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22AllHerd_MNGT+LCMAP_LCcalc.R
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22AllHerd_MNGT+LCMAP_LCcalc.R
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# calculating and visualizing LULC by herd
## input: LCMAP dataframe calculated for all herds 1985-2017 (25 ind and all ind)
####
library(raster)
library(animation)
library(sp)
library(sf)
library(rgdal)
library(dplyr)
library(forcats)
library(ggplot2)
library(ggspatial)
library(hrbrthemes)
library(cowplot)
library(tidyr)
# data prep #
# csv data#
LCPRI_25ind <- read.csv("LCPRI_allHerds_25ind.csv")
# sp data #
HR_25ind_w_agency <- readOGR("AllHerd_25ind_w_Agency_clean.shp")
HR_25ind <- readOGR("C:\\Users\\wenjing.xu\\Google Drive\\RESEARCH\\Elk\\Analysis\\GYE-Elk-LULC\\Data\\FINALwinterRangeSHP_June2020\\25elkYears\\allHerds25.shp")
target.crs <- CRS("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0")
HR_25ind <- spTransform(HR_25ind, target.crs)
## animation of LULC map ##
draw.a.map <- function (index) {
raster.i <- raster(paste0(getwd(),"\\Data\\LCPRI_HR_25ind_1985-2017\\LCPRI_HR_25ind_", index, ".tif"))
cuts=c(0,1,2,3,4,5,6,7) #set breaks
pal <- colorRampPalette(c("#FF5D51", "#FDDC58", "#CAE9B0", "#3bbf4e", "#2E95D4", "#9FDAFF", "#F8FCFF", "#D1D1D1"))
plot(raster.i, breaks=cuts, col = pal(8), axes=FALSE, main=paste0(index, " land cover"))
plot(HR_25ind, col = "#d1d1d150", lwd = 2, add = T)
}
years <- c(1985:2005)
loop.animate <- function () {
lapply(1:length(years), function(i) {
draw.a.map(years[i])
})
}
saveGIF(loop.animate(), interval = .2, movie.name = "LCMAP_allherds_25ind_V2.gif")
######################################################################
## bar plot # reflect a base info ##################################################
######################################################################
## plot 1.1 ## pri-pub ratio across years for each herd
LCPRI_25ind.1 <- LCPRI_25ind %>%
group_by(herd, year, agency) %>% filter (year == 2000) %>%
mutate(total.area.by.agency = (sum(developed + cropland + grass_shrub + tree_cover + water + wetland + ice_snow + barren))*30*30/1000000)
LCPRI_25ind.11 <- LCPRI_25ind.1 %>% group_by(herd) %>% summarise(total.area = sum(total.area.by.agency))
LCPRI_25ind.1 <- LCPRI_25ind.1 %>% left_join(LCPRI_25ind.11, by = "herd")
LCPRI_25ind.1 <- LCPRI_25ind.1 %>% mutate (percent = round( total.area.by.agency/total.area * 100, digits = 1)) %>% mutate (label = paste0(percent, "%"))
LCPRI_25ind.1[LCPRI_25ind.1$agency == "PUBLIC", ]$label <- NA
LCPRI_25ind.1$herd <- with(LCPRI_25ind.1, reorder(herd, total.area))
p1.1 <- ggplot(data = LCPRI_25ind.1, aes(x = herd, y = total.area.by.agency, fill = agency)) +
geom_bar(position="stack", stat="identity") +
geom_text(aes(label = label), position = position_stack(vjust = 0.5), size = 3) +
theme_ipsum() +
theme(axis.text.x = element_text(angle = 90, hjust=1)) +
theme(legend.position = c(0.2, 0.9)) +
theme(legend.title=element_blank()) +
labs(title = "Elk herd winter home range area (km2)", x = element_blank(), y = element_blank()) +
theme(plot.title = element_text(size=14))
p1.1
## plot 1.2 ## averaged % LULC across years for each herd
LCPRI_25ind.2 <- LCPRI_25ind %>%
tidyr::gather ("Land_Cover", "area", 4:11) %>%
group_by(herd, year, Land_Cover) %>% summarise(area = sum(area)*30*30/1000000)
LCPRI_25ind.2 <- LCPRI_25ind.2 %>% group_by(herd, Land_Cover) %>% summarise(area = mean(area))
LCPRI_25ind.22 <- LCPRI_25ind.2 %>% group_by(herd) %>% summarise(total.area = sum(area))
LCPRI_25ind.2 <- LCPRI_25ind.2 %>% left_join(LCPRI_25ind.22, by = "herd")
LCPRI_25ind.2$herd <- with(LCPRI_25ind.2, reorder(herd, total.area))
