Data preparation covariates
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This document provides instructions and code snippets for preparing soil and covariate data for digital soil mapping. It describes downloading climate and soil map data and processing them into raster stacks. It also shows how to extract covariate values to point data on soil properties and export a formatted data table for use in modeling and mapping soil organic carbon. The overall aim is to generate spatially explicit predictions of soil properties by relating point data to landscape covariates.
![CLIMATE
# load the climate covariates from the raster tif files
files <- list.files(path = "covs/", pattern = "CHE3.tif",
full.names = TRUE)
# stack all the files in one RasterStack
climate <- stack(files)
# plot the first 2 layers
plot(climate[[1:2]])](https://image.slidesharecdn.com/ird3-3-datapreparationcovariates-180321133116/85/Data-preparation-covariates-16-320.jpg)
![CLIMATE
# load the climate covariates from the raster tif files
files <- list.files(path = "covs/", pattern = "CHE3.tif",
full.names = TRUE)
# stack all the files in one RasterStack
climate <- stack(files)
# plot the first 2 layers
plot(climate[[1:2]])](https://image.slidesharecdn.com/ird3-3-datapreparationcovariates-180321133116/85/Data-preparation-covariates-17-320.jpg)
![OVERLAYING SOIL DATA AND COVARIATES
covs <- stack(covs, soilmap.r)
# correct the name for layer 14
names(covs)[14] <- "soilmap"
#mask the covariates with the country mask from the data repository
mask <- raster("data/mask.tif")
covs <- mask(x = covs, mask = mask)
plot(covs)](https://image.slidesharecdn.com/ird3-3-datapreparationcovariates-180321133116/85/Data-preparation-covariates-19-320.jpg)
![OVERLAYING SOIL DATA AND COVARIATES
covs <- stack(covs, soilmap.r)
# correct the name for layer 14
names(covs)[14] <- "soilmap"
#mask the covariates with the country mask from the data repository
mask <- raster("data/mask.tif")
covs <- mask(x = covs, mask = mask)
plot(covs)](https://image.slidesharecdn.com/ird3-3-datapreparationcovariates-180321133116/85/Data-preparation-covariates-20-320.jpg)
![OVERLAYING SOIL DATA AND COVARIATES
dat <- as.data.frame(dat)
# The points with NA values has to be removed
dat <- dat[complete.cases(dat),]
# export as a csv table
write.csv(dat, "data/MKD_RegMatrix.csv", row.names = FALSE)](https://image.slidesharecdn.com/ird3-3-datapreparationcovariates-180321133116/85/Data-preparation-covariates-22-320.jpg)
![OVERLAYING SOIL DATA AND COVARIATES
dat <- as.data.frame(dat)
# The points with NA values has to be removed
dat <- dat[complete.cases(dat),]
# export as a csv table
write.csv(dat, "data/MKD_RegMatrix.csv", row.names = FALSE)](https://image.slidesharecdn.com/ird3-3-datapreparationcovariates-180321133116/85/Data-preparation-covariates-23-320.jpg)
Data preparation covariates
- 1. FAO- Global Soil Partnership Training on Digital Soil Organic Carbon Mapping 20-24 January 2018 Tehran/Iran Yusuf YIGINI, PhD - FAO, Land and Water Division (CBL) Guillermo Federico Olmedo, PhD - FAO, Land and Water Division (CBL)
- 3. COVARIATES
- 4. COVARIATES The example covariates from this chapter were prepared by ISRIC (ISRIC, 2017).
- 5. COVARIATES The example covariates from this chapter were prepared by ISRIC (ISRIC, 2017).
- 6. #For handling raster data, we load raster package library(raster) #load DEM from tif file DEM <- raster("covs/DEMENV5.tif") plot(DEM) The example covariates from this chapter were prepared by ISRIC (ISRIC, 2017).
- 7. DEM #For handling raster data, we load raster package library(raster) #load DEM from tif file DEM <- raster("covs/DEMENV5.tif") plot(DEM) The example covariates from this chapter were prepared by ISRIC (ISRIC, 2017).
- 8. Soil Maps Soil maps play a crucial role for upscaling soil property data from point locations. They can be the spatial layer for conventional upscaling, they can also serve as a covariate in digital soil mapping. Predicted soil property maps have lower quality in areas where the covariates such as relief, geology and climate so not correlate well with the dependent variable, here soil carbon stocks. This is especially true for soils groundwater or stagnic water influence. This information is well-represented in soil maps. FAO, IIASA, ISRIC, ISS CAS and JRC produced a gridded 1 km soil class map (HWSD).
- 9. Soil Maps # load the soil map from a shapefile file soilmap <- shapefile("MK_soilmap_simple.shp") # plot the DEM together with the soil types plot(DEM) lines(soilmap) Digitized small-scale national soil maps are the most important spatial layer for soil property mapping. The higher its resolution, the better soil maps contribute to high quality soil property maps - considering that the map should cover the target area/full country coverage.
- 10. DEM #For handling raster data, we load raster package library(raster) #load DEM from tif file DEM <- raster("covs/DEMENV5.tif") plot(DEM) The example covariates from this chapter were prepared by ISRIC (ISRIC, 2017).
