Not to be cited without prior reference to the author

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1 Not to be cited without prior reference to the author ICES CM 2011/G:04 Acoustic studies of benthic habitats in South Baltic Sea using parametric signal processing technique for singlebeam and multibeam echosounders and side scan sonar data Jaroslaw Tegowski a,b, Jaroslaw Nowak a, Aleksandra Kruss c,, Mateusz Moskalik d, Natalia Gorska b, Kazimierz Szefler a a Maritime Institute in Gdansk, Dlugi Targ 41/42, Gdansk, Poland b University of Gdansk, Institute of Oceanography,, al. Marszalka Pilsudskiego 46, Gdynia, Poland, c University of Bath, Department of Physics, Claverton Down, BA2 7AY Bath, United Kingdom d Institute of Geophysics, Polish Academy of Science, Ksiecia Janusza 64, Warsaw, Poland Abstract: The results of a study on the development of efficient and reliable acoustic techniques for classification and monitoring of Baltic seafloor habitats are presented. The comprehensive acoustical, geological and biological investigations were performed in several areas of size up to 10 by 20 km and 220 km by 1 km (narrow euphotic zone) located in the southern Baltic Sea, characterized by diversified types of sediments, geomorphologic forms, and benthic habitats. We used multibeam and singlebeam echosounders, side scan sonar, sub-botom profiler and Van Veen sediment sampler. Such an extensive data collection needs an efficient and fast seafloor classification system, which was created and tested during this research. The basis of the system is a parametric approach to acoustic registrations. Computed spectral, wavelet, statistical and fractal features of echo signals were the input to fuzzy clustering classification and neural network classification algorithms, which produced maps containing morphologically classified seafloor areas. All of the seafloor segmentation schemes have many promising features which allow them to be applied for extracting morphological forms of seabed and habitats. The correctness of the method was verified by the results of underwater video recordings conducted from an underwater vehicle, sedimentological analyses and biological samples taken in situ. Keywords: habitat mapping, seafloor classification methodology, South Baltic Sea Contact author: Jaroslaw Tegowski, Maritime Institute in Gdansk, Dlugi Targ 41/42, Gdansk, Poland, Phone: (+48 58) , j.tegowski@ug.edu.pl

2 1. INTRODUCTION Acoustical methods are very attractive as the main source of knowledge on the shape and geological nature of the seabed itself and the benthic marine organisms present, represent an essential tool for the conservation and management of the seafloor of the Polish Exclusive Economic Zone within the Baltic Sea. Taking it into account, the sets of acoustical backscattering data was collected by used different acoustical tools. Moreover, some of acoustical measurements were accompanied by biological and geological sampling and video inspection. Over the last several years we have developed systems of acoustical classification of bottom types and sediments based on data received from the multibeam echosounders, sidescan sonars, sub-bottom penetrating sonars and single beam echosounders. Our algorithms were based on parameterization of echo signals or wavelet decomposition of acoustic image of bottom bathymetry (multibeam echosounder) or wavelet decomposition of seafloor image (side scan sonar). The algorithms can be divided as follow: Single beam echosounder - echo envelopes parameterization, computation of spectral and wavelet transformations, statistical, fractal and energy parameters of echo envelopes as the input to factor analysis and principal component analysis and subsequently to fuzzy c-means clustering and self-organised neural network classification systems used for classification of bottom sediments and recognition of vegetation coverage of bottom [e.g. 1, 2, 3, 4, 5]. Multi beam echosounder bottom bathymetry image an acoustic identification of morphological forms was performed using technique of spectral, wavelet, and fractal parameters computation in window sliding along the bathymetric transects. Parameters of bottom bathymetric imagery were the input to principal component analysis and subsequently to fuzzy c-means clustering and self-organised neural network classification systems produced classified image of the bottom [e.g. 6]. Multi beam echosounder bottom backscattered intensities - the shape parameters of the angular dependency of backscattering intensities computed for the separated two sides of returning MBSE signals. For each backscattering intensity function were computed 26 spectral, fractal, and wavelet transformation parameters as the input to fuzzy logic classification algorithm producing classified maps of bottom types and sediments [e.g. 7, 8]. Side scan sonar - the method of signal parameterization using sliding window moving along the pulse. The spectral, wavelet and fractal parameters are calculated inside the window forming parameters vectors (for each signal pulse) as the input to the image segmentation procedures (principal component analysis, factor analysis, fuzzy c- means clustering and self-organised neural network) [e.g. 9]. Side scan sonar - The method based on discrete wavelet analysis of echo signals, which creates the image of the investigated seafloor area, where two-dimensional discrete wavelet transformation results in a decomposition of input signals in approximation coefficients and the details in three orientations (horizontal, vertical, and diagonal) [e.g. 10]. Sub-bottom penetrating sonars in the method was developed and tested a classification algorithm containing a limited set of echo envelopes wavelet transformation parameters as input to a Self-organizing Neural Network. The algorithm was successfully tested against the descriptive analysis allowing in a very