LCPRI_25ind.2 <- LCPRI_25ind.2 %>% mutate(Land_Cover = fct_relevel(Land_Cover, "developed", "cropland", "grass_shrub", "tree_cover", "water", "wetland", "ice_snow", "barren"))
p1.2 <- ggplot(LCPRI_25ind.2, aes(x=herd, y = area, fill = Land_Cover)) +
geom_bar(position="stack", stat="identity") +
theme_ipsum() +
scale_fill_manual(values = c("#FF5D51", "#FDDC58", "#CAE9B0", "#3bbf4e", "#2E95D4", "#9FDAFF", "#F8FCFF", "#D1D1D1")) +
theme(axis.text.x = element_text(angle = 90, hjust=1)) +
theme(legend.title=element_blank()) +
labs(title = "Averge LC area (km2) 1985-2007", x = element_blank(), y = element_blank()) +
theme(plot.title = element_text(size=14))
p1.2
# figure out herd rankings in key LC
LCPRI_25ind.rank <- LCPRI_25ind.2 %>% group_by(Land_Cover, herd) %>% mutate(perc = round(area*100/total.area, digits = 2)) %>% arrange(by = Land_Cover, desc(perc))
View(LCPRI_25ind.rank)
############################################################
################ line plot ####################################################
############################################################
LCPRI_25ind.3 <- LCPRI_25ind %>% group_by(herd, year) %>%
mutate(total.area = (sum(developed + cropland + grass_shrub + tree_cover + water + wetland + ice_snow + barren)))
## plot 2.1 # %HR that is developed over years for each herd, all ownership
LCPRI_25IND_DEV <- LCPRI_25ind.3 %>% group_by(herd, year, total.area) %>%
summarise(area = (sum(developed))) %>% mutate(perc = area/total.area*100)
p2.1 <- LCPRI_25IND_DEV %>%
ggplot(aes(x = year, y=perc, group = herd, color = herd)) +
geom_line(size = 1.2) +
theme_ipsum() +
scale_color_brewer(palette="Paired") +
theme(legend.title=element_blank()) +
labs(title = "Developed area (%HR)", x = element_blank(), y = element_blank()) +
theme(plot.title = element_text(size=14))+
theme(legend.position = "none")
## plot 2.2 # %HR that is cropland over years for each herd, all ownership
LCPRI_25IND_AG <- LCPRI_25ind.3 %>% group_by(herd, year, total.area) %>%
summarise(area = (sum(cropland))) %>% mutate(perc = area/total.area*100)
p2.2 <- LCPRI_25IND_AG %>%
ggplot(aes(x = year, y=perc, group = herd, color = herd)) +
geom_line(size = 1.2) +
theme_ipsum() +
scale_color_brewer(palette="Paired") +
theme(legend.title=element_blank()) +
labs(title = "Agriculture area (%HR)", x = element_blank(), y = element_blank()) +
theme(plot.title = element_text(size=14))+
theme(legend.position = "none")
# if without Targhee
# p2.2.noT <-LCPRI_25ind.3 %>% filter(herd != "Targhee") %>% group_by(herd, year, total.area) %>%
# summarise(area = (sum(cropland))) %>% mutate(perc = area/total.area*100) %>%
# ggplot(aes(x = year, y=perc, group = herd, color = herd)) +
# geom_line(size = 1.2) +
# theme_ipsum() +
# scale_color_brewer(palette="Paired") +
# theme(legend.title=element_blank()) +
# labs(title = "Agriculture area (%HR)", x = element_blank(), y = element_blank()) +
# theme(plot.title = element_text(size=14))
## plot 2.3 # %HR that is tree over years for each herd, all ownership
LCPRI_25IND_TREE <- LCPRI_25ind.3 %>% group_by(herd, year, total.area) %>%
summarise(area = (sum(tree_cover))) %>% mutate(perc = area/total.area*100)
p2.3 <- LCPRI_25IND_TREE %>%
ggplot(aes(x = year, y=perc, group = herd, color = herd)) +
geom_line(size = 1.2) +
theme_ipsum() +
scale_color_brewer(palette="Paired") +
theme(legend.title=element_blank()) +
labs(title = "Tree cover (%HR)", x = element_blank(), y = element_blank()) +
theme(plot.title = element_text(size=14))+
theme(legend.position = "none")
## plot 2.4 # %HR that is grass_shrub years for each herd, all ownership
LCPRI_25IND_GRS <- LCPRI_25ind.3 %>% group_by(herd, year, total.area) %>%
summarise(area = (sum(grass_shrub))) %>% mutate(perc = area/total.area*100)
p2.4 <- LCPRI_25IND_GRS %>%
