Abstract
Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.
Highlights
The carbon cycle of the earth has been massively altered by anthropogenic activities [1]
The objectives of the present study were to (1) develop site-specific allometric models to estimate above and belowground biomass of subtropical forest in Guanzhou; (2) reveal the variation of root-shoot ratio between species and with tree size class, and assess the suitability of using root-shoot ratio to predict belowground biomass; (3) examine the fitness of models adding H and wood density (WD) as a second variable following with diameter at breast height (DBH) or combined with DBH as one variable
The fraction of biomass stored in leaves of total biomass had a significant positive correlation with DBH of Castanopsis chinensis, while there was an extremely significant negative correlation of Aleurites montana and Machilus chinensis, but when using all species data for statistics, the relation was negligible (Table 2)
Summary
The carbon cycle of the earth has been massively altered by anthropogenic activities [1]. Quantification of amounts of carbon stored at scales ranging from local to global levels is crucial for accurately predicting future changes in atmospheric carbon dioxide and climate, and to help define management options for the global carbon cycle [2]. An accurate estimation of the magnitude of carbon stocks in various vegetation types is essential for understanding global and regional carbon budgets [1], and is the basis for reporting changes in carbon stock as required in the emerging Reducing Emissions from Deforestation and Forest Degradation in developing countries (REDD+) mechanism. Forest biomass and carbon stock can be estimated by using direct or indirect methods [3]. Remote sensing techniques are ideal indirect methods for quantifying the forest biomass over vast areas, but are limited by technology, cloud cover and fly-over frequency [4]. The allometric technique initially requires an extensive destructive sampling to establish allometric models, and the models can be used as a non-destructive method to estimate the whole or partial weight of a tree from measurable tree dimensions (e.g., stem diameter and height) [5,6]
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