lsm_c_enn_sd | R Documentation |

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

lsm_c_enn_sd(landscape, directions = 8, verbose = TRUE)

`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 |

*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.

Meters

ENN_SD >= 0

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

tibble

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.

`lsm_p_enn`

,
`sd`

,

`lsm_c_enn_mn`

,
`lsm_c_enn_cv`

,

`lsm_l_enn_mn`

,
`lsm_l_enn_sd`

,
`lsm_l_enn_cv`

lsm_c_enn_sd(landscape)

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