Nothing
#' AI (landscape level)
#'
#' @description Aggregation index (Aggregation 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).
#'
#' @details
#' \deqn{AI = \Bigg[\sum\limits_{i=1}^m \Big( \frac{g_{ii}}{max-g_{ii}} \Big) P_{i} \Bigg](100) }
#'
#' where \eqn{g_{ii}} is the number of like adjacencies based on the single-count method and
#' \eqn{max-g_{ii}} is the classwise maximum number of like adjacencies of class i and \eqn{P_{i}}
#' the proportion of landscape compromised of class i.
#'
#' AI is an 'Aggregation metric'. It equals the number of like adjacencies divided
#' by the theoretical maximum possible number of like adjacencies for that class
#' summed over each class for the entire landscape. The metric is based on the
#' adjacency matrix and the single-count method.
#'
#' \subsection{Units}{Percent}
#' \subsection{Range}{0 <= AI <= 100}
#' \subsection{Behaviour}{Equals 0 for maximally disaggregated and 100
#' for maximally aggregated classes.}
#'
#' @return tibble
#' @seealso
#' \code{\link{lsm_c_ai}}
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_l_ai(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
#'
#' He, H. S., DeZonia, B. E., & Mladenoff, D. J. 2000. An aggregation index (AI)
#' to quantify spatial patterns of landscapes. Landscape ecology, 15(7), 591-601.
#'
#' @export
lsm_l_ai <- function(landscape, directions = 8) {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
directions = directions,
FUN = lsm_l_ai_calc)
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_l_ai_calc <- function(landscape, directions, resolution, extras = NULL) {
if (missing(resolution)) resolution <- terra::res(landscape)
if (is.null(extras)){
metrics <- "lsm_l_ai"
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 = "landscape",
class = as.integer(NA),
id = as.integer(NA),
metric = "ai",
value = as.double(NA))))
}
# get aggregation index for each class
ai <- lsm_c_ai_calc(landscape, extras = extras)
# get proportional class area
pland <- lsm_c_pland_calc(landscape,
directions = 8,
resolution = resolution,
extras = extras)
# final AI index
ai <- sum(ai$value * (pland$value / 100), na.rm = TRUE)
return(tibble::new_tibble(list(level = rep("landscape", length(ai)),
class = rep(as.integer(NA), length(ai)),
id = rep(as.integer(NA), length(ai)),
metric = rep("ai", length(ai)),
value = as.double(ai))))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.