3 short time a 2D mapping of the buried features of the area, distinguishing between different types of palaeochannels, buried creeks and no channel areas. [11]. Sub-bottom penetrating sonars the Hilbert-Huang Transform was applied to detect "gassy" anomalies in backscattered signals from the bottom. The transformer decomposes signal into finite and small number of Intrinsic Mode Function (IMF) components with time-dependent amplitudes and frequencies. Certain IMF components carry information on variability of geoacoustic parameters, which can be indicative of presence of gas bubbles in the acoustically penetrated sediment as well as in the water column. Based on the shape of the echo signal envelope and its fading with range we characterized the signal attenuation in areas where gas was present [12]. All presented above algorithms were tested in different areas of Baltic Sea, North Sea, Mediterranean Sea, Lake of Kinneret and Arctic Spitsbergen fjords with positive results. They were helpful in the acoustical classification of sediments, recognition of areas covered by vegetations or gassy bottom sediments and acoustic identification of seafloor morphological forms. Moreover, one algorithm was successfully tested in mapping of the buried features of the area, distinguishing between different types of palaeochannels, buried creeks and no channel areas in the Venice Lagoon [11]. Many activities related to seabed exploration as marine geology, ecology, oil industry, laying of pipelines and cables on the seafloor, designing and construction of hydrotechnical objects and navy operations need tools for fast and effective bottom recognition and habitat mapping. Hence, special attention should be paid to the rapid development of multibeam echosounders (MBES) which provide comprehensive information on the bottom surface. The high-resolution MBES systems produce two types of data - bathymetric and angular dependency of backscattered intensity. The well known dependency of measured and modelled bottom backscattered strength (BBS) on the angle of incidence is a result of relationship between scattered acoustical energy and big set of seafloor geoacoustic features directly related to sediment grain size and compactness. Moreover, the value of BBS in function of acoustical wave incident angle strongly depends on seafloor roughness and slope angle. The angular dependency of the backscatter strength is an inherent property of the seafloor and is the function of geophysical characteristics of sediment. This feature of MBES data is especially useful in the automatic bottom classification algorithms [e.g. 6, 13-16] and developed in commercial bottom classification systems [e.g. 17]. In , the Maritime Institute in Gdansk conducted exhaustive acoustical research of the bottom in the Polish Exclusive Economic Zone, (the Baltic Sea) and collected a large set of swath and subbotom data. A special attention was paid on six different areas named: Kuznica, Rozewie, Rowy, Kolobrzeg, Rewal and Slupsk Bank, which demonstrate typical seafloor features for the southern Baltic. A size of the investigated areas reached up to 10 km by 20 km. Moreover, for the coastal zone were conducted measurements in area 220 km long and more than 1 km width within euphotic zone of the depth between 4-20m parallel to the cost. The acoustical measurements were accompanied by geological sampling and video inspection. For the remote sensing recognition of morphological forms, types of sediments and benthic habitats in the explored area was necessary an effective bottom classification system based on MBES measurements, which allows monitoring, conservation and management of the Baltic shallow water seafloor.

