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fig2_script.R
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fig2_script.R
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#fig2
#2b map of all GEDI
packages <- c("sp","rgdal","sf","rgeos","dplyr","plyr","ggplot2","raster","mapview","stringr",
"maptools","gridExtra","lattice","MASS","foreach","optmatch","doParallel","RItools","gdalUtils",
"rlang","tidyr","magrittr","viridis","ggmap","Hmisc","hrbrthemes","spatialEco","bit64","randomForest", "modelr",
"ranger","caret","scattermore")
package.check <- lapply(packages, FUN = function(x) {
suppressPackageStartupMessages(library(x, character.only = TRUE))
})
fpath <- "/gpfs/data1/duncansongp/GEDI_global_PA/"
dd <- list.files(path = paste(fpath, "iso_full_nodup",sep=""), pattern=".csv",full.names = TRUE, recursive=FALSE)
# bbox=matrix(c(-179.9,-53,179.9,53), nrow = 2, dimnames = list(c("x","y"), c("min", "max"))) #get a worldwide bbox to run globally
bbox <- matrix(c(-83,-27.6,-34, 13), nrow = 2, dimnames = list(c("x","y"), c("min", "max")))
iso_region <- read.csv("/gpfs/data1/duncansongp/GEDI_global_PA/csv/iso3_region_pair.csv")
oPAs <- read.csv("/gpfs/data1/duncansongp/GEDI_global_PA/csv/balanced_paids.csv") %>% .$x
# bc_big <- get_map(location = bbox,maptype="roadmap", source="osm", crop=TRUE)
# top=%quantile(0.999)%>%round_any(10, f=ceiling)
#------------rh98[scattermore method]----------------------
dd <- list.files(path = paste(fpath, "WDPA_gedi_l2a+l2b_clean2/",sep=""), pattern="",full.names = TRUE, recursive=TRUE)
b=c(seq(0,50,10),60)
lab=format(b)
lab[length(b)]="60+ (~top 0.1%)"
lab
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=10),
legend.title=element_text(size=8),
legend.text=element_text(size=8),
rect = element_rect(fill = "transparent"))
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "transparent",
size = 0.15)
country_border <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "transparent", color = "#515151",
size = 0.15)
mapcoords <- coord_fixed(xlim = c(-83.3, -36.5), ylim = c(-26, 12.5))
mbase<- ggplot() + mapcoords + maptheme+ country_shapes+
scale_color_gradientn(
limits = c(0,60),
colours=c("#bfd3e6",
"#9ebcda",
"#8c96c6",
"#8c6bb1",
"#88419d",
"#253494", "#006600"), ##cc0613
na.value="#f5f5f5",
breaks=b, labels=lab,name=paste("Max Canopy Height (m)"))
t=dd[1] %>% read.csv() %>% .[1,]
t[1,]$rh_098 <- NA
mbase=mbase+ geom_point(data=t,
aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098, fill=""), size=0)
set.seed(1234)
index <- c(seq(1, length(dd), 105), length(dd))
for (ind in 2:length(index)){ #length(index)){
print(ind)
start <- index[ind-1]
end <- index[ind]
sub_dd <- dd[start:end]
registerDoParallel(cores=10)
sub_dd_all <- foreach(d=sub_dd,.combine = rbind, .packages=c('sp','magrittr', 'dplyr','tidyr','raster')) %dopar% {
sub_dd_all <- data.frame()
print(d)
rm(dsub)
dsub <- read.csv(d)%>% #dplyr::filter(pa_id %in% oPAs) %>%
dplyr::select(lon_lowestmode, lat_lowestmode,rh_098) %>% dplyr::mutate(rh_098=rh_098/100)
print("alldata")
# if(nrow(dsub)>5000){ #use the sampling to reduce plotted points for worldwide plotting
# dsub <- dsub %>% sample_n(5000)
# }
sub_dd_all <- rbind(sub_dd_all, dsub)
