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#' CORE (patch level)
#'
#' @description Core area (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{CORE = a_{ij}^{core}}
#' where \eqn{a_{ij}^{core}} is the core area in square meters
#'
#' CORE is a 'Core area metric' and equals the area within a patch that is not
#' on the edge of it. A cell is defined as core area if the cell has no
#' neighbour with a different value than itself (rook's case). It describes patch area
#' and shape simultaneously (more core area when the patch is large and the shape is
#' rather compact, i.e. a square).
#'
#' Because the metric is based on distances or areas please make sure your data
#' is valid using \code{\link{check_landscape}}.
#'
#' \subsection{Units}{Hectares}
#' \subsection{Range}{CORE >= 0}
#' \subsection{Behaviour}{Increases, without limit, as the patch area increases
#' and the patch shape simplifies (more core area). CORE = 0 when every cell in
#' the patch is an edge.}
#'
#' @seealso
#' \code{\link{lsm_c_core_mn}},
#' \code{\link{lsm_c_core_sd}},
#' \code{\link{lsm_c_core_cv}},
#' \code{\link{lsm_c_tca}}, \cr
#' \code{\link{lsm_l_core_mn}},
#' \code{\link{lsm_l_core_sd}},
#' \code{\link{lsm_l_core_cv}},
#' \code{\link{lsm_l_tca}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_p_core(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_p_core <- function(landscape, directions = 8,
consider_boundary = FALSE, edge_depth = 1) {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
FUN = lsm_p_core_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_p_core_calc <- function(landscape, directions, consider_boundary, edge_depth, resolution, extras = NULL) {
if (missing(resolution)) resolution <- terra::res(landscape)
if (is.null(extras)){
metrics <- "lsm_p_core"
landscape <- terra::as.matrix(landscape, wide = TRUE)
extras <- prepare_extras(metrics, landscape_mat = landscape,
directions = directions, resolution = resolution)
}
# all values NA
if (all(is.na(landscape))) {
return(tibble::new_tibble(list(level = "patch",
class = as.integer(NA),
id = as.integer(NA),
metric = "core",
value = as.double(NA))))
}
# get common variables
classes <- extras$classes
class_patches <- extras$class_patches
core <- do.call(rbind,
lapply(classes, function(patches_class) {
# get connected patches
landscape_labeled <- class_patches[[as.character(patches_class)]]
# label all edge cells
class_edge <- get_boundaries_calc(landscape_labeled,
edge_depth = edge_depth,
consider_boundary = consider_boundary,
as_NA = FALSE,
patch_id = FALSE)
# count number of edge cells in each patch (edge == 1)
cells_edge_patch <- tabulate(landscape_labeled[class_edge == 1])
# check if no cell is edge, i.e. only one patch is present
if (length(cells_edge_patch) == 0) {
cells_edge_patch <- 0
}
# all cells of the patch
cells_patch <- tabulate(landscape_labeled)
# all cells minus edge cells equal core and convert to ha
core_area <- (cells_patch - cells_edge_patch) * prod(resolution) / 10000
tibble::new_tibble(list(class = rep(patches_class, length(core_area)),
value = core_area))
})
)
tibble::new_tibble(list(
level = rep("patch", nrow(core)),
class = as.integer(core$class),
id = as.integer(seq_len(nrow(core))),
metric = rep("core", nrow(core)),
value = as.double(core$value)
))
}
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