In RS-eco/bdc: Bavarian Data Cube

knitr::opts_chunk$set(
  echo=F, warning=F, message=F, comment = NA, 
  fig.width=10, fig.height=8, fig.path="../figures/"
)

library(dplyr); library(tidyr)
library(lubridate); library(zoo)
library(ggplot2); library(patchwork)
library(scico)

# Load shapefile of Bavaria
data("bavaria", package="bdc")
bavaria_gk <- sf::st_transform(bavaria, sp::CRS("+init=epsg:31468"))
bavaria_laea <- sf::st_transform(bavaria, "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")

General information

CLC - Colour key

# Named vector of clc colours
clc_cols <- c("Continuous urban fabric" = "#e6004d", "Discontinuous urban fabric" = "#ff0000", "Industrial or commercial units" = "#cc4df2", "Road and rail networks and associated land" = "#cc0000", "Port areas" = "#e6cccc", "Airports" = "#e6cce6", "Mineral extraction sites" = "#a600cc", "Dump sites" = "#a64d00", "Construction sites" = "#ff4dff", "Green urban areas" = "#ffa6ff", "Sport and leisure facilities" = "#ffe6ff", "Non-irrigated arable land" = "#ffffa8", "Permanently irrigated land" = "#ffff00", "Rice fields" = "#e6e600", "Vineyards" = "#e68000", "Fruit trees and berry plantations" = "#f2a64d", "Olive groves" = "#e6a600", "Pastures" = "#e6e64d", "Annual crops associated with permanent crops" = "#ffe6a6", "Complex cultivation patterns" = "#ffe64d", "Land principally occupied by agriculture with \n significant areas of natural vegetation" = "#e6cc4d", "Agro-forestry areas" = "#f2cca6", "Broad-leaved forest" = "#80ff00", "Coniferous forest" = "#00a600", "Mixed forest" = "#4dff00", "Natural grasslands" = "#ccf24d", "Moors and heathland" = "#a6ff80", "Sclerophyllous vegetation" = "#a6e64d", "Transitional woodland-shrub" = "#a6f200", "Beaches - dunes - sands" = "#e6e6e6", "Bare rocks" = "#cccccc", "Sparsely vegetated areas" = "#ccffcc", "Burnt areas" = "#000000", "Glaciers and perpetual snow" = "#a6e6cc", "Inland marshes" = "#a6a6ff", "Peat bogs" = "#4d4dff", "Salt marshes" = "#ccccff", "Salines" = "#e6e6ff", "Intertidal flats" = "#a6a6e6", "Water courses" = "#00ccf2", "Water bodies" = "#80f2e6", "Coastal lagoons" = "#00ffa6", "Estuaries" = "#a6ffe6", "Sea and ocean" = "#e6f2ff", "NODATA" = "#ffffff")

Data Analysis -

Here, I perform a first analysis of the Corine Land-Cover (CLC) and Land-Cover Change (CHA) Data for Bavaria.

The Corine CLC data (corine_clc_bav_tk4tel.rda) and CHA data (corine_cha_bav_tk4tel.rda) for Bavaria, which I use here, can be downloaded from https://github.com/RS-eco/bdc/blob/main/data/.

The code for how the CLC and CHA data (corine_clc_bav_tk4tel.rda and corine_cha_bav_tk4tel.rda) was created can be found here: https://github.com/RS-eco/bdc/blob/main/data-raw/corine_lc.R

Map for one year (1990)

data("corine_lc_bav_tk4tel", package="bdc")
colnames(corine_lc_bav_tk4tel) <- c("x", "y", "1990", "2000", "2006", "2012", "2018")

clc_cols_1990 <- clc_cols[names(clc_cols) %in% corine_lc_bav_tk4tel$`1990`]
corine_lc_bav_tk4tel %>% ggplot() + geom_tile(aes (x=x, y=y, fill=`1990`)) +
  scale_fill_manual(name="CLC 1990", values=clc_cols_1990) + 
  geom_sf(data=bavaria_gk, fill=NA) + coord_sf() + 
  theme_bw() + labs(x="", y="")

Faceted maps for all years

clc_all <- corine_lc_bav_tk4tel %>% pivot_longer(names_to="year", values_to="value", -c(x,y))

clc_cols_all <- clc_cols[names(clc_cols) %in% clc_all$value]
clc_all %>% ggplot() + geom_tile(aes (x=x, y=y, fill=value)) +
  facet_wrap(.~year) + scale_fill_manual(name="CLC", values=clc_cols_all) + 
  geom_sf(data=bavaria_gk, fill=NA) + coord_sf() + 
  theme_bw() + labs(x="", y="") + 
  theme(legend.position = "bottom", strip.background = element_blank())

