Introduction to visa"

In visa: Vegetation Imaging Spectroscopy Analyzer

knitr::opts_chunk$set(
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Imaging Spectroscopy (also known as Hyperspectral Remote Sensing, HRS) technology and data are increasingly used in environmental sciences, and nowadays much more beyond that, thus requiring accessible data and analytical tools (especially open source) for students and scientists with a diverse background. Therefore, I come up with such a idea since i was a PhD student at the University of Cologne, inspired by the growing community of R and R users. I have been mainly working with spectral data of plants, and that is reason i use the name VISA for this package, with the aim to facilitate the use of imaging spectroscopy techniques and data for the extraction of vegetation signatures for plant stresses and biodiversity.

Future development of this tool has a long-term goal to include: i) implement the state-of-the-art applications of vegetation spectral indicators, ii) provide a platform to share vegetation spectral data to address certain questions of interest or applications in a broad context, and iii) make it compatible with more data formats and tools, such as the r package hsdar^[ Lukas W. Lehnert, Hanna Meyer, Joerg Bendix (2018). hsdar: Manage, analyse and simulate hyperspectral data in R].

Currently, visa can be installed:

• from CRAN, either from command line 'install.packages("visa")' or from the Rstudio Packages installation tab.

• from my GitHub repository visa, using devtools::install_github("kang-yu/visa").

This vignette will introduces the design and features of visa from the following aspects:

• Data
• Functions
• Compatibility

New features in the updates

• Compute three-band Ratio of Band Difference (RBD) $RBD = (\lambda_k - \lambda_j)/(\lambda_j - \lambda_i)$, and plotting of the three dimensional output array.

Data

The visa package intends to simplify the use and reduce the limit of data format and structure. visa uses two data formats, and a hacking use of R's data.frame and, a S4^[The S4 object system http://adv-r.had.co.nz/S4.html] class specifically for visa.

Built on data.frame

Why i call it a hacking use is because a data.frame is a table organized by variables, and it is ideal to store every spectral band in as a variable. Imaging that you have thousands of columns and you have to refer to thousands of bands when using data.frame. Then, why not just store all the spectral bands, ie. the spectral matrix in a single variable, like the example data NSpec.DF.

This will be ease your coding for analysis, and you write your argument as 'y ~ spectra' instead of 'y ~ band1 + band2 + band3 + ... '

# check the data type of `NSpec.DF`
library(visa)
data(NSpec.DF)
class(NSpec.DF) 
class(NSpec.DF$spectra)
str(NSpec.DF$spectra)

S4 class Spectra and SpectraDatabase

There are already a lot of r packages for spectral data analysis, and some of them use the S4 class, e.g. the hsdar package. visa also supports the S4 format but in a simplified version, using only five slots currently.

# check the data type of `NSpec.Lib`
class(NSpec.Lib) 
class(NSpec.Lib@spectra) 
str(NSpec.Lib@spectra)

Notice that the small difference of accessing data in two types of data, i.e., using $ and @, respectively.

Functions

Computing correlation matrix between 2-band NSR and another variable

The first idea of writing this package was to compute the correlation matrix for the thorough analysis of correlations between, on one hand, the combinations of spectral bands, and on the other hand, the vegetation variables of interest.

Here gives the example using the cm.nsr function, which can be used for non-spectra data as well.

library(visa)
data(NSpec.DF)
x <- NSpec.DF$N # nitrogen
S <- NSpec.DF$spectra[, seq(1, ncol(NSpec.DF$spectra), 10)] # resampled to 10 nm steps
cm <- cm.nsr(S, x, cm.plot = TRUE)

Plotting correlation matrix (2D array)

The correlation matrix plot is the plot of correlation coefficients (r/r2) by bands in x- and y-axis.

# use the output from last example
# cm <- cm.nsr(S, x)
# Plotting the correlation matrix
#ggplot.cm(cm) # deprecated

plt.2dcm(cm) # new function replacing ggplot.cm

More Examples and Details

The computation of SR and NSR follow the equations, e.g.:

2-band combinations

$SR = \lambda_i / \lambda_j$ $NSR = (\lambda_i - \lambda_j)/(\lambda_i + \lambda_j)$

To know more about the NDVI, please also check on Wikipedia^[Normalized difference vegetation index (https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index)].

3-band combinations

$RBD = (\lambda_k - \lambda_j)/(\lambda_j - \lambda_i)$

Example data NSpec.Lib

The first type is the 'NSpec.Lib' in the default S4 class 'Spectra'.

library(visa)
# check the data type
class(NSpec.Lib) 
# data structure
# str(NSpec.Lib)
# print the first 10 columns
knitr::kable(head(NSpec.Lib@spectra[,1:10]))

Example data NSpec.DF

The second type is a data.frame format, i.e., NSpec.DF.

# check the data type
class(NSpec.DF) 
# check whether it contains the same data as 'NSpec.Lib'
knitr::kable(head(NSpec.DF$spectra[,1:10]))

Accessing data

The spectra function access the spectral data stored in the Spectra.Dataframe or Spectra.Library formats.

#library(visa)
#data(NSpec.DF)
spec <- spectra(NSpec.DF)
# check whether it contains the same data as 'NSpec.Lib'
knitr::kable(head(spec[,1:10]))

The wavelength function access the wavelengths of bands stored in the Spectra.Dataframe or Spectra.Library formats.

#library(visa)
#data(NSpec.DF)
spec <- wavelength(NSpec.DF)
# check whether it contains the same data as 'NSpec.Lib'
str(spec)

Compatibility

Data format conversion

The as.spectra function save spectral data in the format of Spectra.Library.

slib <- as.spectra(spectra = matrix(rnorm(100), nrow = 10), 
                   wavelength = 1:10, 
                   w.unit = "nm", 
                   data = data.frame(y=rnorm(10))
                   )
str(slib)

The as.spectra.dataframe function save spectral data in the format of Spectra.Dataframe. The Spectra.Dataframe is actually a data.frame.

s.df <- as.spectra.dataframe(spectra = matrix(rnorm(100), nrow = 10),
                             wavelength = 1:10,
                             w.unit = "nm",
                             data = data.frame(y=rnorm(10))
                             )
str(s.df)

Future development

Regarding compatibility for future development, special focuses will be put on:

• spectral image analysis
• spatial data integration
• Functions for simplifying integrated analysis such as using radiative transfer models and machine learning models

visa documentation built on April 4, 2025, 5:40 a.m.