return(sub_dd_all)
}
stopImplicitCluster()
print("sampledata")
# mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098), size=0.001, alpha=0.3)
mbase=mbase+ geom_scattermore(data=sub_dd_all,
aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098),
pointsize=1,
pixels =c(10000,10000),
interpolate=TRUE)
print("layer added")
}
mapcoords <- mapcoords #coord_fixed(xlim = c(-178, 180), ylim = c(-51.6, 51.6))
mbase1 <- mbase+country_border+ #+labs(title = "Distribution of GEDI shots")
scale_fill_manual(values=NA) + mapcoords+
guides(fill=guide_legend("No valid data", override.aes=list(colour="#f5f5f5"), title.position="bottom"))+
guides(fill = FALSE)
mbase1=mbase1+labs(tag = "C")
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_gedishots_dist_rh98_oct25_amaz_zoom.png",
plot=mbase1, width=12, height=4,
units = "in", dpi="print",bg = "transparent")
#-----------------------rh98-------------------------------------------------
dd <- list.files(path = paste(fpath, "WDPA_gedi_l2a+l2b_clean2/",sep=""), pattern="",full.names = TRUE, recursive=TRUE)
b=c(seq(0,50,10),60)
lab=format(b)
lab[length(b)]="60+ (~top 0.1%)"
lab
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=10),
legend.title=element_text(size=8),
legend.text=element_text(size=8),
rect = element_rect(fill = "transparent"))
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "transparent",
size = 0.15)
country_border <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "transparent", color = "#515151",
size = 0.15)
mapcoords <- coord_fixed(xlim = c(-83.3, -36.5), ylim = c(-26, 12.5)) #coord_fixed(xlim = c(-178, 180), ylim = c(-51.6, 51.6))
mbase<- ggplot() + mapcoords + maptheme+ country_shapes+
scale_color_gradientn(
limits = c(0,60),
colours=c("#bfd3e6",
"#9ebcda",
"#8c96c6",
"#8c6bb1",
"#88419d",
"#6e016b", "#004529"),
na.value="#f5f5f5",
breaks=b, labels=lab,name=paste("Max Canopy Height (m)"))
for (d in dd){
print(d)
dsub=read.csv(d)%>% #dplyr::filter(pa_id %in% oPAs) %>%
dplyr::select(lon_lowestmode, lat_lowestmode,rh_098) %>% dplyr::mutate(rh_098=rh_098/100)
mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098, fill=""), size=0.001, alpha=0.3)
}
mbase1=mbase+country_border+ #+labs(title = "Distribution of GEDI shots")
scale_fill_manual(values=NA) +
guides(fill=guide_legend("No valid data", override.aes=list(colour="#f5f5f5"), title.position="bottom"))+
guides(fill = FALSE)
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_gedishots_dist_rh98_mar26.png", plot=mbase1, width=12, height=8,
units = "in", dpi="print",bg = "transparent")
#--------------------by AGBD---------------------------------
dd <- list.files(path = paste(fpath, "WDPA_gedi_l4a_clean/",sep=""), pattern="",full.names = TRUE, recursive=TRUE)
b=c(seq(0,340,40),345)
lab=format(b)
lab[length(b)]="345+\n(~top 1%)"
# lab=str_wrap(lab, width = 5)
nonforest=c("Montane Grasslands & Shrublands", "Deserts & Xeric Shrublands", "Tundra", "Tropical & Subtropical Grasslands, Savannas & Shrublands",
"Temperate Grasslands, Savannas & Shrublands","Flooded Grasslands & Savannas")
