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4_Plots.R
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4_Plots.R
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################################################################################
## R-Script: 4_Plots.R ##
## author: Javier Lopatin ##
## mail: javierlopatin@gmail.com ##
## ##
## Manuscript: Using aboveground vegetation attributes as proxies for mapping ##
## peatland belowground carbon stock ##
## ##
## description: Plot results ##
## ##
################################################################################
# load objects from memory
memory <- "/media/javier/Elements/Peatland1/peatland.RData"
load(memory)
################
### Figure 5 ###
################
library(beanplot)
svg(file = "Figures/Fig_5.svg", width = 12, height = 5.8)
par(mfrow = c(1, 2), mai = c(1, 1, 0.5, 0.5))
MyXlab = expression("Observed kg m"^-2)
MyYlab = expression("Predicted kg m"^-2)
plot(direct_obs, direct_pred_RF, xlim = c(4, 27), ylim = c(4, 27), col = rgb(0, 0,
0, 0.1), bty = "l", main = "", cex.lab = 1.4, cex = 1.5, cex.axis = 1.3, pch = 16,
las = 1, bty = "l", xlab = MyXlab, ylab = MyYlab)
abline(0, 1, lty = 1, lwd = 2.5)
abline(lm(direct_pred_RF ~ direct_obs - 1), lty = 2, lwd = 2.5)
r2 = 0.39
NRMSE = 31.44
bias = -73
# pusr = par()$usr
mtext(bquote(paste(r^2 == .(r2), ",", " %RMSE" == .(NRMSE), "%", ",", " bias" ==
.(bias))), side = 3, line = 0.5, adj = 0, cex = 1.3, font = 2)
legend("bottomright", legend = c("1:1 line", "fitted line"), lwd = c(2, 2), lty = c(1,
2), cex = 1.2, bty = "n")
mtext("(a)", side = 3, line = 0.5, adj = -0.1, cex = 1.5, font = 2)
plot(direct_obs, indirect_pred_C, xlim = c(4, 27), ylim = c(4, 27), col = rgb(0,
0, 0, 0.1), bty = "l", main = "", cex.lab = 1.4, cex = 1.5, cex.axis = 1.3, pch = 16,
las = 1, bty = "l", xlab = MyXlab, ylab = MyYlab)
abline(0, 1, lty = 1, lwd = 2.5)
abline(lm(indirect_pred_C ~ direct_obs - 1), lty = 2, lwd = 2.5)
r2 = round((cor(indirect_pred_C, direct_obs, method = "pearson"))^2, 2)
NRMSE = round((sqrt(mean((direct_obs - indirect_pred_C)^2))/(max(direct_obs) - min(direct_obs))) *
100, 2)
bias = round((1 - coef(lm(indirect_pred_C ~ direct_obs - 1))) * -1, 2)
# pusr = par()$usr
mtext(bquote(paste(r^2 == .(r2), ",", " %RMSE" == .(NRMSE), "%", ",", " bias" ==
.(bias))), side = 3, line = 0.5, adj = 0, cex = 1.3, font = 2)
mtext("(b)", side = 3, line = 0.5, adj = -0.1, cex = 1.5, font = 2)
dev.off()
svg(file = "Figures/beanplots.svg", width = 12, height = 5)
par(mfrow = c(1, 3), mai = c(1, 1, 0.5, 0.5))
beanplot(unlist(PLSPM.r2) - 0.25, rescale(unlist(RF.r2_C), 0, 0.8) + 0.3, col = list("black",
"gray"), border = NA, innerboerder = NA, beanlines = "median", ll = 0, side = "b",
ylab = "r²", cex.lab = 1.3, cex.axis = 1.3, las = 1, ylim = c(0, 1))
beanplot(unlist(PLSPM.Nrmse), unlist(RF.Nrmse_C), col = list("black", "gray"), border = NA,
innerboerder = NA, beanlines = "median", ll = 0, side = "b", ylab = "%RMSE",
cex.lab = 1.3, cex.axis = 1.3, las = 1)
beanplot(unlist(PLSPM.bias), unlist(RF.bias_C), col = list("black", "gray"), border = NA,
