function to predict efps using sentinel 2 added

DESCRIPTION

@@ -30,11 +30,16 @@ Imports:
   lubridate,
   zoo,
   ArgumentCheck,
-  Rdpack
+  Rdpack,
+  raster,
+  rgdal,
+  randomForest
 Suggests: 
     knitr,
     rmarkdown,
     testthat
 RdMacros: Rdpack
 VignetteBuilder: knitr
 RoxygenNote: 7.1.0
+Depends: 
+    R (>= 2.10)

NAMESPACE

@@ -16,6 +16,7 @@ export(gppsat)
 export(lue)
 export(plotensembles)
 export(predictR)
+export(sentinel2.efps)
 export(ts_gs)
 export(wue)
 importFrom(Rdpack,reprompt)

R/VI_functions.R

@@ -0,0 +1,263 @@
+# VI functions 
+
+bi2 <- function(red, green, nir, red_factor = 1, green_factor = 1, nir_factor =1) {
+  BI2 = sqrt( ( (red_factor * red * red_factor * red) + (green_factor * green * green_factor * green) + (nir_factor * nir * nir_factor * nir) ) / 3 )
+  return(BI2)
+}
+
+bi <- function(red, green, red_factor = 1, green_factor = 1) {
+  BI = sqrt( ( (red_factor * red * red_factor * red) + (green_factor * green * green_factor * green) ) / 2 )
+  return(BI)
+}
+
+ci <- function(red, green, red_factor = 1, green_factor = 1) {
+  CI = (red_factor * red - green_factor * green) / (red_factor * red + green_factor * green) 
+  return(CI)
+}
+
+ri <- function(red, green, red_factor = 1, green_factor = 1) {
+  RI = (red_factor * red * red_factor * red) / (green_factor * green * green_factor * green * green_factor * green)
+  return(RI)
+}
+
+arvi <- function(red, blue, nir, red_factor = 1, blue_factor = 1, nir_factor = 1, gamma = 1) {
+  rb = (red_factor * red) - gamma * (blue_factor * blue - red_factor * red)
+  ARVI = (nir_factor * nir - rb) / (nir_factor * nir + rb)
+  return(ARVI)
+}
+
+dvi <- function(red, nir, red_factor = 1, nir_factor = 1) {
+  DVI = (nir_factor * nir - red_factor * red)
+  return(DVI)
+}
+
+gemi <- function(red, nir, red_factor = 1, nir_factor = 1) {
+  eta = (2 * (nir_factor * nir * nir_factor * nir - red_factor * red * red_factor * red) + 1.5 * nir_factor * nir + 0.5 * red_factor * red) / (nir_factor * nir + red_factor * red + 0.5)
+
+  GEMI = eta * (1 - 0.25 * eta) - (red_factor * red - 0.125) / (1 - red_factor * red) 
+  return(GEMI)
+}
+
+gndvi <- function(green, nir, green_factor = 1, nir_factor = 1) {
+  GNDVI = (nir_factor * nir - green_factor * green) / (nir_factor * nir + green_factor * green)
+  return(GNDVI)
+}
+
+ipvi <- function(red, nir, red_factor = 1, nir_factor = 1) {
+  IPVI = (nir_factor * nir) / (nir_factor * nir + red_factor * red)
+  return(IPVI)
+}
+
+ireci <- function(red_b4, red_b5, red_b6, nir_b7, red_b4_factor = 1, red_b5_factor = 1, red_b6_factor = 1, nir_b7_factor = 1){
+  IRECI = (nir_b7_factor * nir_b7_factor - red_b4_factor * red_b4) / (red_b5_factor * red_b5 / red_b6_factor * red_b6)
+  return(IRECI)
+}
+
+# ireci with B8 by BAA??
+
+mcari <- function(red_b4, red_b5, green, red_b4_factor = 1, red_b5_factor = 1, green_factor = 1){
+  MCARI = ((red_b5_factor * red_b5 - red_b4_factor * red_b4) - 0.2 * (red_b5_factor * red_b5 - green_factor * green)) * (red_b5_factor * red_b5 / red_b4_factor * red_b4)  
+  return(MCARI)
+}
+
+msavi2 <- function(red, nir, red_factor = 1, nir_factor = 1) {
+  MSAVI2 = (1/2) * ( 2 * nir_factor * nir + 1 - sqrt( ( 2 * nir_factor * nir + 1) * ( 2 * nir_factor * nir + 1) - 8 * (nir_factor * nir - red_factor * red) ) )
+}
+
+msavi <- function(red, nir, red_factor = 1, nir_factor = 1, slope = 0.5) {
