Abstract
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.
Highlights
Improved knowledge of carbon stocks and fluxes is needed in order to understand the current state of the carbon cycle and how it might evolve with changing land use and climatic conditions [1].This has led to an increased interest in estimating forest biomass for practical forestry purposes and for scientific purposes
The results of the coefficient of determination indicated that the fitted models explained between 57% and 98% of the observed biomass variance per component for all species, with mean values of 91%, 82%, 83% and 76% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total above-ground biomass (AGB) equations explained on average 93% of the total observed variance in AGB
The results reported here suggest that the best equations for biomass estimation for most species are based on d and h; it is possible to use the biomass equation systems developed for a specific species across different sites in the temperate forests of Durango, assuming that total height is included in the models and that the addition of this variable may take into account different levels of competition induced by different stand density conditions [55]
Summary
Improved knowledge of carbon stocks and fluxes is needed in order to understand the current state of the carbon cycle and how it might evolve with changing land use and climatic conditions [1]. This has led to an increased interest in estimating forest biomass for practical forestry purposes and for scientific purposes. Most states in Mexico are implementing action plans aimed at mitigating the effects of climate change and accessing economic incentives that favour carbon sequestration in forests.
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