Mercurial > repos > ecology > srs_process_data
changeset 0:cf69ad260611 draft default tip
planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
| author | ecology |
|---|---|
| date | Mon, 09 Jan 2023 13:36:02 +0000 |
| parents | |
| children | |
| files | Lib_preprocess_S2.r alpha_beta.r biodiv_indices_global.r comparison_div.r comparison_div.xml functions.r indices_spectral.r macro.xml pca_raster.r preprocess_S2.r prosail-master/R/Lib_PROSAIL.R prosail-master/R/Lib_PROSAIL_HybridInversion.R prosail-master/R/Lib_SpectralIndices.R test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml test-data/Metadata_validation.txt test-data/Mission.csv test-data/NDVI.tabular test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip test-data/S2A_Subset test-data/S2A_Subset.hdr test-data/S2A_T33NUD_Plots.zip val_metadata.r |
| diffstat | 22 files changed, 15610 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Lib_preprocess_S2.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,1335 @@ +# == == == == == == == == == == == == == == == == == == == == == == == == == == == +# preprocS2 +# Lib_preprocess_S2.R +# == == == == == == == == == == == == == == == == == == == == == == == == == == == +# PROGRAMMERS: +# Jean-Baptiste FERET <jb.feret@teledetection.fr> +# Copyright 2021/08 Jean-Baptiste FERET +# == == == == == == == == == == == == == == == == == == == == == == == == == == == +# This Library contains functions to preprocess Sentinel-2 images downloaded from +# different data hubs, such as THEIA, PEPS or SCIHUB +# == == == == == == == == == == == == == == == == == == == == == == == == == == == + +#" This function adjusts information from ENVI header +#" +#" @param dsn character. path where to store the stack +#" @param bands list. should include "bandname", and if possible "wavelength" +#" @param sensor character. Name of the sensor used to acquire the image +#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only +#" +#" @return None +#" @importFrom utils read.table +#" @importFrom raster hdr raster +#" @export +adjust_envi_hdr <- function(dsn, bands, sensor = "Unknown", stretch = FALSE) { + + # Edit hdr file to add metadata + hdr <- read_envi_header(get_hdr_name(dsn)) + hdr$`band names` <- bands$bandname + if (length(bands$wavelength) == length(bands$bandname)) { + hdr$wavelength <- bands$wavelength + }else { + hdr$wavelength <- NULL + } + if (stretch == TRUE) { + hdr$`default stretch` <- "0.000000 1000.000000 linear" + } + hdr$`z plot range` <- NULL + hdr$`data ignore value` <- "-Inf" + hdr$`sensor type` <- sensor + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dsn)) + + # remove unnecessary files + file2remove <- paste(dsn, ".aux.xml", sep = "") + if (file.exists(file2remove)) file.remove(file2remove) + file2remove <- paste(dsn, ".prj", sep = "") + if (file.exists(file2remove)) file.remove(file2remove) + file2remove <- paste(dsn, ".stx", sep = "") + if (file.exists(file2remove)) file.remove(file2remove) + return(invisible()) +} + +#" This function saves reflectance files +#" +#" @param s2sat character. Sentinel-2 mission ("2A" or "2B") +#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters) +#" @param dateacq_s2 double. date of acquisition +#" +#" @return s2mission character. name of the S2 mission (2A or 2B) +#" @importFrom sen2r safe_getMetadata check_scihub_connection s2_list +#" @export +check_s2mission <- function(s2sat, tile_s2, dateacq_s2) { + + # is mission already defined by user? + if (!is.null(s2sat)) { + if (s2sat == "2A") { + s2mission <- "2A" + }else if (s2sat == "2B") { + s2mission <- "2B" + }else { + message("Could not identify if image from Sentinel-2A or -2B") + message("Defining central wavelength of spectral bands based on S2A") + s2mission <- "2A" + } + }else { + message("Could not identify if image from Sentinel-2A or -2B") + message("Defining central wavelength of spectral bands based on S2A") + s2mission <- "2A" + } + return(s2mission) +} + +#" this function aims at computing directory size +#" @param path character. path for directory +#" @param recursive boolean . set T if recursive +#" +#" @return size_files numeric. size in bytes +#" - image stack +#" - path for individual band files corresponding to the stack +#" - path for vector (reprojected if needed) +#" +#" @importFrom raster raster +#" @importFrom tools file_path_sans_ext file_ext +#" @export +dir_size <- function(path, recursive = TRUE) { + stopifnot(is.character(path)) + files <- list.files(path, full.names = TRUE, recursive = recursive) + vect_size <- sapply(files, function(x) file.size(x)) + size_files <- sum(vect_size) + return(size_files) +} + +#" This function reads S2 data from L2A directories downloaded from +#" various data hubs including THEIA, PEPS & SCIHUB (SAFE format & LaSRC) +#" @param path_dir_s2 character. path for S2 directory +#" @param path_vector character. path for vector file +#" @param s2source character. type of directory format (depends on atmospheric correction: SAFE produced from Sen2Cor) +#" @param resolution numeric. buffer applied to vector file (in meters) +#" @param interpolation character. method for resampling. default = "bilinear" +#" @param fre_sre character. SRE or FRE products from THEIA +#" +#" @return listout list. +#" - image stack +#" - path for individual band files corresponding to the stack +#" - path for vector (reprojected if needed) +#" +#" @importFrom raster raster +#" @importFrom tools file_path_sans_ext file_ext +#" @export +extract_from_s2_l2a <- function(path_dir_s2, path_vector = NULL, s2source = "SAFE", + resolution = 10, interpolation = "bilinear", fre_sre = "FRE") { + # Get list of paths corresponding to S2 bands and depending on S2 directory + s2_bands <- get_s2_bands(path_dir_s2 = path_dir_s2, + s2source = s2source, + resolution = resolution, + fre_sre = fre_sre) + + if (length(s2_bands$s2bands_10m) > 0) { + rastmp <- raster::raster(s2_bands$s2bands_10m[[1]]) + } else if (length(s2_bands$s2bands_20m) > 0) { + rastmp <- raster::raster(s2_bands$s2bands_20m[[1]]) + } + # check if vector and raster share the same projection. if not, re-project vector + if (!is.null(path_vector)) { + raster_proj <- raster::projection(rastmp) + path_vector_reproj <- paste(tools::file_path_sans_ext(path_vector), "_reprojected.shp", sep = "") + path_vector <- reproject_shp(path_vector_init = path_vector, + newprojection = raster_proj, + path_vector_reproj = path_vector_reproj) + } + # Extract data corresponding to the vector footprint (if provided) & resample data if needed + if (length(s2_bands$s2bands_10m) > 0) { + stack_10m <- read_s2bands(s2_bands = s2_bands$s2bands_10m, path_vector = path_vector, + resampling = 1, interpolation = interpolation) + } + if (length(s2_bands$s2bands_20m) > 0) { + if (resolution == 10 && s2source != "LaSRC") { + resampling <- 2 + }else { + resampling <- 1 + } + stack_20m <- read_s2bands(s2_bands = s2_bands$s2bands_20m, path_vector = path_vector, + resampling = resampling, interpolation = interpolation) + } + # get full stack including 10m and 20m spatial resolution + if (length(s2_bands$s2bands_10m) > 0 && length(s2_bands$s2bands_20m) > 0) { + diffxstart <- attributes(stack_10m)$dimensions[[1]]$from - attributes(stack_20m)$dimensions[[1]]$from + diffxstop <- attributes(stack_10m)$dimensions[[1]]$to - attributes(stack_20m)$dimensions[[1]]$to + diffystart <- attributes(stack_10m)$dimensions[[2]]$from - attributes(stack_20m)$dimensions[[2]]$from + diffystop <- attributes(stack_10m)$dimensions[[2]]$to - attributes(stack_20m)$dimensions[[2]]$to + if (!diffxstop == 0) { + # size of 20m > size of 10m --> reduce 20m + # size of 10m > size of 20m --> reduce 10m + if (diffxstop > 0) { + stack_10m <- stack_10m[, 1:(dim(stack_10m)[1] - diffxstop), , ] + }else if (diffxstop < 0) { + stack_20m <- stack_20m[, 1:(dim(stack_20m)[1] + diffxstop), , ] + } + } + if (!diffystop == 0) { + if (diffystop > 0) { + stack_10m <- stack_10m[, , 1:(dim(stack_10m)[2] - diffystop), ] + }else if (diffystop < 0) { + stack_20m <- stack_20m[, , 1:(dim(stack_20m)[2] + diffystop), ] + } + } + if (!diffxstart == 0) { + if (diffxstart > 0) { + stack_20m <- stack_20m[, (1 + diffxstart):dim(stack_20m)[1], , ] + }else if (diffxstart < 0) { + stack_10m <- stack_10m[, (1 - diffxstart):dim(stack_10m)[1], , ] + } + } + if (!diffystart == 0) { + if (diffystart > 0) { + stack_20m <- stack_20m[, , (1 + diffystart):dim(stack_20m)[2], ] + }else if (diffystart < 0) { + stack_10m <- stack_10m[, , (1 - diffystart):dim(stack_10m)[2], ] + } + } + # reorder bands with increasing wavelength + s2bands <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12", "Cloud") + namebands <- c(names(s2_bands$s2bands_10m), names(s2_bands$s2bands_20m)) + reorder_bands <- match(s2bands, namebands) + namebands <- namebands[reorder_bands] + listfiles <- c(stack_10m$attr, stack_20m$attr)[reorder_bands] + + # adjust size to initial vector footprint without buffer + # --> buffer is needed in order to ensure that extraction following + # footprint of vector matches for images of different spatial resolution + # get bounding box corresponding to footprint of image or image subset + bb_xycoords <- get_bb(path_raster = listfiles[1], + path_vector = path_vector, buffer = 0) + + # prepare reading data for extent defined by bounding box + nxoff <- bb_xycoords$UL$col + nyoff <- bb_xycoords$UL$row + nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1 + nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1 + nbufxsize <- nxsize + nbufysize <- nysize + s2_stack <- stars::read_stars(listfiles, along = "band", + RasterIO = list(nXOff = nxoff, nYOff = nyoff, + nXSize = nxsize, nYSize = nysize, + nBufXSize = nbufxsize, nBufYSize = nbufysize, + resample = "nearest_neighbour"), proxy = TRUE) + + + names(s2_stack$attr) <- namebands + }else if (length(s2_bands$s2bands_10m) > 0) { + s2_stack <- stack_10m + namebands <- names(s2_bands$s2bands_10m) + names(s2_stack$attr) <- namebands + }else if (length(s2_bands$s2bands_20m) > 0) { + s2_stack <- stack_20m + namebands <- names(s2_bands$s2bands_20m) + names(s2_stack$attr) <- namebands + } + + listout <- list("s2_stack" = s2_stack, "s2_bands" = s2_bands, "path_vector" = path_vector, + "namebands" = namebands) + return(listout) +} + +#" This function gets coordinates of a bounding box defined by a vector (optional) and a raster +#" +#" @param path_raster character. path for raster file +#" @param path_vector character. path for vector file +#" @param buffer numeric. buffer applied to vector file (in meters) +#" +#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box +#" @export +get_bb <- function(path_raster, path_vector = NULL, buffer = 0) { + + if (!is.null(path_vector)) { + # get bounding box with a 50m buffer in order to allow for interpolation + bb_xycoords <- get_bb_from_vector(path_raster = path_raster, + path_vector = path_vector, + buffer = buffer) + }else if (is.null(path_vector)) { + bb_xycoords <- get_bb_from_fullimage(path_raster) + } + return(bb_xycoords) +} + +#" This function gets extreme coordinates of a bounding box corresponding to a full image +#" +#" @param path_raster character. path for raster file +#" +#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box +#" @importFrom raster raster +#" @export +get_bb_from_fullimage <- function(path_raster) { + # get raster coordinates corresponding to Full image + rasterobj <- raster::raster(path_raster) + bb_xycoords <- list() + bb_xycoords[["UL"]] <- data.frame("row" = 1, "col" = 1) + bb_xycoords[["UR"]] <- data.frame("row" = 1, "col" = dim(rasterobj)[2]) + bb_xycoords[["LL"]] <- data.frame("row" = dim(rasterobj)[1], "col" = 1) + bb_xycoords[["LR"]] <- data.frame("row" = dim(rasterobj)[1], "col" = dim(rasterobj)[2]) + return(bb_xycoords) +} + +#" This gets bounding box corresponding to a vector from a raster (UL, UR, LL, LR corners) +#" +#" @param path_raster character. path for raster file +#" @param path_vector character. path for vector file +#" @param buffer numeric. buffer applied to vector file (in meters) +#" +#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box +#" @importFrom sf st_read st_bbox st_crop +#" @importFrom rgeos gbuffer bbox2SP +#" @importFrom sp SpatialPoints bbox +#" @importFrom raster projection extract extent raster +#" @importFrom methods as +#" @export +get_bb_from_vector <- function(path_raster, path_vector, buffer = 0) { + + data_raster <- raster::raster(path_raster) + # extract BB coordinates from vector + bb_vector <- rgeos::gbuffer(spgeom = as(sf::st_read(dsn = path_vector, quiet = TRUE), "Spatial"), + width = buffer, byid = TRUE) + # extract BB coordinates from raster + bb_raster <- rgeos::bbox2SP(bbox = bbox(data_raster)) + # compute intersection + intersect <- rgeos::gIntersection(bb_vector, bb_raster) + bbext <- raster::extent(intersect) + xmin <- bbext[1] + xmax <- bbext[2] + ymin <- bbext[3] + ymax <- bbext[4] + # get coordinates of bounding box corresponding to vector + corners <- list() + corners[["UR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymax)) + corners[["LR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymin)) + corners[["UL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymax)) + corners[["LL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymin)) + raster::projection(corners[["UL"]]) <- raster::projection(corners[["UR"]]) <- + raster::projection(corners[["LL"]]) <- raster::projection(corners[["LR"]]) <- + raster::projection(sf::st_read(dsn = path_vector, quiet = TRUE)) + # get coordinates for corners of bounding box + bb_xycoords <- list() + for (corner in names(corners)) { + ex_df <- as.data.frame(raster::extract(data_raster, corners[[corner]], cellnumbers = TRUE)) + colrow <- ind2sub(data_raster, ex_df$cell) + bb_xycoords[[corner]] <- data.frame("row" = colrow$row, "col" = colrow$col) + } + return(bb_xycoords) +} + +#" get hdr name from image file name, assuming it is BIL format +#" +#" @param impath path of the image +#" +#" @return corresponding hdr +#" @import tools +#" @export +get_hdr_name <- function(impath) { + if (tools::file_ext(impath) == "") { + impathhdr <- paste(impath, ".hdr", sep = "") + }else if (tools::file_ext(impath) == "bil") { + impathhdr <- gsub(".bil", ".hdr", impath) + }else if (tools::file_ext(impath) == "zip") { + impathhdr <- gsub(".zip", ".hdr", impath) + }else { + impathhdr <- paste(tools::file_path_sans_ext(impath), ".hdr", sep = "") + } + + if (!file.exists(impathhdr)) { + message("WARNING : COULD NOT FIND hdr FILE") + print(impathhdr) + message("Process may stop") + } + return(impathhdr) +} + +#" This function returns path for the spectral bands to be used +#" +#" @param path_dir_s2 character. Path for the directory containing S2 data. either L2A .SAFE S2 file or THEIA directory +#" @param s2source character. defines if data comes from SciHub as SAFE directory, from THEIA or from LaSRC +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" @param fre_sre character. SRE or FRE products from THEIA +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution +#" @export +get_s2_bands <- function(path_dir_s2, s2source = "SAFE", resolution = 10, fre_sre = "FRE") { + + if (s2source == "SAFE" || s2source == "Sen2Cor") { + listbands <- get_s2_bands_from_sen2cor(path_dir_s2 = path_dir_s2, resolution = resolution) + }else if (s2source == "THEIA") { + listbands <- get_s2_bands_from_theia(path_dir_s2 = path_dir_s2, resolution = resolution, + fre_sre = fre_sre) + }else if (s2source == "LaSRC") { + listbands <- get_s2_bands_from_lasrc(path_dir_s2 = path_dir_s2, resolution = resolution) + }else { + message("The data source (Atmospheric correction) for Sentinel-2 image is unknown") + message("Please provide S2 images from one of the following data sources:") + message("- LaSRC (atmospheric correction: LaSRC)") + message("- THEIA (atmospheric correction: MAJA)") + message("- SAFE (atmospheric correction: Sen2Cor)") + s2bands_10m <- s2bands_20m <- granule <- mtdfile <- metadata_msi <- metadata_lasrc <- NULL + listbands <- list("s2bands_10m" = s2bands_10m, "s2bands_20m" = s2bands_20m, "GRANULE" = granule, + "metadata" = mtdfile, "metadata_MSI" = metadata_msi, + "metadata_lasrc" = metadata_lasrc) + } + return(listbands) +} + +#" This function returns path for the spectral bands in SAFE / sen2Cor directory +#" +#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule +#" @export +get_s2_bands_from_sen2cor <- function(path_dir_s2, resolution = 10) { + # build path for all bands + if (resolution == 10) { + b10m <- c("B02", "B03", "B04", "B08") + b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12") + }else { + b10m <- c() + b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12") + } + # get granule directory & path for corresponding metadata XML file + granule <- list.dirs(list.dirs(path_dir_s2, recursive = FALSE)[grep(pattern = "GRANULE", + x = list.dirs(path_dir_s2, recursive = FALSE))], recursive = FALSE) + mtdfile <- file.path(granule, "MTD_TL.xml") + if (file.exists(file.path(path_dir_s2, "MTD_MSIL2A.xml"))) { + mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL2A.xml") + } else { + mtd_msi_file <- NULL + } + + # Define path for bands + s2bands_20m_dir <- file.path(granule, "IMG_DATA", "R20m") + s2bands_10m_dir <- file.path(granule, "IMG_DATA", "R10m") + s2bands_10m <- s2bands_20m <- list() + for (band in b20m) { + s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list.files(s2bands_20m_dir, pattern = band)) + } + for (band in b10m) { + s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list.files(s2bands_10m_dir, pattern = band)) + } + # get cloud mask + cloud <- "MSK_CLDPRB_20m" + cloud_20m_dir <- file.path(granule, "QI_DATA") + s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud)) + listbands <- list("s2bands_10m" = s2bands_10m, + "s2bands_20m" = s2bands_20m, + "GRANULE" = granule, + "metadata" = mtdfile, + "metadata_MSI" = mtd_msi_file, + "metadata_lasrc" = NULL) + return(listbands) +} + +#" This function returns path for the spectral bands in LaSRC directory +#" +#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule +#" @importFrom stringr str_subset +#" @export +get_s2_bands_from_lasrc <- function(path_dir_s2, resolution = 10) { + + # get granule directory & path for corresponding metadata XML file + granule <- path_dir_s2 + mtdfile <- file.path(granule, "MTD_TL.xml") + if (file.exists(file.path(path_dir_s2, "MTD_MSIL1C.xml"))) { + mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL1C.xml") + } else { + mtd_msi_file <- NULL + } + + # build path for all bands + b10m <- c("band2", "band3", "band4", "band5", "band6", "band7", "band8", "band8a", "band11", "band12") + b10m_standard <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12") + # Define path for bands + s2bands_10m <- s2bands_20m <- list() + for (i in 1:seq_along(b10m)) { + s2bands_10m[[b10m_standard[i]]] <- file.path(path_dir_s2, + list.files(path_dir_s2, + pattern = paste(b10m[i], ".tif", sep = ""))) + } + + # get metadata file containing offset + mtd_lasrc <- str_subset(list.files(path_dir_s2, pattern = "S2"), ".xml$") + if (file.exists(file.path(path_dir_s2, mtd_lasrc))) { + metadata_lasrc <- file.path(path_dir_s2, mtd_lasrc) + } else { + metadata_lasrc <- NULL + } + # get cloud mask + cloud <- "CLM" + s2bands_10m[["Cloud"]] <- file.path(path_dir_s2, list.files(path_dir_s2, pattern = cloud)) + listbands <- list("s2bands_10m" = s2bands_10m, + "s2bands_20m" = s2bands_20m, + "GRANULE" = granule, + "metadata" = mtdfile, + "metadata_MSI" = mtd_msi_file, + "metadata_lasrc" = metadata_lasrc) + return(listbands) +} + +#" This function returns path for the spectral bands in THEIA directory +#" +#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" @param fre_sre character. SRE or FRE products from THEIA +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule +#" @export +get_s2_bands_from_theia <- function(path_dir_s2, resolution = 10, fre_sre = "FRE") { + + # build path for all bands + if (resolution == 10) { + b10m <- c("B02", "B03", "B04", "B08") + b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12") + } else { + b10m <- c() + b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12") + } + + # get path_tile_s2 directory & path for corresponding metadata XML file + path_tile_s2 <- list.dirs(path_dir_s2, recursive = FALSE) + files_tile_s2 <- list.files(path_tile_s2, recursive = FALSE) + mtdfile <- file.path(path_tile_s2, files_tile_s2[grep(pattern = "MTD_ALL.xml", x = files_tile_s2)]) + + # Define path for bands + s2bands_10m_dir <- s2bands_20m_dir <- path_tile_s2 + s2bands_10m <- s2bands_20m <- list() + for (band in b20m) { + band_20m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE) + list_files_20m <- list.files(s2bands_20m_dir, pattern = band_20m_pattern) + s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list_files_20m)[grep(pattern = fre_sre, + x = file.path(s2bands_20m_dir, list_files_20m))] + } + for (band in b10m) { + band_10m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE) + list_files_10m <- list.files(s2bands_10m_dir, pattern = band_10m_pattern) + s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list_files_10m)[grep(pattern = fre_sre, + x = file.path(s2bands_10m_dir, list_files_10m))] + } + + # get cloud mask 10m + cloud_10m <- "CLM_R1" + cloud_10m_dir <- file.path(path_tile_s2, "MASKS") + s2bands_10m[["Cloud"]] <- file.path(cloud_10m_dir, list.files(cloud_10m_dir, pattern = cloud_10m)) + + # get cloud mask 20m + cloud_20m <- "CLM_R2" + cloud_20m_dir <- file.path(path_tile_s2, "MASKS") + s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud_20m)) + + # return list bands + listbands <- list("s2bands_10m" = s2bands_10m, + "s2bands_20m" = s2bands_20m, + "path_tile_s2" = path_tile_s2, + "metadata" = mtdfile) + return(listbands) +} + +#" This function check S2 data level: +#" - L2A: already atmospherically corrected +#" - L1C: requires atmospheric corrections with sen2cor +#" +#" @param prodname character. original name for the S2 image +#" +#" @return s2level character. S2 level: L1C or L2A +#" @export +get_s2_level <- function(prodname) { + prodname <- basename(prodname) + if (length(grep(pattern = "L1C_", x = prodname)) == 1) { + s2level <- "L1C" + } else if (length(grep(pattern = "L2A_", x = prodname)) == 1) { + s2level <- "L2A" + } + return(s2level) +} + +#" This function gets tile from S2 image +#" +#" @param prodname character. original name for the S2 image +#" +#" @return tilename character +#" @importFrom tools file_path_sans_ext +#" @export +get_tile <- function(prodname) { + prodname <- basename(prodname) + tilename <- tools::file_path_sans_ext(gsub("_.*", "", gsub(".*_T", "", prodname))) + return(tilename) +} + +#" This function gets acquisition date from S2 image +#" +#" @param prodname character. original name for the S2 image +#" +#" @return dateacq character +#" @export +get_date <- function(prodname) { + prodname <- basename(prodname) + dateacq <- as.Date(gsub("T.*", "", gsub(".