Advanced Raster Analysis

WUR Geoscripting WUR logo

Today's items

ToDo in the morning

Go through the whole lesson and try to answer the questions below. We will address the questions in the lesson and discuss if needed.

Learning outcomes of today:

Being able to:

  • publish a documented report using R
  • carry out a supervised and unsupervised classification on a series of raster layers
  • construct a raster sieve using the clump() function
  • deal with thematic (categorical maps)

Creating documented reports

A great way to avoid clicking and copy pasting is to create a script that combines text (explanation of your script, introduction, or something else) and a R script and creates a report as an html file.

You can create an .html from a.Rmd file. Click on the following link for getting started and publishing your html to the www.


  • In RStudio, create a new R Markdown document by choosing File | New | R Markdown
    • select HTML as a default output format (see screenshot)
  • Click the Knit HTML button in the doc toolbar to preview your document.
  • In the preview window, click the Publish button.

Note: It is possible that you obtain the following error on Windows Operation system:

Error in function (type, msg, asError = TRUE)  : 
  SSL certificate problem, verify that the CA cert is OK. Details:
error:14090086:SSL routines:SSL3_GET_SERVER_CERTIFICATE:certificate verify failed
Calls: rpubsUpload ...  -> .postForm -> .Call ->  -> fun
Execution halted

This can be solved by the following solution. More information about creating an .Rprofile file here.

Other R IDEs

It is not necessary to use RStudio for knitting. knitr is an ordinary R package, and so knitting can be done from R itself. The most straight-forward way of using the package is:


However, this will create a Markdown (.md) file, and not an HTML file. For creating this very website, the package knitrBootstrap was used, which outputs HTML files by default. To use it:

render('your-file.Rmd', 'knitrBootstrap::bootstrap_document')

The resulting HTML file then has to be uploaded to the website manually, or via git.

Advanced Raster Analysis

Introduction to Landsat data used here

Since being released to the public, the Landsat data archive has become an invaluable tool for environmental monitoring. With a historical archive reaching back to the 1970's, the release of these data has resulted in a spur of time series based methods. In this tutorial, we will work with time series data from the Landsat 7 Enhanced Thematic Mapper (ETM+) sensor. Landsat scenes are delivered via the USGS as a number of image layers representing the different bands captured by the sensors. In the case of the Landsat 7 Enhanced Thematic Mapper (ETM+) sensor, the bands are shown in the figure below. Using different combination of these bands can be useful in describing land features and change processes.

Part of a landsat scene, including bands 2-4 are included in the data provided here. These data have been processed using the LEDAPS framework, so the values contained in this dataset represent surface reflectance, scaled by 10000 (ie. divide by 10000 to get a reflectance value between 0 and 1).

We will begin exploring these data simply by downloading and visualizing them:

To download the data you can clone the Github repository ( to your local computer. All the required datasets are located within the data folder.

## Libraries
## Load data

## Check out the attributes
## Some basic statistics using cellStats()
cellStats(GewataB2, stat=max)
cellStats(GewataB2, stat=mean)
# This is equivalent to:
## What is the maximum value of all three bands?
max(c(maxValue(GewataB2), maxValue(GewataB3), maxValue(GewataB4)))
## summary() is useful function for a quick overview

## Put the 3 bands into a RasterBrick object to summarize together
gewata <- brick(GewataB2, GewataB3, GewataB4)
# 3 histograms in one window (automatic, if a RasterBrick is supplied)

When we plot the histogram of the RasterBrick, the scales of the axes and the bin sizes are not equivalent, which could be problematic. This can be solved by adjusting these paramters in hist(), which requires extra consideration. The raster hist() function inherits arguments from the function of the same name from the graphics package. To view additional arguments, type:


To ensure that our histograms are of the same scale, we should consider the xlim, ylim and breaks arguments.

par(mfrow = c(1, 1)) # reset plotting window
hist(gewata, xlim = c(0, 5000), ylim = c(0, 750000), breaks = seq(0, 5000, by = 100))

Note that the values of these bands have been rescaled by a factor of 10000. This is done for file storage considerations. For example, a value of 0.5643 stored as a float takes up more disk space than a value of 5643 stored as an integer. If you prefer reflectance values in their original scale (from 0 to 1), this can easily be done using raster algebra or calc().

A scatterplot matrix can be helpful in exploring relationships between raster layers. This can be done with the pairs() function of the raster package, which (like hist()) is a wrapper for the same function found in the graphics packages.


Note that both hist() and pairs() compute histograms and scatterplots based on a random sample of raster pixels. The size of this sample can be changed with the argument maxpixels in either function.

Calling pairs() on a RasterBrick reveals potential correlations between the layers themselves. In the case of bands 2-4 of the gewata subset, we can see that band 2 and 3 (in the visual part of the EM spectrum) are highly correlated, while band 4 contains significant non-redundant information.

Question 1: Given what we know about the location of these bands along the EM spectrum, how could these scatterplots be explained?

ETM+ band 4 (nearly equivalent to band 5 in the Landsat 8 OLI sensor) is situated in the near infrared (NIR) region of the EM spectrum and is often used to describe vegetation-related features.

