Data Carpentry for Biologists

Spatial Data 1

Assignment

Learning Objectives

Following this assignment students should be able to:

  • import, view properties, and plot a raster
  • perform simple raster math
  • import, view properties, and plot vector data
  • extract points from a raster using a shapefile

Reading

Lecture Notes

Setup

install.packages(c("dplyr", "ggplot2", "stars", "sf"))
download.file("www.datacarpentry.org/semester-biology/data/neon-geospatial-data.zip", "neon-geospatial-data.zip", mode = "wb")
unzip("neon-geospatial-data.zip")
file.rename("neon-geospatial-data/", "data/")

Lectures

Place this code at the start of the assignment to load all the required packages.

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

Exercises

  1. Canopy Height from Space (90 pts)

    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 and create a map that shows the canopy height model from (3) with the plot locations on top.
    5. Extract the mean canopy heights at each plot location for SJER and display the values.
    6. Add the canopy height values from (5) to the spatial data frame you created for the plots and display the full data frame.
    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 buffter, 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

  2. Check That Your Code Runs (10 pts)

    Sometimes you think you’re code runs, but it only actually works because of something else you did previously. To make sure it actually runs you should save your work and then run it in a clean environment.

    Follow these steps in RStudio to make sure your code really runs:

    1. Restart R (see above) by clicking Session in the menu bar and selecting Restart R:

    Screenshot showing clicking session from the menu bar and selecting Restart R

    2. If the Environment tab isn’t empty click on the broom icon to clear it:

    Screenshot showing the Environment tab with the cursor hovering over the broom icon

    The Environment tab should now say “Environment Is Empty”:

    Screenshot showing the Environment tab with only the words Environment Is Empty

    3. Rerun your entire homework assignment using “Source with Echo” to make sure it runs from start to finish and produces the expected results.

    Screenshot showing the RStudio Source with Echo item hovered in the Source dropdown

    4. Make sure that you saved your code with the name assignment somewhere in the file name. You should see the file in the Files tab and the name of the file should be black (not red with an * in the tab at the top of the text editor):

    Screenshot showing the Files tab with the cursor hovering over the assignment file

    Screenshot showing the file name in the editor tab and it is black and there is no *

    5. Make sure that your code will run on other computers

    • No setwd() (use RStudio Projects instead)
    • Use / not \ for paths
    Expected outputs for Check That Your Code Runs

Assignment submission & checklist