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% Encoding: UTF-8
@article{Warnholz_Schmid_2016,
title={Simulation Tools for Small Area Estimation: Introducing the R-package saeSim},
volume={45},
url={https://www.ajs.or.at/index.php/ajs/article/view/vol45-1-4},
DOI={10.17713/ajs.v45i1.89},
number={1},
journal={Austrian Journal of Statistics},
author={Warnholz, Sebastian and Schmid, Timo},
year={2016},
month={Feb.},
pages={55-69}
}
@article{Molina2015,
author = {Isabel Molina and Yolanda Marhuenda},
title = {{sae: An R Package for Small Area Estimation}},
year = {2015},
journal = {{The R Journal}},
doi = {10.32614/RJ-2015-007},
url = {https://doi.org/10.32614/RJ-2015-007},
pages = {81--98},
volume = {7},
number = {1}
}
@Manual{Goerg2013,
title = {{LICORS: Light Cone Reconstruction of States - Predictive State
Estimation From Spatio-Temporal Data}},
author = {G. M. Goerg},
year = {2013},
note = {R package version 0.2.0},
url = {https://CRAN.R-project.org/package=LICORS},
}
@Article{Lenth2009,
author = {R. V. Lenth},
journal = {Journal of Statistical Software},
title = {{Response-Surface Methods in {R}, Using {rsm}}},
year = {2009},
number = {7},
pages = {1--17},
volume = {32},
url = {https://www.jstatsoft.org/v32/i07/},
}
@Article{Baillargeon2011,
author = {S. Baillargeon and L.-P. Rivest},
title = {{The construction of stratified designs in R with the package stratification}},
journal = {Survey Methodology},
year = {2011},
volume = {37},
pages = {53--65},
url = {http://www.statcan.gc.ca/pub/12-001-x/2011001/article/11447-eng.pdf},
}
@Manual{Baillargeon2014,
title = {{stratification: Univariate Stratification of Survey Populations}},
author = {S. Baillargeon and L.-P. Rivest},
year = {2014},
note = {R package version 2.2-5},
url = {https://CRAN.R-project.org/package=stratification},
}
@Article{bog99,
author = {P. Bogaert and D. Russo},
title = {Optimal spatial sampling design for the estimation of the variogram based on a least squares approach},
journal = {Water Resources Research},
year = {1999},
volume = {35},
pages = {1275--1289},
}
@Article{bra08,
author = {C. J. F. {ter Braak} and D. J. Brus and E. J. Pebesma},
title = {Comparing sampling patterns for kriging the spatial mean temporal trend},
journal = {Journal of Agricultural, Biological and Environmental Statistics},
year = {2008},
volume = {13},
pages = {159--176},
owner = {brus003},
timestamp = {2008.07.28},
}
@Article{Brus2014b,
author = {D. J. Brus},
title = {Statistical sampling approaches for soil monitoring},
journal = {European Journal of Soil Science},
year = {2014},
volume = {65},
pages = {779--791},
doi = {10.1111/ejss.12176},
owner = {brus003},
timestamp = {2014.03.19},
}
@Article{bru00b,
author = {D. J. Brus and M. J. W. Jansen and W. F. {de Haan}},
title = {Designing efficient sampling schemes for reconnaissance surveys of contaminated bed sediments in water courses},
journal = {Geologie en Mijnbouw/Netherlands Journal of Geosciences},
year = {2000},
volume = {79},
pages = {441--447},
owner = {brus003},
timestamp = {2009.06.11},
}
@Article{Brus2019,
author = {Brus, D. J. and Yang, L. and Zhu, A. X.},
title = {Accounting for differences in costs among sampling locations in optimal stratification},
journal = {European Journal of Soil Science},
year = {2019},
volume = {70},
pages = {200--212},
doi = {10.1111/ejss.12731},
}
@Article{bru00,
author = {D. J. Brus},
title = {Using regression models in design-based estimation of spatial means of soil properties},
journal = {European Journal of Soil Science},
year = {2000},
volume = {51},
pages = {159--172},
}
@Article{bru94a,
author = {D. J. Brus},
title = {Improving design-based estimation of spatial means by soil map stratification. {A} case study of phosphate saturation},
journal = {Geoderma},
year = {1994},
volume = {62},
pages = {233--246},
