Table 1 The confusion matrix of SVM classification results.
The three classification methods demonstrated variability in their results across different tree species (Figure 5). Two common issues were identified (Figure 6): commission and omission, and patch fragmentation. Commission and omission were mainly observed in Artemisia, Salix, and Corn communities. UAV images with high spectral resolution facilitated the identification of tree crown shadows, affecting spectral heterogeneity and leading to misclassification of shadows as Artemisia. Meanwhile, the spectral similarity between Artemisia and Salix resulted in increased misclassification. Within the same vegetation environment, variations in age, growth conditions, sizes, and shapes of plant species contributed to significant within-species variability. Additionally, image errors such as irregular changes in intensity density caused by stitching might cause commission and omission, which are unavoidable. The fragmentation of patches was caused by two factors: high spatial resolution, enabling clear identification of gaps between tree and shrub branches, and sparsely distributed grass, resulting in many small fragmented patches that hindered plant spatial structure. Large spectral variation within the same type introduces uncertainty during computer processing, leading to ‘speckle’ noise. To address these, post-classification analyses (clump analysis and majority/minority analysis) were conducted to refine the results and serve as the basis for calculating plant diversity.