Optimization of sample patterns for universal kriging of environmental variables

By Dick Brus, Gerard B. M. Heuvelink. 2007

350 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7236227369184e095db5263abb8f38d547bb00ef

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In Economics and Econometrics

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

ZipWeave: Towards efficient and reliable measurement based mobile coverage maps (2017, methodology): “Both cases aim to optimize an objective function such as minimizing Mean Kriging Variance [15].”
Seeing the forest beyond the trees (2015, background): “…of the interpolation also deteriorates significantly when sampling patterns are clustered instead of random, unless the sampling is designed initially to minimize the maximum or average kriging predictionerror variance (Brus & Heuvelink, 2007), which is not the case with the research plots.”
A Practical Guide to Geostatistical Mapping (2009, methodology): “User-friendly sampling optimisation packages: Although methodologies both to plan a new sampling design and to optimize additional sampling designs have already been proposed (Minasny and McBratney, 2006; Brus and Heuvelink, 2007), neither simulated annealing neither Latin hypercube sampling are ava”
Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models (2013, background): “The quality of the spatial prediction is also subject to the spatial patterns of the observations (Brus and Heuvelink 2007).”
Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests (2008, methodology): “Likewise, model-based sampling strategies have been applied to the optimal collection of spatial data by Muller (2001); the specification of optimal designs for variogram estimation by Muller and Zimmerman (1999), Warrick and Myers (1987), and Russo (1984); the estimation of spatially referenced LR ”