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sample_strat.R
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sample_strat.R
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#' Stratified sampling
#'
#' @description Sampling based on a stratified raster.
#'
#' @family sample functions
#'
#' @inheritParams sample_srs
#' @inheritParams calculate_allocation
#' @param method Character. Sampling design approach \code{"Queinnec"} (default) or \code{"random"}. \code{"Queinnec"} method is
#' described in notes below. \code{"random"} performs traditional stratified random sampling where probability to sample each
#' cell within each stratum is equal assuming default parameters for \code{mindist}. \code{existing, include, remove} are ignored when \code{method = "random"}.
#' @param sraster spatRaster. Stratification raster to be used for sampling.
#' @param nSamp Numeric. Number of desired samples. \code{existing}, \code{include} and \code{force} influence this value.
#' @param existing sf 'POINT' or data.frame. Existing plot network.
#' @param include Logical. If \code{TRUE} include \code{existing} plots in \code{nSamp} total.
#' @param remove Logical. If \code{TRUE} randomly remove samples from over represented strata to meet allocated sample numbers.
#' Used only when \code{existing} and \code{include} are both \code{TRUE}.
#' @param wrow Numeric. Number of row in the focal window (\code{default = 3}).
#' @param wcol Numeric. Number of columns in the focal window (\code{default = 3}).
#' @param details Logical. If \code{FALSE} (default) output is sf object of
#' stratified samples. If \code{TRUE} return a list
#' where \code{$details} additional sampling information and \code{$raster}
#' is an sf object of stratified samples.
#' @param plot Logical. Plots samples of type `existing` (if provided; croses) and `new` (circles) along with \code{sraster}.
#'
#' @importFrom methods is
#'
#' @return An sf object with \code{nSamp} stratified samples.
#'
#' @examples
#' #--- Load raster and access files ---#
#' r <- system.file("extdata", "sraster.tif", package = "sgsR")
#' sr <- terra::rast(r)
#'
#'
#' #--- perform stratified sampling random sampling ---#
#' sraster <- sample_strat(
#' sraster = sr,
#' nSamp = 50
#' )
#'
#' @author Tristan R.H. Goodbody & Martin Queinnec
#'
#' @note
#' The sampling is performed in 2 stages when \code{method = "Queinnec"}:
#' \enumerate{
#'
#' \item \code{Rule 1} - Sample within grouped stratum pixels defined within the
#' \code{wrow, wcol} parameters
#'
#' \item \code{Rule 2} - If no samples exist to satisfy Rule 1
#' individual stratum pixels are sampled.
#'
#' The rule applied to allocate each sample is defined in the \code{rule} attribute of output samples.
#'
#' }
#'
#' \code{existing} may contain samples that fall in \code{sraster} cells that are `NA`. If this occurs and \code{include = TRUE}, `NA` samples
#' are separated during sampling and re-appended at the end of the sampling process.
#'
#' If the \code{sraster} provided contains factor values, the algorithm will automatically convert these into the numeric factor levels and
#' perform sampling using those values. The categories (factor values) will be extracted and appended to the algorithm output as the `category` attribute.
#'
#' @references
#' Queinnec, M., White, J. C., & Coops, N. C. (2021).
#' Comparing airborne and spaceborne photon-counting LiDAR canopy
#' structural estimates across different boreal forest types.
#' Remote Sensing of Environment, 262 (August 2020), 112510.
