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This tutorial covers how to work with and plot a raster time series, using an R RasterStack object. It also covers practical assessment of data quality in remote sensing derived imagery.

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

Goals / Objectives

After completing this activity, you will:

  • Understand the format of a time series raster dataset.
  • Know how to work with time series rasters.
  • Be able to efficiently import a set of rasters stored in a single directory.
  • Be able to plot and explore time series raster data using the plot() function in R.

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.

Additional Resources

About Raster Time Series Data

A raster data file can contain one single band or many bands. If the raster data contains imagery data, each band may represent reflectance for a different wavelength (color or type of light) or set of wavelengths - for example red, green and blue. A multi-band raster may two or more bands or layers of data collected at different times for the same extent (region) and of the same resolution.

A multi-band raster dataset can contain time series data. Source: National Ecological Observatory Network (NEON).

The raster data that we will use in this tutorial are located in the (NEON-DS-Landsat-NDVI\HARV\2011\NDVI) directory and cover part of the NEON Harvard Forest field site.

In this tutorial, we will:

  1. Import NDVI data in GeoTIFF format.
  2. Import, explore and plot NDVI data derived for several dates throughout the year.
  3. View the RGB imagery used to derived the NDVI time series to better understand unusual / outlier values.


The Normalized Difference Vegetation Index or NDVI is a quantitative index of greenness ranging from 0-1 where 0 represents minimal or no greenness and 1 represents maximum greenness.

NDVI is often used for a quantative proxy measure of vegetation health, cover and phenology (life cycle stage) over large areas. Our NDVI data is a Landsat derived single band product saved as a GeoTIFF for different times of the year.

NDVI is calculated from the visible and near-infrared light reflected by vegetation. Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light. Image & Caption Source: NASA

RGB Data

While the NDVI data is a single band product, the RGB images that contain the red band used to derive NDVI, contain 3 (of the 7) 30m resolution bands available from Landsat data. The RGB directory contains RGB images for each time period that NDVI is available.

A "true" color image consists of 3 bands - red, green and blue. When composited or rendered together in a GIS, or even a image-editor like Photoshop the bands create a color image. Source: National Ecological Observatory Network (NEON).

Getting Started

In this tutorial, we will use the raster and rgdal libraries.

# load packages

To begin, we will create a list of raster files using the list.files() function in R. This list will be used to generate a RasterStack. We will only add files to our list with a .tif extension using the syntax pattern=".tif$".

If we specify full.names=TRUE, the full path for each file will be added to the list.

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

# view list - note the full path, relative to our working directory, is included

##  [1] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/005_HARV_ndvi_crop.tif"
##  [2] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/037_HARV_ndvi_crop.tif"
##  [3] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/085_HARV_ndvi_crop.tif"
##  [4] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/133_HARV_ndvi_crop.tif"
##  [5] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/181_HARV_ndvi_crop.tif"
##  [6] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/197_HARV_ndvi_crop.tif"
##  [7] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/213_HARV_ndvi_crop.tif"
##  [8] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/229_HARV_ndvi_crop.tif"
##  [9] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/245_HARV_ndvi_crop.tif"
## [10] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/261_HARV_ndvi_crop.tif"
## [11] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/277_HARV_ndvi_crop.tif"
## [12] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/293_HARV_ndvi_crop.tif"
## [13] "NEON-DS-Landsat-NDVI/HARV/2011/NDVI/309_HARV_ndvi_crop.tif"

Now we have a list of all GeoTIFF files in the NDVI directory for Harvard Forest. Next, we will create a RasterStack from this list using the stack() function.

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

We can explore the GeoTIFF tags (the embedded metadata) in a stack using the same syntax that we used on single-band raster objects in R including: crs() (coordinate reference system), extent() and res() (resolution; specifically yres() and xres()).

# view crs of rasters

## CRS arguments:
##  +proj=utm +zone=19 +ellps=WGS84 +units=m +no_defs

# view extent of rasters in stack

## class       : Extent 
## xmin        : 239415 
## xmax        : 239535 
## ymin        : 4714215 
## ymax        : 4714365

# view the y resolution of our rasters

## [1] 30

# view the x resolution of our rasters

## [1] 30

Notice that the CRS is +proj=utm +zone=19 +ellps=WGS84 +units=m +no_defs. The CRS is in UTM Zone 19. If you have completed the previous tutorials in this raster data in R series , you may have noticed that the UTM zone for the NEON collected remote sensing data was in Zone 18 rather than Zone 19. Why are the Landsat data in Zone 19?

Landsat imagery swaths are over 170 km N-S and 180 km E-W. As a result a given image may overlap two UTM zones. The designated zone is determined by the zone that the majority of the image is in. In this example, our point of interest is in UTM Zone 18 but the Landsat image will be classified as UTM Zone 19. Source: National Ecological Observatory Network (NEON).

