Tasks and Inputs

Open your RStudio Project which you prepared for this week. Create a new RScript and import the libraries we need for this week. Import your wild boar dataset wildschwein_BE_2056.csv (on moodle) as an sf object

library("readr")
library("sf")


wildschwein_BE <- read_delim("datasets/wildschwein_BE_2056.csv", ",") |>
    st_as_sf(coords = c("E", "N"), crs = 2056, remove = FALSE)

Download the dataset Feldaufnahmen_Fanel.gpkg (on moodle) and save it to your project folder. This is a vector dataset stored in the filetype Geopackage, which is similar to a Shapefile, with some advantages (see the website shapefile must die).

Also download the dataset vegetationshoehe_LFI.tif (on moodle) and store it in your project folder. This is a raster dataset stored in a Geotiff.

Tasks 1: Import and visualize spatial data

Since Feldaufnahmen_Fanel.gpkg is a vector dataset, you can import it using read_sf(). Explore this dataset in R to answer the following questions:

  • What information does the dataset contain?
  • What is the geometry type of the dataset (possible types are: Point, Lines and Polygons)?
  • What are the data types of the other columns?
  • What is the coordinate system of the dataset?

Task 2: Annotate Trajectories from vector data

We would like to know what crop was visited by which wild boar, and at what time. Since the crop data is most relevant in summer, filter your wild boar data to the months may to june first and save the output to a new variable. Overlay the filtered dataset with your fanel data to verify the spatial overlap.

To sematically annotate each wild boar location with crop information, you can use a spatial join with the function st_join(). Do this and explore your annotated dataset.

Task 3: Explore annotated trajectories

Think of ways you could visually explore the spatio-temporal patterns of wild boar in relation to the crops. In our example below we visualize the percentage of samples in a given crop per hour.

Figure 23.1: Percentages of samples in a given crop per hour. Only showing the most common categories

Task 4: Import and visualize vegetationindex (raster data)

In terms of raster data, we have prepared the Vegetation Height Model provided by the Swiss National Forest Inventory (NFI). This dataset contains high resolution information (1x1 Meter) on the vegetation height, which is determined from the difference between the digital surface models DSM and the digital terrain model by swisstopo (swissAlti3D). Buildings are eliminated using a combination of the ground areas of the swisstopo topographic landscape model (TLM) and spectral information from the stereo aerial photos.

Import this dataset using the function rast from the package terra. Visualize the raster data using the base plot function and tmap. We do not recommend using ggplot2 in this case, as is very slow with raster data.

Task 5: Annotate Trajectories from raster data

Semantically annotate your wild boar locations with the vegetation index (similar as you did with the crop data in Task 2). Since you are annotating a vector dataset with information from a raster dataset, you cannot use st_join but need the function extract from the terra package. Read the help on the extract function to see what the function expects. The output should look something like this:

Simple feature collection with 51246 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2568153 ymin: 1202306 xmax: 2575154 ymax: 1207609
Projected CRS: CH1903+ / LV95
First 10 features:
   TierID         DatetimeUTC vegetationshoehe_LFI                geometry
1    002A 2014-08-22 21:00:12                20.09 POINT (2570409 1204752)
2    002A 2014-08-22 21:15:16                23.85 POINT (2570402 1204863)
3    002A 2014-08-22 21:30:43                24.96 POINT (2570394 1204826)
4    002A 2014-08-22 21:46:07                21.59 POINT (2570379 1204817)
5    002A 2014-08-22 22:00:22                15.68 POINT (2570390 1204818)
6    002A 2014-08-22 22:15:10                23.77 POINT (2570390 1204825)
7    002A 2014-08-22 22:30:13                25.09 POINT (2570387 1204831)
8    002A 2014-08-22 22:45:11                24.88 POINT (2570381 1204840)
9    002A 2014-08-22 23:00:27                29.91 POINT (2570316 1204935)
10   002A 2014-08-22 23:15:41                21.52 POINT (2570393 1204815)

You can now explore the spatio-temporal patterns of this new data, similarly to as we did in Figure 23.1.