Research On Remote Sensing Biomass Monitoring Of Picea Schrenkiana Var. Tianshanica

Selected the West Tianshan Mountains in Xinjiang province as study area, Based on the data preprocessing and registration between remote sensing images and forest resource data for subcompartments, to extract the vegetation index information of Picea Schrenkiana var.tianshanica from different scales and different age-classes. Through correlation analysis between biomass which is Measured from the stand volume through BEF (biomass expansion factor) and remote sensing vegetation index, to determine the relationship of the two.According as the Principle of remote sensing vegetation and the measured spectral data,to construct the model of remote sensing monitoring between characteristic genes which can describe Picea Schrenkiana var.tianshanica' biomass and biomass data of different age-classes which is measured from the forest resource data for subcompartments through BEF. Contrast traditional regression analysis and artificial neural network technology,thc model of the second one to estimate the accuracy is better, which reached 91.79 percents. Research shows that the non-linear theory of Artificial neural network is suitable for the remote sensing estimation of Picea Schrenkiana var.tianshanica, which can reflect he actual situation.It is calculated by the model that the total biomass of Picea Schrenkiana var.tianshanica in Nileke forest region is 3729733.345 t. Comparison of various age classes of Picea Schrenkiana var.tianshanica, the mature Picea Schrenkiana var.tianshanica's total biomass is the most one,and the excessive mature Picea Schrenkiana var.tianshanica's biomass per unit area is the other most one. Picea Schrenkiana var.tianshanica's biomass grows and increases with age. Further analysis that the largest proportion of Picea Schrenkiana var.tianshanica forest's biomass is scattered in the altitude of 1800-2600m, the slope of 0°-35°,the direction of middle, and the north aspect,or with north aspect. Its spatial distribution pattern was evident in the central Asian mountain forest characteristics.