3 Results

3.1 Vegetation Classification

3.1.1 Classification Accuracy Assessment and Comparisons

All methods achieved an overall accuracy of at least 79% and Kappa coefficient above 0.74, indicating high repeatability and accuracy (Figure 5). Among three methods, SVM demonstrated superior performance, exhibiting the highest overall accuracy of 84.0% and Kappa coefficient of at least 0.81. This represents a 5% and 10% higher accuracy compared to the other algorithms, respectively. The confusion matrix of SVM classification can be found in Table 1. It reveals that Caragana, Poplar, and Grass have a high classification accuracy of more than 89%. The classification accuracy of Salix and Artemisia is relatively low, at 71.43% and 72.92% respectively; while Corn is the lowest, at 68.54%.