4 Fig.1. Areas of geomorphologic investigations of Southern Baltic bottom: 1 Kuznice area, 2 Rozewie area, 3 Rowy area, 4 Slupsk Bank, 5 Kolobrzeg area, 6 Rewal area. In this work we present results of two algorithms functioning only for the example Rewal (1) and Rowy (3) areas which are located in the western and middle parts of the Polish Baltic coastal zone. The methods used were tested positive in other areas in the Polish Exclusive Economic Zone. 2. MEASUREMENT METHODOLOGY The acoustical measurements of the bottom were conducted onboard the research vessel of the Maritime Institute in Gdansk r/v Imor. The vessel is constructed especially for acoustic, geophysic and geologic marine measurements. The following equipment were used: the multibeam echosounder Reson 8125 operating at a frequency of 455 khz, range from 0,5m to 120m, no. of beams - 248, scan width - 120º and single beam width - 0.5º, chirp dual frequency sidescan sonar EdgeTech 4200SP (300/600 khz), single beam echosounders: Simrad EK-500 (120 khz) and BioSonics DT-X (420 khz) and subbotom profiler OreTech Pipeliner II 3010 with frequency 3.5 khz 14 khz. Samples of sediments (Van Veen grab and vibrosonde) were collected in many sites and additionally in some areas underwater videos were registered with a Remote Operating Vehicle (ROV). For precise calibration measurements of the echo signals scattered at the selected locations the USBL underwater positioning system was employed. At some locations information about seafloor morphological and biological features were also collected, using SCUBA-diving technique. Divers collected biological samples from the chosen areas (Rowy 3 in the Fig.1) limited by frames (1x1m) and made video recordings of benthic habitats. Figure 2 shows the metallic frame surrounding area before pick up of biological samples (left photo) and after sampling (right picture). Based on information from divers, video and photographic documentation,

5 Fig.2: The metallic frame surrounding biological measurement area before samples picking (left photo) and after samples collection (right photo). Transducer of the USBL positioning system is visible attached to the frame. laboratory analysis of benthic material and information from the literature [18], the characteristics of the individual stations were extracted. 3. PARAMETRICAL ANALYSIS OF THE SHAPE OF BATHYMETRIC TRANSECTS The first presented classification method utilises the information included in the shape of bottom surface. From the high resolution bathymetric MBES 3D map of tested Rowy polygon, 150 vertical and 150 horizontal parallel bathymetry cross- sections were extracted. An example of one bathymetric vertical cross-section taken in the middle part of investigated area is presented in Fig.3. Such cross-section was the object of high-pass filtration procedure necessary for elimination of the depth level dependency on the parameters values. Fig.3: Example of one bathymetric vertical cross-section taken in the middle part of investigated area (white rectangle in Fig.1.a). For each consecutive cross-section the shape parameters in sliding window were computed. There were 26 spectral, fractal and wavelet transformation parameters defined in [6]. The spatial resolution of such a parameterised bathymetric map were depended on sliding window width (256, 512 or 1024 samples) and the distance between consecutive crosssections. The set of parameters were object of the PCA process. After the choice of first 6 Principal Components and the computation of the Calinski-Harabasz index [19], Principal

6 Components were input to the FCM [20] classification algorithm. Fig. 6 presents a comparative set of a bathymetric map and segmented bottom imageries. Fig.6: Comparative set of a bathymetric map (a) and segmented bottom images for 3 clusters (b) four clusters (c) and five clusters (d). The MBES bottom imagery segmentation scheme presented in this study have many promising features which allow them to be applied for extracting morphological forms of seabed and habitats. Example of result is presented in Fig. 7. Fig.7: Results of bottom bathymetric transects classification for 5 clusters.

7 4. PARAMETRICAL ANALYSIS OF BOTTOM BACKSCATTER INTENSITY Parametrical analysis of bottom backscattered intensity is based on the assumption that the shape of the backscatter angular profiles reflects morphological features of bottom. Figure 8 presents three curves of backscatter angular response collected at different locations of the Rewal area. Red top line indicates angular response of signals scattered at seafloor covered by medium grained sand, green middle line fine sand and blue bottom line - boulder clay. All presented backscattered intensities are the results of the averaging procedure for 30 consecutive curves. Fig.8: Average backscattered intensity level versus transmission angle. The differences in backscatter angular profiles coming from sound scattered at different seafloor morphological types of the Rewal area are shown in a form of mosaic map of bottom backscattering intensity, which reflects geomorphological forms parallel to the NE-SW direction (Fig.9). In the map are evident patchiness containing different geomorphologic characteristics of the bottom. Fig.9: Bottom backscattering intensity Rewal area; 1 ridges medium-grained sand; 2 boulder clay and gyttja, isolated outcrops of lacustrine gyttja and mud covered with a layer of sand (up to 30cm); 3 slopes of elevations, fine and medium-grained sands.

