DOI: 10.1016/j.jag.2014.01.019
Scopus记录号: 2-s2.0-84897482031
论文题名: Bayesian area-to-point kriging using expert knowledge as informative priors
作者: Truong P ; N ; , Heuvelink G ; B ; M ; , Pebesma E
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 30, 期: 1 起始页码: 128
结束页码: 138
语种: 英语
英文关键词: Area-to-point conditional simulation
; Area-to-point kriging
; Expert knowledge
; Informative bayesian area-to-point estimator
; Spatial disaggregation
; Statistical expert elicitation
Scopus关键词: algorithm
; Bayesian analysis
; geostatistics
; knowledge
; kriging
; Markov chain
; MODIS
; Monte Carlo analysis
; uncertainty analysis
; variogram
英文摘要: Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example. The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79731
Appears in Collections: 气候变化事实与影响
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作者单位: Soil Geography and Landscape Group, Wageningen University, PO Box 47, 6700 AA Wageningen, Netherlands; Institute for Geoinformatics (IFGI), University of Münster, Heisenbertstr 2, 48149 Münster, Germany
Recommended Citation:
Truong P,N,, Heuvelink G,et al. Bayesian area-to-point kriging using expert knowledge as informative priors[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,30(1)