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Forest carbon sinks significantly contribute to mitigation of atmospheric concentrations of carbon dioxide. Thus, estimating forest carbon is becoming important to develop policies for mitigating climate change and trading carbon credits. However, a great challenge is how to quantify uncertainties in estimation of forest carbon. This study investigated uncertainties of mapping aboveground forest carbon due to location errors of sample plots for Lin-An County of China. National forest inventory plot data and Landsat TM images were combined using co-simulation algorithm. The findings show that randomly perturbing plot locations within 10 distance intervals statistically did not result in biased population mean predictions of aboveground forest carbon at a significant level of 0.05, but increased root mean square errors of the maps. The perturbations weakened spatial autocorrelation of aboveground forest carbon and its correlation with spectral variables. The perturbed distances of 800 m or less did not obviously change the spatial distribution of predicted values. However, when the perturbed distances were 1600 m or larger, the correlation coefficients of the predicted values from the perturbed locations with those from the true plot locations statistically did not significantly differ from zero at a level of 0.05 and the spatial distributions became random. [ABSTRACT FROM AUTHOR]

Developing an individual tree diameter increment (ΔDBH) model is the basis of near-natural management of mixed, uneven-aged oak forests. This analysis used remeasurement data (2009–2014) comprising 6154 observations from 112 permanent plots in central China to develop and compare an indicator variable model (IVM) and a mixed-effect model (MEM) to estimate ΔDBH. First, a basic model was estimated using 12 potential explanatory variables. Geographical regions (GR), competition intensities (CI) and species compositions (SC) were introduced into the basal model as indicator variables or mixed effects, step by step, and then the prediction accuracy of IVM and MEM was compared. The results showed that (1) the independent variables significantly affecting ΔDBH included the reciprocal of DBH, basal area, altitude, and mean annual rainfall; (2) the introducing GR could not improve the accuracy of estimating ΔDBH, but the CI and SC could. (3) Compared with the basic model and IVM, the percentage mean absolute deviation of MEM decreased by 2.07% and 1.11%, while the root mean square error decreased by 0.06 and 0.04, respectively. The MEM including CI and SC as a random effect showed the best predictive performance and can be applied to improve the prediction of individual oak trees ΔDBH. [ABSTRACT FROM AUTHOR]