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

Species-specific allometric models for reducing uncertainty in estimating above ground biomass at Moist Evergreen Afromontane Forest of Ethiopia

An allometric equation is used to convert easily measured tree variables into biomass. However, limited species-specific biomass equations are available for native tree species grown in various biomes of Ethiopia. The available pantropic generic equation has resulted in biases owing to the uncertainty of the generic model estimation due to the difference in tree nature and response to growth conditions. The objective of the study is, thus, to develop a species-specific allometric equation for reducing uncertainty in biomass estimation at the Moist Evergreen Afromontane Forest in south-central Ethiopia. Five tree species were selected for model development, these selected trees were harvested and weighed in the field. The measured above-ground biomass data related to easily measured tree variables: diameter at stump height, diameter at breast height (dbh), crown diameter, and total tree height. The developed model evaluated and compared with previously published model by using measures of goodness of fit such as coefficient of determination (R2), total relative error, mean prediction error, root mean square error, and Akaike information criteria. The analysis showed that a model with dbh as a single predictor variable was selected as the best model for the estimation of above-ground biomass. It gives the highest R2 for Syzygium guineense (0.992) and the lowest for Bersama abyssinica (0.879). The additions of other tree variables did not improve the model The pantropic model by Brown overestimates the biomass by 9.6-77.8% while both Chave models resulted in an estimation error of 12-50.3%. Our findings indicated that species-specific allometric equations outperformed both site-specific and pantropic models in estimating above-ground biomass by giving 0.1% up to 7.9% estimation error for the respective tree species.

Carbon stocks of tree plantations in a Western Ghats landscape, India: influencing factors and management implications

Biomass and carbon stock assessments in data-deficient plantations and identifying the factors influencing tree growth, distribution, and carbon stocks are extremely important for implementing sound silvicultural management and monitoring practices to achieve REDD+ goals. We conducted carbon stock assessments in five major plantation types in a regional landscape in the central Western Ghats, India, by establishing fifty 0.1-ha plots across the landscape. We quantified the overall carbon stocks by summing the carbon pools across mature trees, deadwood, and soil (0 -15 cm) components. Allometric equations were compared to address the uncertainty in the tree biomass carbon. The tree biomass carbon and soil organic carbon varied significantly across the plantation types (F = 55.23, p < 0.00). The present study yielded the highest carbon stocks in Pinus plantation (201.91 ± 9.52 Mg ha^-1) and the least in Eucalyptus (122.63 ± 9.73 Mg ha^-1). The correlation analysis displayed a strong influence of mean annual precipitation and edaphic factors on soil organic carbon, while basal area and elevation were good predictors of tree biomass carbon. The principal component analysis revealed an association of predictor variables in the distribution of plantation types. We found a strong association between mean annual precipitation on Pinus plantation and mean annual temperature on Eucalyptus and Acacia plantations. On the other hand, teak pure plantation was associated with structural and topographic variables, while edaphic factors mainly influenced the distribution of teak mixed plantations. The findings of the present study conclude substantial carbon storage ability of the plantations in the studied landscape which can play a significant role in mitigating the effects of climate change and reaching carbon neutrality.

COVID-19 pandemic underscores role of green space in urban carbon dynamics

For Detroit Michigan the arrival of COVID-19 led to intensive measures to prevent further spread of the virus resulting in consequent changes in traffic and energy use. We take advantage of these different emission scenarios to explore CO₂ dynamics in a postindustrial city with a declining population and increasing green space. We present atmospheric CO₂ concentration and net urban ecosystem exchange of CO₂ (NUE) from a typical eddy covariance system and canopy greenness from a field camera on the Wayne State University campus in midtown Detroit. We categorized our study period (January 18, 2020-July 31, 2020) into three subperiods associated with the state-wide shelter-in-place order. Our results support that the city was a net carbon source throughout the period, particularly during the shelter-in-place period, although reduced traffic lowered CO₂ concentrations and NUE. However, during the post-order period when traffic was highest, atmospheric CO₂ concentrations and NUE were lowest, suggesting that the greening of urban vegetation may have greater carbon mitigation potential than lowering anthropogenic carbon emissions through traffic reductions.

Allometric Equations for the Biomass Estimation of Calophyllum inophyllum L. in Java, Indonesia

Reliable data on CO2 quantification is increasingly important to quantify the climate benefits of forest landscape restoration and international commitments, such as the Warsaw REDD+ Framework and Nationally Determined Contributions under the Paris Agreement. Calophyllum inophyllum L. (nyamplung as a local name or tamanu tree for the commercial name) is an increasingly popular tree species in forest landscape restoration and bioenergy production for a variety of reasons. In this paper, we present allometric equations for aboveground biomass (AGB), belowground biomass (BGB), and total above- and belowground biomass (TABGB) predictions of C. inophyllum L. Data collection was carried out twice (2017 and 2021) from 40 trees in Java, Indonesia. Allometric equations using the natural logarithm of diameter at breast height (lnDBH) and ln height (lnH) for biomass prediction qualified the model’s fit with statistical significance at 95% of the confidence interval for AGB, BGB, and TABGB predictions. The results showed that the linear models using both lnDBH and lnH were well fit and accurate. However, the model with lnDBH is more precise than the model using lnH. Using lnDBH as a predictor, the R2 values were 0.923, 0.945, and 0.932, and MAPE were 24.7, 37.0, and 25.8 for AGB, BGB, and TABGB, respectively. Using lnH as a predictor, the R2 values were 0.887, 0.918, and 0.898 and MAPE were 37.4, 49.0, and 39.8 for AGB, BGB, and TABGB, respectively. Consequently, the driven allometric equations can help accurate biomass quantification for carbon-trading schemes of C. inophyllum L.

Allometric equations for selected Acacia species (Vachellia and Senegalia genera) of Ethiopia

BACKGROUND

Allometric equations are used to estimate biomass and carbon stock of forests. In Ethiopia, despite the presence of large floral diversity, only a few site-specific allometric equations have been developed so far. This study was conducted in the Omo-Gibe woodland of south-western Ethiopia to develop an allometric equation to estimate the Above-ground Biomass (AGB) of the four Acacia species (Senegalia polyacantha, Vachellia seyal, Vachellia etbaica and Vachellia tortilis). Fifty-four (54) Acacia trees were sampled and measured within 35 temporarily established square plots. In each plot, dendrometric variables were measured to derive the models based on combinations of Diameter at Breast Height (DBH), height, and wood density as predictor variables. Model performance was evaluated using goodness-of-fit statistics. The biomass was compared using four allometric biomass models that have been widely used in the tropics.

RESULTS

The model containing DBH alone was more accurate to estimate AGB compared to the use of multiple predictor variables. This study, therefore, substantiated the importance of site-specific allometric equations in estimating the AGB of Acacia woodlands. This is because a site-specific allometric equation recognizes the environmental factors, vegetation types and management practices.

CONCLUSIONS

The results of this study contribute to a better understanding of allometric equations and an accurate estimate of AGB of Acacia woodlands in Ethiopia and similar ecosystems elsewhere.

Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations

BACKGROUND

Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation-errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n = 285).

RESULTS

Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30-75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25-70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE] = 50%). This is likely due to low-sample size (10-20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE = 97%) and nationwide (2.2 billion Mg;  % RMSE = 94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg;  % RMSE = 165%).

CONCLUSIONS

Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.