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.
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Aboveground forest carbon mapping;
plot location error;
Document Type: Research Article
Department of Geography and Environmental Resources,Southern Illinois University, CarbondaleIL, USA
Department of Forest and Environmental Resources,Zhejiang A&F University, Zhejiang, China
Department of Natural Resources and Environmental Sciences,University of Illinois at Urbana-Champaign, UrbanaIL, USA
Northern Research Station,U.S. Forest Service, St. PaulMN, USA
Publication date: 2011-08-01
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