This is a test of Andrew Klein's New Document Formt

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Lab Exercise
GEOG 361/651 Dr. Andrew Klein
Lab Exercise 7 – Extracting Information from Thermal Remote Sensing Data
This lab is a combination of labs originally developed by Dr. Liu and modified by Dr. Klein
with help implementing the labs in ENVI from Songgang Gu.
I. Introduction
Many multispectral systems sense radiation in the thermal infrared as well as the visible and reflected
infrared portions of the spectrum. However, remote sensing of energy emitted from the Earth's
surface in the thermal infrared (3μm to 15μm) is different from the sensing of reflected energy.
Multispectral scanning allows the possibility to acquire, display and interpret thermal properties of
the Earth's surface. Thermal energy is generally emitted rather than reflected from the Earth's
surface. The break in wavelength is at about 3 μm. Shorter wavelengths are reflected solar energy,
whereas longer are emitted from the Earth's surface. The Earth behaves overall as a blackbody with
peak energy emission at about 9.7 μm wavelength. However, the radiant temperature of a given
object depends on many thermal factors, such as emissivity, conductivity, capacity, diffusivity and
inertia.
Thermal sensors essentially measure the surface temperature and thermal properties of targets. This
lab aims to develop students’ understanding of thermal imagery. Specific tasks include:
• Visually interpret a daytime Landsat thermal image;
• Calculate the absolute radiance based on the DN values of thermal image;
• Calculate the effective at-satellite temperature;
• Smooth the temperature image using a low-pass filter;
• Visualize the temperature using pseudocolor and 3D perspective views; and
• Interpret and compare daytime and nighttime thermal images.

II. Quantitative Analysis of Landsat Thermal Imagery
I. Conversion of DN values to Absolute Radiance
ETM+ images are acquired in either a low or high gain state (Figure 1). The science goal in
switching gain states is to maximize the instrument's 8-bit radiometric resolution without saturating
the detectors. This requires matching the gain state for a given scene to the expected brightness
conditions. For all bands, the low gain dynamic range is approximately 1.5 times the high gain
dynamic range. It makes sense, therefore, to image in low gain mode when surface brightness is high
and in high gain mode when surface brightness is lower.
Figure 1 Design ETM+ Reflective Band High and Low gain Dynamic Ranges
http://landsathandbook.gsfc.nasa.gov/handbook/handbook_htmls/chapter6/chapter6.html
The per-band sensitivity of the instrument is set by adjusting the gain to conditions expected for that
time of year. Bands 1, 2 and 3 are set to high or low gain as a group. Gain settings for bands 5 and 7
are set similarly. Band 6 acquired in both high and low gain mode, while bands 4 and 8 are set
individually according to land surface brightness conditions. Band gains are set on a month by
month basis. The DN values of each Landsat image band were scaled from the absolute radiance
measure to byte values prior to media output using the gain and bias (offset) values given for each
band. The DN values can be converted back to the radiance units using the following equation:
Which can also be expressed as:
Radiance = gain*DN + offset (1)
Radiance = ((L MAX-L MIN)/(Q CALMAX-Q CALMIN))*(Q CAL-Q CALMIN) + L MIN (2)
Where: Q CALMIN=0, Q CALMAX=255 and Q CAL=Digital Number
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The L MINs and L MAXs are the spectral radiances for each band at digital numbers of Q CALMIN and
Q CALMAX (for example, Q CALMIN = 0 and Q CALMAX = 255). The L MINs and L MAXs are a representation of
how the output Landsat ETM+ Level 1G data products are scaled in radiance units. The L MIN
corresponds to the radiance at the minimum quantized and calibrated data digital number, Q CALMIN,
and L MAX corresponds to the radiance at the maximum quantized and calibrated data digital number,
Q CALMAX. That is, Q CALMIN is the minimum number and Q CALMAX is the maximum number of each
band.
Table 1 Lists two sets of L MINs and L MAXs. The first set should be used for 1G products created
before July 1, 2000 and the second set for 1G products created after July 1, 2000. Use of the
appropriate L MINs and L MAXs will ensure accurate conversion to radiance units. For the band 6, a bias
was found in the pre-launch calibration by a team of independent investigators post launch. For data
processed before this, the image radiances given by the above transform are 0.31
w/(m2*sterandian*μm) too high. You have to subtract this value after the conversion.
Table 1 ETM+ Spectral Radiance Range in watts/(meter squared * ster * μm)
Before July 1, 2000 After July 1, 2000
Band Low Gain High Gain Low Gain High Gain
Number L MIN L MAX L MIN L MAX L MIN L MAX L MIN L MAX
1 -6.2 297.5 -6.2 194.3 -6.2 293.7 -6.2 191.6
2 -6.0 303.4 -6.0 202.4 -6.4 300.9 -6.4 196.5
3 -4.5 235.5 -4.5 158.6 -5.0 234.4 -5.0 152.9
4 -4.5 235.0 -4.5 157.5 -5.1 241.1 -5.1 157.4
5 -1.0 47.70 -1.0 31.76 -1.0 47.57 -1.0 31.06
6 0.0 17.04 3.2 12.65 0.0 17.04 3.2 12.65
7 -0.35 16.60 -0.35 10.932 -0.35 16.54 -0.35 10.80
8 -5.0 244.00 -5.0 158.40 -4.7 243.1 -4.7 158.3
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2. Conversion of Radiance to Temperature
ETM+ Band 6 imagery can also be converted from spectral radiance (as described above) to a more
physically useful variable. This is the effective at-satellite temperature of the viewed Earthatmosphere
system under an assumption of unity emissivity and using pre-launch calibration
constants listed in Table 2. The conversion formula is:
K 2
T =
⎛ K2
⎞
ln⎜
⎟
⎜
+ 1
⎟
⎝ Lλ
⎠
Where:
T = Effective at-satellite temperature in Kelvin
K2 = Calibration constant 2 from Table 2
K1 = Calibration constant 1 from Table 2
L λ = Spectral radiance in watts/(meter squared * ster * μm)
Table 2. ETM+ Thermal Constants and Constant Value Units
K1 666.09 W m -2 ster -1 μm -1
K2 1282.71 °K
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(3)