}
@Article{Brus2015,
author = {Brus, D. J.},
title = {{Balanced sampling: A versatile sampling approach for statistical soil surveys}},
journal = {Geoderma},
year = {2015},
volume = {253-254},
pages = {111--121},
owner = {brus003},
timestamp = {2016.03.02},
}
@Article{bru03a,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables},
journal = {Environmental Monitoring and Assessment},
year = {2003},
volume = {83},
pages = {303--317},
}
@Article{bru93,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {Design-based versus model-based estimates of spatial means. {Theory} and application in environmental soil science},
journal = {Environmetrics},
year = {1993},
volume = {4},
pages = {123--152},
}
@Article{bru94b,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {Estimation of nonergodic variograms and their sampling variance by design-based sampling strategies},
journal = {Mathematical Geology},
year = {1994},
volume = {26},
pages = {437--454},
}
@Article{bru97,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {Random sampling or geostatistical modelling? {C}hoosing between design-based and model-based sampling strategies for soil (with {D}iscussion)},
journal = {Geoderma},
year = {1997},
volume = {80},
pages = {1--59},
}
@Article{Brus2011,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {Design-based {Generalized Least Squares} estimation of status and trend of soil properties from monitoring data},
journal = {Geoderma},
year = {2011},
volume = {164},
pages = {172--180},
doi = {10.1016/j.geoderma.2011.06.001},
owner = {brus003},
timestamp = {2009.12.11},
}
@Article{Brus2012,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {A hybrid design-based and model-based sampling approach to estimate the temporal trend of spatial means},
journal = {Geoderma},
year = {2012},
volume = {173-174},
pages = {241--248},
doi = {10.1016/j.geoderma.2011.12.010},
owner = {brus003},
timestamp = {2009.12.11},
}
@Article{Brus2013,
author = {D. J. Brus and J. J. {de Gruijter}},
title = {Effects of spatial pattern persistence on the performance of sampling designs for regional trend monitoring, analyzed by simulation of space-time fields},
journal = {Computers \& Geosciences},
year = {2013},
volume = {61},
pages = {175--183},
owner = {brus003},
timestamp = {2013.09.09},
}
@InCollection{bru07c,
author = {D. J. Brus and J. J. {de Gruijter} and J. W. {van Groenigen}},
title = {Designing spatial coverage samples using the k-means clustering algorithm},
booktitle = {Digital Soil Mapping. An introductory perspective},
publisher = {Elsevier},
year = {2007},
editor = {P. Lagacherie and A. B. McBratney and M. Voltz},
pages = {183--192},
owner = {brus003},
timestamp = {2008.06.11},
}
@Article{brus2011d,
author = {D. J. Brus and B. Kempen and G. B. M. Heuvelink},
title = {Sampling for validation of digital soil maps},
journal = {European Journal of Soil Science},
year = {2011},
volume = {62(3)},
pages = {394--407},
doi = {10.1111/j.1365-2389.2011.01364.x},
owner = {brus003},
timestamp = {2010.09.17},
}
@Article{bru08b,
author = {Brus, D. J. and Knotters, M.},
title = {{Sampling design for compliance monitoring of surface water quality: A case study in a polder area.}},
journal = {Water Resources Research},
year = {2008},
volume = {44},
pages = {W11410},
doi = {10.1029/2007WR006123},
owner = {brus003},
timestamp = {2008.12.17},
}
@Article{bru01,
author = {D. J. Brus and W. J. M. {te Riele}},
title = {Design-based regression estimators for spatial means of soil properties: the use of two-phase sampling when the means of the auxiliary variables are unknown},
journal = {Geoderma},
year = {2001},
volume = {104},
pages = {257--279},
}
@Article{Brus2016c,
author = {D. J. Brus and N. P. A. Saby},
title = {{Approximating the variance of estimated means for systematic random sampling, illustrated with data of the French Soil Monitoring Network}},
