ClassStat: Landscape Class Statistics

Description

ClassStat calculates the class statistics for patch types identified in a matrix of data or in a raster of class 'asc' (SDMTools & adehabitat packages), 'RasterLayer' (raster package) or 'SpatialGridDataFrame' (sp package).

Usage

ClassStat(mat, cellsize = 1, bkgd = NA, latlon = FALSE)

Arguments

mat

a matrix of data with patches identified as classes (unique integer values) as e.g., a binary lanscape of a species distribution or a vegetation map. Matrix can be a raster of class 'asc' (adehabitat package), 'RasterLayer' (raster package) or 'SpatialGridDataFrame' (sp package)

cellsize

cell size (in meters) is a single value representing the width/height of cell edges (assuming square cells)

bkgd

the background value for which statistics will not be calculated

latlon

boolean value representing if the data is geographic. If latlon == TRUE, matrix must be of class 'asc', 'RasterLayer' or 'SpatialGridDataFrame'

Value

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 lanscape 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.
lanscape.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.

Details

The class statistics are based on statistics calculated by fragstats http://www.umass.edu/landeco/research/fragstats/fragstats.html.

References

McGarigal, K., S. A. Cushman, M. C. Neel, and E. Ene. 2002. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: www.umass.edu/landeco/research/fragstats/fragstats.html

Examples

Run this code

#define a simple binary matrix
tmat = { matrix(c( 0,0,0,1,0,0,1,1,0,1,
                   0,0,1,0,1,0,0,0,0,0,
                   0,1,NA,1,0,1,0,0,0,1,
                   1,0,1,1,1,0,1,0,0,1,
                   0,1,0,1,0,1,0,0,0,1,
                   0,0,1,0,1,0,0,1,1,0,
                   1,0,0,1,0,0,1,0,0,1,
                   0,1,0,0,0,1,0,0,0,1,
                   0,0,1,1,1,0,0,0,0,1,
                   1,1,1,0,0,0,0,0,0,1),nr=10,byrow=TRUE) }

#do the connected component labelling
ccl.mat = ConnCompLabel(tmat)
ccl.mat
image(t(ccl.mat[10:1,]),col=c('grey',rainbow(length(unique(ccl.mat))-1)))

#calculate the patch statistics
ps.data = PatchStat(ccl.mat)
ps.data

#calculate the class statistics
cl.data = ClassStat(tmat)
cl.data

#identify background data is 0
cl.data = ClassStat(tmat,bkgd=0)
cl.data

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