• NEANIAS Underwater Research Community

Advances in remotely-sensed techniques have revolutionized mapping methods and our understanding of the seabed environment. In particular, multibeam backscatter data nowadays allows developing quantitative studies on the composition of the seafloor, which represents an important baseline for habitat mapping. Usually, the acoustic response is considered as a direct proxy of sediment texture, but seepage could affect significantly this relationship. A multibeam data set from the Gulf of Cádiz, was grouped using an ISO-cluster analysis and results were compared with 80 ground-truthing stations taken inside and outside cold seepage areas. Results show significant differences between the acoustic response of sediments with the same texture depending on the presence/absence of fluid emissions. Understanding this relationship is necessary to make image-based backscatter classification that allows the production of sediment and habitat maps in areas with extensive fluid emissions such as the Gulf of Cádiz.

Advances in remotely-sensed techniques have revolutionized mapping methods and our understanding of the seabed environment. In particular, multibeam backscatter data nowadays allows developing quantitative studies on the composition of the seafloor, which represents an important baseline for habitat mapping. Usually, the acoustic response is considered as a direct proxy of sediment texture, but seepage could affect significantly this relationship. A multibeam data set from the Gulf of Cádiz, was grouped using an ISO-cluster analysis and results were compared with 80 ground-truthing stations taken inside and outside cold seepage areas. Results show significant differences between the acoustic response of sediments with the same texture depending on the presence/absence of fluid emissions. Understanding this relationship is necessary to make image-based backscatter classification that allows the production of sediment and habitat maps in areas with extensive fluid emissions such as the Gulf of Cádiz. Versión del editor

Underwater acoustic devices are widely recognized as very effective tools to remotely map and characterize the seabed and overlying habitats. Multibeam echosounder systems (MBES), in particular, deliver high-resolution co-located bathymetry and acoustic backscatter. The processing of MBES signals is a complex problem requiring knowledge of the angular dependence of the reflected signal, the bottom morphology and the water column reverberation. Especially in shallow, tidal environment, such as the lagoon of Venice (Italy), the multipath reflections and the environmental conditions affect the signal quality. Our study aims to find a repeatable methodology for submerged aquatic vegetation (SAV) imaging and its abundance assessment in a very shallow and dynamic environment. Currents, salinity and turbidity influence the acoustic characteristic of water column and benthic backscatter and the SAV detection by multibeam sonar. Combining MBES and ground truth data allowed us to estimate SAV coverage with high accuracy taking into account environmental conditions. We present the preliminary results of a 12 hours experiment in one of the channels of the Venice lagoon partly covered by SAV. The channel was repeatedly surveyed about every hour, using a Kongsberg 2040c dual head multibeam system with two frequencies: 200 kHz and 320 kHz. At the same time CTD profiles were acquired to estimate the environmental conditions, while video sampling was used for ground-truthing. New algorithms were developed for signal and image analysis of MBES signals (including water column data) for efficient and reliable SAV detection and assessment in shallow subtidal environment.

In the course of three years, the CNR-ISMAR of Naples carried out the surveys ("Lampedusa 2015", "Linosa 2016" and "BioGeoLin 2017") with the aim of studying the seabed of the insular shelf of Lampedusa, Linosa and Lampione, the three islands belonging to the Pelagie Archipelago. A common feature of all three surveys was the use of the multibeam Teledyne Reson SeaBat 7125 400 kHz (Innangi et al., 2018; Innangi et al., 2019), providing sub-centimetric resolution in the bathymetric data at that depth range between 5 to 180 m. Furthermore, the vessels employed were equipped with the same auxiliary instruments, i.e. an Oministar DGPS (for position data), an IxSea Octans 3000 (for attitude data), and a Valeport mini-SVS sound velocity probe installed near the transducer (for beam steering). For all surveys, the snippet data was logged (as backscatter information) with the same Absorption and Spread acquisition parameters. Also the data processing was the same, e.g. the snippet data was processed using FMGeocoder Toolbox (FMGT) in Fledermaus 7.6 version (QPS, 2016) to produce mosaic images with the same amplitude range, from -60 dB (lighter tones, corresponding to low backscatter) to -25 dB (dark grey tones, corresponding to high backscatter). Furthermore, ground-truth information, in the form of video-investigation (for all islands) and grab samples (only for Linosa and Lampione), were collected during the surveys. These characteristics made it possible to analyse all islands with RSOBIA (Remote Sensing Object Based Image Analysis) with the integrated information derived from backscatter data and bathy-morphological features, validated by ground-truth data (Innangi et al., 2018; Innangi et al., 2019) to produce three seabed maps, including seagrass distribution and benthoscape classification (according to Lacharité et al., 2017), and comparable to each other. Finally, it must be emphasized that the maps provided the first indication of the occurrence of rhodolith and maërl habitats at Lampione and Linosa, which are among the most important ecosystems in the Mediterranean Sea, while for Lampedusa further ground-truth data are necessary to better characterize the acoustic facies pattern of the island (Innangi et al., 2018; Innangi et al., 2019)

