# Nearshore Benthic Mapping in the Great Lakes:
## 3 Results
### Classification Performance Analysis
#### 3.1.2. Biotic Component Classification Results  
Tier 1 Biotic  
For the biotic component, average overall accuracy for the 24 airborne data combinations was 93.58 \(\pm\) 0.91% (including data combinations with additional ROIs), while for the 11 satellite data combinations it was 92.67 \(\pm\) 0.71% (data combinations with additional ROIs were not included). Similar to the results for the substrate component, KCs were more moderate to low, averaging 0.38 (\(\pm\)0.14) for airborne data combinations and 0.31 (\(\pm\)0.07) for satellite data combinations. Table 10 (Appendix A) lists the percent overall accuracy and KC, as well as the percent area and producer and user accuracies for the two classes (aquatic vegetation bed and biotic null) using airborne and satellite data combinations. As with the substrate component, the Index field in Table 10 (Appendix A) is a numerical value that is referenced in Figures 16 and 17 in which classification performance is plotted (percent overall accuracy compared to percent producer accuracy) to help illustrate patterns with respect to unique data combinations. Generally, the classifications showed that the majority of the area fell into the biotic null class with airborne data combinations estimating 97.42% (\(\pm\)1.26%) and satellite data combinations estimating a higher coverage amount of 98.33% (\(\pm\)1.13%). However, pockets of the aquatic vegetation bed class occurred largely coincident with the coarse unconsolidated substrate class in the central and northern parts of the study area with airborne data combinations estimating 2.58% (\(\pm\)1.26%) and satellite data combinations estimating a little less, 2.3% (\(\pm\)0.43) (Figure 18; Table 10, Appendix A). Figure 15. Tier 2 fine unconsolidated (substrate) classification results using airborne data combinations for the north-central section of the study area: CA MNF WLR + CA MNF FU + AB DE and CA MNF FU + CA MNF WLR. Figure 16: Tier 1 plot of aquatic vegetation bed classification performance, comparing percent overall accuracy and percent producer accuracy for airborne and satellite data combinations. Number labels refer to unique classification input data combinations identified in the Index field in Table 12, Appendix A. The three airborne data combinations with the highest overall classification accuracies included the following (highest to lowest): (1) AB DE + CA MNF FU + CA MNF WLR, (2) CA MNF FU + CA MNF WLR, and (3) AB RF + AB DE + CA MNF FU + CA MNF WLR. These top three data combinations with additional ROIs were also evaluated for accuracy performance. Figure 18 illustrates the six top results for the Tier 1 biotic component. All three classification results using original ROIs tended to misclassify the aquatic vegetation bed class along the immediate shoreline (Figure 18, panels a, c and e). This was likely due to a lack of ground truth available in that part of the study area, and thus, when additional ROIs were added, the classification errors were corrected and likewise resulted in increased aquatic vegetation bed in areas coinciding with coarse unconsolidated substrate where it is known to occur (Figure 18, panels b, d and f). In fact, all classification results using original ROIs generally tended to underestimate the overall amount of the aquatic vegetation bed class in the study area (despite the overclassification along the immediate shoreline), averaging approximately 2.26% (\(\pm\)1.03%) as opposed to 4.53% (\(\pm\)0.33%) with additional ROIs. As with the substrate component, the spectral data inputs played an important role in the classification of the biotic component. However, the data combination with the highest overall accuracy and KC had AB DE as first in combination with spectral data inputs, suggesting that for aquatic vegetation bed discrimination that depth (fused with spectral data) could be more influential than one data type alone [51].