globalAgLand
This project is maintained by agrichick45
Global Cropland Expansion and Soils at Risk
Mandy Liesch
Purpose
The purpose of this vignette is looking at the land use changes around the globe by different land use types using the R Terra Package, masking, zonal statisitics, and visualizations with bivariate cholopleth maps to visualize the cropland gains and losses by country/state.
A Note on file size:
terraOptions(tempDir="F:/temp")
R Packages
library(biscale)
library(cowplot)
library(exactextractr)
library(gdalUtils)
library(geodata)
library(ggplot2)
library(ggridges)
library(rgdal)
library(rmarkdown)
library(sf)
library(terra)
library(tidyverse)
terraOptions(tempDir="F:/temp")
Raw Data and Preparations
Countries
w <- world(path=tempdir())
plot(w)
w
## class : SpatVector
## geometry : polygons
## dimensions : 231, 2 (geometries, attributes)
## extent : -180, 180, -90, 83.65625 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs
## names : GID_0 NAME_0
## type : <chr> <chr>
## values : ABW Aruba
## AFG Afghanistan
## AGO Angola
Soil Grids: Total Soil Carbon Stocks 0-30 cm
SoilGrids is a system for global digital soil mapping that makes use of global soil profile information and covariate data to model the spatial distribution of soil properties across the globe. SoilGrids is a collections of soil property maps for the world produced using machine learning at 250 m resolution. The Soil Grids 2.0 FAQs details the file structure and downloads for both the soil grids data for soil carbon, as well as the uncertainty.
This code process takes you through how to grab the WebDAV protocol modelled off of the SoilGrids Notebook, using the GDAL functionality in R.
library(rgdal)
library(gdalUtils)
link="/vsicurl/https://files.isric.org/soilgrids/latest/data/ocs/ocs_0-30cm_Q0.5.vrt"
gdal_translate(link,
"soilC_Rast.tif",
co=c("TILED=YES","COMPRESS=DEFLATE","BIGTIFF=YES"),
verbose=TRUE)
scStock<-rast("soilC_Rast.tif")
plot(scStock)
project(scStock, w, filename="Reproj_SoilC.tif")
scStock<-rast("Reproj_SoilC.tif")
plot(scStock)
plot(w, bg="transparent", add=TRUE)
Cropland Extent Changes
The dataset represents a globally consistent cropland extent time-series at 30-m spatial resolution. Cropland defined as land used for annual and perennial herbaceous crops for human consumption, forage (including hay), and biofuel. It is on the Global Land Analysis and Discovery, on a project through the University of Maryland.
The chosen rasters downloaded are the Cropland Gain (Global_cropland_3km_netgain.tif).
The net cropland extent change from 2003 to 2019 is shown as the Pixel value: 0-100, percent of cropland dynamic (loss or gain) per pixel.
cropGain<-rast("Global_cropland_3km_netgain.tif")
project(cropGain, scStock, filename="Reproj_cropGain.tif")
gains<-rast("Reproj_cropGain.tif")
plot(gains)
plot(w, bg="transparent", add=TRUE)
Preliminary Analysis
shapeSF <- sf::st_as_sf(w)
regions<-read_csv("regions.csv")
shapes<-merge(shapeSF, regions, by="GID_0")
shapes$TotalCount<-exact_extract(scStock, shapes, 'count')
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shapes$totC_Mean<-exact_extract(scStock, shapes, 'mean')
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shapes$cropGains_Mean<-exact_extract(gains, shapes, 'mean')
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Tables by Region
summary<-na.omit(shapes)
summary$geometry<-NULL
sumStats <- summary %>%
group_by(Region) %>%
summarise_at(vars(totC_Mean, cropGains_Mean), list(name = mean))
sumStats
## # A tibble: 18 x 3
## Region totC_Mean_name cropGains_Mean_name
## <chr> <dbl> <dbl>
## 1 Central America 54.5 1.32
## 2 Central Asia 36.2 1.37
## 3 East Africa 41.1 2.32
## 4 East Asia 47.3 0.920
## 5 Eastern Europe 48.3 3.54
## 6 Indonesia 54.8 0.979
## 7 Middle East 16.9 1.46
## 8 North Africa 13.4 0.816
## 9 North America 55.5 0.423
## 10 Northern South America 44.6 0.744
## 11 Oceania 55.5 0.184
## 12 Russia 52.7 3.39
## 13 South Asia 36.4 3.15
## 14 Southeast Asia 47.6 2.73
## 15 Southern Africa 28.1 3.04
## 16 Southern South America 51.5 3.17
## 17 West Africa 32.1 2.53
## 18 Western Europe 60.0 2.18
Ridgeplots of Regional Data
cropGainPlot<-ggplot(summary, aes(x = cropGains_Mean, y = Region)) +
geom_density_ridges(scale = 4) +
scale_y_discrete(expand = c(0, 0)) + # will generally have to set the `expand` option
scale_x_continuous(expand = c(0, 0)) + # for both axes to remove unneeded padding
