R/lsm_c_core_cv.R

In r-spatialecology/landscapemetrics: Landscape Metrics for Categorical Map Patterns

#' CORE_CV (class level)
#'
#' @description Coefficient of variation of core area (Core area metric)
#' @param directions The number of directions in which patches should be connected: 4 (rook's case) or 8 (queen's case).
#' @param landscape A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.
#' @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_{CV} = cv(CORE[patch_{ij}])}
#' where \eqn{CORE[patch_{ij}]} is the core area in square meters of each patch.
#'
#' CORE_CV is a 'Core area metric'. It equals the Coefficient of variation of the core area
#' of each patch belonging to class i. The core area is defined as all cells that have no
#' neighbour with a different value than themselves (rook's case). The metric describes the
#' differences among patches of the same class i in the landscape and is easily comparable
#' because it is scaled to the mean.
#'
#' \subsection{Units}{Hectares}
#' \subsection{Range}{CORE_CV >= 0}
#' \subsection{Behaviour}{Equals CORE_CV = 0 if all patches have the same core area.
#' Increases, without limit, as the variation of patch core areas increases.}
#'
#' @seealso
#' \code{\link{lsm_p_core}}, \cr
#' \code{\link{lsm_c_core_mn}},
#' \code{\link{lsm_c_core_sd}}, \cr
#' \code{\link{lsm_l_core_mn}},
#' \code{\link{lsm_l_core_sd}},
#' \code{\link{lsm_l_core_cv}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_c_core_cv(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_core_cv <- function(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1) {
    landscape <- landscape_as_list(landscape)

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

    # calculate core for each patch
    core <- lsm_p_core_calc(landscape,
                            directions = directions,
                            consider_boundary = consider_boundary,
                            edge_depth = edge_depth,
                            resolution = resolution,
                            extras = extras)

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

    # summarise for class
    core_cv <- stats::aggregate(x = core[, 5], by = core[, 2],
                                FUN = function(x) stats::sd(x) / mean(x) * 100)

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