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Commit 10a08758 authored by Monnet Jean-Matthieu's avatar Monnet Jean-Matthieu
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WIP 4.0.5

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DESCRIPTION
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Package: lidaRtRee Package: lidaRtRee
Type: Package Type: Package
Version: 4.0.4 Version: 4.0.5
Title: Forest Analysis with Airborne Laser Scanning (LiDAR) Data Title: Forest Analysis with Airborne Laser Scanning (LiDAR) Data
Date: 2022-09-14 Date: 2023-04-05
Authors@R: c( Authors@R: c(
person("Jean-Matthieu", "Monnet", email = "jean-matthieu.monnet@inrae.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9948-9891")), person("Jean-Matthieu", "Monnet", email = "jean-matthieu.monnet@inrae.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9948-9891")),
person("Pascal", "Obstétar", email = "pascal.obstetar@onf.fr", role = c("ctb"), comment = c(ORCID = "0000-0002-2811-7548"))) person("Pascal", "Obstétar", email = "pascal.obstetar@onf.fr", role = c("ctb"), comment = c(ORCID = "0000-0002-2811-7548")))
......
R/tree_detection.R
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− 117
View file @ 10a08758
...@@ -83,14 +83,6 @@ tree_detection <- ...@@ -83,14 +83,6 @@ tree_detection <-
normalize = FALSE, normalize = FALSE,
crown = FALSE, crown = FALSE,
... ...
# nl_filter = "Closing",
# nl_size = 5,
# sigma = 0.3,
# dmin = 0,
# dprop = 0.05,
# hmin = 5,
# crown_prop = 0.3,
# crown_hmin = 2
) )
{ {
if (!is.null(ROI)) if (!is.null(ROI))
...@@ -130,14 +122,6 @@ tree_detection <- ...@@ -130,14 +122,6 @@ tree_detection <-
normalize = normalize, normalize = normalize,
crown = crown, crown = crown,
..., ...,
# nl_filter = nl_filter,
# nl_size = nl_size,
# sigma = sigma,
# dmin = dmin,
# dprop = dprop,
# hmin = hmin,
# crown_prop = crown_prop,
# crown_hmin = crown_hmin,
.options = options .options = options
) )
return(output) return(output)
...@@ -169,14 +153,6 @@ tree_detection <- ...@@ -169,14 +153,6 @@ tree_detection <-
normalize = normalize, normalize = normalize,
crown = crown, crown = crown,
... ...
# nl_filter = nl_filter,
# nl_size = nl_size,
# sigma = sigma,
# dmin = dmin,
# dprop = dprop,
# hmin = hmin,
# crown_prop = crown_prop,
# crown_hmin = crown_hmin
) )
if (is.null(output)) return(NULL) if (is.null(output)) return(NULL)
if (nrow(output) == 0) return(NULL) if (nrow(output) == 0) return(NULL)
...@@ -210,14 +186,6 @@ tree_detection <- ...@@ -210,14 +186,6 @@ tree_detection <-
segms <- lidaRtRee::tree_segmentation( segms <- lidaRtRee::tree_segmentation(
las, las,
... ...
# nl_filter = nl_filter,
# nl_size = nl_size,
# sigma = sigma,
# dmin = dmin,
# dprop = dprop,
# hmin = hmin,
# crown_prop = crown_prop,
# crown_hmin = crown_hmin
) )
# extract apices # extract apices
output <- output <-
...@@ -232,15 +200,6 @@ tree_detection <- ...@@ -232,15 +200,6 @@ tree_detection <-
if (nrow(output) == 0) { if (nrow(output) == 0) {
return(NULL) return(NULL)
} }
# crowns are now handled directly by tree_extraction
# if (crown)
# {
# # segments_id outside of ROI to NA
# if (!is.null(ROI))
# {
# segms$segments_id[!is.element(segms$segments_id, output$id)] <- NA
# }
# }
return(output) return(output)
} }
stop("Not supported input") stop("Not supported input")
...@@ -287,28 +246,32 @@ create_disk <- function(width = 5) { ...@@ -287,28 +246,32 @@ create_disk <- function(width = 5) {
#' SpatRaster object (e.g. obtained with \code{\link[terra]{rast}}) #' SpatRaster object (e.g. obtained with \code{\link[terra]{rast}})
#' @param nl_filter string. type of non-linear filter to apply: "None", "Closing" #' @param nl_filter string. type of non-linear filter to apply: "None", "Closing"
#' or "Median" #' or "Median"
#' @param nl_size numeric. kernel width in pixel for non-linear filtering #' @param nl_size numeric. kernel width in pixels for non-linear filtering
#' @param sigmap numeric or matrix. if a single number is provided, sigmap is #' @param sigma numeric or matrix. If a single number is provided, \code{sigma} is
#' the standard deviation of the Gaussian filter in pixel, 0 corresponds to no #' the standard deviation of the Gaussian filter, 0 corresponds to no
#' smoothing. In case of matrix, the first column corresponds to the standard #' smoothing. Unit is pixel in case \code{dem} is a cimg object, SpatRaster units
#' otherwise. In case of a matrix, the first column corresponds to the standard
#' deviation of the filter, and the second to thresholds for image values (e.g. #' deviation of the filter, and the second to thresholds for image values (e.g.
#' a filter of standard deviation specified in line \code{i} is applied to pixels #' a filter of standard deviation specified in line \code{i} is applied to pixels
#' in image which values are between thresholds indicated in lines \code{i} and #' in image which values are between thresholds indicated in lines \code{i} and
#' \code{i+1}). Threshold values should be ordered in increasing order. #' \code{i+1}). Threshold values should be ordered in increasing order.
