All of the work we have done in this course up until now has used only reflected electromagnetic radiation. In this lab exercise we will explore the use of emitted (not reflected) thermal infrared data from the Thermal Infrared Multispectral Scanner (TIMS). Remember that thermal IR is at the long wavelength end of the IR part of the electromagnetic spectrum.
1.
The TIMS image (tims.img) is in the lab 19 folder in the class data directory. This image is from a flight in August 1990 over a ponderosa pine woodland near Metolius, Oregon. It includes 6 thermal bands that span the far infrared part of the spectrum. Open the image and try various band combinations. (Worksheet question #1).
2.
Another way to look at the relationship between bands is to create feature space graphs of all the band combinations. To do this, go to Multispectral/Supervised/Feature Space
Image. Your input image is the TIMS image, you can use the default output root name of
“tims” and check the “output to viewer” box so that the resulting feature space images are put into their own viewers (you can delete these after you examine the result). Note that each window shows a 2-D feature space for the bands in the filename at the top of that window. Quickly look through the various windows and answer Worksheet question #2.
3.
It is common to decorrelate the bands using a statistical transformation so that you can view a color composite of 3 bands. To do this, go to the Raster tab/Spectral
/Decorrelation Stretch. This procedure uses principal components analysis to create new independent (uncorrelated) bands, so that differences in emissivity of the materials on the ground become more apparent. Perform a decorrelation stretch on the TIMS image, display the result, and try to find band combinations that result in differences in color.
Doing this makes it easier to distinguish ground features using thermal imagery.
4.
Each channel in the TIMS image is calibrated with a low and a high temperature reference blackbody, which for this image was 17.00
 and 34.50
 C, respectively. The digital numbers (8-bit) corresponding to these reference temperatures for the 6 bands are:
Channel Wavelengths (μm) DN (low)
1 8.2 - 8.6
2 8.6 - 9.0
3 9.0 - 9.4
4 9.4 - 10.2
5 10.2 - 11.2
6 11.2 - 12.2
62
41
29
38
42
42
DN (high)
122
103
90
93
108
105
Assume the response of digital number to temperature is linear, and calculate the temperatures over the image for at least two channels (using the Model Maker) (solve the equation: temperature = m(DN) + b for each of two channels using the DNs and the
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temperatures above, where m is slope and b is the y-intercept). Use the Spatial Modeler to difference the temperature images to see if they are identical for the two calculations. (Worksheet question #3, 4, 5)
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Name______________________________
Worksheet
Lab 19: Thermal Remote Sensing
1.
When you open the thermal image as a “true color” image, do you see colors in the image? Does the image appearance change when you change the band combinations?
What does this tell you about the thermal bands?
2.
What shape does the data cloud have in all of the 2-D feature space windows? What does this tell you about the relationship of ALL of the bands with each other in the TIMS image?
3.
Are the calculated temperatures from different bands the same for each pixel? What could cause the same pixel to have different calculated temperatures?
4.
Do places where there were differences in calculated temperature correspond to places where there were differences in color resulting from your decorrelation stretch? What does this tell you about emissivity?
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5.
Does the decorrelated image allow you to pick out differences in the land surface that were harder to see using a 3 band composite from the image that wasn’t decorrelated?
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