Worksheet 2: Landscape metrics part 1

Exercises WS 2

1. The marginal entropy and relative mutual information can be calculated using the landscapemetrics package’s functions: lsm_l_ent() and lsm_l_relmutinf(). Calculate both of these metrics for the exdata/lc_small.tif raster.
2. Read the exdata/lc_europe.tif raster using rast() from the terra package and the exdata/polygons.gpkg vector data using the read_sf() function from the sf package. Calculate the marginal entropy and relative mutual information for each polygon using the sample_lsm() function.
3. Join the calculated values with the polygons (see https://r-spatialecology.github.io/landscapemetrics/articles/irregular_areas.html for more details).
4. Calculate SHDI and AI for the polygons. Compare the values of SHDI and AI with the marginal entropy and relative mutual information (e.g., using a scatterplot or by calculating the correlation coefficient). Are the results similar?
5. (Extra) Create your own polygonal grid using st_make_grid() function from the sf package for the area from the exdata/polygons.gpkg file. Calculate the marginal entropy and relative mutual information for each square using the sample_lsm() function. Visualize the results.