#' @param padding boolean. Whether image should be padded by duplicating edge #' @param padding boolean. Whether image should be padded by duplicating edge
#' values before filtering to avoid border effects #' values before filtering to avoid border effects
#' @param sigmap deprecated (numeric or matrix). (old name for \code{sigma} parameter,
#' retained for backward compatibility, overwrites \code{sigma} if provided, unit is
#' pixel whatever the class of \code{dem})
#' @examples #' @examples
#' data(chm_chablais3) #' data(chm_chablais3)
#' chm_chablais3 <- terra::rast(chm_chablais3) #' chm_chablais3 <- terra::rast(chm_chablais3)
#' #'
#' # filtering with median and Gaussian smoothing #' # filtering with median and Gaussian smoothing
#' im <- dem_filtering(chm_chablais3, nl_filter = "Median", nl_size = 3, sigmap = 0.8) #' im <- dem_filtering(chm_chablais3, nl_filter = "Median", nl_size = 3, sigma = 0.8)
#' #'
#' # filtering with median filter and value-dependent Gaussian smoothing #' # filtering with median filter and value-dependent Gaussian smoothing
#' # (less smoothing for values between 0 and 15) #' # (less smoothing for values between 0 and 15)
#' im2 <- dem_filtering(chm_chablais3, #' im2 <- dem_filtering(chm_chablais3,
#' nl_filter = "Median", nl_size = 3, #' nl_filter = "Median", nl_size = 3,
#' sigmap = cbind(c(0.2, 0.8), c(0, 15)) #' sigma = cbind(c(0.2, 0.8), c(0, 15))
#' ) #' )
#' #'
#' # plot original image #' # plot original image
...@@ -328,22 +291,34 @@ create_disk <- function(width = 5) { ...@@ -328,22 +291,34 @@ create_disk <- function(width = 5) {
#' @return A list of two cimg objects or a SpatRaster object with image after non-linear #' @return A list of two cimg objects or a SpatRaster object with image after non-linear
#' filter and image after both filters #' filter and image after both filters
#' @export #' @export
dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3, dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigma = 0.3,
padding = TRUE) { padding = TRUE, sigmap = NULL) {
# convert raster to cimg object if necessary # convert raster to cimg object if necessary
if (inherits(dem, "SpatRaster")) { if (inherits(dem, "SpatRaster")) {
dem.c <- raster2Cimg(dem) dem.c <- raster2Cimg(dem)
# conversion of Gaussian filter standard deviation from meters to pixels
if (length(sigma == 1)) {
sigma <- sigma / terra::res(dem)[1]
} else {
sigma[, 2] <- sigma[, 2] / terra::res(dem)[1]
}
} else { } else {
dem.c <- dem dem.c <- dem
} }
# if sigmap argument present, its value (in pixels) overrides sigma
if (!is.null(sigmap))
{
sigma <- sigmap
warning("sigmap argument in function dem_filtering is deprecated, please use its new name sigma")
}
# #
if (padding) { if (padding) {
# padding number of cells is maximum of half width of non linear filter or # padding number of cells is maximum of half width of non linear filter or
# ceiling value of three times sigmap # ceiling value of three times sigma
if (!is.null(dim(sigmap))) { if (!is.null(dim(sigma))) {
dummy <- max(sigmap[, 1]) dummy <- max(sigma[, 1])
} else { } else {
dummy <- sigmap dummy <- sigma
} }
border.size <- max((nl_size - 1) / 2 + 1, ceiling(dummy * 3)) border.size <- max((nl_size - 1) / 2 + 1, ceiling(dummy * 3))
# convert to matrix # convert to matrix
...@@ -373,23 +348,23 @@ dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3, ...@@ -373,23 +348,23 @@ dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3,
# linear filtering # linear filtering
# gaussian smoothing # gaussian smoothing
# if several values of sigma are provided, value-dependent smoothin is performed # if several values of sigma are provided, value-dependent smoothin is performed
if (length(sigmap) > 1) { if (length(sigma) > 1) {
dem_gs <- dem_nl dem_gs <- dem_nl
# for each value of sigma # for each value of sigma
for (i in 1:nrow(sigmap)) for (i in 1:nrow(sigma))
{ {
# perform 2D smoothing # perform 2D smoothing
dummy <- imager::deriche(dem_nl, sigmap[i, 1], axis = "x") dummy <- imager::deriche(dem_nl, sigma[i, 1], axis = "x")
dummy <- imager::deriche(dummy, sigmap[i, 1], axis = "y") dummy <- imager::deriche(dummy, sigma[i, 1], axis = "y")
# identify pixels which values are superior to the threshold # identify pixels which values are superior to the threshold
temp <- dem_gs >= sigmap[i, 2] temp <- dem_gs >= sigma[i, 2]
# update only those values in the output # update only those values in the output
dem_gs[temp] <- dummy[temp] dem_gs[temp] <- dummy[temp]
} }
} else { # if only one value of sigma is provided } else { # if only one value of sigma is provided
if (sigmap > 0) { if (sigma > 0) {
dem_gs <- imager::deriche(dem_nl, sigmap, axis = "x") dem_gs <- imager::deriche(dem_nl, sigma, axis = "x")
dem_gs <- imager::deriche(dem_gs, sigmap, axis = "y") dem_gs <- imager::deriche(dem_gs, sigma, axis = "y")
} else { # if 0, no smoothing is performed } else { # if 0, no smoothing is performed
dem_gs <- dem_nl dem_gs <- dem_nl
} }
...@@ -426,12 +401,12 @@ dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3, ...@@ -426,12 +401,12 @@ dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3,
#' @param dem.res numeric. image resolution, in case \code{dem} is a SpatRaster #' @param dem.res numeric. image resolution, in case \code{dem} is a SpatRaster
#' object, \code{dem.res} is extracted from the object by \code{\link[terra]{res}} #' object, \code{dem.res} is extracted from the object by \code{\link[terra]{res}}
#' @param max.width numeric. maximum kernel width to check for local maximum, in #' @param max.width numeric. maximum kernel width to check for local maximum, in
#' pixels if dem is a cimg, in SpatRaster units otherwise #' pixels if \code{dem} is a cimg, in SpatRaster units otherwise
#' @param jitter boolean. indicates if noise should be added to image values to #' @param jitter boolean. indicates if noise should be added to image values to
#' avoid the adjacent maxima due to the adjacent pixels with equal values #' avoid adjacent maxima due to the adjacent pixels with equal values
#' @return A cimg object or SpatRaster object which values are the radius (n) #' @return A cimg object / SpatRaster object which values correspond to the radius
#' in meter of the square window (width 2n+1) where the center pixel is global #' (n) in pixels / meters of the square window (width 2n+1) where the center pixel is global
#' maximum #' maximum (tested up to the \code{max.width} parameter)
#' @examples #' @examples
#' data(chm_chablais3) #' data(chm_chablais3)
#' chm_chablais3 <- terra::rast(chm_chablais3) #' chm_chablais3 <- terra::rast(chm_chablais3)
...@@ -444,7 +419,8 @@ dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3, ...@@ -444,7 +419,8 @@ dem_filtering <- function(dem, nl_filter = "Closing", nl_size = 5, sigmap = 0.3,
#' #'
#' # plot maxima image #' # plot maxima image
#' terra::plot(maxi, main = "Local maxima") #' terra::plot(maxi, main = "Local maxima")
