Information Theory as a consistent framework for quantification and classification of landscape patterns

Abstract

Context: Quantitative grouping of similar landscape patterns is an important part of landscape ecology due to the relationship between a pattern and an underlying ecological process. One of the priorities in landscape ecology is a development of the theoretically consistent framework for quantifying, ordering and classifying landscape patterns. Objective: To demonstrate that the Information Theory as applied to a bivariate random variable provides a consistent framework for quantifying, ordering, and classifying landscape patterns. Methods: After presenting Information Theory in the context of landscapes, information-theoretical metrics were calculated for an exemplar set of landscapes embodying all feasible configurations of land cover patterns. Sequences and 2D parametrization of patterns in this set were performed to demonstrate the feasibility of Information Theory for the analysis of landscape patterns. Results: Universal classification of landscape into pattern configuration types was achieved by transforming landscapes into a 2D space of weakly correlated information-theoretical metrics. An ordering of landscapes by any single metric cannot produce a sequence of continuously changing patterns. In real-life patterns, diversity induces complexity -- increasingly diverse patterns are increasingly complex. Conclusions: Information theory provides a consistent, theory-based framework for the analysis of landscape patterns. Information-theoretical parametrization of landscapes offers a method for their classification.

Cite
Nowosad, J., Stepinski, T. F. (2019). Information Theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecology. https://doi.org/10.1007/s10980-019-00830-x
Date