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

## 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 each class as the standard deviation 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.

## 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.