The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study (original) (raw)
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Modelling of Allometric Equations for Biomass Estimate in Deciduous Forest
FLORESTA, 2018
This paper aimed to test and adjust allometric models to estimate biomass in a Deciduous Forest. The data were obtained from seven 12 x 12 m plots, from which 91 trees were cut down. Only trees with diameter at breast height (DBH) greater than 5 cm were measured, and the fitting of the models was performed based on the DBH, total height (H) and total dry biomass (DAB) for each individual tree. The adjusted equations with no stratification presented adjusted determination coefficients (R 2 aj) ranging from 0.726 to 0.972 and standard errors in percentage (Syx%) from 33.5 to 119.6. The best adjusted model for nonstratified dataset was obtained by the Stepwise procedure, leading to the equation: DAB = β0 + β1.(DBH 3) + β2.H + β3.(DBH 3 .H), with 0.954 of R 2 aj and 44.0 of Syx%. For stratified dataset, only the diameter class higher than 15 cm presented acceptable results, with 0.968 of R 2 aj and 26.5 of Syx%. The current database has shown good quality measurements for fitting stochastic models to estimate the biomass of each tree.
Ecological Indicators, 2020
Developing allometric biomass models is an important process because reliability of forest biomass and carbon estimations largely depend on the accuracy and precision of such models. The effects of tree sampling on tree aboveground biomass (AGB) prediction accuracy and precision are complex and can, therefore, be difficult to quantify. In this paper we use a Monte Carlo simulation to investigate how model prediction accuracy and precision are affected by tree sampling approaches. Because diameter at breast height (D, in cm) is the most common predictor of tree AGB (in kg dry weight), we focused our analysis on the AGB-D relationship. The following sample characteristics were investigated: (i) sample size; (ii) extent of the D-range (difference between the largest and the smallest D value); (iii) position of D-range (characterized by the starting point of D-range); and (iv) the size-distribution (distribution of D) of sample trees. We found that, although the natural variability of AGB-D relationship was a key driver for both prediction accuracy and precision, the above sample characteristics were important for improving prediction accuracy. Although having a negligible effect on precision, both sample size and size-distribution of sample trees, greatly influenced prediction accuracy. We demonstrate that selecting a constant number of trees for each D class (i.e. uniform distribution of the sample trees over the Drange) generally produced models that were more accurate predictors of AGB. The extent and position of Drange, although considerably affecting the goodness of fit and the standard errors of allometric model parameters, had only a marginal effect on AGB prediction accuracy and precision. Furthermore, we showed that R 2 was a poor indicator of model prediction accuracy and precision, due to its sensitivity to changes in D-range. These findings inform certain practical recommendations we report for improving the accuracy and precision of biomass prediction.
Forests
In this paper, site-specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several approaches to minimize the demand for site-specific observations in allometric biomass model development were also investigated. Developing site-specific allometric biomass models requires new measurements of biomass for a sample of trees from that specific site. Yet, measuring biomass is laborious, time consuming, and requires extensive logistics, especially for very large trees. The allometric biomass models were developed for a wide range of diameters at breast height, D (6–86 cm for European beech and 6–93 cm for silver fir) using a logarithmic transformation approach. Two alternative approaches were applied, i.e., random intercept model (RIM) and a Bayesian model with strong informative priors, to enhance the information of the site-specific sample (of biomass ...
CAPITALIZING ON THE INFORMATION IN ALLOMETRIC EQUATION DATA BASES FOR FOREST BIOMASS ESTIMATION
In many countries, inventory data or biomass or volume equations are often incomplete or unavailable. Either taxonomic information is not accurate at the species level, or else no literature exist compiling particular allometric equations for some species. On the other hand, some species are represented by many alternative equations in the database. The vast quantity of information that allometric equation databases such as Globallometree can provide, can be capitalized to inform other, non-available species from the ranges and distributions of aboveground biomass estimates that other, better known species provide. In this study we provide an alternative method that takes those elements to estimate overall plot aboveground biomass from bootstrapping different equations belonging to a certain ecozone. Using a real inventory plot as an example, we prove that such estimates present error levels similar to those of generalized pantropical equations when a minimum set of rules for quality control has been added. This opens the possibility to establish more adequate quality control protocols that end up providing even better estimates than those published pantropical equations.
