| Forest biomass and carbon storage is one of the important factors monitoring of the global greenhouse effect.Remote sensing technology by right of its convenient,rapid and other advantages has been widely used in forest biomass and carbon storage studies. The article research region in Saihanwula national nature reserve, based on local plot measured biomass data, the SPOT and TM data were used as remote sensing information sources, extracting bands of TM images and relaed vegetation indexs, and the geographical data including slope, aspects, and elevation were got from DEM. The correlation analysis between extracted factors and local plot measured biomass data,find the correlation factors, and through multiple regression analysis, The birch, aspen and Mongolia oak remote sensing model for biomass and carbon storage were established respectively. Inversion of remote sensing model and analyze the result. The research results are as follows:(1) The TM image with SPOT image for effective fusion, After the visual interpretation,and based on the local forest species, draw the forest distribution map.(2) Using the multiple regression analysis, The major tree species remote sensing model for biomass of birch, aspen and Mongolia oak were established respectively. Further Proof of the Scientific and Rational remote sensing model through regression test and collinearity diagnosis. Estimation of the model precision,and the model precision is88%-92.7%.(3) Using the established remote sensing model for forest biomass. Inversion of the the total forest biomass is160,8000t and the total carbon storage is80,4000t,and to analyze the total forest biomass distribution change along with slope, aspects, and elevation.(4) Ruling the tree Species factors, It was found that the forest biomass distribution law change along with elevation increasing were in "converse-U"; the forest biomass decrease with the slope degree increase; and the forest biomass along with aspect increasing were in"U"... |
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