mapcoords <- coord_fixed(xlim = c(-83.3, -36.5), ylim = c(-26, 12.5)) #coord_fixed(xlim = c(-178, 180), ylim = c(-55, 55)) #coord_fixed(xlim =c(60, 75) , ylim =c(29, 39) ) #
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "transparent",
size = 0.15)
country_border <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "transparent", color = "#515151",
size = 0.15)
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=10),
legend.title=element_text(size=8),
legend.text=element_text(size=8),
rect = element_rect(fill = "transparent"))
mbase<- ggplot() + mapcoords + country_shapes+
scale_color_gradientn(
limits = c(0,345),
colours=c("olivedrab1",
"olivedrab4", "darkgreen","midnightblue", "red4"),
na.value="#f5f5f5",
breaks=b, labels=lab,name="AGBD (Mg/ha)")
t=dd[1] %>% read.csv() %>% .[1,]
t[1,]$agbd <- NA
mbase=mbase+ geom_point(data=t,
aes(x = lon_lowestmode, y = lat_lowestmode,color = agbd, fill=""), size=0.00001)
set.seed(1234)
index <- c(seq(1, length(dd), 110), length(dd))
for (ind in 2:length(index)){
print(ind)
start <- index[ind-1]
end <- index[ind]
sub_dd <- dd[start:end]
registerDoParallel(cores=10)
sub_dd_all <- foreach(d=sub_dd,.combine = rbind, .packages=c('sp','magrittr', 'dplyr','tidyr','raster')) %dopar% {
sub_dd_all <- data.frame()
print(d)
rm(dsub)
dsub <- read.csv(d) %>% #dplyr::filter(pa_id%in% oPAs) %>%
dplyr::select(lon_lowestmode, lat_lowestmode,agbd)
print("alldata")
# if(nrow(dsub)>5000){
# dsub <- dsub %>% sample_n(5000)
# }
sub_dd_all <- rbind(sub_dd_all, dsub)
return(sub_dd_all)
}
stopImplicitCluster()
print("sampledata")
# mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098), size=0.001, alpha=0.3)
mbase=mbase+ geom_scattermore(data=sub_dd_all,
aes(x = lon_lowestmode, y = lat_lowestmode,color = agbd),
pointsize=1,
pixels=c(10000,10000),
interpolate=TRUE)
print("layer added")
}
mapcoords <-mapcoords # coord_fixed(xlim = c(-178, 180), ylim = c(-51.6, 51.6))
mbase2 <- mbase+country_border+maptheme+mapcoords+
scale_fill_manual(values=NA) +
guides(fill=guide_legend(" No valid data", override.aes=list(colour="#f5f5f5"), title.position="bottom"))
mbase2=mbase2+labs(tag = "B")
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_gedishots_dist_agbd_oct25_amaz_zoom.png",
plot=mbase2, width=12, height=4,
units = "in", dpi="print",bg = "transparent")
#-------------------canopy cover----------------------------------------
dd <- list.files(path = paste(fpath, "WDPA_gedi_l2a+l2b_clean2/",sep=""), pattern="",full.names = TRUE, recursive=TRUE)
b=seq(0,1, 0.1)
lab=format(b)
# lab[length(b)]="345+\n(~top 1%)"
# lab=str_wrap(lab, width = 5)
nonforest=c("Montane Grasslands & Shrublands", "Deserts & Xeric Shrublands", "Tundra", "Tropical & Subtropical Grasslands, Savannas & Shrublands",
"Temperate Grasslands, Savannas & Shrublands","Flooded Grasslands & Savannas")
mapcoords <- coord_fixed(xlim = c(-83.3, -36.5), ylim = c(-26, 12.5))#coord_fixed(xlim = c(-178, 180), ylim = c(-55, 55)) #coord_fixed(xlim =c(60, 75) , ylim =c(29, 39) ) #
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "transparent",
size = 0.15)
country_border <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "transparent", color = "#515151",
