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This tutorial covers how to improve plotting output using the rasterVis package in R. Specifically it covers using levelplot() and adding meaningful custom names to bands within a RasterStack.

R Skill Level: Intermediate - you’ve got the basics of R down.

Goals / Objectives

After completing this activity, you will:

  • Be able to assign custom names to bands in a RasterStack for prettier plotting.
  • Understand advanced plotting of rasters using the rasterVis package and levelplot.

Things You’ll Need To Complete This Tutorial

You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.

Install R Packages

Data to Download

Download NEON Teaching Data Subset: Landsat-derived NDVI raster files

The imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network’s Harvard Forest and San Joaquin Experimental Range field sites.
The imagery was created by the U.S. Geological Survey (USGS) using a multispectral scanner on a Landsat Satellite. The data files are Geographic Tagged Image-File Format (GeoTIFF).

Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets. An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

Get Started

In this tutorial, we are working with the same set of rasters used in the Raster Time Series Data in R tutorial. This data is derived from the Landsat satellite and stored in GeoTIFF format. Each raster covers the NEON Harvard Forest field site.

If you have not already created the RasterStack, originally created in Raster Time Series Data in R , please create it now.


## Loading required package: lattice

## Loading required package: latticeExtra

## Loading required package: RColorBrewer

## Attaching package: 'latticeExtra'

## The following object is masked from 'package:ggplot2':
##     layer

# Create list of NDVI file paths
all_NDVI_HARV <- list.files("NEON-DS-Landsat-NDVI/HARV/2011/NDVI", full.names = TRUE, pattern = ".tif$")

# Create a time series raster stack
NDVI_HARV_stack <- stack(all_NDVI_HARV)

# apply scale factor
NDVI_HARV_stack <- NDVI_HARV_stack/10000

Plot Raster Time Series Data

We can use the plot function to plot our raster time series data.

# view a histogram of all of the rasters
# nc specifies number of columns
     zlim = c(.15, 1), 
     nc = 4)

Data Tip: The range of values for NDVI is 0-1. However, the data stored in our raster ranges from 0 - 10,000. If we view the metadata for the original .tif files, we will see a scale factor of 10,000 is defined. Multiplying values with decimal places by a factor of 10, allows the data to be stored in integer format (no decimals) rather than a floating point format (containing decimals). This keeps the file size smaller.

Our plot is nice however, it’s missing some key elements including, easily readable titles. It also contains a legend that is repeated for each image. We can use levelplot from the rasterVis package to make our plot prettier!

The syntax for the levelplot() function is similar to that for the plot() function. We use main="TITLE" to add a title to the entire plot series.

# create a `levelplot` plot
          main="Landsat NDVI\nNEON Harvard Forest")

Adjust the Color Ramp

Next, let’s adjust the color ramp used to render the rasters. First, we can change the red color ramp to a green one that is more visually suited to our NDVI (greenness) data using the colorRampPalette() function in combination with colorBrewer.

# use colorbrewer which loads with the rasterVis package to generate
# a color ramp of yellow to green
cols <- colorRampPalette(brewer.pal(9,"YlGn"))
# create a level plot - plot
        main="Landsat NDVI -- Improved Colors \nNEON Harvard Forest Field Site",

The yellow to green color ramp visually represents NDVI well given it’s a measure of greenness. Someone looking at the plot can quickly understand that pixels that are more green, have a higher NDVI value.

Data Tip: Cynthia Brewer, the creater of ColorBrewer, offers an online tool to help choose suitable color ramps, or to create your own. ColorBrewer 2.0; Color Advise for Cartography

Refine Plot & Tile Labels

Next, let’s label each raster in our plot with the Julian day that the raster represents. The current names come from the band (layer names) stored in the RasterStack and first part each name is the Julian day.

To create a more meaningful label we can remove the “x” and replace it with “day” using the gsub() function in R. The syntax is as follows: gsub("StringToReplace","TextToReplaceIt", Robject).

First let’s remove “_HARV_NDVI_crop” from each label.

