2 Preparing Raster Data

3 Load data on study area

3.1 Soil samples

Code

# Load soil data from sampling locations
bern_data <- readr::read_csv(
  here::here("data-raw/soildata/berne_soil_sampling_locations.csv")
  )

# Display data
head(bern_data) |> knitr::kable()

site_id_unique	timeset	x	y	dataset	dclass	waterlog.50	waterlog.100	ph.0.10	ph.10.30	ph.30.50	ph.50.100
4_26-In-005	d1968_1974_ptf	2571994	1203001	validation	poor	0	1	6.071733	6.227780	7.109235	7.214589
4_26-In-006	d1974_1978	2572149	1202965	calibration	poor	1	1	6.900000	6.947128	7.203502	7.700000
4_26-In-012	d1974_1978	2572937	1203693	calibration	moderate	1	1	6.200000	6.147128	5.603502	5.904355
4_26-In-014	d1974_1978	2573374	1203710	validation	well	0	0	6.600000	6.754607	7.200000	7.151129
4_26-In-015	d1968_1974_ptf	2573553	1203935	validation	moderate	0	1	6.272715	6.272715	6.718392	7.269008
4_26-In-016	d1968_1974_ptf	2573310	1204328	calibration	poor	0	1	6.272715	6.160700	5.559031	5.161655

The dataset on soil samples from Bern holds 13 variables for 1052 entries (more information here):

site_id_unique: The location’s unique site id.
timeset: The sampling year and information on sampling type for soil pH (no label: CaCl\(_2\) laboratory measurement, field: indicator solution used in field, ptf: H\(_2\)O laboratory measurement transferred by pedotransfer function).
x: The x (easting) coordinates in meters following the (CH1903/LV03) system.
y: The y (northing) coordinates in meters following the (CH1903/LV03) system.
dataset: Specification whether a sample is used for model calibration or validation (this is based on randomization to ensure even spatial coverage).
dclass: Soil drainage class
waterlog.30, waterlog.50, waterlog.100: Specification whether soil was water logged at 30, 50, or 100 cm depth (0 = No, 1 = Yes).
ph.0.10, ph.10.30, ph.30.50, ph.50.100: Average soil pH between 0-10, 10-30, 30-50, and 50-100 cm depth.

3.1.1 Covariate data

Now, let’s load all the covariates that we want to produce our soil maps with.

Code

# Get a list with the path to all raster files
list_raster <- 
  base::list.files(
    here::here("data-raw/geodata/covariates/"),
    full.names = T
  )

# Take a random subset
set.seed(3)
list_raster_subset <- list_raster |> sample(15)

# Display data (lapply to clean names)
lapply(
  list_raster_subset, 
  function(x) sub(".*/(.*)", "\\1", x)
  ) |> 
  unlist() |> 
  head(5) |> 
  print()

[1] "geo500h1id.tif"           "Se_n_aspect2m.tif"       
[3] "mt_rr_y.tif"              "Se_curvplan2m_std_5c.tif"
[5] "vdcn25.tif"

The output above shows the first five raster files with rather cryptic names. The meaning of all 91 raster files are given in Chapter 6. So, make sure to have a look at the list there as it will help you to interpret your model results later on. Let’s look at one of these raster files to get a better feeling for our data. Specifically, let’s look at the slope profile at 2m resolution:

Code

# Load a raster file as example: Picking the slope profile at 2m resolution
raster_example <- terra::rast(list_raster[74])
raster_example

class       : SpatRaster 
dimensions  : 986, 2428, 1  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : 2568140, 2616700, 1200740, 1220460  (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95 
source      : Se_slope2m.tif 
name        : Se_slope2m 
min value   :    0.00000 
max value   :   85.11286

As shown in the output, a raster object has the following properties (among others, see ?terra::rast):

class: The class of the file, here a SpatRaster.
dimensions: The number of rows, columns, years (if temporal encoding).
resolution: The resolution of the coordinate system, here it is 20 in both axes.
extent: The extent of the coordinate system defined by min and max values on the x and y axes.
coord. ref.: Reference coordinate system. Here, the raster is encoded using the LV95 geodetic reference system from which the projected coordinate system CH1903+ is derived.
source: The name of the source file.
names: The name of the raster file (mostly the file name without file-specific ending)
min value: The lowest value of all cells.
max value: The highest value of all cells.

