Decision tree classification algorithms have significant potential in remote sensing data classification. In this research

two popular decision tree algorithms——CART and C4.5 are presented

and two techniques known as boosting and bagging in machine learning area are introduced. We examined these methods to maximize classification accuracies using these decision trees and techniques to map land cover of Huabei area in China from MODIS 250m data. The result indicates that decision tree with abundance training samples has higher classification accuracy than maximum likelihood classifier（MLC）in the land cover classification test

whereas insufficient samples resulted in a lower accuracy for decision tree than MLC. The result also shows CART algorithm has more advantageous than C4.5 algorithm in classification accuracy and tree structure. And the decision tree classification accuracy depends on the optimal structure and pruning process. We also tested the behaviour of boosting and bagging techniques combined with CART and the result shows that adding boosting technique to decision tree can increase classification accuracies by 18.5%—25.6% for the poorly separable classes in MLC.