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annotate comparison_div.r @ 1:9300a492b634 draft default tip

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planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit 0f331043178bbfbe5cda0e31d887971464b3071c
author ecology
date Fri, 07 Apr 2023 15:58:46 +0000
parents 79bcb8c6dff0
children
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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1 #Rscript
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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3 ###########################################
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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4 ## Mapping alpha and beta diversity ##
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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5 ###########################################
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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7 #####Packages : stars
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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8 # utils
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9 # biodivmapr
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10 # raster
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11 # sf
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12 # mapview
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13 # leafpop
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14 # RColorBrewer
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15 # labdsv
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16 # rgdal
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17 # ggplot2
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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18 # gridExtra
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19 ##remotes::install_github("jbferet/biodivMapR")
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20 #####Load arguments
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22 args <- commandArgs(trailingOnly = TRUE)
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24 #####Import the S2 data
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26 if (length(args) < 1) {
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27 stop("This tool needs at least 1 argument")
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28 }else {
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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29 data_raster <- args[1]
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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30 rasterheader <- args[2]
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31 data <- args[3]
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32 plots_zip <- args[4]
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33 choice <- as.character(args[5])
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34 source(args[6])
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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35 # type of PCA:
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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36 # PCA: no rescaling of the data
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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37 # SPCA: rescaling of the data
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38 typepca <- as.character(args[7])
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39 }
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40
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41 ################################################################################
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42 ## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ##
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43 ################################################################################
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44 if (data_raster == "") {
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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45 #Create a directory where to unzip your folder of data
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46 dir.create("data_dir")
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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47 unzip(data, exdir = "data_dir")
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48 # Path to raster
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49 data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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50 input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
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51 input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
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52
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53 } else {
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54 input_image_file <- file.path(getwd(), data_raster, fsep = "/")
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55 input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
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56 }
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57
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58 ################################################################################
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59 ## PROCESS IMAGE ##
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60 ################################################################################
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61 # 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
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62
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63 print("PERFORM PCA ON RASTER")
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64 pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
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65 Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
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66
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67 pca_files <- pca_output$PCA_Files
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68 pix_per_partition <- pca_output$Pix_Per_Partition
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69 nb_partitions <- pca_output$nb_partitions
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70 # path for the updated mask
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71 input_mask_file <- pca_output$MaskPath
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73 # 3- Select principal components from the PCA raster
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74 # Select components from the PCA/SPCA/MNF raster
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75 sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8")
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76 image_name <- tools::file_path_sans_ext(basename(input_image_file))
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77 output_dir_full <- file.path(output_dir, image_name, typepca, "PCA")
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79 write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt"))
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80 sel_pc <- file.path(output_dir_full, "Selected_Components.txt")
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83 ################################################################################
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84 ## MAP ALPHA AND BETA DIVERSITY ##
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85 ################################################################################
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86 print("MAP SPECTRAL SPECIES")
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88 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)
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90 ################################################################################
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91 ## COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS ##
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92 ################################################################################
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93 ## read selected features from dimensionality reduction
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94
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95 ## path for selected components
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96
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97 # location of the directory where shapefiles used for validation are saved
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98 dir.create("VectorDir")
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99 unzip(plots_zip, exdir = "VectorDir")
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100
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101 # list vector data
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102 path_vector <- biodivMapR::list_shp("VectorDir")
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103 name_vector <- tools::file_path_sans_ext(basename(path_vector))
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104
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105 # location of the spectral species raster needed for validation
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106 path_spectralspecies <- kmeans_info$SpectralSpecies
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107 # get diversity indicators corresponding to shapefiles (no partitioning of spectral dibversity based on field plots so far...)
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108 biodiv_indicators <- biodivMapR::diversity_from_plots(Raster_SpectralSpecies = path_spectralspecies, Plots = path_vector, nbclusters = nbclusters, Raster_Functional = pca_files, Selected_Features = FALSE)
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109
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110 shannon_rs <- c(biodiv_indicators$Shannon)[[1]]
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111 fric <- c(biodiv_indicators$FunctionalDiversity$FRic)
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112 feve <- c(biodiv_indicators$FunctionalDiversity$FEve)
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113 fdiv <- c(biodiv_indicators$FunctionalDiversity$FDiv)
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114 # if no name for plots
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115 biodiv_indicators$Name_Plot <- seq(1, length(biodiv_indicators$Shannon[[1]]), by = 1)
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116
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117
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118 ####################################################
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119 # write RS indicators #
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120 ####################################################
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121 # write a table for Shannon index
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122
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123 # write a table for all spectral diversity indices corresponding to alpha diversity
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124 results <- data.frame(name_vector, biodiv_indicators$Richness, biodiv_indicators$Fisher,
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125 biodiv_indicators$Shannon, biodiv_indicators$Simpson,
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126 biodiv_indicators$FunctionalDiversity$FRic,
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127 biodiv_indicators$FunctionalDiversity$FEve,
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128 biodiv_indicators$FunctionalDiversity$FDiv)
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129
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130 names(results) <- c("ID_Plot", "Species_Richness", "Fisher", "Shannon", "Simpson", "fric", "feve", "fdiv")
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131 write.table(results, file = "Diversity.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
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132
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133 if (choice == "Y") {
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134 # write a table for Bray Curtis dissimilarity
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135 bc_mean <- biodiv_indicators$BCdiss
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136 bray_curtis <- data.frame(name_vector, bc_mean)
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137 colnames(bray_curtis) <- c("ID_Plot", bray_curtis[, 1])
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138 write.table(bray_curtis, file = "BrayCurtis.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
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139
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140 ####################################################
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141 # illustrate results
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142 ####################################################
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143 # apply ordination using PCoA (same as done for map_beta_div)
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144
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145 mat_bc_dist <- as.dist(bc_mean, diag = FALSE, upper = FALSE)
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146 betapco <- labdsv::pco(mat_bc_dist, k = 3)
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147
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148 # assign a type of vegetation to each plot, assuming that the type of vegetation
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149 # is defined by the name of the shapefile
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150
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151 nbsamples <- shpname <- c()
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152 for (i in 1:length(path_vector)) {
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153 shp <- path_vector[i]
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154 nbsamples[i] <- length(rgdal::readOGR(shp, verbose = FALSE))
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155 shpname[i] <- tools::file_path_sans_ext(basename(shp))
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156 }
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157
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158 type_vegetation <- c()
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159 for (i in 1: length(nbsamples)) {
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160 for (j in 1:nbsamples[i]) {
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161 type_vegetation <- c(type_vegetation, shpname[i])
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162 }
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163 }
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164
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165 #data frame including a selection of alpha diversity metrics and beta diversity expressed as coordinates in the PCoA space
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166 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)
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167
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168 #plot field data in the PCoA space, with size corresponding to shannon index
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169 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"))
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170
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171 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"))
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172
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173 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"))
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174
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175 #extract legend
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176 get_legend <- function(a_gplot) {
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177 tmp <- ggplot2::ggplot_gtable(ggplot2::ggplot_build(a_gplot))
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178 leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
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179 legend <- tmp$grobs[[leg]]
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180 return(legend)
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181 }
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182
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183 legend <- get_legend(g3)
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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184 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))
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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187 filename <- ggplot2::ggsave("BetaDiversity_PcoA1_vs_PcoA2_vs_PcoA3.png", gall, scale = 0.65, width = 12, height = 9, units = "in", dpi = 200, limitsize = TRUE)
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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189 filename
79bcb8c6dff0 planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
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190 }