ggplot(aes(x = year, y=perc, group = herd, color = herd)) +
geom_line(size = 1.2) +
theme_ipsum() +
scale_color_brewer(palette="Paired") +
theme(legend.title=element_blank()) +
labs(title = "Grass/shrub area (%HR)", x = element_blank(), y = element_blank()) +
theme(plot.title = element_text(size=14))+
theme(legend.position = "none")
plot_grid(p2.1, p2.2, p2.3, p2.4)
############################################################
# change plot 1985 vs 2017################################################################################
############################################################
LCPRI_25IND_DEV.rank <- LCPRI_25IND_DEV %>% filter(year %in% c(1985, 2017)) %>% group_by(herd) %>%
select(herd, total.area, area, year) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25IND_DEV.rank)[3:4] <- c("y1985", 'y2017')
LCPRI_25IND_DEV.rank <- LCPRI_25IND_DEV.rank %>% ungroup() %>%
mutate (r1985 = y1985/total.area, r2017 = y2017/total.area, rate = (y2017 - y1985)*100/y1985) %>%
mutate(mycolor = ifelse(rate > 0, "type1", "type2"),
herd = factor(herd, c("Blacktail", "Clarks Fork", "Cody", "Gooseberry", "Jackson", "Madison", "Northern", "North Madison", "Sand Creek", "Targhee", "Wiggins Fork")))
p3.1 <- ggplot(LCPRI_25IND_DEV.rank, aes (x = herd, y = rate)) +
geom_segment( aes(x=herd, xend=herd, y=0, yend=rate, color=mycolor), size=1.3, alpha=0.9) +
#geom_point( aes(x=herd, y=r1985*100), color=rgb(0.2,0.7,0.1,0.5), size=3 ) +
geom_point( aes(x=herd, y=rate), color=rgb(0.7,0.2,0.1,0.5), size=3 ) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Developed", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
LCPRI_25IND_AG.rank <- LCPRI_25IND_AG %>% filter(year %in% c(1985, 2017)) %>% group_by(herd) %>%
select(herd, total.area, area, year) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25IND_AG.rank)[3:4] <- c("y1985", 'y2017')
LCPRI_25IND_AG.rank <- LCPRI_25IND_AG.rank %>% ungroup() %>%
mutate (r1985 = y1985/total.area, r2017 = y2017/total.area, rate = (y2017 - y1985)*100/y1985) %>%
mutate(mycolor = ifelse(rate > 0, "type1", "type2") ,
herd = factor(herd, c("Blacktail", "Clarks Fork", "Cody", "Gooseberry", "Jackson", "Madison", "Northern", "North Madison", "Sand Creek", "Targhee", "Wiggins Fork")))
p3.2 <- ggplot(LCPRI_25IND_AG.rank, aes (x = herd, y = rate)) +
geom_segment( aes(x=herd, xend=herd, y=0, yend=rate, color=mycolor), size=1.3, alpha=0.9) +
#geom_point( aes(x=herd, y=r1985*100), color=rgb(0.2,0.7,0.1,0.5), size=3 ) +
geom_point( aes(x=herd, y=rate), color=rgb(0.7,0.2,0.1,0.5), size=3 ) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Agriculture", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
LCPRI_25IND_GRS.rank <- LCPRI_25IND_GRS %>% filter(year %in% c(1985, 2017)) %>% group_by(herd) %>%
select(herd, total.area, area, year) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25IND_GRS.rank)[3:4] <- c("y1985", 'y2017')
LCPRI_25IND_GRS.rank <- LCPRI_25IND_GRS.rank %>% ungroup() %>%
mutate (r1985 = y1985/total.area, r2017 = y2017/total.area, rate = (y2017 - y1985)*100/y1985) %>%
mutate(mycolor = ifelse(rate > 0, "type1", "type2") ,
herd = factor(herd, c("Blacktail", "Clarks Fork", "Cody", "Gooseberry", "Jackson", "Madison", "Northern", "North Madison", "Sand Creek", "Targhee", "Wiggins Fork")))
p3.3 <- ggplot(LCPRI_25IND_GRS.rank, aes (x = herd, y = rate)) +
geom_segment( aes(x=herd, xend=herd, y=0, yend=rate, color=mycolor), size=1.3, alpha=0.9) +
#geom_point( aes(x=herd, y=r1985*100), color=rgb(0.2,0.7,0.1,0.5), size=3 ) +
geom_point( aes(x=herd, y=rate), color=rgb(0.7,0.2,0.1,0.5), size=3 ) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Grass/shrub", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
