Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico

• Published in: Forests 2017-08, Vol.8 (8), p.269
• Main Authors: Vargas-Larreta, Benedicto, López-Sánchez, Carlos Antonio, Corral-Rivas, José Javier, López-Martínez, Jorge Omar, Aguirre-Calderón, Cristóbal Gerardo, Álvarez-González, Juan Gabriel
• Format: Article
• Language: English
• Subjects: above-ground biomass; allometry; Bark; Biomass; biomass allocation; Carbon; Carbon content; Economic models; Estimation; Foliage; Forests; Generalized method of moments; GMM; Learning algorithms; Leaves; machine learning technique; Mathematical models; Method of moments; Oak; Pine; Pine trees; Temperate forests; Variance; Wood
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f8080269

This paper presents new equations for estimating above-ground biomass (AGB) and biomass components of seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. The generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. In addition, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The results of cross-validation indicated that the fitted equations accounted for on average 91%, 82%, 83% and 76% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of total height (h) or diameter at breast height2 × total height (d2h) as a predictor in the d-only based equations systems slightly improved estimates for stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The predictive power of the proposed equations is higher than that of existing models for the study area. The fitted equations were used to estimate stand level AGB stocks from data on growing stock in 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were used to map the AGB and carbon distributions in the study area, for which mean values of respectively 129.84 Mg ha−1 and 63.80 Mg ha−1 were obtained.