# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods
### Data Classification and Analysis
#### 2.5.3 Remote Sensing Classification
An accuracy assessment was conducted using an error matrix method comparing resultant classifications with the corresponding 3 and 10 m validation sample datasets (Section 2.5.2. Field Data Classification) to produce overall accuracy, Kappa Coefficient (KC), and producer and user accuracies per class [50]. Upon reviewing initial results, some misclassifications were observed in areas with limited ground truth data, such as areas immediately adjacent to the shoreline (i.e., overclassification of aquatic vegetation bed). Therefore, additional ROIs were manually delineated in misclassified areas using  
Figure 9: Remote sensing workflow illustrating pre-processing, classification inputs, and classification and analysis approach (HSI = hyperspectral; SMARTS = Simple Model of the Atmospheric Radiative Transfer of Sunshine; DEM = digital elevation model; WLR = water-leaving reflectance; FU = fusion; MNF = Minimum Noise Transform; CA = CASI; DE = lidar depth; RF = bathymmetric reflectance; S2 MS = Sentinel-2B Multispectral; DII = Depth Invariant Index; SDB = Satellite-Derived Bathymetry; SVM = Support Vector Machine; and ROIs = regions of interest). local knowledge and applied to input data combinations with the highest overall classification accuracies. More specifically, for the Tier 1 classification of the biotic component using airborne data, the following ROIs were added: 10 biotic null (concentrated along the shoreline and some offshore areas) and 11 aquatic vegetation bed (near existing ground truth, but not overlapping with validation data). While multiple attempts at additional ROIs in the classification of the biotic component using satellite data were made, none were successful in resolving misclassifications and were ultimately not used. For the Tier 1 classification of the substrate component using airborne data, the following ROIs were added: 4 coarse unconsolidated (near existing ground truth, but not overlapping with validation data), 21 fine unconsolidated (concentrated along the shoreline and some offshore areas), and 17 anthropogenic (near existing ground truth or known anthropogenic features that lacked ground truth, such as a submerged breakwater). For the Tier 1 classification of the substrate component using satellite data, the following ROIs were added: 14 coarse unconsolidated (near existing ground truth, but not overlapping with validation data) and 7 fine unconsolidated (concentrated along the shore and some offshore areas). No additional ROIs were added to the anthropogenic class in the satellite data classification because the inputs were insufficient for discriminating those features. The result from the best-performing 3 m airborne Tier 1 classification was used to mask the input data for the Tier 2 classifications of the fine unconsolidated (substrate) and aquatic vegetation bed (biotic) classes. More specifically, the three airborne data combinations with the highest overall accuracy achieved in Tier 1 were evaluated in the SVM classifier for Tier 2 using training samples or ROIs designated in Table 3 (Section 2.5.2. Field Data Classification). No additional ROIs were required in the Tier 2 classification. As with the Tier 1 results, an accuracy assessment was conducted for Tier 2. Satellite data inputs were not able to be further classified in Tier 2 due to spatial resolution limitations associated with the satellite imagery. After accuracy assessments were completed, classification performance (overall and producer accuracies) was evaluated by comparing results to better understand the impact of satellite and airborne data inputs on classification accuracy as related to CMECS class detail.