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.
Highlights
Forest managers are constantly facing new problems and challenges, which include climate change, mitigation and adaptation[1]
The parameters of the model are summarized by the 3 indices of D, H and Wood density (WD) [13,21]
The size of the trees can be described by the forest measurements D and H, and D and H are comprehensive statistics for the volume (TV)[27]
Summary
Forest managers are constantly facing new problems and challenges, which include climate change, mitigation and adaptation[1]. Accurate and precise measurements of forest ecosystem parameters such as biomass will be important for future forest management[2,3]. In addition to climate change, the development of a regional biomass energy industry and artificial forests means that the energy management problems will still exist, so highly accurate forest stand biomass models is of key importance[4]. Current biomass equations mainly use the following methods: the biomass factor method, the allometry growth equation method and the volume source biomass method[5].At present, PLOS ONE | DOI:10.1371/journal.pone.0169747.
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