- 11. DEM # the "Symbol" attribute from the vector layer will be used for the # rasterization process. It has to be a factor soilmap@data$Symbol <- as.factor(soilmap@data$Symbol) #save the levels names in a character vector Symbol.levels <- levels(soilmap$Symbol) # The rasterization process needs a layer with the target grd # system: spatial extent and cell size. soilmap.r <- rasterize(x = soilmap, y = DEM, field = "Symbol") # The DEM raster layer could be used for this. plot(soilmap.r, col=rainbow(21)) legend("bottomright",legend = Symbol.levels, fill=rainbow(21), cex=0.5) Technical Steps - Rasterizing a vector layer in R
- 12. DEM # the "Symbol" attribute from the vector layer will be used for the # rasterization process. It has to be a factor soilmap@data$Symbol <- as.factor(soilmap@data$Symbol) #save the levels names in a character vector Symbol.levels <- levels(soilmap$Symbol) # The rasterization process needs a layer with the target grd # system: spatial extent and cell size. soilmap.r <- rasterize(x = soilmap, y = DEM, field = "Symbol") # The DEM raster layer could be used for this. plot(soilmap.r, col=rainbow(21)) legend("bottomright",legend = Symbol.levels, fill=rainbow(21), cex=0.5) Technical Steps - Rasterizing a vector layer in R
- 13. LAND COVER landcover <- raster("covs/LCEE10.tif") # Land cover is a categorical covariate, this has to be made # explicit using function as.factor() landcover <- as.factor(landcover) landcover ## class : RasterLayer ## dimensions : 182, 310, 56420 (nrow, ncol, ncell) ## resolution : 0.008327968, 0.008353187 (x, y) ## extent : 20.45242, 23.03409, 40.8542, 42.37448 (xmin, xmax, ymin, ymax) ## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 plot(landcover)
- 14. LAND COVER landcover <- raster("covs/LCEE10.tif") # Land cover is a categorical covariate, this has to be made # explicit using function as.factor() landcover <- as.factor(landcover) landcover ## class : RasterLayer ## dimensions : 182, 310, 56420 (nrow, ncol, ncell) ## resolution : 0.008327968, 0.008353187 (x, y) ## extent : 20.45242, 23.03409, 40.8542, 42.37448 (xmin, xmax, ymin, ymax) ## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 plot(landcover)
- 15. CLIMATE WorldClim Climate Data are available at: www.worldclim.org (WorldClim 1.4 (current conditions) by www.worldclim.org; Hijmans et al., 2005. Int. J. of Clim. 25: 1965-1978. Is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License).
- 16. CLIMATE # load the climate covariates from the raster tif files files <- list.files(path = "covs/", pattern = "CHE3.tif", full.names = TRUE) # stack all the files in one RasterStack climate <- stack(files) # plot the first 2 layers plot(climate[[1:2]])
- 17. CLIMATE # load the climate covariates from the raster tif files files <- list.files(path = "covs/", pattern = "CHE3.tif", full.names = TRUE) # stack all the files in one RasterStack climate <- stack(files) # plot the first 2 layers plot(climate[[1:2]])
- 18. OVERLAYING SOIL DATA AND COVARIATES # Load the processed data. This table was prepared in the previous # chapter. dat <- read.csv("dataproc.csv") files <- list.files(path = "covs", pattern = "tif$", full.names = TRUE) covs <- stack(files)
- 19. OVERLAYING SOIL DATA AND COVARIATES covs <- stack(covs, soilmap.r) # correct the name for layer 14 names(covs)[14] <- "soilmap" #mask the covariates with the country mask from the data repository mask <- raster("data/mask.tif") covs <- mask(x = covs, mask = mask) plot(covs)
- 20. OVERLAYING SOIL DATA AND COVARIATES covs <- stack(covs, soilmap.r) # correct the name for layer 14 names(covs)[14] <- "soilmap" #mask the covariates with the country mask from the data repository mask <- raster("data/mask.tif") covs <- mask(x = covs, mask = mask) plot(covs)
- 21. OVERLAYING SOIL DATA AND COVARIATES #upgrade points data frame to SpatialPointsDataFrame coordinates(dat) <- ~ X + Y # extract values from covariates to the soil points dat <- extract(x = covs, y = dat, sp = TRUE) # LCEE10 and soilmap are categorical variables dat@data$LCEE10 <- as.factor(dat@data$LCEE10) dat@data$soilmap <- as.factor(dat@data$soilmap) #levels(soilmap) <- Symbol.levels summary(dat@data)
- 22. OVERLAYING SOIL DATA AND COVARIATES dat <- as.data.frame(dat) # The points with NA values has to be removed dat <- dat[complete.cases(dat),] # export as a csv table write.csv(dat, "data/MKD_RegMatrix.csv", row.names = FALSE)
- 23. OVERLAYING SOIL DATA AND COVARIATES dat <- as.data.frame(dat) # The points with NA values has to be removed dat <- dat[complete.cases(dat),] # export as a csv table write.csv(dat, "data/MKD_RegMatrix.csv", row.names = FALSE)
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