# Nearshore Benthic Mapping in the Great Lakes:
## 3 Results
### Component Classification Comparison  
In addition to comparisons made between data types and tiered classes within components, comparisons were also possible between the components themselves. This provides a better understanding of the potential strengths and weaknesses of classification approaches and data types related to the substrate and biotic components. Such a comparison further illustrates which components may better lend themselves to classification depending upon the data types used in the analysis as well as provide insight into the methods that work best for each one or where improvement and further investigation may be needed. In general, overall classification accuracies were higher for the biotic component than for the substrate component, regardless of data type (airborne vs. satellite) (Figure 21; Tables A1 and A2). More specifically, the biotic component classifications had higher average overall accuracies of 3.48% for airborne data combinations and 2.9% for satellite data combinations. This may be due in part to the additional class complexity involved with the Tier 1 substrate component which had three classes, including coarse unconsolidated and anthropogenic classes that could have spectrally similar materials (i.e., concrete) and lead to potential classification confusion. In contrast, the biotic component had lower average KCs by 0.09 for airborne data combinations, while they remained approximately the same between components for satellite data combinations. Yet, for airborne data combinations, the biotic component had greater variability among KCs (\(\pm\)0.14) as opposed to the substrate component (\(\pm\)0.08), while the top-performing airborne data combinations for the biotic component had KCs on par with the top-performing satellite data combinations for the substrate component (\(\sim\)0.55). Figure 20: Tier 2 aquatic vegetation bed (biotic) classification results using airborne data combinations for the north-central section of the study area: **(a)** AB DE + CA MNF FU + CA MNF WLR, CA MNF FU + CA MNF WLR, and AB RF + AB DE + CA MNF FU + CA MNF WLR) and **(b)** inset panel corresponding to box in panel **(a)**. Regarding data types, the satellite data combinations with the highest accuracies were somewhat consistent between components, and the best results for substrate and biotic classifications included all three three data types: S2 MS + S2 DII + S2 SDB. However, there were some small variations between components, reflecting the impact of changes in data input order. For example, the substrate component appeared to be less influenced by the data input order, whereby S2 MS + S2 SDB + S2 DII and S2 DII + S2 SDB + S2 MS resulted in the same accuracies as the top performer. It was only when S2 SDB was used first in the data order that it resulted in a slight accuracy decrease. In contrast, the biotic component appeared a little more sensitive to data input order in which S2 SDB + S2 DII + S2 MS and S2 DII + S2 SDB + S2 MS experienced a slight decrease in accuracy compared to the top performer, while S2 MS + S2 SDB + S2 DII experienced an additional accuracy decrease. Thus, S2 DII may play an important role for the biotic component relative to the other data inputs. A similar finding was also present in [PERSON] et al. (2020) in which two out of three machine learning approaches evaluated performed best when DII was used in the classification and was especially helpful for separating biotic coral reef and seagrass classes. For the airborne data combinations, there was less consistency between components in terms of data inputs, but both components had top performers with the data combination: CA MNF FU + CA MNF WLR.