journal = {Geoderma},
year = {2016},
volume = {279},
pages = {77--86},
doi = {http://dx.doi.org/10.1016/j.geoderma.2016.05.016},
journaltitle = {Geoderma},
}
@Article{bru99,
author = {D. J. Brus and L. E. E. M. Sp{\"a}tjens and J. J. {de Gruijter}},
title = {A sampling scheme for estimating the mean extractable phosphorus concentration of fields for environmental regulation},
journal = {Geoderma},
year = {1999},
volume = {89},
pages = {129--148},
}
@Article{BURGESS1981,
author = {T. M. Burgess and R. Webster and A. B. McBratney},
title = {{Optimal interpolation and isarithmic mapping of soil properties .4. Sampling strategy}},
journal = {Journal of Soil Science},
year = {1981},
volume = {32},
number = {4},
pages = {643--659},
owner = {brus003},
timestamp = {2011.08.17},
}
@Book{coc77,
title = {Sampling Techniques},
publisher = {Wiley},
year = {1977},
author = {W. G. Cochran},
address = {New York},
owner = {brus003},
timestamp = {2008.06.09},
}
@Article{cor94,
author = {L. C. A. Corsten and A. Stein},
title = {Nested sampling for estimating spatial semivariograms compared to other designs},
journal = {Applied Stochastic Models and Data Analysis},
year = {1994},
volume = {10},
pages = {103--122},
}
@Article{Corwin2010,
author = {D. L. Corwin and S. M. Lesch and E. Segal and T. H. Skaggs and S. A. Bradford},
title = {Comparison of Sampling Strategies for Characterizing Spatial Variability with Apparent Soil Electrical Conductivity Directed Soil Sampling},
journal = {Journal of Environmental and Engineering Geophysics},
year = {2010},
volume = {15},
number = {3},
pages = {147--162},
doi = {10.2113/JEEG15.3.147},
}
@Article{corwin2005,
author = {Corwin, D. L. and Lesch, S. M.},
title = {Characterizing soil spatial variability with apparent soil electrical conductivity: Part {II}. Case study},
journal = {Computers and Electronics in Agriculture},
year = {2005},
volume = {46},
number = {1--3},
pages = {135--152},
owner = {brus003},
timestamp = {2010.03.05},
}
@Article{corwin2006,
author = {Corwin, D. L. and Lesch, S. M. and Oster, J. D. and Kaffka, S. R.},
title = {Monitoring management-induced spatio-temporal changes in soil quality through soil sampling directed by apparent electrical conductivity},
journal = {Geoderma},
year = {2006},
volume = {131},
number = {3-4},
pages = {369--387},
note = {Cited By (since 1996): 10},
owner = {brus003},
timestamp = {2010.03.05},
}
@Article{corwin2003,
author = {Corwin, D. L. and Lesch, S. M. and Shouse, P. J. and Soppe, R. and Ayars, J. E.},
title = {Identifying soil properties that influence cotton yield using soil sampling directed by apparent soil electrical conductivity},
journal = {Agronomy Journal},
year = {2003},
volume = {95},
number = {2},
pages = {352--364},
note = {Cited By (since 1996): 27},
owner = {brus003},
timestamp = {2010.03.05},
}
@Article{Dorazio03,
author = {M. D'Orazio},
title = {Estimating the variance of the sample mean in two-dimensional systematic sampling},
journal = {Journal of Agricultural, Biological, and Environmental Statistics},
year = {2003},
volume = {8},
pages = {280--295},
owner = {brus003},
timestamp = {2009.02.11},
}
@Article{Deville1998,
author = {Deville, J. C. and Till{\'e}, Y.},
title = {Unequal probability sampling without replacement through a splitting method},
journal = {Biometrika},
year = {1998},
volume = {{85}},
number = {{1}},
pages = {89--101},
abstract = {{A very general class of sampling methods without replacement and
with unequal probabilities is proposed. It consists of splitting
the inclusion probability vector into several new inclusion probability
vectors. One of these vectors. is chosen randomly;:thus, the initial
problem is reduced to another sampling problem with unequal probabilities.
