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#' DCORE_SD (landscape level)
#'
#' @description Standard deviation number of disjunct core areas (Core area 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 consider_boundary Logical if cells that only neighbour the landscape
#' boundary should be considered as core
#' @param edge_depth Distance (in cells) a cell has the be away from the patch
#' edge to be considered as core cell
#'
#' @details
#' \deqn{DCORE_{SD} = sd(NCORE[patch_{ij}])}
#' where \eqn{NCORE[patch_{ij}]} is the number of core areas.
#'
#' DCORE_SD is an 'Core area metric'. It summarises the landscape as the standard deviation
#' of all patches. A cell is defined as core if the cell has no neighbour with a different
#' value than itself (rook's case). NCORE counts the disjunct core areas, whereby a core
#' area is a 'patch within the patch' containing only core cells. The metric describes the
#' differences among all patches in the landscape.
#'
#' \subsection{Units}{None}
#' \subsection{Range}{DCORE_SD >= 0}
#' \subsection{Behaviour}{Equals DCORE_SD = 0 if all patches have the same number of disjunct
#' core areas. Increases, without limit, as the variation of number of disjunct core areas
#' increases.}
#'
#' @seealso
#' \code{\link{lsm_p_ncore}},
#' \code{\link[stats]{sd}} \cr
#' \code{\link{lsm_c_dcore_mn}},
#' \code{\link{lsm_c_dcore_sd}}, \cr
#' \code{\link{lsm_c_dcore_cv}},
#' \code{\link{lsm_l_dcore_mn}},
#' \code{\link{lsm_l_dcore_cv}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_l_dcore_sd(landscape)
#'
#' @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
#'
#' @export
lsm_l_dcore_sd <- function(landscape,
directions = 8,
consider_boundary = FALSE,
edge_depth = 1) {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
FUN = lsm_l_dcore_sd_calc,
directions = directions,
consider_boundary = consider_boundary,
edge_depth = edge_depth)
layer <- rep(seq_along(result),
vapply(result, nrow, FUN.VALUE = integer(1)))
result <- do.call(rbind, result)
tibble::add_column(result, layer, .before = TRUE)
}
lsm_l_dcore_sd_calc <- function(landscape, directions, consider_boundary, edge_depth, resolution, extras = NULL){
dcore_patch <- lsm_p_ncore_calc(landscape,
directions = directions,
consider_boundary = consider_boundary,
edge_depth = edge_depth,
resolution = resolution,
extras = extras)
# all values NA
if (all(is.na(dcore_patch$value))) {
return(tibble::new_tibble(list(level = "landscape",
class = as.integer(NA),
id = as.integer(NA),
metric = "dcore_sd",
value = as.double(NA))))
}
dcore_sd <- stats::sd(dcore_patch$value)
return(tibble::new_tibble(list(level = rep("landscape", length(dcore_sd)),
class = rep(as.integer(NA), length(dcore_sd)),
id = rep(as.integer(NA), length(dcore_sd)),
metric = rep("dcore_sd", length(dcore_sd)),
value = as.double(dcore_sd))))
}
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