## 7 Discussion and Conclusions  
In this study, we examined the usefulness of quad-pol X-band TerraSAR-X data for vegetation mapping over the Everglades wetland. We applied the H&W four-component decomposition model to extract scattering behavior for characterizing the wetland vegetation. We hypothesized that each vegetation type is characterized by typical scattering behavior detectable by the decomposition approach and would be helpful for the vegetation classification. We also compared the classification results using [PERSON]'s decomposition to evaluate the performance of our decomposition method. Overall accuracy for our classification results ranged from 84.5% to 95.8%. The best accuracy was achieved using only RapidEye multispectral layers, which indicates that cloud-free optical data are very good for generating maps of general vegetation types. Good accuracy (93.0%) was also achieved with SAR image feature layers, indicating the high potential of polarimetric SAR decomposition products for detecting wetland vegetation, particularly for mangrove forests. The overall pattern of classification results between our decomposition and [PERSON]'s method were very similar, even though the overall accuracy using our decomposition method was much higher than achieved with [PERSON]'s decomposition. Our reference samples were placed in the middle of relatively unchanged vegetation stands to capture their homogeneous characteristics. Consequently, the classification matrix could not validate the classification accuracy over areas transitioning from forest to shrub or edges between two vegetation types. From our visual inspection of the original RapidEye image, we determined that mangrove forests along the tidal canals were underestimated in the classification based \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Scenario 5**} \\ \hline \multicolumn{6}{c}{**Reference**} \\ \hline \multicolumn{6}{c}{**F**} & **S** & **P** & **W** & **PA (\%)** \\ \hline \multirow{4}{*}{class} & F & 20 & 3 & 2 & 1 & 76.9 \\  & S & 2 & 22 & 1 & 1 & 84.6 \\ \cline{1-1}  & P & & & 18 & 1 & 94.7 \\ \cline{1-1}  & W & & & & & 0.0 \\ \cline{1-1}  & N & & & & & \\ \hline \multicolumn{6}{c}{UA} & 90.9 & 88.0 & 85.7 & 0.0 \\ \hline \multicolumn{6}{c}{OA} & 84.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._on optical data, but classified well when SAR features were used. The better performance of SAR decomposition products for detecting mangrove forest is likely due to the radar signal containing physical scattering characteristics over the target surface. The multispectral image contains just reflectance from sun illumination, whereas the SAR signal includes information about the surface geometry in the form of backscattering effects (e.g., surface, double bounce, and volume scattering). We can use the scattering information to ascribe physical meaning to the surface targets. Polarimetric SAR decomposition with the aid of optical imagery could be very useful for vegetation classification. Furthermore, a high accuracy level in the vegetation classification using SAR decomposition features shows a very advantageous benefit, particularly in the case of cloudy weather conditions, in which optical sensors have limited ability to sense the vegetation. However some limitations still remain. Operational space-based X-band quadruple polarimetric observations are not yet available. The returned values of the SAR image are recorded as a power, and therefore are always positive. We can discover the negative power when we apply a model-based decomposition algorithm. Our decomposition approach suffers from a negative power problem similar to those encountered with other model-based decomposition methods [32, 33, 34]. Once these scattering decomposition results are improved by developing a better model, the accuracy of vegetation classification should also improve. The negative value could be due to speckle-like noise in the input image that prohibits the segmentation and subsequent classification.