Abstract As with a spatial variogram or spatial covariance, a principal purpose of estimating and modeling a space-time variogram is to quantify the spatial temporal dependence reflected in a data set. The resulting model might then be used for spatial interpolation and/or temporal prediction which might take several forms, e.g. kriging and Radial Basis functions. There are significant problems...

The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the A(p) horizon of a reed canary-grass field in northern Minnesota. Geostatistical concepts relevant to nematology include semi-variogram modelling, kriging, and change of support calculations. Soil and nematode data generally followed a spherical sem...

Estimating stems per hectare (SPHA) for a given forest area from high spatial resolution remotely sensed data usually follows the identification of individual trees. A common method of tree identification is through local maxima filtering, which in the context of a lidar canopy height model (CHM), seeks to locate the highest value within a specified neighbourhood of pixels. Hence, specifying an...

Since gstat package version 1.0-0, a dependency of gstat on the R package spacetime was introduced, allowing the code in gstat to exploit spatio-temporal data structures from that package. This vignette describes the possibilities and limitations of the package for spatio-temporal geostatistics. To understand some of the possibilities and limitations, some knowledge of the history of the softwa...

The effect of outliers on estimates of the variogram depends on how they are distributed in space. The ‘spatial breakdown point’ is the largest proportion of observations which can be drawn from some arbitrary contaminating process without destroying a robust variogram estimator, when they are arranged in the most damaging spatial pattern. A numerical method is presented to find the spatial bre...

We use a basic stationary and isotropic spatial linear regression model to fit a rainfall dataset collected in central Venezuela. First order and partial second order trend explain the mean function. With a Matérn correlation function with smoothness fixed at one, unknown parameters are estimated through MCMC method. The variogram is also estimated based on the posterior samples. By examining p...

Ž . Using spatial simulated annealing SSA , spatial sampling schemes can be optimised for minimal kriging variance. Two optimisation criteria are presented in this paper. The first criterion minimises the average kriging variance, the second the maximum kriging variance. In a simple case with 23 observations, performances of a sampling scheme obtained with SSA were compared with performances of...

While outcrop models can provide important information on reservoir architecture and heterogeneity, it is not entirely clear how such information can be used exhaustively in geostatistical reservoir modeling. Traditional, variogram-based geostatistics is inadequate in that regard since the variogram is too limiting in capturing geological heterogeneity from outcrops. A new field, termed multipl...