Abstract:

Based on the continuous forest inventory data and remote sensing data from 2002 to 2017,parameter models and BP artificial neural network models were established,and the dynamic analysis was carried out. The effects of time phase and estimation scale of remote sensing data on the inversion accuracy were analyzed by using different monthly,quarterly and annual data in 2002.The results showed that the linear relationship between biomass density,carbon storage density and remote sensing indexes was weak,with Pearson correlation coefficient less than 0.60,and there were significant differences between biomass density and carbon storage density in different periods. The fitting determination coefficient of biomass density and carbon density by BP artificial neural network models increased by 0.0705 and 0.0762 on average compared with the parameter models. During the period of 2002 to 2017,the biomass density and carbon density of forest vegetation in Sichuan Province showed an increasing trend assisting GPS precise positioning for timing data calibration,and the dynamic change law of the two was consistent.There were obvious periods of overestimation and underestimation of biomass density and carbon density in remote sensing data inversion,and the estimation effect of quarterly data and annual data was not as good as that of monthly data. Different estimation scales had significant influence on the relative error of remote sensing data inversion.

Key words: biomass, carbon density, remote sensing retrieval, artificial neural network, time series data calibration