The Application of Directional Univariate Structure Functions Analysis for Studying the Spatial Anisotropy of Environmental Variables

Abstract

As anisotropy is a fundamental property of the real-world environmental spatial variables, the conventional omnidirectional variograms and correlograms do not provide means enough to characterise spatial dependence between observations. The purpose of this article is to introduce directional univariate structure functions analysis to explore and quantify the spatial anisotropy of environmental variables. Analysis of six environmental variables within three physical–geographical regions proved the leading role of relief for landscape differentiation; it also defined the size and extension of major landforms responsible for the organisation of spatial pattern. The arrangement of the vegetation patches demonstrated linkage with the major landforms. The other relief derivatives, being prone to noise and artefacts in the original data, showed a random-variable type of behaviour. In the lack of any particular spatially anisotropic structure, the results of the analysis can provide a clue about meaningful distances of interest at finer scales. The approach can also be an exploratory tool for discrete measurements to recognise the features of spatial continuity.

Keywords

• autocorrelation
• environmental variable
• kanivs’kiy nature reserve
• semivariance
• spatial analysis
• spatial anisotropy