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

Abstract

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 larger, associated with relatively smaller errors and least biased than the BEF-based ones. For stem and stem wood, the percentages of their total errors (as total variance) attributed to BEF model were considerably smaller than those attributed to biomass regression equations.

Biomass allometric equation and expansion factor for a mountain moist evergreen forest in Mozambique

“…However, BAE and BEF functions previously developed in Mozambique were developed for forest types other than moist evergreen forest, e.g. lowland miombo woodland [ 15 – 17 ], mangrove forests [ 18 ] and mecrusse woodlands [ 19 – 21 ]. Moreover, the degree of reliability of the existing general allometric models and BEF functions and those suggested for moist in tropical zones [ 2 , 22 – 24 ] must be checked if applied in a site different than that where they were originally developed [ 25 ].…”

Least squares-based biomass conversion and expansion factors best estimate biomass than ratio-based ones: Statistical evidences based on tropical timber species

“…Biomass regression equations are developed as linear or non-linear functions of one or more tree-level dimensions. When biomass equations are fitted in such a way that they specify tree component biomass as directly proportional to stem volume, the ratios of proportionality are then called biomass conversion and expansion factors (BCEFs) [2].…”

“…National and regional aboveground biomass (AGB) estimates and greenhouse gas (GHG) reporting are generally based on BCEFs [[2], [3]], mainly because of its readiness to convert standing stem volumes from forest inventories into different tree component biomasses [4], including the non-commercial components (foliage, needles, branches, root system, etc.) [5].…”

Site-specific height-diameter and stem volume equations for Lebombo-ironwood

“…When the model used fits reasonably well the sample data, the statistical model error is generally small (Cunia 1986b, McRoberts & Westfall 2015. This error is negligible when the predictors explain a large portion of the variation of the dependent variable (Magalhães 2015a), therefore this source of error can be judged by the coefficient of determination (R 2 ). McRoberts & Westfall (2015) stated that this source of error is typically not a problem when R 2 > 85%.…”

“…McRoberts & Westfall (2015) stated that this source of error is typically not a problem when R 2 > 85%. The error due to uncertainty in the model parameter estimates is expressed by the parameter variance-covariance matrix (Magalhães 2015a). Here, this error is expressed by the standard errors of the regression parameters, as they are the square roots of the respective variances obtained from the variance-covariance matrix.…”