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07_stats_vegIndex_pca_inverse_1stR.R
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07_stats_vegIndex_pca_inverse_1stR.R
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library(hsdar)
library(foreach)
library(doParallel)
setwd("/media/sd19006/data/users/iotte/R-Server/hyperspectral/clean/1st/norm_441/re_norm/pca/pca_inverse/pci_vegIndex/pci_vegIndex_dia_50m/")
filepath_raster <- "/media/sd19006/data/users/iotte/R-Server/hyperspectral/clean/1st/norm_441/re_norm/pca/pca_inverse/pci_vegIndex/pci_vegIndex_dia_50m/"
# vegIndices raster files
fls_hps <- list.files(paste0(filepath_raster), full.names = FALSE,
recursive = FALSE, pattern = "*vegIndex_dia_50m.tif")
fls_hps_stck <- lapply(seq(fls_hps), function(i){
stack(fls_hps[[i]])
})
# one plot aim: all plots
cl <- makeCluster(15)
registerDoParallel(cl)
# cof1
fls_hps_1.1 <- fls_hps_stck[[1]]
vegIndex.Vars.cof1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
# get neames of vegIndices
vegindex_names <- vegindex()
vegindex_names <- vegindex_names[c(4:17, 20:21, 23:25, 28:29, 34, 38:47,
50:57, 59, 61:65, 68:70, 72:83, 85:98,
106:113, 115)]
row.names(vegIndex.Vars.cof1) <- vegindex_names
vegIndex.Vars.cof1$plotID <- "cof1"
#cof2
fls_hps_1.1 <- fls_hps_stck[[2]]
vegIndex.Vars.cof2 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][], probs = .25,
na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][], probs = .75,
na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.cof2) <- vegindex_names
vegIndex.Vars.cof2$plotID <- "cof2"
vegIndex.Vars.all <- rbind(vegIndex.Vars.cof1, vegIndex.Vars.cof2)
# cof3
fls_hps_1.1 <- fls_hps_stck[[3]]
vegIndex.Vars.cof3 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.cof3) <- vegindex_names
vegIndex.Vars.cof3$plotID <- "cof3"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.cof3)
# cof4
fls_hps_1.1 <- fls_hps_stck[[4]]
vegIndex.Vars.cof4 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.cof4) <- vegindex_names
vegIndex.Vars.cof4$plotID <- "cof4"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.cof4)
# cof5
fls_hps_1.1 <- fls_hps_stck[[5]]
vegIndex.Vars.cof5 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.cof5) <- vegindex_names
vegIndex.Vars.cof5$plotID <- "cof5"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.cof5)
# cof6
#fls_hps_1.1 <- fls_hps_stck[[6]]
#vegIndex.Vars.cof6 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
# .combine = "rbind") %dopar% {
# min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
# max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
# mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
# median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
# sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
# stqrt <- quantile(fls_hps_1.1[[i]][],
# probs = .25, na.rm = TRUE)
# rdqrt <- quantile(fls_hps_1.1[[i]][],
# probs = .75, na.rm = TRUE)
# IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
# IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
# (median(fls_hps_1.1[[i]][], na.rm = TRUE))
# data.frame(min, max, mean, median, sd, stqrt,
# rdqrt, IQR, IQR_M)
# }
#row.names(vegIndex.Vars.cof6) <- vegindex_names
#vegIndex.Vars.cof6$plotID <- "cof6"
#vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.cof6)
# flm1
fls_hps_1.1 <- fls_hps_stck[[6]]
vegIndex.Vars.flm1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.flm1) <- vegindex_names
vegIndex.Vars.flm1$plotID <- "flm1"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.flm1)
# flm2
fls_hps_1.1 <- fls_hps_stck[[7]]
vegIndex.Vars.flm2 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.flm2) <- vegindex_names
vegIndex.Vars.flm2$plotID <- "flm2"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.flm2)
# flm3
fls_hps_1.1 <- fls_hps_stck[[8]]
vegIndex.Vars.flm3 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.flm3) <- vegindex_names
vegIndex.Vars.flm3$plotID <- "flm3"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.flm3)
# flm4
fls_hps_1.1 <- fls_hps_stck[[9]]
vegIndex.Vars.flm4 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.flm4) <- vegindex_names
vegIndex.Vars.flm4$plotID <- "flm4"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.flm4)
# flm6
fls_hps_1.1 <- fls_hps_stck[[10]]
vegIndex.Vars.flm6 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.flm6) <- vegindex_names
vegIndex.Vars.flm6$plotID <- "flm6"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.flm6)
# foc1
fls_hps_1.1 <- fls_hps_stck[[11]]
vegIndex.Vars.foc1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.foc1) <- vegindex_names
vegIndex.Vars.foc1$plotID <- "foc1"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.foc1)
# foc6
fls_hps_1.1 <- fls_hps_stck[[12]]
vegIndex.Vars.foc6 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.foc6) <- vegindex_names
vegIndex.Vars.foc6$plotID <- "foc6"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.foc6)
# fod5
fls_hps_1.1 <- fls_hps_stck[[13]]
vegIndex.Vars.fod5 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.fod5) <- vegindex_names
vegIndex.Vars.fod5$plotID <- "fod5"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.fod5)
# gra1
fls_hps_1.1 <- fls_hps_stck[[14]]
vegIndex.Vars.gra1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.gra1) <- vegindex_names
vegIndex.Vars.gra1$plotID <- "gra1"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.gra1)
