Journal
CANADIAN JOURNAL OF FOREST RESEARCH
Volume 47, Issue 8, Pages 1095-1103Publisher
CANADIAN SCIENCE PUBLISHING
DOI: 10.1139/cjfr-2016-0436
Keywords
aboveground biomass estimation; uncertainty assessment; Monte Carlo simulation; model error; sample size for model fitting
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Funding
- National Natural Science Foundation of China [31170588]
- National High Technology Research and Development Program of China from Chinese Academy of Forestry [2012AA12A306]
- Scientific Research Project of Science and Technology Commission of Shanghai Municipality from Shanghai Academy of Landscape Architecture Science and Planning [15dz1208104]
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Uncertainty associated with multiple sources of error exists in biomass estimation over large areas. This uncertainty affects the accuracy of the resultant biomass estimates. A new method that introduces Taylor series principles into a Monte Carlo simulation procedure was proposed and developed for estimating regional-scale aboveground biomass, along with quantifying the corresponding uncertainty arising from both sampling and model predictions. Additionally, the effect of sample size on estimates during model fitting was studied based on the new method to determine whether the effect of the size of the calibration data set can be neglected when the number of simulations is sufficiently large. The results revealed that the proposed method not only produces more reliable estimates of both biomass and uncertainty but also effectively and separately quantifies the uncertainties associated with different sources of error. The new method also reduced the effect of model uncertainty on final estimates. The uncertainty that was associated with model error increased significantly with decreasing sample sizes during model fitting, and the error was not reduced by increasing the number of Monte Carlo simulations.
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