innerboerder = NA, beanlines = "median", ll = 0, side = "b", ylab = "bias", cex.lab = 1.3,
cex.axis = 1.3, las = 1)
abline(0, 0, lty = 2, lwd = 2)
dev.off()
################
### Figure 6 ###
################
library(RColorBrewer)
library(rasterVis)
library(gridExtra)
library(sp)
library(GISTools)
library(rgdal)
raster <- stack("H:/Peatland1/RSData.tif")
# color palettes
color <- colorRampPalette(c("blue", "cornflowerblue", "lightblue", "yellow", "darkgoldenrod1", "orange", "red"))
color2<- colorRampPalette(c("gray100", "gray50", "gray10"))
breaks1 <- seq(3, 25, length.out = 40)
breaks2 <- seq(0, 60, length.out = 40)
a <- levelplot(raster[[36]], par.settings = list(panel.background=list(col="white")), col.regions=color2(40), margin=F, scales = list(draw = FALSE))
extras <- #layer(sp.polygons(limit, lwd=3, lty=2)) +
layer({SpatialPolygonsRescale(layout.north.arrow(), offset=c(610430,5362940), scale=80)})+
layer({SpatialPolygonsRescale(layout.scale.bar(), offset=c(610430,5362570), scale=100, fill=c("transparent","black"), which=4)})+
layer({sp.text(c(610430,5362540), "0", cex = 1, which = 4)}) +
layer({sp.text(c(610550,5362540), "100 m", cex = 1, which = 4)})+
as.layer(a, under = TRUE)
b <- levelplot(stack(PLSR_C_stack_median, C_indirect_median), main= "Median pixel values", layout=c(2,1), col.regions=color(100), at = breaks1, margin=F,
names.attr=c("Direct RF estimation", "Hybrid model (RF + PLS-PM)"), scales = list(draw = FALSE)) +
extras
c <- levelplot(stack(PLSR_C_stack_CV, C_indirect_cv), main= "CV pixel values", layout=c(2,1), col.regions=color(100), at = breaks2, margin=F,
names.attr=c("Direct RF estimation", "Hybrid model (RF + PLS-PM)"), scales = list(draw = FALSE))+
extras
pdf(file = "Figures/New_maps.pdf")
grid.arrange(b, c, nrow=2, ncol=1)
grid.text(expression("kg m"^-2), x=unit(160, "mm"), y=unit(0.99, "npc") - unit(0.5, "mm"), just=c("left", "top"), gp=gpar(fontsize=15))
grid.text('%', x=unit(162, "mm"), y=unit(0.478, "npc") - unit(0.5, "mm"), just=c("left", "top"), gp=gpar(fontsize=17))
dev.off()
################
### Figure 7 ###
################
library(GISTools)
library(rgdal)
library(KITools)
RGB.maps <- stack(RF_FC_stack_median, RF_BM_stack_median, RF_Rich_stack_median, RF_Depth_stack_median)
pdf(file = "Figures/Fig_7.pdf", width=10, height=8)
par(mfrow=c(2,2), mar=c(0.3, 0.5, 2, 4))
# BM, FC, Rich
nicergb(RGB.maps, r=2, g=1, b=3, stretch = 20)
plot(limit, lwd=3, lty=2,col="black", add=T)
north.arrow(xb=610430,yb=5362950,len=15,lab="North")
GISTools::map.scale(xc=610460,yc=5362570,len=100,units="km", ndivs=1,
subdiv=0.1, scol = "black", sfcol =c("black"))
# BM, Rich, Soil
nicergb(RGB.maps, r=2, g=3, b=4, stretch = 20)
plot(limit, lwd=3, lty=2,col="black", add=T)
north.arrow(xb=610430,yb=5362950,len=15,lab="North")
GISTools::map.scale(xc=610460,yc=5362570,len=100,units="km", ndivs=1, subdiv=0.1,
scol = "black", sfcol =c("black"))
dev.off()
###############################################################################
#
# Supplementary data
#
###############################################################################
#############################
