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Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship

1
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
2
Research Center for Ecological Forecasting and Global Change, Northwest Agriculture and Forestry University, Yangling 712100, China
3
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
4
Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada
5
Ecological Modeling and Carbon Science, Department of Biology Science, University of Quebec at Montreal, Montreal, QC H3C 3P8, Canada
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Forests 2019, 10(8), 658; https://doi.org/10.3390/f10080658
Received: 19 May 2019 / Revised: 26 July 2019 / Accepted: 27 July 2019 / Published: 5 August 2019
(This article belongs to the Section Forest Ecology and Management)
The method of forest biomass estimation based on a relationship between the volume and biomass has been applied conventionally for estimating stand above- and below-ground biomass (SABB, t ha−1) from mean growing stock volume (m3 ha−1). However, few studies have reported on the diagnosis of the volume-SABB equations fitted using field data. This paper addresses how to (i) check parameters of the volume-SABB equations, and (ii) reduce the bias while building these equations. In our analysis, all equations were applied based on the measurements of plots (biomass or volume per hectare) rather than individual trees. The volume-SABB equation is re-expressed by two Parametric Equations (PEs) for separating regressions. Stem biomass is an intermediate variable (parametric variable) in the PEs, of which one is established by regressing the relationship between stem biomass and volume, and the other is created by regressing the allometric relationship of stem biomass and SABB. A graphical analysis of the PEs proposes a concept of “restricted zone,” which helps to diagnose parameters of the volume-SABB equations in regression analyses of field data. The sampling simulations were performed using pseudo data (artificially generated in order to test a model) for the model test. Both analyses of the regression and simulation demonstrate that the wood density impacts the parameters more than the allometric relationship does. This paper presents an applicable method for testing the field data using reasonable wood densities, restricting the error in field data processing based on limited field plots, and achieving a better understanding of the uncertainty in building those equations. View Full-Text
Keywords: allometric equation; biomass estimation; forest biomass dataset; observational error; parametric equation; parameter diagnosis; restricted zone; wood density allometric equation; biomass estimation; forest biomass dataset; observational error; parametric equation; parameter diagnosis; restricted zone; wood density
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Liu, C.; Zhou, X.; Lei, X.; Huang, H.; Zhou, C.; Peng, C.; Wang, X. Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship. Forests 2019, 10, 658.

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