基于郁闭度联立方程组模型的森林生物量遥感估测

Abstract

Abstract:

Forest biomass estimation is the basis of carbon cycle in forest ecosystems and carbon dynamic analysis. Therefore, the accurate estimate of biomass is very important. Establishing biomass models is a major means of biomass estimation on a large scale. Based on remote sensing images of Changbai Mountain region in Heilongjiang province and data from continuous forest inventory of 122 permanent sample plots, 171 independent variables was chosed including options include band grayscale value, the different band combinations between the grey value of linear and nonlinear, texture information and environmental factors. Respectively adopting conventional regression model of biomass without canopy density variable, conventional regression model of biomass with canopy density variable, consociation equations model of biomass and canopy density, forest biomass was calculated in the Changbai Mountain region in Heilongjiang province, and precision evaluation was carried out. Research results showed that: simultaneous equations model of biomass and canopy density was the optimal model, with the accuracy of the model as high as 83.1%, and the precision was increased by 6%-7% compared with the other two models. This study provides a new train of thought for the estimate of forest biomass using the remote sensing.

Key words: canopy density, biomass, remote sensing estimation, simultaneous equations model

CLC Number:

S757

Li Mingze, Mao Xuegang, Fan Wenyi. Forest Biomass Estimation Using Remote Sensing Based on Canopy Density Simultaneous Equations Model[J]. Scientia Silvae Sinicae, 2014, 50(2): 85-91.

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Forest Biomass Estimation Using Remote Sensing Based on Canopy Density Simultaneous Equations Model

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