Developing individual tree-based models for estimating aboveground biomass of five key coniferous species in China

Weisheng Zeng, Liyong Fu, Ming Xu, Xuejun Wang, Zhenxiong Chen, Shunbin Yao

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling. In this study, we developed one- (tree diameter only) and two-variable (tree diameter and height) biomass equations, biomass conversion factor (BCF) models, and an integrated simultaneous equation system (ISES) to estimate the aboveground biomass for five conifer species in China, i.e., Cunninghamia lanceolata (Lamb.) Hook., Pinus massoniana Lamb., P. yunnanensis Faranch, P. tabulaeformis Carr. and P. elliottii Engelm., based on the field measurement data of aboveground biomass and stem volumes from 1055 destructive sample trees across the country. We found that all three methods, including the one- and two-variable equations, could adequately estimate aboveground biomass with a mean prediction error less than 5%, except for Pinus yunnanensis which yielded an error of about 6%. The BCF method was slightly poorer than the biomass equation and the ISES methods. The average coefficients of determination (R2) were 0.944, 0.938 and 0.943 and the mean prediction errors were 4.26, 4.49 and 4.29% for the biomass equation method, the BCF method and the ISES method, respectively. The ISES method was the best approach for estimating aboveground biomass, which not only had high accuracy but also could estimate stocking volumes simultaneously that was compatible with aboveground biomass. In addition, we found that it is possible to develop a species-invariant one-variable allometric model for estimating aboveground biomass of all the five coniferous species. The model had an exponent parameter of 7/3 and the intercept parameter a0 could be estimated indirectly from stem basic density (a0 = 0.294 ρ).

Original languageEnglish (US)
Pages (from-to)1251-1261
Number of pages11
JournalJournal of Forestry Research
Issue number5
StatePublished - Sep 1 2018

All Science Journal Classification (ASJC) codes

  • Forestry


  • Allometric equations
  • Biomass conversion factor
  • Biomass models
  • Error-in-variable simultaneous equations


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