Abstract: 【Objective】 Identification of forest stand is critical for forest resources monitoring. 【Method】To study the extraction of forest stand information based on object oriented method, QuickBird remote sensing image with multispectral bands(blue, green, red and near-infrared )was used as the experimental data, and 10 segmentation scales(25-250, step size 25)were carried out using eCognition Developer 8.7. For each segmentation scale, the support vector machine with linear kernel was applied to three combination features(spectrum, spectrum+ texture, spectrum+ texture+ space), respectively. 【Result】The results showed that segmentation scale was significant to forest stand identification, with a highest segmentation quality at segmentation scale of 150. At each of 10 segmentation scales, introducing texture features into spectral features could improve accuracy of classification; however, introducing spatial features into spectral features had no influence on accuracy of classification. So the highest accuracy of classification(OA=85%; Kappa value is 0.86)was obtained based on the integration of spectral and texture features at segmentation scale of 150. 【Conclusion】Segmentation scale plays an important role in tree species classification.At all scales(25-250), overall accuracy of spectral and texture features was higher than that of overall accuracy using spectral features alone. Spatial features did not play a role in forest classification. Matches between segmented and reference objects produced higher classification accurate, and slight over- and under-segmentations did not significantly affect the classifications. The object-based method based on eCognition software can obtain satisfactory results for classification of stand types from QuickBird multi-band remote sensing data.