#' @seealso \code{\link{dem_filtering}}, \code{\link{maxima_selection}} #' @seealso \code{\link{dem_filtering}}, \code{\link{maxima_selection}},
#' \code{\link{tree_segmentation}}
#' @export #' @export
maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
# convert raster to cimg object if necessary # convert raster to cimg object if necessary
...@@ -457,6 +433,7 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -457,6 +433,7 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
} }
# #
# add absolute of gaussian white noise, mean=0, sd=sd(dem_gs)/100000 to non 0 pixels # add absolute of gaussian white noise, mean=0, sd=sd(dem_gs)/100000 to non 0 pixels
# avoids adjacent maxima but output maybe not reproducible
if (jitter) { if (jitter) {
dem_gs <- dem_gs + abs(imager::imnoise( dem_gs <- dem_gs + abs(imager::imnoise(
dim = dim(dem_gs), dim = dim(dem_gs),
...@@ -465,7 +442,7 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -465,7 +442,7 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
# #
max.radius <- max.width %/% 2 max.radius <- max.width %/% 2
# extraction of maxima on variable window size (from 0 to max.radius) # extraction of maxima on variable window size (from 0 to max.radius)
# # using lapply # METHOD 1 using lapply
# maxi <- lapply(1:max.radius, function(i) # maxi <- lapply(1:max.radius, function(i)
# { # {
# # create square structuring element for dilation # # create square structuring element for dilation
...@@ -478,7 +455,8 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -478,7 +455,8 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
# # retain only max value from each image # # retain only max value from each image
# maxi <- imager::parmax(maxi2, na.rm = TRUE) # maxi <- imager::parmax(maxi2, na.rm = TRUE)
# #
# using a for loop (avoid storing multiple images at the same time) # METHOD 2 using a for loop (avoid storing multiple images at the same time)
# used in lidaRtRee <= 4.0.4
# maxi <- NULL # maxi <- NULL
# for (i in 1:max.radius) # for (i in 1:max.radius)
# { # {
...@@ -494,7 +472,7 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -494,7 +472,7 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
# imager::as.cimg(dem_gs == imager::dilate(dem_gs, strel)) * i), na.rm = TRUE) # imager::as.cimg(dem_gs == imager::dilate(dem_gs, strel)) * i), na.rm = TRUE)
# } # }
# } # }
# using a for loop with iterative dilation -> faster # METHOD 3 : using a for loop with iterative dilation -> faster
# create 3x3 square structuring element for dilation # create 3x3 square structuring element for dilation
strel <- imager::imfill(3, 3, val = 1) strel <- imager::imfill(3, 3, val = 1)
# first dilation (radius = 1) # first dilation (radius = 1)
...@@ -523,14 +501,18 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -523,14 +501,18 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
#' Image maxima selection based on values and neighborhood of local maxima #' Image maxima selection based on values and neighborhood of local maxima
#' #'
#' In a maxima image (output of \code{\link{maxima_detection}}), sets values to #' In a maxima image (output of \code{\link{maxima_detection}}), sets values to
#' zero for pixels which #' zero for pixels which:
#' \enumerate{ #' \enumerate{
#' \item value in the initial image (from which maxima were detected) are below #' \item values in the initial image (from which maxima were detected) are below
#' a threshold #' a threshold
#' \item values in the maxima image (corresponding to the radius of the #' \item values in the maxima image (corresponding to the radius of the
#' neighborhood where they are global maxima) are below a threshold depending on #' neighborhood where they are global maxima) are below a threshold depending on
#' the initial image value. #' the initial image value.
#' } #' }
#' Make sure that the \code{max.width} parameter in \code{\link{maxima_detection}}
#' is consistent with the selection parameters (e.g. do not select with
#' \code{dmin = 7} if values were only tested up to \code{max.width} the default
#' value which is approx. 5.5 m).
#' #'
#' @param maxi cimg object or SpatRaster object. image with local maxima #' @param maxi cimg object or SpatRaster object. image with local maxima
#' (typically output from \code{\link{maxima_detection}}, image values correspond #' (typically output from \code{\link{maxima_detection}}, image values correspond
...@@ -553,8 +535,8 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -553,8 +535,8 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
#' #'
#' # several maxima selection settings #' # several maxima selection settings
#' selected_maxi_hmin <- maxima_selection(maxi, chm_chablais3, hmin = 15) #' selected_maxi_hmin <- maxima_selection(maxi, chm_chablais3, hmin = 15)
#' selected_maxi_dm <- maxima_selection(maxi, chm_chablais3, dm = 2.5) #' selected_maxi_dm <- maxima_selection(maxi, chm_chablais3, dmin = 2.5)
#' selected_maxi <- maxima_selection(maxi, chm_chablais3, dm = 1, dprop = 0.1) #' selected_maxi <- maxima_selection(maxi, chm_chablais3, dmin = 1, dprop = 0.1)
#' #'
#' # corresponding count number of remaining maxima #' # corresponding count number of remaining maxima
#' table(terra::values(maxi)) #' table(terra::values(maxi))
...@@ -568,9 +550,9 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) { ...@@ -568,9 +550,9 @@ maxima_detection <- function(dem, dem.res = 1, max.width = 11, jitter = TRUE) {
#' # plot maxima images, original and first case #' # plot maxima images, original and first case
#' terra::plot(maxi, main = "Local maxima") #' terra::plot(maxi, main = "Local maxima")
#' terra::plot(selected_maxi, main = "Selected maxima") #' terra::plot(selected_maxi, main = "Selected maxima")
#' @seealso \code{\link{maxima_detection}} #' @seealso \code{\link{maxima_detection}}, \code{\link{tree_segmentation}}
#' @export #' @export
maxima_selection <- function(maxi, dem_nl, hmin = 0, dmin = 0, dprop = 0) { maxima_selection <- function(maxi, dem_nl, hmin = 5, dmin = 0, dprop = 0.05) {
# convert SpatRaster to cimg object if necessary # convert SpatRaster to cimg object if necessary
if (inherits(maxi, "SpatRaster")) { if (inherits(maxi, "SpatRaster")) {
israster <- TRUE israster <- TRUE
...@@ -609,7 +591,7 @@ maxima_selection <- function(maxi, dem_nl, hmin = 0, dmin = 0, dprop = 0) { ...@@ -609,7 +591,7 @@ maxima_selection <- function(maxi, dem_nl, hmin = 0, dmin = 0, dprop = 0) {
#' # median filter #' # median filter
#' chm_chablais3 <- dem_filtering(chm_chablais3, #' chm_chablais3 <- dem_filtering(chm_chablais3,
#' nl_filter = "Median", nl_size = 3, #' nl_filter = "Median", nl_size = 3,
#' sigmap = 0 #' sigma = 0
#' )$non_linear_image #' )$non_linear_image
#' #'
#' # maxima detection #' # maxima detection
...@@ -652,6 +634,8 @@ segmentation <- function(maxi, dem_nl) { ...@@ -652,6 +634,8 @@ segmentation <- function(maxi, dem_nl) {
