lsm_l_relmutinf(landscape, neighbourhood = 4, ordered = TRUE, base = "log2")
Value
tibble
Arguments
landscape
A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.
neighbourhood
The number of directions in which cell adjacencies are considered as neighbours:
4 (rook's case) or 8 (queen's case). The default is 4.
ordered
The type of pairs considered.
Either ordered (TRUE) or unordered (FALSE).
The default is TRUE.
base
The unit in which entropy is measured.
The default is "log2", which compute entropy in "bits".
"log" and "log10" can be also used.
Details
Due to the spatial autocorrelation, the value of mutual information tends to grow
with a diversity of the landscape (marginal entropy). To adjust this tendency,
it is possible to calculate relative mutual information by dividing the mutual
information by the marginal entropy. Relative mutual information always has a
range between 0 and 1 and can be used to compare spatial data with different
number and distribution of categories. When the value of mutual information equals
to 0, then relative mutual information is 1.
References
Nowosad J., TF Stepinski. 2019. Information theory as a consistent framework
for quantification and classification of landscape patterns. https://doi.org/10.1007/s10980-019-00830-x