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This research dissertation presents a new decision tree induction method, called co-location-based decision tree (CL-DT), to extract exposed carbonate rocks in karst rocky desertification area. The proposed algorithm utilizes co-location characteristics of multiple feature parameters, including landmarks spectral attribute, vegetation fraction, land surface temperature and soil moisture content, etc., and spatial attributes of various landmarks in desertification area. This paper first presented multiple feature parameters co-location mining algorithm, including attributes data selection, determination of rough candidate co-locations, determination of co-locations, pruning non-prevalent co-locations, and inducing co-location rules, and then focused on developing the algorithm of co-location decision tree, which including non-spatial attributes data selection, multiple feature parameters co-location modeling, node merging criteria, and colocation decision tree induction. The paper uses Landsat-5 TM images covering the whole Du. an city in China as the data to verify the proposed method. The experimental results demonstrated that (1) Compared to traditional decision tree, the proposed multiattribute co-location decision tree has higher accuracy and can make better decision; (2) The training data can be fully played roles in contribution to decision tree induction.