/
lsm_l_pd.R
101 lines (87 loc) · 3.67 KB
/
lsm_l_pd.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
#' PD (landscape level)
#'
#' @description Patch density (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{PD = \frac{N} {A} * 10000 * 100}
#' where \eqn{N} is the number of patches and \eqn{A} is the total landscape
#' area in square meters.
#'
#' PD is an 'Aggregation metric'. It describes the fragmentation the landscape, however,
#' does not necessarily contain information about the configuration or composition of the
#' landscape. In contrast to \code{\link{lsm_l_np}} it is standardized to the area and
#' comparisons among landscapes with different total area are possible.
#'
#' \subsection{Units}{Number per 100 hectares}
#' \subsection{Ranges}{0 < PD <= 1e+06}
#' \subsection{Behaviour}{Increases as the landscape gets more patchy. Reaches its maximum
#' if every cell is a different patch.}
#'
#' @seealso
#' \code{\link{lsm_c_np}},
#' \code{\link{lsm_l_ta}}, \cr
#' \code{\link{lsm_c_pd}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_l_pd(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_l_pd <- function(landscape, directions = 8) {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
FUN = lsm_l_pd_calc,
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_l_pd_calc <- function(landscape, directions, resolution, extras = NULL) {
if (missing(resolution)) resolution <- terra::res(landscape)
if (is.null(extras)){
metrics <- "lsm_l_pd"
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 = "pd",
value = as.double(NA))))
}
# get patch area
area_patch <- lsm_p_area_calc(landscape,
directions = directions,
resolution = resolution,
extras = extras)
# summarise for total landscape
area_total <- sum(area_patch$value)
# number of patches for each class
number_patches <- lsm_c_np_calc(landscape,
directions = directions,
extras = extras)
# summarise for total landscape
number_patches <- sum(number_patches$value)
# relative patch density
patch_density <- number_patches / area_total * 100
return(tibble::new_tibble(list(level = rep("landscape", length(patch_density)),
class = rep(as.integer(NA), length(patch_density)),
id = rep(as.integer(NA), length(patch_density)),
metric = rep("pd", length(patch_density)),
value = as.double(patch_density))))
}