Geostatistics For Environmental Scientists

By M. Wagner. 2016

949 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/86123dec962431974292e44d2f0485c4bc8eea50

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Citations in the corpus

Solute transport in low‐heterogeneity sandboxes: The role of correlation length and permeability variance (2014, background): “The three patterns differ in their ‘‘dimensionless correlation length,’’ k/L, defined as the ratio of the spatial correlation length [Kitanidis, 1997; Webster and Oliver, 2007] of the permeability field k over the length of the sandbox in the main flow direction L.”
Accuracy Assessment and Comparative Analysis of IDW, Spline and Kriging in Spatial Interpolation of Landform (Topography): An Experimental Study (2017, unknown): “Matheron derived the formulas, establishing the entire field of linear geostatistics [11].”
A Comparison of Some Interpolation Techniques for Determining Spatial Distribution of Nitrogen Compounds in Groundwater (2019, methodology): “These methods are especially useful in the analysis of spatially continuous phenomena (Webster and Oliver 2007; Zhu 2016).”
Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, methodology): “where I x0 t^ h is the estimated value of an attribute at the point of interest , px0 I xi ^ h is the observed value at the sampled point , pxi wi is the weight assigned to the sampled point , pxi and N represents the number of sampled points used for the interpolation [22].”
Spatial interpolation methods applied in the environmental sciences: A review (2014, background): “Geostatistics is usually believed to have originated from thework in geology and mining by Krige (1951), but it can be traced back to the early 1910s in agronomy and 1930s in meteorology (Webster and Oliver, 2001).”
Establishing the precision and robustness of farmers’ crop experiments (2019, methodology): “Webster and Oliver (2007) suggest that at least 100 observations of a soil variable located on a regular grid are required to estimate a standard variogram model.”
Interpolation in Time Series : An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment (2017, methodology): “Another method, considered as a reﬁnement of inverse distance weighting method by [20], is the natural neighbor interpolation [58]: this method requires additional data (such as topology and by extension, other univariate series) to interpolate the missing value(s).”
A tutorial guide to geostatistics: Computing and modelling variograms and kriging (2014, methodology): “Now the most recent release of GenStat (Payne, 2013) contains the facilities, From Webster and Oliver (2007). as does R (Pinheiro et al., 2013; R Development Core Team, 2010).”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, background): “…an unbiasedness condition (i.e., the weights are forced to sum to124 one).125 The associated prediction error variance, i.e., the OK variance, is given by (Webster and Oliver, 2001,126 p.183):127 σ2OK(s0) = var [ Y (s0)− Ŷ (s0) ] =C(s0,s0)−w(si) T ·C0 −ϕ, (10) where C0 is an n-vector of…”
Multivariate mapping of soil with structural equation modelling (2017, methodology): “To determine if our model results could be improved further by taking spatial autocorrelation into account, we fitted variograms (Webster & Oliver, 2007) to the SEM cross-validation residuals (Figure 12).”
Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale (2014, unknown): “The final predictions exhibited a limited bias (MPE=-0.19 kg m−2 for the Fg model, Table 2), a problem that can arise in lognormal kriging due to the sensitivity of the back-transform to the variogram parameters and to the assumption of a lognormal distribution (Webster and Oliver, 2007).”