This splitting is then repeated on; these new vectors of inclusion
probabilities; at each step,he sampling problem is reduced to a simpler
problem. The simplicity of this technique allows one to generate
easily new sampling procedures with unequal probabilities. The splitting
method also generalises well-known methods such as the Midzuno method,
the elimination procedure and the Chao procedure. Next, a sufficient
condition is given in order that a splitting method satisfies the
Sen-Yates-Grundy condition; Finally, it is shown that the elimination
procedure satisfies the Gabler sufficient condition.}},
doi = {10.1093/biomet/85.1.89},
owner = {brus003},
timestamp = {2013.02.11},
}
@Article{Deville2004,
author = {Deville, J. C. and Till{\'e}, Y.},
title = {Efficient balanced sampling: The cube method.},
journal = {Biometrika},
year = {2004},
volume = {91},
pages = {893--912},
owner = {brus003},
timestamp = {2013.10.07},
}
@Article{Diggle2006,
author = {P. J. Diggle and S. Lophaven},
title = {Bayesian geostatistical design},
year = {2006},
volume = {33},
pages = {53--64},
journaltitle = {Scandinavian Journal of Statistics},
}
@Article{Diggle2010,
author = {P. J. Diggle and R. Menezes and T. Su},
title = {Geostatistical inference under preferential sampling},
journal = {Applied Statistics},
year = {2010},
volume = {59},
number = {Part 2},
pages = {191--232},
}
@Article{dom94,
author = {P. Domburg and J. J. {de Gruijter} and D. J. Brus},
title = {A structured approach to designing soil survey schemes with prediction of sampling error from variograms},
journal = {Geoderma},
year = {1994},
volume = {62},
pages = {151--164},
}
@Article{eng94,
author = {E. J. Englund and N. Heravi},
title = {Phased sampling for soil remediation},
journal = {Environmental and Ecological Statistics},
year = {1994},
volume = {1},
pages = {247--263},
}
@Article{fitzgerald2006,
author = {Fitzgerald, G. J. and Lesch, S. M. and Barnes, E. M. and Luckett, W. E.},
title = {Directed sampling using remote sensing with a response surface sampling design for site-specific agriculture},
journal = {Computers and Electronics in Agriculture},
year = {2006},
volume = {53},
number = {2},
pages = {98--112},
owner = {brus003},
timestamp = {2010.03.05},
}
@Article{Gelfand2012,
author = {Gelfand, A. E. and Sahu, S. K. and Holland, D. M.},
title = {On the effect of preferential sampling in spatial prediction},
journal = {Environmetrics},
year = {2012},
volume = {23},
pages = {565--578},
owner = {brus003},
timestamp = {2014.12.16},
}
@Manual{Grafstrom2016,
title = {BalancedSampling: Balanced and Spatially Balanced Sampling},
author = {A. Grafstr{\"o}m and J. Lisic},
year = {2016},
note = {R package version 1.5.1},
url = {https://CRAN.R-project.org/package=BalancedSampling},
}
@Article{vgr00,
author = {J. W. {van Groenigen} and G. Pieters and A. Stein},
title = {optimising spatial sampling for multivariate contamination in urban areas},
journal = {Environmetrics},
year = {2000},
volume = {11},
pages = {227--244},
owner = {brus003},
timestamp = {2008.05.30},
}
@Article{vgr99,
author = {J. W. {van Groenigen} and W. Siderius and A. Stein},
title = {Constrained optimisation of soil sampling for minimisation of the kriging variance},
journal = {Geoderma},
year = {1999},
volume = {87},
pages = {239--259},
owner = {brus003},
timestamp = {2008.06.09},
}
@Article{vgr98,
author = {J. W. {van Groenigen} and A. Stein},
title = {Constrained optimisation of spatial sampling using continuous simulated annealing},
journal = {Journal of Environmental Quality},
year = {1998},
volume = {27},
pages = {1078--1086},
}
@Article{dgr90,
author = {J. J. {de Gruijter} and C. J. F. {ter Braak}},
title = {Model-free estimation from spatial samples: a reappraisal of classical sampling theory},
journal = {Mathematical Geology},
year = {1990},
volume = {22},
pages = {407--415},
owner = {brus003},
timestamp = {2010.12.22},
}
@Book{gru06,
title = {Sampling for Natural Resource Monitoring},
publisher = {Springer},
year = {2006},
author = {J. J. {de Gruijter} and D. J. Brus and M. F. P. Bierkens and M. Knotters},
address = {Berlin},
owner = {brus003},
timestamp = {2009.02.27},
}
@InProceedings{dgr85,
author = {{de Gruijter}, J. J. and Marsman, B. A.},
title = {Transect sampling for reliable information on mapping units},
booktitle = {Proceedings of the SSSA-ISSS Workshop on Spatial Variability, Las Vegas, U.S.A., Nov. 30 - Dec. 1, 1984},
year = {1985},
editor = {D. R. Nielson and J. Bouma},
pages = {150--163},
publisher = {Pudoc, Wageningen},
}
@Article{deGruijter2015,
author = {{de Gruijter}, J. J. and Minasny, B. and McBratney, A. B.},
title = {optimising stratification and allocation for design-based estimation of spatial means using predictions with error},
journal = {Journal of Survey Statistics and Methodology},
year = {2015},
volume = {3},
pages = {19--42},
}
@InCollection{heu07,
