Quantifying the uncertainty caused by sampling, modeling, and field measurements in the estimation of AGB with information of the national forest inventory in Durango, Mexico

Abstract

Aboveground biomass (AGB) is related to the carbon content of the forest and forest carbon is a core variable for contemporary forest management and policy decisions. A credible and precise estimate of AGB is a prerequisite for the scientifically proper implementation of commitments made regarding the reduction of forest carbon emissions. With datasets of the Mexican National Forest Inventory (MNFI), this thesis estimates AGB in the temperate forests of Durango, a federal state of Mexico, evaluating the uncertainty of the estimate of total AGB and how different error sources contribute to the overall error. In addition to AGB, also the basal area was analyzed which is closely correlated to AGB but does not have the error source of coming from the application of allometric models. SummaryVI almost be ignoredalways. However, assuming that measurement and model errors are random errors and biases are absent.Applying re-measurements was an efficient way to estimate and describe the measurement errors in DBH and TH. Through the application of the GUM Method, error propagation is decomposed into sources and processes, and it is better understood how uncertainties are combined. The Monte-Carlo simulation Method (MCM) also proves to be an effective, practical and reliable way to approximate the total AGB uncertainty estimate with acceptable ranges of probable error at scales of the MNFI. The results in the error propagation by the GUM Method and by the MCM are equivalent.We found that TH measurement errors have a greater contribution than DBH measurement errors at the tree-level. Furthermore, when the main contribution to the uncertainty at tree-level comes from uAM instead of uMes, then the total estimate of uNS at stand-level is proportional to the number of the trees. However, a ratio of uMes>uAM produces a total uNS estimate at stand level that is proportional to the contribution according to the size of the tree. Therefore, in this last relation, a greater contribution to the total NS estimate is made by the trees with the largest AGB estimated.