# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods
### Data Classification and Analysis
#### 2.5.3 Remote Sensing Classification  
Satellite and airborne data inputs were iteratively classified to examine their impact on classification accuracy for discriminating CMECS-defined classes. Figure 9 shows the overall remote sensing workflow, illustrating the pre-processing, classification inputs, and classification and analysis approach. Image classification was conducted using the data inputs described in the Airborne and Satellite Data Pre-processing Sections (Sections 2.2 and 2.3). Table 4 provides a summary of the satellite and airborne classification inputs. While the goal of this study was not to conduct a full evaluation of analytical methods, initial investigation of Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers was conducted to determine the best approach for the study area as well as could be easily applied in other coastal environments. Ultimately, ML was not selected for further analysis because initial results showed consistent over- or under-class estimation, resulting in major errors despite attempts to adjust settings, ROIs, etc. This is consistent with similar studies [22], which explain that there are limitations associated with ML when the distribution of the spectral response is not normal (typically the case for benthic environments). Thus, machine learning techniques, such as SVM, which are not based on distribution assumptions, can help overcome those limitations and have shown success in benthic mapping applications [21; 22; 23; 49]. As with [PERSON] et al. [22], a pixel-based classification approach was selected over OBIA to take advantage of higher spatial resolution data and unique spectral characteristics associated with the airborne and satellite data used as input for identifying detailed benthic features. \begin{table}
\begin{tabular}{l c c} \hline
**Classification Inputs** & **Collection Date** & **Spatial Resolution (m)** \\ \hline _Satellite (S2)_ & & \\ S2 Multispectral (MS) [Bands 2–5, 8] & 10 July 2018 & 10 \\ Depth Invariant Index (DII) & 10 July 2018 & 10 \\ Satellite-Derived Bathymetry (SDB) & 10 July 2018 & 10 \\ _Airborne (AB)_ & & \\ CASI (CA) Minimum Noise Fraction (MNF) Fusion (FU) [Bands 1–10] & 17 July 2018 & 3 \\ CA MNF Water-Leaving Reflectance (WLR) [Bands 1–23] & 17 July 2018 & 3 \\ AB Lidar Bathymetry [Depth] (DE) & 17 July 2018 & 3 \\ AB Lidar Relative Bathymetric Reflectance (RF) & 17 July 2018 & 3 \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of satellite and airborne classification inputs. \begin{table}
\begin{tabular}{c c c} \hline
**Short Name (Index \#)** & **\# of Classification Samples (3 m)** & **\# of Validation Samples (3 m)** \\ \hline \multicolumn{3}{c}{Find Unconstrained (Einstein (cm))} \\ \hline \multicolumn{3}{c}{Slightly Gravelled Sand (1)} \\ Slightly Gravelled Sand/Surt Pattern: Ripple Surface (2) & 3 & 4 \\ Slightly Gravelled Sand/Surt Pattern: Pockmarked & 17 & 16 \\ \multicolumn{3}{c}{Surface Pattern (3)} \\ Slightly Gravelled Sand/COE: Woody Debris (4) & 1 & 0 \\ Slightly Gravelled Sand/COE: Organic Detritus; & 10 & 12 \\ Possible INT: Spotted Surface Color (5) & 2 & 2 \\ Slightly Gravelled Sand/COE: Mussel Shell Hash (6) & 534 & 536 \\ \hline \multicolumn{3}{c}{Actual Variation (Eligible Component)} \\ \hline _Cladophorn_ sp. (1) & 3 & 5 \\ _Cladophorn_ sp. (COE: Attached Mussels (2) & 15 & 17 \\ \multicolumn{3}{c}{TOTAL} \\ \hline \end{tabular}
\end{table} 
Table 3: Number of ground truth points (samples) for classification and validation used for Tier 2 classification of the 3 m airborne data (# = number). Classification training samples or ROIs (Section 2.5.2. Field Data Classification) were designated in the SVM classifier using ENVI v5.5 software (Kernel Type: Radial Basis Function, Gamma in Kernel Function: 0.029, and Penalty Parameter 100) to identify the Tier 1 biotic and substrate classes (Table 2) applied to different combinations of airborne and satellite data inputs.