ClassStat: Landscape Class Statistics

In jjvanderwal/SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises

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'

Details

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

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.

Author(s)

Jeremy VanDerWal [e-mail]

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

jjvanderwal/SDMTools documentation built on May 19, 2019, 11:40 a.m.