<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Physical Geography and Geoecology, Faculty of Geography

Taras Shevchenko National University of Kyiv

Ekológia (Bratislava)

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

As anisotropy is a fundamental property of the real-world environmental spatial variables, the conventional omnidirectional variograms and correlograms do not...

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

Online veröffentlicht: 20. Juni 2019

Seitenbereich: 140 - 153

DOI: https://doi.org/10.2478/eko-2019-0011

Schlüsselwörter
autocorrelation, environmental variable, Kanivs’kiy Nature Reserve, semivariance, spatial analysis, spatial anisotropy

© 2019 Daria Svidzinska, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 20. Juni 2019

Seitenbereich: 140 - 153

DOI: https://doi.org/10.2478/eko-2019-0011

Schlüsselwörterautocorrelation, environmental variable, Kanivs’kiy Nature Reserve, semivariance, spatial analysis, spatial anisotropy

© 2019 Daria Svidzinska, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
autocorrelation, environmental variable, Kanivs’kiy Nature Reserve, semivariance, spatial analysis, spatial anisotropy