@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},
}