+  L = 1 - 2 * slope * ((nir_factor * nir - red_factor * red)/(nir_factor * nir + red_factor * red)) * (nir_factor * nir - slope * red_factor * red)
+  MSAVI = (1 + L) * (nir_factor * nir - red_factor * red) / (nir_factor * nir + red_factor * red + L)
+  return(MSAVI)
+}
+
+mtci <- function(red_b4, red_b5, nir_b6, red_b4_factor = 1, red_b5_factor = 1, nir_b6_factor = 1) {
+  MTCI = (nir_b6_factor * nir_b6 - red_b5_factor * red_b5) / (red_b5_factor * red_b5 - red_b4_factor * red_b4)
+  return(MTCI)
+}
+
+ndi45 <- function(red_b4, nir_b5, red_b4_factor = 1, nir_b5_factor = 1) {
+  NDI45 = (nir_b5_factor * nir_b5 - red_b4_factor * red_b4) / (nir_b5_factor * nir_b5 + red_b4_factor * red_b4)
+  return(NDI45)
+}
+
+pssra <- function(red_b4, nir_b7, red_b4_factor = 1, nir_b7_factor = 1) {
+  PSSRa = (nir_b7_factor * nir_b7) / (red_b4_factor * red_b4)
+  return(PSSRa)
+}
+
+pvi <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1, angle_soil_line = 45){
+  PVI = sin(angle_soil_line) * nir_b8_factor * nir_b8 - cos(angle_soil_line) * red_b4_factor * red_b4
+  return(PVI)
+}
+
+reip <- function(red_b4, red_b5, red_b6, nir_b7, red_b4_factor = 1, red_b5_factor =1, red_b6_factor = 1, nir_b7_factor = 1){
+  REIP = 700 + 40 * ( (red_b4_factor * red_b4 + nir_b7_factor * nir_b7)/2 - red_b5_factor * red_b5) / (red_b6_factor * red_b6 - red_b5_factor * red_b5) 
+  return(REIP)
+}
+
+rvi <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1) {
+  RVI = (nir_b8_factor * nir_b8) / (red_b4_factor * red_b4) 
+  return(RVI)
+}
+
+
+s2rep <- function(red_b4, red_b5, red_b6, nir_b7, red_b4_factor = 1, red_b5_factor = 1, red_b6_factor = 1, nir_b7_factor = 1){
+  S2REP = 705 + 35 * ( (red_b4_factor * red_b4 + nir_b7_factor * nir_b7)/2 - red_b5_factor * red_b5) / (red_b6_factor * red_b6 - red_b5_factor * red_b5)
+  return(S2REP)
+}
+
+savi <- function(red_b4, nir_b8, red_b4_factor =1, nir_b8_factor = 1, soil_correction = 0.5){
+  SAVI = (1 + soil_correction) * (nir_b8_factor * nir_b8 - red_b4_factor * red_b4) / (nir_b8_factor * nir_b8 + red_b4_factor * red_b4 + soil_correction)
+  return(SAVI)
+}
+
+tndvi <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1){
+  TNDVI = sqrt( (nir_b8_factor * nir_b8 - red_b4_factor * red_b4) / (nir_b8_factor * nir_b8 + red_b4_factor * red_b4) + 0.5)
+  return(TNDVI)
+}
+
+tsavi <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1, slope = 0.5, intercept = 0.5, adjustment = 0.08) {
+  TSAVI = slope * (nir_b8_factor * nir_b8 - slope * red_b4_factor * red_b4 - intercept) / (slope * nir_b8_factor * nir_b8 + red_b4_factor * red_b4 - intercept * slope + adjustment * ( 1 + slope * slope ))
+  return(TSAVI)
+}
+
+
+wdvi <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1, slope = 0.5) {
+  WDVI = (nir_b8_factor * nir_b8 - slope * red_b4_factor * red_b4) 
+  return(WDVI)
+}
+
+cir <- function(red_b5, red_b7, red_b5_factor = 1, red_b7_factor = 1) {
+  CIR = ((red_b7_factor * red_b7) / (red_b5_factor * red_b5)) -1
+  return(CIR)
+}
+
+cig <- function(green, red_b7, green_factor = 1, red_b7_factor = 1) {
+  CIG = ((red_b7_factor * red_b7) / (green_factor * green)) -1
+}
+
+ndvi <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1) {
+  NDVI = (nir_b8_factor * nir_b8 - red_b4_factor * red_b4) / (nir_b8_factor + nir_b8 - red_b4_factor * red_b4)
+  return(NDVI)
+}
+
+nirv <- function(red_b4, nir_b8, red_b4_factor = 1, nir_b8_factor = 1) {