# create seed image # create seed image
dummy <- maxi_c dummy <- maxi_c
# initialise seeds with id # initialise seeds with id
# WHY USE SAMPLE HERE ?
# tree id different in each run but segments more visible when plotted
dummy[a] <- sample(1:na, na) dummy[a] <- sample(1:na, na)
# watershed segmentation # watershed segmentation
dem_w <- imager::watershed(dummy, dem_nl) dem_w <- imager::watershed(dummy, dem_nl)
...@@ -681,7 +665,7 @@ segmentation <- function(maxi, dem_nl) { ...@@ -681,7 +665,7 @@ segmentation <- function(maxi, dem_nl) {
#' # median filter #' # median filter
#' chm_chablais3 <- dem_filtering(chm_chablais3, #' chm_chablais3 <- dem_filtering(chm_chablais3,
#' nl_filter = "Median", nl_size = 3, #' nl_filter = "Median", nl_size = 3,
#' sigmap = 0 #' sigma = 0
#' )$non_linear_image #' )$non_linear_image
#' #'
#' # maxima detection #' # maxima detection
...@@ -750,7 +734,7 @@ raster_zonal_stats <- function(segms, dem_nl, fun = max) { ...@@ -750,7 +734,7 @@ raster_zonal_stats <- function(segms, dem_nl, fun = max) {
#' # median filter #' # median filter
#' chm_chablais3 <- dem_filtering(chm_chablais3, #' chm_chablais3 <- dem_filtering(chm_chablais3,
#' nl_filter = "Median", nl_size = 3, #' nl_filter = "Median", nl_size = 3,
#' sigmap = 0 #' sigma = 0
#' )$non_linear_image #' )$non_linear_image
#' #'
#' # maxima detection and selection #' # maxima detection and selection
...@@ -814,28 +798,24 @@ seg_adjust <- function(dem_w, dem_wh, dem_nl, prop = 0.3, min.value = 2, min.max ...@@ -814,28 +798,24 @@ seg_adjust <- function(dem_w, dem_wh, dem_nl, prop = 0.3, min.value = 2, min.max
#' @param dem raster object or string indicating location of raster file #' @param dem raster object or string indicating location of raster file
#' (typically a canopy height model or a digital surface model; in the latter #' (typically a canopy height model or a digital surface model; in the latter
#' case the dtm parameter should be provided) #' case the dtm parameter should be provided)
#' @param nl_filter string. specifies the non-linear filter for image pre-processing,
#' should be an option of function \code{\link{dem_filtering}}
#' @param nl_size numeric. width of kernel of non-linear filter in pixels
#' @param sigma numeric or matrix. if a single number is provided, sigmap is the
#' standard deviation of Gaussian filter in meters, 0 corresponds to no smoothing.
#' In case of matrix, the first column corresponds to the standard deviation of
#' the filter, and the second to thresholds for image values (e.g. a filter of
#' standard deviation specified in line \code{i} is applied to pixels in image
#' which values are between thresholds indicated in lines \code{i} and
#' \code{i+1}). Threshold values should be ordered in increasing order.
#' @param dmin numeric. treetop minimum distance to next higher pixel in meters
#' @param dprop numeric. number defining the treetop minimum distance as
#' proportion of height to next higher pixel
#' @param hmin numeric. minimum treetop height
#' @param crown_prop numeric. minimum height of tree crown as proportion of
#' treetop height
#' @param crown_hmin numeric. minimum crown height
#' @param dtm raster object or string indicating location of raster file with #' @param dtm raster object or string indicating location of raster file with
#' the terrain model. If provided, the maxima extraction and watershed segmentation #' the terrain model. If provided, the maxima extraction and watershed segmentation
#' are performed on the dem (this avoids the deformation of crown because of the #' are performed on the dem (this avoids the deformation of crown because of the
#' normalisation with terrain), but maxima selection and segment adjustment are #' normalisation with terrain), but maxima selection and segment adjustment are
#' performed on 'dem-dtm' because the selection criteria is the height to terrain. #' performed on 'dem-dtm' because the selection criteria are based on the height to terrain.
