Part 2: Explore and Investigate Multispectral Data2

Working with Multispectral Data in ImageJ

In this section, you will learn how satellites and aircraft use a single black and white camera with colored filters to capture data that can be used to re-create both natural and false color images.

Download Landsat data

Before you begin, you must download a set of seven Landsat imagesone image for each of the Landsat Thematic Mapper instrument's 7 bands.

Landsat Band 5 image of the Tierras Bajas region of Bolivia. (Source: NASA/USGS)

  1. Create a subfolder in your Day 4 data folder and name it Bolivia.
  2. Right-click (Win) or control-click (Mac) each of the links below and download each of the following seven TIFF files to the Bolivia folder you created.

Open and explore the seven Landsat bands

Before you merge the bands together to make color images, you'll open and examine them on their own.

  1. Launch ImageJ by double-clicking its icon ImageJ Icon Small on your desktop or by clicking its icon in the dock (Mac) or Launch Bar (Win).
  2. Choose File > Import > Image Sequence..., navigate to the Bolivia folder, and import the seven images, representing the seven Thematic Mapper bands, into ImageJ as a stack.

  3. Move forward and backward through the stack. As you do, note the label of each slice, which lists the TM band it represents. Notice what appears light and what appears dark in each band. Notice which bands have a lot of contrast and which don't. Think about what this is telling you.

  4. For example...

    Band 1 (blue) - Since the atmosphere scatters more blue light than other colors, band 1 images nearly always have low contrast.

    Band 2 (green) - The peak sensitivity of the human eye is in the yellow-green part of the spectrum. The green band most closely represents the brightness and contrast of the scene as you would see it with your eyes.

  5. After you have explored the images, close the stack.

Create a true color image

You are going to use these images to re-create a "true color" version of the scene by combining three bands that represent what is seen in red, green, and blue wavelengths.

  1. Choose File > Open and open all seven of the Bolivia images, band1 through band7.
  2. Choose Image > Color > Merge Channels....
  3. In the Color Merge window, assign the band 3 image to the red channel, the band 2 image to the green channel, and the band 1 image to the blue channel. Leave the Create Composite option unchecked, but check the Keep Source Images option.
  4. color_merge_bolivia
  5. Click OK. The result is a true color RGB image. It is called a 321 image, because of the bands assigned to the red, green, and blue channels of the image. (They are also called RGB321 images.)
  6. Does your true color image seem flat and dull? Does it lack the "punch" you see in satellite images on the Web? If you have time, you may want to check out the Optional section at the end of the Intro to Color Imaging page. This section shows you how to increase the contrast of the band images before you merge them. This increases the overall contrast (and color range) of the final RGB image.

Create a false color image

In your initial examination of the seven Landsat color bands, you noticed that some types of land cover appear very different in some bands than others. You are going to use other bands to create a "false color" version of the scene. In this case, you will create a 432 image.

Vegetation and 432 false color images

You've probably seen this type of image used to show vegetation. In a 432 image, the bands are assigned as follows:
  • The Near IR band (4) is assigned to the Red channel.
  • The Red band (3) is assigned to the Green channel.
  • The Green band (2) is assigned to the Blue channel.

In this type of image, older, forest-type vegetation appears dark red, while crops and younger vegetation appear brighter red. Cities appear blue or gray, and bare ground appears grayish-brown.

Other false color band assignment combinations are used to study specific types of ground coverwater, minerals, vegetation, etc. Some of the links in the Resources section at the bottom of the page show examples of these different false color images and explain how they are used.

Create Your Own RGB True and False Color Images.

  1. Choose ONE set of Landsat images from the collections below.
  2. Download the 6-8 images (representing 6-8 Landsat bands) in that collection to your Week 11 folder.
  3. Open the images in ImageJ.
  4. Use Image > Color > Merge Channels to create both true and false color images by assigning different bands to the Red, Green, and Blue color channels.
  5. Save a copy of your favorite RGB color image AS A JPEG (Be carefulImageJ always defaults to saving images in TIFF format!) to your Day 4 directory.

Landsat Image CollectionsChoose ONE

Choose ONE of the Landsat image collections below, download the images to your computer and use them to create true and false color RGB images using ImageJ

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Las Vegas, Nevada

Sioux City, Iowa

Baton Rouge, Louisiana

Great Salt Lake, Utah

Owens Valley, California

Santa Cruz Valley, Arizona

Mono Lake, California

Your Assignment: Locate a Multispectral Image and Discuss It

  1. Visit the Landsat Event Gallery by going to the Index of Landsat Images.
  2. Find a Landsat image that you think you could use in your teaching.
  3. Click the image and read the description, including the bands used to create the image. (Is it true or false color?)
  4. Go to the Part 2: Share and Discuss page and post the name of the image you selected from the Landsat Event Gallery, along with a description of the spectral bands used to create the image and what land features are highlighted by those spectral bands.
  5. Share one or more ideas about how you might incorporate the use of these multispectral images into your teaching, engaging in an online discussion with colleagues.


1Adapted from Earth Exploration Toolbook chapter instructions under Creative Commons license Attribution-NonCommercial-ShareAlike 1.0.
2Adapted from Eyes in the Sky II online course materials, Copyright 2010, TERC. All rights reserved.
3New material developed for Earth Analysis Techniques, Copyright 2011, TERC. All rights reserved.