The Research Of Rs Model For The Growth Of Picea Schrenkiana In Western Tianshan Mountains

Growth can serve as the site conditions quality testing and evaluation index of management measure effect, correctly analysis and master of forest tree growth rule, using relevant management measures; it can change the growth situation, and improve the growth of the trees. As the research object in Tekes County to combine the measured data with GIS means extract relevant factors, and factors related to the discrimination, analyzed the relationship between each RS factors and successive growth ,and used statistical software for regression analysis, established significant relationship to regression model . After that analyze the results after inspection and then compared the model better fit, and ultimately select the best model, And Picea schrenkiana growth by remote sensing inversion in Tekes County. Through the above research get the main conclusion as follows:(1)According to the distribution of Picea schrenkiana, choose Picea schrenkiana grew better in Tekes County, and adopted the 51 sample plots of 1365 sample wood DBH data in 1996,2001 and 2006 . RS image Select Landsat-7-ETM + data and SRTM-DEM data.(2)Use Diameter at breast height of per plant by Picea schrenkiana data, through statistical calculation, calculate a Picea schrenkiana volume table and volume of experience formulation for V = 0. 00010447D2.54202946 in Tekes County, which calculated the year of the Picea schrenkiana volume and the volume and periodic annual increment growth, the 51 sample plots of the periodic growth for 3.64463m~3 and the annual increment growth for 0.364463m~3 in 1996-2006.(3)Due to the increased growth with the elevation gradient showed a single peak pattern of change, it takes the actual value of the elevation of discrimination that is classification of elevation based on the increased growth, elevation data based on combining a large number of documents, it can be divided into 5 levels. Aspect of discrete data also in need of treatment, and aspect can be divided into eight directions, according to aspect that classification method to divide the 360°, the north aspect for grade 8, and south aspect for grade 1.(4)According to the data on the impact of growth factors correlation analysis, analysis of a total of nine factors are red, green, blue, near infrared, shortwave infrared, NDVI and site factors elevation, slope, aspect. By principal components'analysis and correlation analysis to select factor. Finally four factors are green, NDVI, elevation, aspect. (5)Through four factors of the multivariate linear to compute and conducts the regression equation significant F for inspection and regression coefficients for T inspection, the result shows: Green have not through regression coefficients, so choose three and two factors of the multivariate linear to compute,and Compared to the four equations and the error test. The optimal prediction model for NDVI, elevation, aspect of multiple regression equation: Y=-0.003716+0.018369*N+0.001966*E+0.000666*A.(6)Using the prediction model to Picea schrenkiana growth by remote sensing inversion in Tekes County, it showed successive growth that is 1.332684×10~4m~3, Picea schrenkiana covers an area of 9.523737×10~4hm~2. And elevation, slope to stack analysis, the trend growth performance of the largest in the elevations, Picea schrenkiana growth is 1.126898×10~4m~3in 1900～2599m, accounting for 84.56%.The largest increase on North Slope, growth is 4.65614×10~3m~3, accounting for 34.94%.