vegspec 1.0.0

A compilation of vegetative spectral indices and transformations in Python

vegspec

A compilation of spectral vegetation indices and transformations in Python

The goal of the vegspec package is to encapsulate computations of 1) more than 145 published spectral vegetation indices and 2) several published pretreatment transformations for vegetative spectral reflectance data.

Scientific background material for the methods implemented in the vegspec package is given here.

Source Code

The spectral methodologies are contained in one Python module (i.e., vegspec.py). The module defines a class (i.e., VegSpec) to encapsulate all the computations for one vegetative reflectance spectrum. To analyze sets of multiple spectral measurements, users can develop a list of unique VegSpec instances for each measurement. At minimum, each VegSpec instance requires inputting two 1-dimensional lists that specify the spectral reflecance factors and associated wavelengths.

The source code is available here.

Install

pip install vegspec

Quickstart

Import the package

import vegspec as vs

Instantiate a VegSpec class

spectrum = vs.VegSpec(wl,rf)

where required inputs are defined as follows:

• wl : list, Wavelengths (nm) corresponding to the data in rf
• rf : list, Reflectance factors (0 to 1) for a single spectral scan

Other optional arguments for the VegSpec constructor are as follows:

• fs1 : int, optional, Filter size for Savitsky-Golay first derivatives, (default = 7)
• po1 : int, optional, Polynomial order for Savitsky-Golay first derivatives, (default = 2)
• fs2 : int, optional, Filter size for Savitsky-Golay second derivatives, (default = 15)
• po2 : int, optional, Polynomial order for Savitsky-Golay second derivatives, (default = 2)
• wlblu : float, optional, Primary wavelength of visible blue light (nm), (default = 480.)
• wlgrn : float, optional, Primary wavelength of visible green light (nm), (default = 550.)
• wlred : float, optional, Primary wavelength of visible red light (nm), (default = 670.)
• wlnir : float, optional, Primary wavelength of near-infrared light (nm), (default = 800.)
• solslp : float, optional, Slope of the soil line, (default = 1.166)
• solicpt : float, optional, Intercept of the soil line, (default = 0.042)
• getlirf : boolean, optional, If True, compute log inverse reflectance, (default = True)
• getcrrf : boolean, optional, If True, compute continuum removed spectra, (default = True)

The VegSpec class constructor will automatically compute all spectral vegetation indices and spectral data pretreatment transformations unless optional arguments (i.e., getlirf and getcrrf) are changed to False. If increased computational efficiency is needed, consider changing getcrrf to False.

Access the results

for key in spectrum.indices.keys():
    print(key+',{:f}'.format(spectrum.indices[key]))

print(spectrum.rfd1)

The VegSpec class stores the computational results in the following attribute variables:

• rfd1 : numpy.ndarray, A 1D array of the Savitsky-Golay first derivative of rf
• rfd2 : numpy.ndarray, A 1D array of the Savitsky-Golay second derivative of rf
• lirf : numpy.ndarray, A 1D array of the logarithm of inverse rf (log10(1/R))
• lirfd1 : numpy.ndarray, A 1D array of the Savitsky-Golay first derivative of lirf
• lirfd2 : numpy.ndarry, A 1D array of the Savitsky-Golay second derivative of lirf
• crrf : numpy.ndarray, A 1D array of the continuum removal of rf
• indices : dict, A dictionary of 145+ published spectral vegetation indices

For further information on the keys of the indices attribute, see the documentation here.

Further examples

Further example scripts for using the vegspec package are here.

Further information

The vegspec package is further described in the following article:

Thorp, K. R., 2024. vegspec: A compilation of spectral vegetation indices and transformations in Python. SoftwareX. In prep.

Also, the vegspec package was used to conduct the following research:

Thorp, K. R., Thompson, A.L., 2024. Phenotyping cotton leaf chlorophyll via proximal hyperspectral reflectance sensing, spectral vegetation indices, and machine learning. Frontiers in Plant Science. In prep.