focal.lmetrics: Focal landscape metrics

Description

Calculates a variety of landscape metrics on integer rasters using focal approach

Usage

focal.lmetrics(x, w = 5, bkg = 0, land.value = 1,
  metric = "prop.landscape", latlong = FALSE)

Arguments

raster class object

Size of focal window, a single value or two values defining size of matrix (must be odd values)

bkg

Background value (will be ignored)

land.value

Raster cell value to calculate metrics on

metric

Name of desired metric (see available metrics)

latlong

(FALSE/TRUE) Is raster data in lat-long

Value

RasterLayer class object of specified landscape metric

Details

The following metrics are available:

class - a particular patch type from the original input matrix (mat).
n.patches - the number of patches of a particular patch type or in a class.
total.area - the sum of the areas (m2) of all patches of the corresponding patch type.
prop.landscape - the proportion of the total landscape represented by this class
patch.density - the numbers of patches of the corresponding patch type divided by total landscape area (m2).
total.edge - the total edge length of a particular patch type.
edge.density - edge length on a per unit area basis that facilitates comparison among landscapes of varying size.
landscape.shape.index - a standardized measure of total edge or edge density that adjusts for the size of the landscape.
largest.patch.index - largest patch index quantifies the percentage of total landscape area comprised by the largest patch.
mean.patch.area - average area of patches.
sd.patch.area - standard deviation of patch areas.
min.patch.area - the minimum patch area of the total patch areas.
max.patch.area - the maximum patch area of the total patch areas.
perimeter.area.frac.dim - perimeter-area fractal dimension equals 2 divided by the slope of regression line obtained by regressing the logarithm of patch area (m2) against the logarithm of patch perimeter (m).
mean.perim.area.ratio - the mean of the ratio patch perimeter. The perimeter-area ratio is equal to the ratio of the patch perimeter (m) to area (m2).
sd.perim.area.ratio - standard deviation of the ratio patch perimeter.
min.perim.area.ratio - minimum perimeter area ratio
max.perim.area.ratio - maximum perimeter area ratio.
mean.shape.index - mean of shape index
sd.shape.index - standard deviation of shape index.
min.shape.index - the minimum shape index.
max.shape.index - the maximum shape index.
mean.frac.dim.index - mean of fractal dimension index.
sd.frac.dim.index - standard deviation of fractal dimension index.
min.frac.dim.index - the minimum fractal dimension index.
max.frac.dim.index - the maximum fractal dimension index.
total.core.area - the sum of the core areas of the patches (m2).
prop.landscape.core - proportional landscape core
mean.patch.core.area - mean patch core area.
sd.patch.core.area - standard deviation of patch core area.
min.patch.core.area - the minimum patch core area.
max.patch.core.area - the maximum patch core area.
prop.like.adjacencies - calculated from the adjacency matrix, which shows the frequency with which different pairs of patch types (including like adjacencies between the same patch type) appear side-by-side on the map (measures the degree of aggregation of patch types).
aggregation.index - computed simply as an area-weighted mean class aggregation index, where each class is weighted by its proportional area in the landscape.
landscape.division.index - based on the cumulative patch area distribution and is interpreted as the probability that two randomly chosen pixels in the landscape are not situated in the same patch
splitting.index - based on the cumulative patch area distribution and is interpreted as the effective mesh number, or number of patches with a constant patch size when the landscape is subdivided into S patches, where S is the value of the splitting index.
effective.mesh.size - equals 1 divided by the total landscape area (m2) multiplied by the sum of patch area (m2) squared, summed across all patches in the landscape.
patch.cohesion.index - measures the physical connectedness of the corresponding patch type.

Examples

Run this code

# NOT RUN {
 library(raster)
 library(sp)

 r <- raster(nrows=180, ncols=360, xmn=571823.6, xmx=616763.6, ymn=4423540, 
             ymx=4453690, resolution=270, crs = CRS("+proj=utm +zone=12 +datum=NAD83 
             +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"))

 r[] <- rpois(ncell(r), lambda=1)
 r <- calc(r, fun=function(x) { x[x >= 1] <- 1; return(x) } )  

# proportion landscape class   
pland <- focal.lmetrics(r, w=11)
  plot(pland)

# Aggregation index 
ai <- focal.lmetrics(r, w=11, metric = "aggregation.index")
  plot(ai)
# }
# NOT RUN {
# }

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