ENN_MN (class level)

Mean of euclidean nearest-neighbor distance (Aggregation metric)

Usage

lsm_c_enn_mn(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

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

ENN_MN is an 'Aggregation metric'. It summarises each class as the mean 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.

Units

Meters

Range

ENN_MN > 0

Behaviour

Approaches ENN_MN = 0 as the distance to the nearest neighbour decreases, i.e. patches of the same class i are more aggregated. Increases, without limit, as the distance between neighbouring patches of the same class i increases, i.e. patches are more isolated.

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_c_enn_mn(landscape)
#> # A tibble: 3 × 6
#>   layer level class    id metric value
#>   <int> <chr> <int> <int> <chr>  <dbl>
#> 1     1 class     1    NA enn_mn  3.68
#> 2     1 class     2    NA enn_mn  3.20
#> 3     1 class     3    NA enn_mn  2