lsm_l_enn_sd: ENN_SD (landscape level)

In landscapemetrics: Landscape Metrics for Categorical Map Patterns

ENN_SD (landscape level)

Description

Standard deviation of euclidean nearest-neighbor distance (Aggregation metric)

Usage

lsm_l_enn_sd(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

Details

ENN_{SD} = sd(ENN[patch_{ij}])

where ENN[patch_{ij}] is the euclidean nearest-neighbor distance of each patch.

ENN_CV is an 'Aggregation metric'. It summarises in the landscape as the standard deviation 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_SD >= 0

Behaviour

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

Value

tibble

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: https://www.umass.edu/landeco/

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, sd,
lsm_c_enn_mn, lsm_c_enn_sd, lsm_c_enn_cv,
lsm_l_enn_mn, lsm_l_enn_cv

Examples

lsm_l_enn_sd(landscape)

landscapemetrics documentation built on March 31, 2023, 7:28 p.m.