Minor improvements to spatial data material

exercises/Neon-canopy-height-from-space-R.md

@@ -5,21 +5,21 @@ title: Canopy Height from Space
 language: R
 ---
 
-The [National Ecological Observatory Network](http://www.neonscience.org) has invested in [high-resolution airborne imaging](https://www.neonscience.org/data-collection/airborne-remote-sensing) of their field sites. 
+The [National Ecological Observatory Network](http://www.neonscience.org) has invested in [high-resolution airborne imaging](https://www.neonscience.org/data-collection/airborne-remote-sensing) of their field sites.
 Elevation models generated from [LiDAR](https://www.neonscience.org/resources/learning-hub/tutorials/lidar-basics) 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]({{ site.baseurl }}/data/neon-geospatial-data.zip) 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](http://www.fs.fed.us/psw/ef/san_joaquin/). 
+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](http://www.fs.fed.us/psw/ef/san_joaquin/).
 
 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.
+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 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.
+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 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.
+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.

materials/spatial-data-extracting-raster-values-R.md

@@ -65,6 +65,16 @@ plot_harv_elevations <- extract(dtm_harv, plots_harv_utm, bind = TRUE) |>
   st_as_sf()
 ```
 
+* This produces a version where the extracted values are named based on the raster, so it's often a good idea to rename that column
+* _Add to above_
+
+```r
+plot_harv_elevations <- extract(dtm_harv, plots_harv_utm, bind = TRUE) |>
+  st_as_sf() |>
+  rename(elevations = harv_dtmcrop)
+```
+
+
 > Do Tasks 4-5 of [Canopy Height from Space]({{ site.baseurl }}/exercises/Neon-canopy-height-from-space-R).
 
 ### Extract raster data at points with buffers