## 3 Methods
### Feature Selection Methods  
As mentioned in Section 2, this research used multitemporal PS imagery (Section 2.2.), as well as vegetation indices and texture features derived from satellite imagery. While additional derived input features, such as textural measures and vegetation indices, can provide valuable information for classifying Earth observation image data, they can also lead to a large number of irrelevant and redundant features [55]. In a highly dimensional dataset, variables that are correlated or redundant can introduce noise into the dataset, which can negatively impact prediction accuracy [56]. To address this issue, various feature selection methods are used to select a subset of relevant variables for the classification task [57]. Many R packages provide RF variable selection procedures (e.g., _caret_[58], _VSURF_[59], _boruta_[60], _varSelfRF_[61]); hence, three feature selection (FS) methods were evaluated for mapping allergenic tree species using PS imagery: mean decrease accuracy (MDA), mean decrease Gini (MDG), and variable selection using random forests (VSURF).  
Mean decrease accuracy (MDA) is an FS method that is based on the idea of evaluating how much the accuracy of a machine learning model decreases when a feature is removed from the dataset [31]. The feature that causes the least decrease in accuracy is considered the most important feature, and features that cause a large decrease in accuracy are considered to be less important. The resulting importance scores can be used to rank the features and select a subset of the most important features [62].  
Similar to MDA, mean decrease Gini (MDG) is a feature selection method that uses the Gini impurity as the measure of feature importance instead of using accuracy. Gini impurity is a measure of the degree of homogeneity of the classes in a set of data, and it is commonly used in decision tree-based models [63]. The feature that causes the least decrease in Gini impurity is considered the most important feature, and features that cause a large decrease in Gini impurity are considered to be less important [64].  
Variable selection using random forests (VSURF) belongs to a stepwise selection technique that uses the two-stage strategy based on backward elimination and then forward selection [65]. The method also uses the Gini impurity criterion as the measure of feature importance. It then generates a random forest model to measure the relationship between the features and the target variable and uses this information to select the most important features. The result is a subset of important features, which can be used for further analysis or modeling [59].  
In this research, RF was firstly used as a classifier with the above-mentioned FS methods in order to select the most pertinent input features for mapping of allergenic tree species, i.e., six PS imagery with eight spectral bands, five vegetation indices per date, and three texture features per date and band were used as input features. The result was a subset of the most important features for each FS method, which were then used as input features using machine learning algorithms described in Section 3.1.