E6

Packages

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

Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ purrr     1.0.2
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tmap)

Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
remotes::install_github('r-tmap/tmap')

library(terra)

Warning: Paket 'terra' wurde unter R Version 4.4.3 erstellt

terra 1.8.29

Attache Paket: 'terra'

Das folgende Objekt ist maskiert 'package:tidyr':

    extract

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?

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

Rows: 51246 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): TierID, TierName
dbl  (3): CollarID, E, N
dttm (1): DatetimeUTC

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

fanel <- st_read("data/Feldaufnahmen_Fanel.gpkg")

Reading layer `Feldaufnahmen_Fanel' from data source 
  `C:\Users\yanni\OneDrive - ZHAW\06_PTED\03_Exercises\E6\E6\data\Feldaufnahmen_Fanel.gpkg' 
  using driver `GPKG'
Simple feature collection with 975 features and 2 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2568099 ymin: 1199766 xmax: 2578824 ymax: 1207836
Projected CRS: CH1903+ / LV95

st_crs(fanel)

Coordinate Reference System:
  User input: CH1903+ / LV95 
  wkt:
PROJCRS["CH1903+ / LV95",
    BASEGEOGCRS["CH1903+",
        DATUM["CH1903+",
            ELLIPSOID["Bessel 1841",6377397.155,299.1528128,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4150]],
    CONVERSION["Swiss Oblique Mercator 1995",
        METHOD["Hotine Oblique Mercator (variant B)",
            ID["EPSG",9815]],
        PARAMETER["Latitude of projection centre",46.9524055555556,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8811]],
        PARAMETER["Longitude of projection centre",7.43958333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8812]],
        PARAMETER["Azimuth of initial line",90,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8813]],
        PARAMETER["Angle from Rectified to Skew Grid",90,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8814]],
        PARAMETER["Scale factor on initial line",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8815]],
        PARAMETER["Easting at projection centre",2600000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8816]],
        PARAMETER["Northing at projection centre",1200000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8817]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping (large and medium scale)."],
        AREA["Liechtenstein; Switzerland."],
        BBOX[45.82,5.96,47.81,10.49]],
    ID["EPSG",2056]]

st_geometry_type(fanel)

  [1] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
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18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE

str(fanel)

Classes 'sf' and 'data.frame':  975 obs. of  3 variables:
 $ FieldID: num  1 0 0 2 3 5 6 4 7 0 ...
 $ Frucht : chr  "Roggen" NA NA "Wiese" ...
 $ geom   :sfc_POLYGON of length 975; first list element: List of 1
  ..$ : num [1:12, 1:2] 2570914 2570917 2570985 2571294 2571719 ...
  ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
 - attr(*, "sf_column")= chr "geom"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA
  ..- attr(*, "names")= chr [1:2] "FieldID" "Frucht"

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.

ws_sommer <- wildschwein_BE |> 
  dplyr::filter(
      DatetimeUTC >= "2014-05-01" & DatetimeUTC <= "2014-08-31"
      )

ggplot() +
  geom_sf(data = fanel) +
  geom_sf(data = ws_sommer, aes(color = "red"))

ws_crop <- st_join(ws_sommer, fanel, join = st_within)

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.

wildboars_summary <- ws_crop |> 
  st_drop_geometry() |> 
  mutate(
    hour = hour(round_date(DatetimeUTC, "hour")),
    Frucht = fct_lump(Frucht, 4)
  ) |> 
  group_by(TierName, hour, Frucht) |>
  summarise(n = n()) |> 
  mutate(
    perc = n/sum(n)
    )

`summarise()` has grouped output by 'TierName', 'hour'. You can override using
the `.groups` argument.

wildboars_summary |> 
  ggplot(aes(hour, perc, fill = Frucht)) +
  geom_col() +
    facet_wrap(~TierName)

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.

veg <- rast("data/vegetationshoehe_LFI.tif")
plot(veg)

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:

wildboars_vegetation <- extract(veg, vect(wildschwein_BE))

# Checking number of rows
nrow(wildboars_vegetation)

[1] 51246

nrow(wildschwein_BE)

[1] 51246

wildschwein_BE$veg <- wildboars_vegetation$vegetationshoehe_LFI

wildschwein_BE

Simple feature collection with 51246 features and 7 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2568153 ymin: 1202306 xmax: 2575154 ymax: 1207609
Projected CRS: CH1903+ / LV95
# A tibble: 51,246 × 8
   TierID TierName CollarID DatetimeUTC                E        N
 * <chr>  <chr>       <dbl> <dttm>                 <dbl>    <dbl>
 1 002A   Sabi        12275 2014-08-22 21:00:12 2570409. 1204752.
 2 002A   Sabi        12275 2014-08-22 21:15:16 2570402. 1204863.
 3 002A   Sabi        12275 2014-08-22 21:30:43 2570394. 1204826.
 4 002A   Sabi        12275 2014-08-22 21:46:07 2570379. 1204817.
 5 002A   Sabi        12275 2014-08-22 22:00:22 2570390. 1204818.
 6 002A   Sabi        12275 2014-08-22 22:15:10 2570390. 1204825.
 7 002A   Sabi        12275 2014-08-22 22:30:13 2570387. 1204831.
 8 002A   Sabi        12275 2014-08-22 22:45:11 2570381. 1204840.
 9 002A   Sabi        12275 2014-08-22 23:00:27 2570316. 1204935.
10 002A   Sabi        12275 2014-08-22 23:15:41 2570393. 1204815.
# ℹ 51,236 more rows
# ℹ 2 more variables: geometry <POINT [m]>, veg <dbl>