raster.entropy: Raster Entropy

In spatialEco: Spatial Analysis and Modelling Utilities

Raster Entropy

Description

Calculates entropy on integer raster (i.e., 8 bit 0-255)

Usage

raster.entropy(x, d = 5, categorical = FALSE, global = FALSE, ...)

Arguments

x	A terra SpatRaster object (requires integer raster)
d	Size of matrix (window)
categorical	Is the data categorical or continuous (FALSE/TRUE)
global	Should the model use a global or local n to calculate entropy (FALSE/TRUE)
...	Optional arguments passed terra focal function

Details

Entropy calculated as: H = -sum(Pi*ln(Pi)) where; Pi, Proportion of one value to total values Pi=n(p)/m and m, Number of unique values. Expected range: 0 to log(m) H=0 if window contains the same value in all cells. H increases with the number of different values in the window. The ellipsis arguments can be used to write to disk using the filename argument.

Value

terra SpatRaster class object

Author(s)

Jeffrey S. Evans <[e-mail]>

References

Fuchs M., R. Hoffmann, F. Schwonke (2008) Change Detection with GRASS GIS - Comparison of images taken by different sensor.

Examples

library(terra)
  r <- rast(ncols=100, nrows=100)
    r[] <- round(runif(ncell(r), 1,8), digits=0)

rEnt <- raster.entropy(r, d=5, categorical = TRUE, global = TRUE)
  opar <- par(no.readonly=TRUE)
    par(mfcol=c(2,1))
      plot(r)
        plot(rEnt)
  par(opar)

# Maximum entropy is reached when all values are different, same as log(m)
#   for example; log( length( unique(x) ) ) 

spatialEco documentation built on Nov. 18, 2023, 1:13 a.m.