Study On Forest Carbon Estimation Method Based On Remote Sensing Data And Ground Plot Information

As the largest carbon sink in terrestrial ecosystems, forest ecosystems play an important role in balancing global carbon budget and mitigating the effect of global warming. Recently, the widely used approach for quantifying aboveground forest carbon storage is to use forest resources inventory data (FID) and the relationship between dominant tree species and its biomass. Based on this method, combining forest resources inventory data (FID) and remotely sensed images can lead to spatial distribution of predicted aboveground forest carbon storage at both regional and global scales. This method can well provide the results required by the relevant departments where decisions are made.This paper explores forest carbon estimation models based on remote sensing data and FID, and study the relationships between forest carbon storages and Landsat Thematic Mapper (TM) spectral responses through analyses of the study area in Lin'an county. A total of 930 sample plots obtained from Lin'an county forest resources inventory in 2004 were used. For each plot, aboveground forest biomass was calculated based on allometric equations of four dominant tree species from literatures. The biomass was then converted to forest carbon using standard coefficients. Six TM bands and many vegetation indices were examined through integration of spectral responses and vegetation inventory data. Pearson's correlation coefficients were used to interpret relationships between forest carbon and TM data. Suitable TM data was selected based on the correlation analysis and used as the variable to simulate aboveground forest carbon storage and its spatial distribution. These forest carbon estimation models were established by simple linear regression based on least squares and polynomial, stepwise multiple linear regression, error back-propagation neural network (BP-ANN) and radial-basis-function neural network (RBF-ANN) methods.The results showed the forest carbon storage can be better estimated by the artificial neural networks than by linear regression methods. The obtained estimates with the BP-ANN algorithm were quite similar to the observed values at the sample locations. The mean estimate of carbon density for the whole study area was 0.98Mg (10.89 Mg/hm2) which was smaller than the average from the sample plots with a relative error of only 13%. Although the RMSE remained relatively large, the predictions were more accurate compared to those from previous studies. At the same time, although the obtained estimates with the BRF-ANN algorithm were also quite similar to the observed values at the sample locations, the estimation accuracy was slightly smaller than that with the BP ANN algorithm. However, the simulation results were more stable through adjusting the spread coefficient of RBF-ANN compared to the results of BP-ANN. These findings implied that the artificial neural networks are a promising tool that can be used to estimate and simulate forest carbon storage and can well provide the results required by the relevant departments where decisions are made.