# HG changeset patch # User ecology # Date 1673271574 0 # Node ID a775bae8083bd164c5b33ae16733675cae15c409 # Parent fbffdeefb1460cb8d5ce92cfeec87d259c7a6ef7 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7 diff -r fbffdeefb146 -r a775bae8083b .shed.yml --- a/.shed.yml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,14 +0,0 @@ -categories: - - Ecology -owner: ecology -remote_repository_url: https://github.com/Marie59/Sentinel_2A/srs_tools -long_description: | - Compute biodiversity indicators for remote sensing data from Sentinel 2 -type: unrestricted -auto_tool_repositories: - name_template: "{{ tool_id }}" - description_template: "Wrapper for srs biodiversity indicators tool: {{ tool_name }}." -suite: - name: "sentinel_2" - description: "A suite of biodiversity indicators for Sentinel 2 data" - type: unrestricted diff -r fbffdeefb146 -r a775bae8083b Lib_preprocess_S2.r --- a/Lib_preprocess_S2.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1335 +0,0 @@ -# == == == == == == == == == == == == == == == == == == == == == == == == == == == -# preprocS2 -# Lib_preprocess_S2.R -# == == == == == == == == == == == == == == == == == == == == == == == == == == == -# PROGRAMMERS: -# Jean-Baptiste FERET -# 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()) -} diff -r fbffdeefb146 -r a775bae8083b alpha_beta.r --- a/alpha_beta.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,142 +0,0 @@ -#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) -} diff -r fbffdeefb146 -r a775bae8083b alpha_beta.xml --- a/alpha_beta.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,153 +0,0 @@ - - from remote sensing data - - macro.xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - type == 'alpha' or type == 'all' - - - type == 'beta' or type == 'all' - - - type == 'funct' or type == 'all' - - - - - - - - - - - - - - - - - - - - - - diff -r fbffdeefb146 -r a775bae8083b biodiv_indices_global.r --- a/biodiv_indices_global.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,112 +0,0 @@ -#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) diff -r fbffdeefb146 -r a775bae8083b biodiv_indices_global.xml --- a/biodiv_indices_global.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,123 +0,0 @@ - - from remote sensing data - - macro.xml - - - r-base - r-raster - r-rgdal - r-sp - r-rasterdiv - r-ggplot2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff -r fbffdeefb146 -r a775bae8083b comparison_div.r --- a/comparison_div.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,190 +0,0 @@ -#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 -} diff -r fbffdeefb146 -r a775bae8083b comparison_div.xml --- a/comparison_div.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,138 +0,0 @@ - - with remote sensing data - - macro.xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - choice == 'Y' - - - - choice =='Y' - - - - - - - - - - - - - - - - - - - - - - - - - diff -r fbffdeefb146 -r a775bae8083b functions.r --- a/functions.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,79 +0,0 @@ -#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") -} diff -r fbffdeefb146 -r a775bae8083b indices_spectral.r --- a/indices_spectral.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,105 +0,0 @@ -#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 diff -r fbffdeefb146 -r a775bae8083b indices_spectral.xml --- a/indices_spectral.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,270 +0,0 @@ - - as NDVI from remote sensing data - - macro.xml - - - r-base - r-r.utils - r-raster - r-sp - r-rgdal - r-rasterdiv - r-stars - r-stringr - r-pracma - r-expint - r-matrixstats - r-ggplot2 - r-xml - r-zip - r-dplyr - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - choice == 'Y' - - - - - - - - - - - - - - - - - - - - - - diff -r fbffdeefb146 -r a775bae8083b macro.xml --- a/macro.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,102 +0,0 @@ - - 0.0.1 - - - r-base - r-r.utils - r-sf - r-rgdal - r-rgeos - r-stars - r-stringr - r-biodivmapr - r-xfun - r-rastervis - r-ggplot2 - r-mapview - r-gridextra - r-emstreer - r-filesstrings - - - - - - - - - - - - @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}, - } - - - - - - - @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}, - } - - - - - - - @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}, - } - - - - - - doi:10.1016/j.ecolind.2016.07.039 - doi:10.1101/2021.01.23.427872 - - - - - - @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}, - } - - - - - - doi.org/10.5281/zenodo.5907920 - - - - - doi.org/10.1016/j.rse.2013.09.022 - - - - - topic_0610 - topic_3050 - - - diff -r fbffdeefb146 -r a775bae8083b pca_raster.r --- a/pca_raster.