lsm_l_enn_mn: ENN_MN (landscape level)

In landscapeecology/landscapemetrics: Landscape Metrics for Categorical Map Patterns

ENN_MN (landscape level)

Description

Mean of euclidean nearest-neighbor distance (Aggregation metric)

Usage

lsm_l_enn_mn(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

Details

ENN_{MN} = cv(mean[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 mean 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.

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.

Value

tibble

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, mean,
lsm_c_enn_mn, lsm_c_enn_sd, lsm_c_enn_cv,
lsm_l_enn_sd, lsm_l_enn_cv

Examples

landscape <- terra::rast(landscapemetrics::landscape)
lsm_l_enn_mn(landscape)

landscapeecology/landscapemetrics documentation built on April 7, 2024, 11:11 p.m.