Abstract:

Objective: Based on actual measurement biomass data and weighted average carbon content of the above-ground and below-ground components of Schima superba in Guangdong Province, this study established the above-ground and below-ground biomass models for individual tree. The carbon density and its uncertainty for S. superba were estimated at a regional-scale, which could provide a reference for the estimation of tree species carbon sinks at other regional-scale. Method: According to the inventory data of distribution of S. superba in Guangdong Province, the number of 90 trees of S. superba were cut down, the carbon content and biomass of the above-ground part were measured and the number of 40 trees among were selected to measure the carbon content and biomass of the below-ground part. The above-ground and below-ground biomass relative growth models were constructed respectively based on diameter at breast height (DBH). The model parameters were obtained by non-linear regression. Based on the 8^th National Forest Inventory data of Guangdong Province, Monte Carlo method was used to simulate the process of estimating the carbon density of S. superba components at the regional-scale. Used R-square, root-mean-square error and mean predicted error to evaluate the fitting individual tree biomass model effect. Regional-scale carbon density uncertainty were calculated by root-mean-square error and relative-root-mean-square error. Result: 1) The above-ground carbon content is 0.554 9 and the below-ground carbon content is 0.548 7 for S. superba in Guangdong Province; 2) The individual tree above-ground biomass model's R² is 0.909 8 and the below-ground biomass model's R² is 0.793 1, which showed that the biomass models fitted well and predicted accurately; 3) In the 8^th National Forest Inventory in Guangdong Province, the above-ground carbon density of S. superba was 5.80±0.44 t·hm^-2, uncertainty was 7.62%; the below-ground carbon density was 1.73±0.17 t·hm^-2, uncertainty was 9.76%; the total carbon density was 7.53±0.54 t·hm^-2, uncertainty was 7.23%. Conclusion: The above-ground part and the below-ground part carbon content of S. superba in Guangdong Province is higher than the average level in southern China, and it obviously has regional characteristics. The stable and reliable regional-scale estimates of carbon density and their uncertainty could be obtained by using Monte Carlo method.

Key words: carbon content, biomass model, carbon density, uncertainty quantification, Monte Carlo simulation