## 3 Results  
### Classification Results and Accuracy of Classification Results  
Remote sensing classification algorithms, including SVM, CART, and RF, were used to classify the Sentinel-2 composite images in the QLM and to analyze the ability of the different classifiers to identify land cover types in high spatial resolution images. The land cover results based on the three classification algorithms are shown in Figure 3.  
The results showed that the classification results obtained using the different remote sensing classification algorithms had high consistency in terms of the proportions and the distribution characteristics of the land cover types. Among them, bare land and grassland accounted for the largest proportion of the study area, accounting for more than 90%.  
Figure 3: Land cover maps of the Qilian Mountains obtained using three remote sensing classification algorithms. Land cover maps obtained using: (**a**) the random forest (RF) classification algorithm; (**b**) the classification regression tree (CART) classification algorithm; (**c**) the support vector machine (SVM) classification algorithm.  
The grassland was mainly distributed in the eastern and central regions of the QLM, and the relatively warm and humid valleys in the west were also distributed within a small area. The bare land was widely distributed in the western part of the QLM, where the temperature is low and the precipitation is scarce. In addition, the cropland was concentrated in the eastern basin, the middle reaches of the Datong River, and the plains on the northern slope of the northeastern part of the mountains, where the land is flatter, with abundant water resources and suitable temperatures. The forests were distributed in the valleys in the middle and eastern parts of the study area and on the northern slopes of the QLM in the northeast. The permanent snow and ice were distributed in the mountainous areas with higher elevations (4891 m on average) in the western and central parts of the study area.  
The confusion matrix of the classification results obtained using the different remote sensing classification algorithms is shown in Table 3. In general, in multiple experiments, the OAs of the three classifiers reached 95%, which indicated that the three classification algorithms were suitable for high-spatial resolution image classification in the study area. Among them, the average value of OAs of the RF was 96.51% and the Kappa coefficient was 0.95, and the OAs of the SVM and CART were 94.67% and 94.50% and the Kappa coefficients were 0.92 and 0.91, respectively. This indicated that the OA of the classification results generated by the RF was higher than those of the classification results of the CART and SVM, which showed that the RF classifiers could more accurately identify and classify the land cover types in the remote sensing images. Specifically, all three classification algorithms had high classification accuracies for grassland, water bodies, bare land, and permanent snow and ice, but their classification accuracies for cropland, forests, and construction land were significantly different. The PA and OA of the RF for cropland and construction land were higher than those of the other two classifiers. Compared with the CART and RF, the SVM had a lower recognition and classification accuracy for forests.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & **Land Cover Types** & **CR** & **FO** & GL & SL & WL & WB & CL & BL & PSI & PA \\ \hline \multirow{10}{*}{SVM} & CR & **47** & 5