## 6 Wetland Vegetation Classification
### Classification Results and Accuracy Assessment  
Results differed among the five classification scenarios. When only optical multispectral characteristics were used (Scenario 1), the forest class mostly was assigned along water flows corresponding well with highly vital vegetative areas; the prairie class appeared mostly in the inland areas (and was particularly well recognized in the area marked B in Figure 8d); and the scrub class covered the remaining areas. When RapidEye and TSX features were used together (Scenario 2), the classification results were very similar to those of Scenario 1. When SAR features were based on segmentation type O (Scenario 3), wider areas near tidal canals were assigned to the forest class. For example, vegetation along the narrow tidal canals was classified as forest in Scenario 3, but shown as scrub class in Scenario 1 (particularly recognizable in the area marked C in Figure 8d). These areas showed very high vitality in the original RapidEye image. Thus, we interpreted these areas as the edges of mangrove forest, which were classified as scrub in Scenario 1 using multi-spectral features and as forest in Scenario 3 using SAR features. In this case, the SAR classification was advantageous for identifying the successive mangrove forest. In Scenario 3, more areas were classified as prairie in the north part of the study area (particularly in area A in Figure 8d). The classification results using  
Figure 7: Segmentation results of type O displayed on the TSX image using false-color composite image decomposition layers (red = double bounce scattering, green = volume scattering, and blue = single bounce scattering in [a] and [c]) and on the RapidEye image using false-color composite images (red = near infrared, green = red visible, and blue = green visible bands in [b] and [d]); (a,b) are near tidal canals; (c,d) are near inland areas. SAR features only (Scenario 4) show that the forest class is a bit exaggerated along the tidal canals due to the coarse segmentation. Narrow mangrove forests were not classified as forest and the scrub and prairie classes were found as scattered on the northern part of the study area. From Scenario 5 using [PERSON]'s decomposition method, the overall classification pattern was similar to Scenario 3, as shown Figure 8c; however, more areas were classified as water in the northern part of the study area. In summary, the classification results of five scenarios showed some differences, but shared common patterns of distributed vegetation type such as forests along the tidal canal, prariies behind the forest inward to the land, and scrubs distributed widely in the inland area. We calculated the classification error matrix (Table 4) by using 71 field sample sites (25 for forest, 22 for scrub, 21 for prairie, and three for water). The classification by RapidEye's spectral features (Scenario 1) showed the highest overall classification accuracy (95.8%), followed by the classification developed using SAR features with segmentation type O (Scenario 3; 93.0%). The classification resulting from mixed optical and radar features (Scenario 2) was somewhat less successful (88.7%). The classification based on SAR features only with segmentation type S (Scenario 4) had the lowest classification accuracy among the four scenarios (87.3%), and the prairie class was more often misclassified, compared with forest and scrub classes. The accuracy difference between Scenarios 2 and 4 is statistically not significant. Scenario 5, which was based on [PERSON]'s method, resulted in 84.5% overall accuracy, which was the lowest classification accuracy among scenarios. Figure 8: Classification results. (**a**) Scenario 1 (brown = forest, yellow = scrubs, green = prairie, blue = water); (**b**) Scenario 2; (**c**) Scenario 3; (**d**) RapidEye image with false-color combination (red = NIR, green = red visible, and blue = green visible); (**e**) Scenario 4; and (**f**) Scenario 5.