spatialEco (version 1.3-5)

raster.entropy: Raster Entropy

Description

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

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.

Maximum entropy is reached when all values are different, same as log(m) max.ent <- function(x) log( length( unique(x) ) )

Usage

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

Arguments

x

Object of class raster (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)

filename

Raster file written to disk

...

Optional arguments passed to writeRaster or dataType

Value

raster class object or specified format raster written to disk

References

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

Examples

# NOT RUN {
require(raster)
  r <- raster(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)

# }