*_20", "20", prodname)), format = "%Y%m%d") + return(dateacq) +} + +#" download S2 L1C data from Copernicus hub or Google cloud +#" +#" @param list_safe safe object. produced with sen2r::s2_list +#" @param l1c_path character. path for storage of L1C image +#" @param path_vector path for a vector file +#" @param time_interval dates. time interval for S2 query +#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and +#" @param forcegoogle boolean. set to TRUE if only google requested +#" sen2r configured as an alternative hub for S2 download +#" +#" @return prodname character. S2 Product name +#" @importFrom sen2r safe_is_online s2_list s2_download s2_order check_gcloud +#" @export +get_s2_l1c_image <- function(list_safe, l1c_path, path_vector, time_interval, + googlecloud = FALSE, forcegoogle = FALSE) { + # Check if available from Copernicus hub first + copernicus_avail <- sen2r::safe_is_online(list_safe) + # if available: download + prodname <- attr(list_safe, which = "name") + if (file.exists(file.path(l1c_path, prodname))) { + message("L1C file already downloaded") + message(file.path(l1c_path, prodname)) + } else { + if (copernicus_avail == TRUE && forcegoogle == FALSE) { + sen2r::s2_download(list_safe, outdir = l1c_path) + } else if (copernicus_avail == FALSE || forcegoogle == TRUE) { + # if not available and googlecloud==TRUE + if (googlecloud == TRUE) { + # check if google cloud SDK available from this computer + ggc <- sen2r::check_gcloud() + if (ggc == TRUE) { + message("downloading from Google cloud") + list_safe_ggc <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = path_vector), + time_interval = time_interval, + server = "gcloud") + prodname <- attr(list_safe_ggc, which = "name") + if (file.exists(file.path(l1c_path, prodname))) { + message("L1C file already downloaded") + message(file.path(l1c_path, prodname)) + } else { + sen2r::s2_download(list_safe_ggc, outdir = l1c_path) + # check if QI_DATA exists in DATASTRIP, and create it if not the case + datastrip_path <- file.path(l1c_path, prodname, "DATASTRIP") + dsdir <- list.dirs(datastrip_path, recursive = FALSE) + if (length(match(list.dirs(dsdir, recursive = FALSE, full.names = FALSE), "QI_DATA")) == 0) { + dir.create(file.path(dsdir, "QI_DATA")) + } + } + } else if (ggc == FALSE) { + message("googlecloud set to TRUE but missing") + message("Please install Google cloud SDK") + message("https://cloud.google.com/sdk/docs/install") + message("and/or set configuration of sen2r following instructions") + message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/") + } + } + } + if (copernicus_avail == FALSE && googlecloud == FALSE) { + message("S2 image in Long Term Archive (LTA)") + message("Ordering image from LTA") + message("This may take 1 day, please run your script later") + orders2 <- sen2r::s2_order(list_safe) + message("An alternative is possible with Google cloud SDK") + message("https://cloud.google.com/sdk/docs/install") + message("and/or set configuration of sen2r following instructions") + message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/") + } + } + return(prodname) +} + +#" download S2 L2A data from Copernicus hub or convert L1C to L2A +#" +#" @param l2a_path character. path for storage of L2A image +#" @param spatial_extent path for a vector file +#" @param dateacq character. date of acquisition +#" @param deletel1c Boolean. set TRUE to delete L1C images +#" @param Sen2Cor Boolean. set TRUE to automatically perform atmospheric corrections using sen2Cor +#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and +#" sen2r configured as an alternative hub for S2 download +#" +#" @return pathl2a character. Path for L2A image +#" @importFrom sen2r s2_list s2_download +#" @importFrom R.utils getAbsolutePath + +#" @export +get_s2_l2a_image <- function(l2a_path, spatial_extent, dateacq, + deletel1c = FALSE, sen2cor = TRUE, + googlecloud = FALSE) { + + # Needs to be updated: define path for L1c data + l1c_path <- l2a_path + # define time interval + time_interval <- as.Date(c(dateacq, dateacq)) + # get list S2 products corresponding to study area and date of interest using sen2r package + if (googlecloud == TRUE) { + server <- c("scihub", "gcloud") + } else if (googlecloud == FALSE) { + server <- "scihub" + } + list_safe <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = spatial_extent), + time_interval = time_interval, + server = server, availability = "check") + # download products + sen2r::s2_download(list_safe, outdir = l2a_path) + # name all products + prodname <- attr(list_safe, which = "name") + prodfullpath <- file.path(l2a_path, prodname) + if (sen2cor == TRUE) { + for (imgname in prodname) { + s2level <- get_s2_level(imgname) + if (s2level == "L1C") { + datepattern <- gsub(pattern = "-", replacement = "", x = dateacq) + pathl2a <- s2_from_l1c_to_l2a(prodname = imgname, l1c_path = l2a_path, l2a_path = l2a_path, + datepattern = datepattern, tmp_path = NULL) + if (deletel1c == TRUE) { + unlink(x = R.utils::getAbsolutePath(file.path(l1c_path, prodname)), + recursive = TRUE, force = TRUE) + # delete from full path and add atmospherically corrected + whichimg <- grep(x = prodfullpath, pattern = imgname) + dateacq <- get_date(imgname) + tilename <- get_tile(imgname) + pathl2a <- list.files(path = l2a_path, pattern = tilename, full.names = TRUE) + pathl2a <- pathl2a[grep(x = pathl2a, pattern = dateacq)] + pathl2a <- pathl2a[grep(x = basename(pathl2a), pattern = "L2A")] + prodfullpath[whichimg] <- pathl2a + } + } + } + } + + return(prodfullpath) +} + +#" convert image coordinates from index to X-Y +#" +#" @param Raster image raster object +#" @param image_index coordinates corresponding to the raster +ind2sub <- function(data_raster, image_index) { + c <- ((image_index - 1) %% data_raster@ncols) + 1 + r <- floor((image_index - 1) / data_raster@ncols) + 1 + my_list <- list("col" = c, "row" = r) + return(my_list) +} + +#" mosaicing a set of rasters +#" +#" @param list_rasters character. list of paths corresponding to rasters to mosaic +#" @param dst_mosaic character. path and name of mosaic produced +#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only +#" +#" @return None +#" @importFrom gdalUtils mosaic_rasters +#" @importFrom raster hdr raster +#" @export +mosaic_rasters <- function(list_rasters, dst_mosaic, stretch = FALSE) { + + # produce mosaic + gdalUtils::mosaic_rasters(gdalfile = list_rasters, dst_dataset = dst_mosaic, + separate = FALSE, of = "Ehdr", verbose = TRUE) + + # convert hdr to ENVI format + raster::hdr(raster(dst_mosaic), format = "ENVI") + # add info to hdr based on initial rasters + hdr_init <- read_envi_header(get_hdr_name(list_rasters[1])) + hdr <- read_envi_header(get_hdr_name(dst_mosaic)) + hdr$`band names` <- hdr_init$`band names` + hdr$wavelength <- hdr_init$wavelength + if (stretch == TRUE) { + hdr$`default stretch` <- "0.000000 1000.000000 linear" + } + hdr$`z plot range` <- NULL + hdr$`data ignore value` <- "-Inf" + hdr$`sensor type` <- hdr_init$`sensor type` + hdr$`coordinate system string` <- read.table(paste(file_path_sans_ext(dst_mosaic), ".prj", sep = "")) + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dst_mosaic)) + return(invisible()) +} + +#" Reads ENVI hdr file +#" +#" @param hdrpath Path of the hdr file +#" +#" @return list of the content of the hdr file +#" @export +read_envi_header <- function(hdrpath) { + if (!grepl(".hdr$", hdrpath)) { + stop("File extension should be .hdr") + } + hdr <- readLines(hdrpath) + ## check ENVI at beginning of file + if (!grepl("ENVI", hdr[1])) { + stop("Not an ENVI header (ENVI keyword missing)") + } else { + hdr <- hdr [-1] + } + ## remove curly braces and put multi-line key-value-pairs into one line + hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr) + l <- grep("\\{", hdr) + r <- grep("\\}", hdr) + + if (length(l) != length(r)) { + stop("Error matching curly braces in header (differing numbers).") + } + + if (any(r <= l)) { + stop("Mismatch of curly braces in header.") + } + + hdr[l] <- sub("\\{", "", hdr[l]) + hdr[r] <- sub("\\}", "", hdr[r]) + + for (i in rev(seq_along(l))) { + hdr <- c( + hdr [seq_len(l [i] - 1)], + paste(hdr [l [i]:r [i]], collapse = "\n"), + hdr [-seq_len(r [i])] + ) + } + + ## split key = value constructs into list with keys as names + hdr <- sapply(hdr, split_line, "=", USE.NAMES = FALSE) + names(hdr) <- tolower(names(hdr)) + + ## process numeric values + tmp <- names(hdr) %in% c( + "samples", "lines", "bands", "header offset", "data type", + "byte order", "default bands", "data ignore value", + "wavelength", "fwhm", "data gain values" + ) + hdr [tmp] <- lapply(hdr [tmp], function(x) { + as.numeric(unlist(strsplit(x, ", "))) + }) + + return(hdr) +} + +#" This function reads a list of files corresponding to S2 bands +#" S2 bands are expected to have uniform spatial resolution and footprint +#" @param s2_bands list. list of S2 bands obtained from get_s2_bands +#" @param path_vector path for a vector file +#" @param resampling numeric. resampling factor (default = 1, set to resampling = 2 to convert 20m into 10m resolution) +#" @param interpolation character. method for resampling. default = "bilinear" +#" +#" @return stack_s2 list. contains stack of S2 bands +#" +#" @importFrom stars read_stars +#" @importFrom sf st_bbox st_read st_crop +#" @export + +read_s2bands <- function(s2_bands, path_vector = NULL, + resampling = 1, interpolation = "bilinear") { + # get bounding box corresponding to footprint of image or image subset + bb_xycoords <- get_bb(path_raster = s2_bands[[1]], + path_vector = path_vector, buffer = 50) + + # prepare reading data for extent defined by bounding box + nxoff <- bb_xycoords$UL$col + nyoff <- bb_xycoords$UL$row + nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1 + nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1 + nbufxsize <- resampling * nxsize + nbufysize <- resampling * nysize + if (resampling == 1) { + interpolation <- "nearest_neighbour" + } + # write interpolated individual bands in temp directory + tmpdir <- tempdir() + tmpfile <- list() + for (band in names(s2_bands)) { + stack_s2_tmp <- stars::read_stars(s2_bands[[band]], along = "band", + RasterIO = list(nXOff = nxoff, nYOff = nyoff, + nXSize = nxsize, nYSize = nysize, + nBufXSize = nbufxsize, nBufYSize = nbufysize, + resample = interpolation), proxy = FALSE) + if (!is.null(path_vector)) { + stack_s2_tmp <- sf::st_crop(x = stack_s2_tmp, y = st_bbox(st_read(dsn = path_vector, quiet = TRUE))) + } + tmpfile[[band]] <- file.path(tmpdir, tools::file_path_sans_ext(basename(s2_bands[[band]]))) + if (band == "Cloud") { + stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]], + driver = "ENVI", type = "Byte", overwrite = TRUE) + } else { + stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]], + driver = "ENVI", type = "Int16", overwrite = TRUE) + } + gc() + } + + stack_s2 <- stars::read_stars(tmpfile, along = "band", proxy = TRUE) + return(stack_s2) +} + +#" This function reads a raster stack, and gets footprint as pixel coordinates or vector file as input +#" @param path_raster character. path for raster file +#" @param path_vector character. path for vector file +#" @param bbpix list. coordinates of pixels corresponding to a bounding box +#" +#" @return starsobj stars object corresponding to raster or raster subset +#" +#" @importFrom stars read_stars +#" @importFrom sf st_bbox st_read st_crop +#" @export +read_raster <- function(path_raster, path_vector = NULL, bbpix = NULL) { + # get bounding box corresponding to footprint of image or image subset + if (is.null(bbpix)) { + bb_xycoords <- get_bb(path_raster = path_raster, + path_vector = path_vector, buffer = 0) + } else { + bb_xycoords <- bbpix + } + # prepare reading data for extent defined by bounding box + nxoff <- bb_xycoords$UL$col + nyoff <- bb_xycoords$UL$row + nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1 + nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1 + nbufxsize <- nxsize + nbufysize <- nysize + starsobj <- stars::read_stars(path_raster, along = "band", + RasterIO = list(nXOff = nxoff, nYOff = nyoff, + nXSize = nxsize, nYSize = nysize, + nBufXSize = nbufxsize, nBufYSize = nbufysize), + proxy = FALSE) + return(starsobj) +} + +#" This function reprojects a shapefile and saves reprojected shapefile +#" +#" @param path_vector_init character. path for a shapefile to be reprojected +#" @param newprojection character. projection to be applied to path_vector_init +#" @param path_vector_reproj character. path for the reprojected shapefile +#" +#" @return path_vector character. path of the shapefile +#" - path_vector_init if the vector did not need reprojection +#" - path_vector_reproj if the vector needed reprojection +#" +#" @importFrom rgdal readOGR writeOGR +#" @importFrom sp spTransform +#" @importFrom raster projection +#" @export +reproject_shp <- function(path_vector_init, newprojection, path_vector_reproj) { + + dir_vector_init <- dirname(path_vector_init) + # shapefile extension + fileext <- file_ext(basename(path_vector_init)) + if (fileext == "shp") { + name_vector_init <- file_path_sans_ext(basename(path_vector_init)) + vector_init_ogr <- rgdal::readOGR(dir_vector_init, name_vector_init, verbose = FALSE) + } else if (fileext == "kml") { + vector_init_ogr <- rgdal::readOGR(path_vector_init, verbose = FALSE) + } + vector_init_proj <- raster::projection(vector_init_ogr) + + if (!vector_init_proj == newprojection) { + dir_vector_reproj <- dirname(path_vector_reproj) + name_vector_reproj <- file_path_sans_ext(basename(path_vector_reproj)) + vector_reproj <- sp::spTransform(vector_init_ogr, newprojection) + rgdal::writeOGR(obj = vector_reproj, dsn = dir_vector_reproj, layer = name_vector_reproj, + driver = "ESRI Shapefile", overwrite_layer = TRUE) + path_vector <- path_vector_reproj + } else { + path_vector <- path_vector_init + } + return(path_vector) +} + + +#" perform atmospheric corrections to convert L1C to L2A data with Sen2cor +#" +#" @param prodname character. produced with sen2r::s2_list +#" @param l1c_path character. path of directory where L1C image is stored +#" @param l2a_path character. path of directory where L2A image is stored +#" @param datepattern character. pattern corresponding to date of acquisition to identify L2A directory +#" @param tmp_path character. path of temporary directory where L2A image is stored +#" sen2r configured as an alternative hub for S2 download +#" +#" @return pathl2a character. S2 Product name +#" @importFrom sen2r safe_is_online s2_list s2_download s2_order +#" @importFrom R.utils getAbsolutePath +#" +#" @export +s2_from_l1c_to_l2a <- function(prodname, l1c_path, l2a_path, datepattern, tmp_path = NULL) { + + # define path for tmp directory + if (is.null(tmp_path)) { + tmp_path <- tempdir(check = TRUE) + } + tmp_prodlist <- prodname + # perform Sen2Cor atmospheric corrections + binpath <- sen2r::load_binpaths() + # 2- open a command prompt and directly run sen2cor with following command line + cmd <- paste(binpath$sen2cor, + "--output_dir", R.utils::getAbsolutePath(l2a_path), + R.utils::getAbsolutePath(file.path(l1c_path, prodname)), sep = " ") + system(cmd) + pathl2a <- list.files(path = l2a_path, pattern = datepattern, full.names = TRUE) + + return(pathl2a) +} + +#" This function saves cloud masks. +#" "cloudMask_Binary" is default binary mask with 0 where clouds are detected and 1 for clean pixels +#" "cloudMask_RAW" is the original cloud layer produced by atmospheric correction algorithm +#" --> may be useful to refine cloud mask +#" +#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a +#" @param cloud_path character. +#" @param s2source character. +#" @param footprint character. path for vector file defining footprint of interest in the image +#" @param saveraw boolean. should the original cloud mask layer be saved? +#" @param maxchunk numeric. Size of individual chunks to be written (in Mb) +#" +#" @return list of cloudmasks (binary mask, and raw mask if required) +#" @importFrom sf st_read +#" @importFrom stars write_stars +#" @importFrom raster raster +#" @export +save_cloud_s2 <- function(s2_stars, cloud_path, s2source = "SAFE", + footprint = NULL, saveraw = FALSE, maxchunk = 256) { + + whichcloud <- which(names(s2_stars$attr) == "Cloud") + # Save cloud mask + if (saveraw == TRUE) { + cloudraw <- file.path(cloud_path, "CloudMask_RAW") + obj <- stars::read_stars(s2_stars$attr[whichcloud], proxy = TRUE) + sizeobj <- dim(obj)[1] * dim(obj)[2] / (1024**2) + nbchunks <- ceiling(sizeobj / maxchunk) + stars::write_stars(obj, + dsn = cloudraw, + driver = "ENVI", + type = "Byte", + chunk_size = c(dim(obj)[1], dim(obj)[2] / nbchunks), + progress = TRUE) + } else { + cloudraw <- NULL + } + # Save cloud mask as in biodivMapR (0 = clouds, 1 = pixel ok) + cloudmask <- stars::read_stars(s2_stars$attr[whichcloud], proxy = FALSE) + if (s2source == "SAFE" || s2source == "THEIA") { + cloudy <- which(cloudmask[[1]] > 0) + sunny <- which(cloudmask[[1]] == 0) + } else if (s2source == "LaSRC") { + cloudy <- which(is.na(cloudmask[[1]])) + sunny <- which(cloudmask[[1]] == 1) + } + + cloudmask[[1]][cloudy] <- 0 + cloudmask[[1]][sunny] <- 1 + cloudbin <- file.path(cloud_path, "CloudMask_Binary") + stars::write_stars(cloudmask, dsn = cloudbin, driver = "ENVI", type = "Byte", overwrite = TRUE) + cloudmasks <- list("BinaryMask" = cloudbin, "RawMask" = cloudraw) + # delete temporary file + file.remove(s2_stars$attr[whichcloud]) + if (file.exists(paste(s2_stars$attr[whichcloud], ".hdr", sep = ""))) file.remove(paste(s2_stars$attr[whichcloud], ".hdr", sep = "")) + gc() + return(cloudmasks) +} + +#" This function saves reflectance files +#" +#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a +#" @param refl_path character. path for reflectance file to be stored +#" @param format character. file format for reflectance data +#" @param datatype character. data type (integer, float, 16bits, 32bits...) +#" @param s2sat character. Sentinel-2 mission ("2A" or "2B") +#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters) +#" @param dateacq_s2 double. date of acquisition +#" @param MTD character. path for metadata file +#" @param MTD_MSI character. path for metadata MSI file +#" @param mtd_lasrc character. path for metadata LaSRC file +#" @param maxchunk numeric. Size of individual chunks to be written (in Mb) +#" +#" @return None +#" @importFrom stars write_stars st_apply +#" @importFrom XML xml +#" @export +save_reflectance_s2 <- function(s2_stars, refl_path, format = "ENVI", datatype = "Int16", + s2sat = NULL, tile_s2 = NULL, dateacq_s2 = NULL, + mtd = NULL, mtd_msi = NULL, mtd_lasrc = NULL, + maxchunk = 256) { + # identify if S2A or S2B, if possible + s2mission <- check_s2mission(s2sat = s2sat, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2) + + # define central wavelength corresponding to each spectral band + if (s2mission == "2A") { + wl_s2 <- list("B02" = 496.6, "B03" = 560.0, "B04" = 664.5, + "B05" = 703.9, "B06" = 740.2, "B07" = 782.5, "B08" = 835.1, + "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4) + } else if (s2mission == "2B") { + wl_s2 <- list("B02" = 492.1, "B03" = 559.0, "B04" = 665.0, + "B05" = 703.8, "B06" = 739.1, "B07" = 779.7, "B08" = 833.0, + "B8A" = 864.0, "B11" = 1610.4, "B12" = 2185.7) + } + if (s2mission == "2A") { + sensor <- "Sentinel_2A" + } else if (s2mission == "2B") { + sensor <- "Sentinel_2B" + } + + # apply offset when necessary + listbands_bis <- c("B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12") + if (!is.null(mtd_msi) && is.null(mtd_lasrc)) { + # read XML file containing info about geometry of acquisition + s2xml <- XML::xmlToList(mtd_msi) + xml_offset <- s2xml$General_Info$Product_Image_Characteristics$BOA_ADD_offset_VALUES_LIST + bands <- lapply(s2xml$General_Info$Product_Image_Characteristics$Spectral_Information_List, "[[", 4) + if (!is.null(xml_offset) && !is.null(bands)) { + bandid <- lapply(bands, "[[", 1) + bandname <- lapply(bands, "[[", 2) + offset <- data.frame("bandname" = unlist(bandname), + "bandid" = unlist(bandid), + "offset" = unlist(lapply(xml_offset, "[[", 1))) + selbands <- match(listbands_bis, offset$bandname) + offset <- offset[selbands, ] + boa_quantval <- as.numeric(s2xml$General_Info$Product_Image_Characteristics$QUANTIFICATION_VALUES_LIST$BOA_QUANTIFICATION_VALUE[1]) + } else { + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = 0) + boa_quantval <- 10000 + } + } else if (!is.null(mtd_lasrc)) { + # read XML file containing info about geometry of acquisition + s2xml <- XML::xmlToList(mtd_lasrc) + attributes_lasrc <- s2xml$bands[[14]]$.attrs + attributes_lasrc_df <- data.frame(attributes_lasrc) + if (match("add_offset", rownames(attributes_lasrc_df)) > 0 && match("scale_factor", rownames(attributes_lasrc_df)) > 0) { + xml_offset <- as.numeric(attributes_lasrc[["add_offset"]]) + boa_quantval <- 1 / as.numeric(attributes_lasrc[["scale_factor"]]) + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = xml_offset) + } else { + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = 0) + boa_quantval <- 10000 + } + } else { + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = 0) + boa_quantval <- 10000 + } + + # identify where spectral bands are in the stars object + stars_spectral <- list() + starsnames <- names(s2_stars$attr) + stars_spectral$bandname <- starsnames[which(!starsnames == "Cloud")] + stars_spectral$wavelength <- wl_s2[stars_spectral$bandname] + + sortedwl <- names(wl_s2) + reorder <- match(sortedwl, stars_spectral$bandname) + elim <- which(is.na(reorder)) + if (length(elim) > 0) { + reorder <- reorder[-elim] + } + pathr <- s2_stars$attr[reorder] + + names(pathr) <- NULL + s2_stars2 <- stars::read_stars(pathr, along = "band", proxy = TRUE) + stars_spectral$bandname <- stars_spectral$bandname[reorder] + stars_spectral$wavelength <- stars_spectral$wavelength[reorder] + + uniqueoffset <- as.numeric(unique(offset$offset)) + if (length(uniqueoffset) > 1) { + message("Warning: BOA offset differs between bands.") + message("offset will not be applied to the final S2 reflectance raster") + message("check metadata file to identify the offset applied on each band") + print(mtd_msi) + } else { + message("applying offset to reflectance data") + if (is.null(mtd_lasrc) || uniqueoffset == 0) { + offsets2 <- function(x) (round(x + uniqueoffset) * (10000 / boa_quantval)) + s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2) + } else { + offsets2 <- function(x) (round(10000 * ((x + uniqueoffset * boa_quantval) / boa_quantval))) + s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2) + } + } + write_stack_s2(stars_s2 = s2_stars2, stars_spectral = stars_spectral, refl_path = refl_path, + format = format, datatype = datatype, sensor = sensor, maxchunk = maxchunk) + # save metadata file as well if available + if (!is.null(mtd)) { + if (file.exists(mtd)) { + file.copy(from = mtd, to = file.path(dirname(refl_path), basename(mtd)), overwrite = TRUE) + } + } + # save metadata file as well if available + if (!is.null(mtd_msi)) { + if (file.exists(mtd_msi)) { + file.copy(from = mtd_msi, to = file.path(dirname(refl_path), basename(mtd_msi)), overwrite = TRUE) + } + } + # save LaSRC metadata file as well if available + if (!is.null(mtd_lasrc)) { + if (file.exists(mtd_lasrc)) { + file.copy(from = mtd_lasrc, to = file.path(dirname(refl_path), basename(mtd_lasrc)), overwrite = TRUE) + } + } + # delete temporary file + for (pathtemp in pathr) { + file.remove(pathtemp) + if (file.exists(paste(pathtemp, ".hdr", sep = ""))) file.remove(paste(pathtemp, ".hdr", sep = "")) + } + gc() + return(invisible()) +} + +#" ENVI functions +#" +#" based on https://github.com/cran/hyperSpec/blob/master/R/read.ENVI.R +#" added wavelength, fwhm, ... to header reading +#" Title +#" +#" @param x character. +#" @param separator character +#" @param trim_blank boolean. +#" +#" @return list. +#" @export +split_line <- function(x, separator, trim_blank = TRUE) { + tmp <- regexpr(separator, x) + key <- substr(x, 1, tmp - 1) + value <- substr(x, tmp + 1, nchar(x)) + if (trim_blank) { + blank_pattern <- "^[[:blank:]]*([^[:blank:]]+.