Data Carpentry for Biologists

Canopy Height from Space (NEON)

The National Ecological Observatory Network has invested in high-resolution airborne imaging of their field sites. Elevation models generated from LiDAR can be used to map the topography and vegetation structure at the sites.

Check to see if there is a data directory in your workspace with an SJER subdirectory in it. If not, Download the data and extract it into your working directory. The SJER directory contains raster data for a digital terrain model (sjer_dtmcrop.tif) and a digital surface model (sjer_dsmcrop.tif), and vector data on plot locations (sjer_plots.shp) and the site boundary (sjer_boundar.shp) for the San Joaquin Experimental Range.

  1. Map the digital terrain model for SJER using the viridis color ramp.
  2. Create and map the canopy height model for SJER using the viridis color ramp. To do this subtract the values in the digital terrain model from the values in the digital surface model using raster math (chm = dsm - dtm).
  3. Create a map that shows the SJER boundary and the plot locations colored by plot_type.
  4. Transform the plot data to have the same CRS as the CHM. Show CRS for both objects. Create a map that shows the canopy height model from (3) with the plot locations on top.
  5. Extract the canopy heights at each plot location for SJER and display the values.
  6. Building on (5) create a version of your code that extracts the canopy heights and includes them with the data in plots_sjer_utm. You can do this using either mutate (to add the results of (5) to plots_sjer_utm or bind put the data together in extract and st_as_sf to convert the resulting terra object into a simple features object. Display the full simple features object. Make sure that the column name for the canopy heights is canopy_height. The detailed display will vary depending on your approach.
  7. Create a map that shows the SJER boundary and the plot locations colored by the canopy height values.
  8. Create a map that shows the canopy height model raster, but in cm rather than m (i.e., multiply the canopy height model by 100).
  9. Create a map that shows the digital terrain model raster, the plot locations, and the SJER boundary, using transparency as needed to allow all three layers to be seen. Remember all three layers will need to have the same CRS.
  10. Conduct an analysis of the relationship between elevation and canopy height at the SJER plots. Using a 50m buffer, extract the mean elevations (i.e., the values from the digital terrain model) and the canopy heights at each plot location for SJER and add to the spatial plots data to produce a simple features object that includes both the average elevations (in a 50 m buffer) and the canopy heights (in a 50 m buffer). Then make a scatter plot showing the relationship between elevation and canopy height using this data. Color the points by plot type and fit a single smooth curve through all of the points. Finally, use dplyr to calculate the average canopy height and average elevation for the two different plot types.
Expected outputs for Canopy Height from Space