# Airborne GNSS Reflectometry for Water Body Detection
## 6 Data Analysis
### Water Body Edge Localization
We can also observe in the histogram of Figure 16, a comparison of the number of perfect and imperfect localizations with respect to the total number of water body edge localizations per type. We notice from the second column of Table 4 and from Figure 16 that swamps had the highest localization accuracy with 86.70% (26 perfect localizations out of 30) followed by lakes and rivers with an accuracy of 79.2% for each with the same number of detections. The pool had the lowest edge localization accuracy with 50% but from just 2 measurements. Streams/brooks noted the second lowest edge localization accuracy with 58.3% from 12 localizations. We note from Table 4 that swamps had the least mean localization error of 0.36 m and the lowest localization difference standard deviation of 0.5 m (after the pool). Although \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Water Body**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Detections** \\ \end{tabular} } & \begin{tabular}{c} **Percentage of** \\ **Perfect Edge** \\ **Localization** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Distance** \\ **Localization Error** \\ **(in meters)** \\ \end{tabular} &
\begin{tabular}{c} **Localization** \\ **Difference—Std** \\ **(in meters)** \\ \end{tabular} \\ \hline Lakes & 12 & 79.20\% & 0.59 & 0.69 \\ \hline Oxbow Lakes & 4 & 62.50\% & 1.86 & 1.47 \\ \hline Ponds & 11 & 72.70\% & 0.68 & 0.73 \\ \hline Pools & 1 & 50.00\% & 0.82 & 0.41 \\ \hline Rivers/Canals & 12 & 79.20\% & 0.63 & 0.93 \\ \hline Streams/Brooks & 6 & 58.30\% & 1.75 & 1.38 \\ \hline Swamps & 15 & 86.70\% & 0.36 & 0.50 \\ \hline Wetlands & 4 & 75.00\% & 0.99 & 1.12 \\ \hline
**Total** & **65** & **76.2\%** & **0.96** & **0.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Assessment of the accuracy of proposed radar technique for water body edge localization. Quantitative analysis of the difference between the automatic and manual edge localization for the detected water body surfaces by the traces of the 3 satellites footprints along the whole trajectory per water body type and in total (bold). Figure 16: Statistics of water body edge localization by the proposed radar technique per water body type. lakes and rivers have the same percentage of perfect edge localizations, we notice that the lakes had lower mean distance localization error and localization difference standard deviation, implying better overall localization accuracy for lakes. The oxbow lake and streams reported the highest statistical errors in terms of the recorded mean and std. We notice that the only oxbow lake detected along the trajectory was surrounded by vegetation on its borders. In addition, as previously discussed, the streams along the trajectory are mostly muddy and covered by trees. The results are in agreement with those derived in Table 3, but applied for the whole study area and for the three satellites. We can observe that the edge localization is affected by the type of the water body surface, as well as the characteristics of the landforms surrounding the water body at its edges (vegetation biomass, roughness, etc.) In total, the proposed radar technique achieved an overall water body edge localization accuracy of 76.2% with a mean distance localization error of 0.96 m and localization difference standard deviation of 0.9 m. From this study, we conclude that we can achieve the meter accuracy for water body edge localization with our automatic approach as compared to manual localization using Google Earth taking into consideration the spatial resolution of our GNSS-R application and the approximate distance between two consecutive specular points (\(\sim\)0.5 m) for a gyrocopter average speed of \(\sim\)95 km/h.