DOI: 10.5061/dryad.z08kprrp0
title: High resolution spectral data predicts taxonomic diversity in low diversity grasslands

description: 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.
description: Data collection for this project built on a long-term grassland
 monitoring initiative in the city of Boulder, CO, USA, run by their Open
 Space & Mountain Parks department. The monitoring area covers a
 2,000 ha mixed grass prairie in the Front Range of Colorado, USA (39°56 N,
 105°12 W). Permanent plots (hereafter, ‘transects’) of 50 m x 2 m were
 established in 2009 to implement the city of Boulder’s Grassland Ecosystem
 Management Plan (GEMAP). Transects were surveyed using a panel design for
 the first 8 years and thereafter, every three years  for vegetation cover
 and composition. Cover is surveyed with a point intercept method: along
 the 50 m center line in a transect, species identity (or soil/rock/litter)
 is recorded at each meter mark at 0.5 m from both sides of the centerline,
 resulting in a total of 100 points. Additionally, vascular species that
 are present in the rest of the plot, but not hit during the point
 intercept method, are noted. Here, we utilize data from
 the 17 transects surveyed in 2022, which was collected at peak biomass for
 this system between July 18 and August 23. Vegetation survey data is
 summarized as relative vegetation cover, species richness and an
 abundance-based taxonomic diversity metric, Shannon Index, to protect the
 location and identity of species according to Boulder OSMP policy. To
 calculate vegetation cover, we computed the percentage of points along the
 transect where vegetation was hit by excluding rock, litter, and bare
 soil. To compute species richness, we calculated the number of species
 identified in each transect both as those counted with the point intercept
 method (here, “hits”) and as hits plus those identified as present in the
 rest of the plot (here, “full species list”). We also computed the Shannon
 diversity index of both the hits and the full species list. Species in the
 full species list that were not counted as hits were given an abundance of
 0.1 for incorporation in this abundance-based metric.
 We also collected proximal imagery of the 17 GEMAP transects
 using a handheld Specim IQ hyperspectral camera (Spectral Imaging Ltd.,
 Konica Minolta; www.specim.com). Sampling was conducted from August 11th -
 August 31st, 2022, such that the maximum time between vegetation survey
 and imagery was 24 days (average time between: 7 days). This hyperspectral
 camera is equipped with a built-in pushbroom imaging system (a line scan
 camera system composed of an imaging spectrograph, grayscale camera and
 objective) that records reflectance in 204 bands spanning the visible to
 near infrared regions (400 to 1000 nm; 7 nm spectral resolution FWHM) with
 a 1 mm spatial resolution when mounted on a tripod at 1.5 m from the
 ground. For each image, we included a white reference material provided by
 Specim (with a reflectance close to 100%) in the frame of reference.
 Sampling was conducted on cloud free days and within 3 hours from solar
 noon. Raw data was transformed to reflectance using the reflectance from
 the white reference, the dark reference signal, and the imaging
 integration time. Images were taken of square meter
 plots in 2-meter increments along the transect. We ultimately combined
 pixels from all images within a transect to represent the transect-level
 data in the manuscript, but here archive the spectra at the
 "plot" level. For each plot, there is a .dat file representing
 reflectance in 204 bands across a 512 pixels by 512 pixels. Subsequent
 processing, including removal of bad bands and filtering for
 photosynthetically active vegetation, is detailed in the
 manuscript. 
description: # High resolution spectral data predicts taxonomic diversity in low
 diversity grasslands
 [https://doi.org/10.5061/dryad.z08kprrp0](https://doi.org/10.5061/dryad.z08kprrp0) ## Description of the data and file structure This dataset contains reflectance spectra and species cover data for vegetation plots that are part of a long-term grassland monitoring initiative in the city of Boulder, CO, USA, run by their Open Space & Mountain Parks department. Permanent plots (hereafter, ‘transects’) of 50 m x 2 m were established in 2009 to implement the city of Boulder’s Grassland Ecosystem Management Plan. We collected proximal imagery of 17 of these transects using a handheld Specim IQ hyperspectral camera (Spectral Imaging Ltd., Konica Minolta; [www.specim.com](http://www.specim.com)) in August 2022, coincident with an annual monitoring effort that recorded species cover along each transect. Imagery was taken of square meter plots in 2-meter increments along each transect. There is a .zip folder for each transect (*GEMAP-TRANSECTID.zip*) which contains all reflectance files. There are up to 25 folders for each transect which each represent a vegetation plot (*DATE_PLOTID*). For each vegetation plot, data are organized into "capture", "metadata" and "results", with the structure of files detailed in the *metadata.xml* file. The two primary files for analysis are in the "results" folder (*REFLECTANCE_DATE_PLOTID.dat* and *REFLECTANCE_DATE_PLOTID.hdr*) which contain the spectra (512 x 512 pixels, 204 bands) and image metadata. Reflectance values are expressed as the fraction of light reflected from 0 to 1, and the wavelength units are nm. Detailed measurement information can be found in the associated paper. We also include a file which contains the *in situ* vegetation survey data for the 17 surveyed transects (*Vegetation_**survey.csv) *as well as associated metadata* (**Vegetation_survey_metadata.csv*).  ### Data use The spectra and survey data together have been used to test the relationship between spectral diversity (measured as coefficient of variation, convex hull volume, and spectral species richness) and taxonomic diversity across variable conditions.  ## Sharing/Access information Please contact Meghan Hayden at meghan.hayden [at] colorado [dot] edu with any questions.