scholarly journals Allometric Equation for Aboveground Biomass Estimation for Selected Trees Shrubs in Gesha - Sayilem Moist Afromontane Forest.

Author(s):  
Admassu Addi Merti ◽  
Teshome soromessa Soromessa ◽  
Tura Bareke Kefle

Abstract Background: Allometric equations which are regressions linking the biomass to some independent variables that are used to estimate tree components from the forest. The generic equation developed by many authors may not adequately reveal the tree biomass in a specific region in tropics including in Ethiopia. Therefore, the use of species specific allometric equations is important to achieve higher levels of accuracy because trees of different species may differ in size and biomass. The objective of the study was to develop species-specific allometric equations for Apodytes dimidiata, Ilex mitis, Sapium ellipticum and shrubs (Galiniera saxifraga and Vernonia auriculifera) for estimating the aboveground biomass (AGB). Non-destructive sampling method was used for the measurement of tree biomass, accordingly the trees and shrubs whose Diameter at Breast Height (DBH) is ≥ 5 cm were sampled. For trees serial measurements of the height and diameter of trunk were done at 2 m intervals. For the determination of biomass of shrubs destructively sampled. Four branches were trimmed from tree and the trimmed branches were separated into leaves and wood and oven dried at 1050C and recorded to estimate the biomass of untrimmed small branches. Nested model was used and the best fit model was selected based on higher Adjusted R2, lower residual standard error and Akaike information criterion. Results: All the necessary biomass calculations were done, and biomass equations were developed for each species. The regression equations relate AGB with DBH, height (H), and density (ρ) were computed and the models were tested for accuracy based on observed data. The best model was selected based higher adj R2 and lower residual standard error and Akaike information criterion than rejected models. The relations for all selected models are significant (p<0.000), which showed strong correlation AGB with selected dendrometric variables. Accordingly, the AGB was strongly correlated with DBH and was not significantly correlated with wood density and height individually in Ilex mitis. In combination, AGB was strongly correlated with DBH, height and wood density; are better for carbon assessment than general equations.Conclusions: The specific allometeric equation developed for the Gesha-Sayilem Afromontane Forest which can be used in similar moist forests in Ethiopia for the implementation of Reduced Emission from Deforestation and Degradation (REDD+) activities to benefit the local communities from carbon trade.

2020 ◽  
Author(s):  
Admassu Merti ◽  
Teshome Soromessa ◽  
Tura Bareke

Abstract Background: Allometric equations which are regressions linking the biomass to some independent variables are used to estimate tree components from the forest. The generic equation developed by many authors may not adequately reveal the tree biomass in a specific region in tropics including in Ethiopia. Therefore, the use of species specific allometric equations is important to achieve higher levels of accuracy because trees of different species may differ. The objective of the study was to develop species-specific allometric equations for Apodytes dimidiata, Ilex mitis, Sapium ellipticum and shrubs (Galiniera saxifraga and Vernonia auriculifera) using semi-destructive method for estimating the aboveground biomass (AGB). For purpose of sampling trees, individual species were categorized into trees whose Diameter at breast height (DBH) is ≥ 5 cm.Results: All the necessary biomass calculations were done, and biomass equations were developed for each species. The regression equations relate AGB with DBH, height (H), and density (ρ) were computed and the models were tested for accuracy based on observed data. The best model was selected based higher adj R2 and lower residual standard error and Akaike information criterion than rejected models. The relations for all selected models are significant (p<0.000), which showed strong correlation AGB with selected dendrometric variables. Accordingly, the AGB was strongly correlated with DBH and was not significantly correlated with wood density and height individually in Ilex mitis. In combination, AGB was strongly correlated with DBH, height; DBH and wood density; are better for carbon assessment than general equations.Conclusions: The specific allometeric equation developed for the Gesha-Sayilem Afromontane Forest which can be used in similar moist forests in Ethiopia for the implementation of Reduced Emission from Deforestation and Degradation (REDD+) activities to benefit the local communities from carbon trade.


2020 ◽  
Author(s):  
Getaneh Gebeyehu ◽  
Teshome Soromessa ◽  
Tesfaye Bekele ◽  
Demel Teketay

Abstract 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 (R2). The model selection and validation was based on statistical significance of model parameter estimates, Akaike Information Criterion (AIC), adjusted coefficient of determination (R2), residual standard error (RSE) and mean relative error (MRE). Results: The results showed that the AGB correlated significantly with diameter at breast height (R2 > 0.944, P < 0.001), and tree height (R2 > 0.742, P <0.001). The species-specific allometric models, which include DBH, H and WD predicted AGB with high-model fit (R2 > 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 (R2 > 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 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.