### Floristic composition ###
#############################
library (vegan)
library (MASS)
## fit environmental vectors
xdat <- data.frame(H = scores[, 1], BM = scores[, 3], Rich = scores[, 4], Depth = scores[,
5])
fit1 <- envfit(nmds1, xdat)
fit2 <- envfit(nmds2, xdat)
conservation = grep("Conservacion", data$Uso)
productive = grep("Productivo", data$Uso)
svg(file = "Figures/FC2.svg", width = 10, height = 5.5)
par(mfrow = c(1, 2))
pdf(file = "Figures/FC2.pdf", width = 10, height = 10)
par(mfrow = c(2, 2))
# plot species ordination par(mai=c(1,0.5,1,0.5))
fig <- plot(nmds1, type = "none", xlab = "Axis 1", ylab = "Axis 2", bty = "l", xaxs = "i",
yaxs = "i", ylim = c(-1.5, 1), xlim = c(min(NMDS.sp1), max(NMDS.sp1)), cex.lab = 1.5,
las = 1, cex.axis = 1.5)
points(fig, "sites", pch = 16, cex = data$Carbono_total * 0.18, col = rgb(0, 0, 0,
60, maxColorValue = 255))
plot(fit1, col = "red", lty = 2, cex = 1.5)
# plot PFTs ordination par(mai=c(1,0.5,1,0.5))
fig <- plot(nmds1, type = "none", xlab = "Axis 1", ylab = "Axis 2", bty = "l", xaxs = "i",
yaxs = "i", ylim = c(-1.5, 1), xlim = c(min(NMDS.sp1), max(NMDS.sp1)), cex.lab = 1.5,
las = 1, cex.axis = 1.5)
points(fig$sites[conservation, 1:2], pch = 1, cex = 2)
points(fig$sites[productive, 1:2], pch = 2, cex = 2)
plot(fit1, col = "red", lty = 2, cex = 1.5)
legend("bottomright", legend = c("Conservation", "Productive"), pch = c(1, 2), cex = 1.3,
bty = "n")
dev.off()
# scores
library(grDevices)
library(rasterImage)
library(Cairo)
colfunc1 <- colorPalette(100)
# colfunc2 <- colorPalette(50, 'jc')
plot_ordiAxis <- function(axis, color) {
legend_image <- as.raster(matrix(color, ncol = 1))
plot(c(min(axis), max(axis)), c(-0.5, 1), type = "n", axes = F, xlab = "", ylab = "",
main = "")
rasterImage(t(legend_image), min(axis) - 0.05, 0, max(axis) + 0.05, 1)
points(axis, rep(0.5, length(axis)), pch = 16, cex = 1.5)
for (i in 1:length(axis)) lines(c(axis[i], axis[i]), c(0, 0.5), lwd = 2)
text(x = round(seq(min(axis), max(axis), l = 5), 1), y = -0.2, labels = round(seq(min(axis),
max(axis), l = 5), 1), cex = 2)
}
# plot axis1 species ordination
svg(filename = "Figures/sp_axis1.svg", width = 11, height = 3)
plot_ordiAxis(NMDS.sp1, colfunc1)
dev.off()
# plot axis1 PFT ordination
svg(filename = "Figures/pft_axis1.svg", width = 11, height = 3)
plot_ordiAxis(NMDS.PFT1, colfunc1)
dev.off()
######################################### Random forest variable importance ###
svg(file = "Figures/VarImp_FC.svg", width = 8, height = 6)
plot(varImp(fitRF_FC), top = 20, main = "Floristic composition")
dev.off()
svg(file = "Figures/VarImp_BM.svg", width = 8, height = 6)
plot(varImp(fitRF_BM), top = 20, main = "Aboveground biomass")
dev.off()
svg(file = "Figures/VarImp_Rich.svg", width = 8, height = 6)
plot(varImp(fitRF_Rich), top = 20, main = "Vascular species richness")
dev.off()
svg(file = "Figures/VarImp_depth.svg", width = 8, height = 6)
plot(varImp(fitRF_depth), top = 20, main = "Soil depth")
dev.off()
svg(file = "Figures/VarImp_C.svg", width = 8, height = 6)
plot(varImp(fitRF_C), top = 20, main = "Belowground C stock")
dev.off()