DEW2.3. Color in Surface Features


DigitalImageBasics software and computer

Part 1: Representative Color

Since digital sensors in satellites can capture wavelengths of radiation beyond those that we can see, we are faced with the problem of how to represent that data in a digital image. We must choose some colors that we can see to represent the data for the invisible radiation that is captured. For instance, we might choose visible red to represent infrared light in a digital image. Such substitutions have also been called “false color” but that could be misleading because the colors are not so much false as they are representing invisible wavelengths of radiation.

As an example, start the DigitalImageBasics program, click the False Color button, and open Satellite image 4. This was a 1973 picture of Mt. St. Helens in southwestern Washington recorded by sensors on Landsat, the first satellite to measure and monitor land resources. The sensors could measure a number of narrow bands of electromagnetic radiation, but only three are available for this image: infrared, red and green light. The only visible energies though are red and green. 

Start with the following settings for Computer Color and satellite Measured Light:

  • Computer Color: Red set for Measured Light: Red 
  • Computer Color: Green set for Measured Light: Green 
  • Computer Color: Blue set for Measured Light: none (off)

Question 4.1. 
In this “blue-less” world, what features can you see and how can you tell what they are? 

Just for fun, make the satellite Measured Light red show up as Computer Color green and vice versa— satellite Measured Light green show as Computer Color red. You’re starting to create “representative colors!”

Now make the invisible energy, infrared, visible. Assign the invisible infrared energy Measured Light data to a Computer Color as a “false color” that we can see: red, blue, or green. Try different ones.

Question 4.2. 
How does the intensity of the infrared data compare to that of the visible red and green light?

Part 2: Reflectance of Surface Features

Use the following pictures to complete the table below. Change the way you display the satellite data to see how much each set of measurements is contributing to the combined intensities within the image.

  • Satellite image 4
    MtStHelens_1973: volcano in Washington State
  • Satellite image 5
    Rondonia_1975: tropical rain forest in Brazil
  • Satellite image 6
    GreatSaltLakeUtah_1987: city, lake, and desert in Utah
  • OrlandoDisney_1986.jpeg
    city and large developed area in Florida

Common way to view Landsat imagery 

Although Landsat satellite measured many wavelengths of the electromagnetic spectrum, three energy bands are commonly used: infrared, red, and green. Most scientists use the following false color scheme that makes ALL the energies false color:

Computer Color: Red set for Measured Light: Infrared

Computer Color: Green set for Measured Light: Red

Computer Color: Blue set for Measured Light: Green

Task: Using the DigitalImageBasics program, False Color button, rank the intensities of reflected infrared, red, and green light from the list of surfaces. 5 = maximum reflected values, 0 = no reflected light.

Surface CoverInfraredVisible RedVisible GreenColor in Standard Landsat imagery
Paved Roads    

Part 3: Analysis with Color Subtraction

  1. Using the AnalyzingDigitalImages software, open MtStHelens_1973.jpeg with either File: Open Picture or by clicking the Open a Picture button. The volcano violently erupted in 1980, creating dramatic changes to the vegetation around the volcano.
  2. Click the “Enhance Colors” button and try different selections in the dropdown menu “Show Original or Select Enhancement” to examine the relative intensities of infrared (IR), and Red (R) and Green (G) light reflected from the Earth’s surface and displayed in the image on your screen.

The first option (Original) is a standard color composite of Landsat imagery with infrared (IR) displayed as the computer display’s Red color, visible Red displayed as the computer’s Green, and visible Green displayed as the computer’s Blue.

The next 3 options show IR, Red, or Green as a gray shade and allow unbiased viewing of the intensities of each of these satellite measured light values individually.

The next option, Gray Image of Average Intensities, is a composite gray scale image.

The remaining options allow you to quickly see which surface features reflect greater amounts of IR (computer red), Red (computer green), or Green (computer blue) light. They display the difference between two colors, with the color of the greater value is displayed and brighter colors showing larger differences and darker colors indicating little difference. The term “normalized” indicates that the color value for each pixel is not simply one color subtracted from another, but a formula is used to minimize difference in illumination of the surface caused by shadows of clouds and slope of the land surface that cause uneven illumination of the surface by the Sun.

Example: In “Red vs Green (Normalized)” the formula is 

(Intensity Red – Intensity Green) 

(Intensity Red + Intensity Green). 

Keep in mind:

(a) the color of the greater value is displayed.

(b) the displayed colors are Computer colors and represent actual satellite data by the Landsat standard scheme:

Computer Red = Satellite sensor IR.

Computer Green = Satellite sensor Red.

Computer Blue = Satellite sensor Green.

In the example Red vs Green, if a pixel has 10% IR and 20% Red, the difference is 10% and will be displayed in the computer’s Red, since the larger value is IR which corresponds to computer Red. The normalized difference is 10% divided by 30% = 0.33. This value is scaled to 33 and will be displayed in the computer’s Red. Compare this to 10 displayed earlier.

Question 4.3. 
See if you can use the enhancement functions to better discern features in the Mt. St. Helens image. Explain what features are easier to see and what you think those features are. 

Part 4: Back to Spatial Analysis

Click on the Spatial Analysis button. By way of review, see if you can use the Pixel, Line, Area, and Polygon tools to answer these questions:

Question 4.4. 
What are the maximum and minimum x and y values you can find on the satellite image?

Question 4.5.  
Using the small white square, which represents one mile along each edge, in the lower left of the image, what is the number of pixels that represents 1 mile? Assuming the edge of one pixel touches the edge of the neighboring pixel, what is the size of one pixel? How many pixels represent 10 miles? 

Question 4.6. 
This image is oriented so that north is up and east is to the right. The east-to-west and north-to-south extents of the satellite image are how many miles? What is the distance from the upper-left corner to the lower-right corner of the image? Hint: you will need to use the Pythagorean Theorem if you are using the pixel analysis tool or you may use the line length in pixels output from the line analysis tool.

Question 4.7.  
What is the greatest distance across the snow cover observed on Mt. St. Helens in the lower left corner of the satellite image?

Question 4.8.  
What is the greatest width across the lake observed in the left center of the satellite image? What is the greatest length across the lake?