## 9 Discussion  
A thorough evaluation process clearly showed that automated acoustic habitat mapping can be highly accurate when using multi-platform swath-sonars for shallow habitat mapping. MBES alone proved capable of separating most seafloor types, but it suffered from low coverage in shallow waters. This made lower-frequency SSS a very important complementary system, as its transmit geometries allow for full coverage even in very shallow habitats. We take advantage of the very low grazing angles that low-frequency SSS can achieve to maximize the scanned area. At the same time, we gain dense bathymetric information along with additional backscatter data from the MBES. This combines into four layers of information; three backscatter mosaics at different frequencies and bathymetry. Although SSS and MBES data cannot be precisely co-registered, how important this is for habitat mapping studies needs to be compared with the desired resolution. For each dataset, the accuracy in the positions of sonar measurements on the ground must ideally be smaller than their spatial resolution. In our case, where quite large seafloor segments define the baseline spatial scale for the analysis, positional accuracies are always comparatively higher. Regarding the system selection per mapping subject and area, SSS offers the fastest possible mappings in terms of survey time, retaining wide swaths in shallow depths at lower frequencies and it preserves high textural content in the acquired images. The above makes it suitable for shallow habitats with highly textured patchy seagrasses on a plain seafloor. At the same time, common dual-frequency SSS offer the opportunity for a multi-spectral perception of the seafloor. While low frequencies show the highest overall contrast between different seafloor types, a consideration of all frequencies permits for an improved interpretation of subtle sea-floor features. Multi-spectra backscatter proved to be advantageous for the separation of seabed types where the volume scattering versus the isotopic  
Figure 14: Per habitat type classification accuracy comparison between the _data models_ that correspond to single-sonar mapping using either SSS (_m3_) or MBES (_m4_, _m6_ and _m8_) system) and the _data model m10_ which regards the multi-sonar mapping scenario. _Data models_ are described in Figure 5 while “Bs” stands for backscatter. scattering is of importance, i.e., when there is some shallow layering just below the seafloor as in the case of sparse seagrasses with extended rhizomes (e.g., _Cymodocean nodosa_). However, due to the lack of auxiliary depth and incidence angle information, traditional SSS offers less capabilities for the application of a full range of geometric and radiometric corrections and the classification of habitats in rough terrains (e.g., reefs and steep slopes) is less accurate. In the contrary, despite the lower resolution and coverage that common MBES provide, they have been proven to offer the best trade-off between habitat discrimination and survey cost, as it is a many in one system. Given the recent advances in MBES Mills-Cross technology, offering snippet imagery with centimeter-accuracy and wideband operation e.g., [32], one can get much more predictive results than the ones derived by the older Elac system used in this study. One should consider though that it would have been impossible for any MBES to provide a full coverage mapping without the object-oriented analysis that was made feasible through the full coverage low frequency SSS mosaic. The above arguments imply that while MBES takes the lead in habitat mapping works at present, SSS still remains an invaluable tool for higher resolutions and coverages in shallow waters that is always worth employing. Regarding the habitat composition in NMPZ, measurements show that \"Posidonia beds\" cover 27% (22.5 km\({}^{2}\)) of the NMPZ seafloor, _C.