R/lsm_c_ndca.R

In landscapeecology/landscapemetrics: Landscape Metrics for Categorical Map Patterns

#' NDCA (class level)
#'
#' @description 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{NDCA = \sum \limits_{j = 1}^{n} n_{ij}^{core}}
#' where \eqn{n_{ij}^{core}} is the number of disjunct core areas.
#'
#' NDCA is a 'Core area metric'. The metric summarises class i as the sum of all
#' patches belonging to class i. A cell is defined as core if the cell has no
#' neighbour with a different value than itself (rook's case). NDCA counts the disjunct
#' core areas, whereby a core area is a 'patch within the patch' containing only core cells.
#' It describes patch area and shape simultaneously (more core area when the patch is large,
#' however, the shape must allow disjunct core areas). Thereby, a compact shape (e.g. a square)
#' will contain less disjunct core areas than a more irregular patch.
#'
#' \subsection{Units}{None}
#' \subsection{Range}{NDCA >= 0}
#' \subsection{Behaviour}{NDCA = 0 when TCA = 0, i.e. every cell in patches of class i is
#' an edge. NDCA increases, with out limit, as core area increases and patch shapes allow
#' disjunct core areas (i.e. patch shapes become rather complex).}
#'
#' @seealso
#' \code{\link{lsm_c_tca}}, \cr
#' \code{\link{lsm_p_ncore}},
#' \code{\link{lsm_l_ndca}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_c_ndca(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_c_ndca <- function(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1) {
    landscape <- landscape_as_list(landscape)

    result <- lapply(X = landscape,
                     FUN = lsm_c_ndca_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_c_ndca_calc <- function(landscape, directions, consider_boundary, edge_depth, resolution, extras = NULL){

    # get number of core areas for each patch
    ndca <- lsm_p_ncore_calc(landscape,
                             directions = directions,
                             consider_boundary = consider_boundary,
                             edge_depth = edge_depth,
                             resolution = resolution,
                             extras = extras)

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

    # summarise for each class
    ndca <- stats::aggregate(x = ndca[, 5], by = ndca[, 2], FUN = sum)

    return(tibble::new_tibble(list(level = rep("class", nrow(ndca)),
                              class = as.integer(ndca$class),
                              id = rep(as.integer(NA), nrow(ndca)),
                              metric = rep("ndca", nrow(ndca)),
                              value = as.double(ndca$value))))
}