lsm_p_enn: ENN (patch level)

In landscapemetrics: Landscape Metrics for Categorical Map Patterns

ENN (patch level)

Description

Euclidean Nearest-Neighbor Distance (Aggregation metric)

Usage

lsm_p_enn(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 = h_{ij}

where h_{ij} is the distance to the nearest neighbouring patch of the same class i in meters

ENN is an 'Aggregation metric'. 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 the metric is based on distances or areas please make sure your data is valid using check_landscape.

Units

Meters

Range

ENN > 0

Behaviour

Approaches ENN = 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_c_enn_mn, lsm_c_enn_sd, lsm_c_enn_cv,
lsm_l_enn_mn, lsm_l_enn_sd, lsm_l_enn_cv

Examples

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

landscapemetrics documentation built on Sept. 12, 2024, 7:42 a.m.