Comparing spatial patterns in raster data using R

Abstract

Spatial patterns are a crucial aspect of geographical studies and play a significant role in various fields, such as remote sensing, ecology, and geology. Comparing spatial patterns is a common task in these fields, and using raster data is a popular approach. This work aims to provide a comprehensive overview of existing R packages for comparing spatial patterns in rasters. First, we discuss various methods for comparing spatial patterns, including visual inspection, correlation coefficient between focal regions of two rasters, spatial autocorrelation analysis, structural similarity index, and comparison of spatial signatures. These methods can be applied to continuous or categorical raster data, and the choice of method depends on the type of data and the specific research question. Next, we present several R packages that implement these methods, including terra, SSIMmap, and spatialEco, and show how they can be applied. These packages provide a wide range of tools for comparing spatial patterns in rasters, offering researchers a powerful means to study change, similarity, association, and model assessment. However, we also highlight issues with the current software, such as the absence of standardization and inadequate documentation. Importantly, there is still a lack of studies that systematically compare different methods and suggest good practices in their use. The growing number of FOSS tools for spatial raster comparison offers a promising avenue for testing various methods and their application to real-life scenarios. Further research is needed to evaluate the performance of these methods and establish best practices for their use.