SDMTools (version 1.1-11)

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 'SpatialGri

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

• a data.frame listing
• 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

See Also

PatchStat, ConnCompLabel

Examples

#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