ClassStat {SDMTools} | R Documentation |

`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).

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

`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' |

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

a data.frame listing

`class` |
a particular patch type
from the original input matrix ( |

`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. |

Jeremy VanDerWal jjvanderwal@gmail.com

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

#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

[Package *SDMTools* version 1.1-221 Index]