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corine2uscie.r
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library(data.table)
library(raster)
library(rgdal)
### Reading in data: USCIE, CORINE ####
#CORINE raster
corine_dir <- "\\\\ies-ud01.jrc.it\\D5_agrienv\\Data\\Corine_Land_Cover\\clc2018_v20_incl_turkey\\7ac95361f9ac3cecdf37785bc183ff02dd765a16\\clc2018_clc2018_v2018_20_raster100m/"
corine100m <- paste0(corine_dir, "/CLC2018_CLC2018_V2018_20.tif")
corine100m <- raster(corine100m)
corine100m
unique(corine100m@data@attributes)
corine_cats <- read.csv(paste0(corine_dir, "/CLC2018_CLC2018_V2018_20.txt"), header = FALSE)
corine_cats
#USCIE
uscie_dir <- "\\\\ies\\d5\\agrienv\\Data\\uscie\\uscie_raster_FSU"
uscie1km <- paste0(uscie_dir, "/refras_FSU_land.tif")
uscie1km <- raster(uscie1km)
uscie1km
# clipping corine
corine100m_crop <- crop(corine100m, uscie1km)
#corine100m_crop_kk <- corine100m_crop
#corine100m_crop <- corine100m_crop_kk
#plot(corine100m_crop)
activate_this1 <- 0 # to make checkings
### Aggregating corine to 1km: NoGo and FOREST ####
## All nogo's toghether
#View(corine_cats[, c(1,6)])
rcl_mat <- corine_cats[, 1:2]
rcl_mat[, 2] <- 0
rcl_mat[c(1:11, 30, 31, 34, 38, 39, 40:44), 2] <- 1
#rcl_mat[nrow(rcl_mat), 2] <- NA
nogolist <- corine_cats[, c(1, 6)]
nogolist <- nogolist[c(1:11, 30, 31, 34, 38, 39, 40:44),]
nogolist$nogo <- paste0(nogolist$V1, ": ", nogolist$V6)
nogolist <- paste(nogolist$nogo, collapse=" - ")
# Flag nogo areas at 100 m resolution
corine100m_crop_recl <- reclassify(corine100m_crop, rcl_mat)
plotsmallwindow(corine100m_crop_recl)
# Calculate NOGO shares at 1 km scale
corine1km <- aggregate(corine100m_crop_recl, fact = 10, fun = sum)
plotsmallwindow(corine1km)
#corine1km
#sum(freq(corine1km)[91:101 , 2])
#plot(corine1km)
# Define threshold for full NOGO spatial units (90% of area classified NOGO)
rcl_mat1 <- matrix(nrow = 2, ncol = 3, c(0, 90, 1, 90, 100, 0), byrow = TRUE)
corine1km_nogo_all <- reclassify(corine1km, rcl_mat1, right = FALSE, include.lowest = TRUE)
plotsmallwindow(corine1km_nogo_all)
#plot(corine1km_nogo_all)
#corine1km_nogo_all
#freq(corine1km_nogo_all)
### Saving raster NOGO at 1km ####
dir2save <- "E:\\FSUs/final"
dir2save <- "x:\\adrian/data/fsu"
dir2save <- "\\\\tsclient/x/adrian/data/fsu"
# raster with shares of NoGo
writeRaster(corine1km, filename = paste0(dir2save, "/nogo_shares_1km.tif"), format = "GTiff", overwrite = TRUE)
# raster with NoGo (threshhold = 90%)
writeRaster(corine1km_nogo_all, filename = paste0(dir2save, "/nogo_1km.tif"),
format = "GTiff", overwrite = TRUE)
nogoshares <- raster(paste0(dir2save, "/nogo_shares_1km.tif"))
nogodt <- convertRaster2datatable(nogoshares, uscie1km)
save(nogodt, file=paste0(dir2save, "/corineCLASSNOGOs_share100m_uscie.rdata"))
### FOREST shares ####
#View(corine_cats[, c(1,6)])
rcl_mat <- corine_cats[, 1:2]
rcl_mat[, 2] <- 0
rcl_mat[c(23:25), 2] <- 1
#rcl_mat[nrow(rcl_mat), 2] <- NA
