# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 2 Materials and Methods
### Satellite Image Processing  
Planet (formerly Planet Labs, Inc.) has manufactured and launched numerous miniature satellites called \"Dove\". With a constellation of over 150 satellites, the Doves offer 3.7 m spatial resolution and collect daily scenes. A total of 18 sun-synchronous Dove images from three satellite sensors were selected to map benthic habitats over the study area. The single scene dimensions are approximately 25 km \(\times\) 8 km with a spatial resolution of 3.7 m. These images were collected from 22 January to 30 January 2018 and were selected based on minimal cloud cover, sun-glint, waves, and water turbidity. The Dove top-of-atmosphere (TOA) radiance in Blue (470 nm), Green (540 nm), Red (610 nm) and Near-Infrared (NIR;780 nm) bands were used to correct atmospheric effects by using the 6S atmospheric correction model [17]. The resulting surface reflectance \(\rho(\lambda)\) in visible bands were subtracted by the NIR band in order to minimize sea surface effects to derive marine reflectance \(\rho_{m}(\lambda)\) as [18]:  
\[\rho_{m}(\lambda)=\rho(\lambda)-\rho(\mathrm{NIR}), \tag{1}\]  
Below-surface remote sensing reflectance (\(r_{rs}\)) was then calculated using method by [19]:  
\[r_{rs}(\lambda)=\frac{\rho_{m}(\lambda)/\pi}{0.52+1.7(\rho_{m}(\lambda)/\pi)}, \tag{2}\] \begin{table}
\begin{tabular}{c c c} \hline
**Benthic Habitat Type** & **Description**[16] & **Field Video Example** \\ \hline \hline \multirow{4}{*}{Seagrass Dense} & Dense meadows of seagrass (\(>\)60\% cover) & \\  & dominated by _Thalassia testatum_. Other & \\  & seagrasses (e.g., _Siruginatum filiform_) and & \\  & macroalgae (e.g., _Halimeda_ sp.) are also & \\  & present but at lower density. & \\ \end{tabular}
\end{table} 
Table 1: _Cont._With below-surface reflectance calculated, water bathymetry was predicted using an adaptive bathymetry estimation method with self-tuning parameters (\(m_{0},\ m_{1}\)) as described in [PERSON] et al. (2016):  
\[H=m_{0}\frac{\ln(1000*r_{s}blue)}{\ln(1000*r_{s}green)}-m_{1}, \tag{3}\]  
Using the modeled bathymetry, we estimated bottom reflectance by using bathymetry as input information. We masked out deep open ocean according to the bathymetry (\(H>15\) m). The total below-surface remote sensing reflectance is the combination of both water column contribution (\(r_{sC}(\lambda)\)) and bottom contribution at the water surface (\(r_{sB}(\lambda)\)) as ([PERSON] et al., 2016):  
\[r_{s}(\lambda)=r_{sC}(\lambda)+r_{sB}(\lambda), \tag{4}\]  
where bottom contribution at the water surface (\(r_{sB}(\lambda)\)) is contributed by bottom reflectance (\(r_{b}(\lambda)\)) as:  
\[r_{sB}(\lambda)=\ \frac{1}{\pi}r_{b}(\lambda)e^{-kH}, \tag{5}\]  
where \(k\) is an attenuation coefficient for the water column ([PERSON] et al., 2016). After deriving surface reflectance \(\rho(\lambda)\), bottom reflectance \(r_{sB}(\lambda)\), and bathymetry \(H\), we used eCognition Developer 9.4 (Munich, Germany) software to segment the images using the following parameters: Scale (150), Shape (0.1), and Compactness (0.5). We tested different combinations of segmentation parameters (\(50<\text{Scale}<200\), \(0.05<\text{Shape}<0.2\), \(0.1<\text{Compactness}<1.0\)) to arrive at the combination that best captured the representation of benthic features based on the visual examination. These processing steps are illustrated in Figure 2.  
Figure 2: Bathymetric, bottom reflectance, and benthic map retrival. The interpreted GPS-referenced video transects were used to identify objects that represented each habitat type, and the nearest neighbor classifier was applied to classify the objects. The nearest neighbor classifier was selected because it offered the best results after comparison of multiple classifiers (e.g., nearest neighbor, support vector machine, etc.). The mean values of the blue and green bands for both surface reflectance and bottom reflectance and mean depth were used as object attributes for the classifier.