Mapped canopy boundaries of single species plotsIn December 2013 and July 2014, we mapped the canopy boundaries of 87 single species plots, using a handheld GPS unit (Bad Elf GPS Pro, manufactured by Bad Elf, LLC; http://bad-elf.com) and tablet displaying the aerial image. The attached kml file includes the boundaries of those plots, named according to the plot number of the PRORENA field planting trial plots. This plot number can be used to merge the tree growth data and this spatial data on plot boundaries.plot_boundaries.kmlTree growth data for PRORENA plots 2008-2010This file presents the growth data from the PRORENA field planting trials for the 87 plots used in the growth study. The field codes are as follows: SPEC_ID (the species code), TREE_ID (unique tree identification number), DIAM_2008 (Diameter in mm measured at 1.3 m high for all trees in 2008. For trees with multiple stems, the value represents the square root of the sum of all squared trunk stem diameters). DIAM_2010 (same as DIAM_2008, but for 2010), BLOCK (the treatment groups of plots), PLOT (a numeric identifier for each plot. Note that this is the same PLOT value in the spatial data of mapped canopy boundaries), ABSOLUTE GROWTH (the difference between diameter in mm between 2000 and 2010), STD.GROWTH (growth rate standardized within species by centering around the mean absolute growth rate for that species and dividing by two standard deviations of the absolute growth rate for that species), Species (the scientific name for each species).growth_data.csv

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.