author = {Heuvelink, G. B. M. and Brus, D. J. and de Gruijter, J. J.},
title = {optimisation of sample configurations for digital mapping of soil properties},
booktitle = {Digital Soil Mapping. An introductory perspective},
publisher = {Elsevier},
year = {2007},
editor = {P. Lagacherie and A. B. McBratney and M. Voltz},
pages = {1020--1030},
owner = {brus003},
timestamp = {2008.06.11},
}
@Article{Knotters2010d,
author = {M. Knotters and D. J. Brus},
title = {Estimating space-time mean concentrations of nutrients in surface waters of variable depth},
journal = {Water Resources Research},
year = {2010},
volume = {46},
pages = {W08502},
art_number = {W08502},
doi = {10.1029/2009WR008350},
owner = {brus003},
timestamp = {2011.02.25},
}
@Article{Knotters2013,
author = {M. Knotters and D. J. Brus},
title = {{Purposive versus random sampling for map validation: a case study on ecotope maps of floodplains in the Netherlands}},
journal = {Ecohydrology},
year = {2013},
volume = {6},
pages = {425--434},
doi = {10.1002/eco.1289},
owner = {brus003},
timestamp = {2012.04.12},
}
@Article{lar00,
author = {R. M. Lark},
title = {Designing sampling grids from imprecise information on soil variability, an approach based on the fuzzy kriging variance},
journal = {Geoderma},
year = {2000},
volume = {98},
pages = {35--59},
owner = {brus003},
timestamp = {2008.06.04},
}
@Article{Lark2016,
author = {R. M. Lark},
journal = {Spatial Statistics},
title = {Multi-objective optimisation of spatial sampling},
year = {2016},
issn = {2211-6753},
pages = {412--430},
volume = {18, Part B},
abstract = {Abstract The optimisation of spatial sampling by simulated annealing has been demonstrated and applied for a range of objective functions. In practice more than one objective function may be important for sampling, and there may be complex trade-offs between them. In this paper it is shown how a multi-objective optimisation algorithm can be applied to a spatial sampling problem. This generates a set of solutions which is non-dominated (no one solution does better than any other on all objective functions). These solutions represent different feasible trade-offs between the objective functions, and a subset might be practically acceptable. The algorithm is applied to a hypothetical example of sampling for a regional mean with the variance of the mean and the total distance travelled between sample points as the two objective functions. The solutions represent a transition of sample arrays from a loose grid to a tight loop. The potential to develop this approach and apply it to other spatial sampling problems is discussed.},
doi = {http://dx.doi.org/10.1016/j.spasta.2016.09.001},
keywords = {Spatial sampling, Multi-objective, Pareto set},
url = {http://www.sciencedirect.com/science/article/pii/S2211675316300562},
}
@Article{lar00b,
author = {Lark, R. M.},
title = {Estimating variograms of soil properties by the method-of-moments and maximum likelihood},
journal = {European Journal of Soil Science},
year = {2000},
volume = {51},
pages = {717--728},
}
@Article{lar02,
author = {R. M. Lark},
title = {optimised spatial sampling of soil for estimation of the variogram by maximum likelihood},
journal = {Geoderma},
year = {2002},
volume = {105},
pages = {49--80},
}
@Article{lark2009,
author = {R. M. Lark},
title = {Estimating the regional mean status and change of soil properties: two distinct objectives for soil survey},
journal = {European Journal of Soil Science},
year = {2009},
volume = {60},
pages = {748--756},
owner = {brus003},
timestamp = {2009.12.08},
}
@Article{Lark2012,
author = {Lark, R. M.},
title = {{Some considerations on aggregate sample supports for soil inventory and monitoring}},
journal = {European Journal of Soil Science},
year = {2012},
volume = {63},
number = {1},
pages = {86--95},
abstract = {{Soil monitoring and inventory require a sampling strategy. One component
of this strategy is the support of the basic soil observation: the
size and shape of the volume of material that is collected and then
analysed to return a single soil datum. Many, but not all, soil sampling
schemes use aggregate supports in which material from a set of more
than one soil cores, arranged in a given configuration, is aggregated
and thoroughly mixed prior to analysis. In this paper, it is shown
how the spatial statistics of soil information, collected on an aggregate
support, can be computed from the covariance function of the soil
variable on a core support (treated as point support). This is done
via what is called here the discrete regularization of the core-support
function. It is shown how discrete regularization can be used to
compute the variance of soil sample means and to quantify the consistency
of estimates made by sampling then re-sampling a monitoring network,
given uncertainty in the precision with which sample sites are relocated.
