scholarly journals Allometric equations for aboveground biomass estimation ofOlea europaeaL. subsp.cuspidatain Mana Angetu Forest

2018 ◽  
Vol 4 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Birhanu Kebede ◽  
Teshome Soromessa
Keyword(s):  
Aboveground Biomass ◽  
Allometric Equations ◽  
Biomass Estimation

scholarly journals The accuracy of species-specific allometric equations for estimating aboveground biomass in tropical moist montane forests: case study of Albizia grandibracteata and Trichilia dregeana

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Damena Edae Daba ◽  
Teshome Soromessa
Keyword(s):  
Forest Ecosystem ◽  
Aboveground Biomass ◽  
Forest Type ◽  
Model Performance ◽  
Allometric Equation ◽  
Allometric Equations ◽  
Coefficient Of Determination ◽  
Biomass Estimation ◽  
Moist Forest ◽  
Species Specific

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.

Climate-based Allometric Biomass Equations for Five Major Canadian Timber Species

2021 ◽  
Author(s):  
Kun Xu ◽  
Jinghe Jiang ◽  
Fangliang He
Keyword(s):  
Aboveground Biomass ◽  
Forest Biomass ◽  
Allometric Equations ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Change Scenario ◽  
Mean Annual Temperature ◽  
Timber Species ◽  
Trembling Aspen ◽  
Allometric Models

Accurate estimation of forest biomass is essential to quantify the role forests play at balancing terrestrial carbon. Allometric equations based on tree size have been used for this purpose worldwide. There is little quantitative understanding on how environmental variation may affect tree allometries. Even less known is how to incorporate environmental factors into such equations to improve estimation. Here we tested the effects of climate on tree allometric equations and proposed to model forest biomass by explicitly incorporating climatic factors. Among the five major Canadian timber species tested, the incorporation of climate was not found to improve the allometric models. For trembling aspen and tamarack, the residuals of their conventional allometric models were found strongly related to frost-free period and mean annual temperature, respectively. The predictions of the two best climate-based models were significantly improved, which indicate that trembling aspen and tamarack store more aboveground biomass when growing in warmer than in colder regions. We showed that, under the RCP4.5 modest climate change scenario, there would be a 10% underestimation of aboveground biomass for these two species if the conventional non-climate models would still be in use in 2030. This study suggests the necessity to proactively develop climate-based allometric equations for more accurate and reliable forest biomass estimation.

Allometric equations for aboveground biomass estimation by size class for Pinus brutia Ten. trees growing in North and South Aegean Islands, Greece

2010 ◽  
Vol 130 (2) ◽  
pp. 145-160 ◽  
Author(s):  
Dimitris Zianis ◽  
Gavriil Xanthopoulos ◽  
Kostas Kalabokidis ◽  
George Kazakis ◽  
Dany Ghosn ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Size Class ◽  
Allometric Equations ◽  
Biomass Estimation ◽  
Pinus Brutia ◽  
Aegean Islands ◽  
North And South

scholarly journals Allometric equation for aboveground biomass estimation in moist Afromontane forest in Gesha and Sayilem district in Kaffa zone, southwestern Ethiopia

2020 ◽  
Author(s):  
Admassu Merti ◽  
Teshome Soromessa ◽  
Tura Bareke
Keyword(s):  
Wood Density ◽  
Aboveground Biomass ◽  
Allometric Equation ◽  
Allometric Equations ◽  
Individual Species ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Strongly Correlated ◽  
Afromontane Forest ◽  
Species Specific

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.

scholarly journals Allometric Equations for Aboveground Biomass Estimations of Four Dry Afromontane Tree Species

2020 ◽  
Author(s):  
Getaneh Gebeyehu ◽  
Teshome Soromessa ◽  
Tesfaye Bekele ◽  
Demel Teketay
Keyword(s):  
Wood Density ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Model Fit ◽  
Allometric Equations ◽  
Biomass Estimation ◽  
Parameter Estimates ◽  
Total Biomass ◽  
Allometric Models ◽  
Species Specific

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.

scholarly journals Improved allometric equations for tree aboveground biomass estimation in tropical dipterocarp forests of Kalimantan, Indonesia

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Solichin Manuri ◽  
Cris Brack ◽  
Fatmi Noor’an ◽  
Teddy Rusolono ◽  
Shema Mukti Anggraini ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Allometric Equations ◽  
Biomass Estimation ◽  
Dipterocarp Forests

Biomass of understory species of Pinus halepensis Mill. forests in the Kassandra Peninsula - Chalkidiki

1998 ◽  
Vol 63 ◽  
Author(s):  
P. Smiris ◽  
F. Maris ◽  
K. Vitoris ◽  
N. Stamou ◽  
P. Ganatsas
Keyword(s):  
Moisture Content ◽  
Aboveground Biomass ◽  
Quercus Ilex ◽  
Pinus Halepensis ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Pistacia Lentiscus ◽  
Quercus Coccifera ◽  
Understory Species ◽  
Cistus Incanus

This  study deals with the biomass estimation of the understory species of Pinus halepensis    forests in the Kassandra peninsula, Chalkidiki (North Greece). These  species are: Quercus    coccifera, Quercus ilex, Phillyrea media, Pistacia lentiscus, Arbutus  unedo, Erica arborea, Erica    manipuliflora, Smilax aspera, Cistus incanus, Cistus monspeliensis,  Fraxinus ornus. A sample of    30 shrubs per species was taken and the dry and fresh weights and the  moisture content of    every component of each species were measured, all of which were processed  for aboveground    biomass data. Then several regression equations were examined to determine  the key words.

scholarly journals Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

2021 ◽  
Vol 13 (8) ◽  
pp. 1595
Author(s):  
Chunhua Li ◽  
Lizhi Zhou ◽  
Wenbin Xu
Keyword(s):  
Aboveground Biomass ◽  
Carbon Accounting ◽  
Dynamic Monitoring ◽  
Gradient Boosting ◽  
Biomass Estimation ◽  
Floodplain Wetlands ◽  
Red Edge ◽  
Extreme Gradient Boosting ◽  
Ensemble Algorithms ◽  
Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

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