stat.summary.plot: Statistical summary plot of reflectance values

In spectralR: Obtain and Visualize Spectral Reflectance Data for Earth Surface Polygons

Statistical summary plot of reflectance values

Description

Make a plot with statistical summary of reflectance values (mean, mean-standard deviation, mean+standard deviation) for defined classes of surface.

Usage

stat.summary.plot(
  data,
  target_classes = NULL,
  point_size = 0.6,
  fatten = 4,
  x_dodge = 0.2
)

Arguments

data	reflectance data as dataframe with pixel values for Sentinel optical bands B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12
target_classes	list of the classes of surface which should be highlighted, others will be turned in gray, as a background. Defaults is NULL.
point_size	Size of points on a plot
fatten	A multiplicative factor used to increase the size of points in comparison with standard deviation lines
x_dodge	Position adjustment of points along the X-axis

Value

ggplot2 object with basic visual aesthetics. Default aesthetics are line with statistical summary for each satellite band ([geom_line()] + [geom_pointrange()]). See [geom_linerange](https://ggplot2.tidyverse.org/reference/geom_linerange.html) and [geom_path](https://ggplot2.tidyverse.org/reference/geom_path.html) documentation for more details.

Wavelengths values (nm) acquired from mean known value for each optical band of Sentinel 2 sensor https://en.wikipedia.org/wiki/Sentinel-2

Examples

# Load example data
load(system.file("testdata/reflectance_test_data.RData", package = "spectralR"))

# Create a summary plot
p <- stat.summary.plot(data = reflectance)

# Customize a plot
p +
  ggplot2::labs(x = 'Sentinel-2 bands', y = 'Reflectance',
      colour = "Surface classes",
      title = "Reflectance for different surface classes",
      caption='Data: Sentinel-2 Level-2A\nmean ± standard deviation')+
  ggplot2::theme_minimal()

# Highlight only specific target classes
stat.summary.plot(
   data = reflectance,
   target_classes = list("meadow", "coniferous_forest")
  )

spectralR documentation built on Aug. 24, 2023, 5:06 p.m.