Effectively fitting the major features of experimental transiograms (or variograms) is crucial in characterizing spatial patterns and reproducing them in geostatistical simulations. Landscape patterns usually tend to contain neighboring structures. The experimental cross-transiograms of frequent neighboring landscape categories normally demonstrate apparent peaking features at low lag distances...

In this study, we suggest a new approach to the detection of edge orientation in order to guide a choice of the appropriate directional filter. This approach aims at analysing variograms properties such as the directionality, the renewal period of texture pattern and size of objet. The use of variograms enable to define directional MAP and to determine the edge orientation of the object in orde...

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...

Accurate characterization of near-surface soil water content is vital for guiding agricultural management decisions and for reducing the potential negative environmental impacts of agriculture. Characterizing the near-surface soil water content can be difficult, as this parameter is often both spatially and temporally variable, and obtaining sufficient measurements to describe the heterogeneity...

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...

Categorical geographical variables are normally classified into multinomial classes which are mutually exclusive and visualized as area-class maps. Typical categorical variables such as soil types and land cover classes are multinomial and exhibit complex interclass relationships. Interclass relationships may include three situations: cross-correlation (i.e. interdependency), neighbouring situa...