spatialEco (version 1.3-0)

PatchStat: Landscape Patch Statistics

Description

Patch-level landscape metrics

Usage

PatchStat(mat, cellsize = 1, latlon = FALSE)

Arguments

mat

A matrix of data with individual patches identified as with connected.pixels The matrix can be a raster of class 'asc' (this & 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)

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 with all available landscape patch metrics

Details

Available landscape metrics

• patchID - the unique ID for each patch.

• n.cell - the number of cells for each patch, specified in square meters.

• n.core.cell - the number of cells in the core area, without the edge area.

• n.edges.perimeter - the number of outer perimeter cell edges of the patch.

• n.edges.internal - the number of internal cell edges of the patch.

• area - the area of each patch comprising a landscape mosaic.

• core.area - represents the interior area of the patch, greater than the specified depth-of-edge distance from the perimeter.

• perimeter - the perimeter of the patch, including any internal holes in the patch, specified in meters.

• perim.area.ratio - the ratio of the patch perimeter (m) to area (m2).

• shape.index - the shape complexity, sum of each patches perimeter divided by the square root of patch area.

• frac.dim.index - fractal dimension index reflects shape complexity across a range of spatial scales, approaches 2 times the logarithm of patch perimeter (m) divided by the logarithm of patch area (m2).

• core.area.index - quantifies core area as a percentage of patch area.

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.

Examples

# NOT RUN {
library(raster)
library(sp)
#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 = connected.pixels(tmat) )
image(t(ccl.mat[10:1,]),col=c('grey',rainbow(length(unique(ccl.mat))-1)))

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

# }