Evaluating a non-destructive method for calibrating tree biomass equations derived from tree branching architecture
- 577 Downloads
Functional branch analysis (FBA) is a promising non-destructive method that can produce accurate tree biomass equations when applied to trees which exhibit fractal branching architecture.
Functional branch analysis (FBA) is a promising non-destructive alternative to the standard destructive method of tree biomass equation development. In FBA, a theoretical model of tree branching architecture is calibrated with measurements of tree stems and branches to estimate the coefficients of the biomass equation. In this study, species-specific and mixed-species tree biomass equations were derived from destructive sampling of trees in Western Kenya and compared to tree biomass equations derived non-destructively from FBA. The results indicated that the non-destructive FBA method can produce biomass equations that are similar to, but less accurate than, those derived from standard methods. FBA biomass prediction bias was attributed to the fact that real trees diverged from fractal branching architecture due to highly variable length–diameter relationships of stems and branches and inaccurate scaling relationships for the lengths of tree crowns and trunks assumed under the FBA model.
KeywordsTree biomass Functional branch analysis Fractal geometry Allometry
The authors would like to thank the World Wildlife Fund and the Global Environmental Facility of the United Nations, members of the Carbon Benefits Project at Michigan State University and ICRAF offices in Kisumu and Nairobi, Kenya, without whose support the data used in this study would not have been generated. The authors would also like to thank two anonymous reviewers for suggestions to improve the quality of this manuscript.
Conflict of interest
The authors declare that they have no conflict of interest to report regarding this submission.
- Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer. FAO Forestry Paper-134. FAO, RomeGoogle Scholar
- Brown SAJ, Gillespie JR, Lugo AE (1989) Biomass estimation methods for tropical forests with application to forest inventory data. For Sci 35(4):881–902Google Scholar
- Burgess I (1988) Provenance trials of Eucalyptus grandis and E. saligna in Australia. Silvae Genetica 37:221–227Google Scholar
- Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure J-P, Nelson BW, Ogawa H, Puig H, Riéra B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145(1):78–99CrossRefGoogle Scholar
- Clark NA, Wynne RA, Schmoldt DL (2000) A review of past research on dendrometers. For Sci 46(4):570–576Google Scholar
- Gibbs HK, Brown S, Niles JO, Foley JA (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2:045023. doi: 10.1088/1748-9326/2/4/045023
- Shinozaki K, Yoda K, Hozumi K, Kira T (1964a) A quantitative analysis of plant form—the pipe model theory I. Basic analysis. Jpn J Ecol 14:97–105Google Scholar
- Shinozaki K, Yoda K, Hozumi K, Kira T (1964b) A quantitative analysis of plant form—the pipe model theory II. Further evidence of the theory and its application in forest ecology. Jpn J Ecol 14:133–139Google Scholar
- Wiemann MC, Williamson GB (2011) Testing a novel method to approximate wood specific gravity of trees. For Sci 58(6):577–591Google Scholar