ANALYSIS OF SINGLE BEAM, MULTIBEAM AND SIDESCAN SONAR DATA FOR BENTHIC HABITAT CLASSIFICATION IN THE

SOUTHERN BALTIC SEA

Jaroslaw Tegowskia, Natalia Gorskab,a, Aleksandra Krussb, Jaroslaw Nowakc, Piotr Blenskia

a Institute of Oceanography, University of Gdansk, al. Marszalka Pilsudskiego 46, 81-378 Gdynia, Poland, b Institute of Oceanology Polish Academy of Science, Powstancow Warszawy 55, 81-712 Sopot, Poland, c Maritime Institute in Gdansk, Dlugi Targ 41/42, 80-830 Gdansk, Poland

Jaroslaw Tegowski, Institute of Oceanography, University of Gdansk, al. Marszalka Pilsudskiego 46, 81-378 Gdynia, Poland, tel: (+48 58) 523 68 82, fax: (+48 58) 523 66 78, e-mail: tegowski@iopan.gda.pl Abstract: Benthic habitat characterization is important for the study and conservation of the biodiversity of the Baltic ecosystem. The main objective of this paper is the development of complementary acoustic techniques for monitoring of Baltic benthic habitats. The study area was located in the southern Baltic Sea and characterised by a considerable diversity of geomorphologic forms and benthic assemblages. The simultaneous registration of the acoustical data was conducted with two single-beam echosounders working at different frequencies, a multibeam echosounder and a sidescan sonar. The high resolution multibeam data were used to estimate seabed corrugation, a crucial feature for bottom surface characterization. To identify morphological forms and benthic habitats, a parametric approach was applied to the multibeam data. Firstly, spectral, wavelet, and fractal parameters were computed in windows sliding along the separated bathymetric transects. The vectors of computed parameters were then used as an input into Principal Component Analysis and subsequently to fuzzy C-means clustering classification system. Moreover, angular dependency of the backscattering intensity was analysed. Also the information from single beam echosounders and sidescan sonar was utilised. The classification algorithms were validated with video records and biological sampling.

Keywords: benthic habitats, multibeam echosounder, classification algorithms

1. INTRODUCTION

The acoustical maps, containing the information 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 and allow to predict accurately the impact of anthropogenic activities on the habitats. Taking it into account, the sets of acoustical backscattering data was collected by complementarily used different acoustical tools as multibeam echosounder, sidescan sonar, sub-bottom profiling system and single beam echosounders. Special attention was focused on the narrow euphotic zone of the depth between 4-20m elongated parallel to the Polish cost and containing different forms of benthic habitats. The total length of the surveyed area was about 220 km and of a width slightly above 1 km. For the habitat mapping purpose a special test polygon was chosen. The surveyed test site featured a diversified seafloor geomorphologic forms and associated habitats and was located 1.2 km NE of the Rowy harbour (see Fig.1.).

a) b)

Fig.1: The study area - measurement polygon located 1.2 km NE of the Rowy harbour (f=5440'02N, l= 1703'10E).

In the Polish marine areas, bottom covered with boulders and pebbles is rare, and therefore

the area of boulder field located near the Rowy harbor stands out against the practically bare of benthic fauna and vegetations Polish inshore zone. The high biological values of this area are undoubtedly a great impact on the varied morphology of abrasion platform with boulders and pebbles scattered over the surface of the bottom, allowing attachment of organisms [1]. Moreover, the small depth of the area provides favorable light conditions for plants and indispensable conditions for photosynthesis.

The acoustical measurements were accompanied by biological and geological sampling and video inspection. The classification methodology were concentrated on the multibeam echosounder data, which have been developed in two directions. The first classical method utilised the shape parameters of the angular dependency of the backscattering intensity, when the second method used the shape parameters of the bathymetric transects computed in a sliding window. The acoustical data were integrated with the collected biological and geological data in order to verify the developed classification algorithms.

1.1. MORPHOLOGY OF SEAFLOOR TEST AREA

The polygon is located in the inshore area within the zone of bottom relief having polygenetic origin with relicts of periglacial forms together with contemporary forms of marine origin. The polygon depth varies from ~4m up to ~14m. The right map in Fig.1. shows the investigated area with depicted MBES imagery of measurement polygon.

The bottom surface is rough and varied with clearly formed embankment of ~300m in width stretching in the NW-SE direction. The highest part of the embankment at depths of about 4-7m, is a abrasive platform, with many young inselbergs and stony gravely abrasive pavement on the bottom surface. The embankment slopes are furrowed with numerous, relatively broad erosion gorges. The south slope is relatively short (up to 50m) and adjoins to the shore slope, which arose as a result of sand accumulation. The north slope 100m long at a depth of approximately 15m becomes a nearly flat surface of accumulated marine sands and fine-grained sands and muddy sands. This is an area of relict moraine embankment occurrence, made of till covered in stony gravely abrasive pavement and numerous relict and contemporary erosion gorges with surfaces covered with contemporary accumulative marine sands.

