• In a previous lesson we learned how to extract the raster values at points in the landscape
  • We can also extract the values inside of polygons
  • We’ll start by loading the sf and stars packages and the Harvard Forest soils the elevation data
  • But this time we’ll load a DTM file that covers the entire site

harv_dtm <- read_stars("data/harv/harv_dtmfull.tif")
harv_soils <- read_sf("data/harv/harv_soils.shp")

ggplot() +
  geom_stars(data = harv_dtm) +
  geom_sf(data = harv_soils, fill = "transparent", color = "white")
  • If we want to understand whether the polygon fields are related to the raster we need to extract data about the raster within each polygon
  • We do this using the aggregate function just like for points
  • The arguments are: the raster, the vector we want to aggregate the values to, and the function we want to use to combine the different pixels
  • Let’s calculate the average elevation in each soil polygon
elevs_by_soil <- aggregate(harv_dtm, harv_soils, mean)
  • You can think of this as similar to group_by and summarize in dplyr, but spatial
  • The function groups the digital terrain model into groups of pixels that fall inside each soil polygon
  • It then calculates the mean value inside each polygon
  • These values are stored in
  • You can add them to our simple features object using your favorite approach
  • I’ll use mutate from dplyr
harv_soils <- mutate(harv_soils, elevation = elevs_by_soil$harv_dtmfull.tif)
  • Once we done this then we can make maps using this information
  • So let’s plot the our soils map but colored by average elevation within the region
ggplot() +
  geom_sf(data = harv_soils, mapping = aes(fill = elevation)) +

Do Tasks 3-5 of Harvard Forest Soils Analysis.