size = 0.15)
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=10),
legend.title=element_text(size=8),
legend.text=element_text(size=8),
rect = element_rect(fill = "transparent"))
mbase<- ggplot() + mapcoords + country_shapes+
scale_color_gradientn(
limits = c(0,1),
colours=c("#ffffe5",
"#f7fcb9",
'#d9f0a3',
"#addd8e",
"#78c679",
"#41ab5d",
'#238443',
"#006837",
"#004529","#253494"),
na.value="#f5f5f5",
breaks=b, labels=lab,name="Canopy cover ")
t=dd[1] %>% read.csv() %>% .[1,]
t[1,]$cover <- NA
mbase=mbase+ geom_point(data=t,
aes(x = lon_lowestmode, y = lat_lowestmode,color = cover, fill=""), size=0)
set.seed(1234)
index <- c(seq(1, length(dd), 110), length(dd))
for (ind in 2:length(index)){
print(ind)
start <- index[ind-1]
end <- index[ind]
sub_dd <- dd[start:end]
registerDoParallel(cores=15)
sub_dd_all <- foreach(d=sub_dd,.combine = rbind, .packages=c('sp','magrittr', 'dplyr','tidyr','raster')) %dopar% {
sub_dd_all <- data.frame()
print(d)
rm(dsub)
dsub <- read.csv(d) %>% #dplyr::filter(pa_id%in% oPAs) %>%
dplyr::select(lon_lowestmode, lat_lowestmode,cover)
print("alldata")
# if(nrow(dsub)>5000){
# dsub <- dsub %>% sample_n(5000)
# }
sub_dd_all <- rbind(sub_dd_all, dsub)
return(sub_dd_all)
}
stopImplicitCluster()
print("sampledata")
# mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098), size=0.001, alpha=0.3)
mbase=mbase+ geom_scattermore(data=sub_dd_all,
aes(x = lon_lowestmode, y = lat_lowestmode,color = cover),
pointsize=1,
pixels=c(10000,10000),
interpolate=TRUE)
print("layer added")
}
mapcoords <- mapcoords #coord_fixed(xlim = c(-178, 180), ylim = c(-51.6, 51.6))
mbase3 <- mbase+country_border+maptheme+
scale_fill_manual(values=NA) + mapcoords+
guides(fill=guide_legend("Data quality not suitable", override.aes=list(colour="#f5f5f5"), title.position="bottom"))
mbase3=mbase3+labs(tag="E")
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_gedishots_dist_cover_oct25_amaz_zoom2.png",
plot=mbase3, width=12, height=4,
units = "in", dpi="print",bg = "transparent")
#-----PAI--------------------
dd <- list.files(path = paste(fpath, "WDPA_gedi_l2a+l2b_clean2/",sep=""), pattern="",full.names = TRUE, recursive=TRUE)
b=c(seq(0, 9, 1), 10)
lab=format(b)
lab[length(b)]="10+"
lab=str_wrap(lab, width = 5)
mapcoords <- coord_fixed(xlim = c(-83.3, -36.5), ylim = c(-26, 12.5)) #coord_fixed(xlim = c(-178, 180), ylim = c(-55, 55)) #coord_fixed(xlim =c(60, 75) , ylim =c(29, 39) ) #
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "transparent",
size = 0.15)
country_border <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "transparent", color = "#515151",
size = 0.15)
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=10),
legend.title=element_text(size=8),
legend.text=element_text(size=8),
rect = element_rect(fill = "transparent"))
mbase<- ggplot() + mapcoords + country_shapes+
scale_color_gradientn(
limits = c(0,10),
colours=c("#fff7ec",
"#fdd49e",
"#fdbb84",
"#fc8d59",
"#ef6548",
'#d7301f',
'#b30000',"#49006a"),
na.value="#f5f5f5",
breaks=b, labels=lab,name="PAI ")
t=dd[1] %>% read.csv() %>% .[1,]
t[1,]$pai <- NA
mbase=mbase+ geom_point(data=t,