We observe a strong correlation between two of the Landsat bands of the gewata subset, but a very different distribution of values in band 4 (NIR). This distribution stems from the fact that vegetation reflects very highly in the NIR range, compared to the visual range of the EM spectrum. A commonly used metric for assessing vegetation dynamics, the normalized difference vegetation index (NDVI), explained in the previous lesson, takes advantage of this fact and is computed from Landsat bands 3 (visible red) and 4 (near infra-red).

In the previous lesson, we explored several ways to calculate NDVI, using direct raster algebra, calc() or overlay(). Since we will be using NDVI again later in this tutorial, let's calculate it again and store it in our workspace using overlay().

ndvi <- overlay(GewataB4, GewataB3, fun=function(x,y){(x-y)/(x+y)})

Aside from the advantages of calc() and overlay() regarding memory usage, an additional advantage of these functions is the fact that the result can be written immediately to file by including the filename = "..." argument, which will allow you to write your results to file immediately, after which you can reload in subsequent sessions without having to repeat your analysis.

Classifying raster data

One of the most important tasks in analysis of remote sensing image analysis is image classification. In classifying the image, we take the information contained in the various bands (possibly including other synthetic bands such as NDVI or principal components). In this tutorial we will explore two approaches for image classification:

  • supervised (random forest) classification,
  • unsupervised (k-means).

Supervised classification: Random Forest

The Random Forest classification algorithm is an ensemble learning method that is used for both classification and regression. In our case, we will use the method for classification purposes. Here, the random forest method takes random subsets from a training dataset and constructs classification trees using each of these subsets. Trees consist of branches and leaves.

Branches represent nodes of the decision trees, which are often thresholds defined for the measured (known) variables in the dataset. Leaves are the class labels assigned at the termini of the trees. Sampling many subsets at random will result in many trees being built. Classes are then assigned based on classes assigned by all of these trees based on a majority rule, as if each class assigned by a decision tree were considered to be a vote.

Figure 1 gives a simple demonstration of how the random forest method works in principle. For an introduction to the random forest algorithm, see this presentation.

One major advantage of the Random Forest method is the fact that an Out of the Bag (OOB) error estimate and an estimate of variable performance are performed. For each classification tree assembled, a fraction of the training data are left out and used to compute the error for each tree by predicting the class associated with that value and comparing with the already known class. This process results in a confusion matrix, which we will explore in our analysis. In addition an importance score is computed for each variable in two forms: the mean decrease in accuracy for each variable, and the Gini impurity criterion, which will also be explored in our analysis.

We should first prepare the data on which the classification will be done. So far, we have prepared three bands from a ETM+ image in 2001 (bands 2, 3 and 4) as a RasterBrick, and have also calculated NDVI. In addition, there is a Vegetation Continuous Field (VCF) product available for the same period (2000).

For more information on the Landsat VCF product, see here. This product is also based on Landsat ETM+ data, and represents an estimate of tree cover (in %). Since this layer could also be useful in classifying land cover types, we will also include it as a potential covariate in the Random Forest classification.

## Load the data and check it out
## class       : RasterLayer 
## dimensions  : 1177, 1548, 1821996  (nrow, ncol, ncell)
## resolution  : 30, 30  (x, y)
## extent      : 808755, 855195, 817635, 852945  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=utm +zone=36 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## data source : in memory
## names       : vcf2000Gewata 
## values      : 0, 254  (min, max)
##         vcf2000Gewata
## Min.                0
## 1st Qu.            32
## Median             64
## 3rd Qu.            75
## Max.              254
## NA's             8289

In the vcfGewata rasterLayer there are some values much greater than 100 (the maximum tree cover), which are flags for water, cloud or cloud shadow pixels. To avoid these values, we can assign a value of NA to these pixels so they are not used in the classification.

vcfGewata[vcfGewata > 100] <- NA
##         vcf2000Gewata
## Min.                0
## 1st Qu.            32
## Median             64
## 3rd Qu.            75
## Max.              100
## NA's            13712

To perform the classification in R, it is best to assemble all covariate layers (ie. those layers contaning predictor variable values) into one RasterBrick object. In this case, we can simply append these new layers (NDVI and VCF) to our existing RasterBrick (currently consisting of bands 2, 3, and 4).

First, let's rescale the original reflectance values to their original scale. This step is not required for the RF classification, but it might help with the interpretation, if you are used to thinking of reflectance as a value between 0 and 1. (On the other hand, for very large raster bricks, it might be preferable to leave them in their integer scale, but we won't go into more detail about that here.)

gewata <- calc(gewata, fun=function(x) x / 10000)
## Make a new RasterBrick of covariates by adding NDVI and VCF layers
covs <- addLayer(gewata, ndvi, vcfGewata)

You'll notice that we didn't give our NDVI layer a name yet. It's good to make sure that the raster layer names make sense, so you don't forget which band is which later on. Let's change all the layer names (make sure you get the order right!).

names(covs) <- c("band2", "band3", "band4", "NDVI", "VCF")