Abstract; ; ; Mitigating impacts of global change on biodiversity is a pressing goal for land managers, but understanding impacts is often limited by the spatial and temporal constraints of traditional in situ data. Advances in remote sensing address this challenge, in part, by enabling standardized mapping of biodiversity at large spatial scales and through time. In particular, hyperspectral imagery can detect functional and compositional characteristics of vegetation by measuring subtle differences in reflected light.; ; The spectral variance hypothesis (SVH) expects spectral diversity, or variability in reflectance across pixels, to predict vegetation diversity. However, the majority of research testing the SVH to date has been conducted in systems with controlled conditions or spatially homogenous assemblages, with little generalizability to heterogeneous real‐world systems.; ; Here, we move the field forward by testing the SVH in a species‐rich system with high heterogeneity resulting from variable species composition and a recent fire. We use very high spatial resolution (~1 mm) hyperspectral imagery to compare spectrally derived estimates of vegetation diversity with in situ measures collected in Boulder, CO, USA.; ; We find that spectral diversity and taxonomic diversity are positively correlated only for low to moderate diversity transects, or in transects that were recently burned where vegetation diversity is low and composed primarily of C3 grasses. Additionally, we find that the relationship between spectral and taxonomic diversity depends on spatial resolution, indicating that pixel size should remain a priority for biodiversity monitoring.; ; Practical implication: The context dependency of this relationship, even with high spatial resolution data, confirms previous work that the SVH does not hold across landscapes and demonstrates the necessity for repeated, high‐resolution data in order to tease apart the biological conditions underpinning the SVH. With refinement, however, the remote sensing techniques described here will offer land managers a cost‐effective approach to monitor biodiversity across space and time.; ;
