#' TE (class level)
#'
#' @description Total (class) edge (Area and Edge metric)
#'
#' @param landscape A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.
#' @param count_boundary Include landscape boundary in edge length
#' @param directions The number of directions in which patches should be
#' connected: 4 (rook's case) or 8 (queen's case).
#'
#' @details
#' \deqn{TE = \sum \limits_{k = 1}^{m} e_{ik}}
#' where \eqn{e_{ik}} is the edge lengths in meters.
#' TE is an 'Area and edge metric'. Total (class) edge includes all edges between class i and
#' all other classes k. It measures the configuration of the landscape because a highly
#' fragmented landscape will have many edges. However, total edge is an absolute measure,
#' making comparisons among landscapes with different total areas difficult. If
#' \code{count_boundary = TRUE} also edges to the landscape boundary are included.
#'
#' \subsection{Units}{Meters}
#' \subsection{Range}{TE >= 0}
#' \subsection{Behaviour}{Equals TE = 0 if all cells are edge cells. Increases, without limit,
#' as landscape becomes more fragmented}
#'
#' @seealso
#' \code{\link{lsm_p_perim}}
#' \code{\link{lsm_l_te}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_c_te(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_te <- function(landscape,
count_boundary = FALSE, directions = 8) {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
FUN = lsm_c_te_calc,
count_boundary = count_boundary,
directions = directions)
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_te_calc <- function(landscape, count_boundary, directions, resolution, extras = NULL) {
if (missing(resolution)) resolution <- terra::res(landscape)
if (is.null(extras)){
metrics <- "lsm_c_te"
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 = "class",
class = as.integer(NA),
id = as.integer(NA),
metric = "te",
value = as.double(NA))))
}
# get class id
classes <- extras$classes
class_patches <- extras$class_patches
resolution_x <- resolution[[1]]
resolution_y <- resolution[[2]]
if (length(classes) == 1 && !count_boundary) {
tibble::new_tibble(list(
level = "class",
class = as.integer(classes),
id = as.integer(NA),
metric = "te",
value = as.double(0)))
} else {
# resolution not identical in x and y direction
if (resolution_x != resolution_y) {
top_bottom_matrix <- matrix(c(NA, NA, NA,
1, 0, 1,
NA, NA, NA), 3, 3, byrow = TRUE)
left_right_matrix <- matrix(c(NA, 1, NA,
NA, 0, NA,
NA, 1, NA), 3, 3, byrow = TRUE)
}
te_class <- lapply(X = classes, function(patches_class) {
# get connected patches
landscape_labeled <- class_patches[[as.character(patches_class)]]
# set all non-class patches, but not NAs, to -999
edge_cells <- which(!is.na(landscape) & landscape != patches_class)
landscape_labeled[edge_cells] <- -999
# add one row/column to count landscape boundary
if (count_boundary) {
landscape_labeled <- pad_raster_internal(landscape = landscape_labeled,
pad_raster_value = -999,
pad_raster_cells = 1,
global = FALSE)
# set NA to -999
landscape_labeled[is.na(landscape_labeled)] <- -999
}
# resolution identical in x and y direction
if (resolution_x == resolution_y) {
# get adjacencies
neighbor_matrix <- rcpp_get_coocurrence_matrix_single(landscape_labeled,
directions = as.matrix(4),
single_class = -999)
# sum of all adjacencies between patch id and non-class patches (-999) converted to edge length
edge_ik <- (sum(neighbor_matrix[2:nrow(neighbor_matrix), 1])) * resolution_x
}
else {
# get adjacencies
left_right_neighbours <- rcpp_get_coocurrence_matrix_single(landscape_labeled,
directions = as.matrix(left_right_matrix),
single_class = -999)
# sum of all adjacencies between patch id and non-class patches (-999) converted to edge length
edge_ik_left_right <- sum(left_right_neighbours[2:nrow(left_right_neighbours), 1]) * resolution_x
# get adjacencies
top_bottom_neighbours <- rcpp_get_coocurrence_matrix_single(landscape_labeled,
directions = as.matrix(top_bottom_matrix),
single_class = -999)
# sum of all adjacencies between patch id and non-class patches (-999) converted to edge length
edge_ik_top_bottom <- sum(top_bottom_neighbours[2:nrow(top_bottom_neighbours), 1]) * resolution_y
# add edge length in x- and y-direction
edge_ik <- edge_ik_left_right + edge_ik_top_bottom
}
tibble::new_tibble(list(
level = rep("class", length(edge_ik)),
class = rep(as.integer(patches_class), length(edge_ik)),
id = rep(as.integer(NA), length(edge_ik)),
metric = rep("te", length(edge_ik)),
value = as.double(edge_ik)))
})
do.call("rbind", te_class)
}
}
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