Forest Ecology and Management, 2001
Estimates of forest biomass are needed for tracking changes in C stocks, as well as for other purposes. A common method for estimating forest biomass is through use of allometric equations which relate the biomass of individual trees to easily obtainable non-destructive measurements, such as diameter. A common form is BaD b for biomass B, diameter D and parameters a and b. Field data collected in Sumatra and compared with previously published data show that the values of a and b vary between sites. This variation is likely to be the major source of uncertainty if biomass estimates are produced using equations that are not calibrated for individual sites. However, calibration by collection of B and D data for each site is unrealistic, requiring destructive measures. Methods of choosing values for a and b are, therefore, proposed that do not require destructive measurements. The parameter b can be estimated from the site-speci®c relationship between height (H) and diameter, HkD c as b2c. The parameter a can be estimated from the average wood density (r) at the site as arr, where r is expected to be relatively stable across sites. The allometric equation proposed is therefore BrrD 2c . #
On simplifying allometric analyses of forest biomass
Forest Ecology and Management, 2004
Tree biomass plays a key role in sustainable management and in estimating forest carbon stocks. The most common mathematical model in biomass studies takes the form of the power function M=aDb where a and b are the allometric coefficients to be determined by empirical data, and M the total aboveground tree dry biomass for a specific diameter at breast height, D.In this study the development and comparison of three methods for simplifying allometric equations of aboveground biomass estimation are reported. Based on the criterion of the relative difference (RD) between observed and predicted biomass data, the small trees sampling scheme (SSS) predicted quite accurate estimates for raw data reported in 10 studies. The SSS equation was based on the hypothesis that information provided in published allometric equations, in conjunction with two pairs of empirical M–D values, are enough to obtain reliable predictions for aboveground stand biomass. In addition, predictions of M based on theoretical values of b were also tested with the RD criterion, but reliability of predictions in 10 studies is questioned. Finally, fractal geometry was used to develop a ‘reductionist’ model for M estimation and implications from its implementation in biomass studies are discussed. We totally based our investigation on a metadata set derived from published aboveground biomass allometric studies conducted for different species spanning the globe.
Forest Ecology and Management, 2006
Allometric relations for tree growth modelling have been subject to research for decades, partly as empirical models, and partly as process models such as the pipe model, hydraulic architecture, mechanical approaches or the fractal-like nature of plant architecture. Unlike empirical studies, process models aim at explaining the scaling within tree architecture as a function of biological, physical or mechanical factors and at modelling their effect on functionality and growth of different parts of an individual tree. The goal of the underlying study is to link theoretical explanation to empirical approaches of tree biomass estimation by the example of Norway spruce (Picea abies [L.] Karst.). Decisively, this article tries to take allometry out of the purely curve-fitting exercise common in literature and derives implications for the use of allometric biomass functions.
Minimizing Bias in Biomass Allometry: Model Selection and Log-Transformation of Data
Biotropica, 2011
Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the traditional approach of logtransformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models. Here, we show that such models may bias stand-level biomass estimates by up to 100 percent in young forests, and we present an alternative nonlinear fitting approach that conforms with allometric theory.
Forest Ecology and Management
Secondary forests are a major terrestrial carbon sink and reliable estimates of their carbon stocks are pivotal for understanding the global carbon balance and initiatives to mitigate CO2 emissions through forest management and reforestation. A common method to quantify carbon stocks in forests is the use of allometric regression models to convert forest inventory data to estimates of aboveground biomass (AGB). The use of allometric models implies decisions on the selection of extant models or the development of a local model, the predictor variables included in the selected model, and the number of trees and species for destructive biomass measurements. We assess uncertainties associated with these decisions using data from 94 secondary forest plots in central Panama and 244 harvested trees belonging to 26 locally abundant species. AGB estimates from species-specific models were used to assess relative errors of estimates from multispecies models. To reduce uncertainty in the estim...
Allometric Equations for Aboveground Biomass Estimations of Four Dry Afromontane Tree Species
2020
Background: Tree species based developing allometric equations are important because they contain the largest proportion of total biomass and carbon stocks of forests. Studies on developing and validating the species-specific allometric models (SSAM) remain insufficient that may result to biomass estimation errors in the forests. The purpose of this study is to determine the wood density of four tree species and develop and validate the accuracy of allometry for biomass estimations. A total of 103 sample trees representing four species were harvested semi-destructively. The species specific allometric equations (SSAM) were developed using aboveground biomass (AGB in kg) as dependent variable, and three of the predictor's variables: diameter at beast height (DBH in cm), height (H in m) and wood density (WD in g cm-3). The relation between dependent and independent variables were tested using multiple correlations (R 2). The model selection and validation was based on statistical significance of model parameter estimates, Akaike Information Criterion (AIC), adjusted coefficient of determination (R 2), residual standard error (RSE) and mean relative error (MRE). Results: The results showed that the AGB correlated significantly with diameter at breast height (R 2 > 0.944, P < 0.001), and tree height (R 2 > 0.742, P <0.001). The species-specific allometric models, which include DBH, H and WD predicted AGB with high-model fit (R 2 > 93.6%, P < 0.001). These models for biomass estimations produced small MRE (1.50-3.40%) and AIC (-7.04-12.84) compared to a single predictor (MRE:-0.4-20.1%; AIC:-7.25-35.29). The SSAM also predicted AGB against predictors with high-model fit (R 2 > 93.6%, P < 0.001) and small MRE: 1.50-3.40% compared to existing general allometric models (MRE:-31.3-11.31%). Conclusions: The research confirmed that the inclusion of DBH, H, and WD in the SSAM predicted AGB with small bias than a single or two predictors. The wood density values of those studied species can be used as the references for biomass estimations using general allometric 2 equations. The study contributes to species-specific allometric models for understanding the total biomass estimation of species. Therefore, the application of species-specific allometric models should be considered in biomass estimations of forests.