Tip

The code chunks filtered for a random sub-sample of 15 variables. As described in Chapter 5, your task will be to investigate all covariates and find the ones that can best be used for your modelling task.

4 Mapping the study area

Now, let’s look at a visualisation of this raster file. Since we selected the slope at 2m resolution, we expect a relief-like map with a color gradient that indicates the steepness of the terrain.

Code

# Plot raster example
terra::plot(raster_example)

Code

# To have some more flexibility, we can plot this in the ggplot-style as such:
ggplot2::ggplot() +
  tidyterra::geom_spatraster(data = raster_example) +
  ggplot2::scale_fill_viridis_c(
    na.value = NA,
    option = "magma",
    name = "Slope (%) \n"
    ) +
  ggplot2::theme_bw() +
  ggplot2::scale_x_continuous(expand = c(0, 0)) +  # avoid gap between plotting area and axis
  ggplot2::scale_y_continuous(expand = c(0, 0)) +
  ggplot2::labs(title = "Slope of the Study Area")

Tip

Note that the second plot has different coordinates than the upper one. That is because the data was automatically projected to the World Geodetic System (WGS84, ESPG: 4326).

This looks already interesting but we can put our data into a bit more context. For example, a larger map background would be useful to get a better orientation of our location. Also, it would be nice to see where our sampling locations are and to differentiate these locations by whether they are part of the calibration or validation dataset. Bringing this all together requires some more understanding of plotting maps in R. So, don’t worry if you do not understand everything in the code chunk below and enjoy the visualizations:

Code

# To get our map working correctly, we have to ensure that all the input data
# is in the same coordinate system. Since our Bern data is in the Swiss 
# coordinate system, we have to transform the sampling locations to the 
# World Geodetic System first.
# To look up EPSG Codes: https://epsg.io/
# World Geodetic System 1984:  4326
# Swiss CH1903+ / LV95: 2056

# For the raster:
r <- terra::project(raster_example, "+init=EPSG:4326")

# Let's make a function for transforming the sampling locations:
change_coords <- function(data, from_CRS, to_CRS) {
  
  # Check if data input is correct
  if (!all(names(data) %in% c("id", "lat", "lon"))) {
    stop("Input data needs variables: id, lat, lon")
  }
  
  # Create simple feature for old CRS
  sf_old_crs <- sf::st_as_sf(data, coords = c("lon", "lat"), crs = from_CRS)
  
  # Transform to new CRS
  sf_new_crs     <- sf::st_transform(sf_old_crs, crs = to_CRS)
  sf_new_crs$lat <- sf::st_coordinates(sf_new_crs)[, "Y"]
  sf_new_crs$lon <- sf::st_coordinates(sf_new_crs)[, "X"]
  
  sf_new_crs <- sf_new_crs |> dplyr::as_tibble() |> dplyr::select(id, lat, lon)
  
  # Return new CRS
  return(sf_new_crs)
}

# Transform dataframes
coord_cal <- 
  bern_data |> 
  dplyr::filter(dataset == "calibration") |> 
  dplyr::select(site_id_unique, x, y) |> 
  dplyr::rename(id = site_id_unique, lon = x, lat = y) |> 
  change_coords(
    from_CRS = 2056, 
    to_CRS = 4326
    )

coord_val <- 
  bern_data |> 
  dplyr::filter(dataset == "validation") |> 
  dplyr::select(site_id_unique, x, y) |> 
  dplyr::rename(id = site_id_unique, lon = x, lat = y) |> 
  change_coords(
    from_CRS = 2056, 
    to_CRS = 4326
    )

Code

# Notes: 
# - This code may only work when installing the development branch of {leaflet}:
# remotes::install_github('rstudio/leaflet')
# - You might have to do library(terra) for R to find functions needed in the backend
library(terra)

# Let's get a nice color palette now for easy reference
pal <- leaflet::colorNumeric(
  "magma",
  terra::values(r),
  na.color = "transparent"
  )