Map of % cover of certain land-cover classes

data("corine_lc_perc_bav", package="bdc")

# Map of fruit trees and berry plantations
corine_lc_perc_bav %>% ggplot() + 
  geom_tile(aes(x=x, y=y, fill=`Fruit trees and berry plantations`)) + 
  scale_fill_scico(name="% Cover", palette="roma") + 
  ggtitle("Fruit trees and berry plantations") + 
  geom_sf(data=bavaria_laea, fill=NA) + coord_sf() + 
  labs(x="", y="") + theme_bw()

# Map of mixed forest
corine_lc_perc_bav %>% ggplot() + 
  geom_tile(aes (x=x, y=y, fill=`Mixed forest`)) + 
  scale_fill_scico(name="% Cover", palette="roma") + 
  ggtitle("Mixed forest") + 
  geom_sf(data=bavaria_laea, fill=NA) + coord_sf() + 
  labs(x="", y="") + theme_bw()

# Map of different forest types across years
corine_lc_perc_bav %>% 
  pivot_longer(names_to="var", values_to="value", -c(x,y,year)) %>%
  filter(var %in% c("Pastures", "Broad-leaved forest", "Coniferous forest", 
                    "Mixed forest", "Natural grasslands")) %>% 
  ggplot() + geom_tile(aes(x=x, y=y, fill=value)) +
  facet_grid(year~var) + scale_fill_scico(name="% Cover", palette="roma") + 
  geom_sf(data=bavaria_laea, fill=NA) + coord_sf() + theme_bw() + 
  theme(strip.background = element_blank(), axis.title = element_blank())

Forest map

Forest = Agro-forestry areas + Coniferous forest + Mixed forest + Broad-leaved forest

corine_lc_perc_bav$forest <- corine_lc_perc_bav$`Agro-forestry areas` + 
  corine_lc_perc_bav$`Coniferous forest` + corine_lc_perc_bav$`Mixed forest` + 
  corine_lc_perc_bav$`Broad-leaved forest`

# Plot individual land-cover percentage cover
corine_lc_perc_bav %>% ggplot() + geom_tile(aes(x=x, y=y, fill=forest)) + 
  facet_wrap(.~year) + scale_fill_scico(name="% Cover", palette="roma") + 
  geom_sf(data=bavaria_laea, fill=NA) + coord_sf() + ggtitle("Forest") + 
  theme_bw() + labs(x="", y="") + theme(legend.text = element_text(size=10))

Calculate dominant land-cover type

# Calculate dominant land-cover class for 2018
dominant_clc <- corine_lc_perc_bav %>% 
  pivot_longer(names_to="var", values_to="value", -c(x,y,year)) %>%
  pivot_wider(names_from="year", values_from="value") %>%
  group_by(x,y) %>% slice(which.max(`2018`)) %>% 
  select(x,y,var) %>% mutate(var = as.factor(var))

# Plot map
clc_cols_sub <- clc_cols[names(clc_cols) %in% dominant_clc$var]
dominant_clc %>% ggplot() + geom_tile(aes(x=x, y=y, fill=var)) + 
  scale_fill_manual(name="CLC 2018", values=clc_cols_sub) + 
  geom_sf(data=bavaria_laea, fill=NA) + coord_sf() + 
  labs(x="", y="") + theme_bw() + theme(legend.position="bottom")

Land-cover change

data("corine_cha_bav_tk4tel", package="bdc")
corine_cha_bav <- corine_cha_bav_tk4tel %>% 
  unite("9000", c("9000_90", "9000_00"), sep=" - ", na.rm=T) %>% 
  unite("0006", c("0006_00", "0006_06"), sep=" - ", na.rm=T) %>% 
  unite("0612", c("0612_06", "0612_12"), sep=" - ", na.rm=T) %>% 
  unite("1218", c("1218_12", "1218_18"), sep=" - ", na.rm=T)
#head(corine_cha_bav)  

#clc_cols_1990 <- clc_cols[names(clc_cols) %in% corine_lc_bav_tk4tel$`1990`]
corine_cha_bav %>% ggplot() + geom_tile(aes (x=x, y=y, fill=`0612`)) +
  #scale_fill_manual(name="CLC 1990", values=clc_cols_1990) + 
  geom_sf(data=bavaria_gk, fill=NA) + coord_sf() + theme_bw() + labs(x="", y="") + 
  theme(legend.position="bottom", legend.text = element_text(size=6))