Standard deviation of euclidean nearest-neighbor distance (Aggregation metric)
lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)tibble
A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.
The number of directions in which patches should be connected: 4 (rook's case) or 8 (queen's case).
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 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.
Because the metric is based on distances or areas please make sure your data
is valid using check_landscape.
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.
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.
lsm_p_enn,
sd
lsm_c_enn_mn,
lsm_c_enn_sd,
lsm_c_enn_cv,
lsm_l_enn_mn,
lsm_l_enn_cv
landscape <- terra::rast(landscapemetrics::landscape)
lsm_l_enn_sd(landscape)
Run the code above in your browser using DataLab