The following activity is available as a template github repository at the following link: https://github.com/VT-Hydroinformatics/13-Geospatial-Raster-Hydro.git
To read in detail about any of the WhiteboxTools used in this activity, check out the user manual: https://jblindsay.github.io/wbt_book/intro.html
In this activity we are going to explore how to work with raster data in R while computing several hydrologically-relevant landscape metrics using the R package whitebox tools. Whitebox is very powerful and has an extensive set of tools, but it is not on CRAN. You must install it with the commented-out line at the top of the next code chunk.
Install/Load necessary packages and data:
library(tidyverse) library(raster) library(sf) library(whitebox) library(tmap) #If installing/using whitebox for the first time #install_whitebox() ::wbt_init() whitebox theme_set(theme_classic())
First, we will set tmap to either map or view depending on how we want to see our maps. I’ll often set to map unless I specifically need to view the maps interactively because if they are all set to view it makes scrolling through the document kind of a pain: every time you hit a map the scroll zooms in or out on the map rather than scrolling the document.
For this activity we are going to use a 5-meter DEM of a portion of a Brush Mountain outside Blacksburg, VA.
What does DEM stand for? What does it show?
What does it mean that the DEM is “5-meter”?
We will use raster() to load the DEM. We let R know that coordinate system is WGS84 by setting the crs argument equal to ‘+init=EPSG:4326’, where 4326 is the EPSG number for WGS84.
Next, an artifact of outputting the DEM for this analysis is that there are a bunch of errant cells around the border that don’t belong in the DEM. If we make a map with them, it really throws off the scale. So we are going to set any elevation values below 1500 ft to NA. Note how this is done as if the dem was just a normal vector. COOL!
## tmap mode set to interactive viewing
<- raster("McDonaldHollowDEM/brushDEMsm_5m.tif", crs = '+init=EPSG:4326') dem writeRaster(dem, "McDonaldHollowDEM/brushDEMsm_5m_crs.tif", overwrite = TRUE) < 1500] <- NAdem[dem
Now let’s plot the DEM. We will use the same syntax as we did in the previous lecture about vector data. Give tm_shape the raster, then visualize it with tm_raster. We will tell tmap that the scale on the raster is continuous, which color palette to use, whether or not to show the legend, and then add a scale bar.
tm_shape(dem)+ tm_raster(style = "cont", palette = "PuOr", legend.show = TRUE)+ tm_scale_bar()
Since the DEM is just elevation values, it is tough to see much about the landscape. We can see the stream network and general featuers, but not much more. BUT those features are there! We just need other ways to illuminate them. One common way to visualize these kind of data is a hillshade. This basically “lights” the landscape with a synthetic sun, casting shadows and illuminating other features. You can control the angle of the sun and from what direction it is shining to control the look of the image. We will position the sun in the south-south east so it illuminates the south side of Brush Mountain well.
We will use the whitebox tools function wbt_hillshade() to produce a hillshade.
The whitebox tools functions work can be a little tricky to work with at first. You might want to pass R objects to them and get R objects back, but that’s not how they are set up.
Basically for your input, you well the wbt function the name of the file that has the input data.
For output, you tell it what to name the output.
The wbt function then outputs the calculated data to your working directory, or whatever directory you give it in the output argument.
This means if you want to do something with the output of the wbt function, you have to read it in separately.
In the chunk below we will read in the brush mountain 5m DEM and output a hillshade with wbt_hillshad().
We will then read the output hillshade in with raster() and make a map with tmap. We will use the “Greys” palette in revers by adding a negative sign in front of it. This is just to make the hillshade look nice.
Notice how much more you can see in the landscape!
wbt_hillshade(dem = "McDonaldHollowDEM/brushDEMsm_5m_crs.tif", output = "McDonaldHollowDEM/brush_hillshade.tif", azimuth = 115) <- raster("McDonaldHollowDEM/brush_hillshade.tif") hillshade tm_shape(hillshade)+ tm_raster(style = "cont", palette = "-Greys", legend.show = FALSE)+ tm_scale_bar()
Alright, we are cooking now.
But we have to cool our jets for a second. If we are going to do hydrologic analyses, we have to prep our DEM a bit.
Our hydrologic tools often work on the premise of following water down the hillslope based on the elevation of the cells in the DEM. If, along a flowpath, there is no cell lower than a location, the algorithm we are using will stop there. This is called a sink, pit, or depression. See the figure below.
We can deal with these features two ways. The first is to “fill” them. Which means the dead end cells will have their elevations raised until the pit is filled and water can flow downhill. See below.
The second way we can deal with these features is to “breach” them. This means the side of the feature that is blocking flow will be lowered to allow water to flow downhill. See below.
We are going to do both to prep our DEM. We will first breach depressions using wbt_breach_depressions_least_cost(), which will lower the elevation of the cells damming depressions.
To use this function we will also give it a maximum distance to search for a place to breach the depression (dist) and tell with whether to fill any depressions leftover after it does its thing (fill).
Then, because this algorithm can leave some remaining depressions, we wil use wbt_fill_depressions_wang_and_liu() to clean up any remaining issues.
Be careful to give wbt_fill_depressions_wang_and_liu() the result of the breach depressions function, not the original DEM!
wbt_breach_depressions_least_cost( dem = "McDonaldHollowDEM/brushDEMsm_5m_crs.tif", output = "McDonaldHollowDEM/bmstationdem_breached.tif", dist = 5, fill = TRUE) wbt_fill_depressions_wang_and_liu( dem = "McDonaldHollowDEM/bmstationdem_breached.tif", output = "McDonaldHollowDEM/bmstationdem_filled_breached.tif" )
Now let’s look at what this did. This is a great example of how easily you can use multiple rasters together.
If we want to see how the fill and breach operations changed the DEM, we can just subtract the filled and breached DEM from the original. Then, in areas where nothing changed, the values will be zero, areas that were filled will be positive, and areas that were decreased in elevation to “breach” a depression will be negative.
To more easily see where stuff happened, we will set all the cells that equal zero to NA. Then we will plot them on the hillshade.
Where where changes made?
What do you think these pit/depression features represent in real life?
<- raster("McDonaldHollowDEM/bmstationdem_filled_breached.tif") filled_breached ## What did this do? <- dem - filled_breached difference == 0] <- NA difference[difference tm_shape(hillshade)+ tm_raster(style = "cont",palette = "-Greys", legend.show = FALSE)+ tm_scale_bar()+ tm_shape(difference)+ tm_raster(style = "cont",legend.show = TRUE)+ tm_scale_bar()
## Variable(s) "NA" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.