湖北林业科技

Abstracts:With the fast development of remote sensing technology,the method based on remote sensing image and sample plots has become the major means of accurate estimation of forest carbon density.However,there were still no universal factors and optimal models for the estimation of forest carbon density.The objective of this paper was to study the estimation of forest carbon density by combining plots data and remote sensing images of Landsat-5 using the methods of stepwise regression,partial least squares regression and nonlinear regression respectively.First, various remote sensing factors derived from Landsat 5 images were generated using different transformations such as band ratios,vegetation indices calculation,principal component analysis and texture transformation.Then,effective remote sensing factors were selected to conduct the estimation of forest carbon density,according to the correlation analysis between the fixed sample plots and factors derived from Landsat 5 images.Finally, the accuracy of Landsat 5 derived maps was assessed using R2,Root Mean Square Error and Relative Error. The results showed that ①the correlation coefficients of 1/TM3 with plot values was the highest.② Among the built models, the effect of nonlinear model built by four remote sensing factors was the best with R2 of 0.74.③The mean value of forest carbon density of research area was 14.36 t/hm2,ranging from 0.00 to 38.38 t/hm2.This implied that nonlinear regression showed a certain potential in the aspect of region estimation of forest carbon density.