关键词: 森林碳储量, 碳密度, 碳分布, TM影像, 序列高斯协同模拟, 临安

Abstract:

Estimation of the forest carbon distribution is an important subject in study of forest carbon. Based on National Forest Inventory (NFI) data and the Landsat TM image data collected in Lin'an County, Zhejiang in 2004, this research applied two methods, namely unary quadratic nonlinear modeling and Sequential Gaussian co-Simulation to reproduce the distribution of above ground forest carbon, and compared and analyzed the estimation results of above ground forest carbon density. The estimation results with unary quadratic nonlinear regression estimation show that the sum of above ground carbon is 2 365 404.37 t, the mean carbon density is 9.000 0 t ·hm^-2, with the maximum carbon density of 73.714 4 t ·hm^-2, and the minimum carbon density of 0.715 6 t ·hm^-2. With the Sequential Gaussian co-Simulation, the sum of the carbon is 3 291 659.83 t, the mean carbon density is 12.523 3 t ·hm^-2, with the maximum carbon density of 78.913 3 t ·hm^-2, and the minimum carbon density of 0.083 3 t ·hm^-2. According to the NFI data in 2004 , the carbon storage for the study area is estimated with the random sampling method. The total carbon is 2 708 897.90 t, the mean carbon density is 10.306 5 t ·hm^-2, with the maximum carbon density of 96.962 5 t ·hm^-2, the minimum carbon density of 0.000 0 t ·hm^-2. The carbon density from the Sequential Gaussian co-Simulation are closer to that calculated from the NFI data, and the carbon density distribution is more reasonable. The sum of squares of differences between unary quadratic nonlinear regression result and the sample plot data is 9 857.461 9, while that between the results from the Sequential Gauss co-Simulation and the sample plot data is 8 018.462 5. The Sequential Gaussian co-Simulation is relatively better than unary quadratic nonlinear regression on regional forest carbon density estimation.

Key words: forest carbon storage, carbon density, carbon distribution, TM images, Sequential Gauss co-Simulation, Lin'an