+  NIRV = ((nir_b8_factor * nir_b8 - red_b4_factor * red_b4) / (nir_b8_factor + nir_b8 - red_b4_factor * red_b4)) * nir_b8_factor * nir_b8
+}
+
+all_vi <- function(red, blue, green, red_b4, red_b5, red_b6, red_b7, nir, nir_b5, nir_b6, nir_b7, nir_b8){
+  r1 <- bi2(red = red, green = green, nir = nir)
+  r2 <- bi(red = red, green = green)
+  r3 <- ci(red = red, green = green)
+  r4 <- ri(red = red, green = green)
+  r5 <- arvi(red = red, blue = blue, nir = nir)
+  r6 <- dvi(red = red, nir = nir)
+  r7 <- gemi(red = red, nir = nir)
+  r8 <- gndvi(green = green, nir = nir)
+  r9 <- ipvi(red = red, nir = nir)
+  r10 <- ireci(red_b4 = red_b4, red_b5 = red_b5, red_b6 = red_b6, nir_b7 = nir_b7)
+  r11 <- mcari(red_b4 = red_b4, red_b5 = red_b5, green = green)
+  r12 <- msavi2(red = red, nir = nir)
+  r13 <- msavi(red = red, nir = nir)
+  r14 <- mtci(red_b4 = red_b4, red_b5 = red_b5, nir_b6 = nir_b6)
+  r15 <- ndi45(red_b4 = red_b4, nir_b5 = nir_b5)
+  r16 <- pssra(red_b4 = red_b4, nir_b7 = nir_b7)
+  r17 <- pvi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r18 <- reip(red_b4 = red_b4, red_b5 = red_b5, red_b6 = red_b6, nir_b7 = nir_b7)
+  r19 <- rvi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r20 <- s2rep(red_b4 = red_b4, red_b5 = red_b5, red_b6 = red_b6, nir_b7 = nir_b7)
+  r21 <- savi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r22 <- tndvi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r23 <- tsavi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r24 <- wdvi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r25 <- cir(red_b5 = red_b5, red_b7 = red_b7)
+  r26 <- cig(green = green, red_b7 = red_b7)
+  r27 <- ndvi(red_b4 = red_b4, nir_b8 = nir_b8)
+  r28 <- nirv(red_b4 = red_b4, nir_b8 = nir_b8)
+  output <- stack(r1,
+                  r2,
+                  r3,
+                  r4,
+                  r5,
+                  r6,
+                  r7,
+                  r8,
+                  r9,
+                  r10,
+                  r11,
+                  r12,
+                  r13,
+                  r14,
+                  r15,
+                  r16,
+                  r17,
+                  r18,
+                  r19,
+                  r20,
+                  r21,
+                  r22,
+                  r23,
+                  r24,
+                  r25,
+                  r26,
+                  r27,
+                  r28)
+  names(output) <- c("bi2_mean",
+                     "bi_mean",
+                     "ci_mean",
+                     "ri_mean",
+                     "arvi_mean",
+                     "dvi_mean",
+                     "gemi_mean",
+                     "gndvi_mean",
+                     "ipvi_mean",
+                     "ireci_mean",
+                     "mcari_mean",
+                     "msavi2_mean",
+                     "msavi_mean",
+                     "mtci_mean",
+                     "ndi45_mean",
+                     "pssra_mean",
+                     "pvi_mean",
+                     "reip_mean",
+                     "rvi_mean",
+                     "s2rep_mean",
+                     "savi_mean",
+                     "tndvi_mean",
+                     "tsavi_mean",
+                     "wdvi_mean",
+                     "cir_mean",
+                     "cig_mean",
+                     "ndvi_mean",
+                     "nirv_mean")
+  return(output)
+}
+
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R/sentinel2_efps.R

