## 4 Multifrequency Backscatter Mosaics and Texture Analysis Features  
### Mosaicking MBES (180 kHz) and Dual Frequency (100 and 400 kHz) SSS Backscatter data  
In this work, we used both a textural and mean angular response approach. The textural approach is less sensitive to absolute backscatter calibration [20] but it heavily relies on radiometric and geometric corrections to produce consistent backscatter responses across the full swaths. Thus, these corrections were considered both for the MBES and SSS backscatter mosaics. MBES backscatter data were derived from the \"sidescan\" option of the Elac system, i.e., digitization of the received beams from the receive beamformer into 2048 samples. The backscatter values have been normalized to an arbitrarily chosen reference level to remove the angular dependencies, here set to the middle-range incidence angle of 45\({}^{\circ}\).  
Acoustic returns from MBES produce intense geometric and radiometric artefacts in the backscatter mosaics, which in turn affect the accuracy of facies delineation. To identify and correct radiometric and geometric MBES backscatter artifacts, we used the Geocoder tool, part of the Hypack 2016 suite. Geocoder, e.g., [18; 40; 47], uses advanced slant range correction, beam pattern compensation, speckle removal, overlap and feathering options. This leads to a drastically improved MBES mosaic of the NMPZ. Individual MBES time series were slant-range corrected based on the bathymetric model of the survey area, replacing the flat bottom assumption with an estimation of the average slope in the across-track direction of each ping. The effective incident angle (based on the bathymetric model) was corrected using Lambert's law, taking in account the changes in volume scattering and slope. A residual beam pattern correction was removed by calculating a moving average of angular responses  
Figure 4: The detailed manual habitat map generated taking all available marine acoustic and ground truth data into account. Bottom: Example underwater images of each habitat type.  
(in our case 100 pings). Overlapping used a data quality indicator, attributing low values to samples closer to and further from the nadir and high values to mid-ranges, allowing selection of the highest quality samples. A speckle noise filter and a feathering algorithm were used to reduce nadir artifacts and the seam artifact between overlapping acquisition lines. All the above processes led to a drastically improved mosaic (see Figure 3), with a pixel size of 50 \(\times\) 50 cm, suitable for producing spatially consistent results through any image segmentation or classification approach. Further details about the processes implemented in Geocoder can be found in [48]. In [40], the importance of Geocoder's acoustic backscatter corrections describe for texture-based seafloor characterization. No absolute dB values are given in the backscatter mosaic, as recommended by the GeoHab community, e.g., [9].  
The raw SSS records underwent radiometric and geometric corrections using the Isis software (Triton Imaging Inc., Capitola, CA, USA). Beam-pattern correction and ping energy level normalization were followed with slant range correction and ping by ping spatial registration. Mosaicking of both frequencies of the SSS data used the Triton Map software (Triton Imaging Inc., Capitola, CA, USA), producing mosaic with 50 cm \(\times\) 50 cm pixel size (see Figure 3).  
In Figure 3 the final seafloor backscatter mosaics at each individual frequency are shown, where clear differences in area coverage are evident between them. A closer comparison between the three individual frequencies is later presented in Figure 11, revealing clear differences in backscatter texture and intensities between the various bottom types, especially regarding the seagrass cover.