Research on Average Tree Height Inversion Model of Cunninghamia lanceolata Plantation

Taking the high resolution SPOT5 image and 1∶10000 topographic map as data sources, 14 factors including the elevation, slope gradient, slope aspect, canopy density, the reflectivity of B1(1st band), B2(2nd band), B3(3rd band),B4(4th band), B1/B4, B2/B4, B3/B1, EVI, NDVI and RVI that affected on the tree height estimation of Cunninghamia lanceolata plantation at Huangfengqiao Stateowned Forest Farm, Youxian County, Hunan Province were extracted, and finally B2, B4, slope aspect, canopy density and NDVI were determined as the 5 principal factors influencing on the tree height estimation by means of the Principal Component Analysis method and the Ridge Estimation method to eliminate the lowcorrelation variable factors. The inversion model was built based on the Least Squares method, and the model was testified with field survey data, the correlation coefficient, coefficient of determination, adjustment correlation coefficient and the error of standard assessment of the regression model were obtained respectively as 08910, 07930, 07740 and 08422. The results showed that the imitation effect of the tree height estimation model for C. lanceolata plantation was pretty good, and the average tree height estimation model was set up.