@@ -0,0 +1,75 @@
+#' Predicting Ecosystem Functional Properties using Sentinel-2
+#'
+#' This function use pre-trained regression trees to predict GPP and GPPsat.
+#'
+#' @param sentinel2_product The path to a Sentinel-2 L2A product downloaded from https://scihub.copernicus.eu/ or generated with sen2cor. (String)
+#' @param product Can be "gpp", "gppsat", or "all".
+#'
+#' @return
+#'  An object of class raster if product = "gpp" or "gppsat".If class = "all" the output is a stack raster.
+#' @export
+#'
+#' @examples
+sentinel2.efps <- function(sentinel2_product, product = "gpp") {
+  library(rgdal)
+  library(raster)
+  library(randomForest)
+
+  images <- Sys.glob(file.path(sentinel2_product, "GRANULE", "*", "IMG_DATA", "*", "*"))
+  length(images)
+  bands_20m_for_forest <- c("B2_mean", "B3_mean", "B4_mean", "B5_mean", "B6_mean", "B7_mean", "B11_mean", "B12_mean", "B8A_mean")
+
+  images.position <- c(9:17)
+
+  bands20.raster <- stack(readGDAL(images[images.position[1]]))
+
+  for (i in 2:length(images.position)){
+    bands20.raster <- addLayer(bands20.raster, stack(readGDAL(images[images.position[i]])))
+  }
+
+  names(bands20.raster) <- bands_20m_for_forest
+
+  # bands to resample
+
+  B08 <- raster(readGDAL(images[5]))
+  B08 <- resample(B08, bands20.raster$B2_mean, method = "ngb")
+  bands20.raster <- addLayer(bands20.raster, B08)
+  names(bands20.raster)[length(names(bands20.raster))] <- "B8_mean"
+  rm(B08)
+  gc()
+
+  B01 <- raster(readGDAL(images[22]))
+  B09 <- raster(readGDAL(images[29]))
+
+  B01 <- resample(B01, bands20.raster$B2_mean, method = "ngb")
+  B09 <- resample(B09, bands20.raster$B2_mean, method = "ngb")
+
+  bands20.raster <- addLayer(bands20.raster, B01, B09)
+  names(bands20.raster)[length(names(bands20.raster))-1] <- "B1_mean"
+  names(bands20.raster)[length(names(bands20.raster))] <- "B9_mean"
+  rm(B01)
+  rm(B09)
+  gc()
+
+  names(bands20.raster)
+
+  stack.vi <- all_vi(red = bands20.raster$B4_mean, blue = bands20.raster$B2_mean, green = bands20.raster$B3_mean, red_b4 = bands20.raster$B4_mean, red_b5 = bands20.raster$B5_mean, red_b6 = bands20.raster$B6_mean, red_b7 = bands20.raster$B7_mean, nir = bands20.raster$B8_mean, nir_b5 = bands20.raster$B5_mean, nir_b6 = bands20.raster$B6_mean, nir_b7 = bands20.raster$B7_mean, nir_b8 = bands20.raster$B8_mean)
+
+  stack.vi <- addLayer(stack.vi, bands20.raster)
+
+  rm(bands20.raster)
+  gc()
+
+  if(product == "gpp"){
+    output.gpp <- raster::predict(stack.vi, ecofunr:::gpp.rf, na.rm = T, inf.rm = T)
+  }
+  else if (product == "gppsat") {
+    output.gpp <- raster::predict(stack.vi, ecofunr:::gppsat.rf, na.rm = T, inf.rm = T)
+  }
+  else if (product == "all") {
+    output1.gpp <- raster::predict(stack.vi, ecofunr:::gpp.rf, na.rm = T, inf.rm = T)
+    output2.gpp <- raster::predict(stack.vi, ecofunr:::gppsat.rf, na.rm = T, inf.rm = T)
+    output.gpp <- stack(output1.gpp, output2.gpp)
+  }
+  return(output.gpp)
+}