corine100m_crop_recl <- reclassify(corine100m_crop, rcl_mat)
corine1km_FOR <- aggregate(corine100m_crop_recl, fact = 10, fun = sum)
#corine1km_FOR
#sum(freq(corine1km_FOR)[91:101 , 2])
#plot(corine1km_FOR)
rcl_mat1 <- matrix(nrow = 2, ncol = 3, c(0, 90, 0, 90, 100, 1), byrow = TRUE)
corine1km_forest_only <- reclassify(corine1km_FOR, rcl_mat1, right = FALSE, include.lowest = TRUE)
#plot(corine1km_forest_only)
#corine1km_forest_only
#freq(corine1km_forest_only)
### Saving raster FOREST at 1km ####
dir2save <- "E:\\FSUs/final"
dir2save <- "\\\\ies-ud01.jrc.it/5_agrienv/Data/FSU/"
dir2save <- "\\\\tsclient/x/adrian/data/fsu"
# raster with shares of NoGo
writeRaster(corine1km_FOR, filename = paste0(dir2save, "/forest_shares_1km.tif"), format = "GTiff", overwrite = TRUE)
# raster with NoGo (threshhold = 90%)
writeRaster(corine1km_forest_only, filename = paste0(dir2save, "/forest_only_1km.tif"), format = "GTiff", overwrite = TRUE)
forsh <- raster(paste0(dir2save, "/forest_shares_1km.tif"))
fordt <- convertRaster2datatable(forsh, uscie1km)
save(fordt, file=paste0(dir2save, "/corineCLASSall31forests_share100m_uscie.rdata"))
### Reading in data: DEM ####
dem_dir <- "E:\\\\DEM_copernicus\\90b8668f52552b7f9202fc885807420e219e1c3b\\"
dem25m <- paste0(dem_dir, "/eudem_dem_3035_europe.tif")
dem25m <- raster(dem25m)
#dem25m
# clipping DEM
dem25m_crop <- crop(dem25m, uscie1km)
#dem25m_crop_kk <-dem25m_crop
#dem25m_crop <- dem25m_crop_kk
#plot(dem25m_crop)
#writeRaster(dem25m_crop, filename = "dem_crop_kk.tif", format = "GTiff", overwrite = TRUE)
activate_this1 <- 0 # to make checkings
if(activate_this1 == 1){
etnt <- extent(dem25m_crop)
etnt@xmin <- 4000000
etnt@xmax <- 4500000
etnt@ymin <- 2500000
etnt@ymax <- 3000000
dem25m_crop <- crop(dem25m_crop, etnt)
plot(dem25m_crop)
}
#dem25m_crop
# 1st quartile
dem1km_1quart <- aggregate(dem25m_crop, fact = 40, fun = function(i, ...) quantile(i, probs = 0.25, na.rm = TRUE))
dem1km_1quart@data@names <- "quart25"
#dem1km_1quart
#values(dem1km_1quart)[1]
#cc <- c()
#for(i in 1:40){
# cc <- c(cc, getValues(dem25m_crop, row = i)[1:40])
#}
#quantile(cc)
# 2nd quartile
dem1km_2quart <- aggregate(dem25m_crop, fact = 40, fun = function(i, ...) quantile(i, probs = 0.5, na.rm = TRUE))
dem1km_2quart@data@names <- "quart50"
#values(dem1km_2quart)
# 3rd quartile
dem1km_3quart <- aggregate(dem25m_crop, fact = 40, fun = function(i, ...) quantile(i, probs = 0.75, na.rm = TRUE))
dem1km_3quart@data@names <- "quart75"
#values(dem1km_3quart)
# max
dem1km_4quart <- aggregate(dem25m_crop, fact = 40, fun = max)
dem1km_4quart
dem1km_4quart@data@names <- "quart100"
#values(dem1km_4quart)
# min
dem1km_0quart <- aggregate(dem25m_crop, fact = 40, fun = min)
dem1km_0quart@data@names <- "quart0"
dem1km_0quart
dem1km_quartiles <- stack(dem1km_0quart, dem1km_1quart, dem1km_2quart, dem1km_3quart, dem1km_4quart)
dem1km_quartiles
writeRaster(dem1km_quartiles, filename = paste0(dir2save, "/quartiles_1km.tif"), format = "GTiff", overwrite = TRUE)