LCPRI_25IND_TREE.rank <- LCPRI_25IND_TREE %>% filter(year %in% c(1985, 2017)) %>% group_by(herd) %>%
select(herd, total.area, area, year) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25IND_TREE.rank)[3:4] <- c("y1985", 'y2017')
LCPRI_25IND_TREE.rank <- LCPRI_25IND_TREE.rank %>% ungroup() %>%
mutate (r1985 = y1985/total.area, r2017 = y2017/total.area, rate = (y2017 - y1985)*100/y1985) %>%
mutate(mycolor = ifelse(rate > 0, "type1", "type2") ,
herd = factor(herd, c("Blacktail", "Clarks Fork", "Cody", "Gooseberry", "Jackson", "Madison", "Northern", "North Madison", "Sand Creek", "Targhee", "Wiggins Fork")))
p3.4 <- ggplot(LCPRI_25IND_TREE.rank, aes (x = herd, y = rate)) +
geom_segment( aes(x=herd, xend=herd, y=0, yend=rate, color=mycolor), size=1.3, alpha=0.9) +
#geom_point( aes(x=herd, y=r1985*100), color=rgb(0.2,0.7,0.1,0.5), size=3 ) +
geom_point( aes(x=herd, y=rate), color=rgb(0.7,0.2,0.1,0.5), size=3 ) +
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Tree Cover", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
plot_grid(p3.1, p3.2, p3.3, p3.4)
##################################################
## stacked bar plot ####################################################
##################################################
LCPRI_25ind.4.dev <- LCPRI_25ind.3 %>% filter(year %in% c(1985, 2017)) %>% group_by(herd, agency, year) %>%
summarise(area = (sum(developed))) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25ind.4.dev)[3:4] <- c("y1985", 'y2017')
LCPRI_25ind.4.dev <- LCPRI_25ind.4.dev %>% mutate(rate = (y2017 - y1985)*100/y1985)
p4.1 <- ggplot(LCPRI_25ind.4.dev, aes(fill=agency, y=rate, x=herd)) +
geom_bar(position="stack", stat="identity", width=.4)+
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Developed", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
LCPRI_25ind.4.crop <- LCPRI_25ind.3 %>% filter(year %in% c(1985, 2017)) %>% group_by(herd, agency, year) %>%
summarise(area = (sum(cropland))) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25ind.4.crop)[3:4] <- c("y1985", 'y2017')
LCPRI_25ind.4.crop <- LCPRI_25ind.4.crop %>% mutate(rate = (y2017 - y1985)*100/y1985)
p4.2 <- ggplot(LCPRI_25ind.4.crop, aes(fill=agency, y=rate, x=herd)) +
geom_bar(position="stack", stat="identity", width=.4)+
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Cropland", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
LCPRI_25ind.4.GRS <- LCPRI_25ind.3 %>% filter(year %in% c(1985, 2017)) %>% group_by(herd, agency, year) %>%
summarise(area = (sum(grass_shrub))) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25ind.4.GRS)[3:4] <- c("y1985", 'y2017')
LCPRI_25ind.4.GRS <- LCPRI_25ind.4.GRS %>% mutate(rate = (y2017 - y1985)*100/y1985)
p4.3 <- ggplot(LCPRI_25ind.4.GRS, aes(fill=agency, y=rate, x=herd)) +
geom_bar(position="stack", stat="identity", width=.4)+
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
theme(legend.position = "none") +
labs(title = "Grass/shrub", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
LCPRI_25ind.4.tree <- LCPRI_25ind.3 %>% filter(year %in% c(1985, 2017)) %>% group_by(herd, agency, year) %>%
summarise(area = (sum(tree_cover))) %>% pivot_wider(names_from = year, values_from = area)
names(LCPRI_25ind.4.tree)[3:4] <- c("y1985", 'y2017')
LCPRI_25ind.4.tree <- LCPRI_25ind.4.tree %>% mutate(rate = (y2017 - y1985)*100/y1985)
p4.4 <- ggplot(LCPRI_25ind.4.tree, aes(fill=agency, y=rate, x=herd)) +
geom_bar(position="stack", stat="identity", width=.4)+
theme_ipsum(base_size = 12, axis_title_size = 14) +
theme(axis.text.x = element_text(angle = 0, hjust=1)) +
#theme(legend.position = "none") +
labs(title = "Tree Cover", x = element_blank(), y = element_blank()) +
coord_flip() +
ylim(-65, 50)
plot_grid(p4.1, p4.2, p4.3, p4.4)