Correcting the overestimate of forest biomass carbon on the national scale

Abstract

Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

“…It implies that PEs with a parameter ρ is advantageous for improving the accuracy of biomass prediction. This supports previous analyses of using wood density as a parameter in biomass equations [25,40,57,58].…”

“…In the bottom right part of each scatter plot, Bs and V have a significant linear relationship with all coefficients of determination greater than 0.6, although a few samples are located in the restricted zones for some species and forest types (No. 8,10,15,18,25,27,29,30). All regression curves (Bs-to-Bt) and lines (V-to-Bs) avoid falling in the "restricted zones."…”

“…For instance, the tree-level models with independent variables in diameter at breast height (DBH) and tree height [18][19][20][21] are suitable for predicting the biomass using tree-scale data. If only statistics of the volume information are available, the stand-level models can be employed to estimate the biomass per unit area [22][23][24][25]. As for the types of indirect methods (ii) and (iii), they are helpful for addressing the mechanism or dynamics of forest carbon sequestration on large temporal-spatial scales [3].…”

“…Using these stand-level models may be the appropriate way, especially in cases where plot data are not available at large scales, to convert forest biomass from growing stock volume per unit area [16]. The reasons for using hectare-based volume as a predictor is due to the data sources, which are often lacking in data for DBH and height for global-scale biomass estimation [25]. In many countries, the data of DBH and height are not released [30].…”

Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years

“…However, the uncertainties in FCS estimates are hard to remove on the national scale. Based on a partial literature review (Zhou et al 2016), existing studies suggest large differences between China's FCS estimates. It is necessary to explore a way to reduce such gaps that could appear in FCS estimates of some regions or countries, as the gaps may influence understanding for the FCS itself as well as many study results associated with the FCS on national or global scales.…”