diff comparison_div.r @ 0:9adccd3da70c draft default tip

planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/comparison_div.r	Mon Jan 09 13:37:37 2023 +0000
@@ -0,0 +1,190 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+##remotes::install_github("jbferet/biodivMapR")
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    plots_zip <- args[4]
+    choice <- as.character(args[5])
+    source(args[6])
+    # type of PCA:
+    # PCA: no rescaling of the data
+    # SPCA: rescaling of the data
+    typepca <- as.character(args[7])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+pca_files <- pca_output$PCA_Files
+pix_per_partition <- pca_output$Pix_Per_Partition
+nb_partitions <- pca_output$nb_partitions
+# path for the updated mask
+input_mask_file <- pca_output$MaskPath
+
+# 3- Select principal components from the PCA raster
+# Select components from the PCA/SPCA/MNF raster
+sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8")
+image_name <- tools::file_path_sans_ext(basename(input_image_file))
+output_dir_full <- file.path(output_dir, image_name, typepca, "PCA")
+
+write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt"))
+sel_pc <-  file.path(output_dir_full, "Selected_Components.txt")
+
+
+################################################################################
+##                      MAP ALPHA AND BETA DIVERSITY                          ##
+################################################################################
+print("MAP SPECTRAL SPECIES")
+
+kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters, TypePCA = typepca)
+
+################################################################################
+##          COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS                 ##
+################################################################################
+## read selected features from dimensionality reduction
+
+## path for selected components
+
+# location of the directory where shapefiles used for validation are saved
+dir.create("VectorDir")
+unzip(plots_zip, exdir = "VectorDir")
+
+# list vector data
+path_vector <- biodivMapR::list_shp("VectorDir")
+name_vector <- tools::file_path_sans_ext(basename(path_vector))
+
+# location of the spectral species raster needed for validation
+path_spectralspecies <- kmeans_info$SpectralSpecies
+# get diversity indicators corresponding to shapefiles (no partitioning of spectral dibversity based on field plots so far...)
+biodiv_indicators <- biodivMapR::diversity_from_plots(Raster_SpectralSpecies = path_spectralspecies, Plots = path_vector, nbclusters = nbclusters, Raster_Functional = pca_files, Selected_Features = FALSE)
+
+shannon_rs <- c(biodiv_indicators$Shannon)[[1]]
+fric <- c(biodiv_indicators$FunctionalDiversity$FRic)
+feve <- c(biodiv_indicators$FunctionalDiversity$FEve)
+fdiv <- c(biodiv_indicators$FunctionalDiversity$FDiv)
+# if no name for plots
+biodiv_indicators$Name_Plot <- seq(1, length(biodiv_indicators$Shannon[[1]]), by = 1)
+
+
+####################################################
+# write RS indicators                              #
+####################################################
+# write a table for Shannon index
+
+# write a table for all spectral diversity indices corresponding to alpha diversity
+results <- data.frame(name_vector, biodiv_indicators$Richness, biodiv_indicators$Fisher,
+                      biodiv_indicators$Shannon, biodiv_indicators$Simpson,
+                      biodiv_indicators$FunctionalDiversity$FRic,
+                      biodiv_indicators$FunctionalDiversity$FEve,
+                      biodiv_indicators$FunctionalDiversity$FDiv)
+
+names(results) <- c("ID_Plot", "Species_Richness", "Fisher", "Shannon", "Simpson", "fric", "feve", "fdiv")
+write.table(results, file = "Diversity.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+if (choice == "Y") {
+# write a table for Bray Curtis dissimilarity
+bc_mean <- biodiv_indicators$BCdiss
+bray_curtis <- data.frame(name_vector, bc_mean)
+colnames(bray_curtis) <- c("ID_Plot", bray_curtis[, 1])
+write.table(bray_curtis, file = "BrayCurtis.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+####################################################
+# illustrate results
+####################################################
+# apply ordination using PCoA (same as done for map_beta_div)
+
+mat_bc_dist <- as.dist(bc_mean, diag = FALSE, upper = FALSE)
+betapco <- labdsv::pco(mat_bc_dist, k = 3)
+
+# assign a type of vegetation to each plot, assuming that the type of vegetation
+# is defined by the name of the shapefile
+
+nbsamples <- shpname <- c()
+for (i in 1:length(path_vector)) {
+  shp <- path_vector[i]
+  nbsamples[i] <- length(rgdal::readOGR(shp, verbose = FALSE))
+  shpname[i] <- tools::file_path_sans_ext(basename(shp))
+}
+
+type_vegetation <- c()
+for (i in 1: length(nbsamples)) {
+  for (j in 1:nbsamples[i]) {
+    type_vegetation <- c(type_vegetation, shpname[i])
+  }
+}
+
+#data frame including a selection of alpha diversity metrics and beta diversity expressed as coordinates in the PCoA space
+results <- data.frame("vgtype" = type_vegetation, "pco1" = betapco$points[, 1], "pco2" = betapco$points[, 2], "pco3" = betapco$points[, 3], "shannon" = shannon_rs, "fric" = fric, "feve" = feve, "fdiv" = fdiv)
+
+#plot field data in the PCoA space, with size corresponding to shannon index
+g1 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco2, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+g2 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+g3 <- ggplot2::ggplot(results, ggplot2::aes(x = pco2, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+#extract legend
+get_legend <- function(a_gplot) {
+    tmp <- ggplot2::ggplot_gtable(ggplot2::ggplot_build(a_gplot))
+    leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+    legend <- tmp$grobs[[leg]]
+    return(legend)
+}
+
+legend <- get_legend(g3)
+gall <- gridExtra::grid.arrange(gridExtra::arrangeGrob(g1 + ggplot2::theme(legend.position = "none"), g2 + ggplot2::theme(legend.position = "none"), g3 + ggplot2::theme(legend.position = "none"), nrow = 1), legend, nrow = 2, heights = c(3, 2))
+
+
+filename <- ggplot2::ggsave("BetaDiversity_PcoA1_vs_PcoA2_vs_PcoA3.png", gall, scale = 0.65, width = 12, height = 9, units = "in", dpi = 200, limitsize = TRUE)
+
+filename
+}