aes(x = lon_lowestmode, y = lat_lowestmode,color = pai, fill=""), size=0)
set.seed(1234)
index <- c(seq(1, length(dd), 110), length(dd))
for (ind in 2:length(index)){
print(ind)
start <- index[ind-1]
end <- index[ind]
sub_dd <- dd[start:end]
registerDoParallel(cores=10)
sub_dd_all <- foreach(d=sub_dd,.combine = rbind, .packages=c('sp','magrittr', 'dplyr','tidyr','raster')) %dopar% {
sub_dd_all <- data.frame()
print(d)
rm(dsub)
dsub <- read.csv(d) %>% #dplyr::filter(pa_id%in% oPAs) %>%
dplyr::select(lon_lowestmode, lat_lowestmode,pai)
print("alldata")
# if(nrow(dsub)>5000){
# dsub <- dsub %>% sample_n(5000)
# }
sub_dd_all <- rbind(sub_dd_all, dsub)
return(sub_dd_all)
}
stopImplicitCluster()
print("sampledata")
# mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098), size=0.001, alpha=0.3)
mbase=mbase+ geom_scattermore(data=sub_dd_all,
aes(x = lon_lowestmode, y = lat_lowestmode,color = pai),
pointsize=1,
pixels=c(10000,10000),
interpolate=TRUE)
print("layer added")
}
mapcoords <- mapcoords #coord_fixed(xlim = c(-178, 180), ylim = c(-51.6, 51.6))
mbase4 <- mbase+country_border+maptheme+
scale_fill_manual(values=NA) + mapcoords+
guides(fill=guide_legend(" Valid data not available", override.aes=list(colour="#f5f5f5"), title.position="bottom", order = 5))+
guides(fill = FALSE)
mbase4=mbase4+labs(tag = "D")
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_gedishots_dist_pai_oct25_amaz_zoom.png",
plot=mbase4, width=12, height=4,
units = "in", dpi="print",bg = "transparent")
#------------pa polygons--------------------
# b=c(seq(0,340,40),345)
# lab=format(b)
# lab[length(b)]="345+\n(~top 1%)"
# lab=str_wrap(lab, width = 5)
dd <- list.files(path = paste(fpath, "WDPA_GEDI_extract4/iso_full_nodup",sep=""), pattern=".csv",full.names = TRUE, recursive=FALSE)
nonforest=c("Montane Grasslands & Shrublands", "Deserts & Xeric Shrublands", "Tundra", "Tropical & Subtropical Grasslands, Savannas & Shrublands",
"Temperate Grasslands, Savannas & Shrublands","Flooded Grasslands & Savannas")
forestBiomKey <- c(1:6, 12,14)
biomRas <- raster()
mapcoords <- coord_fixed(xlim = c(-178, 180), ylim = c(-52, 52))
country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "#CECECE", color = "transparent",
size = 0.1)
country_border <- geom_polygon(aes(x = long, y = lat, group = group),
data = map_data('world'),
fill = "transparent", color = "#515151",
size = 0.1)
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=12),
legend.title=element_text(size=12),
legend.text=element_text(size=10),
rect = element_rect(fill = "transparent"))
f.path <- "/gpfs/data1/duncansongp/GEDI_global_PA/"
ecoregion_list <- read.csv(paste(f.path,"wwf_ecoregions_key.csv",sep=""))
biomKey <- unique(ecoregion_list[,1:2])
mbase<- ggplot() + mapcoords + country_shapes
`%notin%` <- Negate(`%in%`)
countryContinent <- read.csv("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_L4b_output/progress_l4bAGB_countryxBiome_byCountry_jan13.csv") %>%
dplyr::filter(iso3 %notin% c("COK","BHR","FSM","SYR","SOM","SPM","PSE","MDA","LBY","PRK","ATF","ABW","AIA","AND","ASM","USA"))
for (d in countryContinent$iso3){
print(d)
country <- d %>% gsub(".*full_nodup/(.+)_country_full.*", "\\1", .)
