Variation in Carbon Concentration and Allometric Equations for Estimating Tree Carbon Contents of 10 Broadleaf Species in Natural Forests in Northeast China

Abstract

In this study, the effects of tree species, tissue types, and tree size on the carbon concentration were studied, and the two additive systems, one with tree diameter (D), and the other with both D and tree height (H), were developed to estimate the stem, root, branch, and foliage carbon content of 10 broadleaf species in northeast China. The coefficients of the two systems were estimated with the nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residual was solved with the weight function. Our results showed that carbon concentrations varied along with tree species and size; the tissues and foliage contained higher carbon concentration than other observed tissues. The two additive carbon equation systems exhibited good predictive and fitting performance, with Ra2 > 0.87, average prediction error of approximately 0, and small average absolute error and absolute error percentage. The carbon equation system constructed with D and H exhibited better fit and performance, particularly for the stem and total carbon. Thus, the additive carbon equation systems estimated the tree carbon of 10 broadleaf species more accurately. These carbon equations can be used to monitor the carbon pool sizes for natural forests in the Chinese National Forest Inventory.

Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

“…On the contrary, some authors prefer to multiply the biomass value with the total weighted mean carbon concentration (TWMCc) of an individual tree [50,51], while others prefer to used respective mean component carbon concentration (RMCCc) as a multiplying factor to obtain carbon stock estimates [52]. These second forms of approaches are considered a better approach since many studies have reported that carbon concentration varies greatly depending upon the type of tree species and tissues [37,53]. Accordingly, using a generic carbon concentration conversion factor (0.50) may cause serious errors in estimating carbon stock.…”

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

“…Dong L.H. et al researched the carbon content of 10 broadleaf species in natural forests in northeast China and observed significantly different carbon contents among the tree species [13]. Marín P. G. et al measured the carbon content of 175 species in Mexican forests and found that tree species from different biomes significantly differed in their carbon contents (42.8% to 44.3% for species from the tropics and subtropics and from 43.8% to 44.2% for species in arid and semiarid zones; F = 9.07, p = 0.0002) [14].…”