# view names for each raster layer

##  [1] "X005_HARV_ndvi_crop" "X037_HARV_ndvi_crop" "X085_HARV_ndvi_crop"
##  [4] "X133_HARV_ndvi_crop" "X181_HARV_ndvi_crop" "X197_HARV_ndvi_crop"
##  [7] "X213_HARV_ndvi_crop" "X229_HARV_ndvi_crop" "X245_HARV_ndvi_crop"
## [10] "X261_HARV_ndvi_crop" "X277_HARV_ndvi_crop" "X293_HARV_ndvi_crop"
## [13] "X309_HARV_ndvi_crop"

# use gsub to modify label names.that we'll use for the plot 
rasterNames  <- gsub("X","Day ", names(NDVI_HARV_stack))

# view Names

##  [1] "Day 005_HARV_ndvi_crop" "Day 037_HARV_ndvi_crop"
##  [3] "Day 085_HARV_ndvi_crop" "Day 133_HARV_ndvi_crop"
##  [5] "Day 181_HARV_ndvi_crop" "Day 197_HARV_ndvi_crop"
##  [7] "Day 213_HARV_ndvi_crop" "Day 229_HARV_ndvi_crop"
##  [9] "Day 245_HARV_ndvi_crop" "Day 261_HARV_ndvi_crop"
## [11] "Day 277_HARV_ndvi_crop" "Day 293_HARV_ndvi_crop"
## [13] "Day 309_HARV_ndvi_crop"

# Remove HARV_NDVI_crop from the second part of the string 
rasterNames  <- gsub("_HARV_ndvi_crop","",rasterNames)

# view names for each raster layer

##  [1] "Day 005" "Day 037" "Day 085" "Day 133" "Day 181" "Day 197" "Day 213"
##  [8] "Day 229" "Day 245" "Day 261" "Day 277" "Day 293" "Day 309"

Data Tip: Instead of substituting “x” and “_HARV_NDVI_crop” separately, we could have used use the vertical bar character ( | ) to replace more than one element. For example “X|_HARV” tells R to replace all instances of both “X” and “_HARV” in the string. Example code to remove “x” an “_HARV…”: gsub("X|_HARV_NDVI_crop"," | ","rasterNames")

Once the names for each band have been reassigned, we can render our plot with the new labels using names.attr=rasterNames.

# use level plot to create a nice plot with one legend and a 4x4 layout.
          layout=c(4, 4), # create a 4x4 layout for the data
          col.regions=cols, # add a color ramp
          main="Landsat NDVI - Julian Days \nHarvard Forest 2011",

We can adjust the columns of our plot too using layout=c(cols,rows). Below we adjust the layout to be a matrix of 5 columns and 3 rows.

# use level plot to create a nice plot with one legend and a 4x4 layout.
          layout=c(5, 3), # create a 5x3 layout for the data
          col.regions=cols, # add a color ramp
          main="Landsat NDVI - Julian Days \nHarvard Forest 2011",

Finally, scales allows us to modify the x and y-axis scale. Let’s simply remove the axis ticks from the plot with scales =list(draw=FALSE).

# use level plot to create a nice plot with one legend and a 4x4 layout.
          layout=c(5, 3), # create a 5x3 layout for the data
          col.regions=cols, # add a color ramp
          main="Landsat NDVI - Julian Days \nHarvard Forest 2011",
          scales=list(draw=FALSE )) # remove axes labels & ticks

Challenge: Divergent Color Ramps

When we used gsub to modify the tile labels we replaced the beginning of each tile title with “Day”. A more descriptive name could be “Julian Day”.

  1. Create a plot and label each tile “Julian Day” with the julian day value following.
  2. Change the colorramp to a divergent brown to green color ramp to represent the data. Hint: Use the brewerpal page to help choose a color ramp.

Questions: Does having a divergent color ramp represent the data better than a sequential color ramp (like “YlGn”)? Can you think of other data sets where a divergent color ramp may be best?

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