Classification of Beijing Afforestation Species Based on Multi-temporal Images

In order to solve the problem that traditional remote sensing classification method is difficult to distinguish tree species of plain afforestation, high spatial images of four different periods were selected to present the distribution of forest resource with explicit clarity. The optimal segmentation parameters were obtained by combining the calculation of change rate in variance and visual interpretation and by using a tool named ESP. Feature selection was used to reduce a large number of features to simplify the process. The region was classified by the object-based classification by using mutli-temporal images and single image respectively. Pixel-based classifications were applied to compare with the accuracy of object-based classification method. The results show that the accuracy of object-based classification in mutli-temporal images is 64%, which is better than the single image results with the accuracy of 51%. SVM and MLC reaches even lower accuracy of 49% and 43% respectively. The precision of object-based KNN classifications is better than that of pixel-based classification, indicating that the object-based classification adding mutli-temporal images has superiority in identifying those afforestation tree species in ecological landscape of forest with a complex distribution.