A huge amount of seabed acoustic reflectivity data has been acquired from the east to the west side of the southern Adriatic Sea (Mediterranean Sea) in the last 18 years by CNR-ISMAR. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of them has never been carried out. Here, we aimed at coherently interpreting acoustic data images of the seafloor to produce a benthic habitat map of the southern Adriatic Sea showing the spatial distribution of substrates and biological communities within the basin. The methodology here applied consists of a semi-automated classification of acoustic reflectivity, bathymetry and bathymetric derivatives images through object-based image analysis (OBIA) performed by using the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each cruise dataset separately, then classified and validated through comparison with bottom samples, images, and prior knowledge of the study areas.

Abstract The use of data, including bathymetry, backscatter intensity and the angular response of backscatter intensity, collected using multibeam sonar (MBS) systems to recognise seabed types was evaluated in the inner shelf of central western Sardinia (western Mediterranean sea), a site characterised by a complex seabed including sandy and gravelly sediments, Posidonia oceanica seagrass beds growing on hardgrounds (i.e. biogenic carbonates) and sedimentary substrates. A MBS survey was carried out in order to collect bathymetric and acoustic backscatter data. Ground-truth data in the form of grab samples and diver observations were collected at 57 stations, 41 of which were in non-vegetated sedimentary substrate and 16 of which were in P. oceanica beds. Sediment samples were analysed for grain size. Statistical comparison between MBS backscatter and grain size data showed that backscatter intensity is strongly affected by sediment grain size, being directly correlated to the weight percent of the coarse fraction (1–16 mm) and inversely correlated to weight percent of the finer sediments (0.016–0.5 mm). Factor analysis of grain size and acoustic data identified three sedimentary facies: sandy gravels (SG), gravelly sands (GS) and slightly gravelly muddy sands (SGMS). Each facies showed different acoustic responses in terms of backscatter intensity and shape of the angular response curves. Differences in backscatter intensity among five seabed types, including the three sediment types identified by factor analysis and P. oceanica beds, growing on hardgrounds and sedimentary substrates, were tested by analysis of variance. P. oceanica on hardgrounds, P. oceanica on sedimentary substrates and GS exhibited values of relative backscatter intensity within the same range and, consequently, it was not possible to distinguish Posidonia beds and gravelly sands only on the basis of backscatter data. The similarity of backscatter intensity from P. oceanica growing on different substrates suggests that the acoustic response is mainly linked to the leaf canopy rather than the substrate it is growing on. A map of seabed classification, including sediment types and seagrass distribution was produced through a combination of information derived from backscatter data and morphological features derived from multibeam bathymetry, which were validated by ground-truth data.

This thesis focuses on two major topics regarding acoustic seafloor classification techniques. The first topic is about acoustic class separation which affects the discriminative power of classification techniques and the quality of final results. The second topic is the spatial resolution of seafloor acoustic maps that is fundamentally coupled with acoustic class separation. The approach followed here, a) employs an advanced unsupervised classification technique and b) analyzes its implications on the angular response analysis (ARA) of acoustic backscatter. Moreover, a novel approach for improving the ARA technique is described. Applying an unsupervised Bayesian technique that performs an internal cluster validation test, we obtain objective classification of the entire backscatter dataset. This technique utilizes single-angle backscatter measurements from the middle range of the sonar swath offering better discrimination of acoustic classes. The main advantages of the Bayesian technique are that it does not require sonar calibration, it resolves along-swath seafloor variations and that it outputs ordinal categorical values for acoustic classes. Furthermore, the concept of the Hyper-Angular Cube (HAC) is applied and its results are compared with the Bayesian classification results. The HAC is built by several angular backscatter layers which can result either by interpolation of dense soundings or by normalization of backscatter mosaics at different incidence angles. The high dimensional data of the HAC is suitable for supervised classification using machine learning techniques and restricted amount of ground truth information. This approach takes angular dependence of backscatter into consideration and utilizes hydro-acoustic and ground truth data in a more efficient way than it was possible until now.

