High resolution spectral data predicts taxonomic diversity in low diversity grasslands

Mitigating impacts of global change on biodiversity is a pressing goal for
land managers but understanding these 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. Especially when assessing herbaceous ecosystems, however, there
is inconsistent evidence for the SVH, potentially due to a mismatch
between plant size and the traditionally coarse pixels of satellite and
airborne imagery or variation in the biological characteristics of the
observed ecosystems, such as vegetation structure and composition, which
can impact spectral variability. 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. 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.