#' @param ... arguments passed to functions \code{\link{dem_filtering}}
#' (e.g. \code{nl_filter}, \code{nl_size}, \code{sigma}),
#' \code{\link{maxima_detection}}, \code{\link{maxima_selection}},
#' \code{\link{maxima_selection}} (\code{dmin}: treetop minimum distance to next
#' higher pixel in meters, \code{dprop}: number defining the treetop minimum
#' distance as proportion of its height to next higher pixel, \code{hmin}:
#' minimum treetop height), \code{\link{seg_adjust}} (\code{prop}: minimum
#' height of tree crown base as proportion of treetop height, \code{min.value}:
#' minimum crown base height)
#' @param crown_prop (deprecated) numeric. (overrides \code{prop} parameter
#' passed to \code{\link{seg_adjust}}, for backward compatibility)
#' @param crown_hmin (deprecated) numeric. (overrides \code{min.value} parameter
#' passed to \code{\link{seg_adjust}}, for backward compatibility)
#' @references Monnet, J.-M. 2011. Using airborne laser scanning for mountain #' @references Monnet, J.-M. 2011. Using airborne laser scanning for mountain
#' forests mapping: Support vector regression for stand parameters estimation #' forests mapping: Support vector regression for stand parameters estimation
#' and unsupervised training for treetop detection. Ph.D. thesis. University of #' and unsupervised training for treetop detection. Ph.D. thesis. University of
...@@ -879,12 +859,34 @@ seg_adjust <- function(dem_w, dem_wh, dem_nl, prop = 0.3, min.value = 2, min.max ...@@ -879,12 +859,34 @@ seg_adjust <- function(dem_w, dem_wh, dem_nl, prop = 0.3, min.value = 2, min.max
#' distance to higher pixel), segments, non-linear preprocessed dem, smoothed #' distance to higher pixel), segments, non-linear preprocessed dem, smoothed
#' preprocessed dem #' preprocessed dem
#' @export #' @export
tree_segmentation <- function(dem, nl_filter = "Closing", nl_size = 5, sigma = 0.3, tree_segmentation <- function(dem, dtm = NULL, crown_prop = NULL, crown_hmin = NULL, ...) {
dmin = 0, dprop = 0.05, hmin = 5, crown_prop = 0.3,
crown_hmin = 2, dtm = NULL) {
# #
if (crown_hmin > hmin) { # sort dots arguments
stop("minimum tree height lower than minimum crown height") # expected arguments for subsequent functions
dem_filtering.args <- names(formals(dem_filtering)) # nl_filter = "Closing", nl_size = 5, sigma = 0.3
maxima_detection.args <- names(formals(maxima_detection))
maxima_selection.args <- names(formals(maxima_selection)) # dmin = 0, dprop = 0.05, hmin = 5
seg_adjust.args <- names(formals(seg_adjust)) # crown_prop = 0.3, crown_hmin = 2
# dots arguments
dots <- list(...)
# set default values
# handle deprecated values
if (!is.null(crown_prop))
{
dots$prop <- crown_prop
warning("Argument crown_prop is deprecated, please use its new name: prop which is passed to function seg_adjust")
}
if (!is.null(crown_hmin))
{
dots$min.value <- crown_hmin
warning("Argument crown_hmin is deprecated, please use its new name: min.value which is passed to function seg_adjust")
}
#
if (all(is.element(c("min.value", "hmin"), names(dots))))
{
if (dots$min.value > dots$hmin) {
stop("minimum tree height lower than minimum crown base height")
}
} }
dem <- convert_raster(dem, "terra") dem <- convert_raster(dem, "terra")
# convert NAs to terrain altitude # convert NAs to terrain altitude
...@@ -896,40 +898,52 @@ tree_segmentation <- function(dem, nl_filter = "Closing", nl_size = 5, sigma = 0 ...@@ -896,40 +898,52 @@ tree_segmentation <- function(dem, nl_filter = "Closing", nl_size = 5, sigma = 0
dem[is.na(dem)] <- dtm dem[is.na(dem)] <- dtm
} }
# #
# conversion of Gaussian filter standard deviation from meters to pixels
if (length(sigma == 1)) {
sigma <- sigma / terra::res(dem)[1]
} else {
sigma[, 2] <- sigma[, 2] / terra::res(dem)[1]
}
# dem filtering # dem filtering
temp <- dem_filtering(dem, nl_filter = nl_filter, nl_size = nl_size, sigmap = sigma) # temp <- dem_filtering(dem, nl_filter = nl_filter, nl_size = nl_size, sigma = sigma)
temp <- do.call('dem_filtering', c(list(dem), dots[names(dots) %in% dem_filtering.args]))
dem_nl <- temp$non_linear_image dem_nl <- temp$non_linear_image
dem_gs <- temp$smoothed_image dem_gs <- temp$smoothed_image
# #
# maxima detection # maxima detection
maxi <- maxima_detection(dem_gs, terra::res(dem)[1]) # maxi <- maxima_detection(dem_gs, terra::res(dem)[1])
maxi <- do.call('maxima_detection', c(list(dem_gs), dots[names(dots) %in% maxima_detection.args]))
# #
# chm # chm
chm <- dem_nl - dtm chm <- dem_nl - dtm
# maxima selection # maxima selection
# check detection parameters (window size) with selection parameters # check detection parameters (window size) with selection parameters
if (dmin + dprop * terra::global(chm, fun="max", na.rm = TRUE) >= (11 / terra::res(dem)[1]) %/% 2) temp_dmin <- ifelse(methods::hasArg("dmin"), dots$dmin, formals(maxima_selection)$dmin)
warning("Window size for detecting local maxima might be too small to ensure that function maxima_selection returns a correct output. Make sure that parameter max.width temp_dprop <- ifelse(methods::hasArg("dprop"), dots$dprop, formals(maxima_selection)$dprop)
in function maxima_detection is appropriate, or consider reducing dmin or dprop parameters in maxima_selection") temp_max.width <- ifelse(methods::hasArg("max.width"), dots$max.width, formals(maxima_detection)$max.width)
maxi <- maxima_selection(maxi, chm, 0, dmin, dprop) # no selection based on top height at this stage, otherwise some artefacts occur in the watershed step if (temp_dmin + temp_dprop * terra::global(chm, fun="max", na.rm = TRUE) >= (temp_max.width / terra::res(dem)[1]) %/% 2)
warning("Window size for detecting local maxima might be too small to ensure that function maxima_selection returns a correct output. Make sure that parameter max.width in function maxima_detection is appropriate, or consider reducing dmin or dprop parameters in maxima_selection")
