R/lsm_c_cai_mn.R

In landscapeecology/landscapemetrics: Landscape Metrics for Categorical Map Patterns

#' CAI_MN (class level)
#'
#' @description Mean of core area index (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{CAI_{MN} = mean(CAI[patch_{ij}]}
#' where \eqn{CAI[patch_{ij}]} is the core area index of each patch.
#'
#' CAI_MN is a 'Core area metric'. The metric summarises each class
#' as the mean of the core area index of all patches belonging to class i.
#' The core area index is the percentage of core area in relation to patch area.
#' A cell is defined as core area if the cell has no neighbour with a different
#' value than itself (rook's case).
#'
#' \subsection{Units}{Percent}
#' \subsection{Range}{0 <= CAI_MN <= 100}
#' \subsection{Behaviour}{CAI_MN = 0 when all patches have no core area and
#' approaches CAI_MN = 100 with increasing percentage of core area within patches.}
#'
#' @seealso
#' \code{\link{lsm_p_cai}},
#' \code{\link{mean}}, \cr
#' \code{\link{lsm_c_cai_sd}},
#' \code{\link{lsm_c_cai_cv}}, \cr
#' \code{\link{lsm_l_cai_mn}},
#' \code{\link{lsm_l_cai_sd}},
#' \code{\link{lsm_l_cai_cv}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_c_cai_mn(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_cai_mn <- function(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1) {
    landscape <- landscape_as_list(landscape)

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

    # calculate core area index for each patch
    cai <- lsm_p_cai_calc(landscape,
                          directions = directions,
                          consider_boundary = consider_boundary,
                          edge_depth = edge_depth,
                          resolution = resolution,
                          extras = extras)

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

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

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