Does feature selections matter to Decision Tree algorithms?
For ensembles of decision trees, feature selection is generally not that important. During the induction of decision trees, the optimal feature is selected to split the data based on metrics like information gain, so if you have some non-informative features, they simply won't be selected. 

Feature selection can still be important for small datasets, where spurious relationships between features and class labels are more commonly seen. 

As a side note: it's common practice to remove some features from datasets for all learning algorithms, like ID fields. These features are especially bad for decision trees, as they usually end up being the feature with the highest information gain (and therefore selected to be split on) while actually containing no generalisable information. So in this way, feature selection can be useful when using decision trees, although it's arguable that the ID field should be considered a feature in the first place.