#
# no selection based on top height at this stage, otherwise some artefacts occur in the watershed step
# maxi <- maxima_selection(maxi, chm, 0, dmin, dprop)
maxi <- do.call('maxima_selection', c(list(maxi, chm, hmin = 0), dots[names(dots) %in% setdiff(maxima_selection.args, "hmin")]))
# #
# segmentation # segmentation
dem_w <- segmentation(maxi, dem_nl) dem_w <- segmentation(maxi, dem_nl)
dem_wh <- raster_zonal_stats(dem_w, chm, fun = max) dem_wh <- raster_zonal_stats(dem_w, chm, fun = max)
# #
# segmentation adjustment # segmentation adjustment
dem_w <- seg_adjust(dem_w, dem_wh, chm, crown_prop, crown_hmin, hmin) # removal of trees with top heigth < hmin # pass hmin parameter for selection to min.maxvalue parameter for seg_adjust
# remove trees from r_maxi temp_hmin <- ifelse(methods::hasArg("hmin"), dots$hmin, formals(maxima_selection)$hmin)
dots$min.maxvalue <- temp_hmin
if (methods::hasArg("min.maxvalue"))
{
warning(paste0("min.maxvalue parameter value in seg_adjust in set to the same value as hmin parameter (", temp_hmin, ") in maxima_selection for constitency. To manually specify the minimum tree height please use only the hmin parameter"))
}
# dem_w <- seg_adjust(dem_w, dem_wh, chm, crown_prop, crown_hmin, hmin) # removal of trees with top heigth < hmin
dem_w <- do.call('seg_adjust', c(list(dem_w, dem_wh, chm), dots[names(dots) %in% seg_adjust.args]))# prop = crown_prop, min.value = hmin)))
# remove local maxima from r_maxi where segments may have been removed because of
# height criterion
maxi[dem_w == 0] <- 0 maxi[dem_w == 0] <- 0
# #
output <- c(maxi, dem_w, dem_nl, dem_gs) output <- c(maxi, dem_w, dem_nl, dem_gs)
names(output) <- c("local_maxima", "segments_id", "filled_dem", "smoothed_dem") names(output) <- c("local_maxima", "segments_id", "filled_dem", "smoothed_dem")
print(dots)
output output
} }
...@@ -1091,7 +1105,7 @@ tree_extraction <- function(r_dem_nl, r_maxi = NULL, r_dem_w = NULL, r_mask = NU ...@@ -1091,7 +1105,7 @@ tree_extraction <- function(r_dem_nl, r_maxi = NULL, r_dem_w = NULL, r_mask = NU
#' chm_cim_filt <- dem_filtering(chm_cim, #' chm_cim_filt <- dem_filtering(chm_cim,
#' nl_filter = "Closing", #' nl_filter = "Closing",
#' nl_size = 3, #' nl_size = 3,
#' sigmap = 0 #' sigma = 0
#' )$non_linear_image #' )$non_linear_image
#' #'
#' # convert to SpatRaster #' # convert to SpatRaster
......
README.md
+ 2
− 0
View file @ 10a08758
...@@ -21,6 +21,8 @@ ...@@ -21,6 +21,8 @@
# Changelog # Changelog
4.0.5 (April 2023) max.width parameter in function maxima_detection is expect to be in raster units if a SpatRaster is provided; default value is now 11. Function tree_segmentation now expects arguments passed to sub function as ... rather than names arguments; default values for those sub-functions were modified to match with the default values which were used in tree_segmentation ; a warning is issued in case max.width argument (passed to maxima_detection) is not consistent with the dmin or dprop arguments (passed to maxima_selection).
4.0.3 (July 2022) fixes the number of decimals for WKT coordinates of crown in function `tree_extraction`. 4.0.3 (July 2022) fixes the number of decimals for WKT coordinates of crown in function `tree_extraction`.
4.0.2 (July 2022) adds a global function `tree_detection` which includes both the segmentation and extraction, and can be applied to SpatRaster, LAS or LAS-catalog objects. The function `gap_detection` now also accepts LAS and LAS-catalog objects as input. 4.0.2 (July 2022) adds a global function `tree_detection` which includes both the segmentation and extraction, and can be applied to SpatRaster, LAS or LAS-catalog objects. The function `gap_detection` now also accepts LAS and LAS-catalog objects as input.
......
man/cimg2Raster.Rd
+ 1
− 1
View file @ 10a08758
...@@ -28,7 +28,7 @@ chm_cim <- raster2Cimg(chm_chablais3) ...@@ -28,7 +28,7 @@ chm_cim <- raster2Cimg(chm_chablais3)
chm_cim_filt <- dem_filtering(chm_cim, chm_cim_filt <- dem_filtering(chm_cim,
nl_filter = "Closing", nl_filter = "Closing",
nl_size = 3, nl_size = 3,
sigmap = 0 sigma = 0
)$non_linear_image )$non_linear_image
# convert to SpatRaster # convert to SpatRaster
......
man/dem_filtering.Rd
+ 14
− 8
View file @ 10a08758
...@@ -8,8 +8,9 @@ dem_filtering( ...@@ -8,8 +8,9 @@ dem_filtering(
dem, dem,
nl_filter = "Closing", nl_filter = "Closing",
nl_size = 5, nl_size = 5,
sigmap = 0.3, sigma = 0.3,
padding = TRUE padding = TRUE,
sigmap = NULL
) )
} }
\arguments{ \arguments{
...@@ -19,11 +20,12 @@ SpatRaster object (e.g. obtained with \code{\link[terra]{rast}})} ...@@ -19,11 +20,12 @@ SpatRaster object (e.g. obtained with \code{\link[terra]{rast}})}
\item{nl_filter}{string. type of non-linear filter to apply: "None", "Closing" \item{nl_filter}{string. type of non-linear filter to apply: "None", "Closing"
or "Median"} or "Median"}
\item{nl_size}{numeric. kernel width in pixel for non-linear filtering} \item{nl_size}{numeric. kernel width in pixels for non-linear filtering}
\item{sigmap}{numeric or matrix. if a single number is provided, sigmap is \item{sigma}{numeric or matrix. If a single number is provided, \code{sigma} is
the standard deviation of the Gaussian filter in pixel, 0 corresponds to no the standard deviation of the Gaussian filter, 0 corresponds to no
smoothing. In case of matrix, the first column corresponds to the standard smoothing. Unit is pixel in case \code{dem} is a cimg object, SpatRaster units
otherwise. In case of a matrix, the first column corresponds to the standard
deviation of the filter, and the second to thresholds for image values (e.g. deviation of the filter, and the second to thresholds for image values (e.g.