if (countryContinent$iso3Status[match(d, countryContinent$iso3)]=="analyzed"){
country_all_pa <- read_sf(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "all_PAs_",country,"_v3.shp",sep="")) %>%
st_transform("+proj=longlat +datum=WGS84 +no_defs")
country_ana_pa <- read_sf(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "analyzed_PAs_",country,"_v3.shp",sep="")) %>%
st_transform("+proj=longlat +datum=WGS84 +no_defs")
# diff <- gDifference(country_all_pa, country_ana_pa)
foreBiomRas <- raster(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_input_vars_iso3_v2/", country,"/wwf_biomes.tif",sep=""))
foreBiomdf <- as.data.frame(foreBiomRas, xy = TRUE) %>% mutate(wwf_biomes=ifelse(wwf_biomes %in% forestBiomKey, wwf_biomes,"NA"))
# dsub <- read.csv(d) %>% dplyr::filter(pa_id%in% oPAs) %>% dplyr::select(lon_lowestmode, lat_lowestmode,agbd, wwfbiom)
# dsub=dsub[which(dsub$wwfbiom %notin% nonforest),]
# if (!is.null(diff)){
country_ana_pa$level <- "Analyzed PAs"
country_all_pa$level <- "Other PAs"
# country_ana_pa_df <- broom::tidy(country_ana_pa, region = "level")
#
# country_all_pa_df <- broom::tidy(country_ana_pa, region = "level")
mbase <- mbase+ #geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = agbd, fill=""), size=0.0001, alpha=1)+
geom_tile(data = foreBiomdf, aes(x = x, y = y, fill = as.character(wwf_biomes)))+
# geom_sf(aes(colour=level),
# data =country_all_pa,size=0.15,
# fill = "transparent")+
# geom_sf(aes(colour=level),
# data =country_ana_pa,size=0.15,
# fill = "transparent")+
coord_sf(xlim = c(-178, 180), ylim = c(-53, 53),expand = FALSE)
} else{
country_all_pa <- read_sf(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "all_PAs_",country,"_v3.shp",sep="")) %>%
st_transform("+proj=longlat +datum=WGS84 +no_defs")
# diff <- gDifference(country_all_pa, country_ana_pa)
foreBiomRas <- raster(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_input_vars_iso3_v2/", country,"/wwf_biomes.tif",sep=""))
foreBiomdf <- as.data.frame(foreBiomRas, xy = TRUE) %>% mutate(wwf_biomes=ifelse(wwf_biomes %in% forestBiomKey, wwf_biomes,"NA"))
# dsub <- read.csv(d) %>% dplyr::filter(pa_id%in% oPAs) %>% dplyr::select(lon_lowestmode, lat_lowestmode,agbd, wwfbiom)
# dsub=dsub[which(dsub$wwfbiom %notin% nonforest),]
# if (!is.null(diff)){
country_all_pa$level <- "Other PAs"
# country_ana_pa_df <- broom::tidy(country_ana_pa, region = "level")
#
# country_all_pa_df <- broom::tidy(country_ana_pa, region = "level")
# mbase <- mbase+ #geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = agbd, fill=""), size=0.0001, alpha=1)+
# geom_tile(data = foreBiomdf, aes(x = x, y = y, fill = as.character(wwf_biomes)))+
# geom_sf(aes(colour=level),
# data =country_all_pa,size=0.2,
# fill = "transparent")
#
}
}
for (d in countryContinent$iso3){
print(d)
country <- d %>% gsub(".*full_nodup/(.+)_country_full.*", "\\1", .)
