Many physiological processes are spatially variable across leaf surfaces. While maps of photosynthesis, stomatal conductance, gene expression, water transport, and the production of reactive oxygen species (ROS) for individual leaves are readily obtained, analytical methods for quantifying spatial heterogeneity and combining information gathered from the same leaf but with different instruments are not widely used. We present a novel application of tools from the field of geographical imaging to the multivariate analysis of physiological images. Procedures for registration and resampling, cluster analysis, and classification provide a general framework for the analysis of spatially resolved physiological data. Two experiments were conducted to illustrate the utility of this approach. Quantitative analysis of images of chlorophyll fluorescence and the production of ROS following simultaneous exposure of soybean leaves to atmospheric O3 and soybean mosaic virus revealed that areas of the leaf where the operating quantum efficiency of PSII was depressed also experienced an accumulation of ROS. This correlation suggests a causal relationship between oxidative stress and inhibition of photosynthesis. Overlaying maps of leaf surface temperature and chlorophyll fluorescence following a photoinhibition treatment indicated that areas with low operating quantum efficiency of PSII also experienced reduced stomatal conductance (high temperature). While each of these experiments explored the covariance of two processes by overlaying independent images gathered with different instruments, the same procedures can be used to analyze the covariance of information from multiple images. The application of tools from geographic image analysis to physiological processes occurring over small spatial scales will help reveal the mechanisms generating spatial variation across leaves.