These methods are illustrated using data on soil organic carbon content
from a transect in central England. Two aggregate supports, both
based on a 20 m 20 m square, are compared with core support. It is
shown that both the precision and the consistency of data collected
on an aggregate support are better than data on a core support. This
has implications for the design of sampling schemes for soil inventory
and monitoring.}},
doi = {{10.1111/j.1365-2389.2011.01415.x}},
owner = {brus003},
timestamp = {2012.08.30},
}
@Article{Lark2011CAGEO,
author = {R. M. Lark},
title = {{Spatially nested sampling schemes for spatial variance components: Scope for their optimisation}},
journal = {Computers \& Geosciences},
year = {2011},
volume = {{37}},
number = {{10}},
pages = {1633--1641},
month = {{OCT}},
issn = {{0098-3004}},
abstract = {{Efficient designs for nested sampling are needed in many areas of
science. In the geosciences they are used to discover the important
spatial scales on which properties vary. However, while the practical
advantages and disadvantages of various nested designs have been
discussed, no attempt has been made to optimise nested sampling schemes.
This paper shows how an optimal nested sampling design can be found by a
method of numerical combinatorial optimisation: simulated annealing. The
sample design is optimised over a space of possible designs for a fixed
sample size and predetermined levels (spatial scales). The objective
function for optimisation is based on the expected covariance matrix for
errors in the estimates of variance components, and so depends on what
those components are. By simulation it was shown that optimised sampling
schemes can detect scale-dependent variance components with common
requirements for statistical power on smaller total sample sizes than
are required with commonly used spatially nested sample designs such as
the balanced design. Although the optimised design depends on the
underlying covariance structure, sampling designs can be identified that
perform better than the commonly used ones over a wide range of
conditions. (C) 2011 Elsevier Ltd. All rights reserved.}},
doi = {{10.1016/j.cageo.2010.12.010}},
orcid-numbers = {{Lark, Richard/0000-0003-2571-8521}},
researcherid-numbers = {{Lark, Richard/G-6744-2012}},
unique-id = {{ISI:000295768900012}},
}
@Article{lark2006,
author = {R. M. Lark and B. R. Cullis and S. J. Welham},
title = {{On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E-BLUP) with REML}},
journal = {European Journal of Soil Science},
year = {2006},
volume = {57},
pages = {787--799},
owner = {brus003},
timestamp = {2015.06.30},
}
@Article{Lark2017,
author = {R. M. Lark and E. M. Hamilton and B. Kaninga and K. K. Maseka and M. Mutondo and G. M. Sakala and M. J. Watts},
title = {Planning spatial sampling of the soil from an uncertain reconnaissance variogram},
journal = {SOIL},
year = {2017},
}
@Article{Lark2017b,
author = {Lark, R. M. and Hamilton, E. M. and Kaninga, B. and Maseka, K. K. and Mutondo, M. and Sakala, G. M. and Watts, M. J.},
title = {{Nested sampling and spatial analysis for reconnaissance investigations of soil: an example from agricultural land near mine tailings in Zambia}},
journal = {European Journal of Soil Science},
year = {2017},
volume = {{68}},
number = {{5}},
pages = {605--620},
month = {{SEP}},
issn = {{1351-0754}},
abstract = {{A reconnaissance survey was undertaken on soil near mine tailings to
investigate variation in the content of copper, chromium and uranium. A
nested sampling design was used. The data showed significant relations
between the content of copper and uranium in the soil and its organic
matter content, and a significant spatial trend in uranium content with
distance from the tailings. Soil pH was not significantly related to any
of the metals. The variance components associated with different scales
of the sample design had large confidence intervals, but it was possible
to show that the random variation was spatially dependent for all
spatial models, whether for variation around a constant mean, or with a
mean given by a linear effect of organic matter or distance to the
tailings. For copper, we showed that a fractal or multifractal random
model, with equal variance components for scales in a logarithmic
progression, could be rejected for the model of variation around the
fixed mean. The inclusion of organic matter as an explanatory factor
meant that the fractal model could no longer be rejected, suggesting
that the effect of organic matter results in spatial variation that is
not scale invariant. It was shown, taking uranium as a case study, that
further spatially nested sampling to estimate scale-dependent variance
components, or to test a non-fractal model with adequate power, would
require in the order of 200-250 samples in total.