if (countryContinent$iso3Status[match(d, countryContinent$iso3)]=="analyzed"){
country_all_pa <- read_sf(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "all_PAs_",country,"_v3.shp",sep="")) %>%
st_transform("+proj=longlat +datum=WGS84 +no_defs")
country_ana_pa <- read_sf(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "analyzed_PAs_",country,"_v3.shp",sep="")) %>%
st_transform("+proj=longlat +datum=WGS84 +no_defs")
# diff <- gDifference(country_all_pa, country_ana_pa)
# foreBiomRas <- raster(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_input_vars_iso3_v2/", country,"/wwf_biomes.tif",sep=""))
# foreBiomdf <- as.data.frame(foreBiomRas, xy = TRUE) %>% mutate(wwf_biomes=ifelse(wwf_biomes %in% forestBiomKey, wwf_biomes,"NA"))
#
# dsub <- read.csv(d) %>% dplyr::filter(pa_id%in% oPAs) %>% dplyr::select(lon_lowestmode, lat_lowestmode,agbd, wwfbiom)
# dsub=dsub[which(dsub$wwfbiom %notin% nonforest),]
# if (!is.null(diff)){
country_ana_pa$level <- "Analyzed PAs"
country_all_pa$level <- "Other PAs"
# country_ana_pa_df <- broom::tidy(country_ana_pa, region = "level")
#
# country_all_pa_df <- broom::tidy(country_ana_pa, region = "level")
mbase <- mbase+ #geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = agbd, fill=""), size=0.0001, alpha=1)+
# geom_tile(data = foreBiomdf, aes(x = x, y = y, fill = as.character(wwf_biomes)))+
geom_sf(aes(colour=level),
data =country_all_pa,size=0.1,
fill = "#FF00FF")+
geom_sf(aes(colour=level),
data =country_ana_pa,size=0.1,
fill = "blue")+
coord_sf(xlim = c(-178, 180), ylim = c(-53, 53),expand = FALSE)
} else{
country_all_pa <- read_sf(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "all_PAs_",country,"_v3.shp",sep="")) %>%
st_transform("+proj=longlat +datum=WGS84 +no_defs")
country_all_pa$level <- "Other PAs"
mbase <- mbase+ #geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = agbd, fill=""), size=0.0001, alpha=1)+
geom_tile(data = foreBiomdf, aes(x = x, y = y, fill = as.character(wwf_biomes)))+
geom_sf(aes(colour=level),
data =country_all_pa,size=0.2,
fill = "transparent")
#
}
}
mbase5 <- mbase+country_border+
scale_fill_manual(values = c("1" = "#B2FF64","2"="#CCFE64", "3"="#86CC64","4"="#ABAB66","5"="#CBCB60",
"6"="#6A9C6E","12"="#FFCD61", "14"="#7CAC60","NA"="transparent"),
labels=c(as.character(biomKey$BIOME_NAME[forestBiomKey]),"Non-forest biomes"),name=str_wrap("WWF Biomes", width = 5), drop = FALSE)+
scale_colour_manual(values=c("Analyzed PAs"="blue", "Other PAs"="#FF00FF"), name="PA\nTypes") +
coord_sf(xlim = c(-178, 180), ylim = c(-53, 53),expand = FALSE)+
# guides(colour=guide_legend("No data", override.aes=list(colour="#f5f5f5"),title.position="bottom"))+
guides(fill=guide_legend(ncol=2))+ guides(colour=guide_legend(ncol=1, override.aes = list(fill = c("blue", "#FF00FF"))))+
maptheme
source("/gpfs/data1/duncansongp/amberliang/scripts/scale_bar_north_arrow.R")
mbase5 <- mbase5+ scale_bar(lon = -160, lat = -45,
distance_lon = 2000, distance_lat = 300, distance_legend = 620,
dist_unit = "km", orientation = FALSE, legend_size=3.5)+labs(tag = "A")
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_pa_locations_apr05_v2.png",
plot=mbase5, width=12, height=8,
units = "in", dpi="print",bg = "transparent")
######arranging the five figures into one figure##########
mbase2=readRDS("/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_agbd_final_22.rds")
