# Nearshore Benthic Mapping in the Great Lakes:
## 4 Discussion
(2019) states that coastal variability precludes a consistent system, especially given that most systems are driven by optical signatures which often do not align with classes requested by coastal managers. However, CMECS may overcome these issues and offers a flexible solution that would not only increase opportunities for comparative studies, but also, expand exploration of methodological portability, which is one of the primary needs for advancing the state-of-the-science surrounding benthic mapping. Furthermore, CMECS' hierarchical structure ensures a level of flexibility and customization that likewise satisfies wide-ranging coastal management needs while striking a balance with ease of application--another point raised in [PERSON] et al. (2018). Another benefit of a standardized, hierarchical system is that it is data type- and sensor-agnostic meaning that it can be applied to any data, including from field observations as well as active or passive remote sensing. Additionally, while this study explores the implications for data types to resolve various levels within the CMECS hierarchy, such seamless system implementation would be a big step forward in benthic mapping research and toward the overall goal for basin-wide mapping. Another challenge in nearshore coastal benthic mapping includes the need for more frequent, high-resolution bathymetry data, and thus, there is an increased opportunity for modeled data such as SDB to supplement airborne and vessel-based data collection. The Sentinel-2 satellite specifically offers advantages for deriving SDB and benthic habitat classification, owing to the 10-20 m spatial resolution, 5-day revisit cycle allowing rapid response mapping, coastal aerosol band, and free, open data access ([PERSON] et al., 2018; [PERSON] et al., 2019). While there is increased use of SDB in benthic mapping applications, the implications of the data usage to coastal zone management and decision making remains unclear, especially considering varying spatial scales of projects. Recent evaluations of SDB algorithms (e.g., Lyzenga, Stumpf, Traganos, and Dierssen) using Sentinel-2 imagery within Google Earth Engine showed a wide range of root mean square error (RMSE) when modeled data were compared to in situ measurements from a single-beam echosounder with the highest accuracy resulting in an RMSE of 1.67 m, explaining 90% of the variation in the validation dataset ([PERSON] et al., 2019). A similar validation assessment was conducted in our study area in which bathymetry points from the airborne bathymetric lidar point cloud (30 cm vertical accuracy) were used to validate the modeled SDB, resulting in an RMSE of 1.02 m, explaining 76% of the variation in the validation dataset (Figure 22). The map in Figure 23 illustrates the tendency for the SDB to overestimate depths offshore (making it appear deeper) as opposed to underestimate depths nearshore (making it appear shallower). This trend also happens to coincide with areas that were prone to misclassification, such as the northeast section of the study area with optically deeper water and may exacerbate or confound classification issues associated with particular features or in certain areas. While this study utilized the algorithms associated with Sen2 Coral process, it is understood that there are additional SDB inputs and algorithms available that could be evaluated and may provide better results; however, the comparison of these methods was not the focus of this study. Thus, it's important to understand how these data can impact not only the products resulting from their analytical use, but also the use of those resultant products in decision making, such as better understanding habitat changes pre- and post-placement of in-water shoreline protection structures, impacts to known fish spawning grounds (e.g., littoral sedimentation at Buffalo Reef in Lake Superior (2018)), and the relationship between invasive species and changes to the benthic community at Sleeping Bear Dunes in Lake Michigan.