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Color Composite Images

In one single band from the Landsat 5 or 7 sensor, the difference in energy levels between various land cover classifications may not be discernible. Since comparing the spectral characteristics of land features in multiple bands provides a better separation, or contrast, between different land surfaces, Landsat data from multiple bands can be combined to create a data product known as a color composite image. Landsat color composite images are often called three-band composite images since they are created using the measured energy level in each of three spectral bands to control the amount of red, blue, and green in a color output image.

Figure 1. Landsat Band 3 (Visible Red) for Howard County, Maryland

Mapping Landsat Data to an RGB Display

Computer monitors use red, green, and blue (RGB) “guns” to create color images. In an RGB display, all of the colors that comprise an image are made up of a combination of red, green, and blue at varying levels of intensity, each ranging from 0-255. Each unique color has its own combination of red, green, and blue levels. With all of the possible combinations of red, green, and blue values, a computer monitor can display millions of colors. In the diagram below, each unique color is displayed with its red, green, and blue values.

 

Figure 2. Examples of RGB Color Combinations

The way the Landsat data are mapped into three colors in the output image depends on the information that one wishes to be highlighted in the images. The spectral characteristics of the target being observed and the type of information an analyst hopes to extract from the raw data determine which bands will be used in the composite and which color (red, green, or blue) will be assigned to each band. For example, in some applications, it may be desirable that land cover classes be associated with familiar colors (e.g., grass is green). In other cases contrasting colors are preferred to highlight objects of interest from the background.

Regardless of the combination of bands used, the mapping of Landsat data to the RGB color display is the same. Three bands are selected, each is assigned to one of the three primary RGB colors, and the value of each color level is mapped to the measured value of each pixel in the appropriate band. For example, to create a composite image that maps the measured values of Landsat bands 3, 2, and 1 to the colors red, green, and blue respectively, the color of each pixel would be calculated using the following logic:

    • the red value of the pixel would be set equal to the measured energy level (digital number, or DN) of that pixel in band 3;
    • the green value of the pixel would be set equal to the measured energy level of that pixel in band 2;
    • the blue value of the pixel would be set equal to the measured energy level of that pixel in band 1.

 

For example, assume one pixel location in the Landsat 7 scene has the following measured energy levels:

    • measured energy level in band 3 = 18 (which translates into a red value of 18);
    • measured energy level in band 2 = 18 (which translates into a green value of 18);
    • measured energy level in band 1 = 133 (which translates into a blue value of 133).

This results is a RGB value of (18, 18, 133) for that pixel location in the color composite image, which is a deep blue color. This color can be seen in Figure 2. This logic is repeated for every pixel in the scene being processed, until an entire image is produced with pixel values derived from a combination of three bands.

Landsat three-band composite images are usually named using the three bands used to create the image in order from red to green to blue. Thus, the above example would be called a “321 composite” image, since it was derived from mapping bands 3, 2, and 1 to the red, green, and blue guns of a color monitor, respectively.

The following sections will discuss several of the three band composites that are commonly derived from Landsat data.

True Color Composite Images (Landsat Bads 3, 2, and 1). True color composite images are created by combining the spectral bands that most closely resemble the range of vision of the human eye. A true color composite image uses the visible red (band 3), visible green (band 2), and visible blue (band 1) channels to create an image that is very close to what a person would expect to see in a photograph of the same scene. The band to color mapping for a 321 color composite is:

    • Landsat band 3 (visible red) = red;
    • Landsat band 2 (visible green) = green;
    • Landsat band 1 (visible blue-green) = blue.

 

Figure 3. Landsat True Color Color Composite Image of Howard County, Maryland

True color images are based entirely on reflected solar radiation in the visible portion of the electromagnetic spectrum. Haze in the atmosphere, shadows, clouds, and scattering all affect the quality and usefulness of a true color composite. True color composite images are often low in contrast and hazy in appearance because blue light is more easily scattered by the atmosphere.

True color composite images can be very useful, especially when studying coastal regions, since energy in the visible bands can penetrate water surfaces. Particles in the water, such as sediment or algae, will reflect visible light and can therefore be detected by the visible sensors on the Landsat 5 or 7 sensor. Using true color composite imagery, we can observe and measure the amount of sediment flowing from rivers into larger bodies of water such as the Chesapeake Bay following storm events. We can also locate and measure large blooms of algae that threaten the water quality and fishery production in coastal waterways.

Near Infrared Color Composite Images (Landsat Bands 4, 3, and 2). A near infrared composite eliminates the visible blue band and uses a near infrared (NIR) band to produce the image. The resulting composite does not resemble what the human eye will see (for example, vegetation is red instead of green). However it can be very useful to the analyst. The mapping of color to band is:

    • Landsat band 4 (NIR) = red;
    • Landsat band 3 (visible red) = green;
    • Landsat band 2 (visible green) = blue.

 

Figure 4. Landsat Near Infrared Composite Image of Howard County, Maryland

Vegetation has a very high albedo in the NIR band since chlorophyll–the pigment in leaves that give plants their green color–reflects energy at this wavelength. Thus, in a 432 composite image, vegetation is vividly depicted as varying shades of red. Since different types of vegetation have different levels of chlorophyll in their leaves, each type of plant has its own shade of red. This makes a 432 composite image very useful in determining the extent of vegetation and in classifying different vegetation types as seen from space.

Water, which absorbs nearly all of the NIR energy that reaches its surface, appears very dark (nearly black) in a 432 composite image. As such, this type of imagery would not be useful for studying underwater features.

Shortwave Infrared Composite (Landsat Bands 7, 4, and 2). A shortwave infrared composite contains at least one band in the shortwave infrared (SWIR) portion of the electromagnetic spectrum. The other bands used can vary depending on the use of the composite data. Some examples of SWIR composite images would include the following bands mapped to RGB colors:

    • Landsat band 7 (SWIR) = red;
    • Landsat band 4 (NIR) = green;
    • Landsat band 2 (visible red) = blue.

 

Figure 5. Landsat Shortware Infrared Composite Image of Howard County, Maryland

The albedo of surface materials in the SWIR portion of the spectrum is determined primarily by the moisture content of the surfaces being measured. Vegetation that is under stress (due to drought, pests, climate change, pollution, etc.) will generally have less moisture content than healthy vegetation. Therefore, in a SWIR composite image, vegetation stress can be detected and appropriate measures can be taken to protect vegetation in stressed areas. SWIR bands composites are also very useful in detecting soil types and soil disturbance since moisture is an important characteristic of soil structure.

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