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,75 +0,0 @@ -#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 diff -r fbffdeefb146 -r a775bae8083b pca_raster.xml --- a/pca_raster.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,120 +0,0 @@ - - from remote sensing data - - macro.xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff -r fbffdeefb146 -r a775bae8083b preprocess_S2.r --- a/preprocess_S2.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,121 +0,0 @@ -#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) diff -r fbffdeefb146 -r a775bae8083b preprocess_S2.xml --- a/preprocess_S2.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,109 +0,0 @@ - - read, crop, resample and write it as a raster stack - - macro.xml - - - r-base - r-sf - r-rgeos - r-stars - r-stringr - r-jqr - r-protolite - r-sen2r - r-progress - r-gdalutils - r-fasterize - r-xml - r-xml2 - r-zip - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff -r fbffdeefb146 -r a775bae8083b prosail-master/R/Lib_PROSAIL.R --- a/prosail-master/R/Lib_PROSAIL.R Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1233 +0,0 @@ -# ============================================================================= = -# prosail -# Lib_PROSAIL.R -# ============================================================================= = -# PROGRAMMERS: -# Jean-Baptiste FERET -# Florian de BOISSIEU -# 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 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) -} diff -r fbffdeefb146 -r a775bae8083b prosail-master/R/Lib_PROSAIL_HybridInversion.R --- a/prosail-master/R/Lib_PROSAIL_HybridInversion.R Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,554 +0,0 @@ -# ============================================================================= = -# prosail -# Lib_PROSAIL_HybridInversion.R -# ============================================================================= = -# PROGRAMMERS: -# Jean-Baptiste FERET -# Florian de BOISSIEU -# 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()) -} diff -r fbffdeefb146 -r a775bae8083b prosail-master/R/Lib_SpectralIndices.R --- a/prosail-master/R/Lib_SpectralIndices.R Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,679 +0,0 @@ -# ============================================================================== = -# prosail -# Lib_spectralindices.R -# ============================================================================== = -# PROGRAMMERS: -# Jean-Baptiste FERET -# Florian de BOISSIEU -# 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 (CRmin0 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) -} diff -r fbffdeefb146 -r a775bae8083b repository_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/repository_dependencies.xml Mon Jan 09 13:39:34 2023 +0000 @@ -0,0 +1,10 @@ + + + + + + + + + + \ No newline at end of file diff -r fbffdeefb146 -r a775bae8083b test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml --- a/test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,661 +0,0 @@ - - - - 12a0b625-9ad5-4251-a57a-305e22edef2e - - - - - - - - - - - - - - Santiago Poggio - - - IFEVA/ Catédra de producción vegetal, facultad de agronomia, Universidad de Buenos Aires/ CONICET - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - spoggio@agro.uba.ar - - - - - - - - - - - - - - Jacques Baudry - - - BAGAP UMR 0980 INRAE Agrocampus Ouest ESA - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - jacques.baudry@inrae.fr - - - - - - - - - - - - - - Audrey Alignier - - - BAGAP UMR 0980 INRAE Agrocampus Ouest ESA - - - - - - - audrey.alignier@inrae.fr - - - - - - - - - - - - - - - - - Zone Atelier Armorique (ZAAr) - - - - - - - - - - - - - - - - - - - 2021-03-27T00:00:09 - - - ISO 19115:2003/19139 - - - 1.0 - - - - - - - RGF Lambert-93 - - - - - - - - - - - Relevés floristiques en maïs (Pleine-Fougères, 2010) - Zone Atelier Armorique ZAAr - - - - - 2010-09-14 - - - - - - - - - - - - - https://www.osuris.fr/geonetwork/srv/metadata/12a0b625-9ad5-4251-a57a-305e22edef2e - - - - - - - - - - "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) - - - - - - - - - - - Santiago Poggio - - - IFEVA/ Catédra de producción vegetal, facultad de agronomia, Universidad de Buenos Aires/ CONICET - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - spoggio@agro.uba.ar - - - - - - - - - - - - - - Jacques Baudry - - - BAGAP UMR 0980 INRAE Agrocampus Ouest ESA - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - jacques.baudry@inrae.fr - - - - - - - - - - - - - - - - - - - - - https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21_s.png - - - thumbnail - - - png - - - - - - - https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21.png - - - large_thumbnail - - - png - - - - - - - Biodiversité - - - - - - - - - - Maïs - - - - - - - - - - Forêt / milieu végétal - - - Agriculture - - - Ecologie et environnement - - - Paysages et sites - - - - - - - - theme.EnvironnementFR.rdf - - - - - 2017-03-20 - - - - - - - - - - geonetwork.thesaurus.external.theme.EnvironnementFR - - - - - - - - - - - Installations de suivi environnemental - - - Habitats et biotopes - - - - - - - - Registre de thème INSPIRE - - - - - 2019-06-25 - - - - - - - - - - geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme - - - - - - - - - - - - - - - - - licence CC-BY-NC 4.0 - - - - - - - Utilisation libre sous réserve de mentionner la source (a minima le nom du producteur) et la date de sa dernière mise à jour - - - - - - - - - - 20 - - - - - - - - - - - farming - - - - - - - - 2010-09-08 - 2008-09-14 - - - - - - - - - - - - -1.6259765625 - - - -0.9777832031250001 - - - 48.092651367188 - - - 48.696899414063 - - - - - - - France, Pleine-Fougères - - - - - - - - - ESRI Shapefile - - - 1.0 - - - - - - - - - - - - - - - - - - - - - - - - - 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. - - - - - 2013-10-21 - - - - - - - - - - - - - true - - - - - - - - - Relevé sur le terrain des adventices du maïs - - - - - - \ No newline at end of file diff -r fbffdeefb146 -r a775bae8083b test-data/Metadata_validation.txt --- a/test-data/Metadata_validation.txt Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,5 +0,0 @@ - -Validation of metadata according to ISO 19139 - -TRUE - according to ISO 19139 XML schemas! diff -r fbffdeefb146 -r a775bae8083b test-data/Mission.csv --- a/test-data/Mission.csv Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -"","x" -"1","SAFE" diff -r fbffdeefb146 -r a775bae8083b test-data/NDVI.tabular --- a/test-data/NDVI.tabular Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,10001 +0,0 @@ -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 -234.61 13.7241040508311 3.17885362698105 0.811580900151433 -225.7 13.7250038767545 3.17885474367895 0.820858536885687 -243.54 13.7259037029954 3.17885585958953 0.805156185849842 -238.98 13.7268035295538 3.17885697471281 0.814407496583588 -224.3 13.7277033564293 3.17885808904877 0.806504430597208 -229.94 13.7286031836218 3.17885920259741 0.814004957858205 -230.32 13.7295030111309 3.17886031535874 0.834195663335835 -221.94 13.7304028389565 3.17886142733275 0.830148226182174 -221.75 13.7313026670984 3.17886253851944 0.817970615615008 -231.52 13.7322024955563 3.17886364891882 0.826119282944004 -230.78 13.73310232433 3.17886475853087 0.837485155719081 -237.15 13.7340021534193 3.1788658673556 0.807358464103723 -240.64 13.7349019828239 3.178866975393 0.834367581945824 -240.36 13.7358018125437 3.17886808264308 0.814613959603203 -234.42 13.7367016425784 3.17886918910583 0.823334743713733 -232.98 13.7376014729278 3.17887029478125 0.812603727061501 -223.55 13.7385013035916 3.17887139966934 0.817053245524247 -228.45 13.7394011345697 3.1788725037701 0.820890651769377 -234.76 13.7403009658618 3.17887360708353 0.81472573939151 -241.63 13.7412007974676 3.17887470960963 0.800657983613983 -242.54 13.7421006293871 3.17887581134839 0.804551773444016 -233.03 13.7430004616199 3.17887691229981 0.818208709568542 -234.33 13.7439002941658 3.1788780124639 0.828198556142271 -224.33 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3.08939772975906 0.809455691014441 -246.08 13.799792558354 3.08939875143481 0.808684324455036 -251.94 13.8006923337187 3.08939977234536 0.827205483763792 diff -r fbffdeefb146 -r a775bae8083b test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip Binary file test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip has changed diff -r fbffdeefb146 -r a775bae8083b test-data/S2A_Subset Binary file test-data/S2A_Subset has changed diff -r fbffdeefb146 -r a775bae8083b test-data/S2A_Subset.hdr --- a/test-data/S2A_Subset.hdr Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,35 +0,0 @@ -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} diff -r fbffdeefb146 -r a775bae8083b test-data/S2A_T33NUD_Plots.zip Binary file test-data/S2A_T33NUD_Plots.zip has changed diff -r fbffdeefb146 -r a775bae8083b val_metadata.r --- a/val_metadata.r Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,41 +0,0 @@ -#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 -# -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) diff -r fbffdeefb146 -r a775bae8083b val_metadata.xml --- a/val_metadata.xml Sun Jan 08 23:03:35 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,67 +0,0 @@ - - metadata documents - - macro.xml - - - r-base - r-r.utils - r-raster - r-sf - r-rgdal - r-rgeos - r-stars - r-stringr - r-ncdf4 - r-geometa - r-httr - r-xml - r-xml2 - - Metadata_validation.txt - ]]> - - - - - - - - - - - - - - - -