## Materials and Methods
### Classification Method
Benthic communities found at these depths include coral, hardbottom with algae, and sandy bottoms. Since the spectral response is greatly diminished in deep water, the classification within those areas had to be highly fine-tuned. This is especially true for detecting the deep ocean edge boundary (areas >30 m depth), where bottom reflectance from the seafloor is no longer recorded by the sensor. At these depths, the object-specific range and subsequent threshold method was less accurate when trying to distinguish between coral/algae and hardbottom. The automated results often required extensive manual corrections based on image interpretation using other high-resolution satellite image databases such as Esri, Google Earth, and Microsoft Bing, Table 3 presents several of the issues and problems that were encountered during the testing of the classification and the corresponding solutions that were employed. Figure 8: The eCognition classification workflow that integrates RGB spectral data, bathymetry, and geomorphic zone vector data. This method employs two parallel tracks for mapping areas with and without bathymetric data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Issue/Problem** & **Solution** \\ \hline Areas > 20 m depth did not have accurate bathymetry & Employ a classification approach using only RGB values and subsequent manual editing \\ data, only RGB spectral values. & where needed. \\ \hline Shallow reef features around the reef crest needed more & Use geomorphic zones as “parental guidance” and object-specific range and thresholding \\ detail and accuracy. & to refine and improve the boundaries for reef crest, fore reef, and back reef features. \\ \hline Seagrass features classified into dense and sparse beds. & Use object-specific range and thresholding of RGB values within lagoon areas to separate \\ sparse and dense seagrass beds. & sparse and dense seagrass beds. \\ \hline Hardbottom features in deeper areas needed more & Use object-specific range and thresholding of RGB values to separate sparse and dense \\ detail and accuracy. & algae hardbottom. \\ \hline Removing objects that were less than the minimum & Objects with less or equal to area threshold were dissolved by majority length of the \\ mapping unit. & common border of the neighboring objects. \\ \hline Missing reef areas. & Search for missing reefs using an object-specific range and thresholding detection \\  & method to refine and improve reef boundaries. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Issues/problems encountered during the development of the eCognition classification approach and corresponding solutions employed. Once all objects were classified, the last step was to remove any objects that were less than an adaptively set minimum mapping unit. This was done using an area threshold that varied depending on the respective class with broad-scale classes (e.g., sand, seagrass, hardbottom), having a larger minimum mapping area compared to fine-scale classes (e.g., reef classes) in order to better preserve these smaller class occurrences. Objects with less or equal to the area were dissolved by majority length of the common border of the neighboring objects. This process ensured a clean dissolve of any object that was too small and considerably reduced the overall number of objects. Due to the large amount of data, eCognition Server was used to process all image tiles, which were structured into workspaces, based on geographically defined areas. An import routine was defined to create an automatic workflow which imported and processed each dataset within a specified folder structure. A custom import routine was used to ensure the spectral images, depth data, and auxiliary vector layers had the same data structure and alias names used throughout the workflow. The PS image composite was divided into 314 individual tiles from which the eCognition Server engine created 602 projects.