## 4 Discussion  
### Comparison of the Performances of the Different Classification Algorithms  
Comparing the performances of the different classification algorithms used for the land cover classification of QLM was one of the important research goals of this study. Three classification algorithms, including the support vector machine (SVM), classification regression tree (CART), and random forest (RF) algorithms were selected for analysis in this research, and the classifications were performed using the feature variable combinations with the best classification effect. The classification accuracies were compared, and it was found that the three remote sensing classification algorithms produced accurate classification results. Among them, the RF had the best OA, followed by the SVM and the CART. This result was inconsistent with the results of several existing studies. For example, [PERSON] used Sentinel-2 remote sensing data and remote sensing classifiers, including the SVM, RF, extreme gradient boosting (Xgboost), and deep neural network (DNN) algorithms, for land \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Land Cover Types** & **RF** & **CART** & **SVM** & \begin{tabular}{c} **FROM-** \\ **GLC30** \\ \end{tabular} & \begin{tabular}{c} **FROM-** \\ **GLC10** \\ \end{tabular} & \
\begin{tabular}{c} **LCD-QLM** \\ **(V2.0)** \\ \end{tabular} &
\begin{tabular}{c} **GlobeLand30** \\ \end{tabular} \\ \hline CR & 0.97 & 1.06 & 1.02 & 2.81 & 1.73 & 0.29 & 3.53 \\ FO & 1.67 & 1.92 & 3.75 & 2.98 & 4.53 & 1.72 & 2.72 \\ GL & 37.19 & 37.11 & 34.53 & 43.73 & 42.59 & 50.54 & 55.34 \\ SL & 0.02 & 0.43 & 0.02 & 0.14 & 0.06 & 0.003 & 0.63 \\ WL & 0.12 & 0.32 & 0.04 & 0.32 & 0.05 & 0.19 & 0.37 \\ WB & 3.24 & 3.56 & 3.15 & 3.73 & 3.02 & 3.12 & 3.11 \\ CL & 0.16 & 0.43 & 0.16 & 0.59 & 0.03 & 0.03 & 0.31 \\ BL & 55.5 & 53.91 & 56.21 & 43.81 & 46.7 & 38.63 & 31.36 \\ PSI & 1.14 & 1.24 & 1.15 & 1.87 & 1.28 & 5.21 & 2.63 \\ \hline \hline \end{tabular} CR = croplands; FO = forests; GL = grasslands; SL = shrulands; WL = wetlands; WB = water bodies; CL = construction lands; BL = bare lands; PS = permanent snow and ice.  
\end{table} 
Table 4: Ratios (%) of different land cover type areas of different classification maps. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Land Cover** & \multicolumn{2}{c}{**This Study**} & \multicolumn{2}{c}{**FROM-GLC10**} & \multicolumn{2}{c}{**FROM-GLC30**} & \multicolumn{2}{c}{**GlobeLand30**} & \multicolumn{2}{c}{**LCD-QLM (V2.0)**} \\ \cline{2-10}
**Types** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** \\ \hline CR & 71.25 & 85.52 & 59.09 & 64.11 & 64.04 & 57.48 & 86.18 & 51.21 & 80.00 & 91.30 \\ FO & 94.02 & 94.15 & 87.50 & 58.79 & 89.22 & 65.16 & 52.86 & 53.78 & 46.67 & 47.14 \\ GL & 98.19 & 96.75 & 92.63 & 9  
cover classification mapping of a 10 km \(\times\) 12 km area in Uppsala County, south-central Sweden, and determined that the SVM had the highest OA, followed by the Xgboost and RF, and the DNN had the lowest OA [56]. [PERSON] et al. used Sentinel-2 remote sensing data to compare the effectiveness of the traditional and principal component analysis (PCA)-based methods of different classification algorithms, including the maximum likelihood estimation (MLE), RF, and SVM. Their results showed that whether the PCA was used, the SVM had a higher accuracy than the RF [57]. These results were also similar to the previous reported studies. For example, [PERSON] et al. used Landsat 8 remote sensing data and classification methods such as the artificial neural network (ANN), SVM, fuzzy ARTMAP (FA), RF, Mahalanobis distance (MD), and spectral angle mapper (SAM) for classification, and their results showed that the RF had the highest classification accuracy, followed by the ANN, SVM, fuzzy ARTMAP, and SAM; and the MD was the lowest [4]. [PERSON] et al.