Interpreting image texture metrics applied to landscape gradient data

Context Pattern metrics drawn from image processing and remote sensing have been applied as descriptors of the texture of landscape gradient data. Like some classical pattern metrics in ecology, texture has several facets which are measured by examining an adjacency matrix—the frequencies of co-occurring pixel values on a map—in different ways. Objectives To improve the interpretation and application of such metrics in landscape ecology we reformulate and interpret several of them by analogy to traditional metrics used with categorical data. Results and conclusions 1. Four of the eight classical texture metrics measure attraction—the tendency for the same or similar values to be adjacent. Four others measure dispersion—the diversity of adjacencies relative to the entire adjacency matrix, the diagonal of the matrix, or the origin of the matrix. 2. The attraction metrics ( dissimilarity , contrast , inverse difference , and homogeneity ) differ only in the algebraic weights applied to different parts of an adjacency matrix. 3. The dispersion metrics ( entropy , uniformity , difference entropy , and sum entropy ) can be made more comparable by rescaling them to their maximum possible values. 4. While the metrics may be applied to any adjacency matrix, the choices about the method used to create an adjacency matrix have subtle yet important implications for the use and comparability of some metrics.