Standard deviation of euclidean nearest-neighbor distance (Aggregation metric)
lsm_l_enn_sd(landscape, directions, verbose)# S3 method for RasterLayer
lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)
# S3 method for RasterStack
lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)
# S3 method for RasterBrick
lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)
# S3 method for stars
lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)
# S3 method for list
lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)
Raster* Layer, Stack, Brick or a list of rasterLayers.
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
tibble
$$ENN_{SD} = sd(ENN[patch_{ij}])$$ 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.
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.
lsm_p_enn
,
sd
,
lsm_c_enn_mn
,
lsm_c_enn_sd
,
lsm_c_enn_cv
,
lsm_l_enn_mn
,
lsm_l_enn_cv
# NOT RUN {
lsm_l_enn_sd(landscape)
# }
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