## 4 Results  
In Kaski district, Pokhara is open and has mountains in the southern part, making the Landsat scene mostly covered with clouds. Hence, getting a cloud-free scene from a single day is challenging. Hence, we masked out the cloud and its shadow from images using pixel_qa band and made the median composite of the images, as shown in Figure 5. In the figure, the false-color composite image, we can observe that most of the Kaski district is covered by forest. The reference data were created by dividing the classes based on the area of the unsupervised classification classes. The number of reference data on each class was proportional to the area of the unsupervised classes. The OA and Kappa, which was derived based on validation samples, was used to evaluate the performance of RF and XGBoost classifier in Landsat 8 and Landsat 5 images for the year 2020 and 2010, respectively. Before performing the LC classification, hyperparameter tuning was executed. For both the algorithms, the classifier manual grid search hyperparameter tuning was performed. In the RF function of the caret package, the accuracy mainly depends on _mtry_ and _ntree_, where _mtry_ is the number of variables to randomly sample as candidates at each split, and _ntree_ is the number of decision tree ensembled. In our study, we kept the _mtry_ as default for classification, i.e., square root of the number of predictors. In our case, there was a total of 11 predictors in the data set, so we used 3 as _mtry_ approximately. In contrast, the manual hyperparameter tuning was conducted for the _ntree_. We tested the _ntree_ from 500 to 2500, increasing 500 at a time. The repeated CV was used as a train control method. The hyperparameter tuning was done by considering a confidence interval of 95%. The dot plot of accuracy obtained by using different _ntree_ to model is shown in Figure 13 from Appendix A. For XGBoost parameter tuning, we did manual grid tuning for the _max tree depth_, _nrounds_ and _eta_, where _max tree depth_ is the maximum depth of a binary tree to the number of nodes from the root down to the furthest leaf node, _nround_ is boosting iteration that specifies the number of times the tree-making process is continued, and _eta_ is the learning rate, as shown in Figure 14. We kept the other parameters like gamma, colsample_byte tree and subsample as constant. The best hyperparameters obtained for XGBoost are provided in Table 13 from Appendix A. After the data training procedure, the RF model and XGBoost model produced classified images with five classes. The snow area that was masked earlier was joined after training to the classified map. The map obtained is shown in Figure 7. The accuracy of the obtained map was measured by using different statistical accuracy measurement units. The LC map was then assessed for classification accuracy as the map validation goal was to determine the OA of the LC map compared to available reference data. The validation was performed from the randomly divided data from the reference data. All the maps from 2010 and 2020 were validated separately as both have different reference data points. The OA and Kappa, along with no-information data and _p_-value for the hypothesis test, were measured for the accuracy assessment. For the LC map of the year 2010, XGBoost performed better than the RF classifier, whereas, for the year 2020, the RF classifier performed better. The accuracy of the RF model was 0.8792 +/\(-\) 0.0472 at the 95% confidence level for the 2010 LC map, whereas for 2020, the accuracy was 0.875 +/\(-\) 0.0575 at the 95% confidence level. Similarly, the accuracy of the XGBoost model was 0.8926 +/\(-\) 0.0448 at the 95% confidence level for the 2010 LC map, whereas for 2020, the accuracy was 0.8603 +/\(-\) 0.053 at the 95% confidence level. Since the Kappa of the classifier performance for both the years is greater than 0.80, we can conclude that there is a high agreement between the ground truth and agreement value.