a filter of standard deviation specified in line \code{i} is applied to pixels a filter of standard deviation specified in line \code{i} is applied to pixels
in image which values are between thresholds indicated in lines \code{i} and in image which values are between thresholds indicated in lines \code{i} and
...@@ -31,6 +33,10 @@ in image which values are between thresholds indicated in lines \code{i} and ...@@ -31,6 +33,10 @@ in image which values are between thresholds indicated in lines \code{i} and
\item{padding}{boolean. Whether image should be padded by duplicating edge \item{padding}{boolean. Whether image should be padded by duplicating edge
values before filtering to avoid border effects} values before filtering to avoid border effects}
\item{sigmap}{deprecated (numeric or matrix). (old name for \code{sigma} parameter,
retained for backward compatibility, overwrites \code{sigma} if provided, unit is
pixel whatever the class of \code{dem})}
} }
\value{ \value{
A list of two cimg objects or a SpatRaster object with image after non-linear A list of two cimg objects or a SpatRaster object with image after non-linear
...@@ -50,13 +56,13 @@ data(chm_chablais3) ...@@ -50,13 +56,13 @@ data(chm_chablais3)
chm_chablais3 <- terra::rast(chm_chablais3) chm_chablais3 <- terra::rast(chm_chablais3)
# filtering with median and Gaussian smoothing # filtering with median and Gaussian smoothing
im <- dem_filtering(chm_chablais3, nl_filter = "Median", nl_size = 3, sigmap = 0.8) im <- dem_filtering(chm_chablais3, nl_filter = "Median", nl_size = 3, sigma = 0.8)
# filtering with median filter and value-dependent Gaussian smoothing # filtering with median filter and value-dependent Gaussian smoothing
# (less smoothing for values between 0 and 15) # (less smoothing for values between 0 and 15)
im2 <- dem_filtering(chm_chablais3, im2 <- dem_filtering(chm_chablais3,
nl_filter = "Median", nl_size = 3, nl_filter = "Median", nl_size = 3,
sigmap = cbind(c(0.2, 0.8), c(0, 15)) sigma = cbind(c(0.2, 0.8), c(0, 15))
) )
# plot original image # plot original image
......
man/maxima_detection.Rd
+ 7
− 6
View file @ 10a08758
...@@ -14,15 +14,15 @@ SpatRaster object (e.g. obtained with \code{\link[terra]{rast}})} ...@@ -14,15 +14,15 @@ SpatRaster object (e.g. obtained with \code{\link[terra]{rast}})}
object, \code{dem.res} is extracted from the object by \code{\link[terra]{res}}} object, \code{dem.res} is extracted from the object by \code{\link[terra]{res}}}
\item{max.width}{numeric. maximum kernel width to check for local maximum, in \item{max.width}{numeric. maximum kernel width to check for local maximum, in
pixels if dem is a cimg, in SpatRaster units otherwise} pixels if \code{dem} is a cimg, in SpatRaster units otherwise}
\item{jitter}{boolean. indicates if noise should be added to image values to \item{jitter}{boolean. indicates if noise should be added to image values to
avoid the adjacent maxima due to the adjacent pixels with equal values} avoid adjacent maxima due to the adjacent pixels with equal values}
} }
\value{ \value{
A cimg object or SpatRaster object which values are the radius (n) A cimg object / SpatRaster object which values correspond to the radius
in meter of the square window (width 2n+1) where the center pixel is global (n) in pixels / meters of the square window (width 2n+1) where the center pixel is global
maximum maximum (tested up to the \code{max.width} parameter)
} }
\description{ \description{
Variable window size maxima detection is performed on the image to extract Variable window size maxima detection is performed on the image to extract
...@@ -44,5 +44,6 @@ terra::plot(chm_chablais3, main = "Initial image") ...@@ -44,5 +44,6 @@ terra::plot(chm_chablais3, main = "Initial image")
terra::plot(maxi, main = "Local maxima") terra::plot(maxi, main = "Local maxima")
} }
\seealso{ \seealso{
\code{\link{dem_filtering}}, \code{\link{maxima_selection}} \code{\link{dem_filtering}}, \code{\link{maxima_selection}},
\code{\link{tree_segmentation}}
} }
man/maxima_selection.Rd
+ 10
− 6
View file @ 10a08758
...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
\alias{maxima_selection} \alias{maxima_selection}
\title{Image maxima selection based on values and neighborhood of local maxima} \title{Image maxima selection based on values and neighborhood of local maxima}
\usage{ \usage{
maxima_selection(maxi, dem_nl, hmin = 0, dmin = 0, dprop = 0) maxima_selection(maxi, dem_nl, hmin = 5, dmin = 0, dprop = 0.05)
} }
\arguments{ \arguments{
\item{maxi}{cimg object or SpatRaster object. image with local maxima \item{maxi}{cimg object or SpatRaster object. image with local maxima
...@@ -28,14 +28,18 @@ maximum and which fulfill the selection criteria ...@@ -28,14 +28,18 @@ maximum and which fulfill the selection criteria
} }
\description{ \description{
In a maxima image (output of \code{\link{maxima_detection}}), sets values to In a maxima image (output of \code{\link{maxima_detection}}), sets values to
zero for pixels which zero for pixels which:
\enumerate{ \enumerate{
\item value in the initial image (from which maxima were detected) are below \item values in the initial image (from which maxima were detected) are below
a threshold a threshold
\item values in the maxima image (corresponding to the radius of the \item values in the maxima image (corresponding to the radius of the
neighborhood where they are global maxima) are below a threshold depending on neighborhood where they are global maxima) are below a threshold depending on
the initial image value. the initial image value.
} }
Make sure that the \code{max.width} parameter in \code{\link{maxima_detection}}
is consistent with the selection parameters (e.g. do not select with
\code{dmin = 7} if values were only tested up to \code{max.width} the default
value which is approx. 5.5 m).
