LarocqueDutilleulPelletierEtAl2006

Référence

Larocque, G., Dutilleul, P., Pelletier, B. and Fyles, J.W. (2006) Conditional Gaussian co-simulation of regionalized components of soil variation. Geoderma, 134(1-2):1-16.

Résumé

Stochastic simulations are increasingly used to represent and characterize the spatial structure and uncertainty of soil properties. Due to the potential presence of scale dependencies, simulations of the total variables can represent a mixture of spatial components operating at different scales, which may be better interpreted separately. While coregionalization analysis and factorial kriging provide means to characterize and estimate scale-specific components of variation, no methods are available that allow a proper representation of their spatial structure and an assessment of their spatial uncertainty. In this paper, the formulation of cokriging of regionalized components and regionalized factors is first reviewed, after which a method for the conditional Gaussian co-simulation of regionalized components and regionalized factors is presented. We highlight the need for performing conditional simulations for all structures jointly to reduce the correlation between components for different structures and avoid any bias on the sum of simulated components. Simulations obtained with this method adequately represent both the specific features of, and the uncertainty associated with, each scale of variation, as modeled in a coregionalization analysis. The method is applied to an agronomic dataset to characterize the spatial uncertainty of regionalized components of plant available phosphorous and potassium in the soil and illustrate advantages of this new simulation approach. © 2005 Elsevier B.V. All rights reserved.