*[^[:blank:]]+)[[:blank:]]*$" + key <- sub(blank_pattern, "\\1", key) + value <- sub(blank_pattern, "\\1", value) + } + value <- as.list(value) + names(value) <- key + return(value) +} + +#" save raster footprint as vector file +#" +#" @param path_raster character. path for a raster file +#" @param path_vector character. path for a vector file +#" @param driver character. driver for vector +#" +#" @return None +#" @importFrom raster raster extent projection +#" @importFrom sf st_as_sf st_write +#" @export +vectorize_raster_extent <- function(path_raster, path_vector, driver = "ESRI Shapefile") { + rast <- raster(path_raster) + e <- extent(rast) + # coerce to a SpatialPolygons object + p <- as(e, "SpatialPolygons") + projection(p) <- projection(rast) + p <- sf::st_as_sf(p) + sf::st_write(obj = p, path_vector, driver = driver) # create to a shapefile + return(invisible()) +} + +#" writes ENVI hdr file +#" +#" @param hdr content to be written +#" @param hdrpath Path of the hdr file +#" +#" @return None +#" @importFrom stringr str_count +#" @export +write_envi_header <- function(hdr, hdrpath) { + h <- lapply(hdr, function(x) { + if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w+") > 1)) { + x <- paste0("{", paste(x, collapse = ", "), "}") + } + # convert last numerics + x <- as.character(x) + }) + writeLines(c("ENVI", paste(names(hdr), h, sep = " = ")), con = hdrpath) + return(invisible()) +} + +#" This function writes a raster Stack object into a ENVI raster file +#" +#" @param stackobj list. raster stack +#" @param stackpath character. path where to store the stack +#" @param bands list. should include "bandname", and if possible "wavelength" +#" @param datatype character. should be INT2S or FLT4S for example +#" @param sensor character. Name of the sensor used to acquire the image +#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only +#" +#" @return None +#" @importFrom utils read.table +#" @export +write_rasterstack_envi <- function(stackobj, stackpath, bands, datatype = "INT2S", + sensor = "Unknown", stretch = FALSE) { + + r <- raster::writeRaster(x = stackobj, filename = stackpath, format = "Ehdr", overwrite = TRUE, datatype = datatype) + raster::hdr(r, format = "ENVI") + # Edit hdr file to add metadata + hdr <- read_envi_header(get_hdr_name(stackpath)) + hdr$`band names` <- bands$bandname + if (length(bands$wavelength) == length(bands$bandname)) { + hdr$wavelength <- bands$wavelength + } else { + hdr$wavelength <- NULL + } + if (stretch == TRUE) { + hdr$`default stretch` <- "0.000000 1000.000000 linear" + } + hdr$`z plot range` <- NULL + hdr$`data ignore value` <- "-Inf" + hdr$`coordinate system string` <- read.table(paste(stackpath, ".prj", sep = "")) + proj <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][1], split = "=")[[1]][2] + zone <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][2], split = "=")[[1]][2] + datum <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][3], split = "=")[[1]][2] + oldproj <- hdr$`map info` + newproj <- gsub(pattern = "projection", replacement = proj, x = oldproj) + newproj <- paste(newproj, zone, datum, sep = ", ") + hdr$`map info` <- newproj + hdr$`sensor type` <- sensor + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(stackpath)) + + # remove unnecessary files + file2remove <- paste(stackpath, ".aux.xml", sep = "") + file.remove(file2remove) + file2remove <- paste(stackpath, ".prj", sep = "") + file.remove(file2remove) + file2remove <- paste(stackpath, ".stx", sep = "") + file.remove(file2remove) + return(invisible()) +} + + +#" This function writes a stars object into a raster file +#" +#" @param stars_s2 list. stars object containing raster data. Can be produced with function Crop_n_resample_S2 +#" @param stars_spectral list. band name to be saved in the stack and spectral bands corresponding to the image +#" @param refl_path character. path where to store the image +#" @param format character. default = ENVI BSQ. otherwise use gdal drivers +#" @param datatype character. should be Int16 or Float64 for example +#" @param sensor character. Name of the sensor used to acquire the image +#" @param maxchunk numeric. Size of individual chunks to be written (in Mb) +#" +#" @return None +#" @export +write_stack_s2 <- function(stars_s2, stars_spectral, refl_path, format = "ENVI", + datatype = "Int16", sensor = "Unknown", maxchunk = 256) { + + # write raster file from proxy using chunks + sizeobj <- 2 * dim(stars_s2)[1] * dim(stars_s2)[2] * dim(stars_s2)[3] / (1024**2) + nbchunks <- ceiling(sizeobj / maxchunk) + stars::write_stars(obj = stars_s2, + dsn = refl_path, + driver = format, + type = datatype, + chunk_size = c(dim(stars_s2)[1], ceiling(dim(stars_s2)[2] / nbchunks)), + progress = TRUE) + + if (format == "ENVI") { + adjust_envi_hdr(dsn = refl_path, bands = stars_spectral, + sensor = sensor, stretch = TRUE) + } + return(invisible()) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/alpha_beta.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,142 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : stars +# utils +# biodivmapr +# raster +# sf +# mapview +# leafpop +# RColorBrewer +# labdsv +# rgdal +# ggplot2 +# gridExtra + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + rasterheader <- args[2] + data <- args[3] + # type of PCA: + # PCA: no rescaling of the data + # SPCA: rescaling of the data + typepca <- as.character(args[4]) + alpha <- as.logical(args[5]) + beta <- as.logical(args[6]) + funct <- as.logical(args[7]) + all <- as.logical(args[8]) + nbcpu <- as.integer(args[9]) + source(args[10]) +} + +################################################################################ +## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ## +################################################################################ +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1]) + input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2]) + +} else { + input_image_file <- file.path(getwd(), data_raster, fsep = "/") + input_header_file <- file.path(getwd(), rasterheader, fsep = "/") +} + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels + +print("PERFORM PCA ON RASTER") +pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file, + Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram) + +pca_files <- pca_output$PCA_Files +pix_per_partition <- pca_output$Pix_Per_Partition +nb_partitions <- pca_output$nb_partitions +# path for the updated mask +input_mask_file <- pca_output$MaskPath + + +selected_pcs <- seq(1, dim(raster::stack(input_image_file))[3]) + +selected_pcs <- all(selected_pcs) +################################################################################ +## MAP ALPHA AND BETA DIVERSITY ## +################################################################################ +print("MAP SPECTRAL SPECIES") + +kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, SelectedPCs = selected_pcs, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, TypePCA = typepca, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters) + +image_name <- tools::file_path_sans_ext(basename(input_image_file)) +if (alpha == TRUE || beta == TRUE || all == TRUE) { +## alpha + print("MAP ALPHA DIVERSITY") + index_alpha <- c("Shannon") + alpha_div <- biodivMapR::map_alpha_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, Index_Alpha = index_alpha, nbclusters = nbclusters, FullRes = TRUE, LowRes = FALSE, MapSTD = FALSE) + + alpha_zip <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres.zip") + alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA") + unzip(alpha_zip, exdir = alpha_path) + alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres") + alpha_raster <- raster::raster(alpha_path) + get_alpha <- convert_raster(alpha_raster) + + if (alpha == TRUE || all == TRUE) { + colnames(get_alpha) <- c("Alpha", "longitude", "latitude") + plot_indices(get_alpha, titre = "Alpha") + + write.table(get_alpha, file = "alpha.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) +} + if (beta == TRUE || all == TRUE) { +## beta + print("MAP BETA DIVERSITY") + beta_div <- biodivMapR::map_beta_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters) + + beta_path <- file.path(output_dir, image_name, typepca, "BETA", "BetaDiversity_BCdiss_PCO_10") + beta_raster <- raster::raster(beta_path) + get_beta <- convert_raster(beta_raster) + + colnames(get_beta) <- c("Beta", "longitude", "latitude") + plot_indices(get_beta, titre = "Beta") + + write.table(get_beta, file = "beta.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + } +} + + +################################################################################ +## COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS ## +################################################################################ + +if (funct == TRUE || all == TRUE) { + mapper <- biodivMapR::map_functional_div(Original_Image_File = input_image_file, Functional_File = pca_files, Selected_Features = selected_pcs, Output_Dir = output_dir, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, TypePCA = typepca, FullRes = TRUE, LowRes = FALSE, MapSTD = FALSE) + + funct_zip <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres.zip") + funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL") + unzip(funct_zip, exdir = funct_path) + funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres") + funct_raster <- raster::raster(funct_path) + get_funct <- convert_raster(funct_raster) + + colnames(get_funct) <- c("Functionnal", "longitude", "latitude") + plot_indices(get_funct, titre = "Functionnal") + + write.table(get_funct, file = "Functionnal.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/biodiv_indices_global.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,112 @@ +#Rscript + +########################################### +## Mapping biodiversity indicators ## +########################################### + +#####Packages : raster, +# rgdal, +# sp, +# rasterdiv, +# ggplot2, + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + data_header <- args[2] + data <- args[3] + alpha <- as.numeric(args[4]) + source(args[5]) + +} + +######################################################################## +## COMPUTE BIODIVERSITY INDICES ## +######################################################################## + +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + data_raster <- file.path("data_dir/results/Reflectance", data_raster[1]) +} + +# Read raster +copndvi <- raster::raster(data_raster) + +copndvilr <- raster::reclassify(copndvi, cbind(252, 255, NA), right = TRUE) + +#Resample using raster::aggregate and a linear factor of 10 +copndvilr <- raster::aggregate(copndvilr, fact = 20) +#Set float numbers as integers to further speed up the calculation +storage.mode(copndvilr[]) <- "integer" + +# Convert raster to SpatialPointsDataFrame +r_pts <- convert_raster(copndvilr) + +#Shannon's Diversity +sha <- rasterdiv::Shannon(copndvilr, window = 9, np = 1) +sha_df <- data.frame(raster::rasterToPoints(sha, spatial = TRUE)) +sha_name <- "Shannon" +r_pts[, sha_name] <- sha_df[, 1] + +#Renyi's Index +ren <- rasterdiv::Renyi(copndvilr, window = 9, alpha = alpha, np = 1) +ren_df <- data.frame(raster::rasterToPoints(ren[[1]])) +ren_name <- "Renyi" +r_pts[, ren_name] <- ren_df[, 3] + +#Berger-Parker's Index +ber <- rasterdiv::BergerParker(copndvilr, window = 9, np = 1) +ber_df <- data.frame(raster::rasterToPoints(ber, spatial = TRUE)) +ber_name <- "Berger-Parker" +r_pts[, ber_name] <- ber_df[, 1] + +#Pielou's Evenness +pie <- rasterdiv::Pielou(copndvilr, window = 9, np = 1) +pie_df <- data.frame(raster::rasterToPoints(pie)) +if (length(pie_df[, 1]) == length(r_pts[, 1])) { + pie_name <- "Pielou" + r_pts[, pie_name] <- pie_df[, 1] +} + +#Hill's numbers +hil <- rasterdiv::Hill(copndvilr, window = 9, alpha = alpha, np = 1) +hil_df <- data.frame(raster::rasterToPoints(hil[[1]])) +hil_name <- "Hill" +r_pts[, hil_name] <- hil_df[, 3] + +#Parametric Rao's quadratic entropy with alpha ranging from 1 to 5 +prao <- rasterdiv::paRao(copndvilr, window = 9, alpha = alpha, dist_m = "euclidean", np = 1) +prao_df <- data.frame(raster::rasterToPoints(prao$window.9[[1]])) +prao_name <- "Prao" +r_pts[, prao_name] <- prao_df[, 3] + +#Cumulative Residual Entropy +cre <- rasterdiv::CRE(copndvilr, window = 9, np = 1) +cre_df <- data.frame(raster::rasterToPoints(cre)) +if (length(cre_df[, 1]) == length(r_pts[, 1])) { + cre_name <- "CRE" + r_pts[, cre_name] <- cre_df[, 1] +} + +if (length(cre_df[, 1]) == length(r_pts[, 1]) || length(pie_df[, 1]) == length(r_pts[, 1])) { +list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Pielou", "Hill", "Prao", "CRE") +} else { +list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Hill", "Prao") +} +## Plotting all the graph and writing a tabular +for (indice in list_indice) { + plot_indices(data = r_pts, titre = indice) +} + +write.table(r_pts, file = "BiodivIndex.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/comparison_div.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,190 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : stars +# utils +# biodivmapr +# raster +# sf +# mapview +# leafpop +# RColorBrewer +# labdsv +# rgdal +# ggplot2 +# gridExtra +##remotes::install_github("jbferet/biodivMapR") +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + rasterheader <- args[2] + data <- args[3] + plots_zip <- args[4] + choice <- as.character(args[5]) + source(args[6]) + # type of PCA: + # PCA: no rescaling of the data + # SPCA: rescaling of the data + typepca <- as.character(args[7]) +} + +################################################################################ +## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ## +################################################################################ +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1]) + input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2]) + +} else { + input_image_file <- file.path(getwd(), data_raster, fsep = "/") + input_header_file <- file.path(getwd(), rasterheader, fsep = "/") +} + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels + +print("PERFORM PCA ON RASTER") +pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file, + Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram) + +pca_files <- pca_output$PCA_Files +pix_per_partition <- pca_output$Pix_Per_Partition +nb_partitions <- pca_output$nb_partitions +# path for the updated mask +input_mask_file <- pca_output$MaskPath + +# 3- Select principal components from the PCA raster +# Select components from the PCA/SPCA/MNF raster +sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8") +image_name <- tools::file_path_sans_ext(basename(input_image_file)) +output_dir_full <- file.path(output_dir, image_name, typepca, "PCA") + +write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt")) +sel_pc <- file.path(output_dir_full, "Selected_Components.txt") + + +################################################################################ +## MAP ALPHA AND BETA DIVERSITY ## +################################################################################ +print("MAP SPECTRAL SPECIES") + +kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters, TypePCA = typepca) + +################################################################################ +## COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS ## +################################################################################ +## read selected features from dimensionality reduction + +## path for selected components + +# location of the directory where shapefiles used for validation are saved +dir.create("VectorDir") +unzip(plots_zip, exdir = "VectorDir") + +# list vector data +path_vector <- biodivMapR::list_shp("VectorDir") +name_vector <- tools::file_path_sans_ext(basename(path_vector)) + +# location of the spectral species raster needed for validation +path_spectralspecies <- kmeans_info$SpectralSpecies +# get diversity indicators corresponding to shapefiles (no partitioning of spectral dibversity based on field plots so far...) +biodiv_indicators <- biodivMapR::diversity_from_plots(Raster_SpectralSpecies = path_spectralspecies, Plots = path_vector, nbclusters = nbclusters, Raster_Functional = pca_files, Selected_Features = FALSE) + +shannon_rs <- c(biodiv_indicators$Shannon)[[1]] +fric <- c(biodiv_indicators$FunctionalDiversity$FRic) +feve <- c(biodiv_indicators$FunctionalDiversity$FEve) +fdiv <- c(biodiv_indicators$FunctionalDiversity$FDiv) +# if no name for plots +biodiv_indicators$Name_Plot <- seq(1, length(biodiv_indicators$Shannon[[1]]), by = 1) + + +#################################################### +# write RS indicators # +#################################################### +# write a table for Shannon index + +# write a table for all spectral diversity indices corresponding to alpha diversity +results <- data.frame(name_vector, biodiv_indicators$Richness, biodiv_indicators$Fisher, + biodiv_indicators$Shannon, biodiv_indicators$Simpson, + biodiv_indicators$FunctionalDiversity$FRic, + biodiv_indicators$FunctionalDiversity$FEve, + biodiv_indicators$FunctionalDiversity$FDiv) + +names(results) <- c("ID_Plot", "Species_Richness", "Fisher", "Shannon", "Simpson", "fric", "feve", "fdiv") +write.table(results, file = "Diversity.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + +if (choice == "Y") { +# write a table for Bray Curtis dissimilarity +bc_mean <- biodiv_indicators$BCdiss +bray_curtis <- data.frame(name_vector, bc_mean) +colnames(bray_curtis) <- c("ID_Plot", bray_curtis[, 1]) +write.table(bray_curtis, file = "BrayCurtis.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + +#################################################### +# illustrate results +#################################################### +# apply ordination using PCoA (same as done for map_beta_div) + +mat_bc_dist <- as.dist(bc_mean, diag = FALSE, upper = FALSE) +betapco <- labdsv::pco(mat_bc_dist, k = 3) + +# assign a type of vegetation to each plot, assuming that the type of vegetation +# is defined by the name of the shapefile + +nbsamples <- shpname <- c() +for (i in 1:length(path_vector)) { + shp <- path_vector[i] + nbsamples[i] <- length(rgdal::readOGR(shp, verbose = FALSE)) + shpname[i] <- tools::file_path_sans_ext(basename(shp)) +} + +type_vegetation <- c() +for (i in 1: length(nbsamples)) { + for (j in 1:nbsamples[i]) { + type_vegetation <- c(type_vegetation, shpname[i]) + } +} + +#data frame including a selection of alpha diversity metrics and beta diversity expressed as coordinates in the PCoA space +results <- data.frame("vgtype" = type_vegetation, "pco1" = betapco$points[, 1], "pco2" = betapco$points[, 2], "pco3" = betapco$points[, 3], "shannon" = shannon_rs, "fric" = fric, "feve" = feve, "fdiv" = fdiv) + +#plot field data in the PCoA space, with size corresponding to shannon index +g1 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco2, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6")) + +g2 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6")) + +g3 <- ggplot2::ggplot(results, ggplot2::aes(x = pco2, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6")) + +#extract legend +get_legend <- function(a_gplot) { + tmp <- ggplot2::ggplot_gtable(ggplot2::ggplot_build(a_gplot)) + leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") + legend <- tmp$grobs[[leg]] + return(legend) +} + +legend <- get_legend(g3) +gall <- gridExtra::grid.arrange(gridExtra::arrangeGrob(g1 + ggplot2::theme(legend.position = "none"), g2 + ggplot2::theme(legend.position = "none"), g3 + ggplot2::theme(legend.position = "none"), nrow = 1), legend, nrow = 2, heights = c(3, 2)) + + +filename <- ggplot2::ggsave("BetaDiversity_PcoA1_vs_PcoA2_vs_PcoA3.png", gall, scale = 0.65, width = 12, height = 9, units = "in", dpi = 200, limitsize = TRUE) + +filename +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/comparison_div.xml Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,138 @@ +<tool id="srs_process_data" name="Compare diversity indicators" version="@VERSION@" profile="20.01"> + <description>with remote sensing data</description> + <macros> + <import>macro.xml</import> + </macros> + <expand macro="SRS_requirements"/> + <command detect_errors="exit_code"><![CDATA[ + #import re + #if $method.type == 'envi_bil': + #set input_raster = $method.input_raster + #set input_raster_identifier = re.sub('[^\s\w\-]', '_', str($input_raster.element_identifier)) + #set input_header = $method.input_header + #set input_header_identifier = re.sub('[^\s\w\-]+[^.hdr]', '_', str($input_header.element_identifier)) + cp '${input_raster}' '${input_raster_identifier}' && + cp '${input_header}' '${input_header_identifier}' && + #end if + Rscript + '$__tool_directory__/comparison_div.r' + #if $method.type == 'envi_bil': + '$input_raster_identifier' + '$input_header_identifier' + '' + #else: + '' + '' + '$method.input' + #end if + '$input_plot' + '$choice' + '$__tool_directory__/functions.r' + '$typepca' + '$output_div' + '$output_beta' + '$plots' + ]]> + </command> + <inputs> + <conditional name="method"> + <param name="type" type="select" label="In which format are your data ?"> + <option value="zipper">The data you are using are in a zip folder Reflectance</option> + <option value="envi_bil">Your already have the files in ENVI BIL format</option> + </param> + <when value="zipper"> + <param name="input" type="data" format="zip" multiple="true" label="Input data"/> + </when> + <when value="envi_bil"> + <param name="input_raster" type="data" format="bil" label="Input raster" help="It can be the raw data in bil or the PCA raster layer in bil"/> + <param name="input_header" type="data" format="hdr" label="Input header"/> + </when> + </conditional> + <param name="input_plot" type="data" format="data" label="Plots folder zip"/> + <param name="choice" type="select" label="Do you want to compute beta diversity (needs mutliple locations) ?" display="radio"> + <option value="Y">Yes</option> + <option value="N">No</option> + </param> + <param name="typepca" type="select" label="Do you want to do a PCA or a SPCA ?" display="radio" help="If you choose PCA there is no rescaling of the data as oppposed as if you choose SPCA"> + <option value="SPCA">SPCA</option> + <option value="PCA">PCA</option> + </param> + </inputs> + <outputs> + <data name="output_div" from_work_dir="Diversity.tabular" format="tabular" label="Global diversity"> + </data> + <data name="output_beta" from_work_dir="BrayCurtis.tabular" format="tabular" label="Bray Curtis"> + <filter>choice == 'Y'</filter> + </data> + <collection type="list" name="plots" label="Comparison plot"> + <discover_datasets pattern="(?P<designation>.+)\.png" visible="false" format="png"/> + <filter>choice =='Y'</filter> + </collection> + </outputs> + <tests> + <test> + <param name="type" value="envi_bil"/> + <param name="input_raster" value="S2A_Subset"/> + <param name="input_header" value="S2A_Subset.hdr"/> + <param name="input_plot" value="S2A_T33NUD_Plots.zip"/> + <param name="choice" value="Y"/> + <output name="output_div"> + <assert_contents> + <has_n_lines n="25"/> + </assert_contents> + </output> + <output name="output_beta"> + <assert_contents> + <has_n_lines n="25"/> + </assert_contents> + </output> + <output_collection name="plots" type="list" count="1"/> + </test> + </tests> + <help><![CDATA[ +======================================================================== +Process satellite remote sensing data to produce biodiversity indicators +======================================================================== + + +**What it does** + +Féret and Asner (2014) developed a method for **tropical forest** diversity mapping based on very high spatial resolution airborne imaging spectroscopy. + +The goal of this tool using the package biodivMapR is to compute diversity indices over each spatial polygon of a shapefile of plots, if available, in order to compare field inventories with diversity indices estimated from remotely-sensed images. + +**Input description** + +It expects an image file as input, with a specific data format. ENVI HDR image with BIL interleave required. +The image is an ENVI raster including : + +- A binary file (which has no extension here). + +- A header file (with .hdr extension). + +The header file is a text file including all necessary metadata which can be read with a text editor. It includes image dimensions, projection, and the name and central wavelength for each spectral band. + +In order to get such input we advise to use the tool preprocessing sentinel 2 data. + ++--------------+----------+--------------+ +| BIL | ENVI HDR | Shapefiles | ++==============+==========+==============+ +| raster stack | Metadata | plots.zip | ++--------------+----------+--------------+ +| ... | ... | ... | ++--------------+----------+--------------+ + +**Output** + +- Two tabulars : + - One matrix for Bray-Curtis indicator + - One table for the following indicators; Species richness, shannon, fisher, simpson, richness, eveness, divergence + +- One comparison png plot in the Pcoa space that summarizes α- and β-diversity in scatterplots and illustrates that the combination of the three components computed with PCoA allows proper differentiation among vegetation types: + - PCoA#1 allows differentiating medium and high diversity forests from low diversity forest and low vegetation, but does not discriminate medium and high diversity forests. + - PCoA#2 allows differentiating low diversity forest from medium/high diversity forests and low vegetation + - PCoA#3 allows differentiating medium diversity forests from high diversity forests and low vegetation. + + ]]> </help> + <expand macro="SRS_BDMRref"/> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/functions.