## 3 Results  
### Traditional CT Models Based on Measured Samples and Validation  
Classification trees constructed for the three image dates all based the first split on SF1 to identify emergent vegetation, the second split on SF2 to identify floating-leaved vegetation, and the third split on SF3 to identify submerged vegetation and water (Figure 3). The thresholds of SFs in the traditional CT models for July, August, and September were determined based on field data from these months, respectively. Classification accuracies for the three models are shown in Table 5. Overall accuracies for traditional CT models for July, August and September were 82.1%, 79.7% and 84.4%, respectively. Among the misclassified samples, 83% were floating-leaved and submerged vegetation, the areal coverage of which was less than 20% in areas of occurrence and resulted in them being interpreted as water. These results suggest that it might be difficult to identify areas with low density of submerged and floating-leaved vegetation with moderate resolution images, such as from HJ-CCD and Landsat sensor data. Figure 4 shows the classified maps resulting from models for the three images dates. The maps show little emergent vegetation, primarily distributed along the eastern shore of Taihu Lake. Floating-leaved was mainly distributed in sections IV and V and submerged vegetation was most prevalent in sections II and III (Figure 5). From 11 July to 16 August to 26 September, the area of floating-leaved vegetation was stable. submerged vegetation in section II increased in extent from 11 July to 16 August and  
Figure 3: Traditional classification tree (TCT) model structure and thresholds of spectral features (SFs) resulting from the three image dates. TCT\({}_{1}\) model is the model developed for the July image using field data from July (group I). TCT\({}_{2}\) and TCT\({}_{3}\) models were similarly developed for August and September images and their respective ground sample groups (sample groups II and III). TCT\({}_{1}\)-SF\({}_{1}\), TCT\({}_{1}\)-SF\({}_{2}\) and TCT\({}_{1}\)-SF\({}_{3}\) are SF\({}_{1}\), SF\({}_{2}\) and SF\({}_{3}\) values (see Table 3) obtained from the July image. TCT\({}_{2}\)-SF\({}_{1}\), TCT\({}_{2}\)-SF\({}_{2}\) and TCT\({}_{2}\)-SF\({}_{3}\) are SF\({}_{1}\), SF\({}_{2}\) and SF\({}_{3}\) values obtained from the August image. TCT\({}_{3}\)-SF\({}_{1}\), TCT\({}_{3}\)-SF\({}_{2}\) and TCT\({}_{3}\)-SF\({}_{3}\) are SF\({}_{1}\), SF\({}_{2}\) and SF\({}_{3}\) values obtained from the September image. decreased in extent from 16 August to 26 September. In section III, the extent of floating-leaved vegetation increased while extent of submerged vegetation decreased over time. Floating-leaved vegetation in section IV increased from 11 July to 16 August, then decreased from 16 August to 26 September; Floating-leaved vegetation in section V decreased in extent over time while area of submerged vegetation increased from 11 July to 16 August, then stabilized (Figure 5). These results were consistent with information from the field survey. Figure 4: Maps of aquatic vegetation types in Taihu Lake resulting from traditional classification tree models constructed for image acquired on 11 July (**A**), 16 August (**B**) and 26 September (**C**).