Description Usage Arguments Details Value Note Author(s) See Also Examples

Calculates a variety of landscape metrics, on binary rasters, for polygons or points with a buffer distance

1 2 |

`x` |
SpatalPointsDataFrame or SpatalPolgonsDataFrame class object |

`y` |
raster class object |

`bkgd` |
Background value (will be ignored) |

`metrics` |
Numeric index of desired metric (see available metrics) |

`bw` |
Buffer distance (ignored if x is SpatalPolgonsDataFrame) |

`latlon` |
Is raster data in lat-long (TRUE/FALSE) |

`trace` |
Plot raster subsets and echo object ID at each iteration (TRUE | FALSE) |

The following metrics are available:

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 landscape 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.

landscape.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.

If multiple classes are evaluated a list object with a data.frame for each class containing specified metrics in columns. The data.frame is ordered and shares the same row.names as the input feature class and can be directly joined to the @data slot. For single class problems a data.frame object is returned.

Modifications to the function incorporate multi-class metrics by fetching the unique values of the raster and creating a list object containing a data.frame for each class. Unfortunately, retrieving unique values is a very slow function.

depends: sp, raster, rgeos, SDMTools

Jeffrey S. Evans <[email protected]>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(raster)
library(sp)
r <- raster(nrows=180, ncols=360, xmn=571823.6, xmx=616763.6, ymn=4423540,
ymx=4453690, resolution=270, crs = CRS("+proj=utm +zone=12 +datum=NAD83
+units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"))
r[] <- rpois(ncell(r), lambda=1)
r <- calc(r, fun=function(x) { x[x >= 1] <- 1; return(x) } )
x <- sampleRandom(r, 10, na.rm = TRUE, sp = TRUE)
lmet <- c("prop.landscape", "edge.density", "prop.like.adjacencies", "aggregation.index")
( class.1 <- land.metrics(x=x, y=r, bw=1000, bkgd = 0, metrics = lmet) )
( all.class <- land.metrics(x=x, y=r, bw=1000, bkgd = NA, metrics = lmet ) )
# Pull metrics associated with class "0"
all.class[["0"]]
``` |

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