Nonparametric Model for Remote Sensing Estimating the Volume of Spruce-Fir Forest in Shangri-La

Selected the typical forest types of spruce-fir forests in Shangri-La City, located in Diqing of Northwest Yunnan, as the research objects. Using Landsat 8 remote sensing image data combined with ground angle control plot survey data, the BPNN and SVM estimation models of spruce-fir were established and compared. The results show that the precision of the SVM model is obviously better than that of the BPNN model, its R²、rRMSE and P are 0.67, 27.91% and 77.09% respectively. The statistical error between the total volume of spruce-fir forests in Shangri-La City and the results of the second-class survey of traditional forest resources is only 1.14% by using SVM remote sensing estimation model, which shows that SVM estimation model could provide support for forest resources.