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,79 @@ +#Rscript + +######################### +## Functions ## +######################### + +#####Packages : raster +# sp +# ggplot2 + +####Set paramaters for all tools using BiodivMapR + +# path for the Mask raster corresponding to image to process +# expected to be in ENVI HDR format, 1 band, integer 8bits +# expected values in the raster: 0 = masked, 1 = selected +# set to FALSE if no mask available +input_mask_file <- FALSE + +# relative or absolute path for the Directory where results will be stored +# For each image processed, a subdirectory will be created after its name +output_dir <- "RESULTS" + +# SPATIAL RESOLUTION +# resolution of spatial units for alpha and beta diversity maps (in pixels), relative to original image +# if Res.Map = 10 for images with 10 m spatial resolution, then spatial units will be 10 pixels x 10m = 100m x 100m surfaces +# rule of thumb: spatial units between 0.25 and 4 ha usually match with ground data +# too small window_size results in low number of pixels per spatial unit, hence limited range of variation of diversity in the image +window_size <- 10 + +# PCA FILTERING: Set to TRUE if you want second filtering based on PCA outliers to be processed. Slower +filterpca <- TRUE + +################################################################################ +## DEFINE PARAMETERS FOR METHOD ## +################################################################################ +nbcpu <- 4 +maxram <- 0.5 +nbclusters <- 50 + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels +ndvi_thresh <- 0.5 +blue_thresh <- 500 +nir_thresh <- 1500 +continuum_removal <- TRUE + + + +#### Convert raster to dataframe + +# Convert raster to SpatialPointsDataFrame +convert_raster <- function(data_raster) { +r_pts <- raster::rasterToPoints(data_raster, spatial = TRUE) + +# reproject sp object +geo_prj <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0" +r_pts <- sp::spTransform(r_pts, sp::CRS(geo_prj)) + + +# Assign coordinates to @data slot, display first 6 rows of data.frame +r_pts@data <- data.frame(r_pts@data, longitude = sp::coordinates(r_pts)[, 1], + latitude = sp::coordinates(r_pts)[, 2]) + +return(r_pts@data) +} + + +#### Potting indices + +plot_indices <- function(data, titre) { + graph_indices <- ggplot2::ggplot(data) + + ggplot2::geom_point(ggplot2::aes_string(x = data[, 2], y = data[, 3], color = data[, titre]), shape = "square", size = 2) + ggplot2::scale_colour_gradient(low = "blue", high = "orange", na.value = "grey50") + + ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) + ggplot2::ggtitle(titre) + +ggplot2::ggsave(paste0(titre, ".png"), graph_indices, width = 12, height = 10, units = "cm") +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/indices_spectral.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,105 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : expint, +# pracma, +# R.utils, +# raster, +# sp, +# matrixStats, +# ggplot2, +# expandFunctions, +# stringr, +# XML, +# rgdal, +# stars, +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + data_header <- args[2] + data <- args[3] + source(args[4]) + source(args[5]) + source(args[6]) + indice_choice <- strsplit(args[7], ",")[[1]] + source(args[8]) + output_raster <- as.character(args[9]) + +} + +######################################################################## +## COMPUTE SPECTRAL INDEX : NDVI ## +######################################################################## + +if (data != "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + + # Read raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + data_raster <- file.path("data_dir/results/Reflectance", data_raster[1]) + refl <- raster::brick(data_raster) + refl2 <- raster::raster(data_raster) +} else { + # Read raster + refl <- raster::brick(data_raster) + refl2 <- raster::raster(data_raster) +} +# get raster band name and clean format. Expecting band name and wav +# reflFactor = 10000 when reflectance is coded as INT16 +refl <- raster::aggregate(refl, fact = 10) + +# Convert raster to SpatialPointsDataFrame +refl2 <- raster::aggregate(refl2, fact = 10) +r_pts <- convert_raster(refl2) +table_ind <- r_pts +# create directory for Spectralelength to be documented in image +hdr_refl <- read_envi_header(get_hdr_name(data_raster)) +sensorbands <- hdr_refl$wavelength +# compute a set of spectral indices defined by indexlist from S2 data indices +si_path <- file.path("SpectralIndices") +dir.create(path = si_path, showWarnings = FALSE, recursive = TRUE) +# Save spectral indices + +indices <- lapply(indice_choice, function(x) { + indices_list <- computespectralindices_raster(refl = refl, sensorbands = sensorbands, + sel_indices = x, + reflfactor = 10000, stackout = FALSE) + + index_path <- file.path(si_path, paste(basename(data_raster), "_", x, sep = "")) + spec_indices <- stars::write_stars(stars::st_as_stars(indices_list$spectralindices[[1]]), dsn = index_path, driver = "ENVI", type = "Float32") + + # write band name in HDR + hdr <- read_envi_header(get_hdr_name(index_path)) + hdr$`band names` <- x + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(index_path)) + # Writting the tabular and the plot + r_pts[, x] <- as.data.frame(sapply(spec_indices, c)) + plot_indices(data = r_pts, titre = x) + return(r_pts) +}) + +new_table <- as.data.frame(indices) +new_table <- new_table[, !grepl("longitude", names(new_table))] +new_table <- new_table[, !grepl("latitude", names(new_table))] +new_table <- new_table[, !grepl(basename(data_raster), names(new_table))] + +table_ind <- cbind(table_ind, new_table) +if (length(indice_choice) == 1) { + colnames(table_ind) <- c(basename(data_raster), "longitude", "latitude", indice_choice) +} + +write.table(table_ind, file = "Spec_Index.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + +# Get the raster layer of the indice as an output
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macro.xml Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,102 @@ +<macros> + <token name="@VERSION@">0.0.1</token> + <xml name="SRS_requirements"> + <requirements> + <requirement type="package" version="4.2.2">r-base</requirement> + <requirement type="package" version="2.12.2">r-r.utils</requirement> + <requirement type="package" version="1.0_7">r-sf</requirement> + <requirement type="package" version="1.5_32">r-rgdal</requirement> + <requirement type="package" version="0.6_1">r-rgeos</requirement> + <requirement type="package" version="0.5_5">r-stars</requirement> + <requirement type="package" version="1.5.0">r-stringr</requirement> + <requirement type="package" version="1.9.8">r-biodivmapr</requirement> + <requirement type="package" version="0.36">r-xfun</requirement> + <requirement type="package" version="0.51.4">r-rastervis</requirement> + <requirement type="package" version="3.4.0">r-ggplot2</requirement> + <requirement type="package" version="2.11.0">r-mapview</requirement> + <requirement type="package" version="2.3">r-gridextra</requirement> + <requirement type="package" version="3.0.0">r-emstreer</requirement> + <requirement type="package" version="3.2.3">r-filesstrings</requirement> + <!--requirement type="package" version="9.0.1">proj</requirement--> + <yield/> + </requirements> + </xml> + <xml name="SRS_input"> + <param name="input" type="data" format="tabular,csv,txt" label="Input table"/> + <param name="colnames" type="boolean" label="First line is a header line" checked="true"/> + </xml> + <xml name="SRS_bibref"> + <citations> + <citation type="bibtex"> + @Manual{, + title = {obisindicators: Develop marine biodiversity indicators from OBIS data}, + author = {Ben Ben and Pieter Provoost and Tylar Murray}, + year = {2022}, + note = {https://marinebon.org/obisindicators, + https://github.com/marinebon/obisindicators}, + } + </citation> + </citations> + </xml> + <xml name="SRS_S2ref"> + <citations> + <citation type="bibtex"> + @Manual{, + title = {preprocS2: preprocessing of Sentinel-2 data downloaded from various data hubs / produced from various atmospheric correction methods}, + author = {Jean-Baptiste Feret}, + year = {2022}, + note = {R package version 1.1.3}, + url = {https://gitlab.com/jbferet/preprocS2}, + } + </citation> + </citations> + </xml> + <xml name="SRS_BDMRref"> + <citations> + <citation type="bibtex"> + @Manual{, + title = {biodivMapR: biodivMapR: an R package for a- and ß-diversity mapping using remotely-sensed images}, + author = {Jean-Baptiste Feret and Florian {de Boissieu}}, + year = {2022}, + note = {R package version 1.9.4}, + url = {https://github.com/jbferet/biodivMapR}, + } + </citation> + </citations> + </xml> + <xml name="SRS_rasterdivref"> + <citations> + <citation type="doi">doi:10.1016/j.ecolind.2016.07.039</citation> + <citation type="doi">doi:10.1101/2021.01.23.427872</citation> + </citations> + </xml> + <xml name="SRS_prosailref"> + <citations> + <citation type="bibtex"> + @Manual{, + title = {prosail: PROSAIL leaf and canopy radiative transfer model and inversion routines}, + author = {Jean-Baptiste Feret and Florian {de Boissieu}}, + year = {2022}, + note = {R package version 1.1.1}, + url = {https://gitlab.com/jbferet/prosail}, + } + </citation> + </citations> + </xml> + <xml name="SRS_metaref"> + <citations> + <citation type="doi">doi.org/10.5281/zenodo.5907920</citation> + </citations> + </xml> + <xml name="SRS_vegetpheno"> + <citations> + <citation type="doi">doi.org/10.1016/j.rse.2013.09.022</citation> + </citations> + </xml> + <xml name="topic"> + <edam_topics> + <edam_topic>topic_0610</edam_topic> + <edam_topic>topic_3050</edam_topic> + </edam_topics> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pca_raster.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,75 @@ +#Rscript + +########################################### +## Getting PCA raster ## +########################################### + +#####Packages : stars +# utils +# biodivmapr +# raster +# sf +# mapview +# leafpop +# RColorBrewer +# labdsv +# rgdal +# ggplot2 +# gridExtra +## remotes::install_github("jbferet/biodivMapR") +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + rasterheader <- args[2] + data <- args[3] + typepca <- as.character(args[4]) + source(args[5]) +} + +################################################################################ +## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ## +################################################################################ +# expected to be in ENVI HDR + +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1]) + input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2]) + +} else { + input_image_file <- file.path(getwd(), data_raster, fsep = "/") + input_header_file <- file.path(getwd(), rasterheader, fsep = "/") +} + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels +print("PERFORM PCA ON RASTER") +pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file, + Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram) + + +pca_path <- file.path(output_dir, basename(data_raster), typepca, "PCA", "OutputPCA_8_PCs") +pca_raster <- raster::raster(pca_path) +get_pca <- convert_raster(pca_raster) + +colnames(get_pca) <- c("PCA", "longitude", "latitude") +plot_indices(get_pca, titre = "PCA") + +write.table(get_pca, file = "PCA.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) +#### Get the raster layer files +pca_files <- file.path("RESULTS", basename(data_raster), typepca, "PCA") +to_dir_short <- output_dir +file.copy(pca_files, to_dir_short) #copy files from long to short paths
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/preprocess_S2.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,121 @@ +#Rscript + +########################################### +## Preprocessing Sentinel 2 data ## +########################################### + +#####Packages : sen2r, +# jqr, +# protolite, +# raster, +# sf, +# rgeos, +# sp, +# raster, +# stars, +# stringr, +# progress, +# rgdal, +# R.utils, +# gdalUtils, +# fasterize, +# XML, +# XML2 + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data <- args[1] + source(args[2]) + data_source <- as.character(args[3]) + sat_type <- as.character(args[4]) +} + +##____________________________________________________________________## +## Define where data is stored and where to write results ## +##--------------------------------------------------------------------## + +#Create a directory where to unzip your folder of data +dir.create("data_dir") +unzip(data, exdir = "data_dir") + +# Result directory +result_path <- "results" +dir.create(path = result_path, showWarnings = FALSE, recursive = TRUE) + +#Csv file for output useless but needed for linter +write.csv(data_source, "Mission.csv") + +# define raster path +if (data_source == "SAFE") { + path_s2 <- file.path("data_dir", list.files("data_dir", pattern = ".SAFE")) + #To define the level and know if a correction is needed (convert not ready yet) + level_info <- get_s2_level(path_s2) + if (level_info == "L1C") { + stop("! This tool works for data of L2A level and NOT for the L1C level which is currently a work in progress !") + } +}else { + path_s2 <- file.path("data_dir") +} + +##____________________________________________________________________## +## Extract, resample & stack data ## +##--------------------------------------------------------------------## +# define resolution +resolution <- 10 +# define source of data +s2source <- data_source + +s2obj <- extract_from_s2_l2a(path_dir_s2 = path_s2, + path_vector = NULL, + s2source = s2source, + resolution = resolution) + +##____________________________________________________________________## +## Write CLOUD MASK ## +##--------------------------------------------------------------------## + +# directory for cloud mask +cloud_path <- file.path(result_path, "CloudMask") +dir.create(path = cloud_path, showWarnings = FALSE, recursive = TRUE) +# Filename for cloud mask +cloudmasks <- save_cloud_s2(s2_stars = s2obj$s2_stack, + cloud_path = cloud_path, + s2source = s2source, saveraw = TRUE) + +zip_cloud <- file.path("Cloud.zip") +zip::zip(zip_cloud, cloud_path) +##____________________________________________________________________## +## Write REFLECTANCE ## +##--------------------------------------------------------------------## + +# directory for Reflectance +refl_dir <- file.path(result_path, "Reflectance") +dir.create(path = refl_dir, showWarnings = FALSE, recursive = TRUE) + +if (data_source == "SAFE") { + # filename for Reflectance + refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$GRANULE), "_Refl", sep = "")) + + # Save Reflectance file as ENVI image with BIL interleaves + tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][2], 2) + dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][4], 1, 8), format = "%Y%m%d") +}else { + # filename for Reflectance + refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$path_tile_s2), "_Refl", sep = "")) + + # Save Reflectance file as ENVI image with BIL interleaves + tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][2], 2) + dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][4], 1, 8), format = "%Y%m%d") +} + +save_data <- save_reflectance_s2(s2_stars = s2obj$s2_stack, refl_path = refl_path, + s2sat = sat_type, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2, + format = "ENVI", datatype = "Int16", mtd = s2obj$s2_bands$metadata, mtd_msi = s2obj$s2_bands$metadata_MSI) + +zip_files <- file.path("Refl.zip") +zip::zip(zip_files, refl_dir)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/prosail-master/R/Lib_PROSAIL.R Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,1233 @@ +# ============================================================================= = +# prosail +# Lib_PROSAIL.R +# ============================================================================= = +# PROGRAMMERS: +# Jean-Baptiste FERET <jb.feret@teledetection.fr > +# Florian de BOISSIEU <fdeboiss@gmail.com > +# copyright 2019 / 11 Jean-Baptiste FERET +# ============================================================================= = +# This Library includes functions dedicated to PROSAIL simulation +# SAIL versions available are 4SAIL and 4SAIL2 +# ============================================================================= = + +#" computes bidirectional reflectance factor based on outputs from PROSAIL and sun position +#" +#" The direct and diffuse light are taken into account as proposed by: +#" Francois et al. (2002) conversion of 400-1100 nm vegetation albedo +#" measurements into total shortwave broadband albedo using a canopy +#" radiative transfer model, Agronomie +#" Es = direct +#" Ed = diffuse +#" +#" @param rdot numeric. Hemispherical-directional reflectance factor in viewing direction +#" @param rsot numeric. Bi-directional reflectance factor +#" @param tts numeric. Solar zenith angle +#" @param specatm_sensor list. direct and diffuse radiation for clear conditions +#" @return brf numeric. Bidirectional reflectance factor +#" @export +compute_brf <- function(rdot, rsot, tts, specatm_sensor) { + + ############################## # + ## direct / diffuse light ## + ############################## # + es <- specatm_sensor$Direct_Light + ed <- specatm_sensor$Diffuse_Light + rd <- pi / 180 + skyl <- 0.847 - 1.61 * sin((90 - tts) * rd) + 1.04 * sin((90 - tts) * rd) * sin((90 - tts) * rd) # diffuse radiation (Francois et al., 2002) + pardiro <- (1 - skyl) * es + pardifo <- skyl * ed + brf <- (rdot * pardifo + rsot * pardiro) / (pardiro + pardifo) + return(brf) +} + +#" Performs PROSAIL simulation based on a set of combinations of input parameters +#" @param spec_sensor list. Includes optical constants required for PROSPECT +#" refractive index, specific absorption coefficients and corresponding spectral bands +#" @param input_prospect list. PROSPECT input variables +#" @param n numeric. Leaf structure parameter +#" @param chl numeric. chlorophyll content (microg.cm-2) +#" @param car numeric. carotenoid content (microg.cm-2) +#" @param ant numeric. anthocyain content (microg.cm-2) +#" @param brown numeric. brown pigment content (Arbitrary units) +#" @param ewt numeric. Equivalent Water Thickness (g.cm-2) +#" @param lma numeric. Leaf Mass per Area (g.cm-2) +#" @param prot numeric. protein content (g.cm-2) +#" @param cbc numeric. nonprotcarbon-based constituent content (g.cm-2) +#" @param alpha numeric. Solid angle for incident light at surface of leaf (simulation of roughness) +#" @param typelidf numeric. Type of leaf inclination distribution function +#" @param lidfa numeric. +#" if typelidf == 1, controls the average leaf slope +#" if typelidf == 2, corresponds to average leaf angle +#" @param lidfb numeric. +#" if typelidf == 1, unused +#" if typelidf == 2, controls the distribution"s bimodality +#" @param lai numeric. Leaf Area Index +#" @param q numeric. Hot Spot parameter +#" @param tts numeric. Sun zeith angle +#" @param tto numeric. Observer zeith angle +#" @param psi numeric. Azimuth Sun / Observer +#" @param rsoil numeric. Soil reflectance +#" @param fraction_brown numeric. Fraction of brown leaf area +#" @param diss numeric. Layer dissociation factor +#" @param cv numeric. vertical crown cover percentage +#" = % ground area covered with crowns as seen from nadir direction +#" @param zeta numeric. Tree shape factor +#" = ratio of crown diameter to crown height +#" @param sailversion character. choose between 4SAIL and 4SAIL2 +#" @param brownvegetation list. Defines optical properties for brown vegetation, if not nULL +#" - WVL, reflectance, Transmittance +#" - Set to nULL if use PROSPECT to generate it +#" +#" @return list. rdot, rsot, rddt, rsdt +#" rdot: hemispherical-directional reflectance factor in viewing direction +#" rsot: bi-directional reflectance factor +#" rsdt: directional-hemispherical reflectance factor for solar incident flux +#" rddt: bi-hemispherical reflectance factor +#" @import prospect +#" @export +pro4sail <- function(spec_sensor, input_prospect = nULL, n = 1.5, chl = 40.0, + car = 8.0, ant = 0.0, brown = 0.0, ewt = 0.01, + lma = 0.008, prot = 0.0, cbc = 0.0, alpha = 40.0, + typelidf = 2, lidfa = nULL, lidfb = nULL, lai = nULL, + q = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL, + fraction_brown = 0.0, diss = 0.0, cv = 1, zeta = 1, + sailversion = "4SAIL", brownvegetation = nULL) { + + ############################ # + # LEAF OPTICAL PROPERTIES ## + ############################ # + if (is.null(input_prospect)) { + input_prospect <- data.frame("chl" = chl, "car" = car, "ant" = ant, "brown" = brown, "ewt" = ewt, + "lma" = lma, "prot" = prot, "cbc" = cbc, "n" = n, "alpha" = alpha) + } + greenvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor, + n = input_prospect$n[1], + chl = input_prospect$chl[1], + car = input_prospect$car[1], + ant = input_prospect$ant[1], + brown = input_prospect$brown[1], + ewt = input_prospect$ewt[1], + lma = input_prospect$lma[1], + prot = input_prospect$prot[1], + cbc = input_prospect$cbc[1], + alpha = input_prospect$alpha[1]) + + if (sailversion == "4SAIL2") { + # 4SAIL2 requires one of the following combination of input parameters + # Case #1: valid optical properties for brown vegetation + if (!is.null(brownvegetation)) { + # need to define reflectance and Transmittance for brownvegetation + if (length(grep("reflectance", names(brownvegetation))) == 0 || length(grep("Transmittance", names(brownvegetation))) == 0) { + message("Please define brownvegetation as a list including reflectance and Transmittance") + stop() + } + # check if spectral domain for optical properties of brown vegetation match + # with spectral domain for optical properties of green vegetation + if (length(setdiff(spec_sensor$lambda, brownvegetation$wvl)) > 0) { + message("Please define same spectral domain for brownvegetation and SpecPROSPECT") + stop() + } + if (length(unique(lengths(input_prospect))) == 1) { + if (!unique(lengths(input_prospect)) == 1) { + message("brownvegetation defined along with multiple leaf chemical properties") + message("Only first set of leaf chemical properties will be used to simulate green vegetation") + } + } + # if no leaf optical properties brown vegetation defined + } else if (is.null(brownvegetation)) { + # if all PROSPECT input parameters have the same length + if (length(unique(lengths(input_prospect))) == 1) { + # if all PROSPECT input parameters are unique (no possibility to simulate 2 types of leaf optics) + if (unique(lengths(input_prospect)) == 1) { + # if fraction_brown set to 0, then assign green vegetation optics to brown vegetation optics + if (fraction_brown == 0) { + brownvegetation <- greenvegetation + # else run 4SAIL + } else { + message("4SAIL2 needs two sets of optical properties for green and brown vegetation") + message("Currently one set is defined. will run 4SAIL instead of 4SAIL2") + sailversion <- "4SAIL" + } + # if all PROSPECT parameters have at least 2 elements + } else if (unique(lengths(input_prospect)) >= 2) { + # compute leaf optical properties + brownvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor, + n = input_prospect$n[2], + chl = input_prospect$chl[2], + car = input_prospect$car[2], + ant = input_prospect$ant[2], + brown = input_prospect$brown[2], + ewt = input_prospect$ewt[2], + lma = input_prospect$lma[2], + prot = input_prospect$prot[2], + cbc = input_prospect$cbc[2], + alpha = input_prospect$alpha[2]) + if (unique(lengths(input_prospect)) > 2) { + message("4SAIL2 needs two sets of optical properties for green and brown vegetation") + message("Currently more than 2 sets are defined. will only use the first 2") + } + } + } + } + } + if (sailversion == "4SAIL") { + if (length(unique(lengths(input_prospect))) == 1) { + if (unique(lengths(input_prospect)) > 1) { + message("4SAIL needs only one set of optical properties") + message("Currently more than one set of leaf chemical constituents is defined.") + message("Will run 4SAIL with the first set of leaf chemical constituents") + } + } + } + + if (sailversion == "4SAIL") { + # run 4SAIL + ref <- foursail(leafoptics = greenvegetation, + typelidf, lidfa, lidfb, lai, q, tts, tto, psi, rsoil) + } else if (sailversion == "4SAIL2") { + # run 4SAIL2 + ref <- foursail2(leafgreen = greenvegetation, leafbrown = brownvegetation, + typelidf, lidfa, lidfb, lai, q, tts, tto, psi, rsoil, + fraction_brown, diss, cv, zeta) + } + return(ref) +} + +#" Performs PROSAIL simulation based on a set of combinations of input parameters +#" @param leafoptics list. Includes leaf optical properties (reflectance and transmittance) +#" and corresponding spectral bands +#" @param typelidf numeric. Type of leaf inclination distribution function +#" @param lidfa numeric. +#" if