title: Models for Predicting the Biomass of Cunninghamialanceolata Trees and Stands in Southeastern China.
abstract: Using existing equations to estimate the biomass of a single tree or a forest stand still involves large uncertainties. In this study, we developed individual-tree biomass models for Chinese Fir (Cunninghamia lanceolata.) stands in Fujian Province, southeast China, by using 74 previously established models that have been most commonly used to estimate tree biomass. We selected the best fit models and modified them. The results showed that the published model ln(B(Biomass)) = a + b * ln(D) + c * (ln(H))2 + d * (ln(H))3 + e * ln(WD) had the best fit for estimating the tree biomass of Chinese Fir stands. Furthermore, we observed that variables D(diameter at breast height), H (height), and WD(wood density)were significantly correlated with the total tree biomass estimation model. As a result, a natural logarithm structure gave the best estimates for the tree biomass structure. Finally, when a multi-step improvement on tree biomass model was performed, the tree biomass model with Tree volume(TV), WD and biomass wood density conversion factor (BECF),achieved the highest simulation accuracy, expressed as ln(TB) = -0.0703 + 0.9780 * ln(TV) + 0.0213 * ln(WD) + 1.0166 * ln(BECF). Therefore, when TV, WD and BECF were combined with tree biomass volume coefficient bi for Chinese Fir, the stand biomass (SB)model included both volume(SV) and coefficient bi variables of the stand as follows: bi = Exp(-0.0703+0.9780*ln(TV)+0.0213 * ln(WD)+1.0166*ln(BECF)). The stand biomass model is SB = SV/TV * bi.
authors.affiliation: Laboratory for Silviculture and Conservation, Beijing Forestry University, Beijing, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Guangyi
authors.affiliation: Laboratory for Silviculture and Conservation, Beijing Forestry University, Beijing, China.
authors.forename: Sun
authors.identifier: http://orcid.org/0000-0001-9281-0809
authors.initials: S
authors.lastname: Yujun
authors.affiliation: Laboratory for Silviculture and Conservation, Beijing Forestry University, Beijing, China.
authors.forename: Sajjad
authors.initials: S
authors.lastname: Saeed
mesh_terms: D018533:Biomass; D002681:China; D032486:Cunninghamia / Q000254:growth & development*; D065928:Forests; D008962:Models, Theoretical; D014197:Trees / Q000254:growth & development*