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Live above-and belowground biomass of a Mozambican evergreen forest:a comparison of estimates based on regression equations and biomass expansion factors

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Forest Ecosystems 2016年第1期3卷 1-12页

作者： Tarquinio Mateus Magalhāes Departamento de Engenharia Florestal Universidade Eduardo MondlaneCampus Universitario

Background： Biomass regression equations are claimed to yield the most accurate biomass estimates than biomass expansion factors （BEFs）. Yet, national and regional biomass estimates are generally calculated based on BEFs, especially when using national forest inventory data. Comparison of regression equations based and BEF-based biomass estimates are scarce. Thus, this study was intended to compare these two commonly used methods for estimating tree and forest biomass with regard to errors and biases. Methods： The data were collected in 2012 and 2014. In 2012, a two-phase sampling design was used to fit tree component biomass regression models and determine tree BEFs. In 2014, additional trees were felled outside sampling plots to estimate the biases associated with regression equation based and BEF-based biomass estimates; those estimates were then compared in terms of the following sources of error： plot selection and variability, biomass model, model parameter estimates, and residual variability around model prediction. Results： The regression equation based below-, aboveground and whole tree biomass stocks were, approximately, 7.7, 8.5 and 8.3 % larger than the BEF-based ones. For the whole tree biomass stock, the percentage of the total error attributed to first phase （random plot selection and variability） was 90 and 88 % for regression- and BEF-based estimates, respectively, being the remaining attributed to biomass models （regression and BEF models, respectively）. The percent bias of regression equation based and BEF-based biomass estimates for the whole tree biomass stock were -2.7 and 5.4 %, respectively. The errors due to model parameter estimates, those due to residual variability around model prediction, and the percentage of the total error attributed to biomass model were larger for BEF models （than for regression models）, except for stem and stem wood components. Conclusions＂ The regression equation based biomass stocks were found to be slightly larg

关键词： androstachysjohnsonii prain Mecrusse Root growth Biomass additivity Double sampling Forest biomassinventory Carbon allocation

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