Title: Partitioning plant spectral diversity into alpha and beta components
Cite this dataset
Laliberté, Etienne; Schweiger, Anna; Legendre, Pierre (2020). Title: Partitioning plant spectral diversity into alpha and beta components [Dataset]. Dryad. https://doi.org/10.5061/dryad.gxd2547gd
Leaf spectra were measured with a portable field spectrometer (ASD FieldSpec 4, Malvern Panalytical, Cambridge, UK), covering the wavelength range from 350 nm to 2500 nm and an integrating sphere with internal light source (ASD RTS-3ZC, Malvern Panalytical, Cambridge, UK) in the summer of 2017, following the leaf spectroscopy protocol from the Carnegie Spectranomics project (https://cao.carnegiescience.edu/spectranomics-protocols), with some modifications (Laliberté & Soffer 2018). The tree species sampled (with five individual plants per species, each representing the average spectrum from six mature leaves) were Betula alleghaniensis Britton (yellow birch), Populus deltoides W. Bartram ex Marshall subsp. deltoides Marsh (eastern cottonwood), and Populus tremuloides Michaux (trembling aspen). Processing of spectra consisted of applying a third-order Savitzky-Golay filter (length = 55) to reduce noise, reducing spectral resolution from 1 nm to 10 nm wide to reduce the number of bands, trimming the spectra between 410 and 2400 nm to remove regions with low signal-to-noise, and brightness-normalizing spectra. This vector normalization emphasizes differences in the shape of spectra as opposed to differences in amplitude (i.e. albedo or brightness). The R code to perform the analyses is available online (https://github.com/elaliberte/specdiv).
NEON imaging spectroscopy data
Second, we applied our method for partitioning spectral diversity to imaging spectroscopy data collected by NEON’s Airborne Observation Platform (AOP; Kampe et al. 2010) over the Bartlett Experimental Forest (https://www.neonscience.org/field-sites/field-sites-map/BART). The AOP Data Product used was NEON_D01_BART_DP3_314000_4880000_reflectance.h5, and was downloaded from http://data.neonscience.org on 25 February 2018. In this case study, we used a scene measuring 280 m (east-west) x 1000 m (north-south), acquired in August 2017. Spectral data were processed to surface reflectance and subsampled to 1-m pixel size by NEON. The R code to perform the analyses is available online (https://github.com/elaliberte/specdiv).
Laliberté, E. & Soffer, R. (2018). Measuring spectral reflectance and transmittance (350-2500 nm) of large leaves using the Spectra Vista Corporation (SVC) DC-R/T Integrating Sphere. protocols.io. https://dx.doi.org/10.17504/protocols.io.p8pdrvn
Natural Sciences and Engineering Research Council, Award: RGPIN-2014-06106
Natural Sciences and Engineering Research Council, Award: RGPIN-2019-04537
Natural Sciences and Engineering Research Council, Award: 509190-2017