Classification trees: An alternative to traditional land cover classifiers

Classification trees are a powerful alternative to more traditional approaches of land cover classification. Trees provide a hierarchical and nonlinear classification method and are suited to handling non-parametric training data as well as categorical or missing data. By revealing the predictive hierarchical structure of the independent variables, the tree allows for great flexibility in data analysis and interpretation. In this Letter, we compare a tree's performance to that of a maximum likelihood classifier using a 1 degrees by 1 degrees global data set. The tree's accuracy in classifying a validation data set is comparable to that when using maximum likelihood (82 per cent). The tree also may be used to reduce the dimensionality of data sets and to find those metrics that are most useful for discriminating among cover types.