} }
\examples{ \examples{
data(chm_chablais3) data(chm_chablais3)
...@@ -46,8 +50,8 @@ maxi <- maxima_detection(chm_chablais3) ...@@ -46,8 +50,8 @@ maxi <- maxima_detection(chm_chablais3)
# several maxima selection settings # several maxima selection settings
selected_maxi_hmin <- maxima_selection(maxi, chm_chablais3, hmin = 15) selected_maxi_hmin <- maxima_selection(maxi, chm_chablais3, hmin = 15)
selected_maxi_dm <- maxima_selection(maxi, chm_chablais3, dm = 2.5) selected_maxi_dm <- maxima_selection(maxi, chm_chablais3, dmin = 2.5)
selected_maxi <- maxima_selection(maxi, chm_chablais3, dm = 1, dprop = 0.1) selected_maxi <- maxima_selection(maxi, chm_chablais3, dmin = 1, dprop = 0.1)
# corresponding count number of remaining maxima # corresponding count number of remaining maxima
table(terra::values(maxi)) table(terra::values(maxi))
...@@ -63,5 +67,5 @@ terra::plot(maxi, main = "Local maxima") ...@@ -63,5 +67,5 @@ terra::plot(maxi, main = "Local maxima")
terra::plot(selected_maxi, main = "Selected maxima") terra::plot(selected_maxi, main = "Selected maxima")
} }
\seealso{ \seealso{
\code{\link{maxima_detection}} \code{\link{maxima_detection}}, \code{\link{tree_segmentation}}
} }
man/raster_zonal_stats.Rd
+ 1
− 1
View file @ 10a08758
...@@ -27,7 +27,7 @@ chm_chablais3 <- terra::rast(chm_chablais3) ...@@ -27,7 +27,7 @@ chm_chablais3 <- terra::rast(chm_chablais3)
# median filter # median filter
chm_chablais3 <- dem_filtering(chm_chablais3, chm_chablais3 <- dem_filtering(chm_chablais3,
nl_filter = "Median", nl_size = 3, nl_filter = "Median", nl_size = 3,
sigmap = 0 sigma = 0
)$non_linear_image )$non_linear_image
# maxima detection # maxima detection
......
man/seg_adjust.Rd
+ 1
− 1
View file @ 10a08758
...@@ -38,7 +38,7 @@ chm_chablais3 <- terra::rast(chm_chablais3) ...@@ -38,7 +38,7 @@ chm_chablais3 <- terra::rast(chm_chablais3)
# median filter # median filter
chm_chablais3 <- dem_filtering(chm_chablais3, chm_chablais3 <- dem_filtering(chm_chablais3,
nl_filter = "Median", nl_size = 3, nl_filter = "Median", nl_size = 3,
sigmap = 0 sigma = 0
)$non_linear_image )$non_linear_image
# maxima detection and selection # maxima detection and selection
......
man/segmentation.Rd
+ 1
− 1
View file @ 10a08758
...@@ -26,7 +26,7 @@ chm_chablais3 <- terra::rast(chm_chablais3) ...@@ -26,7 +26,7 @@ chm_chablais3 <- terra::rast(chm_chablais3)
# median filter # median filter
chm_chablais3 <- dem_filtering(chm_chablais3, chm_chablais3 <- dem_filtering(chm_chablais3,
nl_filter = "Median", nl_size = 3, nl_filter = "Median", nl_size = 3,
sigmap = 0 sigma = 0
)$non_linear_image )$non_linear_image
# maxima detection # maxima detection
......
man/tree_segmentation.Rd
+ 18
− 38
View file @ 10a08758
...@@ -4,54 +4,34 @@ ...@@ -4,54 +4,34 @@
\alias{tree_segmentation} \alias{tree_segmentation}
\title{Preprocessing and segmentation of raster image for tree identification} \title{Preprocessing and segmentation of raster image for tree identification}
\usage{ \usage{
tree_segmentation( tree_segmentation(dem, dtm = NULL, crown_prop = NULL, crown_hmin = NULL, ...)
dem,
nl_filter = "Closing",
nl_size = 5,
sigma = 0.3,
dmin = 0,
dprop = 0.05,
hmin = 5,
crown_prop = 0.3,
crown_hmin = 2,
dtm = NULL
)
} }
\arguments{ \arguments{
\item{dem}{raster object or string indicating location of raster file \item{dem}{raster object or string indicating location of raster file
(typically a canopy height model or a digital surface model; in the latter (typically a canopy height model or a digital surface model; in the latter
case the dtm parameter should be provided)} case the dtm parameter should be provided)}
\item{nl_filter}{string. specifies the non-linear filter for image pre-processing,
should be an option of function \code{\link{dem_filtering}}}
\item{nl_size}{numeric. width of kernel of non-linear filter in pixels}
\item{sigma}{numeric or matrix. if a single number is provided, sigmap is the
standard deviation of Gaussian filter in meters, 0 corresponds to no smoothing.
In case of matrix, the first column corresponds to the standard deviation of
the filter, and the second to thresholds for image values (e.g. a filter of
standard deviation specified in line \code{i} is applied to pixels in image
which values are between thresholds indicated in lines \code{i} and
\code{i+1}). Threshold values should be ordered in increasing order.}
\item{dmin}{numeric. treetop minimum distance to next higher pixel in meters}
\item{dprop}{numeric. number defining the treetop minimum distance as
proportion of height to next higher pixel}
\item{hmin}{numeric. minimum treetop height}
\item{crown_prop}{numeric. minimum height of tree crown as proportion of
treetop height}
\item{crown_hmin}{numeric. minimum crown height}
\item{dtm}{raster object or string indicating location of raster file with \item{dtm}{raster object or string indicating location of raster file with
the terrain model. If provided, the maxima extraction and watershed segmentation the terrain model. If provided, the maxima extraction and watershed segmentation
are performed on the dem (this avoids the deformation of crown because of the are performed on the dem (this avoids the deformation of crown because of the
normalisation with terrain), but maxima selection and segment adjustment are normalisation with terrain), but maxima selection and segment adjustment are
performed on 'dem-dtm' because the selection criteria is the height to terrain.} performed on 'dem-dtm' because the selection criteria are based on the height to terrain.}
\item{crown_prop}{(deprecated) numeric. (overrides \code{prop} parameter
passed to \code{\link{seg_adjust}}, for backward compatibility)}
\item{crown_hmin}{(deprecated) numeric. (overrides \code{min.value} parameter
passed to \code{\link{seg_adjust}}, for backward compatibility)}
\item{...}{arguments passed to functions \code{\link{dem_filtering}}
(e.g. \code{nl_filter}, \code{nl_size}, \code{sigma}),
\code{\link{maxima_detection}}, \code{\link{maxima_selection}},
\code{\link{maxima_selection}} (\code{dmin}: treetop minimum distance to next
higher pixel in meters, \code{dprop}: number defining the treetop minimum
distance as proportion of its height to next higher pixel, \code{hmin}:
minimum treetop height), \code{\link{seg_adjust}} (\code{prop}: minimum
height of tree crown base as proportion of treetop height, \code{min.value}:
minimum crown base height)}
} }
\value{ \value{
A SpatRaster with 4 layers: selected local maxima (values = A SpatRaster with 4 layers: selected local maxima (values =
......
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