This repository contains functions for a few standard steps in spectral data processing, visualizaiton, and analysis. The aim is to enable people with little experience in spectral data analysis and R programming to perform basic quality control analysis of spectral datasets.
Jonas Anderegg
Crop Science Group
ETH Zürich
The R-packages data.table, mvoutlier, prospectr, ggsci and tidyverse are required. These are automatically loaded, if necessary, in the demo workflow.
load_spectra() Loads spectral data from .asd or .sed files into R;
preprocess_spc() Implements different forms of signal pre-processing, such as smoothing, calculation of derivatives, calculation of standard normal variate, signal binning;
detect_outlier_spectra() Performs multivariate outlier detection based on robust PCA, using the mvoutlier::pcoutfunction;
Calculate_SVI() Calculates a large number of published spectral vegetation indices;
scale_SVI() Scales spectral vegetation indices at the plot level to range from 10 to 0, representing the highest and lowest value recorded during a measurement campaign comprising several consecutive measurements over time;
plot_spectra() Creates plots of spectra or derived data (such as e.g., their derivatives);
plot_SVI() Creates plots of spectral vegetation indices;
get_svi_dynamics()Extracts parameters describing the dynamics of SVI values over time.
The file workflow.R implements an example workflow.
The file demo.pdf demonstrates some of the capabilities of the functions and carries out an example analysis.