Highlights
Sampling was undertaken to investigate spatial variation of metal
content in soil near mine tailings.
Chromium and uranium were related to soil organic matter content;
uranium showed a spatial trend.
Spatial variation was scale dependent, variation of copper was not
scale-invariant.
Characterizing random spatial variation requires substantial sample
effort.}},
doi = {{10.1111/ejss.12449}},
eissn = {{1365-2389}},
orcid-numbers = {{BGS University Funding Initiative, BUFI/0000-0003-3097-5530}},
researcherid-numbers = {{BGS University Funding Initiative, BUFI/H-4822-2011}},
unique-id = {{ISI:000409483900003}},
}
@Article{Lark2018,
author = {Lark, R. M. and Marchant, B.},
title = {How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?},
journal = {Geoderma},
year = {2018},
volume = {319},
pages = {89--99},
doi = {https://doi.org/10.1016/j.geoderma.2017.12.022},
}
@Article{lar06,
author = {R. M. Lark and R. Webster},
title = {Geostatistical mapping of geomorphic variables in the presence of trend},
journal = {Earth Surface Processes and Landforms},
year = {2006},
volume = {31},
pages = {862--874},
owner = {brus003},
timestamp = {2008.06.09},
}
@Manual{Lesch2000ESAPManual,
title = {{ESAP-95 Version 2.01R User Manual and Tutorial Guide}},
author = {Lesch, S. M. and Rhoades, J. D. and Corwin, D. L.},
year = {2000},
publisher = {{USDA-ARS, George E. Brown, Jr., Salinity Laboratory}},
}
@Article{lesch2005,
author = {Lesch, S. M.},
title = {Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties},
journal = {Computers and Electronics in Agriculture},
year = {2005},
volume = {46},
number = {1--3},
pages = {153--179},
owner = {brus003},
timestamp = {2010.03.05},
}
@Article{lesch95,
author = {Lesch, S. M. and Strauss, D. J. and Rhoades, J. D.},
title = {Spatial prediction of soil salinity using electromagnetic induction techniques 2. {An} efficient spatial sampling algorithm suitable for multiple linear regression model identification and estimation.},
journal = {Water Resources Research},
year = {1995},
volume = {31},
pages = {387--398},
owner = {brus003},
timestamp = {2009.11.19},
}
@Book{loh99,
title = {Sampling: Design and Analysis},
publisher = {Duxbury Press},
year = {1999},
author = {S. L. Lohr},
address = {Pacific Grove, USA},
owner = {brus003},
timestamp = {2008.06.09},
}
@InBook{Fitzgerald2010,
chapter = {10},
pages = {121--129},
title = {Response Surface Sampling of Remotely Sensed Imagery for Precision Agriculture},
publisher = {Springer Netherlands},
year = {2010},
author = {Fitzgerald, G. J.},
editor = {{Viscarra Rossel}, R. A. and McBratney, A. B. and Minasny, B.},
address = {Sydney, Australia},
booktitle = {Proximal Soil Sensing},
owner = {brus003},
timestamp = {2009.11.19},
}
@InBook{degruijter10,
chapter = {1},
pages = {3--14},
title = {Sampling for high resolution soil mapping},
publisher = {Springer Netherlands},
year = {2010},
author = {de Gruijter, J. J. and McBratney, A. B. and Taylor, J.},
editor = {{Viscarra Rossel}, R. A. and McBratney, A. B. and Minasny, B.},
address = {Sydney, Australia},
booktitle = {Proximal Soil Sensing},
owner = {brus003},
timestamp = {2009.11.19},
}
@Article{nan04,
author = {A. Nanthakumar and K. Selvavel},
title = {Estimation of proportion of success from a stratified population: a comparative study},
journal = {Communications in Statistics},
year = {2004},
volume = {33},
pages = {2245--2257},
owner = {brus003},
