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61_map_soil_properties.R
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61_map_soil_properties.R
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#------------------------------------------------------------------------------
# Name: 61_map_soil_properties.R
#
# Content: - Load stack of predicted target soil property rasters and visualize
# results as plots using "rasterVis" pkg:
# - 5th, 50th (median) and 95th quantile of QRF
# - 90% prediction interval (PI90)
# - GSM accuracy thresholds
#
# Inputs: - out/maps/target/[TARGET]/GeoTIFFs/
#
# Output: - out/maps/target/[TARGET]/pdf/
#
# Project: BIS+
# Author: Anatol Helfenstein
# Updated: June 2020
#------------------------------------------------------------------------------
### empty memory and workspace; load required packages -------------------------
gc()
rm(list=ls())
pkgs <- c("raster", "rasterVis", "viridisLite", "grid", "gridExtra", "foreach",
"RColorBrewer", "tidyverse")
lapply(pkgs, library, character.only = TRUE)
### Designate script parameters and load data ----------------------------------
# Specify DSM target soil property:
TARGET = "pH_KCl"
# GlobalSoilMap (GSM) depth layers
# (see out/data/covariates/target_GSM_depths):
D_MID = c("d_0_5_mid", "d_5_15_mid", "d_15_30_mid",
"d_30_60_mid", "d_60_100_mid", "d_100_200_mid")
# locate, read in and stack rasters of response soil properties
# prediction mean and quantiles
v_response_names_pred <- dir(paste0("out/maps/target/", TARGET, "/GeoTIFFs"),
pattern = "pred[059_mean]{,5}.tif$", recursive = FALSE)
ls_r_response_pred <- foreach(r = 1:length(v_response_names_pred)) %do%
raster(paste0("out/maps/target/", TARGET, "/GeoTIFFs/", v_response_names_pred[[r]]))
r_stack_response_pred <- stack(ls_r_response_pred)
# PI90
v_response_names_PI90 <- dir(paste0("out/maps/target/", TARGET, "/GeoTIFFs"),
pattern = "\\PI90.tif$", recursive = FALSE)
ls_r_response_PI90 <- foreach(r = 1:length(v_response_names_PI90)) %do%
raster(paste0("out/maps/target/", TARGET, "/GeoTIFFs/", v_response_names_PI90[[r]]))
r_stack_response_PI90 <- stack(ls_r_response_PI90)
# thresholds
v_response_names_thresh <- dir(paste0("out/maps/target/", TARGET, "/GeoTIFFs"),
pattern = "\\holds.tif$", recursive = FALSE)
ls_r_response_thresh <- foreach(r = 1:length(v_response_names_thresh)) %do%
raster(paste0("out/maps/target/", TARGET, "/GeoTIFFs/", v_response_names_thresh[[r]]))
r_stack_response_thresh <- stack(ls_r_response_thresh)
# extract min and max values so we can use same color legend for all maps
response_min = round(min(minValue(r_stack_response_pred)))
response_max = round(max(maxValue(r_stack_response_pred)))
# extract min and max values so we can use same color legend for all maps
PI90_min = round(min(minValue(r_stack_response_PI90)))
PI90_max = round(max(maxValue(r_stack_response_PI90)))
# define interval (smallest step cm to visualize on map and in color scheme)
interval = 0.1
# vector that will define global color scheme for prediction quantiles and PI90
v_col_pred <- seq(response_min, response_max, interval)
v_col_PI90 <- seq(PI90_min, PI90_max, interval)
### Maps of mean & median (50th quantile) predictions over all depth layers ----
# mean predictions over all GSM depth layers
m_pred_mean <- levelplot(stack(r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred_mean,
r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred_mean,
r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred_mean,
r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred_mean,
r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred_mean,
r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred_mean),
margin = FALSE,
# main = expression(paste("Soil pH [KCl] (mean)")),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression("0 cm to 5 cm"),
expression("5 cm to 15 cm"),
expression("15 cm to 30 cm"),
expression("30 cm to 60 cm"),
expression("60 cm to 100 cm"),
expression("100 cm to 200 cm"))
# colorkey = list(title = expression("pH [KCl] 100-200cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_pred_mean_all_depths.pdf"),
# height = 10, width = 10)
# m_pred_mean
# dev.off()
# median predictions (50th quantile) over all GSM depth layers
m_pred50 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred50),
margin = FALSE,
# main = expression(paste("Soil pH [KCl] (median)")),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression("0 cm to 5 cm"),
expression("5 cm to 15 cm"),