# Next, we build a leaflet map
leaflet::leaflet() |> 
  # As base maps, use two provided by ESRI
  leaflet::addProviderTiles(leaflet::providers$Esri.WorldImagery, group = "World Imagery") |>
  leaflet::addProviderTiles(leaflet::providers$Esri.WorldTopoMap, group = "World Topo") |>
  # Add our raster file
  leaflet::addRasterImage(
    r,
    colors = pal,
    opacity = 0.6,
    group = "raster"
    ) |>
  # Add markers for sampling locations
  leaflet::addCircleMarkers(
    data = coord_cal,
    lng = ~lon,  # Column name for x coordinates
    lat = ~lat,  # Column name for y coordinates
    group = "training",
    color = "black"
  ) |>
    leaflet::addCircleMarkers(
    data = coord_val,
    lng = ~lon,  # Column name for x coordinates
    lat = ~lat,  # Column name for y coordinates
    group = "validation",
    color = "red"
  ) |>
  # Add some layout and legend
  leaflet::addLayersControl(
    baseGroups = c("World Imagery","World Topo"),
    position = "topleft",
    options = leaflet::layersControlOptions(collapsed = FALSE),
    overlayGroups = c("raster", "training", "validation")
    ) |>
  leaflet::addLegend(
    pal = pal,
    values = terra::values(r),
    title = "Slope (%)")

Note

This plotting example is based to the one shown in the AGDS 2 tutorial “Handful of Pixels” on phenology. More information on using spatial data in R can be found there in the Chapter on Geospatial data in R.

That looks great! At first glance, it is a bit crowded but once you scroll in you can investigate our study area quite nicely. You can check whether the slope raster file makes sense by comparing it against the base maps. Can you see how cliffs along the Aare river, hills, and even gravel quarries show high slopes? We also see that our validation dataset is nicely distributed across the area covered by the training dataset.

Now that we have played with a few visualizations, let’s get back to preparing our data. The {terra} package comes with the very useful tool to stack multiple raster on top of each other, if they are of the same spatial format. To do so, we just have to feed in the vector of file names list_raster_subset:

Code

# Load all files as one batch
all_rasters <- terra::rast(list_raster_subset)
all_rasters

class       : SpatRaster 
dimensions  : 986, 2428, 15  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : 2568140, 2616700, 1200740, 1220460  (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95 
sources     : geo500h1id.tif  
              Se_n_aspect2m.tif  
              mt_rr_y.tif  
              ... and 12 more source(s)
names       : geo500h1id, Se_n_~ect2m, mt_rr_y, Se_cu~td_5c,   vdcn25,      lsf, ... 
min values  :          1,          -1,  9804.2,      0.0000,   0.0000,   0.0650, ... 
max values  :         99,           1, 15392.6,    467.5855, 324.2082, 152.3634, ...

Now, we do not want to have the covariates’ data from all cells in the raster file. Rather, we want to reduce our stacked rasters to the x and y coordinates for which we have soil sampling data. We can do this using the terra::extract() function. Then, we want to merge the two dataframes of soil data and covariates data by their coordinates. Since the order of the covariate data is the same as the Bern data, we can simply bind their columns with cbind():

Code

# Extract coordinates from sampling locations
sampling_xy <- bern_data |> dplyr::select(x, y)

# From all rasters, extract values for sampling coordinates
covar_data <- 
  terra::extract(all_rasters,  # The raster we want to extract from
                 sampling_xy,  # A matrix of x and y values to extract for
                 ID = FALSE    # To not add a default ID column to the output
                 )

final_data <- cbind(bern_data, covar_data)
head(final_data) |> knitr::kable()