typelidf == 1, controls the average leaf slope +#" if typelidf == 2, corresponds to average leaf angle +#" @param lidfb numeric. +#" if typelidf == 1, unused +#" if typelidf == 2, controls the distribution"s bimodality +#" @param lai numeric. Leaf Area Index +#" @param q numeric. Hot Spot parameter +#" @param tts numeric. Sun zeith angle +#" @param tto numeric. Observer zeith angle +#" @param psi numeric. Azimuth Sun / Observer +#" @param rsoil numeric. Soil reflectance +#" +#" @return list. rdot, rsot, rddt, rsdt +#" rdot: hemispherical-directional reflectance factor in viewing direction +#" rsot: bi-directional reflectance factor +#" rsdt: directional-hemispherical reflectance factor for solar incident flux +#" rddt: bi-hemispherical reflectance factor +#" @export + +foursail <- function(leafoptics, typelidf = 2, lidfa = nULL, lidfb = nULL, lai = nULL, + q = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL) { + + ############################## # + # LEAF OPTICAL PROPERTIES ## + ############################## # + rho <- leafoptics$Reflectance + tau <- leafoptics$Transmittance + + # Geometric quantities + rd <- pi / 180 + cts <- cos(rd * tts) + cto <- cos(rd * tto) + ctscto <- cts * cto + tants <- tan(rd * tts) + tanto <- tan(rd * tto) + cospsi <- cos(rd * psi) + dso <- sqrt(tants * tants + tanto * tanto - 2. * tants * tanto * cospsi) + + # Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters + if (typelidf == 1) { + foliar_distrib <- dladgen(lidfa, lidfb) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + + } else if (typelidf == 2) { + foliar_distrib <- campbell(lidfa) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + } + + # angular distance, compensation of shadow length + # Calculate geometric factors associated with extinction and scattering + # Initialise sums + ks <- 0 + ko <- 0 + bf <- 0 + sob <- 0 + sof <- 0 + + # Weighted sums over LIDF + na <- length(litab) + for (i in 1:na) { + ttl <- litab[i] # leaf inclination discrete values + ctl <- cos(rd * ttl) + # SAIL volume scattering phase function gives interception and portions to be + # multiplied by rho and tau + resvolscatt <- volscatt(tts, tto, psi, ttl) + chi_s <- resvolscatt$chi_s + chi_o <- resvolscatt$chi_o + frho <- resvolscatt$frho + ftau <- resvolscatt$ftau + + #******************************************************************************** + #* SUITS SYSTEM coEFFICIEnTS + #* + #* ks : Extinction coefficient for direct solar flux + #* ko : Extinction coefficient for direct observed flux + #* att : Attenuation coefficient for diffuse flux + #* sigb : Backscattering coefficient of the diffuse downward flux + #* sigf : Forwardscattering coefficient of the diffuse upward flux + #* sf : Scattering coefficient of the direct solar flux for downward diffuse flux + #* sb : Scattering coefficient of the direct solar flux for upward diffuse flux + #* vf : Scattering coefficient of upward diffuse flux in the observed direction + #* vb : Scattering coefficient of downward diffuse flux in the observed direction + #* w : Bidirectional scattering coefficient + #******************************************************************************** + + # Extinction coefficients + ksli <- chi_s / cts + koli <- chi_o / cto + + # Area scattering coefficient fractions + sobli <- frho * pi / ctscto + sofli <- ftau * pi / ctscto + bfli <- ctl * ctl + ks <- ks + ksli * lidf[i] + ko <- ko + koli * lidf[i] + bf <- bf + bfli * lidf[i] + sob <- sob + sobli * lidf[i] + sof <- sof + sofli * lidf[i] + } + + # Geometric factors to be used later with rho and tau + sdb <- 0.5 * (ks + bf) + sdf <- 0.5 * (ks - bf) + dob <- 0.5 * (ko + bf) + dof <- 0.5 * (ko - bf) + ddb <- 0.5 * (1. + bf) + ddf <- 0.5 * (1. - bf) + + # Here rho and tau come in + sigb <- ddb * rho + ddf * tau + sigf <- ddf * rho + ddb * tau + att <- 1 - sigf + m2 <- (att + sigb) * (att - sigb) + m2[which(m2 <= 0)] <- 0 + m <- sqrt(m2) + + sb <- sdb * rho + sdf * tau + sf <- sdf * rho + sdb * tau + vb <- dob * rho + dof * tau + vf <- dof * rho + dob * tau + w <- sob * rho + sof * tau + + # Here the LAI comes in + # Outputs for the case LAI = 0 + if (lai < 0) { + tss <- 1 + too <- 1 + tsstoo <- 1 + rdd <- 0 + tdd <- 1 + rsd <- 0 + tsd <- 0 + rdo <- 0 + tdo <- 0 + rso <- 0 + rsos <- 0 + rsod <- 0 + + rddt <- rsoil + rsdt <- rsoil + rdot <- rsoil + rsodt <- 0 * rsoil + rsost <- rsoil + rsot <- rsoil + } else { + # Other cases (LAI > 0) + e1 <- exp(-m * lai) + e2 <- e1 * e1 + rinf <- (att - m) / sigb + rinf2 <- rinf * rinf + re <- rinf * e1 + denom <- 1. - rinf2 * e2 + + j1ks <- jfunc1(ks, m, lai) + j2ks <- jfunc2(ks, m, lai) + j1ko <- jfunc1(ko, m, lai) + j2ko <- jfunc2(ko, m, lai) + + ps <- (sf + sb * rinf) * j1ks + qs <- (sf * rinf + sb) * j2ks + pv <- (vf + vb * rinf) * j1ko + qv <- (vf * rinf + vb) * j2ko + + rdd <- rinf * (1. - e2) / denom + tdd <- (1. - rinf2) * e1 / denom + tsd <- (ps - re * qs) / denom + rsd <- (qs - re * ps) / denom + tdo <- (pv - re * qv) / denom + rdo <- (qv - re * pv) / denom + + tss <- exp(-ks * lai) + too <- exp(-ko * lai) + z <- jfunc3(ks, ko, lai) + g1 <- (z - j1ks * too) / (ko + m) + g2 <- (z - j1ko * tss) / (ks + m) + + tv1 <- (vf * rinf + vb) * g1 + tv2 <- (vf + vb * rinf) * g2 + t1 <- tv1 * (sf + sb * rinf) + t2 <- tv2 * (sf * rinf + sb) + t3 <- (rdo * qs + tdo * ps) * rinf + + # Multiple scattering contribution to bidirectional canopy reflectance + rsod <- (t1 + t2 - t3) / (1. - rinf2) + + # Treatment of the hotspot-effect + alf <- 1e6 + # Apply correction 2 / (K + k) suggested by F.-M. Breon + if (q > 0) { + alf <- (dso / q) * 2. / (ks + ko) + } + if (alf > 200) { + # inserted H. Bach 1 / 3 / 04 + alf <- 200 + } + if (alf == 0) { + # The pure hotspot - no shadow + tsstoo <- tss + sumint <- (1 - tss) / (ks * lai) + } else { + # Outside the hotspot + fhot <- lai * sqrt(ko * ks) + # Integrate by exponential Simpson method in 20 steps + # the steps are arranged according to equal partitioning + # of the slope of the joint probability function + x1 <- 0 + y1 <- 0 + f1 <- 1 + fint <- (1. - exp(-alf)) * 0.05 + sumint <- 0 + for (i in 1:20) { + if (i < 20) { + x2 <- -log(1. - i * fint) / alf + } else { + x2 <- 1 + } + y2 <- -(ko + ks) * lai * x2 + fhot * (1. - exp(-alf * x2)) / alf + f2 <- exp(y2) + sumint <- sumint + (f2 - f1) * (x2 - x1) / (y2 - y1) + x1 <- x2 + y1 <- y2 + f1 <- f2 + } + tsstoo <- f1 + } + # Bidirectional reflectance + # Single scattering contribution + rsos <- w * lai * sumint + # Total canopy contribution + rso <- rsos + rsod + # Interaction with the soil + dn <- 1. - rsoil * rdd + # rddt: bi-hemispherical reflectance factor + rddt <- rdd + tdd * rsoil * tdd / dn + # rsdt: directional-hemispherical reflectance factor for solar incident flux + rsdt <- rsd + (tsd + tss) * rsoil * tdd / dn + # rdot: hemispherical-directional reflectance factor in viewing direction + rdot <- rdo + tdd * rsoil * (tdo + too) / dn + # rsot: bi-directional reflectance factor + rsodt <- rsod + ((tss + tsd) * tdo + (tsd + tss * rsoil * rdd) * too) * rsoil / dn + rsost <- rsos + tsstoo * rsoil + rsot <- rsost + rsodt + } + my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt) + return(my_list) +} + +#" Performs pro4sail2 simulation based on a set of combinations of input parameters +#" @param leafgreen list. includes relfectance and transmittance for vegetation #1 (e.g. green vegetation) +#" @param leafbrown list. includes relfectance and transmittance for vegetation #2 (e.g. brown vegetation) +#" @param typelidf numeric. Type of leaf inclination distribution function +#" @param lidfa numeric. +#" if typelidf == 1, controls the average leaf slope +#" if typelidf == 2, corresponds to average leaf angle +#" @param lidfb numeric. +#" if typelidf == 1, unused +#" if typelidf == 2, controls the distribution"s bimodality +#" @param lai numeric. Leaf Area Index +#" @param hot numeric. Hot Spot parameter = ratio of the correlation length of leaf projections in the horizontal plane and the canopy height (doi:10.1016 / j.rse.2006.12.013) +#" @param tts numeric. Sun zeith angle +#" @param tto numeric. Observer zeith angle +#" @param psi numeric. Azimuth Sun / Observer +#" @param rsoil numeric. Soil reflectance +#" @param fraction_brown numeric. Fraction of brown leaf area +#" @param diss numeric. Layer dissociation factor +#" @param cv numeric. vertical crown cover percentage +#" = % ground area covered with crowns as seen from nadir direction +#" @param zeta numeric. Tree shape factor +#" = ratio of crown diameter to crown height +#" +#" @return list. rdot, rsot, rddt, rsdt +#" rdot: hemispherical-directional reflectance factor in viewing direction +#" rsot: bi-directional reflectance factor +#" rsdt: directional-hemispherical reflectance factor for solar incident flux +#" rddt: bi-hemispherical reflectance factor +#" alfast: canopy absorptance for direct solar incident flux +#" alfadt: canopy absorptance for hemispherical diffuse incident flux +#" @export + +foursail2 <- function(leafgreen, leafbrown, + typelidf = 2, lidfa = nULL, lidfb = nULL, + lai = nULL, hot = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL, + fraction_brown = 0.5, diss = 0.5, cv = 1, zeta = 1) { + + # This version does not include non-Lambertian soil properties. + # original codes do, and only need to add the following variables as input + rddsoil <- rdosoil <- rsdsoil <- rsosoil <- rsoil + + # Geometric quantities + rd <- pi / 180 + + # Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters + if (typelidf == 1) { + foliar_distrib <- dladgen(lidfa, lidfb) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + + } else if (typelidf == 2) { + foliar_distrib <- campbell(lidfa) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + } + + if (lai < 0) { + message("Please define positive LAI value") + rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil + alfast <- alfadt <- 0 * rsoil + } else if (lai == 0) { + tss <- too <- tsstoo <- tdd <- 1.0 + rdd <- rsd <- tsd <- rdo <- tdo <- 0.0 + rso <- rsos <- rsod <- rsodt <- 0.0 + rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil + alfast <- alfadt <- 0 * rsoil + } else if (lai > 0) { + cts <- cos(rd * tts) + cto <- cos(rd * tto) + ctscto <- cts * cto + tants <- tan(rd * tts) + tanto <- tan(rd * tto) + cospsi <- cos(rd * psi) + dso <- sqrt(tants * tants + tanto * tanto - 2.0 * tants * tanto * cospsi) + + # Clumping effects + cs <- co <- 1.0 + if (cv <= 1.0) { + cs <- 1.0 - (1.0 - cv)^(1.0 / cts) + co <- 1.0 - (1.0 - cv)^(1.0 / cto) + } + overlap <- 0.0 + if (zeta > 0.0) { + overlap <- min(cs * (1.0 - co), co * (1.0 - cs)) * exp(-dso / zeta) + } + fcd <- cs * co + overlap + fcs <- (1.0 - cs) * co - overlap + fod <- cs * (1.0 - co) - overlap + fos <- (1.0 - cs) * (1.0 - co) + overlap + fcdc <- 1.0 - (1.0 - fcd)^(0.5 / cts + 0.5 / cto) + + # Part depending on diss, fraction_brown, and leaf optical properties + # First save the input fraction_brown as the old fraction_brown, as the following change is only artificial + # Better define an fraction_brown that is actually used: fb, so that the input is not modified! + + fb <- fraction_brown + # if only green leaves + if (fraction_brown == 0.0) { + fb <- 0.5 + leafbrown$Reflectance <- leafgreen$Reflectance + leafbrown$Transmittance <- leafgreen$Transmittance + } + if (fraction_brown == 1.0) { + fb <- 0.5 + leafgreen$Reflectance <- leafbrown$Reflectance + leafgreen$Transmittance <- leafbrown$Transmittance + } + s <- (1.0 - diss) * fb * (1.0 - fb) + # rho1 && tau1 : green foliage + # rho2 && tau2 : brown foliage (bottom layer) + rho1 <- ((1 - fb - s) * leafgreen$Reflectance + s * leafbrown$Reflectance) / (1 - fb) + tau1 <- ((1 - fb - s) * leafgreen$Transmittance + s * leafbrown$Transmittance) / (1 - fb) + rho2 <- (s * leafgreen$Reflectance + (fb - s) * leafbrown$Reflectance) / fb + tau2 <- (s * leafgreen$Transmittance + (fb - s) * leafbrown$Transmittance) / fb + + # angular distance, compensation of shadow length + # Calculate geometric factors associated with extinction and scattering + # Initialise sums + ks <- ko <- bf <- sob <- sof <- 0 + + # Weighted sums over LIDF + + for (i in 1:seq_along(litab)) { + ttl <- litab[i] + ctl <- cos(rd * ttl) + # SAIL volscatt function gives interception coefficients + # and two portions of the volume scattering phase function to be + # multiplied by rho and tau, respectively + resvolscatt <- volscatt(tts, tto, psi, ttl) + chi_s <- resvolscatt$chi_s + chi_o <- resvolscatt$chi_o + frho <- resvolscatt$frho + ftau <- resvolscatt$ftau + # Extinction coefficients + ksli <- chi_s / cts + koli <- chi_o / cto + # Area scattering coefficient fractions + sobli <- frho * pi / ctscto + sofli <- ftau * pi / ctscto + bfli <- ctl * ctl + ks <- ks + ksli * lidf[i] + ko <- ko + koli * lidf[i] + bf <- bf + bfli * lidf[i] + sob <- sob + sobli * lidf[i] + sof <- sof + sofli * lidf[i] + } + # Geometric factors to be used later in combination with rho and tau + sdb <- 0.5 * (ks + bf) + sdf <- 0.5 * (ks - bf) + dob <- 0.5 * (ko + bf) + dof <- 0.5 * (ko - bf) + ddb <- 0.5 * (1. + bf) + ddf <- 0.5 * (1. - bf) + + # LAIs in two layers + lai1 <- (1 - fb) * lai + lai2 <- fb * lai + + tss <- exp(-ks * lai) + ck <- exp(-ks * lai1) + alf <- 1e6 + if (hot > 0.0) { + alf <- (dso / hot) * 2.0 / (ks + ko) + } + if (alf > 200.0) { + alf <- 200.0 # inserted H. Bach 1 / 3 / 04 + } + if (alf == 0.0) { + # The pure hotspot + tsstoo <- tss + s1 <- (1 - ck) / (ks * lai) + s2 <- (ck - tss) / (ks * lai) + } else { + # Outside the hotspot + fhot <- lai * sqrt(ko * ks) + # Integrate 2 layers by exponential simpson method in 20 steps + # the steps are arranged according to equal partitioning + # of the derivative of the joint probability function + x1 <- y1 <- 0.0 + f1 <- 1.0 + ca <- exp(alf * (fb - 1.0)) + fint <- (1.0 - ca) * .05 + s1 <- 0.0 + for (istep in 1:20) { + if (istep < 20) { + x2 <- -log(1. - istep * fint) / alf + } else { + x2 <- 1. - fb + } + y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf + f2 <- exp(y2) + s1 <- s1 + (f2 - f1) * (x2 - x1) / (y2 - y1) + x1 <- x2 + y1 <- y2 + f1 <- f2 + } + fint <- (ca - exp(-alf)) * .05 + s2 <- 0.0 + for (istep in 1:20) { + if (istep < 20) { + x2 <- -log(ca - istep * fint) / alf + } else { + x2 <- 1.0 + } + y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf + f2 <- exp(y2) + s2 <- s2 + (f2 - f1) * (x2 - x1) / (y2 - y1) + x1 <- x2 + y1 <- y2 + f1 <- f2 + } + tsstoo <- f1 + } + + # Calculate reflectances and transmittances + # Bottom layer + tss <- exp(-ks * lai2) + too <- exp(-ko * lai2) + sb <- sdb * rho2 + sdf * tau2 + sf <- sdf * rho2 + sdb * tau2 + + vb <- dob * rho2 + dof * tau2 + vf <- dof * rho2 + dob * tau2 + + w2 <- sob * rho2 + sof * tau2 + + sigb <- ddb * rho2 + ddf * tau2 + sigf <- ddf * rho2 + ddb * tau2 + att <- 1.0 - sigf + m2 <- (att + sigb) * (att - sigb) + m2[m2 < 0] <- 0 + m <- sqrt(m2) + which_ncs <- which(m > 0.01) + which_cs <- which(m <= 0.01) + + tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m + rsod <- 0 * m + if (length(which_ncs) > 0) { + resncs <- nonconservativescattering(m[which_ncs], lai2, att[which_ncs], sigb[which_ncs], + ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too) + tdd[which_ncs] <- resncs$tdd + rdd[which_ncs] <- resncs$rdd + tsd[which_ncs] <- resncs$tsd + rsd[which_ncs] <- resncs$rsd + tdo[which_ncs] <- resncs$tdo + rdo[which_ncs] <- resncs$rdo + rsod[which_ncs] <- resncs$rsod + } + if (length(which_cs) > 0) { + rescs <- conservativescattering(m[which_cs], lai2, att[which_cs], sigb[which_cs], + ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too) + tdd[which_cs] <- rescs$tdd + rdd[which_cs] <- rescs$rdd + tsd[which_cs] <- rescs$tsd + rsd[which_cs] <- rescs$rsd + tdo[which_cs] <- rescs$tdo + rdo[which_cs] <- rescs$rdo + rsod[which_cs] <- rescs$rsod + } + + # Set background properties equal to those of the bottom layer on a black soil + rddb <- rdd + rsdb <- rsd + rdob <- rdo + rsodb <- rsod + tddb <- tdd + tsdb <- tsd + tdob <- tdo + toob <- too + tssb <- tss + # Top layer + tss <- exp(-ks * lai1) + too <- exp(-ko * lai1) + + sb <- sdb * rho1 + sdf * tau1 + sf <- sdf * rho1 + sdb * tau1 + + vb <- dob * rho1 + dof * tau1 + vf <- dof * rho1 + dob * tau1 + + w1 <- sob * rho1 + sof * tau1 + + sigb <- ddb * rho1 + ddf * tau1 + sigf <- ddf * rho1 + ddb * tau1 + att <- 1.0 - sigf + + m2 <- (att + sigb) * (att - sigb) + m2[m2 < 0] <- 0 + m <- sqrt(m2) + which_ncs <- which(m > 0.01) + which_cs <- which(m <= 0.01) + + tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m + rsod <- 0 * m + if (length(which_ncs) > 0) { + resncs <- nonconservativescattering(m[which_ncs], lai1, att[which_ncs], sigb[which_ncs], + ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too) + tdd[which_ncs] <- resncs$tdd + rdd[which_ncs] <- resncs$rdd + tsd[which_ncs] <- resncs$tsd + rsd[which_ncs] <- resncs$rsd + tdo[which_ncs] <- resncs$tdo + rdo[which_ncs] <- resncs$rdo + rsod[which_ncs] <- resncs$rsod + } + if (length(which_cs) > 0) { + rescs <- conservativescattering(m[which_cs], lai1, att[which_cs], sigb[which_cs], + ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too) + tdd[which_cs] <- rescs$tdd + rdd[which_cs] <- rescs$rdd + tsd[which_cs] <- rescs$tsd + rsd[which_cs] <- rescs$rsd + tdo[which_cs] <- rescs$tdo + rdo[which_cs] <- rescs$rdo + rsod[which_cs] <- rescs$rsod + } + + # combine with bottom layer reflectances and transmittances (adding method) + rn <- 1.0 - rdd * rddb + tup <- (tss * rsdb + tsd * rddb) / rn + tdn <- (tsd + tss * rsdb * rdd) / rn + rsdt <- rsd + tup * tdd + rdot <- rdo + tdd * (rddb * tdo + rdob * too) / rn + rsodt <- rsod + (tss * rsodb + tdn * rdob) * too + tup * tdo + + rsost <- (w1 * s1 + w2 * s2) * lai + + rsot <- rsost + rsodt + + # Diffuse reflectances at the top and the bottom are now different + rddt_t <- rdd + tdd * rddb * tdd / rn + rddt_b <- rddb + tddb * rdd * tddb / rn + + # Transmittances of the combined canopy layers + tsst <- tss * tssb + toot <- too * toob + tsdt <- tss * tsdb + tdn * tddb + tdot <- tdob * too + tddb * (tdo + rdd * rdob * too) / rn + tddt <- tdd * tddb / rn + + # Apply clumping effects to vegetation layer + rddcb <- cv * rddt_b + rddct <- cv * rddt_t + tddc <- 1 - cv + cv * tddt + rsdc <- cs * rsdt + tsdc <- cs * tsdt + rdoc <- co * rdot + tdoc <- co * tdot + tssc <- 1 - cs + cs * tsst + tooc <- 1 - co + co * toot + + # new weight function fcdc for crown contribution (W. Verhoef, 22-05-08) + rsoc <- fcdc * rsot + tssooc <- fcd * tsstoo + fcs * toot + fod * tsst + fos + # Canopy absorptance for black background (W. Verhoef, 02-03-04) + alfas <- 1. - tssc - tsdc - rsdc + alfad <- 1. - tddc - rddct + # Add the soil background + rn <- 1 - rddcb * rddsoil + tup <- (tssc * rsdsoil + tsdc * rddsoil) / rn + tdn <- (tsdc + tssc * rsdsoil * rddcb) / rn + + rddt <- rddct + tddc * rddsoil * tddc / rn + rsdt <- rsdc + tup * tddc + rdot <- rdoc + tddc * (rddsoil * tdoc + rdosoil * tooc) / rn + rsot <- rsoc + tssooc * rsosoil + tdn * rdosoil * tooc + tup * tdoc + + # Effect of soil background on canopy absorptances (W. Verhoef, 02-03-04) + alfast <- alfas + tup * alfad + alfadt <- alfad * (1. + tddc * rddsoil / rn) + } + my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt, + "alfast" = alfast, "alfadt" = alfadt) + return(my_list) +} + + + +#" computes non conservative scattering conditions +#" @param m numeric. +#" @param lai numeric. Leaf Area Index +#" @param att numeric. +#" @param sigb numeric. +#" @param ks numeric. +#" @param ko numeric. +#" @param sf numeric. +#" @param sb numeric. +#" @param vf numeric. +#" @param vb numeric. +#" @param tss numeric. +#" @param too numeric. +#" +#" @return list. tdd, rdd, tsd, rsd, tdo, rdo, rsod +#" +#" @export +nonconservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) { + + e1 <- exp(-m * lai) + e2 <- e1 * e1 + rinf <- (att - m) / sigb + rinf2 <- rinf * rinf + re <- rinf * e1 + denom <- 1. - rinf2 * e2 + + j1ks <- jfunc1(ks, m, lai) + j2ks <- jfunc2(ks, m, lai) + j1ko <- jfunc1(ko, m, lai) + j2ko <- jfunc2(ko, m, lai) + + ps <- (sf + sb * rinf) * j1ks + qs <- (sf * rinf + sb) * j2ks + pv <- (vf + vb * rinf) * j1ko + qv <- (vf * rinf + vb) * j2ko + + tdd <- (1. - rinf2) * e1 / denom + rdd <- rinf * (1. - e2) / denom + tsd <- (ps - re * qs) / denom + rsd <- (qs - re * ps) / denom + tdo <- (pv - re * qv) / denom + rdo <- (qv - re * pv) / denom + + z <- jfunc2(ks, ko, lai) + g1 <- (z - j1ks * too) / (ko + m) + g2 <- (z - j1ko * tss) / (ks + m) + + tv1 <- (vf * rinf + vb) * g1 + tv2 <- (vf + vb * rinf) * g2 + + t1 <- tv1 * (sf + sb * rinf) + t2 <- tv2 * (sf * rinf + sb) + t3 <- (rdo * qs + tdo * ps) * rinf + + # Multiple scattering contribution to bidirectional canopy reflectance + rsod <- (t1 + t2 - t3) / (1. - rinf2) + my_list <- list("tdd" = tdd, "rdd" = rdd, "tsd" = tsd, + "rsd" = rsd, "tdo" = tdo, "rdo" = rdo, + "rsod" = rsod) + return(my_list) +} + +#" computes conservative scattering conditions +#" @param m numeric. +#" @param lai numeric. Leaf Area Index +#" @param att numeric. +#" @param sigb numeric. +#" @param ks numeric. +#" @param ko numeric. +#" @param sf numeric. +#" @param sb numeric. +#" @param vf numeric. +#" @param vb numeric. +#" @param tss numeric. +#" @param too numeric. +#" +#" @return list. tdd, rdd, tsd, rsd, tdo, rdo, rsod +#" +#" @export +conservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) { + + # near or complete conservative scattering + j4 <- jfunc4(m, lai) + amsig <- att - sigb + apsig <- att + sigb + rtp <- (1 - amsig * j4) / (1 + amsig * j4) + rtm <- (-1 + apsig * j4) / (1 + apsig * j4) + rdd <- 0.5 * (rtp + rtm) + tdd <- 0.5 * (rtp - rtm) + + dns <- ks * ks - m * m + dno <- ko * ko - m * m + cks <- (sb * (ks - att) - sf * sigb) / dns + cko <- (vb * (ko - att) - vf * sigb) / dno + dks <- (-sf * (ks + att) - sb * sigb) / dns + dko <- (-vf * (ko + att) - vb * sigb) / dno + ho <- (sf * cko + sb * dko) / (ko + ks) + + rsd <- cks * (1 - tss * tdd) - dks * rdd + rdo <- cko * (1 - too * tdd) - dko * rdd + tsd <- dks * (tss - tdd) - cks * tss * rdd + tdo <- dko * (too - tdd) - cko * too * rdd + # Multiple scattering contribution to bidirectional canopy reflectance + rsod <- ho * (1 - tss * too) - cko * tsd * too - dko * rsd + + my_list <- list("tdd" = tdd, "rdd" = rdd, "tsd" = tsd, + "rsd" = rsd, "tdo" = tdo, "rdo" = rdo, + "rsod" = rsod) + return(my_list) +} + + + + + + +#" computes the leaf angle distribution function value (freq) +#" +#" Ellipsoidal distribution function characterised by the average leaf +#" inclination angle in degree (ala) +#" Campbell 1986 +#" @param ala average leaf angle +#" @return foliar_distrib list. lidf and litab +#" @export +campbell <- function(ala) { + + tx1 <- c(10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88., 90.) + tx2 <- c(0., 10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88.) + litab <- (tx2 + tx1) / 2 + n <- length(litab) + tl1 <- tx1 * (pi / 180) + tl2 <- tx2 * (pi / 180) + excent <- exp(-1.6184e-5 * ala**3 + 2.1145e-3 * ala**2 - 1.2390e-1 * ala + 3.2491) + sum0 <- 0 + + freq <- c() + for (i in 1:n) { + x1 <- excent / (sqrt(1. + excent**2. * tan(tl1[i])**2)) + x2 <- excent / (sqrt(1. + excent**2. * tan(tl2[i])**2)) + if (excent == 1) { + freq[i] <- abs(cos(tl1[i]) - cos(tl2[i])) + } else { + alpha <- excent / sqrt(abs(1 - excent**2)) + alpha2 <- alpha**2 + x12 <- x1**2 + x22 <- x2**2 + alpx1 <- 0 * alpha2 + alpx2 <- 0 * alpha2 + almx1 <- 0 * alpha2 + almx2 <- 0 * alpha2 + if (excent > 1) { + alpx1 <- sqrt(alpha2[excent > 1] + x12[excent > 1]) + alpx2[excent > 1] <- sqrt(alpha2[excent > 1] + x22[excent > 1]) + dum <- x1 * alpx1 + alpha2 * log(x1 + alpx1) + freq[i] <- abs(dum - (x2 * alpx2 + alpha2 * log(x2 + alpx2))) + } else { + almx1 <- sqrt(alpha2 - x12) + almx2 <- sqrt(alpha2 - x22) + dum <- x1 * almx1 + alpha2 * asin(x1 / alpha) + freq[i] <- abs(dum - (x2 * almx2 + alpha2 * asin(x2 / alpha))) + } + } + } + sum0 <- sum(freq) + freq0 <- freq / sum0 + foliar_distrib <- list("lidf" = freq0, "litab" = litab) + return(foliar_distrib) +} + +#" computes the leaf angle distribution function value (freq) +#" +#" Using the original bimodal distribution function initially proposed in SAIL +#" references +#" ---------- +#" (Verhoef1998) Verhoef, Wout. Theory of radiative transfer models applied +#" in optical remote sensing of vegetation canopies. +#" nationaal Lucht en Ruimtevaartlaboratorium, 1998. +#" http: / / library.wur.nl / WebQuery / clc / 945481. +#" @param a controls