expression("15 cm to 30 cm"),
expression("30 cm to 60 cm"),
expression("60 cm to 100 cm"),
expression("100 cm to 200 cm"))
# colorkey = list(title = expression("pH [KCl] 100-200cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_pred50_all_depths.pdf"),
# height = 10, width = 10)
# m_pred50
# dev.off()
# out of curiosity, map of mean - median values (subtract the two maps)
# 0-5cm
r_mean_minus_median_0_5 <- r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred_mean -
r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred50
names(r_mean_minus_median_0_5) <- "r_mean_minus_median_0_5"
# 5-15cm
r_mean_minus_median_5_15 <- r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred_mean -
r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred50
names(r_mean_minus_median_5_15) <- "r_mean_minus_median_5_15"
# 15-30cm
r_mean_minus_median_15_30 <- r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred_mean -
r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred50
names(r_mean_minus_median_15_30) <- "r_mean_minus_median_15_30"
# 30-60cm
r_mean_minus_median_30_60 <- r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred_mean -
r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred50
names(r_mean_minus_median_30_60) <- "r_mean_minus_median_30_60"
# 60-100cm
r_mean_minus_median_60_100 <- r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred_mean -
r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred50
names(r_mean_minus_median_60_100) <- "r_mean_minus_median_60_100"
# 100-200cm
r_mean_minus_median_100_200 <- r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred_mean -
r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred50
names(r_mean_minus_median_100_200) <- "r_mean_minus_median_100_200"
# mean - median over all depth layers
r_stack_mean_minus_median <- stack(r_mean_minus_median_0_5,
r_mean_minus_median_5_15,
r_mean_minus_median_15_30,
r_mean_minus_median_30_60,
r_mean_minus_median_60_100,
r_mean_minus_median_100_200)
# subtracted maps (mean - median) over all GSM depth layers
m_mean_minus_median <- levelplot(
r_stack_mean_minus_median,
margin = FALSE,
main = expression(paste("Subtracted Maps of Mean - Median for Soil pH [KCl]")),
scales = list(draw = FALSE),
col.regions = colorRampPalette(colors = c("#a50026", "#ffffbf", "#313695")),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression("0 cm to 5 cm"),
expression("5 cm to 15 cm"),
expression("15 cm to 30 cm"),
expression("30 cm to 60 cm"),
expression("60 cm to 100 cm"),
expression("100 cm to 200 cm"))
# colorkey = list(title = expression("pH [KCl] 100-200cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET,
# "_mean_minus_median_all_depths.pdf"),
# height = 10, width = 10)
# m_mean_minus_median
# dev.off()
# save prediction maps as GeoTIFFs to disk
system.time(
foreach(n = 1:nlayers(r_stack_mean_minus_median)) %do%
writeRaster(r_stack_mean_minus_median[[n]],
paste0("out/maps/target/", TARGET, "/GeoTIFFs/", TARGET, "_",
D_MID[n], "_QRF_mean_minus_median.tif"),
overwrite = TRUE)
) # time elapse sequential: 2.5 min
### Maps of 5th, 50th & 95th prediction quantiles at each GSM depth layer ------
# 0-5 cm
m_pred_0_5 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred5,
r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_0_5_mid_QRF_pred95),
margin = FALSE,
# main = expression("Soil pH [KCl]: 0 cm to 5 cm depth"),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression(paste("q"[0.05], " (0.05 quantile)")),
expression(paste("q"[0.50], " (median)")),
expression(paste("q"[0.95], " (0.95 quantile)")))
#colorkey = list(title = expression("pH [KCl] 0-5cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_0_5_pred.pdf"),
# height = 6, width = 12)
# m_pred_0_5
# dev.off()
# 5-15 cm
m_pred_5_15 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred5,
r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_5_15_mid_QRF_pred95),
margin = FALSE,
# main = expression("Soil pH [KCl]: 5 cm to 15 cm depth"),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression(paste("q"[0.05], " (0.05 quantile)")),
expression(paste("q"[0.50], " (median)")),
expression(paste("q"[0.95], " (0.95 quantile)")))
#colorkey = list(title = expression("pH [KCl] 0-5cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_5_15_pred.pdf"),
# height = 6, width = 12)
# m_pred_5_15
# dev.off()
# 15-30 cm
m_pred_15_30 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred5,
r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_15_30_mid_QRF_pred95),
margin = FALSE,