#' https://doi.org/10.1016/j.rse.2021.112510
#'
#' @export
sample_strat <- function(sraster,
nSamp,
allocation = "prop",
method = "Queinnec",
weights = NULL,
force = FALSE,
mraster = NULL,
mindist = NULL,
existing = NULL,
include = FALSE,
remove = FALSE,
access = NULL,
buff_inner = NULL,
buff_outer = NULL,
wrow = 3,
wcol = 3,
plot = FALSE,
details = FALSE,
filename = NULL,
overwrite = FALSE) {
#--- Set global vars ---#
x <- y <- cell <- cats <- rule <- NULL
#--- Error management ---#
if (!inherits(sraster, "SpatRaster")) {
stop("'sraster' must be type SpatRaster.", call. = FALSE)
}
if (!is.numeric(nSamp)) {
stop("'nSamp' must be type numeric.", call. = FALSE)
}
if (!is.character(method)) {
stop("'method' must be type character.", call. = FALSE)
}
if (!method %in% c("random", "Queinnec")) {
stop("'method' must be one of 'random' or 'Queinnec'.", call. = FALSE)
}
if (!is.null(mindist)) {
if (!is.numeric(mindist)) {
stop("'mindist' must be type numeric.", call. = FALSE)
}
}
if (!is.logical(include)) {
stop("'include' must be type logical.", call. = FALSE)
}
if (!is.logical(remove)) {
stop("'remove' must be type logical.", call. = FALSE)
}
if (!is.logical(force)) {
stop("'force' must be type logical.", call. = FALSE)
}
if (!is.numeric(wrow)) {
stop("'wrow' must be type numeric.", call. = FALSE)
}
if (!is.numeric(wcol)) {
stop("'wcol' must be type numeric.", call. = FALSE)
}
if ((wrow %% 2) == 0) {
stop("'wrow' must be an odd number.", call. = FALSE)
}
if ((wcol %% 2) == 0) {
stop("'wcol' must be an odd number.", call. = FALSE)
}
if (!is.logical(plot)) {
stop("'plot' must be type logical.", call. = FALSE)
}
if (!is.logical(details)) {
stop("'details' must be type logical.", call. = FALSE)
}
#--- check if `sraster` contains factor values and if so generate its category list to amend later ---#
if (!is.null(terra::cats(sraster)[[1]])) {
message("'sraster' has factor values. Converting to allow mapping.")
#--- change suggested by R Hijmans ---#
sraster_cats <- cats(sraster) %>%
as.data.frame()
colnames(sraster_cats)[1] <- "value"
}
#--- determine crs of input sraster ---#
crs <- terra::crs(sraster)
if (method == "Queinnec") {
message("Using 'Queinnec' sampling method.")
#--- if existing samples are provided ensure they are in the proper format ---#
if (is.null(existing)) {
if (isTRUE(include)) {
stop("'existing' must be provided when 'include = TRUE'.", call. = FALSE)
}
if (isTRUE(remove)) {
stop("'existing' must be provided when 'remove = TRUE'.", call. = FALSE)
}
#--- if existing samples do not exist make an empty data.frame called addSamples ---#
addSamples <- data.frame(cell = NA, strata = NA, X = NA, Y = NA)
extraCols <- character(0)
} else {
#--- existing must be either a data.frame or an sf object with columns names 'X' 'Y' 'strata' ---#
if (!inherits(existing, "data.frame") && !inherits(existing, "sf")) {
stop("'existing' must be a data.frame or sf object.", call. = FALSE)
}
if (inherits(existing, "sf")) {
if (inherits(sf::st_geometry(existing), "sfc_POINT")) {
#--- determine crs of existing ---#
crs <- sf::st_crs(existing)
#--- if existing is an sf object extract the coordinates and the strata vector ---#
exist_xy <- sf::st_coordinates(existing)
strata <- existing$strata
existing <- as.data.frame(cbind(strata, exist_xy))
} else {
stop("'existing' geometry type must be 'sfc_POINT'.", call. = FALSE)
}
}
if (any(!c("strata") %in% names(existing))) {
stop("'existing' must have an attribute named 'strata'. Consider using extract_strata().", call. = FALSE)
}
#--- if existing samples do exist ensure proper naming convention ---#
if (any(!c("X", "Y") %in% colnames(existing))) {
#--- if coordinate column names are lowercase change them to uppercase to match requirements ---#
if (any(c("x", "y") %in% colnames(existing))) {
existing <- existing %>%
dplyr::rename(
X = x,
Y = y
)
message("'existing' column coordinate names are lowercase - converting to uppercase.")
} else {
#--- if no x/y columns are present stop ---#
stop("'existing' must have columns named 'X' and 'Y'.", call. = FALSE)
}
}
#--- add cell value for future checking for duplicate samples ---#
existing$cell <- NA
addSamples <- existing
}
extraCols <- colnames(existing)[!colnames(existing) %in% c("cell", "X", "Y", "strata")]
} else {
message("Using 'random' sampling method. Ignoring 'existing', 'include', 'remove' if provided.")
addSamples <- data.frame(cell = NA, strata = NA, X = NA, Y = NA)
extraCols <- character(0)
existing <- NULL
include <- NULL
remove <- NULL
}
#--- determine number of samples for each strata ---#
if (isTRUE(include)) {
message("'existing' samples being included in 'nSamp' total.")