8 For each registered angular dependency the 26 spectral and wavelet transformation parameters were computed [8]. The acoustical classification of bottom sediments and separately seafloor geomorphologic forms were made for input vector contained wavelet energies only in the first case and spectral parameters in the second one. As the classification algorithm was used the self-organised Kohonen s neural network with learning set containing 25% of the input data. Results of classification are presented in Fig. 10. a) b) Fig.10: Results of classification procedure, for seafloor geomorphologic forms a) and types of bottom sediments b). The top graph of Fig.10 presents results of classification for four clusters and correctly indicates such geomorphologic forms as ridges, slopes of elevations and outcrops of mud, where the fourth cluster can not be accurately interpreted as the geomorphologic form and is considered the artefact. The bottom graph shows seafloor areas covered by different types of sediments as medium, fine and medium and fine-grained sands and boulder clay and gyttja. Spectral parameters are not sufficiently sensitive to the weaknesses of MBES registrations as wavelet transformation registrations (lack of artefacts). Verification of classification algorithm was made on the base of sediment grab, sidescan sonar and video recordings and geological maps analyses.

9 5. CONCLUSIONS The both MBES bottom imagery segmentation schemes presented in this study have many promising features which allow them to be applied for extracting morphological forms of seabed and habitats. The first method based on the shapes of bathymetry cross-sections delivers precise information about the seafloor morphological forms, when the second method is strongly associated with the sedimentological features of the investigated polygon. The good correlation of spectral parameters with mean grain diameter of sediments indicates the possibility of its use for the sediment type classification, whereas the qualitative correspondence between seafloor forms and spatial distribution of wavelet energy allows using wavelet transformation parameters for classification of seafloor geomorphological forms [7]. Both techniques precisely indicated areas of relicts of periglacial forms as well as contemporary forms of marine origin. The results of sidescan sonar bottom imageries, echograms made using single beam echosounders, sedimentological and biological sampling, and video frames analyses, confirm precision and effectiveness of both supplementary segmentation systems. The benthic flora and fauna settled in bottom geomorphologic forms create separated habitats detectable by both systems. The correctness of the method was verified by the results of underwater video recordings, single beam echosounder registrations and biological samples taken in situ. Results of classification conducted in many areas of the Polish Exclusive Economic Zone in the Baltic Sea confirmed its usefulness for the large area marine geological and biological habitat mapping. REFERENCES [1] Tegowski J., 2006, Acoustical classification of bottom sediments, Dissertations And Monographs IO PAS, 220. [2] Tegowski J.,2005, Acoustical Classification of the Bottom Sediments in the Southern Baltic Sea, Quaternary International, 130, [3] van Walree P.A., Tegowski J., Laban C., Simons D.G., 2005, Acoustical seabed discrimination with echo shape parameters: a comparison with ground truth, Continental Shelf Research, 25, [4] Ostrovsky I., Tegowski J., 2010, Hydroacoustic analysis of spatial and temporal variability of bottom sediment characteristics in Lake Kinneret in relation to water level fluctuation, Geo-Mar Letters, 30, [5] Tegowski J., Gorska N., Klusek Z., 2003, Statistical analysis of acoustic echoes from underwater meadows in the eutrophic Puck Bay (southern Baltic Sea), Aquat. Living Resour., 16, [6] Tegowski J., Gorska N., Kruss A., Nowak J., Blenski J., 2009, Analysis of single beam, multibeam and sidescan sonar data for benthic habitat classification in the southern Baltic Sea, Proceedings of 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies & Results, July, 2009, Nafplion, Greece, Edited by: John S. Papadakis & Leif Bjorno, Volume I, II & III, ISBN: , , , , [7] Tegowski J., Nowak J., Hac B., Zamaryka M., Szefler K., 2010, Mapping seabed features from multibeam echosounder data using autocorrelation and multi-scale wavelet analyses, Hydroacoustics, 13, [8] Tegowski J., Nowak J., Moskalik M., Szefler K., Seabed classification from multibeam echosounder backscatter data using wavelet transformation and neural network approach,

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