# main = expression("Soil pH [KCl]: 15 cm to 30 cm depth"),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression(paste("q"[0.05], " (0.05 quantile)")),
expression(paste("q"[0.50], " (median)")),
expression(paste("q"[0.95], " (0.95 quantile)")))
#colorkey = list(title = expression("pH [KCl] 0-5cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_15_30_pred.pdf"),
# height = 6, width = 12)
# m_pred_15_30
# dev.off()
# 30-60 cm
m_pred_30_60 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred5,
r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_30_60_mid_QRF_pred95),
margin = FALSE,
# main = expression("Soil pH [KCl]: 30 cm to 60 cm depth"),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression(paste("q"[0.05], " (0.05 quantile)")),
expression(paste("q"[0.50], " (median)")),
expression(paste("q"[0.95], " (0.95 quantile)")))
#colorkey = list(title = expression("pH [KCl] 0-5cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_30_60_pred.pdf"),
# height = 6, width = 12)
# m_pred_30_60
# dev.off()
# 60-100 cm
m_pred_60_100 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred5,
r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_60_100_mid_QRF_pred95),
margin = FALSE,
# main = expression("Soil pH [KCl]: 60 cm to 100 cm depth"),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression(paste("q"[0.05], " (0.05 quantile)")),
expression(paste("q"[0.50], " (median)")),
expression(paste("q"[0.95], " (0.95 quantile)")))
#colorkey = list(title = expression("pH [KCl] 0-5cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_60_100_pred.pdf"),
# height = 6, width = 12)
# m_pred_60_100
# dev.off()
# 100-200 cm
m_pred_100_200 <- levelplot(stack(r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred5,
r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred50,
r_stack_response_pred$pH_KCl_d_100_200_mid_QRF_pred95),
margin = FALSE,
# main = expression("Soil pH [KCl]: 100 cm to 200 cm depth"),
scales = list(draw = FALSE),
at = v_col_pred,
col.regions = viridis(n = length(v_col_pred),
option = "magma"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression(paste("q"[0.05], " (0.05 quantile)")),
expression(paste("q"[0.50], " (median)")),
expression(paste("q"[0.95], " (0.95 quantile)")))
#colorkey = list(title = expression("pH [KCl] 0-5cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_100_200_pred.pdf"),
# height = 6, width = 12)
# m_pred_100_200
# dev.off()
### Maps of PI90 over all GSM depth layers -----------------------------------
# PI90 over all depths
m_PI90 <- levelplot(stack(r_stack_response_PI90$pH_KCl_d_0_5_mid_QRF_PI90,
r_stack_response_PI90$pH_KCl_d_5_15_mid_QRF_PI90,
r_stack_response_PI90$pH_KCl_d_15_30_mid_QRF_PI90,
r_stack_response_PI90$pH_KCl_d_30_60_mid_QRF_PI90,
r_stack_response_PI90$pH_KCl_d_60_100_mid_QRF_PI90,
r_stack_response_PI90$pH_KCl_d_100_200_mid_QRF_PI90),
margin = FALSE,
# main = expression("PI90 of pH [KCl]"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression("0 cm to 5 cm"),
expression("5 cm to 15 cm"),
expression("15 cm to 30 cm"),
expression("30 cm to 60 cm"),
expression("60 cm to 100 cm"),
expression("100 cm to 200 cm"))
#colorkey = list(title = expression("pH [KCl] 100-200cm"),
# row = 1, column = 1, vjust = 2))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_PI90_all_depths.pdf"),
# height = 10, width = 10)
# m_PI90
# dev.off()
### Maps of GSM accuracy thresholds over all GSM depth layers ------------------
# Accuracy thresholds over all depths
m_thresholds <- levelplot(stack(r_stack_response_thresh$pH_KCl_d_0_5_mid_QRF_PI90_thresholds,
r_stack_response_thresh$pH_KCl_d_5_15_mid_QRF_PI90_thresholds,
r_stack_response_thresh$pH_KCl_d_15_30_mid_QRF_PI90_thresholds,
r_stack_response_thresh$pH_KCl_d_30_60_mid_QRF_PI90_thresholds,
r_stack_response_thresh$pH_KCl_d_60_100_mid_QRF_PI90_thresholds,
r_stack_response_thresh$pH_KCl_d_100_200_mid_QRF_PI90_thresholds),
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds of PI90"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white")),
names.attr = c(expression("0 cm to 5 cm"),
expression("5 cm to 15 cm"),
expression("15 cm to 30 cm"),
expression("30 cm to 60 cm"),
expression("60 cm to 100 cm"),
expression("100 cm to 200 cm"))
)
# save to disk
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_thresh_all_depths.pdf"),
# height = 10, width = 10)
# m_thresholds
# dev.off()
### Maps of PI90 and GSM accuracy thresholds -----------------------------------
# 0-5 cm PI90
m_PI90_0_5 <- levelplot(r_stack_response_PI90$pH_KCl_d_0_5_mid_QRF_PI90,
