Abstract
Context Comparing a large number of landscapes calls for using the smallest possible set of landscape metrics. The overall complexity of land-scape pattern is the single most important metric, but the standard set of landscape metrics lacks the bona fide indicator of complexity.
Objective Demonstrate that information theory provides a natural frame-work for a systematic analysis of landscape complexity. Organize landscape pattern types using a minimal number of information-theoretical metrics.
Methods Using the concept of entropy of a random variable consisting of pairs of adjacent cells we analytically derive four theoretical metrics of landscape complexity: an overall spatio-thematic complexity, a thematic complexity, a configurational complexity, and a disambiguator of pattern types having the same overall complexity. We use sets of natural and neutral landscapes to demonstrate the utility of these metrics.
Results There is a simple, additive relation between three types of complexity, total = thematic + configurational. Thematic and configurational complexities are highly dependent leading to a simple rule for landscape patterns: class diversity induces complexity. Two metrics, an overall complexity and a pattern type disambiguator, are sufficient to organize landscape types.
Conclusions Long-standing issue of a relative importance of composition and configuration to an overall description of landscape pattern finds an elegant solution within a framework of information theory. We demonstrated that increasing the complexity of composition must be accompanied by increasing the complexity of configuration. Landscape types cannot be compared by using only the complexity metric; the disambiguator metric must be added for an unambiguous comparison.