coord_cartesian(clip = "off") + # to avoid clipping of the very top of the top ridgeline
theme_ridges()
cropGainPlot
SOCPlot<-ggplot(summary, aes(x = totC_Mean, y = Region)) +
geom_density_ridges(scale = 4) +
scale_y_discrete(expand = c(0, 0)) + # will generally have to set the `expand` option
scale_x_continuous(expand = c(0, 0)) + # for both axes to remove unneeded padding
coord_cartesian(clip = "off") + # to avoid clipping of the very top of the top ridgeline
theme_ridges()
SOCPlot
Bivariate Chloropleth Maps By Region
shapes<-na.omit(shapes)
shapes <- bi_class(shapes, x = cropGains_Mean, y = totC_Mean, style = "jenks", dim = 3)
map <- ggplot() +
geom_sf(data = shapes, mapping = aes(fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
bi_scale_fill(pal = "DkBlue", dim = 3) +
labs(
title = "Cropland Gains and 30 cm Carbon Stocks",
) +
bi_theme()
legend <- bi_legend(pal = "DkBlue",
dim = 3,
xlab = "Cropland Gain",
ylab = "Carbon Stocks",
size = 12)
finalPlot <- ggdraw() +
draw_plot(map, 0, 0, 1, 1) +
draw_plot(legend, 0, 0.2, 0.2, 0.2)
finalPlot
Dyanamic Country Results
newShape<-shapes
newShape$geometry<-NULL
highRisk<-newShape %>%
filter(bi_class == '3-3'|bi_class == '3-2') %>%
select(NAME_0, bi_class, Continent, totC_Mean, cropGains_Mean)
highRisk
## NAME_0 bi_class Continent totC_Mean cropGains_Mean
## 1 Azerbaijan 3-2 Asia 42.91636 7.251426
## 2 Belarus 3-2 Europe 35.43983 6.096657
## 3 Barbados 3-3 North America 53.69674 11.969048
## 4 Estonia 3-3 Europe 66.96027 6.431501
## 5 Cambodia 3-2 Asia 41.92271 10.016696
## 6 Lithuania 3-2 Europe 40.46164 13.716304
## 7 Latvia 3-3 Europe 53.71252 8.178349
## 8 Rwanda 3-3 Africa 61.77108 8.167065
## 9 Tanzania 3-2 Africa 33.02401 7.414059
## 10 Uganda 3-2 Africa 51.27988 11.502319
## 11 Uruguay 3-3 South America 59.96348 8.791050
Detailed Analysis
target<-gains>=10
targMask<-classify(
#define the raster you are clipping
target,
#define the reclassification values (id is 0= not in the 10% or more)
cbind(id=c(0, 1),
#these are the raster values, with an id of 0 returning as an NA, otherwise it is 1.
v=c(NA, 1)),
filename='targMask.tif')
targetMask<-rast("targMask.tif")
highCrop<-mask(
#the first raster is the full raster we are cutting (the soil carbon stocks)
scStock,
#the second raster is the mask we created in the previous step
targetMask,
#If TRUE, areas on mask that are _not_ the maskvalue are masked, since we classified them as NA, this can be set to false
inverse=FALSE,
#The value that cells of x should become if they are not covered by mask, set to NA here.
updatevalue=NA,
#new creation of a .tif file, with the cut values of soil carbon remaining.
filename="highCrop_SOC_Stocks.tif")
highCropCh<-rast("highCrop_SOC_Stocks.tif")
plot(highCropCh)
plot(w, bg="transparent", add=TRUE)
shapes$hCCount<-exact_extract(highCropCh, shapes, 'count')
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shapes$hcMean<-exact_extract(highCropCh, shapes, 'mean')
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shapes$perChange<-(shapes$hCCount/shapes$TotalCount)*100
ggplot(data = shapes) +
geom_sf(aes(fill = perChange)) +
scale_fill_viridis_c(option = "plasma")
shapes2<-na.omit(shapes)
shapes2 <- bi_class(shapes2, x = perChange, y = hcMean, style = "jenks", dim = 3)
map <- ggplot() +
geom_sf(data = shapes2, mapping = aes(fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
bi_scale_fill(pal = "DkBlue", dim = 3) +
labs(
title = "High Risk Countries and Carbon",
) +
bi_theme()
legend <- bi_legend(pal = "DkBlue",
dim = 3,
xlab = "High Change Areas",
ylab = "Carbon Stocks",
size = 12)
finalPlot2 <- ggdraw() +
draw_plot(map, 0, 0, 1, 1) +
draw_plot(legend, 0, 0.2, 0.2, 0.2)
finalPlot2
newShape1<-shapes2
newShape1$geometry<-NULL
highRisk<-newShape1 %>%
filter(bi_class == '3-3'|bi_class == '3-2'|bi_class == '2-3'|bi_class == '3-3') %>%
select(NAME_0, bi_class, Continent, totC_Mean, perChange)
highRisk
## NAME_0 bi_class Continent totC_Mean perChange
## 1 Azerbaijan 3-2 Asia 42.91636 25.11263
## 2 Barbados 3-2 North America 53.69674 58.37006
## 3 Estonia 3-3 Europe 66.96027 27.10306
## 4 United Kingdom 2-3 Europe 72.97118 10.38440
## 5 Isle of Man 2-3 Europe 72.40746 10.89843
## 6 Cambodia 3-2 Asia 41.92271 30.22134
## 7 Lithuania 3-2 Europe 40.46164 57.63790
## 8 Latvia 3-2 Europe 53.71252 32.76434
## 9 Netherlands 2-3 Europe 59.25650 14.37528
## 10 Rwanda 3-2 Africa 61.77108 31.66588
## 11 Uganda 3-2 Africa 51.27988 45.13052
## 12 Uruguay 3-3 South America 59.96348 33.77368