## 2 Materials and Methods
### Random Forest Classification Processing  
The random forest classifier is a machine-learning procedure focused on the ensemble learning method. [PERSON] was the one who first proposed this technique. In RF, various tree predictors are combined. The number of classification trees used, the number of variables used at each node, the random seed variable for decision tree construction, the minimum leaf population, the fraction of input variables that are bagged for each decision tree, and the out-of-bag mode are the six input parameters that are typically used by RF classifiers [52]. The overall classification accuracy rises with the rise in the number of trees. With replacement from the training data, each tree is parameterized using essentially a randomly selected collection of values. Assisting in the de-correlation of the trees reduces multicollinearity. This approach builds a training subset using random binary trees, above and beyond the clustering procedure. Additionally, the model is created from the primary database using a random sample of the training dataset. Using out-of-bag (OOB) outputs is one method for selecting optimum parameters [53]. Optimal parameter values are computed based on the RF method by analyzing the training data and testing it on the test data to ensure the best possible classification accuracy.  
Figure 3: Significance of RF tree number on the accuracy of classification.  
In order to better comprehend how various input features, such as Landsat image bands, many established vegetation and bare soil indices, and supplementary data, contribute to the final output, this study analyzed their relative values for extracting a land use and land cover (LULC) map. We accomplished this by using the RF classifier for the study area. In line with the suggestions of related studies [54], and the outcome to exclude the possibility of the OOB error in our data (Figure 4), we chose to focus on a sample size of 50 trees (ntree = 50). To determine which of these characteristics is most crucial, we used metrics such as the Gini index. Important variable characteristics are considered by RF algorithms through a mean decline in the Gini index (MDG). A characteristic with a higher frequency of occurrence across a larger number of nodes is more significant. At the node, the defined attribute is used to execute a splitting operation. In order to determine how many times each attribute was iterated until the RF algorithm's classification was successful, the final count is taken once the classification is complete. Furthermore, the default value for the number of trials (mtry) was set to be the square root of the entire number of features. Based on Figure 4, the classifier appears to perform as well, with 50 and 100 trees to choose. The number of parameters per split that is specified by default was used, and the square root of the total number of variables was calculated. The default value was used for the minimum number of leaf populations. For all other random forest parameters, default values were used, including the fraction of input to bag per tree, the maximum number of leaf nodes, and the randomization seeds. Figure 3 indicates the significance of the RF tree number on classification accuracy.  
Environmental issues such as disaster mitigation and management of water resources have been resolved using random forest (RF) methods. It can process a wide variety of data, including quantitative data and satellite images. The use of the RF classifier for LULC classification is shown to be extremely efficient in various studies of remote sensing applications [42; 55]. This technique uses many trees to increase the precision of image classification and land use analysis [23; 56; 57]. The research community has become interested in it due to its remarkable classification outcomes and processing speed.