The use of multibeam echosounder systems (MBES) for detailed seafloor mapping is increasing at a fast pace. Due to their design, enabling continuous high-density measurements and the coregistration of seafloor’s depth and reflectivity, MBES has become a fundamental instrument in the advancing field of acoustic seafloor classification (ASC). With these data becoming available, recent seafloor mapping research focuses on the interpretation of the hydroacoustic data and automated predictive modeling of seafloor composition. While a methodological consensus on which seafloor sediment classification algorithm and routine does not exist in the scientific community, it is expected that progress will occur through the refinement of each stage of the ASC pipeline: ranging from the data acquisition to the modeling phase. This research focuses on the stage of the feature extraction; the stage wherein the spatial variables used for the classification are, in this case, derived from the MBES backscatter data. This contribution explored the sediment classification potential of a textural feature based on the recently introduced Weyl transform of 300 kHz MBES backscatter imagery acquired over a nearshore study site in Belgian Waters. The goodness of the Weyl transform textural feature for seafloor sediment classification was assessed in terms of cluster separation of Folk’s sedimentological categories (4-class scheme). Class separation potential was quantified at multiple spatial scales by cluster silhouette coefficients. Weyl features derived from MBES backscatter data were found to exhibit superior thematic class separation compared to other well-established textural features, namely: (1) First-order Statistics, (2) Gray Level Co-occurrence Matrices (GLCM), (3) Wavelet Transform and (4) Local Binary Pattern (LBP). Finally, by employing a Random Forest (RF) categorical classifier, the value of the proposed textural feature for seafloor sediment mapping was confirmed in terms of global and by-class classification accuracies, highest for models based on the backscatter Weyl features. Further tests on different backscatter datasets and sediment classification schemes are required to further elucidate the use of the Weyl transform of MBES backscatter imagery in the context of seafloor mapping.

Recent mapping programmes in Irish territorial waters, such as the Irish National Seabed Survey and the Integrated Mapping for the Sustainable Development of Ireland's Marine Resource programme, have generated high resolution multibeam bathymetry, backscatter and sediment sample datasets at an unprecedented resolution and coverage. Building upon previous mapping of glacial landforms on the northwest Irish continental shelf, a 1:225,000 scale map identifying contemporary bedforms has been produced between 54 degrees 40 ' N/56 degrees 10 ' N and 10 degrees 2 ' W/6 degrees 45 ' W. The analysis of bathymetric derivatives and backscatter interpretation has enabled the classification of several types of depositional feature including six sediment wave assemblages. Erosional features have also been identified across the shelf in the form of surface sediment lineations, as well as more spatially confined formations such as furrows. Based on wave asymmetry, sedimentary composition and orientation, in agreement with published modelled hydrodynamic conditions, these bedforms are assumed to be contemporary features. Data interpretation, particularly of backscatter imagery reveals that these sediments mask the acoustic signatures of an underlying glacial architecture and may alter their apparent morphology due to burying.

In this paper, a seafloor characterization technique to unravel the number of data classes using multibeam echo-sounding backscatter data has been demonstrated. An application of self-organizing maps (SOMs) to backscatter profile data has been developed to determine the number of classes. Consequently, the fuzzy C-means (FCM) method is employed thereafter, using the number of class information of the backscatter profiles segmentation. The use of the soft-computational technique for seafloor backscatter data facilitates in achieving stationary profile data sets suitable for seafloor roughness model application. The power spectral density (PSD) function of the segmented profiles provides the power law parameters ( $\beta$ and $a'$ ) through curve fitting, using power law expression. The data acquired from western continental margin of India (WCMI), off Goa, reveal five distinct classes of backscatter strength having different segment lengths. The estimated roughness parameters ( $\beta$ and $a'$ ) of the segmented profiles provide quantitative information about the area seafloor roughness. A gridded map of the estimated roughness parameter $(\beta)$ is generated using the “krigging” method. The gridded map and the class of the segmented profiles overlaid on the backscatter map have been presented, which is a first time application relevant for the understanding of the seafloor. The present study underscores a combination of soft-computational (SOM and FCM) and numerical techniques (power spectral density at short and fine scales) to effectively recognize the seafloor processes and the associated sedimentological dynamics in a complex geographical environment (including the pockmarks and faulted structures), which is subjected to strong bottom currents and seasonal upwelling.