Two contrasting spatial processes with a common variogram: inference about spatial models from higher-order statistics

Geostatistical analysis of soil properties is undertaken to allow prediction of values of these properties over regions or at unsampled locations. A key step in geostatistical analysis is the estimation of a variogram function that describes the spatial covariance structure of the variable in questi...

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Vydané v:European journal of soil science Ročník 61; číslo 4; s. 479 - 492
Hlavný autor: Lark, R. M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford, UK Blackwell Publishing Ltd 01.08.2010
Blackwell
Predmet:
Agronomy. Soil science and plant productions
Biological and medical sciences
covariance
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
geostatistics
land use
population structure
prediction
soil analysis
soil properties
Soil science
Soils
Surficial geology
processes
Variograms
Inference
Soil science
Spatial model
Earth science
statistics
ISSN:1351-0754, 1365-2389
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Shrnutí:Geostatistical analysis of soil properties is undertaken to allow prediction of values of these properties over regions or at unsampled locations. A key step in geostatistical analysis is the estimation of a variogram function that describes the spatial covariance structure of the variable in question. If it can be assumed plausibly that the data are a realization of a second‐order stationary multivariate normal random function then this function is entirely characterized by its mean (expectation) and spatially dependent covariance. Because of this, the variogram is sometimes computed as a general ‘descriptor’ of spatial variation, and used, for example, to compare the spatial structure at within‐aggregate scales of soils under different management, or to compare soils from different land uses with respect to the spatial structure of their microbial populations. The objective of this paper is to draw attention to the limited value of the variogram for characterizing spatial variation (as opposed to deriving best linear unbiased predictions). Specifically, it is shown how two contrasting processes, one of which gives rise to a multivariate normal random function (a convolution filter applied to independent identically distributed random values) and one which does not (a partition of space into random sets), may have the same variogram function. A diagnostic is proposed that indicates which of these two processes is most plausible as a model for a data set. This will allow the spatial analysis of soil data to give greater insight into the factors underlying the variation of a soil property, and may permit more realistic simulation of soil properties.
Bibliografia:istex:52AF4AC0490515A87C5415560BEDB5DE5FE1B655
ark:/67375/WNG-1C5TBDR8-1
ArticleID:EJSS1258
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ISSN:1351-0754
1365-2389
DOI:10.1111/j.1365-2389.2010.01258.x