Allometric models to biomass in restoration areas in the Atlantic rain forest

Abstract

The objective of the study was to present mathematical models and strategies for fitting equations to estimate dry biomass for tree species in forest restoration areas. The presence of outliers was analyzed in each fitted equation using values of the matrix H, leverage points, means of standard and studentized residuals, and of influential points through DFFITS, DFBETAS and COOK distance values. Furthermore, the normality, homoscedasticity and independence of residuals were checked. The accuracy of the fitted equations was evaluated by means of the R 2 adj., S yx , analysis of residuals, and AIC and BIC criteria. The results showed that the model for estimating dry biomass as a function of the variables Dc 2 , DBH 2 , Hc 2 and DBH provides the more accurate solution, with S yx = 40.91% and R 2 adj.= 0.92. We concluded that the performance of this equation improves when adjusted to data stratified by classes of height-diameter ratio, which reduces the value of the estimated error.

Improving the Modeling of the Height–Diameter Relationship of Tree Species with High Growth Variability: Robust Regression Analysis of Ochroma pyramidale (Balsa-Tree)

“…(0.709) of the Chapman-Richards fitted model, it is possible to state that, in O. pyramidale stands, the independent variable (age) explained the behavior of the dependent variable (hdom) very well. On the other hand, regarding the SEE% (12.71%), although it can be considered high (>10%) [42]), the result was satisfactory for the estimation of hdom in the context of the variability presented by the data (overall cv% 24.3%, Table 2).…”

Allometric Equations for Predicting Agave lechuguilla Torr. Aboveground Biomass in Mexico

“…> 0.98 [56]. Araujo et al [61] used the same equation in 111 trees belonging to 50 tropical species in restoration areas in Rio de Janeiro, Brazil, with an R 2 of only 0.65, a β 1 lower than that found in our study (0.91), and a higher β 2 (1.62), using diameter at breast height (Dbh) and height (H) as predictors.…”

Path Analysis Applied to Evaluation of Biomass Estimates in Subtropical Forests at Brazil

“…Linear correlation studies are important for modeling biometric variables, especially the biomass of trees with dbh and h. When these linear correlations between independent and dependent variables are significant, they are appropriate to compose allometric models, usually fitted by means of linear or non-linear regression. Several studies found in the literature use regression to express biomass allometric relationship with dbh and h (ZHAO; KANE, 2017, ARAÚJO et al, 2018, but few studies show the magnitude of these correlations between these variables (BURGER; DELITTI, 2008).…”

“…Návar (2014) mentioned error estimates between 50 and 60% for the best adjusted equations for aboveground biomass modeling in tropical forests in Mexico, using as independent variables dbh, h, wood density and basal area. Araújo et al (2018), evaluating biomass allometric equations in a restoration area of the Atlantic Forest, observed error estimates between 40 and 50% for the best equations when the data set was stratified by diameter classes and ecological groups. In this context, the present work intends to elucidate the reasons why the variables dbh and h, despite having a high correlation with the biomass, when included in the regression models do not concomitantly result in low errors for the biomass estimates.…”

“…In this case, to find the variable that has high correlation with the variable biomass, the modeler can progress faster testing all the independent variables and their combinations to reach such aim. These studies apply estimation techniques by regression that normally uses the simple correlation between variables (ZHAO; KANE, 2017;OLIVEIRA et al, 2017;ARAÚJO et al, 2018).…”