mbase4=readRDS("/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_pai_final_22.rds")
mbase1=readRDS("/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_rh98_final_22.rds")
mbase3=readRDS("/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_cover_final.rds")
mbase5=readRDS("/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/mbase5_scale.rds")
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width=unit(1.5,"cm"))+
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))+
theme(text=element_text(family="Helvetica", face="bold", size=12),
legend.title=element_text(size=12),
legend.text=element_text(size=10),
rect = element_rect(fill = "transparent"))
mbase5=mbase5+labs(tag = "A")+maptheme
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_pa_locations_mar26_4.png",
plot=mbase5, width=12, height=6,
units = "in", dpi="print",bg = "transparent")
ggplot2::ggsave(filename="/gpfs/data1/duncansongp/GEDI_global_PA/figures/JAN21_FIGS/fig2_3panels.pdf",
plot=ggpubr::ggarrange( mbase5,mbase2, mbase1, mbase4, mbase3,
labels = c("A", "B", "C","D","E"),
ncol = 1, nrow = 5), width=12, height=18,
units = "in", dpi="print",bg = "transparent")
# +
# scale_colour_manual(values=NA) +
# guides(fill=guide_legend(ncol=2))
# +
# guides(fill=guide_legend("Non-forest biomes", override.aes=list(colour=rgb(0,0,0,0)), title.position="bottom", ncol=2))
# mbase2
# global_pa <- readOGR(paste("/gpfs/data1/duncansongp/GEDI_global_PA/WDPA_shapefiles/dissolved_by_region/", "all_PAs_",iso3,"_v3.shp",sep=""))
# mbase_papoly <- mapbase+geom_polygon(data = global_pa, aes(x = long, y = lat, group = group), colour = "red", fill = NA)
##previous version
# mbase=ggmap(bc_big) +
# scale_color_gradientn(limits = c(0,60),
# colours=c("olivedrab1",
# "olivedrab4", "darkgreen","midnightblue", "red4"),
# na.value="grey50",
# breaks=b, labels=lab,name=paste("Max Canopy Height, n=150,465,105")) +
# labs(title = "Distribution of GEDI shots")+
# theme_bw()+
# theme(text=element_text(family="Times", face="bold", size=14),
# legend.title=element_text(size=12),
# legend.text=element_text( size=12),
# rect = element_rect(fill = "transparent"))
# for (d in dd){
# print(d)
# dsub=read.csv(d)%>% dplyr::select(lon_lowestmode, lat_lowestmode,rh_098)
# mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon_lowestmode, y = lat_lowestmode,color = rh_098), size=0.001, alpha=0.3)
# }
#2a map of all matched
# matched_PAs <- Sys.glob(paste(f.path,"WDPA_matching_results/*_wk",gediwk,"/*.RDS", sep=""))
# matched_PAs <- list.files(paste(f.path,"WDPA_matching_results/",sep=""), pattern="wk106", full.names = TRUE, recursive = TRUE)
#
# bbox=matrix(c(-179.9,-52,179.9,83), nrow = 2, dimnames = list(c("x","y"), c("min", "max"))) #get a worldwide bbox to run globally
# bc_big <- get_map(location = bbox, source="stamen", maptype="terrain", crop=FALSE)
# b=c(seq(0,100,10),550)
# lab=format(b)
# lab[length(b)]="100+"
# lab
#
# mbase=ggmap(bc_big) +
# theme(legend.text = element_text(color = "black", size = 8),plot.title = element_text(size=15, face="bold"),
# rect = element_rect(fill = "transparent"))
#
# for (f in matched_PAs){
# print(f)
# dsub=readRDS(f)
# # %>% dplyr::select(lon, lat,status)
# mbase=mbase+ geom_point(data = dsub, mapping = aes(x = lon, y = lat,colour =as.factor(as.numeric(status))), size=0.01, alpha=0.3)+
# scale_colour_manual(values=c("#bf812d","#35978f", rgb(0,0,0,0)))
# }
#