# Nearshore Benthic Mapping in the Great Lakes:
## 3 Results
### Classification Performance Analysis
#### 3.1.1 Substrate Component Classification Results
The top three data combinations with the additional ROIs were also evaluated for accuracy performance, and in cases where data combinations using the same data inputs resulted in redundant classification accuracies (e.g., varying only by data input order), only one of the data combinations was evaluated (i.e., CA MNF FU + CA MNF WLR and CA MNF WLR + CA MNF FU + AB DE). Figure 13 illustrates the classification results of the top three performing data combinations with and without additional ROIs for the north-central section of the study area. The southern section is not shown since it is mostly fine unconsolidated substrate. Two data combinations with additional ROIs (Figure 13, panels b and d) experienced a slight decrease in overall accuracy percent by 0.16% and 1.29%, respectively, while one remained unchanged (Figure 13, panel f), as a result of a lack of validation ground truth data in misclassified areas. However, the amount of area originally misclassified as coarse unconsolidated and anthropogenic in the nearshore (Figure 13, panels a, c and e) decreased, resulting in increased area correctly classified as fine unconsolidated (Table A1, Appendix A). As a result, the producer accuracy for the fine unconsolidated class increased, and although it decreased for the coarse unconsolidated and anthropogenic classes, the user accuracies for those two classes increased (Figures 10-12). Despite the lack of data, the performance of the proposed approach is not as good as the  
of validation ground truth data in areas that experienced misclassification, upon confirming with local experts from ISGS and NOAA, the results with the additional ROIs visually appeared to be most accurate and thus, the additional ROIs helped account for misclassified areas both in the nearshore and in cases of confusion between coarse unconsolidated and anthropogenic classes. However, the CA MNF FU with additional ROIs yielded an increase in pixels classified as fine unconsolidated in offshore areas following the flight line pattern (Figure 13, panel b), suggesting that the single data input was not sufficient for overcoming misclassifications even with additional training data. In general, classifications combining three or more of the airborne data inputs (except for CA MNF FU and CA MNF FU + CA MNF WLR) performed consistently and had overall accuracies of 90% or higher (KCs > 0.49). In addition, the use of spectral data inputs, CA MNF FU and WLR, was especially important for producing higher overall accuracies for the substrate component with 13 out of 15 data combinations including both spectral data inputs for combinations with overall accuracies of 90% or higher. Classification results from the satellite data combinations generally showed the same substrate patterns as the airborne data combinations, except that anthropogenic features could not be resolved (Figure 14). The satellite data combinations with the highest overall classification accuracy included ones in which all three data inputs were used and in which either S2 MS or S2 DII was first in the data order. They included the following: S2 MS + S2 DII + S2 SDB (and two additional combinations using the same data but in a different order, S2 MS + S2 SDB + S2 DII and S2 DII + S2 SDB + S2 MS). Only one of the top-performing data combinations was further evaluated with additional ROIs since all had the same overall accuracy. Figure 14 illustrates the two Tier 1 substrate classification results without and with additional ROIs (panels a and b, respectively). Overall and class-specific accuracies remained unchanged due to a lack of ground truth available for validation in select areas (Table A1, Appendix A), yet the addition of ROIs helped to visibly reduce the misclassification of coarse unconsolidated substrate in the northeast offshore part of the study area and discriminate coarse unconsolidated substrate that was missed just south of the intake pipeline. Similar to [PERSON] et al. (2020), the classification results using DII tended to be highest.