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ENN_CV (class level)

Source: R/lsm_c_enn_cv.R
lsm_c_enn_cv.Rd

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

Usage

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

Arguments

landscape

A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.

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 each class as the Coefficient of variation of each patch belonging to class i. 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.

Because the metric is based on distances or areas please make sure your data is valid using check_landscape.

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. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors; available at the following web site: https://www.fragstats.org

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.

See also

lsm_p_enn,
lsm_c_enn_mn, lsm_c_enn_sd,
lsm_l_enn_mn, lsm_l_enn_sd, lsm_l_enn_cv

Examples

landscape <- terra::rast(landscapemetrics::landscape)
lsm_c_enn_cv(landscape)
#> # A tibble: 3 × 6
#>   layer level class    id metric value
#>   <int> <chr> <int> <int> <chr>  <dbl>
#> 1     1 class     1    NA enn_cv  44.5
#> 2     1 class     2    NA enn_cv  30.7
#> 3     1 class     3    NA enn_cv   0