2018 ◽  
Vol 30 (5) ◽  
pp. 1619-1632
Author(s):  
Amsalu Abich ◽  
Tadesse Mucheye ◽  
Mequanent Tebikew ◽  
Yohanns Gebremariam ◽  
Asmamaw Alemu

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Damena Edae Daba ◽  
Teshome Soromessa

Abstract Background Application of allometric equations for quantifying forests aboveground biomass is a crucial step related to efforts of climate change mitigation. Generalized allometric equations have been applied for estimating biomass and carbon storage of forests. However, adopting a generalized allometric equation to estimate the biomass of different forests generates uncertainty due to environmental variation. Therefore, formulating species-specific allometric equations is important to accurately quantify the biomass. Montane moist forest ecosystem comprises high forest type which is mainly found in the southwestern part of Ethiopia. Yayu Coffee Forest Biosphere Reserve is categorized into Afromontane Rainforest vegetation types in this ecosystem. This study was aimed to formulate species-specific allometric equations for Albizia grandibracteata Tuab. and Trichilia dregeana Sond. using the semi-destructive method. Results Allometric equations in form of power models were developed for each tree species by evaluating the statistical relationships of total aboveground biomass (TAGB) and dendrometric variables. TAGB was regressed against diameter at breast height (D), total height (H), and wood density (ρ) individually and in a combination. The allometric equations were selected based on model performance statistics. Equations with the higher coefficient of determination (adj.R2), lower residual standard error (RSE), and low Akaike information criterion (AIC) values were found best fitted. Relationships between TAGB and predictive variables were found statistically significant (p ≤ 0.001) for all selected equations. Higher bias was reported related to the application of pan-tropical or generalized allometric equations. Conclusions Formulating species-specific allometric equations is found important for accurate tree biomass estimation and quantifying the carbon stock. The developed biomass regression models can be applied as a species-specific equation to the montane moist forest ecosystem of southwestern Ethiopia.


2019 ◽  
Vol 8 (6) ◽  
pp. 245 ◽  
Author(s):  
Rita Libertad Adame-Campos ◽  
Adrian Ghilardi ◽  
Yan Gao ◽  
Jaime Paneque-Gálvez ◽  
Jean-François Mas

It is still a major challenge to select appropriate variables from remote sensing sensors, which implicates finding reliable selection methods that can maximize the performance of chosen variables in regression models. In this study, we compare the performance of stepwise variable selection based on Akaike information criterion and an approach that integrates relative importance techniques and the decomposition criteria of R 2 using two different remote sensing data: SPOT-5 and RapidEye images, with the purpose of selecting suitable variables in multiple linear regression models to estimate aboveground biomass. The obtained accuracy of the regression models was evaluated by triple cross-validation. We carried out this study in a mixed pine–oak forest of central Mexico where intensive wood extraction occurs and therefore different levels of degradation are found. We estimated aboveground biomass from field inventory data at the plot level (n = 52) and used well-established allometric equations. The results showed that a better fit was obtained with the explanatory variables selected from the RapidEye image ( R 2 = 0.437 with stepwise variable selection based on the Akaike information criterion approach and R 2 = 0.420 with relative importance techniques) and the approach that integrates the relative importance can generate better regression models to estimate forest biomass with a reduced number of variables and less error in the estimates.


2021 ◽  
Vol 3 ◽  
pp. 100050
Author(s):  
Athanase Mukuralinda ◽  
Shem Kuyah ◽  
Marcel Ruzibiza ◽  
Alain Ndoli ◽  
Nsharwasi Leon Nabahungu ◽  
...  

2016 ◽  
Vol 135 (5) ◽  
pp. 963-979 ◽  
Author(s):  
Wenhua Xiang ◽  
Jing Zhou ◽  
Shuai Ouyang ◽  
Shengli Zhang ◽  
Pifeng Lei ◽  
...  

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.


2014 ◽  
Vol 14 (2) ◽  
Author(s):  
Lauro Rodrigues Nogueira Júnior ◽  
Vera Lex Engel ◽  
John A. Parrotta ◽  
Antonio Carlos Galvão de Melo ◽  
Danilo Scorzoni Ré

Restoration of Atlantic Forests is receiving increasing attention because of its role in both biodiversity conservation and carbon sequestration for global climate change mitigation. This study was carried out in an Atlantic Forest restoration project in the south-central region of São Paulo State - Brazil to develop allometric equations to estimate tree biomass of indigenous tree species in mixed plantations. Above and below-ground biomass (AGB and BGB, respectively), stem diameter (DBH: diameter at 1.3 m height), tree height (H: total height) and specific wood density (WD) were measured for 60 trees of 19 species. Different biomass equations (linear and nonlinear-transformed) were adjusted to estimate AGB and BGB as a function of DBH, H and WD. For estimating AGB and BGB, the linear biomass equation models were the least accurate. The transformed nonlinear biomass equation that used log DBH2, log H and log WD as predictor variables were the most accurate for AGB and the transformed nonlinear biomass equations that used log DBH2*WD as predictor variables were the most accurate for BGB. It is concluded that these adjusted equations can be used to estimate the AGB and BGB in areas of the studied project. The adjusted equations can be recommended for use elsewhere in the region for forest stands of similar age, tree size ranges, species composition and site characteristics.


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