habitats in UK waters. The functional and ecological importance of the Dogger Bank as well as potential anthropogenic impacts is discussed. A scientific justification underlying the proposed Dogger Bank dSAC boundary is also given (Appendix 1). This is followed by a discussion of the suitability and cost-effectiveness of techniques utilised for seabed investigations of the Dogger Bank. Finally, recommendations for strategies and techniques employed for investigation of Annex I sandbanks are provided

Acoustic data optimisation for seabed mapping with visual and computational data mining

Oceans cover 70% of Earth’s surface but little is known about their waters. While the echosounders, often used for exploration of our oceans, have developed at a tremendous rate since the WWII, the methods used to analyse and interpret the data still remain the same. These methods are inefficient, time consuming, and often costly in dealing with the large data that modern echosounders produce. This PhD project will examine the complexity of the de facto seabed mapping technique by exploring and analysing acoustic data with a combination of data mining and visual analytic methods. First we test the redundancy issues in multibeam echosounder (MBES) data by using the component plane visualisation of a Self Organising Map (SOM). A total of 16 visual groups were identified among the 132 statistical data descriptors. The optimised MBES dataset had 35 attributes from 16 visual groups and represented a 73% reduction in data dimensionality. A combined Principal Component Analysis (PCA) + k-means was used to cluster both the datasets. The cluster results were visually compared as well as internally validated using four different internal validation methods. Next we tested two novel approaches in singlebeam echosounder (SBES) data processing and clustering – using visual exploration for outlier detection and direct clustering of time series echo returns. Visual exploration identified further outliers the automatic procedure was not able to find. The SBES data were then clustered directly. The internal validation indices suggested the optimal number of clusters to be three. This is consistent with the assumption that the SBES time series represented the subsurface classes of the seabed. Next the SBES data were joined with the corresponding MBES data based on identification of the closest locations between MBES and SBES. Two algorithms, PCA + k-means and fuzzy c-means were tested and results visualised. From visual comparison, the cluster boundary appeared to have better definitions when compared to the clustered MBES data only. The results seem to indicate that adding SBES did in fact improve the boundary definitions. Next the cluster results from the analysis chapters were validated against ground truth data using a confusion matrix and kappa coefficients. For MBES, the classes derived from optimised data yielded better accuracy compared to that of the original data. For SBES, direct clustering was able to provide a relatively reliable overview of the underlying classes in survey area. The combined MBES + SBES data provided by far the best accuracy for mapping with almost a 10% increase in overall accuracy compared to that of the original MBES data. The results proved to be promising in optimising the acoustic data and improving the quality of seabed mapping. Furthermore, these approaches have the potential of significant time and cost saving in the seabed mapping process. Finally some future directions are recommended for the findings of this research project with the consideration that this could contribute to further development of seabed mapping problems at mapping agencies worldwide