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Coefficient of variation of euclidean nearest-neighbor distance (Aggregation metric)

Usage

lsm_l_enn_cv(landscape, directions = 8, verbose = TRUE)

Arguments

landscape

Raster* Layer, Stack, Brick, SpatRaster (terra), stars, or a list of rasterLayers.

directions

The number of directions in which patches should be connected: 4 (rook's case) or 8 (queen's case).

verbose

Print warning message if not sufficient patches are present

Value

tibble

Details

$$ENN_{CV} = cv(ENN[patch_{ij}])$$ where \(ENN[patch_{ij}]\) is the euclidean nearest-neighbor distance of each patch.

ENN_CV is an 'Aggregation metric'. It summarises the landscape as the Coefficient of variation of all patches in the landscape. ENN measures the distance to the nearest neighbouring patch of the same class i. The distance is measured from edge-to-edge. The range is limited by the cell resolution on the lower limit and the landscape extent on the upper limit. The metric is a simple way to describe patch isolation. Because it is scaled to the mean, it is easily comparable among different landscapes.

Units

Meters

Range

ENN_CV >= 0

Behaviour

Equals ENN_CV = 0 if the euclidean nearest-neighbor distance is identical for all patches. Increases, without limit, as the variation of ENN increases.

References

McGarigal, K., SA Cushman, and E Ene. 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html

McGarigal, K., and McComb, W. C. (1995). Relationships between landscape structure and breeding birds in the Oregon Coast Range. Ecological monographs, 65(3), 235-260.

Examples

lsm_l_enn_cv(landscape)
#> # A tibble: 1 × 6
#>   layer level     class    id metric value
#>   <int> <chr>     <int> <int> <chr>  <dbl>
#> 1     1 landscape    NA    NA enn_cv  39.8