# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 4 Discussion  
The accuracy of the benthic habitat classification is impacted by multiple factors, including habitat heterogeneity, bathymetry, and water column attenuation. Mean bathymetric values, surface reflectance (blue and green bands), and bottom reflectance (blue and green bands) for different benthic classes are shown in Table 3. Regarding the heterogeneous spatial patterns of benthic habitats, although the Dove imagery provides a 3.7 m spatial resolution, it is sometimes not sufficient for detecting complex mixed benthic composition classes, such as gorgonian/soft coral habitats [(24)]. As expected, habitats that are more homogenous, such as seagrass and sandy bottom, were more easily identified (Table 2). Classification accuracy can also be affected by the level of detail of the classification scheme [(11)], with lower reported accuracy observed in fine classification types (coral reef crest, coral back-reef/flat, coral fore-reef, and gorgonian/soft coral). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**User Class\(\backslash\)Sample** & **CR** & **CP** & **CB** & **CF** & **GS** & **H** & **SD** & **SS** & **S** & **SM** & **W** \\ \hline Coral Reef Crest & 46 & 0 & 4 & 1 & 0 & 0 & 18 & 7 & 3 & 0 & 0 \\ Coral Patch Deep & 0 & 38 & 1 & 6 & 3 & 0 & 2 & 0 & 2 & 7 & 0 \\ Coral Back-reef/Flat & 18 & 0 & 110 & 5 & 0 & 0 & 12 & 4 & 11 & 0 & 0 \\ Coral Fore-reef & 5 & 6 & 1 & 161 & 4 & 1 & 2 & 0 & 19 & 10 & 0 \\ Gorgonian/Soft Coral & 0 & 3 & 0 & 6 & 55 & 0 & 10 & 12 & 14 & 12 & 0 \\ Hardbottom/Algae & 0 & 0 & 0 & 0 & 0 & 13 & 0 & 0 & 0 & 7 & 0 \\ Seagrass Dense & 2 & 4 & 2 & 5 & 0 & 0 & 453 & 20 & 6 & 0 & 0 \\ Seagrass Sparse & 1 & 0 & 0 & 0 & 0 & 0 & 33 & 214 & 6 & 0 & 0 \\ Sand Shallow & 0 & 0 & 6 & 1 & 0 & 0 & 2 & 32 & 502 & 5 & 0 \\ Sand Deep/Macroalgae & 0 & 0 & 0 & 8 & 6 & 2 & 0 & 0 & 14 & 299 & 38 \\ Water & 0 & 5 & 0 & 14 & 2 & 4 & 0 & 0 & 3 & 34 & 476 \\ \hline \end{tabular}
\end{table} 
Table 2: (**a**) Confusion matrix results for accuracy assessment for Coral Reef Crest (CR), Coral Patch Deep (CP), Coral Back-reef/flat (CB), Coral Fore-reef (CF), Gorgonian/Soft Coral (GS), Hardbottom/Algae (H), Seagrass Dense (SD), Seagrass Sparse (SS), Sand Shallow (S), Sand Deep/Macroalgae (SM), Water (W). Orange color indicates correct classification while green color represents wrong classification. (**b**) Classification accuracy assessment. Bathymetry is an important predictor of habitat classes, especially when reflectance values are very similar between two classes (e.g., coral reef crest and coral back-reef/flat) (Figure 6). However, bathymetry results for habitats located in very shallow waters (\(\leq\)1 m) are prone to erroneous results since water column attenuation is too low to detect depth [25]. Consequently, this resulted in low classification accuracy for the coral reef crest class (mean depth = 1.09 m). In contrast, the bathymetry provided a suitable model to identify deeper water regions (open ocean, depth \(>\)15 m). Moreover, in shallow coastal environments, water column contributions to surface reflectance increase as depth increases [15; 26; 27]. Surface reflectance contains large portions of non-bottom contribution (water column backscatter light) in deep water (depth \(>\)10 m). Therefore, it is critical that bottom reflectance is derived to properly classify benthic compositions [2; 4]. For example, sandy bottom located at greater depths (\(>\)10 m) has a similar surface reflectance in the Dove's bands when compared with deep coral patch (Table 3, Figure 6). However, the bottom reflectance of deep sand is distinct from other benthic compositions which helps to accurately identify it. The combined inputs of surface reflectance, bottom reflectance, and bathymetry contribute to superior image segmentation results (Figure 4) [4; 14]. For instance, the different benthic types ranging from shallow coastal waters to open ocean could be segmented using bathymetry [28].