margin = FALSE,
# main = expression("PI90"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# 0-5 cm thresholds
m_thresh_0_5 <- levelplot(r_stack_response_thresh$pH_KCl_d_0_5_mid_QRF_PI90_thresholds,
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# save to disk; combine using gridExtra pkg
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_0_5_PI90_thresh.pdf"),
# height = 6, width = 12)
# grid.arrange(m_PI90_0_5, m_thresh_0_5, ncol = 2)
# # top = textGrob("pH [KCl]: 0 cm to 5 cm depth",
# # gp = gpar(fontsize = 16)))
# dev.off()
# 5-15 cm PI90
m_PI90_5_15 <- levelplot(r_stack_response_PI90$pH_KCl_d_5_15_mid_QRF_PI90,
margin = FALSE,
# main = expression("PI90"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# 5-15 cm thresholds
m_thresh_5_15 <- levelplot(r_stack_response_thresh$pH_KCl_d_5_15_mid_QRF_PI90_thresholds,
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# save to disk; combine using gridExtra pkg
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_5_15_PI90_thresh.pdf"),
# height = 6, width = 12)
# grid.arrange(m_PI90_5_15, m_thresh_5_15, ncol = 2)
# # top = textGrob("pH [KCl]: 5 cm to 15 cm depth",
# # gp = gpar(fontsize = 16)))
# dev.off()
# 15-30 cm PI90
m_PI90_15_30 <- levelplot(r_stack_response_PI90$pH_KCl_d_15_30_mid_QRF_PI90,
margin = FALSE,
# main = expression("PI90"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# 15-30 cm thresholds
m_thresh_15_30 <- levelplot(r_stack_response_thresh$pH_KCl_d_15_30_mid_QRF_PI90_thresholds,
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# save to disk; combine using gridExtra pkg
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_15_30_PI90_thresh.pdf"),
# height = 6, width = 12)
# grid.arrange(m_PI90_15_30, m_thresh_15_30, ncol = 2)
# # top = textGrob("pH [KCl]: 15 cm to 30 cm depth",
# # gp = gpar(fontsize = 16)))
# dev.off()
# 30-60 cm PI90
m_PI90_30_60 <- levelplot(r_stack_response_PI90$pH_KCl_d_30_60_mid_QRF_PI90,
margin = FALSE,
# main = expression("PI90"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# 30-60 cm thresholds
m_thresh_30_60 <- levelplot(r_stack_response_thresh$pH_KCl_d_30_60_mid_QRF_PI90_thresholds,
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# save to disk; combine using gridExtra pkg
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_30_60_PI90_thresh.pdf"),
# height = 6, width = 12)
# grid.arrange(m_PI90_30_60, m_thresh_30_60, ncol = 2)
# # top = textGrob("pH [KCl]: 30 cm to 60 cm depth",
# # gp = gpar(fontsize = 16)))
# dev.off()
# 60-100 cm PI90
m_PI90_60_100 <- levelplot(r_stack_response_PI90$pH_KCl_d_60_100_mid_QRF_PI90,
margin = FALSE,
# main = expression("PI90"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# 60-100 cm thresholds
m_thresh_60_100 <- levelplot(r_stack_response_thresh$pH_KCl_d_60_100_mid_QRF_PI90_thresholds,
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# save to disk; combine using gridExtra pkg
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_60_100_PI90_thresh.pdf"),
# height = 6, width = 12)
# grid.arrange(m_PI90_60_100, m_thresh_60_100, ncol = 2)
# # top = textGrob("pH [KCl]: 60 cm to 100 cm depth",
# # gp = gpar(fontsize = 16)))
# dev.off()
# 100-200 cm PI90
m_PI90_100_200 <- levelplot(r_stack_response_PI90$pH_KCl_d_100_200_mid_QRF_PI90,
margin = FALSE,
# main = expression("PI90"),
scales = list(draw = FALSE),
at = v_col_PI90,
col.regions = viridis(n = length(v_col_PI90),
option = "viridis"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# 100-200 cm thresholds
m_thresh_100_200 <- levelplot(r_stack_response_thresh$pH_KCl_d_100_200_mid_QRF_PI90_thresholds,
att = "category",
margin = FALSE,
# main = expression("Accuracy thresholds"),
scales = list(draw = FALSE),
col.regions = c("#db4325", "#eda247", "#e6e1bc", "#006164"),
par.settings = list(axis.line = list(col = 0),
strip.background = list(col = "white"))
)
# save to disk; combine using gridExtra pkg
# pdf(paste0("out/maps/target/", TARGET, "/pdf/m_", TARGET, "_d_100_200_PI90_thresh.pdf"),
# height = 6, width = 12)
# grid.arrange(m_PI90_100_200, m_thresh_100_200, ncol = 2)
# # top = textGrob("pH [KCl]: 100 cm to 200 cm depth",
# # gp = gpar(fontsize = 16)))
# dev.off()
### Retrieve % of pixels for each accuracy thresholds per depth layer ----------
# list of percentages of each accuracy threshold for each depth layer
system.time(
ls_thresh_per <- map(ls_r_response_thresh,
~prop.table(table(as.vector(.x)))) %>%
map(., ~as_tibble(as.data.frame(.x)))
) # time elapse: min