Uncertainties in above ground tree biomass estimation
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ORIGINAL PAPER
Uncertainties in above ground tree biomass estimation Lihou Qin1 · Shengwang Meng2 · Guang Zhou3 · Qijing Liu1 · Zhenzhao Xu1
Received: 10 June 2020 / Accepted: 30 August 2020 © Northeast Forestry University 2020
Abstract Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data. However, many sources of uncertainty affect above-ground biomass estimation and are challenging to assess. In this study, the uncertainties associated with the measurement error in independent variables (diameter at breast height, tree height), residual variability, variances of the parameter estimates, and the sampling variability of national inventory data are estimated for five above-ground biomass models. The results show sampling variability is the most significant source of uncertainty. The measurement error and residual variability have negligible effects on forests above-ground biomass estimations. Thus, a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty. The power model containing only the diameter at breast height has the smallest uncertainty. The findings of this study provide Project funding: This work was supported financially by the National Key R&D Program of China (Grant No. 2017YFC0506503-02). The online version is available at https://www.springerlink.com. Corresponding editor: Zhu Hong. * Qijing Liu [email protected] 1
College of Forestry, Beijing Forestry University, Beijing 100083, People’s Republic of China
2
Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China
3
Jiangxi Academy of Forestry, Nanchang 330022, People’s Republic of China
suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work. Keywords Above-ground biomass · Measurement error · Residual variability · Parameter estimates · Sampling variability
Introduction Forests sequester and store large amounts of carbon and play an important role in the carbon cycle (Foley et al. 2005). Above-ground biomass (AGB), which is a crucial indicator of the carbon storage capacity of forest ecosystems (Bonan et al. 1992), is an important parameter for evaluating carbon sinks and analyzing matter and energy flow in forest ecosystems (Alongi et al. 2003). However, many sources of uncertainty can affect forest biomass prediction (Temesgen et al. 2015). One of the challenges confronting scientists is the estimation of uncertainties in forest biomass prediction (Wang et al. 2009). The traditional method to estimate forest AGB from the individual tree scale to larger scales involves three steps (van Breugel et al. 2011): (1) models are used to predict the AGB of individual trees in forest inventory plots; (2) AGB at the plot level is estimated by summing the AGB of all tre
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