the average leaf slope +#" @param b controls the distribution"s bimodality +#" LIDF type a b +#" Planophile 1 0 +#" Erectophile -1 0 +#" Plagiophile 0 -1 +#" Extremophile 0 1 +#" Spherical -0.35 -0.15 +#" Uniform 0 0 +#" requirement: ||lidfa|| + ||lidfb|| < 1 +#" +#" @return foliar_distrib list. lidf and litab +#" @export +dladgen <- function(a, b) { + litab <- c(5., 15., 25., 35., 45., 55., 65., 75., 81., 83., 85., 87., 89.) + freq <- c() + for (i1 in 1:8) { + t <- i1 * 10 + freq[i1] <- dcum(a, b, t) + } + for (i2 in 9:12) { + t <- 80. + (i2 - 8) * 2. + freq[i2] <- dcum(a, b, t) + } + freq[13] <- 1 + for (i in 13:2) { + freq[i] <- freq[i] - freq[i - 1] + } + foliar_distrib <- list("lidf" = freq, "litab" = litab) + return(foliar_distrib) +} + +#" dcum function +#" @param a numeric. controls the average leaf slope +#" @param b numeric. controls the distribution"s bimodality +#" @param t numeric. angle +#" @return f +#" @export +dcum <- function(a, b, t) { + rd <- pi / 180 + if (a >= 1) { + f <- 1 - cos(rd * t) + } else { + eps <- 1e-8 + delx <- 1 + x <- 2 * rd * t + p <- x + while (delx >= eps) { + y <- a * sin(x) + .5 * b * sin(2. * x) + dx <- .5 * (y - x + p) + x <- x + dx + delx <- abs(dx) + } + f <- (2. * y + p) / pi + } + return(f) +} + +#" J1 function with avoidance of singularity problem +#" +#" @param k numeric. Extinction coefficient for direct (solar or observer) flux +#" @param l numeric. +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc1 <- function(k, l, t) { + # J1 function with avoidance of singularity problem + del <- (k - l) * t + jout <- 0 * l + jout[which(abs(del) > 1e-3)] <- (exp(-l[which(abs(del) > 1e-3)] * t) - exp(-k * t)) / (k - l[which(abs(del) > 1e-3)]) + jout[which(abs(del) <= 1e-3)] <- 0.5 * t * (exp(-k * t) + exp(-l[which(abs(del) <= 1e-3)] * t)) * (1 - del[which(abs(del) <= 1e-3)] * del[which(abs(del) <= 1e-3)] / 12) + return(jout) +} + +#" J2 function with avoidance of singularity problem +#" +#" @param k numeric. Extinction coefficient for direct (solar or observer) flux +#" @param l numeric. +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc2 <- function(k, l, t) { + # J2 function + jout <- (1. - exp(-(k + l) * t)) / (k + l) + return(jout) +} + +#" J3 function with avoidance of singularity problem +#" +#" @param k numeric. Extinction coefficient for direct (solar or observer) flux +#" @param l numeric. +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc3 <- function(k, l, t) { + out <- (1. - exp(-(k + l) * t)) / (k + l) + return(out) +} + + +#" j4 function for treating (near) conservative scattering +#" +#" @param m numeric. Extinction coefficient for direct (solar or observer) flux +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc4 <- function(m, t) { + + del <- m * t + out <- 0 * del + out[del > 1e-3] <- (1 - exp(-del)) / (m * (1 + exp(-del))) + out[del <= 1e-3] <- 0.5 * t * (1. - del * del / 12.) + return(out) +} + + +#" compute volume scattering functions and interception coefficients +#" for given solar zenith, viewing zenith, azimuth and leaf inclination angle. +#" +#" @param tts numeric. solar zenith +#" @param tto numeric. viewing zenith +#" @param psi numeric. azimuth +#" @param ttl numeric. leaf inclination angle +#" @return res list. includes chi_s, chi_o, frho, ftau +#" @export +volscatt <- function(tts, tto, psi, ttl) { + #******************************************************************************** + #* chi_s = interception functions + #* chi_o = interception functions + #* frho = function to be multiplied by leaf reflectance rho + #* ftau = functions to be multiplied by leaf transmittance tau + #******************************************************************************** + # Wout Verhoef, april 2001, for CROMA + + rd <- pi / 180 + costs <- cos(rd * tts) + costo <- cos(rd * tto) + sints <- sin(rd * tts) + sinto <- sin(rd * tto) + cospsi <- cos(rd * psi) + psir <- rd * psi + costl <- cos(rd * ttl) + sintl <- sin(rd * ttl) + cs <- costl * costs + co <- costl * costo + ss <- sintl * sints + so <- sintl * sinto + + #c .............................................................................. + #c betas -bts- and betao -bto- computation + #c Transition angles (beta) for solar (betas) and view (betao) directions + #c if thetav + thetal > pi / 2, bottom side of the leaves is observed for leaf azimut + #c interval betao + phi<leaf azimut<2pi-betao + phi. + #c if thetav + thetal<pi / 2, top side of the leaves is always observed, betao=pi + #c same consideration for solar direction to compute betas + #c .............................................................................. + + cosbts <- 5 + if (abs(ss) > 1e-6) { + cosbts <- -cs / ss + } + cosbto <- 5 + if (abs(so) > 1e-6) { + cosbto <- -co / so + } + + if (abs(cosbts) < 1) { + bts <- acos(cosbts) + ds <- ss + } else { + bts <- pi + ds <- cs + } + chi_s <- 2. / pi * ((bts - pi * .5) * cs + sin(bts) * ss) + if (abs(cosbto) < 1) { + bto <- acos(cosbto) + doo <- so + } else if (tto < 90) { + bto <- pi + doo <- co + } else { + bto <- 0 + doo <- -co + } + chi_o <- 2. / pi * ((bto - pi * .5) * co + sin(bto) * so) + + #c .............................................................................. + #c computation of auxiliary azimut angles bt1, bt2, bt3 used + #c for the computation of the bidirectional scattering coefficient w + #c ............................................................................. + + btran1 <- abs(bts - bto) + btran2 <- pi - abs(bts + bto - pi) + + if (psir <= btran1) { + bt1 <- psir + bt2 <- btran1 + bt3 <- btran2 + } else { + bt1 <- btran1 + if (psir <= btran2) { + bt2 <- psir + bt3 <- btran2 + } else { + bt2 <- btran2 + bt3 <- psir + } + } + t1 <- 2. * cs * co + ss * so * cospsi + t2 <- 0 + if (bt2 > 0) { + t2 <- sin(bt2) * (2. * ds * doo + ss * so * cos(bt1) * cos(bt3)) + } + + denom <- 2. * pi * pi + frho <- ((pi - bt2) * t1 + t2) / denom + ftau <- (-bt2 * t1 + t2) / denom + + if (frho < 0) { + frho <- 0 + } + if (ftau < 0) { + ftau <- 0 + } + res <- list("chi_s" = chi_s, "chi_o" = chi_o, "frho" = frho, "ftau" = ftau) + return(res) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/prosail-master/R/Lib_PROSAIL_HybridInversion.R Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,554 @@ +# ============================================================================= = +# prosail +# Lib_PROSAIL_HybridInversion.R +# ============================================================================= = +# PROGRAMMERS: +# Jean-Baptiste FERET <jb.feret@teledetection.fr> +# Florian de BOISSIEU <fdeboiss@gmail.com> +# Copyright 2019 / 11 Jean-Baptiste FERET +# ============================================================================= = +# This Library includes functions dedicated to PROSAIL inversion using hybrid +# approach based on SVM regression +# ============================================================================= = + + +#" This function applies SVR model on raster data in order to estimate +#" vegetation biophysical properties +#" +#" @param raster_path character. path for a raster file +#" @param hybridmodel list. hybrid models produced from train_prosail_inversion +#" each element of the list corresponds to a set of hybrid models for a vegetation parameter +#" @param pathout character. path for directory where results are written +#" @param selectedbands list. list of spectral bands to be selected from raster (identified by name of vegetation parameter) +#" @param bandname character. spectral bands corresponding to the raster +#" @param maskraster character. path for binary mask defining ON (1) and OFF (0) pixels in the raster +#" @param multiplyingfactor numeric. multiplying factor used to write reflectance in the raster +#" --> PROSAIL simulates reflectance between 0 and 1, and raster data expected in the same range +#" +#" @return None +#" @importFrom progress progress_bar +#" @importFrom stars read_stars +#" @importFrom raster raster brick blockSize readStart readStop getValues writeStart writeStop writeValues +#" @import rgdal +#" @export +apply_prosail_inversion <- function(raster_path, hybridmodel, pathout, + selectedbands, bandname, + maskraster = FALSE, multiplyingfactor = 10000) { + + # explain which biophysical variables will be computed + bpvar <- names(hybridmodel) + print("The following biophysical variables will be computed") + print(bpvar) + + # get image dimensions + if (attr(rgdal::GDALinfo(raster_path, returnStats = FALSE), "driver") == "ENVI") { + hdr <- read_envi_header(get_hdr_name(raster_path)) + dimsraster <- list("rows" = hdr$lines, "cols" = hdr$samples, "bands" = hdr$bands) + } else { + dimsraster <- dim(read_stars(raster_path)) + dimsraster <- list("rows" = as.numeric(dimsraster[2]), "cols" = as.numeric(dimsraster[1]), "bands" = as.numeric(dimsraster[3])) + } + + # Produce a map for each biophysical property + for (parm in bpvar) { + print(paste("Computing", parm, sep = " ")) + # read by chunk to avoid memory problem + blk <- blockSize(brick(raster_path)) + # reflectance file + r_in <- readStart(brick(raster_path)) + # mask file + r_inmask <- FALSE + if (maskraster == FALSE) { + selectpixels <- "ALL" + } else if (!maskraster == FALSE) { + if (file.exists(maskraster)) { + r_inmask <- readStart(raster(maskraster)) + } else if (!file.exists(maskraster)) { + message("WARNING: Mask file does not exist:") + print(maskraster) + message("Processing all image") + selectpixels <- "ALL" + } + } + # initiate progress bar + pgbarlength <- length(hybridmodel[[parm]]) * blk$n + pb <- progress_bar$new( + format = "Hybrid inversion on raster [:bar] :percent in :elapsedfull, estimated time remaining :eta", + total = pgbarlength, clear = FALSE, width = 100) + + # output files + bpvarpath <- file.path(pathout, paste(basename(raster_path), parm, sep = "_")) + bpvarsdpath <- file.path(pathout, paste(basename(raster_path), parm, "STD", sep = "_")) + r_outmean <- writeStart(raster(raster_path), filename = bpvarpath, format = "ENVI", overwrite = TRUE) + r_outsd <- writeStart(raster(raster_path), filename = bpvarsdpath, format = "ENVI", overwrite = TRUE) + selbands <- match(selectedbands[[parm]], bandname) + + # loop over blocks + for (i in seq_along(blk$row)) { + # read values for block + # format is a matrix with rows the cells values and columns the layers + blockval <- getValues(r_in, row = blk$row[i], nrows = blk$nrows[i]) + fulllength <- dim(blockval)[1] + + if (typeof(r_inmask) == "logical") { + blockval <- blockval[, selbands] + # automatically filter pixels corresponding to negative values + selectpixels <- which(blockval[, 1] > 0) + blockval <- blockval[selectpixels, ] + } else if (typeof(r_inmask) == "S4") { + maskval <- getValues(r_inmask, row = blk$row[i], nrows = blk$nrows[i]) + selectpixels <- which(maskval == 1) + blockval <- blockval[selectpixels, selbands] + } + mean_estimatefull <- NA * vector(length = fulllength) + std_estimatefull <- NA * vector(length = fulllength) + if (length(selectpixels) > 0) { + blockval <- blockval / multiplyingfactor + modelsvr_estimate <- list() + for (modind in 1:seq_along(hybridmodel[[parm]])) { + pb$tick() + modelsvr_estimate[[modind]] <- predict(hybridmodel[[parm]][[modind]], blockval) + } + modelsvr_estimate <- do.call(cbind, modelsvr_estimate) + # final estimated value = mean parm value for all models + mean_estimate <- rowMeans(modelsvr_estimate) + # "uncertainty" = STD value for all models + std_estimate <- rowSds(modelsvr_estimate) + mean_estimatefull[selectpixels] <- mean_estimate + std_estimatefull[selectpixels] <- std_estimate + } else { + for (modind in 1:seq_along(hybridmodel[[parm]])) { + pb$tick() + } + } + r_outmean <- writeValues(r_outmean, mean_estimatefull, blk$row[i], format = "ENVI", overwrite = TRUE) + r_outsd <- writeValues(r_outsd, std_estimatefull, blk$row[i], format = "ENVI", overwrite = TRUE) + } + # close files + r_in <- readStop(r_in) + if (typeof(r_inmask) == "S4") { + r_inmask <- readStop(r_inmask) + } + r_outmean <- writeStop(r_outmean) + r_outsd <- writeStop(r_outsd) + # write biophysical variable name in headers + hdr <- read_envi_header(get_hdr_name(bpvarpath)) + hdr$`band names` <- paste("{", parm, "}", sep = "") + write_envi_header(hdr, get_hdr_name(bpvarpath)) + } + print("processing completed") + return(invisible()) +} + +#" get hdr name from image file name, assuming it is BIL format +#" +#" @param impath path of the image +#" +#" @return corresponding hdr +#" @importFrom tools file_ext file_path_sans_ext +#" @export +get_hdr_name <- function(impath) { + if (tools::file_ext(impath) == "") { + impathhdr <- paste(impath, ".hdr", sep = "") + } else if (tools::file_ext(impath) == "bil") { + impathhdr <- gsub(".bil", ".hdr", impath) + } else if (tools::file_ext(impath) == "zip") { + impathhdr <- gsub(".zip", ".hdr", impath) + } else { + impathhdr <- paste(tools::file_path_sans_ext(impath), ".hdr", sep = "") + } + + if (!file.exists(impathhdr)) { + message("WARNING : COULD NOT FIND hdr FILE") + print(impathhdr) + message("Process may stop") + } + return(impathhdr) +} + +#" This function applies the regression models trained with prosail_hybrid_train +#" +#" @param regressionmodels list. List of regression models produced by prosail_hybrid_train +#" @param refl numeric. LUT of bidirectional reflectances factors used for training +#" +#" @return hybridres list. Estimated values corresponding to refl. Includes +#" - meanestimate = mean value for the ensemble regression model +#" - stdestimate = std value for the ensemble regression model +#" @importFrom stats predict +#" @importFrom matrixStats rowSds +#" @importFrom progress progress_bar +#" @export + +prosail_hybrid_apply <- function(regressionmodels, refl) { + + # make sure refl is right dimensions + refl <- t(refl) + nbfeatures <- regressionmodels[[1]]$dim + if (!ncol(refl) == nbfeatures && nrow(refl) == nbfeatures) { + refl <- t(refl) + } + nbensemble <- length(regressionmodels) + estimatedval <- list() + pb <- progress_bar$new( + format = "Applying SVR models [:bar] :percent in :elapsed", + total = nbensemble, clear = FALSE, width = 100) + for (i in 1:nbensemble) { + pb$tick() + estimatedval[[i]] <- predict(regressionmodels[[i]], refl) + } + estimatedval <- do.call(cbind, estimatedval) + meanestimate <- rowMeans(estimatedval) + stdestimate <- rowSds(estimatedval) + hybridres <- list("meanestimate" = meanestimate, "stdestimate" = stdestimate) + return(hybridres) +} + +#" This function trains a suppot vector regression for a set of variables based on spectral data +#" +#" @param brf_lut numeric. LUT of bidirectional reflectances factors used for training +#" @param inputvar numeric. biophysical parameter corresponding to the reflectance +#" @param figplot Boolean. Set to TRUE if you want a scatterplot +#" @param nbensemble numeric. Number of individual subsets should be generated from brf_lut +#" @param withreplacement Boolean. should subsets be generated with or without replacement? +#" +#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut +#" @importFrom liquidSVM svmRegression +#" @importFrom stats predict +#" @importFrom progress progress_bar +#" @importFrom graphics par +#" @importFrom expandFunctions reset.warnings +#" @importFrom stringr str_split +#" @importFrom simsalapar tryCatch.W.E +#" @import dplyr +#" @import ggplot2 +# @" @import caret +#" @export + +prosail_hybrid_train <- function(brf_lut, inputvar, figplot = FALSE, nbensemble = 20, withreplacement = FALSE) { + + x <- y <- ymean <- ystdmin <- ystdmax <- NULL + # split the LUT into nbensemble subsets + nbsamples <- length(inputvar) + if (dim(brf_lut)[2] == nbsamples) { + brf_lut <- t(brf_lut) + } + + # if subsets are generated from brf_lut with replacement + if (withreplacement == TRUE) { + subsets <- list() + samples_per_run <- round(nbsamples / nbensemble) + for (run in (1:nbensemble)) { + subsets[[run]] <- sample(seq(1, nbsamples), samples_per_run, replace = TRUE) + } + # if subsets are generated from brf_lut without replacement + } else if (withreplacement == FALSE) { + subsets <- split(sample(seq(1, nbsamples, by = 1)), seq(1, nbensemble, by = 1)) + } + + # run training for each subset + modelssvr <- list() + predictedyall <- list() + tunedmodelyall <- list() + pb <- progress_bar$new( + format = "Training SVR on subsets [:bar] :percent in :elapsed", + total = nbensemble, clear = FALSE, width = 100) + for (i in 1:nbensemble) { + pb$tick() + Sys.sleep(1 / 100) + trainingset <- list() + trainingset$X <- brf_lut[subsets[i][[1]], ] + trainingset$Y <- inputvar[subsets[i][[1]]] + # liquidSVM + r1 <- tryCatch.W.E(tunedmodel <- liquidSVM::svmRegression(trainingset$X, trainingset$Y)) + if (!is.null(r1$warning)) { + msg <- r1$warning$message + valgamma <- str_split(string = msg, pattern = "gamma=")[[1]][2] + vallambda <- str_split(string = msg, pattern = "lambda=")[[1]][2] + if (!is.na(as.numeric(valgamma))) { + message("Adjusting Gamma accordingly") + valgamma <- as.numeric(valgamma) + tunedmodel <- liquidSVM::svmRegression(trainingset$X, trainingset$Y, min_gamma = valgamma) + } + if (!is.na(as.numeric(vallambda))) { + message("Adjusting Lambda accordingly") + vallambda <- as.numeric(vallambda) + tunedmodel <- liquidSVM::svmRegression(trainingset$X, trainingset$Y, min_lambda = vallambda) + } + } + modelssvr[[i]] <- tunedmodel + } + + # if scatterplots needed + if (figplot == TRUE) { + # predict for full brf_lut + for (i in 1:nbensemble) { + tunedmodely <- stats::predict(modelssvr[[i]], brf_lut) + tunedmodelyall <- cbind(tunedmodelyall, matrix(tunedmodely, ncol = 1)) + } + # plot prediction + df <- data.frame(x = rep(1:nbsamples, nbensemble), y = as.numeric(matrix(tunedmodelyall, ncol = 1))) + df_summary <- df %>% + dplyr::group_by(x) %>% + summarize(ymin = min(y), ystdmin = mean(y) - sd(y), + ymax = max(y), ystdmax = mean(y) + sd(y), + ymean = mean(y)) + par(mar = rep(.1, 4)) + p <- ggplot(df_summary, aes(x = inputvar, y = ymean)) + + geom_point(size = 2) + + geom_errorbar(aes(ymin = ystdmin, ymax = ystdmax)) + meanpredict <- rowMeans(matrix(as.numeric(tunedmodelyall), ncol = nbensemble)) + print(p) + } + return(modelssvr) +} + +#" Reads ENVI hdr file +#" +#" @param hdrpath Path of the hdr file +#" +#" @return list of the content of the hdr file +#" @export +read_envi_header <- function(hdrpath) { + if (!grepl(".hdr$", hdrpath)) { + stop("File extension should be .hdr") + } + hdr <- readLines(hdrpath) + ## check ENVI at beginning of file + if (!grepl("ENVI", hdr[1])) { + stop("Not an ENVI header (ENVI keyword missing)") + } else { + hdr <- hdr [-1] + } + ## remove curly braces and put multi-line key-value-pairs into one line + hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr) + l <- grep("\\{", hdr) + r <- grep("\\}", hdr) + + if (length(l) != length(r)) { + stop("Error matching curly braces in header (differing numbers).") + } + + if (any(r <= l)) { + stop("Mismatch of curly braces in header.") + } + + hdr[l] <- sub("\\{", "", hdr[l]) + hdr[r] <- sub("\\}", "", hdr[r]) + + for (i in rev(seq_along(l))) { + hdr <- c( + hdr [seq_len(l [i] - 1)], + paste(hdr [l [i]:r [i]], collapse = "\n"), + hdr [-seq_len(r [i])] + ) + } + + ## split key = value constructs into list with keys as names + hdr <- sapply(hdr, split_line, " = ", USE.NAMES = FALSE) + names(hdr) <- tolower(names(hdr)) + + ## process numeric values + tmp <- names(hdr) %in% c( + "samples", "lines", "bands", "header offset", "data type", + "byte order", "default bands", "data ignore value", + "wavelength", "fwhm", "data gain values" + ) + hdr [tmp] <- lapply(hdr [tmp], function(x) { + as.numeric(unlist(strsplit(x, ","))) + }) + + return(hdr) +} + +#" ENVI functions +#" +#" based on https: / / github.com / cran / hyperSpec / blob / master / R / read.ENVI.R +#" added wavelength, fwhm, ... to header reading +#" Title +#" +#" @param x character. +#" @param separator character +#" @param trim_blank boolean. +#" +#" @return list. +#" @export +split_line <- function(x, separator, trim_blank = TRUE) { + tmp <- regexpr(separator, x) + key <- substr(x, 1, tmp - 1) + value <- substr(x, tmp + 1, nchar(x)) + if (trim_blank) { + blank_pattern <- "^[[:blank:]]*([^[:blank:]] + .*[^[:blank:]] + )[[:blank:]]*$" + key <- sub(blank_pattern, "\\1", key) + value <- sub(blank_pattern, "\\1", value) + } + value <- as.list(value) + names(value) <- key + return(value) +} + +#" This function performs full training for hybrid invrsion using SVR with +#" values for default parameters +#" +#" @param minval list. minimum value for input parameters sampled to produce a training LUT +#" @param maxval list. maximum value for input parameters sampled to produce a training LUT +#" @param typedistrib list. Type of distribution. Either "Uniform" or "Gaussian" +#" @param gaussiandistrib list. Mean value and STD corresponding to the parameters sampled with gaussian distribution +#" @param parmset list. list of input parameters set to a specific value +#" @param nbsamples numeric. number of samples in training LUT +#" @param nbsamplesperrun numeric. number of training sample per individual regression model +#" @param nbmodels numeric. number of individual models to be run for ensemble +#" @param replacement bolean. is there replacement in subsampling? +#" @param sailversion character. Either 4SAIL or 4SAIL2 +#" @param parms2estimate list. list of input parameters to be estimated +#" @param bands2select list. list of bands used for regression for each input parameter +#" @param noiselevel list. list of noise value added to reflectance (defined per input parm) +#" @param specprospect list. Includes optical constants required for PROSPECT +#" @param specsoil list. Includes either dry soil and wet soil, or a unique soil sample if the psoil parameter is not inverted +#" @param specatm list. Includes direct and diffuse radiation for clear conditions +#" @param path_results character. path for results +#" @param figplot boolean. Set TRUE to get scatterplot of estimated biophysical variable during training step +#" @param force4lowlai boolean. Set TRUE to artificially reduce leaf chemical constituent content for low LAI +#" +#" +#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut +#" @export + +train_prosail_inversion <- function(minval = NULL, maxval = NULL, + typedistrib = NULL, gaussiandistrib = NULL, parmset = NULL, + nbsamples = 2000, nbsamplesperrun = 100, nbmodels = 20, replacement = TRUE, + sailversion = "4SAIL", + parms2estimate = "lai", bands2select = NULL, noiselevel = NULL, + specprospect = NULL, specsoil = NULL, specatm = NULL, + path_results = "./", figplot = FALSE, force4lowlai = TRUE) { + + ###===================================================================### + ### 1- PRODUCE A LUT TO TRAIN THE HYBRID INVERSION ### + ###===================================================================### + # Define sensor characteristics + if (is.null(specprospect)) { + specprospect <- prosail::specprospect + } + if (is.null(specsoil)) { + specsoil <- prosail::specsoil + } + if (is.null(specprospect)) { + specatm <- prosail::specatm + } + # define distribution for parameters to be sampled + if (is.null(typedistrib)) { + typedistrib <- data.frame("CHL" = "Uniform", "CAR" = "Uniform", "EWT" = "Uniform", "ANT" = "Uniform", "LMA" = "Uniform", "N" = "Uniform", "BROWN" = "Uniform", + "psoil" = "Uniform", "LIDFa" = "Uniform", "lai" = "Uniform", "q" = "Uniform", "tto" = "Uniform", "tts" = "Uniform", "psi" = "Uniform") + } + if (is.null(gaussiandistrib)) { + gaussiandistrib <- list("Mean" = NULL, "Std" = NULL) + } + if (is.null(minval)) { + minval <- data.frame("CHL" = 10, "CAR" = 0, "EWT" = 0.01, "ANT" = 0, "LMA" = 0.005, "N" = 1.0, "psoil" = 0.0, "BROWN" = 0.0, + "LIDFa" = 20, "lai" = 0.5, "q" = 0.1, "tto" = 0, "tts" = 20, "psi" = 80) + } + if (is.null(maxval)) { + maxval <- data.frame("CHL" = 75, "CAR" = 15, "EWT" = 0.03, "ANT" = 2, "LMA" = 0.03, "N" = 2.0, "psoil" = 1.0, "BROWN" = 0.5, + "LIDFa" = 70, "lai" = 7, "q" = 0.2, "tto" = 5, "tts" = 30, "psi" = 110) + } + # define min and max values + # fixed parameters + if (is.null(parmset)) { + parmset <- data.frame("TypeLidf" = 2, "alpha" = 40) + } + # produce input parameters distribution + if (sailversion == "4SAIL") { + inputprosail <- get_distribution_input_prosail(minval, maxval, parmset, nbsamples, + typedistrib = typedistrib, + Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std, + force4lowlai = force4lowlai) + } else if (sailversion == "4SAIL2") { + inputprosail <- get_distribution_input_prosail2(minval, maxval, parmset, nbsamples, + typedistrib = typedistrib, + Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std, + force4lowlai = force4lowlai) + } + if (sailversion == "4SAIL2") { + # Definition of Cv && update LAI + maxlai <- min(c(maxval$lai), 4) + inputprosail$Cv <- NA * inputprosail$lai + inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1 + inputprosail$Cv[which(inputprosail$lai <= maxlai)] <- (1 / maxlai) + inputprosail$lai[which(inputprosail$lai <= maxlai)] / (maxlai + 1) + inputprosail$Cv <- inputprosail$Cv * matrix(rnorm(length(inputprosail$Cv), mean = 1, sd = 0.1)) + inputprosail$Cv[which(inputprosail$Cv < 0)] <- 0 + inputprosail$Cv[which(inputprosail$Cv > 1)] <- 1 + inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1 + inputprosail$fraction_brown <- 0 + 0 * inputprosail$lai + inputprosail$diss <- 0 + 0 * inputprosail$lai + inputprosail$Zeta <- 0.2 + 0 * inputprosail$lai + inputprosail$lai <- inputprosail$lai * inputprosail$Cv + } + + # generate LUT of BRF corresponding to inputprosail, for a sensor + brf_lut <- Generate_LUT_BRF(sailversion = sailversion, inputprosail = inputprosail, + specprospect = specprospect, specsoil = specsoil, specatm = specatm) + + # write parameters LUT + output <- matrix(unlist(inputprosail), ncol = length(inputprosail), byrow = FALSE) + filename <- file.path(path_results, "PROSAIL_LUT_InputParms.txt") + write.table(x = format(output, digits = 3), file = filename, append = FALSE, quote = FALSE, + col.names = names(inputprosail), row.names = FALSE, sep = "\t") + # Write BRF LUT corresponding to parameters LUT + filename <- file.path(path_results, "PROSAIL_LUT_reflectance.txt") + write.table(x = format(t(brf_lut), digits = 5), file = filename, append = FALSE, quote = FALSE, + col.names = specprospect$lambda, row.names = FALSE, sep = "\t") + + # Which bands will be used for inversion? + if (is.null(bands2select)) { + bands2select <- list() + for (parm in parms2estimate) { + bands2select[[parm]] <- seq(1, length(specprospect$lambda)) + } + } + # Add gaussian noise to reflectance LUT: one specific LUT per parameter + if (is.null(noiselevel)) { + noiselevel <- list() + for (parm in parms2estimate) { + noiselevel[[parm]] <- 0.01 + } + } + + # produce LIT with noise + brf_lut_noise <- list() + for (parm in parms2estimate) { + brf_lut_noise[[parm]] <- brf_lut[bands2select[[parm]], ] + brf_lut[bands2select[[parm]], ] * matrix(rnorm(nrow(brf_lut[bands2select[[parm]], ]) * ncol(brf_lut[bands2select[[parm]], ]), + 0, noiselevel[[parm]]), nrow = nrow(brf_lut[bands2select[[parm]], ])) + } + + ###===================================================================### + ### PERFORM HYBRID INVERSION ### + ###===================================================================### + # train SVR for each variable and each run + modelsvr <- list() + for (parm in parms2estimate) { + colparm <- which(parm == names(inputprosail)) + inputvar <- inputprosail[[colparm]] + modelsvr[[parm]] <- prosail_hybrid_train(brf_lut = brf_lut_noise[[parm]], inputvar = inputvar, + figplot = figplot, nbensemble = nbmodels, withreplacement = replacement) + } + return(modelsvr) +} + +#" writes ENVI hdr file +#" +#" @param hdr content to be written +#" @param hdrpath Path of the hdr file +#" +#" @return None +#" @importFrom stringr str_count +#" @export +write_envi_header <- function(hdr, hdrpath) { + h <- lapply(hdr, function(x) { + if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w + ") > 1)) { + x <- paste0("{", paste(x, collapse = ","), "}") + } + # convert last numerics + x <- as.character(x) + }) + writeLines(c("ENVI", paste(names(hdr), h, sep = "=")), con = hdrpath) + return(invisible()) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/prosail-master/R/Lib_SpectralIndices.R Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,679 @@ +# ============================================================================== = +# prosail +# Lib_spectralindices.R +# ============================================================================== = +# PROGRAMMERS: +# Jean-Baptiste FERET <jb.feret@teledetection.fr> +# Florian de BOISSIEU <fdeboiss@gmail.com> +# Copyright 2019/11 Jean-Baptiste FERET +# ============================================================================== = +# This Library includes aims at computing spectral indices from reflectance data +# ============================================================================== = + +#" This function computes Area under curve for continuum removed reflectances +#" See Malenovský et al. (2013) for details +#" http://dx.doi.org/10.1016/j.rse.2012.12.015 +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image +#" @param aucminmax list. wavelengths of lower and upper boundaries ("CRmin" and "CRmax") +#" @param reflfactor numeric. multiplying factor used to write reflectance in image (==10000 for S2) +#" +#" @return aucval raster +#" @export +auc <- function(refl, sensorbands, aucminmax, reflfactor = 1) { + + aucbands <- list() + aucbands[["CRmin"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmin"]])] + aucbands[["CRmax"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmax"]])] + bands <- get_closest_bands(sensorbands, aucbands) + for (i in bands[["CRmin"]]:bands[["CRmax"]]) { + if (is.na(match(i, bands))) { + aucbands[[paste("B", i, sep = "")]] <- sensorbands[i] + } + } + # compute continuum removal for all spectral bands + cr <- cr_wl(refl = refl, sensorbands = sensorbands, + crbands = aucbands, reflfactor = reflfactor) + + wl <- sort(unlist(aucbands), decreasing = FALSE) + aucval <- 0.5 * (1 - cr[[1]]) * (wl[2] - wl[1]) + for (i in 2:length(cr)) { + aucval <- aucval + 0.5 * (2 - cr[[i - 1]] - cr[[i]]) * (wl[i + 1] - wl[i]) + } + aucval <- aucval + 0.5 * (1 - cr[[length(cr)]]) * (wl[i + 2] - wl[i + 1]) + return(aucval) +} + +#" This function extracts boundaries to be used to compute continuum from reflectance data +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image +#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return crminmax list. list of rasters corresponding to minimum and maximum wavelengths +#" @export +crbound <- function(refl, sensorbands, crbands, reflfactor = 1) { + + # get closest spectral bands from CR1 and CR2 + bands <- get_closest_bands(sensorbands, list(crbands[["CRmin"]], crbands[["CRmax"]])) + wl <- sensorbands[bands] + # get equation for line going from CR1 to CR2 + crminmax <- readrasterbands(refl = refl, bands = bands, reflfactor = reflfactor) + names(crminmax) <- paste("wl_", as.character(wl), sep = "") + return(crminmax) +} + +#" This function extracts boundaries to be used to compute continuum from reflectance data +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image +#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2 +#" @importFrom progress progress_bar +#" @export +cr_wl <- function(refl, sensorbands, crbands, reflfactor = 1) { + + # Make sure CRmin and CRmax are correctly defined + if (is.na(match("CRmin", names(crbands))) || is.na(match("CRmax", names(crbands)))) { + stop("Please define CRmin and CRmax (CRmin<CRmax) as spectral bands in crbands") + } + if (crbands[["CRmax"]] < crbands[["CRmin"]]) { + stop("Please define CRmin < CRmax in crbands") + } + # extract CRmin and CRmax + crminmax <- crbound(refl, sensorbands, crbands, reflfactor = reflfactor) + # extract other bands and compute CR + crmin <- sensorbands[get_closest_bands(sensorbands, crbands[["CRmin"]])] + crmax <- sensorbands[get_closest_bands(sensorbands, crbands[["CRmax"]])] + crbands[["CRmin"]] <- NULL + crbands[["CRmax"]] <- NULL + cr <- list() + # initiate progress bar + pgbarlength <- length(crbands) + pb <- progress_bar$new( + format = "Computing continuum removal [:bar] :percent in :elapsedfull, estimated time remaining :eta", + total = pgbarlength, clear = FALSE, width = 100) + # computation for each band + for (band in crbands) { + pb$tick() + bandrank <- get_closest_bands(sensorbands, band) + raster2cr <- readrasterbands(refl = refl, bands = bandrank, reflfactor = reflfactor) + cr[[as.character(band)]] <- computecr(wlmin = crmin, wlmax = crmax, + wltarget = band, boundaries = crminmax, + target = raster2cr) + } + return(cr) +} + +#" This function computes continuum removal value for a spectral band of interest, +#" based on lower and upper wavelengths corresponding to boundaries of the continuum +#" +#" @param wlmin numeric. wavelength of the spectral band corresponding to minimum boundary +#" @param wlmax numeric. wavelength of the spectral band corresponding to maximum boundary +#" @param wltarget numeric. wavelength of the spectral band for which cr is computed +#" @param boundaries list. raster objects corresponding to minimum and maximum wavelengths +#" @param target list. raster object corresponding target wavelength +#" +#" @return cr list. raster object corresponding to continuum removed value +#" @export +computecr <- function(wlmin, wlmax, wltarget, boundaries, target) { + + cr <- target / (boundaries[[1]] + (wltarget - wlmin) * (boundaries[[2]] - boundaries[[1]]) / (wlmax - wlmin)) + return(cr) +} + +#" this function produces a spectral index from an expression defining a spectral index +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. wavelength in nanometers of the spectral bands of refl. +#" @param expressindex character. expression corresponding to the spectral index to compute +#" @param listbands list. list of spectral bands defined in the "expressindex" variable +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" @param nameindex character. name for the index to be computed, provided in the raster layer +#" +#" @return numeric. band numbers of original sensor corresponding to S2 +#" @export +spectralindices_fromexpression <- function(refl, sensorbands, expressindex, listbands, reflfactor = 1, nameindex = NULL) { + + # define which bands to be used in the spectral index + bands <- get_closest_bands(sensorbands, listbands) + + classraster <- class(refl) + if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") { + # if !reflfactor == 1 then apply a reflectance factor + if (classraster == "stars") { + refl <- refl[bands] + } else { + refl <- raster::subset(refl, bands) + } + if (!reflfactor == 1) { + refl <- refl / reflfactor + } + } else if (is.list(refl)) { + refl <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent + if (!reflfactor == 1) { + refl <- refl / reflfactor + } + } else { + stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters") + } + names(refl) <- gsub(pattern = "B", replacement = "Band", x = names(bands)) + + nbbands <- unique(as.numeric(gsub(pattern = "B", + replacement = "", + x = unlist(regmatches(expressindex, + gregexpr("B[[:digit:]] + ", + expressindex)))))) + sortband <- sort(nbbands, index.return = TRUE, decreasing = TRUE) + matches <- unique(unlist(regmatches(expressindex, gregexpr("B[[:digit:]] + ", expressindex))))[sortband$ix] + replaces <- paste("refl[['Band", gsub(pattern = "B", replacement = "", x = matches), "']]", sep = "") + expressindex_final <- expressindex + for (bb in 1:seq_along(matches)) { + expressindex_final <- gsub(pattern = matches[bb], replacement = replaces[bb], x = expressindex_final) + } + si <- eval(parse(text = expressindex_final)) + if (!is.null(nameindex)) { + names(si) <- nameindex + } + return(si) +} + +#" this function aims at computing spectral indices from Sensor reflectance data in raster object +#" it computes the spectral indices based on their computation with Sentinel-2 +#" and assumes that the bands of the S2 data follow this order +#" wavelength = {496.6, 560.0, 664.5, 703.9, 740.2, 782.5, 835.1, 864.8, 1613.7, 2202.4} +#" Full description of the indices can be found here: +#" https://www.sentinel-hub.com/eotaxonomy/indices +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. wavelength in nanometers of the spectral bands of refl. +#" @param sel_indices list. list of spectral indices to be computed +#" @param stackout logical. If TRUE returns a stack, otherwise a list of rasters. +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return list. includes +#" - spectralindices: List of spectral indices computed from the reflectance initially provided +#" - listindices: list of spectral indices computable with the function +#" @importFrom methods is +#" @importFrom raster stack brick +#" @export + +computespectralindices_raster <- function(refl, sensorbands, sel_indices = "ALL", stackout = TRUE, reflfactor = 1) { + + s2bands <- c("B2" = 496.6, "B3" = 560.0, "B4" = 664.5, "B5" = 703.9, "B6" = 740.2, + "B7" = 782.5, "B8" = 835.1, "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4) + + spectralindices <- list() + sen2s2 <- get_closest_bands(sensorbands, s2bands) + classraster <- class(refl) + if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") { + # if !reflfactor == 1 then apply a reflectance factor + if (classraster == "stars") { + refl <- refl[sen2s2] + } else { + refl <- raster::subset(refl, sen2s2) + } + if (!reflfactor == 1) { + refl <- refl / reflfactor + } + } else if (is.list(refl)) { + refl <- raster::stack(refl[sen2s2]) # checks that all rasters have same crs/extent + if (!reflfactor == 1) { + refl <- refl / reflfactor + } + } else { + stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters") + } + names(refl) <- names(sen2s2) + + indexall <- list() + + # inelegant but meeeeh + listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CCCI", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2", + "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2", + "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDSI", "NDVI", "NDVI_G", + "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP", + "SAVI", "SIPI", "SR", "CR_SWIR") + if (sel_indices[1] == "ALL") { + sel_indices <- listindices + } + if ("ARI1" %in% sel_indices) { + ari1 <- (1 / refl[["B3"]]) - (1 / refl[["B5"]]) + spectralindices$ARI1 <- ari1 + } + if ("ARI2" %in% sel_indices) { + ari2 <- (refl[["B8"]] / refl[["B2"]]) - (refl[["B8"]] / refl[["B3"]]) + spectralindices$ARI2 <- ari2 + } + if ("ARVI" %in% sel_indices) { + arvi <- (refl[["B8"]] - (2 * refl[["B4"]] - refl[["B2"]])) / (refl[["B8"]] + (2 * refl[["B4"]] - refl[["B2"]])) + spectralindices$ARVI <- arvi + } + if ("BAI" %in% sel_indices) { + bai <- (1 / ((0.1 - refl[["B4"]])**2 + (0.06 - refl[["B8"]])**2)) + spectralindices$BAI <- bai + } + if ("BAIS2" %in% sel_indices) { + bais2 <- (1 - ((refl[["B6"]] * refl[["B7"]] * refl[["B8A"]]) / refl[["B4"]])**0.5) * ((refl[["B12"]] - refl[["B8A"]]) / ((refl[["B12"]] + refl[["B8A"]])**0.5) + 1) + spectralindices$BAIS2 <- bais2 + } + if ("CCCI" %in% sel_indices) { + ccci <- ((refl[["B8"]] - refl[["B5"]]) / (refl[["B8"]] + refl[["B5"]])) / ((refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]])) + spectralindices$CCCI <- ccci + } + if ("CHL_RE" %in% sel_indices) { + chl_re <- refl[["B5"]] / refl[["B8"]] + spectralindices$CHL_RE <- chl_re + } + if ("CRI1" %in% sel_indices) { + cri1 <- (1 / refl[["B2"]]) - (1 / refl[["B3"]]) + spectralindices$CRI1 <- cri1 + } + if ("CRI2" %in% sel_indices) { + cri2 <- (1 / refl[["B2"]]) - (1 / refl[["B5"]]) + spectralindices$CRI2 <- cri2 + } + if ("EVI" %in% sel_indices) { + evi <- 2.5 * (refl[["B8"]] - refl[["B4"]]) / ((refl[["B8"]] + 6 * refl[["B4"]] - 7.5 * refl[["B2"]] + 1)) + spectralindices$EVI <- evi + } + if ("EVI2" %in% sel_indices) { + evi2 <- 2.5 * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + 2.4 * refl[["B4"]] + 1) + spectralindices$EVI2 <- evi2 + } + if ("GRVI1" %in% sel_indices) { + grvi1 <- (refl[["B4"]] - refl[["B3"]]) / (refl[["B4"]] + refl[["B3"]]) + spectralindices$GRVI1 <- grvi1 + } + if ("GNDVI" %in% sel_indices) { + gndvi <- (refl[["B8"]] - refl[["B3"]]) / (refl[["B8"]] + refl[["B3"]]) + spectralindices$GNDVI <- gndvi + } + if ("IRECI" %in% sel_indices) { + ireci <- (refl[["B7"]] - refl[["B4"]]) * (refl[["B6"]] / (refl[["B5"]])) + spectralindices$IRECI <- ireci + } + if ("LAI_SAVI" %in% sel_indices) { + lai_savi <- - log(0.371 + 1.5 * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]] + 0.5)) / 2.4 + spectralindices$LAI_SAVI <- lai_savi + } + if ("MCARI" %in% sel_indices) { + mcari <- (1 - 0.2 * (refl[["B5"]] - refl[["B3"]]) / (refl[["B5"]] - refl[["B4"]])) + spectralindices$MCARI <- mcari + } + if ("mNDVI705" %in% sel_indices) { + mndvi705 <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B6"]] + refl[["B5"]] - 2 * refl[["B2"]]) + spectralindices$mNDVI705 <- mndvi705 + } + if ("MSAVI2" %in% sel_indices) { + msavi2 <- ((refl[["B8"]] + 1) - 0.5 * sqrt(((2 * refl[["B8"]]) - 1)**2 + 8 * refl[["B4"]])) + spectralindices$MSAVI2 <- msavi2 + } + if ("MSI" %in% sel_indices) { + msi <- refl[["B11"]] / refl[["B8A"]] + spectralindices$MSI <- msi + } + if ("mSR705" %in% sel_indices) { + msr705 <- (refl[["B6"]] - refl[["B2"]]) / (refl[["B5"]] - refl[["B2"]]) + spectralindices$mSR705 <- msr705 + } + if ("MTCI" %in% sel_indices) { + mtci <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B5"]] + refl[["B4"]]) + spectralindices$MTCI <- mtci + } + if ("nBR_RAW" %in% sel_indices) { + nbr_raw <- (refl[["B8"]] - refl[["B12"]]) / (refl[["B8"]] + refl[["B12"]]) + spectralindices$nBR_RAW <- nbr_raw + } + if ("NDI_45" %in% sel_indices) { + ndi_45 <- (refl[["B5"]] - refl[["B4"]]) / (refl[["B5"]] + refl[["B4"]]) + spectralindices$NDI_45 <- ndi_45 + } + if ("NDII" %in% sel_indices) { + ndii <- (refl[["B8A"]] - refl[["B11"]]) / (refl[["B8A"]] + refl[["B11"]]) + spectralindices$NDII <- ndii + } + if ("NDSI" %in% sel_indices) { + ndsi <- (refl[["B3"]] - refl[["B11"]]) / (refl[["B3"]] + refl[["B11"]]) + spectralindices$NDSI <- ndsi + } + if ("NDVI" %in% sel_indices) { + ndvi <- (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]]) + spectralindices$NDVI <- ndvi + } + if ("NDVI_G" %in% sel_indices) { + ndvi_g <- refl[["B3"]] * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]]) + spectralindices$NDVI_G <- ndvi_g + } + if ("NDVI705" %in% sel_indices) { + ndvi705 <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B6"]] + refl[["B5"]]) + spectralindices$NDVI705 <- ndvi705 + } + if ("NDWI" %in% sel_indices) { + ndwi <- (refl[["B3"]] - refl[["B8"]]) / (refl[["B3"]] + refl[["B8"]]) + spectralindices$NDWI <- ndwi + } + if ("NDWI1" %in% sel_indices) { + ndwi1 <- (refl[["B8A"]] - refl[["B11"]]) / (refl[["B8A"]] + refl[["B11"]]) + spectralindices$NDWI1 <- ndwi1 + } + if ("NDWI2" %in% sel_indices) { + ndwi2 <- (refl[["B8A"]] - refl[["B12"]]) / (refl[["B8A"]] + refl[["B12"]]) + spectralindices$NDWI2 <- ndwi2 + } + if ("PSRI" %in% sel_indices) { + psri <- (refl[["B4"]] - refl[["B2"]]) / (refl[["B5"]]) + spectralindices$PSRI <- psri + } + if ("PSRI_NIR" %in% sel_indices) { + psri_nir <- (refl[["B4"]] - refl[["B2"]]) / (refl[["B8"]]) + spectralindices$PSRI_NIR <- psri_nir + } + if ("RE_NDVI" %in% sel_indices) { + re_ndvi <- (refl[["B8"]] - refl[["B6"]]) / (refl[["B8"]] + refl[["B6"]]) + spectralindices$RE_NDVI <- re_ndvi + } + if ("RE_NDWI" %in% sel_indices) { + re_ndwi <- (refl[["B4"]] - refl[["B6"]]) / (refl[["B4"]] + refl[["B6"]]) + spectralindices$RE_NDWI <- re_ndwi + } + if ("S2REP" %in% sel_indices) { + s2rep <- 705 + 35 * (0.5 * (refl[["B8"]] + refl[["B5"]]) - refl[["B6"]]) / (refl[["B7"]] - refl[["B6"]]) + spectralindices$S2REP <- s2rep + } + if ("SAVI" %in% sel_indices) { + savi <- 1.5 * (refl[["B8"]] - refl[["B5"]]) / (refl[["B8"]] + refl[["B5"]] + 0.5) + spectralindices$SAVI <- savi + } + if ("SIPI" %in% sel_indices) { + sipi <- (refl[["B8"]] - refl[["B2"]]) / (refl[["B8"]] - refl[["B4"]]) + spectralindices$SIPI <- sipi + } + if ("SR" %in% sel_indices) { + sr <- refl[["B8"]] / refl[["B4"]] + spectralindices$SR <- sr + } + if ("TCARI" %in% sel_indices) { + sr <- refl[["B8"]] / refl[["B4"]] + spectralindices$SR <- sr + } + if ("CR_SWIR" %in% sel_indices) { + cr_swir <- refl[["B11"]] / (refl[["B8A"]] + (s2bands["B11"] - s2bands["B8A"]) * (refl[["B12"]] - refl[["B8A"]]) / (s2bands["B12"] - s2bands["B8A"])) + spectralindices$CR_SWIR <- cr_swir + } + + if (stackout) + spectralindices <- raster::stack(spectralindices) + + res <- list("spectralindices" = spectralindices, "listindices" = listindices) + return(res) +} + +#" this function aims at computing spectral indices from Sensor reflectance data. +#" it computes the spectral indices based on their computation with Sentinel-2 +#" and assumes that the bands of the S2 data follow this order +#" wavelength = {496.6, 560.0, 664.5, 703.9, 740.2, 782.5, 835.1, 864.8, 1613.7, 2202.4} +#" Full description of the indices can be found here: +#" https://www.sentinel-hub.com/eotaxonomy/indices +#" +#" @param refl numeric. reflectance dataset defined in matrix +#" @param sel_indices list. list of spectral indices to be computed +#" @param sensorbands numeric. wavelength of the spectral bands corresponding to the spectral index +#" +#" @return list. includes +#" - spectralindices: List of spectral indices computed from the reflectance initially provided +#" - listindices: list of spectral indices computable with the function +#" @export + +computespectralindices_hs <- function(refl, sensorbands, sel_indices = "ALL") { + + spectralindices <- list() + s2bands <- data.frame("B2" = 496.6, "B3" = 560.0, "B4" = 664.5, "B5" = 703.9, "B6" = 740.2, + "B7" = 782.5, "B8" = 835.1, "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4) + + sen2s2 <- get_closest_bands(sensorbands, s2bands) + indexall <- list() + # set zero vaues to >0 in order to avoid problems + selzero <- which(refl == 0) + refl[selzero] <- 0.005 + if (dim(refl)[1] == length(sensorbands)) { + refl <- t(refl) + } + + # inelegant but meeeeh + listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2", + "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2", + "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDVI", "NDVI_G", + "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP", + "SAVI", "SIPI", "SR", "CR_SWIR") + if (sel_indices == "ALL") { + sel_indices <- listindices + } + if ("ARI1" %in% sel_indices) { + ari1 <- (1 / refl[, sen2s2[["B3"]]]) - (1 / refl[, sen2s2[["B5"]]]) + spectralindices$ARI1 <- ari1 + } + if ("ARI2" %in% sel_indices) { + ari2 <- (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B2"]]]) - (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B3"]]]) + spectralindices$ARI2 <- ari2 + } + if ("ARVI" %in% sel_indices) { + arvi <- (refl[, sen2s2[["B8"]]] - (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]])) / (refl[, sen2s2[["B8"]]] + (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]])) + spectralindices$ARVI <- arvi + } + if ("BAI" %in% sel_indices) { + bai <- (1 / ((0.1 - refl[, sen2s2[["B4"]]])**2 + (0.06 - refl[, sen2s2[["B8"]]])**2)) + spectralindices$BAI <- bai + } + if ("BAIS2" %in% sel_indices) { + bais2 <- (1 - ((refl[, sen2s2[["B6"]]] * refl[, sen2s2[["B7"]]] * refl[, sen2s2[["B8A"]]]) / refl[, sen2s2[["B4"]]])**0.5) * ((refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / ((refl[, sen2s2[["B12"]]] + refl[, sen2s2[["B8A"]]])**0.5) + 1) + spectralindices$BAIS2 <- bais2 + } + if ("CCCI" %in% sel_indices) { + ccci <- ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]])) / ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]])) + spectralindices$CCCI <- ccci + } + if ("CHL_RE" %in% sel_indices) { + chl_re <- refl[, sen2s2[["B5"]]] / refl[, sen2s2[["B8"]]] + spectralindices$CHL_RE <- chl_re + } + if ("CRI1" %in% sel_indices) { + cri1 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B3"]]]) + spectralindices$CRI1 <- cri1 + } + if ("CRI2" %in% sel_indices) { + cri2 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B5"]]]) + spectralindices$CRI2 <- cri2 + } + if ("EVI" %in% sel_indices) { + evi <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / ((refl[, sen2s2[["B8"]]] + 6 * refl[, sen2s2[["B4"]]] - 7.5 * refl[, sen2s2[["B2"]]] + 1)) + spectralindices$EVI <- evi + } + if ("EVI2" %in% sel_indices) { + evi2 <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + 2.4 * refl[, sen2s2[["B4"]]] + 1) + spectralindices$EVI2 <- evi2 + } + if ("GRVI1" %in% sel_indices) { + grvi1 <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B3"]]]) + spectralindices$GRVI1 <- grvi1 + } + if ("GNDVI" %in% sel_indices) { + gndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B3"]]]) + spectralindices$GNDVI <- gndvi + } + if ("IRECI" %in% sel_indices) { + ireci <- (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B4"]]]) * (refl[, sen2s2[["B6"]]] / (refl[, sen2s2[["B5"]]])) + spectralindices$IRECI <- ireci + } + if ("LAI_SAVI" %in% sel_indices) { + lai_savi <- - log(0.371 + 1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]] + 0.5)) / 2.4 + spectralindices$LAI_SAVI <- lai_savi + } + if ("MCARI" %in% sel_indices) { + mcari <- (1 - 0.2 * (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]])) + spectralindices$MCARI <- mcari + } + if ("mNDVI705" %in% sel_indices) { + mndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]] - 2 * refl[, sen2s2[["B2"]]]) + spectralindices$mNDVI705 <- mndvi705 + } + if ("MSAVI2" %in% sel_indices) { + msavi2 <- ((refl[, sen2s2[["B8"]]] + 1) - 0.5 * sqrt(((2 * refl[, sen2s2[["B8"]]]) - 1)**2 + 8 * refl[, sen2s2[["B4"]]])) + spectralindices$MSAVI2 <- msavi2 + } + if ("MSI" %in% sel_indices) { + msi <- refl[, sen2s2[["B11"]]] / refl[, sen2s2[["B8"]]] + spectralindices$MSI <- msi + } + if ("mSR705" %in% sel_indices) { + msr705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B2"]]]) + spectralindices$mSR705 <- msr705 + } + if ("MTCI" %in% sel_indices) { + mtci <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$MTCI <- mtci + } + if ("nBR_RAW" %in% sel_indices) { + nbr_raw <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B12"]]]) + spectralindices$nBR_RAW <- nbr_raw + } + if ("NDI_45" %in% sel_indices) { + ndi_45 <- (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$NDI_45 <- ndi_45 + } + if ("NDII" %in% sel_indices) { + ndii <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B11"]]]) + spectralindices$NDII <- ndii + } + if ("NDSI" %in% sel_indices) { + ndisi <- (refl[, sen2s2[["B3"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B3"]]] + refl[, sen2s2[["B11"]]]) + spectralindices$NDSI <- ndsi + } + if ("NDVI" %in% sel_indices) { + ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$NDVI <- ndvi + } + if ("NDVI_G" %in% sel_indices) { + ndvi_g <- refl[, sen2s2[["B3"]]] * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$NDVI_G <- ndvi_g + } + if ("NDVI705" %in% sel_indices) { + ndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]]) + spectralindices$NDVI705 <- ndvi705 + } + if ("NDWI1" %in% sel_indices) { + ndwi1 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B11"]]]) + spectralindices$NDWI1 <- ndwi1 + } + if ("NDWI2" %in% sel_indices) { + ndwi2 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B12"]]]) + spectralindices$NDWI2 <- ndwi2 + } + if ("PSRI" %in% sel_indices) { + psri <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]]) + spectralindices$PSRI <- psri + } + if ("PSRI_NIR" %in% sel_indices) { + psri_nir <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]]) + spectralindices$PSRI_NIR <- psri_nir + } + if ("RE_NDVI" %in% sel_indices) { + re_ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B6"]]]) + spectralindices$RE_NDVI <- re_ndvi + } + if ("RE_NDWI" %in% sel_indices) { + re_ndwi <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B6"]]]) + spectralindices$RE_NDWI <- re_ndwi + } + if ("S2REP" %in% sel_indices) { + s2rep <- 705 + 35 * (0.5 * (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]]) - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B6"]]]) + spectralindices$S2REP <- s2rep + } + if ("SAVI" %in% sel_indices) { + savi <- 1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]] + 0.5) + spectralindices$SAVI <- savi + } + if ("SIPI" %in% sel_indices) { + sipi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) + spectralindices$SIPI <- sipi + } + if ("SR" %in% sel_indices) { + sr <- refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B4"]]] + spectralindices$SR <- sr + } + if ("CR_SWIR" %in% sel_indices) { + cr_swir <- refl[, sen2s2[["B11"]]] / (refl[, sen2s2[["B8A"]]] + (s2bands$B11 - s2bands$B8A) * (refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / (s2bands$B12 - s2bands$B8A)) + spectralindices$CR_SWIR <- cr_swir + } + res <- list("spectralindices" = spectralindices, "listindices" = listindices) + return(res) +} + +#" this function identifies the bands of a given sensor with closest match to its spectral characteristics +#" +#" @param sensorbands numeric. wavelength in nanometer of the sensor of interest +#" @param listbands numeric or list. Named vector or list of spectral bands corresponding to sensor +#" +#" @return numeric. band numbers of original sensor +#" @export +get_closest_bands <- function(sensorbands, listbands) { + sapply(listbands, function(x) { + b <- which.min(abs(sensorbands - x)) + names(b) <- "" + b + }) +} + +#" This function computes interquartile range (IQR) criterion, which can be used +#" as a criterion for outlier detection +#" +#" @param distval numeric. vector of distribution of values +#" @param weightirq numeric. weighting factor appplied to IRQ to define lower and upper boudaries for outliers +#" +#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2 +#" @importFrom stats IQR quantile +#" @export +iqr_outliers <- function(distval, weightirq = 1.5) { + iqr <- IQR(distval, na.rm = TRUE) + range_iqr <- c(quantile(distval, 0.25, na.rm = TRUE), quantile(distval, 0.75, na.rm = TRUE)) + outlier_iqr <- c(range_iqr[1] - weightirq * iqr, range_iqr[2] + weightirq * iqr) + return(outlier_iqr) +} + +#" This function selects bands from a raster or stars object +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param bands numeric. rank of bands to be read in refl +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return robj list. R object (default is raster, stars if refl is stars object) +#" @importFrom raster subset stack +#" @export +readrasterbands <- function(refl, bands, reflfactor = 1) { + + # get equation for line going from CR1 to CR2 + classraster <- class(refl) + if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") { + # if !reflfactor == 1 then apply a reflectance factor + if (classraster == "stars") { + robj <- refl[bands] + } else { + robj <- raster::subset(refl, bands) + } + if (!reflfactor == 1) { + robj <- robj / reflfactor + } + } else if (is.list(refl)) { + robj <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent + if (!reflfactor == 1) { + robj <- robj / reflfactor + } + } else { + stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters") + } + return(robj) +}
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</gmd:CI_Citation> + </gmd:citation> + <gmd:abstract> + <gco:CharacterString>"Relevés des adventices en cultures de maïs (2010) ZAAr" + +Relevés des adventices dans 28 parcelles de maïs pour séparer les effets de la séquence des cultures de l'hétérogénéité spatio-temporelle du paysage des 10 dernières années. + +Ce projet a été financé en partenariat par : +MINCyT: Ministerio de ciencia, tecnologia e innovation productiva (Argentine) +ECOS-SUD (France)</gco:CharacterString> + </gmd:abstract> + <gmd:purpose gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:purpose> + <gmd:status> + <gmd:MD_ProgressCode codeListValue="completed" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_ProgressCode"/> + </gmd:status> + <gmd:pointOfContact> + <gmd:CI_ResponsibleParty> + <gmd:individualName> + <gco:CharacterString>Santiago Poggio</gco:CharacterString> + </gmd:individualName> + <gmd:organisationName> + <gco:CharacterString>IFEVA/ Catédra de producción vegetal, facultad de agronomia, Universidad de Buenos Aires/ CONICET</gco:CharacterString> + </gmd:organisationName> + <gmd:positionName gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:positionName> + <gmd:contactInfo> + <gmd:CI_Contact> + <gmd:phone> + <gmd:CI_Telephone> + <gmd:voice gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:voice> + <gmd:facsimile gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:facsimile> + </gmd:CI_Telephone> + </gmd:phone> + <gmd:address> + <gmd:CI_Address> + <gmd:deliveryPoint gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:deliveryPoint> + <gmd:city gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:city> + <gmd:administrativeArea gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:administrativeArea> + <gmd:postalCode gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:postalCode> + <gmd:country gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:country> + <gmd:electronicMailAddress> + <gco:CharacterString>spoggio@agro.uba.ar</gco:CharacterString> + </gmd:electronicMailAddress> + </gmd:CI_Address> + </gmd:address> + </gmd:CI_Contact> + </gmd:contactInfo> + <gmd:role> + <gmd:CI_RoleCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_RoleCode" + codeListValue="author"/> + </gmd:role> + </gmd:CI_ResponsibleParty> + </gmd:pointOfContact> + <gmd:pointOfContact> + <gmd:CI_ResponsibleParty> + <gmd:individualName> + <gco:CharacterString>Jacques Baudry</gco:CharacterString> + </gmd:individualName> + <gmd:organisationName> + <gco:CharacterString>BAGAP UMR 0980 INRAE Agrocampus Ouest ESA</gco:CharacterString> + </gmd:organisationName> + <gmd:positionName gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:positionName> + <gmd:contactInfo> + <gmd:CI_Contact> + <gmd:phone> + <gmd:CI_Telephone> + <gmd:voice gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:voice> + <gmd:facsimile gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:facsimile> + </gmd:CI_Telephone> + </gmd:phone> + <gmd:address> + <gmd:CI_Address> + <gmd:deliveryPoint gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:deliveryPoint> + <gmd:city gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:city> + <gmd:administrativeArea gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:administrativeArea> + <gmd:postalCode gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:postalCode> + <gmd:country gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:country> + <gmd:electronicMailAddress> + <gco:CharacterString>jacques.baudry@inrae.fr</gco:CharacterString> + </gmd:electronicMailAddress> + </gmd:CI_Address> + </gmd:address> + </gmd:CI_Contact> + </gmd:contactInfo> + <gmd:role> + <gmd:CI_RoleCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_RoleCode" + codeListValue="author"/> + </gmd:role> + </gmd:CI_ResponsibleParty> + </gmd:pointOfContact> + <gmd:resourceMaintenance> + <gmd:MD_MaintenanceInformation> + <gmd:maintenanceAndUpdateFrequency> + <gmd:MD_MaintenanceFrequencyCode codeListValue="notPlanned" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_MaintenanceFrequencyCode"/> + </gmd:maintenanceAndUpdateFrequency> + </gmd:MD_MaintenanceInformation> + </gmd:resourceMaintenance> + <gmd:graphicOverview xmlns:srv="http://www.isotc211.org/2005/srv"> + <gmd:MD_BrowseGraphic> + <gmd:fileName> + <gco:CharacterString>https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21_s.png</gco:CharacterString> + </gmd:fileName> + <gmd:fileDescription> + <gco:CharacterString>thumbnail</gco:CharacterString> + </gmd:fileDescription> + <gmd:fileType> + <gco:CharacterString>png</gco:CharacterString> + </gmd:fileType> + </gmd:MD_BrowseGraphic> + </gmd:graphicOverview> + <gmd:graphicOverview xmlns:srv="http://www.isotc211.org/2005/srv"> + <gmd:MD_BrowseGraphic> + <gmd:fileName> + <gco:CharacterString>https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21.png</gco:CharacterString> + </gmd:fileName> + <gmd:fileDescription> + <gco:CharacterString>large_thumbnail</gco:CharacterString> + </gmd:fileDescription> + <gmd:fileType> + <gco:CharacterString>png</gco:CharacterString> + </gmd:fileType> + </gmd:MD_BrowseGraphic> + </gmd:graphicOverview> + <gmd:descriptiveKeywords> + <gmd:MD_Keywords> + <gmd:keyword> + <gco:CharacterString>Biodiversité</gco:CharacterString> + </gmd:keyword> + <gmd:type> + <gmd:MD_KeywordTypeCode codeListValue="theme" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode"/> + </gmd:type> + </gmd:MD_Keywords> + </gmd:descriptiveKeywords> + <gmd:descriptiveKeywords> + <gmd:MD_Keywords> + <gmd:keyword> + <gco:CharacterString>Maïs</gco:CharacterString> + </gmd:keyword> + <gmd:type> + <gmd:MD_KeywordTypeCode codeListValue="theme" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode"/> + </gmd:type> + </gmd:MD_Keywords> + </gmd:descriptiveKeywords> + <gmd:descriptiveKeywords> + <gmd:MD_Keywords> + <gmd:keyword> + <gco:CharacterString>Forêt / milieu végétal</gco:CharacterString> + </gmd:keyword> + <gmd:keyword> + <gco:CharacterString>Agriculture</gco:CharacterString> + </gmd:keyword> + <gmd:keyword> + <gco:CharacterString>Ecologie et environnement</gco:CharacterString> + </gmd:keyword> + <gmd:keyword> + <gco:CharacterString>Paysages et sites</gco:CharacterString> + </gmd:keyword> + <gmd:type> + <gmd:MD_KeywordTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode" + codeListValue="theme"/> + </gmd:type> + <gmd:thesaurusName> + <gmd:CI_Citation> + <gmd:title> + <gco:CharacterString>theme.EnvironnementFR.rdf</gco:CharacterString> + </gmd:title> + <gmd:date> + <gmd:CI_Date> + <gmd:date> + <gco:Date>2017-03-20</gco:Date> + </gmd:date> + <gmd:dateType> + <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode" + codeListValue="publication"/> + </gmd:dateType> + </gmd:CI_Date> + </gmd:date> + <gmd:identifier> + <gmd:MD_Identifier> + <gmd:code> + <gmx:Anchor xmlns:gmx="http://www.isotc211.org/2005/gmx" + xmlns:xlink="http://www.w3.org/1999/xlink" + xlink:href="https://www.osuris.fr/geonetwork/srv/api/registries/vocabularies/external.theme.EnvironnementFR">geonetwork.thesaurus.external.theme.EnvironnementFR</gmx:Anchor> + </gmd:code> + </gmd:MD_Identifier> + </gmd:identifier> + </gmd:CI_Citation> + </gmd:thesaurusName> + </gmd:MD_Keywords> + </gmd:descriptiveKeywords> + <gmd:descriptiveKeywords xmlns:gn="http://www.fao.org/geonetwork" + xmlns:gmx="http://www.isotc211.org/2005/gmx" + xmlns:srv="http://www.isotc211.org/2005/srv" + xmlns:xlink="http://www.w3.org/1999/xlink"> + <gmd:MD_Keywords> + <gmd:keyword> + <gco:CharacterString>Installations de suivi environnemental</gco:CharacterString> + </gmd:keyword> + <gmd:keyword> + <gco:CharacterString>Habitats et biotopes</gco:CharacterString> + </gmd:keyword> + <gmd:type> + <gmd:MD_KeywordTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode" + codeListValue="theme"/> + </gmd:type> + <gmd:thesaurusName> + <gmd:CI_Citation> + <gmd:title> + <gco:CharacterString>Registre de thème INSPIRE</gco:CharacterString> + </gmd:title> + <gmd:date> + <gmd:CI_Date> + <gmd:date> + <gco:Date>2019-06-25</gco:Date> + </gmd:date> + <gmd:dateType> + <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode" + codeListValue="publication"/> + </gmd:dateType> + </gmd:CI_Date> + </gmd:date> + <gmd:identifier> + <gmd:MD_Identifier> + <gmd:code> + <gmx:Anchor xlink:href="https://www.osuris.fr/geonetwork/srv/api/registries/vocabularies/external.theme.httpinspireeceuropaeutheme-theme">geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme</gmx:Anchor> + </gmd:code> + </gmd:MD_Identifier> + </gmd:identifier> + </gmd:CI_Citation> + </gmd:thesaurusName> + </gmd:MD_Keywords> + </gmd:descriptiveKeywords> + <gmd:resourceConstraints> + <gmd:MD_LegalConstraints> + <gmd:accessConstraints> + <gmd:MD_RestrictionCode codeListValue="license" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_RestrictionCode"/> + </gmd:accessConstraints> + <gmd:useConstraints> + <gmd:MD_RestrictionCode codeListValue="restricted" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_RestrictionCode"/> + </gmd:useConstraints> + <gmd:otherConstraints> + <gco:CharacterString>licence CC-BY-NC 4.0</gco:CharacterString> + </gmd:otherConstraints> + </gmd:MD_LegalConstraints> + </gmd:resourceConstraints> + <gmd:resourceConstraints xmlns:gn="http://www.fao.org/geonetwork" + xmlns:gmx="http://www.isotc211.org/2005/gmx" + xmlns:srv="http://www.isotc211.org/2005/srv" + xmlns:xlink="http://www.w3.org/1999/xlink"> + <gmd:MD_Constraints> + <gmd:useLimitation> + <gco:CharacterString>Utilisation libre sous réserve de mentionner la source (a minima le nom du producteur) et la date de sa dernière mise à jour</gco:CharacterString> + </gmd:useLimitation> + </gmd:MD_Constraints> + </gmd:resourceConstraints> + <gmd:spatialRepresentationType> + <gmd:MD_SpatialRepresentationTypeCode codeListValue="textTable" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_SpatialRepresentationTypeCode"/> + </gmd:spatialRepresentationType> + <gmd:spatialResolution xmlns:gn="http://www.fao.org/geonetwork" + xmlns:gmx="http://www.isotc211.org/2005/gmx" + xmlns:srv="http://www.isotc211.org/2005/srv" + xmlns:xlink="http://www.w3.org/1999/xlink"> + <gmd:MD_Resolution> + <gmd:distance> + <gco:Distance uom="m">20</gco:Distance> + </gmd:distance> + </gmd:MD_Resolution> + </gmd:spatialResolution> + <gmd:language> + <gmd:LanguageCode codeList="http://www.loc.gov/standards/iso639-2/" codeListValue="fre"/> + </gmd:language> + <gmd:characterSet> + <gmd:MD_CharacterSetCode codeListValue="utf8" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_CharacterSetCode"/> + </gmd:characterSet> + <gmd:topicCategory> + <gmd:MD_TopicCategoryCode>farming</gmd:MD_TopicCategoryCode> + </gmd:topicCategory> + <gmd:extent> + <gmd:EX_Extent> + <gmd:temporalElement> + <gmd:EX_TemporalExtent> + <gmd:extent> + <gml:TimePeriod gml:id="d2536736e509a1052958"> + <gml:beginPosition>2010-09-08</gml:beginPosition> + <gml:endPosition>2008-09-14</gml:endPosition> + </gml:TimePeriod> + </gmd:extent> + </gmd:EX_TemporalExtent> + </gmd:temporalElement> + </gmd:EX_Extent> + </gmd:extent> + <gmd:extent> + <gmd:EX_Extent> + <gmd:geographicElement> + <gmd:EX_GeographicBoundingBox> + <gmd:westBoundLongitude> + <gco:Decimal>-1.6259765625</gco:Decimal> + </gmd:westBoundLongitude> + <gmd:eastBoundLongitude> + <gco:Decimal>-0.9777832031250001</gco:Decimal> + </gmd:eastBoundLongitude> + <gmd:southBoundLatitude> + <gco:Decimal>48.092651367188</gco:Decimal> + </gmd:southBoundLatitude> + <gmd:northBoundLatitude> + <gco:Decimal>48.696899414063</gco:Decimal> + </gmd:northBoundLatitude> + </gmd:EX_GeographicBoundingBox> + </gmd:geographicElement> + </gmd:EX_Extent> + </gmd:extent> + <gmd:supplementalInformation> + <gco:CharacterString>France, Pleine-Fougères</gco:CharacterString> + </gmd:supplementalInformation> + </gmd:MD_DataIdentification> + </gmd:identificationInfo> + <gmd:distributionInfo> + <gmd:MD_Distribution> + <gmd:distributionFormat> + <gmd:MD_Format> + <gmd:name> + <gco:CharacterString>ESRI Shapefile</gco:CharacterString> + </gmd:name> + <gmd:version> + <gco:CharacterString>1.0</gco:CharacterString> + </gmd:version> + </gmd:MD_Format> + </gmd:distributionFormat> + <gmd:transferOptions> + <gmd:MD_DigitalTransferOptions/> + </gmd:transferOptions> + </gmd:MD_Distribution> + </gmd:distributionInfo> + <gmd:dataQualityInfo> + <gmd:DQ_DataQuality> + <gmd:scope> + <gmd:DQ_Scope> + <gmd:level> + <gmd:MD_ScopeCode codeListValue="series" + codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_ScopeCode"/> + </gmd:level> + </gmd:DQ_Scope> + </gmd:scope> + <gmd:report xmlns:gn="http://www.fao.org/geonetwork" + xmlns:gmx="http://www.isotc211.org/2005/gmx" + xmlns:srv="http://www.isotc211.org/2005/srv" + xmlns:xlink="http://www.w3.org/1999/xlink"> + <gmd:DQ_DomainConsistency> + <gmd:result> + <gmd:DQ_ConformanceResult> + <gmd:specification> + <gmd:CI_Citation> + <gmd:title> + <gco:CharacterString>L’article 7, paragraphe 1, de la directive 2007/2/CE correspond aux modalités techniques de l’interopérabilité : il s’agit du règlement relatif à l’interopérabilité : règlement n°1253/2013 du 21 octobre 2013 modifiant et complétant le règlement n°1089/2010 du 23 novembre 2010.</gco:CharacterString> + </gmd:title> + <gmd:date> + <gmd:CI_Date> + <gmd:date> + <gco:Date>2013-10-21</gco:Date> + </gmd:date> + <gmd:dateType> + <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode" + codeListValue="publication"/> + </gmd:dateType> + </gmd:CI_Date> + </gmd:date> + </gmd:CI_Citation> + </gmd:specification> + <gmd:explanation gco:nilReason="missing"> + <gco:CharacterString/> + </gmd:explanation> + <gmd:pass> + <gco:Boolean>true</gco:Boolean> + </gmd:pass> + </gmd:DQ_ConformanceResult> + </gmd:result> + </gmd:DQ_DomainConsistency> + </gmd:report> + <gmd:lineage> + <gmd:LI_Lineage> + <gmd:statement> + <gco:CharacterString>Relevé sur le terrain des adventices du maïs</gco:CharacterString> + </gmd:statement> + </gmd:LI_Lineage> + </gmd:lineage> + </gmd:DQ_DataQuality> + </gmd:dataQualityInfo> +</gmd:MD_Metadata> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Metadata_validation.txt Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,5 @@ + +Validation of metadata according to ISO 19139 + +TRUE + according to ISO 19139 XML schemas!
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Mission.csv Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,2 @@ +"","x" +"1","SAFE"
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/NDVI.tabular Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,10001 @@ +S2A_Subset longitude latitude NDVI +234.29 13.7115065213769 3.17883791054367 0.809475381273922 +233.06 13.7124063428304 3.17883903826377 0.814533326859802 +231.57 13.7133061646046 3.17884016519658 0.808063813546791 +228.73 13.7142059866993 3.17884129134209 0.814678963045019 +230.6 13.7151058091144 3.17884241670032 0.812649359747791 +228.85 13.7160056318495 3.17884354127124 0.806605795437084 +229.59 13.7169054549044 3.17884466505487 0.810899033966968 +239.74 13.7178052782789 3.1788457880512 0.808152735556578 +228.84 13.7187051019728 3.17884691026023 0.81364672485546 +217.93 13.7196049259859 3.17884803168196 0.821532180501305 +221.14 13.720504750318 3.17884915231639 0.813394469354217 +226.34 13.7214045749687 3.17885027216352 0.804053528518387 +233.05 13.722304399938 3.17885139122333 0.802659325445255 +237.25 13.7232042252255 3.17885250949585 0.809442899205139 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/S2A_Subset.hdr Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,35 @@ +ENVI +description = { + File Resize Result, x resize factor: 1.000000, y resize factor: 1.000000. + [Thu Jul 11 09:00:28 2019]} +samples = 1000 +lines = 1000 +bands = 10 +header offset = 0 +file type = ENVI Standard +data type = 2 +interleave = bil +sensor type = Unknown +byte order = 0 +x start = 998 +y start = 766 +map info = {UTM, 1.000, 1.000, 356780.000, 351500.000, 1.0000000000e+001, 1.0000000000e+001, 33, North, WGS-84, units=Meters} +coordinate system string = {PROJCS["WGS_1984_UTM_Zone_33N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["false_easting",500000.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",15.0],PARAMETER["scale_factor",0.9996],PARAMETER["latitude_of_origin",0.0],UNIT["Meter",1.0]]} +wavelength units = Nanometers +z plot range = {-32768.00, 32767.00} +data ignore value = 0 +default stretch = 0.000000 1000.000000 linear +band names = { + Resize (band 02:S2A_T33NUD_20180104_Subset), + Resize (band 03:S2A_T33NUD_20180104_Subset), + Resize (band 04:S2A_T33NUD_20180104_Subset), + Resize (band 05:S2A_T33NUD_20180104_Subset), + Resize (band 06:S2A_T33NUD_20180104_Subset), + Resize (band 07:S2A_T33NUD_20180104_Subset), + Resize (band 08:S2A_T33NUD_20180104_Subset), + Resize (band 08A:S2A_T33NUD_20180104_Subset), + Resize (band 11:S2A_T33NUD_20180104_Subset), + Resize (band 12:S2A_T33NUD_20180104_Subset)} +wavelength = { + 496.600006, 560.000000, 664.500000, 703.900024, 740.200012, 782.500000, + 835.099976, 864.799988, 1613.699951, 2202.399902}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/val_metadata.r Mon Jan 09 13:36:02 2023 +0000 @@ -0,0 +1,41 @@ +#Rscript + +############################################ +## Validate ISO 19139 metadata documen ## +############################################ + +#####Packages : ncdf4, +# geometa, +# httr +# xml +# xml2 +library(geometa) + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + input_data <- args[1] +} + +##------------------------------------------## +## Read ISO 19139 from a file or url ## +##------------------------------------------## + +# Test depuis catalogue Indores http://indores-tmp.in2p3.fr/geonetwork/srv/fre/catalog.search#/metadata/112ebeea-e79c-422c-8a43-a5a8323b446b +# <!--ISO 19139 XML compliance: NO--> +input_data <- xml2::read_xml(input_data) + +dir.create("results") +file.create("results/meta.xml") + +xml2::write_xml(input_data, file = "results/meta.xml") + +md <- geometa::readISO19139("results/meta.xml") + + +# validate iso +cat("\nValidation of metadata according to ISO 19139\n", md$validate(), file = "Metadata_validation.txt", fill = 1, append = FALSE)