# gra2
fls_hps_1.1 <- fls_hps_stck[[15]]
vegIndex.Vars.gra2 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.gra2) <- vegindex_names
vegIndex.Vars.gra2$plotID <- "gra2"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.gra2)
# gra3
fls_hps_1.1 <- fls_hps_stck[[16]]
vegIndex.Vars.gra3 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.gra3) <- vegindex_names
vegIndex.Vars.gra3$plotID <- "gra3"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.gra3)
# gra4
fls_hps_1.1 <- fls_hps_stck[[17]]
vegIndex.Vars.gra4 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.gra4) <- vegindex_names
vegIndex.Vars.gra4$plotID <- "gra4"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.gra4)
# gra5
fls_hps_1.1 <- fls_hps_stck[[18]]
vegIndex.Vars.gra5 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.gra5) <- vegindex_names
vegIndex.Vars.gra5$plotID <- "gra5"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.gra5)
# gra6
fls_hps_1.1 <- fls_hps_stck[[19]]
vegIndex.Vars.gra6 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.gra6) <- vegindex_names
vegIndex.Vars.gra6$plotID <- "gra6"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.gra6)
# hom1
fls_hps_1.1 <- fls_hps_stck[[20]]
vegIndex.Vars.hom1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.hom1) <- vegindex_names
vegIndex.Vars.hom1$plotID <- "hom1"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.hom1)
# hom2
fls_hps_1.1 <- fls_hps_stck[[21]]
vegIndex.Vars.hom2 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.hom2) <- vegindex_names
vegIndex.Vars.hom2$plotID <- "hom2"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.hom2)
# hom3
fls_hps_1.1 <- fls_hps_stck[[22]]
vegIndex.Vars.hom3 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.hom3) <- vegindex_names
vegIndex.Vars.hom3$plotID <- "hom3"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.hom3)
# hom4
fls_hps_1.1 <- fls_hps_stck[[23]]
vegIndex.Vars.hom4 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.hom4) <- vegindex_names
vegIndex.Vars.hom4$plotID <- "hom4"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.hom4)
# hom5
fls_hps_1.1 <- fls_hps_stck[[24]]
vegIndex.Vars.hom5 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.hom5) <- vegindex_names
vegIndex.Vars.hom5$plotID <- "hom5"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.hom5)
# mai1
fls_hps_1.1 <- fls_hps_stck[[25]]
vegIndex.Vars.mai1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.mai1) <- vegindex_names
vegIndex.Vars.mai1$plotID <- "mai1"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.mai1)
# mai2
fls_hps_1.1 <- fls_hps_stck[[26]]
vegIndex.Vars.mai2 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.mai2) <- vegindex_names
vegIndex.Vars.mai2$plotID <- "mai2"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.mai2)
# mai3
fls_hps_1.1 <- fls_hps_stck[[27]]
vegIndex.Vars.mai3 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.mai3) <- vegindex_names
vegIndex.Vars.mai3$plotID <- "mai3"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.mai3)
# mai4
fls_hps_1.1 <- fls_hps_stck[[28]]
vegIndex.Vars.mai4 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.mai4) <- vegindex_names
vegIndex.Vars.mai4$plotID <- "mai4"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.mai4)
# mai5
fls_hps_1.1 <- fls_hps_stck[[29]]
vegIndex.Vars.mai5 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.mai5) <- vegindex_names
vegIndex.Vars.mai5$plotID <- "mai5"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.mai5)
# sav1
fls_hps_1.1 <- fls_hps_stck[[30]]
vegIndex.Vars.sav1 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.sav1) <- vegindex_names
vegIndex.Vars.sav1$plotID <- "sav1"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.sav1)
# sav2
fls_hps_1.1 <- fls_hps_stck[[31]]
vegIndex.Vars.sav2 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.sav2) <- vegindex_names
vegIndex.Vars.sav2$plotID <- "sav2"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.sav2)
# sav3
fls_hps_1.1 <- fls_hps_stck[[32]]
vegIndex.Vars.sav3 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.sav3) <- vegindex_names
vegIndex.Vars.sav3$plotID <- "sav3"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.sav3)
# sav4
fls_hps_1.1 <- fls_hps_stck[[33]]
vegIndex.Vars.sav4 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.sav4) <- vegindex_names
vegIndex.Vars.sav4$plotID <- "sav4"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.sav4)
# sav5
fls_hps_1.1 <- fls_hps_stck[[34]]
vegIndex.Vars.sav5 <- foreach(i = 1:nlayers(fls_hps_1.1), .packages = "raster",
.combine = "rbind") %dopar% {
min <- min(fls_hps_1.1[[i]][], na.rm = TRUE)
max <- max(fls_hps_1.1[[i]][], na.rm = TRUE)
mean <- mean(fls_hps_1.1[[i]][], na.rm = TRUE)
median <- median(fls_hps_1.1[[i]][], na.rm = TRUE)
sd <- sd(fls_hps_1.1[[i]][], na.rm = TRUE)
stqrt <- quantile(fls_hps_1.1[[i]][],
probs = .25, na.rm = TRUE)
rdqrt <- quantile(fls_hps_1.1[[i]][],
probs = .75, na.rm = TRUE)
IQR <- IQR(fls_hps_1.1[[i]][], na.rm = TRUE)
IQR_M <- (IQR(fls_hps_1.1[[i]][], na.rm = TRUE))/
(median(fls_hps_1.1[[i]][], na.rm = TRUE))
data.frame(min, max, mean, median, sd, stqrt,
rdqrt, IQR, IQR_M)
}
row.names(vegIndex.Vars.sav5) <- vegindex_names
vegIndex.Vars.sav5$plotID <- "sav5"
vegIndex.Vars.all <- rbind(vegIndex.Vars.all, vegIndex.Vars.sav5)
write.csv2(vegIndex.Vars.all,
file = "pci_vegIndex_dia_50m_stats_1st.csv",
row.names = TRUE)
stopCluster(cl)