Data from: A hyperspectral image can predict tropical tree growth rates in single-species stands

Remote sensing is increasingly needed to meet the critical demand for
estimates of forest structure and composition at landscape to continental
scales. Hyperspectral images can detect tree canopy properties, including
species identity, leaf chemistry and disease. Tree growth rates are
related to these measurable canopy properties but whether growth can be
directly predicted from hyperspectral data remains unknown. We used a
single hyperspectral image and LiDAR-derived elevation to predict growth
rates for twenty tropical tree species planted in experimental plots. We
asked whether a consistent relationship between spectral data and growth
rates exists across all species and which spectral regions, associated
with different canopy chemical and structural properties, are important
for predicting growth rates. We found that a linear combination of
narrowband indices and elevation is correlated with standardized growth
rates across all twenty tree species (R2=53.70%). Although wavelengths
from the entire visible-to-shortwave infrared spectrum were involved in
our analysis, results point to relatively greater importance of visible
and near-infrared regions for relating canopy reflectance to tree growth
data. Overall, we demonstrate the potential for hyperspectral data to
quantify tree demography over a much larger area than possible with
field-based methods in forest inventory plots.