# R/lsm_c_enn_sd.R In landscapemetrics: Landscape Metrics for Categorical Map Patterns

#### Documented in lsm_c_enn_sd

```#' ENN_SD (class level)
#'
#' @description Standard deviation of euclidean nearest-neighbor distance (Aggregation metric)
#'
#' @param landscape A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.
#' @param directions The number of directions in which patches should be connected: 4 (rook's case) or 8 (queen's case).
#' @param verbose Print warning message if not sufficient patches are present
#'
#' @details
#' \deqn{ENN_{SD} = sd(ENN[patch_{ij}])}
#' where \eqn{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.
#'
#' \subsection{Units}{Meters}
#' \subsection{Range}{ENN_SD >= 0}
#' \subsection{Behaviour}{Equals ENN_SD = 0 if the euclidean nearest-neighbor distance is
#' identical for all patches. Increases, without limit, as the variation of ENN increases.}
#'
#' @seealso
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_c_enn_sd(landscape)
#'
#' @aliases lsm_c_enn_sd
#' @rdname lsm_c_enn_sd
#'
#' @references
#' 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.
#'
#' @export
lsm_c_enn_sd <- function(landscape, directions = 8, verbose = TRUE) {
landscape <- landscape_as_list(landscape)

result <- lapply(X = landscape,
FUN = lsm_c_enn_sd_calc,
directions = directions,
verbose = verbose)

layer <- rep(seq_along(result),
vapply(result, nrow, FUN.VALUE = integer(1)))

result <- do.call(rbind, result)

}

lsm_c_enn_sd_calc <- function(landscape, directions, verbose,
points = NULL) {

enn <- lsm_p_enn_calc(landscape,
directions = directions,
verbose = verbose,
points = points)

# all cells are NA
if (all(is.na(enn\$value))) {
return(tibble::tibble(level = "class",
class = as.integer(NA),
id = as.integer(NA),
metric = "enn_sd",
value = as.double(NA)))
}

enn_sd <- stats::aggregate(x = enn[, 5], by = enn[, 2], FUN = stats::sd)

return(tibble::tibble(level = "class",
class = as.integer(enn_sd\$class),
id = as.integer(NA),
metric = "enn_sd",
value = as.double(enn_sd\$value)))
}
```

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landscapemetrics documentation built on Oct. 3, 2023, 5:06 p.m.