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@ARTICLE { LarocqueDutilleulPelletierEtAl2006,
    AUTHOR = { Larocque, G. and Dutilleul, P. and Pelletier, B. and Fyles, J.W. },
    TITLE = { Conditional Gaussian co-simulation of regionalized components of soil variation },
    JOURNAL = { Geoderma },
    YEAR = { 2006 },
    VOLUME = { 134 },
    PAGES = { 1-16 },
    NUMBER = { 1-2 },
    NOTE = { 00167061 (ISSN) Export Date: 26 April 2007 Source: Scopus CODEN: GEDMA doi: 10.1016/j.geoderma.2005.08.008 Language of Original Document: English Correspondence Address: Larocque, G.; Department of Natural Resource Sciences; McGill University; Macdonald Campus, 21, 111 Lakeshore Ste-Anne-de-Bellevue, Que. H9X 3V9, Canada; email: guillaume.larocque@mcgill.ca References: Alabert, F., The practice of fast conditional simulations through the LU decomposition of the covariance matrix (1987) Math. Geol., 19 (5), pp. 369-386; Bocchi, S., Castrignano, A., Fornaro, F., Maggiore, T., Application of factorial kriging for mapping soil variation at field scale (2000) Eur. J. Agron., 13 (4), pp. 295-308; Bourennane, H., Salvador-Blanes, S., Cornu, S., King, D., Scale of spatial dependence between chemical properties of topsoil and subsoil over a geologically contrasted area (Massif central, France) (2003) Geoderma, 112 (3-4), pp. 235-251; Bourgault, G., Probability field for the post-processing of stochastic simulations (1996) Math. Geol., 28 (6), pp. 723-734; Box, G.E.P., Cox, D.R., An analysis of transformations (1964) J. Roy. Stat. Soc., B Met., 26 (2), pp. 211-252; Carter, M.R., (1993) Soil Sampling and Methods of Analysis, , Lewis Publishers, Boca Raton; Castrignano?, A., Giugliarini, L., Risaliti, R., Martinelli, N., Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics (2000) Geoderma, 97 (1-2), pp. 39-60; Davis, M., Production of conditional simulations via the LU triangular decomposition of the covariance matrix (1987) Math. Geol., 19 (2), pp. 91-98; Dobermann, A., Goovaerts, P., George, T., Sources of soil variation in an acid Ultisol of the Philippines (1995) Geoderma, 68 (3), pp. 173-191; Dobermann, A., Goovaerts, P., Neue, H.U., Scale-dependent correlations among soil properties in two tropical lowland rice fields (1997) Soil Sci. Soc. Am. J., 61 (5), pp. 1483-1496; Go?mez-Herna?ndez, J.J., Journel, A.G., Joint sequential simulation of multiGaussian fields (1994) Geostatistics Tro?ia '92, pp. 85-94. , Soares A. (Ed), Kluwer Academic Publishers, Dordrecht; Goovaerts, P., Factorial kriging analysis: a useful tool for exploring the structure of multivariate spatial soil information (1992) J. Soil Sci., 43 (4), pp. 597-619; Goovaerts, P., (1997) Geostatistics for Natural Resources Evaluation, , Oxford University Press, New York; Goovaerts, P., Estimation or simulation of soil properties? An optimization problem with conflicting criteria (2000) Geoderma, 97 (3-4), pp. 186-195; Goovaerts, P., Geostatistical modelling of uncertainty in soil science (2001) Geoderma, 103 (1-2), pp. 3-26; Goovaerts, P., Webster, R., Scale-dependent correlation between topsoil copper and cobalt concentrations in Scotland (1994) Eur. J. Soil Sci., 45 (1), pp. 79-95; Goovaerts, P., Sonnet, P., Navarre, A., Factorial kriging analysis of springwater contents in the Dyle River basin, Belgium (1993) Water Resour. Res., 29 (7), pp. 2115-2125; Goulard, M., Voltz, M., Linear coregionalization model: tools for estimation and choice of cross-variogram matrix (1992) Math. Geol., 24 (3), pp. 269-286; Isaaks, E.H., Srivastava, R.M., (1989) An Introduction to Applied Geostatistics, , Oxford University Press, New York; Journel, A.G., Huijbregts, C.J., (1978) Mining Geostatistics, , Academic Press, London; Lin, Y.P., Multivariate geostatistical methods to identify and map spatial variations of soil heavy metals (2002) Environ. Geol., 42 (1), pp. 1-10; Marcotte, D., Conditional simulation with data subject to measurement error: post-simulation filtering with modified factorial kriging (1995) Math. Geol., 27 (6), pp. 749-762; Matheron, G., (1982) Pour une analyse krigeante des donne?es re?gionalise?es, Rapport N-732, , Centre de Ge?ostatistique, Fontainebleau, France; Pebesma, E.J., Weeseling, C.G., Gstat: a program for geostatistical modelling, prediction and simulation (1998) Comput. Geosci., 24 (1), pp. 17-31; Pelletier, B., Dutilleul, P., Larocque, G., Fyles, J.W., Fitting the linear model of coregionalization by generalized least squares (2004) Math. Geol., 36 (3), pp. 323-343; Soares, A., Direct sequential simulation and cosimulation (2001) Math. Geol., 33 (8), pp. 911-926; ; Vargas-Guzma?n, J.A., Dimitrakopoulos, R., Conditional simulation of random fields by successive residuals (2002) Math. Geol., 34 (5), pp. 597-611; Wackernagel, H., (2003) Multivariate Geostatistics: An Introduction with Applications, , Springer-Verlag, Berlin; Webster, R., Atteia, O., Dubois, J.P., Coregionalization of trace metals in the soil in the Swiss Jura (1994) Eur. J. Soil Sci., 45 (2), pp. 205-218. },
    ABSTRACT = { Stochastic simulations are increasingly used to represent and characterize the spatial structure and uncertainty of soil properties. Due to the potential presence of scale dependencies, simulations of the total variables can represent a mixture of spatial components operating at different scales, which may be better interpreted separately. While coregionalization analysis and factorial kriging provide means to characterize and estimate scale-specific components of variation, no methods are available that allow a proper representation of their spatial structure and an assessment of their spatial uncertainty. In this paper, the formulation of cokriging of regionalized components and regionalized factors is first reviewed, after which a method for the conditional Gaussian co-simulation of regionalized components and regionalized factors is presented. We highlight the need for performing conditional simulations for all structures jointly to reduce the correlation between components for different structures and avoid any bias on the sum of simulated components. Simulations obtained with this method adequately represent both the specific features of, and the uncertainty associated with, each scale of variation, as modeled in a coregionalization analysis. The method is applied to an agronomic dataset to characterize the spatial uncertainty of regionalized components of plant available phosphorous and potassium in the soil and illustrate advantages of this new simulation approach. © 2005 Elsevier B.V. All rights reserved. },
    KEYWORDS = { Conditional stochastic simulations Coregionalization analysis Factorial kriging Scale dependence Spatial uncertainty },
    OWNER = { brugerolles },
    TIMESTAMP = { 2007.12.05 },
}

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