# Nearshore Benthic Mapping in the Great Lakes:
## 4 Discussion
Supplemental or tangential analyses such as the one presented in Figures 22 and 23, comparing SDB to high-fidelity in situ measurements or high-accuracy bathymetric lidar data, helps to better understand SDB accuracy in conjunction with benthic classification outputs and in relation to locally specific coastal management activities. Other challenges specific to the progress in benthic mapping stem from the variety and increasing amounts of in situ observations, yet this also poses a tremendous opportunity, especially for moving towards semi-automation and machine learning approaches that rely on extensive ground verification data. To that end, the use of machine learning techniques for benthic mapping is a growing area of study with many documented benefits despite the tradeoffs associated with computation time and added ground validation requirements [7; 21; 22; 23; 56]. Ultimately, a compromise is needed to balance the desire for basin-wide, regionally consistent map products and the requirement for improved and increasing amounts of ground validation or ground truth data to satisfy machine learning and more automated approaches. In this study, the partnership with local experts was critical for gaining a priori knowledge to improve classification results. For example, without the cultural context of the shoreline impacts specific to the study area (Figure 24), which were provided in the ground validation data and captured within the CMECS hierarchy, the anthropogenic features identified offshore could have easily been identified as geologic coarse substrate given the spectrally similarity and shape of the materials. Figure 24 offers a historical perspective of changes in this highly dynamic area further illustrating how the data used in this study in combination with high-quality ground truth data and local knowledge can be used to monitor specific changes that have direct implications for natural resource policy-making and to inform restoration practices. Thus, it is important to think about strategic integration and application of the increasing amounts of field observations made by academia, state, local, and federal agencies, as well as many stakeholder and partner organizations to improve basin-wide mapping approaches. This ties back to the need for more consistent or standardized classification systems, in which CMECS may offer a consistent and flexible way for the application of field observation data. [PERSON] et al. [22] posits that in situ training or ground data are no longer necessary, which may be true in large homogeneous environments or areas with common benthic  
Figure 22: Plot comparison of SDB generated from Sentinel-2 imagery to airborne bathymetric lidar point cloud (30 cm vertical accuracy). features. However, in the case of the Great Lakes, there is a wide variety in nearshore substrate and biotic compositions in which the incorporation of field observations is essential for the development of accurate and reliable benthic habitat information and mapping products to support coastal and natural resources management. Figure 23: Map comparison of SDB generated from Sentinel-2 imagery to airborne bathymetric lidar point cloud (30 cm vertical accuracy), highlighting the tendency for the SDB to overestimate depths, offshore (red sites, making it appear deeper) as opposed to underestimate depths nearshore (blue sites, making it appear shallower). Note that validation points within the RMSE range of \(\pm\)1.02 m were removed from the map graphic.