toSample <- calculate_allocation(
sraster = sraster,
nSamp = nSamp,
weights = weights,
existing = existing,
force = force,
allocation = allocation,
mraster = mraster
)
} else {
toSample <- calculate_allocation(
sraster = sraster,
nSamp = nSamp,
weights = weights,
force = force,
allocation = allocation,
mraster = mraster
)
}
#--- determine access buffers ---#
if (!missing(access)) {
access_buff <- mask_access(raster = sraster, access = access, buff_inner = buff_inner, buff_outer = buff_outer)
raster_masked <- access_buff$rast
}
#--- Define focal window ---#
w <- matrix(1 / (wrow * wcol), wrow, wcol)
####################################
#--- Start of sampling function ---#
####################################
for (i in 1:nrow(toSample)) {
s <- as.numeric(toSample[i, 1])
n <- as.numeric(toSample[i, 2])
message(paste0("Processing strata : ", s))
#--- use stratified RANDOM sampling or "Queinnec" method ---#
if (method == "random") {
if (n == 0) {
message("No samples needed.")
} else if (n > 0) {
strata_m <- terra::mask(sraster,
mask = sraster,
maskvalues = s,
inverse = TRUE
)
names(strata_m) <- "strata"
#--- if access line polygon is specified create inner and outer buffers
if (!missing(access)) {
strata_m_buff <- terra::mask(strata_m,
mask = access_buff$buff
)
sampAvail <- sum(!is.na(terra::values(strata_m_buff)))
if (sampAvail > n) {
message(
paste0("Buffered area contains ", sampAvail, " available candidates. Sampling to reach ", n, " starting.")
)
#--- rename to original strata sraster that will be used for sampling ---#
strata_m <- strata_m_buff
#--- if there are no samples to take within the specified 'buff_outer' distance extend buffer until values are found ---#
} else {
stop("Insufficient candidate samples within the buffered access extent. Consider altering buffer widths.", call. = FALSE)
}
}
#--- initiate number of sampled cells ---#
add_strata <- addSamples %>%
dplyr::filter(strata == s)
#--- ensure that sample units from previous strata are appended for distance checking ---#
if(!is.null(mindist)){
if(exists("out")){
add_strata <- rbind(out, add_strata)
}
}
add_strata <- strat_rule2(
n = n,
s = s,
add_strata = add_strata,
nCount = 0,
strata_m = strata_m,
extraCols = extraCols,
mindist = mindist
) %>%
dplyr::filter(strata == s)
}
}
if (method == "Queinnec") {
#--- if the number of samples required is equal to zero (if `include = TRUE`) just keep existing samples only ---#
if (n == 0) {
#--- Initiate number of sampled cells ---#
add_strata <- addSamples %>%
dplyr::filter(strata == s)
if (nrow(add_strata) > 0) {
add_strata$type <- "existing"
if (!"rule" %in% colnames(add_strata)) {
add_strata$rule <- "existing"
}
}
message(paste0("Strata : ", s, " required no sample additions. Keeping all existing samples."))
} else if (n > 0) {
#--- mask for individual strata ---#
strata_m <- terra::mask(sraster,
mask = sraster,
maskvalues = s,
inverse = TRUE
)
names(strata_m) <- "strata"
#--- if access line polygon is specified create inner and outer buffers
if (!missing(access)) {
strata_m_buff <- terra::mask(strata_m,
mask = access_buff$buff
)
sampAvail <- sum(!is.na(terra::values(strata_m_buff)))
if (sampAvail > n) {
message(
paste0("Buffered area contains ", sampAvail, " available candidates. Sampling to reach ", n, " starting.")
)
#--- rename to original strata sraster that will be used for sampling ---#
strata_m <- strata_m_buff
#--- if there are no samples to take within the specified 'buff_outer' distance extend buffer until values are found ---#
} else {
stop("Insufficient candidate samples within the buffered access extent. Consider altering buffer widths.", call. = FALSE)
}
}
### --- sampling ---###
suppressWarnings(strat_mask <-
terra::focal(
strata_m,
w = w,
na.rm = FALSE
))
names(strat_mask) <- "strata"
#--- Initiate number of sampled cells ---#
add_strata <- addSamples %>%
dplyr::filter(strata == s)
if (nrow(add_strata) > 0) {
add_strata$type <- "existing"
if (!"rule" %in% colnames(add_strata)) {
add_strata$rule <- "existing"
}
}
#--- ensure that sample units from previous strata are appended for distance checking ---#
if(!is.null(mindist)){
if(exists("out")){
add_strata <- rbind(out, add_strata)
}
}
#--- Rule 1 sampling ---#
r1 <- strat_rule1(
n = n,
i = i,
s = s,
strat_mask = strat_mask,
add_strata = add_strata,
extraCols = extraCols,
mindist = mindist
)
#--- Rule 2 sampling ---#
add_strata <- strat_rule2(
n = n,
s = s,
add_strata = r1$add_strata,
nCount = r1$nCount,
strata_m = strata_m,
extraCols = extraCols,
mindist = mindist
) %>%
dplyr::filter(strata == s)
#--- if number of samples is < 0 based on `include` parameter ---#
} else if (n < 0) {
if (isTRUE(remove)) {
#--- need to remove samples from over represented strata ---#
#--- sample total needed from existing ---#
need <- as.numeric(toSample[i, 3])
message(paste0("'include = TRUE & remove = TRUE' - Stratum ", s, " overrepresented - ", abs(n), " samples removed."))
