Lesson 2: Raster Operations

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Tasks

Task 1

The following command returns the path to a tif file on your hard drive:

system.file("ex/elev.tif", package="terra")

Use this path to import the tif file using rast(), store it as r. It contains the elevation of Luxembourg.

Task 2

Calculate the (global) mean, maximum and minimum elevation values.

              mean max min
elevation 348.3366 547 141

Task 3

Calculate slope and aspect (see terrain functions)

Task 4

Determine suitable locations of solar panels using the following conditions:

  • Elevation: above 300 MASL
  • Slope: Between 1 and 3Ā°
  • Aspect: Southfacing (between 135 and 235Ā°)

Task 5

Reclassify the elevation raster into three categories using classify():

  • Low: below the 33rd percentile
  • Mid: between the 33rd and 66th percentile
  • High: above the 66th percentile

Use global() with quantile to compute the cut points.

Task 6

Import the municipalities of Luxembourg and calculate the minimum, maximum, and mean elevation per municipality. Then visualize the mean elevation per municipality as a map.

library(tmap)
lux <- system.file("ex/lux.shp", package="terra") |>
  vect()
zones <- rasterize(lux, r, "NAME_2")

Task 7

Smooth the elevation raster using a 3Ɨ3 and an 11Ɨ11 mean focal window. Compare the two results visually side by side.

Task 8

Reproject the elevation raster to EPSG:2169 (Luxembourg national grid, units: metres) and resample to 1 km resolution. Then apply a circular focal filter with a 5 km radius using focalMat().

Task 9

Import the multispectral Landsat 7 image as a raster:

system.file("tif/L7_ETMs.tif", package = "stars")

Task 10

Calculate the NDVI using the formula below and visualize the result with a diverging color scale centered at 0.

\[\text{NDVI} = \frac{\text{NIR}-\text{red}}{\text{NIR}+\text{red}}\]