Cropping and Masking Spatial Data

Spatial data is often larger than we need it to be
E.g., Raster data is typically a large rectangular block of data
And we are often only interested in the portion of that data that is located inside our study region
To get the piece of spatial data that we want for our maps or analysis we can “crop” the data
We’ll look at this using the data from Harvard forest we’ve been working with so far
Including the DTM file that covers the entire site

library(stars)
library(sf)
library(ggplot2)

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

If we plot this data we see that we have elevation data for a large square surrounding the site

ggplot() +
  geom_stars(data = harv_dtm) +
  scale_fill_viridis_c() +
  geom_sf(data = harv_boundary, fill = "transparent")

Cropping a raster using to a vector polygon

There are two general approaches to cropping data
The first is to crop a raster to only keep the portion that falls inside some vector data
For example, we often only want the portion of a raster dataset that falls inside the boundar of our study site
We can do this using the st_crop function
The first argument is the raster we want to crop
The second argument is the vector data we want to crop it to
Let’s crop our raster to only include points inside the site boundary

harv_dtm_cropped <- st_crop(harv_dtm, harv_boundary)

We can see that the data has been cropped by looking at the extents

harv_dtm
harv_dtm_cropped

harv_dtm_cropped has smaller values for from and to in both the x and y dimensions
Let’s plot the cropped raster to see what it looks like

ggplot() +
  geom_stars(data = harv_dtm_cropped) +
  scale_fill_viridis_c() +
  geom_sf(data = harv_boundary, fill = "transparent")

We now see colored values only for the part of the part of the raster inside the boundary
But we still see gray boxes outside of it
These boxes indicate that the values have been replaced with null values
The raster has been “cropped” to the limits of the vector object but rasters are always rectangular
So these null values fill the space outside of the vector object but within it’s x and y extent
We can choose to not show these null values by setting their color to be transparent

ggplot() +
  geom_stars(data = harv_dtm_cropped) +
  scale_fill_viridis_c(na.value = "transparent") +
  geom_sf(data = harv_boundary, fill = "transparent")

Cropping removes the portion of the raster that is outside the x/y extent of the vector
If we want to keep the full dimensions of the raster but convert all values outside the vector to NA we “mask” the data instead of cropping it
Do this with an optional argument crop = FALSE

harv_dtm_masked <- st_crop(harv_dtm, harv_boundary, crop = FALSE)
harv_dtm_masked

We can see that it still has the same dimensions as the original raster, 150 x 150
But if we plot it in the same way as the cropped data we will only see the values inside the vector

ggplot() +
  geom_stars(data = harv_dtm_masked) +
  scale_fill_viridis_c(na.value = "transparent") +
  geom_sf(data = harv_boundary, fill = "transparent")

Do Task 3 of Cropping NEON Data.

Cropping to a bounding box

The other common approach to cropping is to crop spatial objects to only include those within a bounding box
E.g., we might only want to explore a specific area within Harvard Forest
We still use st_crop to do this but we pass it a square region instead of a polygon
To do this we need to know the values for the region we want to crop
To figure out these values let’s modify our plot to plot in the CRS of our data

ggplot() +
  geom_stars(data = harv_dtm_cropped) +
  scale_fill_viridis_c(na.value = "transparent") +
  geom_sf(data = harv_boundary, fill = "transparent") +
  coord_sf(datum = st_crs(harv_dtm))

We create a bounding box using the st_bbox function
We describe the square based on the largest and smallest values of both x and y
We provide this information in a named vector
We create this using the c function, but giving names to each value using the name we want and the =
The names for the bounding box are xmin, xmax, ymin, ymax
And we also need to provide the CRS

bbox <- st_bbox(c(xmin = 731000, ymin = 4713000, xmax = 732000, ymax = 4714000), crs = st_crs(harv_dtm))

So let’s crop a square in this region here

harv_dtm_small <- st_crop(harv_dtm, bbox)

We can also perform bounding box cropping on vector data
Let’s load and then crop our soils data

harv_soils <- read_sf("data/harv/harv_soils.shp")
harv_soils_small <- st_crop(harv_soils, bbox)

For sf vector data we can also skip the st_bbox function and just pass the named vector with xmin, xmax, ymin, and ymax directly
Let’s plot our cropped regions together

ggplot() +
  geom_stars(data = harv_dtm_small) +
  scale_fill_viridis_c(na.value = "transparent") +
  geom_sf(data = harv_soils_small, fill = "transparent")

Do Task 4 of Cropping NEON Data.

Notes

Cropping a raster using to a vector polygon

Cropping to a bounding box