Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space

编号 030035004

推送时间 20220704

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space

来源期刊 CANADIAN JOURNAL OF FOREST RESEARCH

期 第350期

发表时间 20220117

关键词 forest inventory;  hierarchical Bayes model;  Kalman filter;  Markov chain Monte Carlo (MCMC);  credible interval; 

摘要 Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry.

服务人员 付贺龙

服务院士 唐守正

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