add_strata <- addSamples %>%
dplyr::filter(strata == s) %>%
dplyr::sample_n(need)
#--- add type and rule attributes ---#
add_strata$type <- "existing"
add_strata$rule <- "existing"
} else {
message(paste0("'include = TRUE & remove = FALSE' - Stratum ", s, " overrepresented by ", abs(n), " samples but have not been removed. Expect a higher total 'nSamp' in output."))
#--- keep over represented samples in dataset ---#
add_strata <- addSamples %>%
dplyr::filter(strata == s)
if (nrow(add_strata) > 0) {
add_strata$type <- "existing"
if (!"rule" %in% colnames(add_strata)) {
add_strata$rule <- "existing"
}
}
}
}
}
# Create out object if first iteration of loop
# Else just rbind output with what has been processed in the loop
if (i == 1) {
out <- add_strata
} else {
out <- rbind(out, add_strata)
}
}
#--- check if samples fall in areas where stratum values are NA ---#
if (!is.null(existing)) {
if (any(!complete.cases(existing$strata))) {
na_only <- existing %>%
dplyr::filter(!complete.cases(strata)) %>%
dplyr::select(-cell)
samples_NA <- na_only %>%
dplyr::mutate(
type = "existing",
rule = NA
)
#--- convert coordinates to a spatial points object ---#
samples <- out %>%
dplyr::select(-cell) %>%
as.data.frame() %>%
rbind(., samples_NA) %>%
sf::st_as_sf(., coords = c("X", "Y"), crs = crs)
} else {
#--- convert coordinates to a spatial points object ---#
samples <- out %>%
dplyr::select(-cell) %>%
as.data.frame() %>%
sf::st_as_sf(., coords = c("X", "Y"), crs = crs)
}
} else {
#--- convert coordinates to a spatial points object ---#
samples <- out %>%
dplyr::select(-cell) %>%
as.data.frame() %>%
sf::st_as_sf(., coords = c("X", "Y"), crs = crs)
if(method == "random"){
samples <- samples %>%
dplyr::select(-rule)
}
}
#--- plot the raster and samples if desired ---#
if (isTRUE(plot)) {
if (method == "random") {
if (missing(access)) {
terra::plot(sraster[[1]])
suppressWarnings(terra::plot(samples, add = T, col = "black"))
#--- if access is provided plot the masked access sraster ---#
} else {
terra::plot(sraster[[1]])
suppressWarnings(terra::plot(access_buff$buff, add = T, border = c("gray30"), col = "gray10", alpha = 0.1))
suppressWarnings(terra::plot(samples, add = T, col = "black"))
}
} else {
#--- if existing is not provided plot the masked raster ---#
if (missing(existing)) {
#--- if access is also missing plot the full sraster extent ---#
if (missing(access)) {
terra::plot(sraster[[1]])
suppressWarnings(terra::plot(samples, add = T, col = "black"))
#--- if access is provided plot the masked access sraster ---#
} else {
terra::plot(sraster[[1]])
suppressWarnings(terra::plot(access_buff$buff, add = T, border = c("gray30"), col = "gray10", alpha = 0.1))
suppressWarnings(terra::plot(samples, add = T, col = "black"))
}
#--- if existing is provided plot the full raster ---#
} else {
#--- plot input sraster and random samples ---#
terra::plot(sraster[[1]])
suppressWarnings(terra::plot(samples, add = T, col = "black", pch = ifelse(samples$type == "existing", 3, 1)))
}
}
}
if (exists("sraster_cats")) {
#--- match label to value from categorical raster ---#
samples$category <- sraster_cats$label[match(samples$strata,sraster_cats$value)]
}
#--- write outputs if desired ---#
write_samples(samples = samples, filename = filename, overwrite = overwrite)
if (isTRUE(details)) {
#--- output metrics details along with stratification raster ---#
output <- list(sampleDist = toSample, samples = samples)
#--- output samples dataframe ---#
return(output)
} else {
#--- just output raster ---#
return(samples)
}
}