A generic composite measure of similarity between geospatial variables

Abstract

The comparison between spatial or temporal patterns is often needed for model evaluation and change detection in ecological studies. The statistics developed for image quality assessment, such as the structural similarity index (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), have been introduced for comparing ecological patterns. However, these measures can be applied only when a positive relationship is expected between patterns having the same scale. We propose a new index, generic composite similarity measure (GCSM), to meet a wide range of potential applications. A set of numerical experiments was performed to illustrate the properties of GCSM in comparison with SSIM and CMSC. Two case studies were conducted examining the (dis)agreement between two products of gross primary production (GPP), and the relative (dis)similarity between GPP and precipitation, respectively. GCSM has advantages over both SSIM and CMSC, including higher sensitivity and the ability to quantify the dissimilarity, which cannot be properly revealed with the latter two indices. The normalization preprocessing constructs universal criteria for assessing the relative (dis)similarity between patterns having unequal scales. The GCSM, overcoming the limi-tations of preexisting composite measures in quantifying the similarity or dissimilarity between patterns, would aid assessment of heterogeneous relationship between ecological factors over space or time.