Modeling and propagating inventory-based sampling uncertainty in the large-scale forest demographic model "MARGOT"

编号 030037805

推送时间 20230116

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Modeling and propagating inventory-based sampling uncertainty in the large-scale forest demographic model "MARGOT"

来源期刊 NATURAL RESOURCE MODELING

期 第378期

发表时间 20220816

关键词 bootstrap;  demographic model;  error propagation;  forest dynamic;  matrix model;  national forest inventory;  sampling;  uncertainty; 

摘要 Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance-covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug-in option to any inventory-based initial condition. Forty-year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.

服务人员 付贺龙

服务院士 唐守正

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