2. EXPERIMENT METODOLOGY

The measurements were conducted from the r/v IMOR equipped with precise navigational instrumentation, multibeam echosounder Reson 8125 (455kHz, range: 0,5m - 120m, no. of beams: 248, scan width: 120 , beam width: 0.5), chirp dual frequency sidescan sonar EdgeTech 4200SP (300/600 kHz), single beam echosounders: Simrad EK-500 (120 kHz) and BioSonics DT-X (420 kHz). The USBL underwater positioning system was used to calibrate measurements, where the acoustical signals were backscatter from biologically recognized areas. Divers collected biological samples from the eight areas 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

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.

documentation, laboratory analysis of benthic material and information from the literature [1], the characteristics of the individual stations were extracted.

It should be noted, that for the purpose of resolution enhancement, the number of acoustical transects exceeds the number which assures the needs of polygon area coverage. For that reason the spatial resolution of the bathymetric map obtained from multibeam echosounder measurements reaches 0.05m.

3. DATA PROCESSING

The MBES data processing delivers segmented maps of different geomorphologic and associated habitable areas. For this targets realisation were made two classification algorithms based on different ideas the parameterisation of the shape of angular dependency of backscattering intensity and the second method the parameterisation of the high resolution bathymetric transects.

3.1. PARAMETRICAL ANALYSIS OF BOTTOM BACKSCATTER INTENSITY

The idea of MBSE seafloor classification based on angular dependency of the backscattered intensity features is known in several classification systems [e.g. 2,3,4,5]. In contrast to these systems, the method presented here utilises only the shape parameters of the backscattering intensity computed for the separated two sides of returning MBSE signals. For each backscattering intensity function were computed 26 spectral, fractal, and wavelet transformation parameters.

The normalized power spectrum of backscattering intensity angular dependency in logarithmic form is defined as [6]:

1Alog1log 10max10

S

SAC f , (1)

where A=105 const., S() - power spectral density function and its maximum value Smax(). The classification parameters were defined as the relationships between parts of spectral density functions:

Ny

1

f

0f dfCS f ,

Ny

1

m

fm1

0ff

1 dfCS

S f , (2)

where m=2, 4, 8, 16 and fNy is the Nyquist frequency.

The spectral moments of the r-th order are very sensitive for signal shape variation are defined as:

dSm rr 0

, (3)

where S is the Fourier power spectral density with moment order of r=0, 1,..,7. The spectral widths 2, 2 and spectral skewness are defined as [7]:

40

22402

mmmmm , 1-2

1

202

mmm , 23

2

3~~

mm . (4)

The other parameter based on power spectral density function is the fractal dimension computed from the spectrum slope and defined by Mandelbrot [8] as DFFT= (5-)/2.

The next group of very useful parameters in data segmentation process are wavelet transformation coefficients and related wavelet energies. For the backscattering intensity signal, the wavelet energy content was computed using the 7-channel dyadic decomposition (scale a=2j, j=1,..,7) with a 3th-order Coiflet wavelet:

maxmin

d,23,b

bCoifj bbaCE , (6)

where C(a,b) is the wavelet transformation coefficient, bmin and bmax are boundary values of scale b (time). The energy distribution indicator - entropy hCoif3 is defined as [9]:

7

13,3,3 ln

jCoifjCoifjCoif EEh , (7)

The technique of Hurst exponent determination via the averaged wavelet coefficient method [10, 7] was used in this calculation. The Hurst exponent H and subsequently the Hausdorff dimension is equal DH,Daub7=2-H. The above defined parameters formed 26-element vectors for either side of backscattering intensity. For elimination of the excessive fluctuation of parameters values the moving average procedure was used. The strong correlation between some of the parameters required the elimination of redundant information. A Principal Component Analysis (PCA) has been applied to the data for the removal of this redundancy. The number of chosen Principal Components is determined by their summed variations. In successive calculations we used seven firsts PCs, which ensured above 96% (96.40%) of cumulative variation and resulted in a loss of less than 4% of information (Fig.3.b).

a) b)

Fig.3: PCA plot containing four clusters indicating separate features of seafloor sound reflectivity a), summed variations of first seven PCAs b).

In the next step, the Calinski-Harabasz index [11] was calculated to determine the number of clusters centers needed for the classification procedures. The Principal Components were the input to fuzzy c-means (FCM) data clustering algorithm [12]. The example of the result of this algorithm product is presented below (Fig.4).

Fig.4: Example of result of segmentation using shape parameters of the angular dependency of backscattering intensity.

3.1. PARAMETRICAL ANALYSIS OF THE SHAPE OF BATHYMETRIC TRANSECTS The second classification method utilises the information included in the shape of bottom surface. From the high resolution bathymetric 3D map of tested polygon (the white rectangle area in Fig 1.b) 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.5. Such cross-section was the object of high-pass filtration procedure necessary for elimination of the depth level dependency on the parameters values.

Fig.5: 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 section 3.1. 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 cross-sections. The segmentation procedure was almost the same as method presented in section 3.1. 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 [11], Principal Components were input to the FCM [12] 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).

4.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 angular dependency of the backscattered intensity delivers information about the reflectivity of the measured area, when the second method is strongly associated with the morphology of the investigated polygon. 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.

4. ACKNOWLEDGEMENTS

This work was supported by the Ministry of Science and Higher Education of Poland (research project no. N306296933).

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