文献类型：期刊文献

英文题名：Uncertainty assessment in aboveground biomass estimation at the regional scale using a new method considering both sampling error and model error

作者：Fu, Yu[1,2] Lei, Yuancai[2] Zeng, Weisheng[3] Hao, Ruijun[1] Zhang, Guilian[1] Zhong, Qicheng[1] Xu, Mingshan[1]

第一作者：Fu, Yu

通信作者：Lei, YC[1]

机构：[1]Shanghai Acad Landscape Architecture Sci & Planni, Shanghai Engn Res Ctr Landscaping Challenging Urb, Shanghai 200232, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]State Forestry Adm, Acad Forest Inventory & Planning, Beijing 100714, Peoples R China

年份：2017

卷号：47

期号：8

起止页码：1095-1103

外文期刊名：CANADIAN JOURNAL OF FOREST RESEARCH

收录：;EI(收录号：20173204018493);Scopus(收录号：2-s2.0-85026641866);WOS:【SCI-EXPANDED(收录号:WOS:000406997200010)】；

基金：The authors thank Hao Zang for helpful suggestions that improved the manuscript. We also thank the National Natural Science Foundation of China (grant No. 31170588), the National High Technology Research and Development Program of China (grant No. 2012AA12A306) from the Chinese Academy of Forestry, and the Scientific Research Project of Science and Technology Commission of Shanghai Municipality (grant No. 15dz1208104) from the Shanghai Academy of Landscape Architecture Science and Planning for fiscal support and the Academy of Forest Inventory and Planning, State Forestry Administration of China, for supplying the research data.

语种：英文

外文关键词：aboveground biomass estimation; uncertainty assessment; Monte Carlo simulation; model error; sample size for model fitting

摘要：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.