timestamp = {2008.06.09},
}
@Article{walvoort2010,
author = {D. J. J. Walvoort and D. J. Brus and J. J. de Gruijter},
title = {An {R} package for spatial coverage sampling and random sampling from compact geographical strata by k-means},
journal = {Computers and Geosciences},
year = {2010},
volume = {36},
pages = {1261--1267},
doi = {10.1016/j.cageo.2010.04.005},
owner = {brus003},
timestamp = {2010.08.11},
}
@Article{matei2005,
author = {A. Matei and Y. Till{\'e}},
title = {Evaluation of Variance Approximations and Estimators in Maximum Entropy Sampling with Unequal Probability and Fixed Sample Size},
journal = {Journal of Official Statistics},
year = {2005},
volume = {21},
pages = {543--570},
owner = {brus003},
timestamp = {2010.03.05},
}
@Book{Tille2006,
title = {Sampling algorithms},
publisher = {Springer},
year = {2006},
author = {Y. Till{\'e}},
owner = {brus003},
timestamp = {2013.02.06},
}
@Article{Grafstrom2012,
author = {Grafstr{\"o}m, A. and Lundstr{\"o}m, N. L. P. and Schelin, L.},
title = {Spatially balanced sampling through the pivotal method},
journal = {Biometrics},
year = {2012},
volume = {68},
pages = {514--520},
owner = {brus003},
timestamp = {2014.09.30},
}
@Article{Grafstrom2013,
author = {A. Grafstr{\"o}m and Y. Till{\'e}},
title = {Doubly balanced spatial sampling with spreading and restitution of auxiliary totals},
journal = {Environmetrics},
year = {2013},
volume = {24},
number = {2},
pages = {120--131},
issn = {1099-095X},
doi = {10.1002/env.2194},
keywords = {balanced sampling, pivotal method, spatially balanced sampling, spatial correlation},
owner = {brus003},
publisher = {John Wiley \& Sons, Ltd},
timestamp = {2013.03.07},
url = {http://dx.doi.org/10.1002/env.2194},
}
@Article{Pebesma2005,
author = {E. J. Pebesma and R. S. Bivand},
title = {{Classes and methods for spatial data in R}},
journal = {R News},
year = {2005},
volume = {5},
number = {2},
pages = {9--13},
owner = {brus003},
timestamp = {2013.06.07},
url = {http://cran.r-project.org/doc/Rnews/},
}
@Book{web90,
title = {{Statistical Methods in Soil and Land Resource Survey}},
publisher = {Oxford University Press},
year = {1990},
author = {R. Webster and M. A. Oliver},
address = {Oxford},
}
@Article{McKay1979,
author = {McKay, M. D. and Beckman, R. J. and Conover, W. J.},
title = {{A comparison of three methods for selecting values of input variables in the analysis of output from a computer code}},
journal = {Technometrics},
year = {1979},
volume = {{21}},
number = {{2}},
pages = {239--245},
issn = {{0040-1706}},
doi = {10.2307/1268522},
owner = {brus003},
timestamp = {2014.03.05},
unique-id = {{ISI:A1979GW16700012}},
}
@Article{Minasny2006,
author = {Minasny, B. and McBratney, A. B.},
title = {A conditioned {L}atin hypercube method for sampling in the presence of ancillary information},
journal = {Computers \& Geosciences},
year = {2006},
volume = {32},
pages = {1378--1388},
doi = {10.1016/j.cageo.2005.12.009},
owner = {brus003},
timestamp = {2009.12.10},
}
@Manual{Alessandro2016,
title = {{spsann: optimisation of Sample Configurations using Spatial Simulated Annealing}},
author = {A. {Samuel-Rosa}},
year = {2016},
note = {R package version 2.0-0},
url = {https://CRAN.R-project.org/package=spsann},
}
@Manual{roudier2011,
title = {{clhs: a R package for conditioned Latin hypercube sampling}},
author = {P. Roudier},
year = {2011},
url = {https://CRAN.R-project.org/package=clhs},
}
@Book{myers2002,