Mean of euclidean nearest-neighbor distance (Aggregation metric)
lsm_l_enn_mn(landscape, directions = 8, verbose = TRUE)tibble
Raster* Layer, Stack, Brick, SpatRaster (terra), stars, 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
$$ENN_{MN} = cv(mean[patch_{ij}])$$ where \(ENN[patch_{ij}]\) is the euclidean nearest-neighbor distance of each patch.
ENN_CV is an 'Aggregation metric'. It summarises the landscape as the mean 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.
Meters
ENN_MN > 0
Approaches ENN_MN = 0 as the distance to the nearest neighbour decreases, i.e. patches of the same class i are more aggregated. Increases, without limit, as the distance between neighbouring patches of the same class i increases, i.e. patches are more isolated.
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,
mean,
lsm_c_enn_mn,
lsm_c_enn_sd,
lsm_c_enn_cv,
lsm_l_enn_sd,
lsm_l_enn_cv
lsm_l_enn_mn(landscape)
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