site_id_unique	timeset	x	y	dataset	dclass	waterlog.50	waterlog.100	ph.0.10	ph.10.30	ph.30.50	ph.50.100	geo500h1id	Se_n_aspect2m	mt_rr_y	Se_curvplan2m_std_5c	vdcn25	lsf	Se_conv2m	Se_slope2m	Se_MRVBF2m	Se_curvprof2m_fmean_50c	Se_diss2m_50c	Se_vrm2m	Se_curvplan2m	Se_SAR2m	Se_curv6m
4_26-In-005	d1968_1974_ptf	2571994	1203001	validation	poor	0	1	6.071733	6.227780	7.109235	7.214589	6	-0.2402939	9931.120	0.6229440	65.62196	0.0770846	-40.5395088	1.1250136	6.950892	-0.0382753	0.3934371	0.0002450	-1.0857303	4.000910	-0.5886537
4_26-In-006	d1974_1978	2572149	1202965	calibration	poor	1	1	6.900000	6.947128	7.203502	7.700000	6	0.4917848	9931.672	2.2502327	69.16074	0.0860347	19.0945148	1.3587183	6.984581	-0.0522900	0.4014700	0.0005389	-0.3522736	4.001326	0.1278165
4_26-In-012	d1974_1978	2572937	1203693	calibration	moderate	1	1	6.200000	6.147128	5.603502	5.904355	6	-0.9633239	9935.438	0.2292406	63.57096	0.0737963	-9.1396294	0.7160403	6.990917	-0.0089129	0.6717541	0.0000124	-0.2168447	4.000320	-0.0183221
4_26-In-014	d1974_1978	2573374	1203710	validation	well	0	0	6.600000	6.754607	7.200000	7.151129	6	-0.4677161	9939.923	0.1029889	64.60535	0.0859686	-0.9318936	0.8482135	6.964162	-0.0331309	0.4988544	0.0000857	-0.0272214	4.000438	-0.0706228
4_26-In-015	d1968_1974_ptf	2573553	1203935	validation	moderate	0	1	6.272715	6.272715	6.718392	7.269008	6	0.5919228	9942.032	0.9816071	61.16533	0.0650000	4.2692256	1.2301254	6.945287	-0.0202268	0.6999696	0.0002062	0.2968794	4.000948	0.0476020
4_26-In-016	d1968_1974_ptf	2573310	1204328	calibration	poor	0	1	6.272715	6.160700	5.559031	5.161655	6	0.5820994	9940.597	0.3455668	55.78354	0.0731646	-0.1732794	1.0906221	6.990967	-0.0014042	0.3157751	0.0001151	0.0100844	4.000725	0.0400775

Great that worked without problems!

Now, not all our covariates may be continuous variables and therefore have to be encoded as factors. As an easy check, we can take the original corvariates data and check for the number of unique values in each raster. If the variable is continuous, we expect that there are a lot of different values - at maximum 1052 different values because we have that many entries. So, let’s have a look and assume that variables with 10 or less different values are categorical variables.

Code

cat_vars <- 
  covar_data |> 
  # Get number of distinct values per variable
  dplyr::summarise(dplyr::across(dplyr::everything(), ~ dplyr::n_distinct(.))) |> 
  # Turn df into long format for easy filtering
  tidyr::pivot_longer(dplyr::everything(), 
                      names_to = "variable", 
                      values_to = "n") |> 
  # Filter out variables with 10 or less distinct values
  dplyr::filter(n <= 10) |>
  # Extract the names of these variables
  dplyr::pull('variable')

cat("Variables with less than 10 distinct values:", 
    ifelse(length(cat_vars) == 0, "none", cat_vars))

Variables with less than 10 distinct values: geo500h1id

Now that we have the names of the categorical values, we can mutate these columns in our df using the base function as.factor():

Code

final_data <- 
  final_data |> 
  dplyr::mutate(dplyr::across(cat_vars, ~ as.factor(.)))

5 Checking missing data

We are almost done with our data preparation, we just need to reduce it to sampling locations for which we have a decent amount of data on the covariates. Else, we blow up the model calibration with data that is not informative enough.

Code

# Get number of rows to calculate percentages
n_rows <- nrow(final_data)

# Get number of distinct values per variable
final_data |> 
  dplyr::summarise(dplyr::across(dplyr::everything(), 
                                 ~ length(.) - sum(is.na(.)))) |> 
  tidyr::pivot_longer(dplyr::everything(), 
                      names_to = "variable", 
                      values_to = "n") |>
  dplyr::mutate(perc_available = round(n / n_rows * 100)) |> 
  dplyr::arrange(perc_available) |> 
  head(10) |> 
  knitr::kable()

variable	n	perc_available
ph.30.50	856	81
ph.10.30	866	82
ph.50.100	859	82
timeset	871	83
ph.0.10	870	83
dclass	1006	96
site_id_unique	1052	100
x	1052	100
y	1052	100
dataset	1052	100

This looks good, we have no variable with a substantial amount of missing data. Generally, only pH measurements are lacking, which we should keep in mind when making predictions and inferences. Another great way to explore your data, is using the {visdat} package:

Code

final_data |> visdat::vis_miss()

Alright, we see that we are not missing any data in the covariate data. Mostly sampled data, specifically pH and timeset data is missing. We also see that this missing data is mostly from the same entry, so if we keep only entries where we have pH data - which is what we are interested here - we have a dataset with pracitally no missing data.

6 Save data

Code

saveRDS(final_data, 
        here::here("data/bern_sampling_locations_with_covariates.rds"))