The width of a Landsat scene is extremely wide - spanning over 170km north to south and 180km east to west. This means that Landsat data often cover multiple UTM zones. When the data are processed, the zone in which the majority of the data cover, is the zone which is used for the final CRS. Thus, our field site at Harvard Forest is located in UTM Zone 18, but the Landsat data is in a CRS of UTM Zone 19.

Challenge: Raster Metadata

Answer the following questions about our RasterStack.

  1. What is the CRS?
  2. What is the x and y resolution of the data?
  3. What units is the above resolution in?

Plotting Time Series Data

Once we have created our RasterStack, we can visualize our data. We can use the plot() command to quickly plot a RasterStack.

# view a plot of all of the rasters
# 'nc' specifies number of columns (we will have 13 plots)
     zlim = c(1500, 10000), 
     nc = 4)

Have a look at the range of NDVI values observed in the plot above. We know that the accepted values for NDVI range from 0-1. Why does our data range from 0 - 10,000?

Scale Factors

The metadata for this NDVI data specifies a scale factor: 10,000. A scale factor is sometimes used to maintain smaller file sizes by removing decimal places. Storing data in integer format keeps files sizes smaller.

Let’s apply the scale factor before we go any further. Conveniently, we can quickly apply this factor using raster math on the entire stack as follows:

raster_stack_object_name / 10000

Data Tip: We can make this plot
even prettier by fixing the individual tile names, adding an plot title and by using the (levelplot) function. This is covered in the NEON Data Skills Plot Time Series Rasters in R tutorial.

# apply scale factor to data
NDVI_HARV_stack <- NDVI_HARV_stack/10000
# plot stack with scale factor applied
# apply scale factor to limits to ensure uniform plottin
     zlim = c(.15, 1),  
     nc = 4)

Take a Closer Look at Our Data

Let’s take a closer look at the plots of our data. Note that Massachusettes, where the NEON Harvard Forest Field Site is located has a fairly consistent fall, winter, spring and summer season where vegetation turns green in the spring, continues to grow throughout the summer, and begins to change colors and senesce in the fall through winter. Do you notice anything that seems unusual about the patterns of greening and browning observed in the plots above?

Hint: the number after the “X” in each tile title is the Julian day which in this case represents the number of days into each year. If you are unfamiliar with Julian day, check out the NEON Data Skills Converting to Julian Day tutorial.

View Distribution of Raster Values

In the above exercise, we viewed plots of our NDVI time series and noticed a few images seem to be unusually light. However this was only a visual representation of potential issues in our data. What is another way we can look at these data that is quantitative?

Next we will use histograms to explore the distribution of NDVI values stored in each raster.

# create histograms of each raster
     xlim = c(0, 1))

It seems like things get green in the spring and summer like we expect, but the data at Julian days 277 and 293 are unusual. It appears as if the vegetation got green in the spring, but then died back only to get green again towards the end of the year. Is this right?

Explore Unusual Data Patterns

The NDVI data that we are using comes from 2011, perhaps a strong freeze around Julian day 277 could cause a vegetation to senesce early, however in the eastern United States, it seems unusual that it would proceed to green up again shortly thereafter.

Let’s next view some temperature data for our field site to see whether there were some unusual fluctuations that may explain this pattern of greening and browning seen in the NDVI data.

There are no significant peaks or dips in the temperature during the late summer or early fall time period that might account for patterns seen in the NDVI data.

What is our next step?

Let’s have a look at the source Landsat imagery that was partially used used to derive our NDVI rasters to try to understand what appears to be outlier NDVI values.

Challenge: Examine RGB Raster Files

  1. View the imagery located in the /NEON-DS-Landsat-NDVI/HARV/2011 directory.
  2. Plot the RGB images for the Julian days 277 and 293 then plot and compare those images to jdays 133 and 197.
  3. Does the RGB imagery from these two days explain the low NDVI values observed on these days?

HINT: if you want to plot 4 images in a tiled set, you can use par(mfrow=c(2,2)) to create a 2x2 tiled layout. When you are done, be sure to reset your layout using: par(mfrow=c(1,1)).

Explore The Data’s Source

The third challenge question, “Does the RGB imagery from these two days explain the low NDVI values observed on these days?” highlights the importance of exploring the source of a derived data product. In this case, the NDVI data product was derived from (created using) Landsat imagery - specifically the red and near-infrared bands.

When we look at the RGB collected at Julian days 277 and 293 we see that most of the image is filled with clouds. The very low NDVI values resulted from cloud cover — a common challenge that we encounter when working with satellite remote sensing imagery.

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