A nearest neighbor classifier was used for the three segmentation schemes to classify the images into five categories, including Moso bamboo, broadleaf, conifer, nonforest, and water. The object-based features used in the classification were the mean and standard deviations of the pixels within each image object in all the input layers. Nine input layers based on TM/SPOT5, three based on SPOT5, and six based on TM were used (Table 1). Therefore, the number of object features used in the classification for the three schemes was 18, 6, and 12, respectively. According to the measuring grid (Fig. 3), sampling points at an interval of 500 m were used for validation in the accuracy assessment.29,30 Confusion matrices for the accuracy assessment of the results were conducted based on the 170 sampling points of validation for TM/SPOT5, SPOT5, and TM classifications. Visual interpretation of the 170 sampling points was implemented for accuracy assessment. This was based on 67 sample sites through a field survey which enabled us to obtain knowledge of the land cover types and the referenced forest map.