Abstract: The landuse classification in Liaoning Province was conducted with MODIS NDVI data between March and December in 2010 (by taking the top 3 principal components of MODIS NDVI data, and the top 5 principal components of MODIS NDVI data of this time duration individually), and with MODIS NDVI data from June to October in 2010 by means of six kinds of classification methods, i.e., the Maximum Likelihood Method, the Mahalanobis Distance Method, the Spectral Angle Mapping Method, the Support Vector Machines Method, the Neural Network Method and the Minimum Distance Method. The classification results showed that the Maximum Likelihood Method, the Mahalanobis Distance Method and the Minimum Distance Method were comparatively more suitable for MODIS NDVI data information extraction, whose overall classification accuracy was 8263%, 8029% and 79.17% individually, and whose information extraction precision of arbor forests reached 8191%, 7854% and 8002%, respectively. In terms of growing phases, the best results of vegetation data transformation by these three methods were obtained from June to October, whose overall classification accuracy reached 8263%, and whose information extraction precision of arbor forests could reach 7854%.