III. Instructions
Task 1: The thermal image etm6_1999 and panchromatic image etm_pan1999 are located in the
ETM subdirectory on the class shared drive for lab07. These two Landsat 7 ETM+ image bands
were acquired over the Houston area at 16:43:32 in September 20, 1999. The sun elevation is 54.88
degree, and sun azimuth is 140.69 degree. Visually interpret and compare the thermal image with the
panchromatic image, and answer the following questions:
Question 1. What is the spectral range of thermal image etm6_1999 in terms of wavelength?
Question 2. How does this wavelength differ from the non-thermal bands of Landsat ETM+ in
terms of the recorded radiation energy?
Question 3. Do you expect strong correlation between DN values in the thermal band and other
non-thermal image bands? Why or why not? Please explain.
Question 4. What is the spatial resolution of the thermal image? Why is the spatial resolution of the
thermal image much lower than that of non-thermal bands?
Question 5. Compare the panchromatic and thermal band images, and explain the tonal differences
between the visible (panchromatic) and thermal images. What types of surface features generally
have high or low emission in the thermal band (assuming surface temperatures are the same across
the image)?
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Task 2. The thermal image band that we have is low gain representation. Given a subset of Landsat
ETM+ band 6 image sub_etm6, you are required to perform the following quantitative analysis:
Question 6. Determine the L MAX, L MIN, Q CALMAX, Q CALMIN values for the low gain representation of
ETM+ band6, and construct the correct formula for converting the DN values in band 6 to the
absolute radiance.
Question 7. Now apply (using band math) this formula to the entire image to calculate the
absolute radiance for each pixel, and determine the range of radiance values for this sub image using
the compute statistics function.
Question 8. Determine average radiance values for typical pixels of water, grass, forest, buildings
and roads.
Question 9. Write out the formula to convert the absolute radiance to temperature. Using band
math apply this formula to each pixel in the image to calculate the effective at-satellite temperatures,
and find out the temperature range for this sub image and also determine the temperatures for the
typical pixels of water, grass, forest, buildings and roads you analyzed in Question 8.
Question 10. Smooth the temperature image using a low-pass filter and display the temperature
image using the pseudo-color mode and select an appropriate color table to display surface
temperature. Make a hardcopy map to show the spatial distribution of the temperature.
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Tips
Much of what is required here you have done before so you should try to remember how to do this
again.
1. To convert the DN values to at-satellite radiance, you first need to construct a formula of the
type listed in equation (1). You can then apply this formula using either band math or the
gain/offset preprocessing function. [To do a log use the alog function and be sure to use your parentheses
correctly].
2. You can use ROI tool to determine both the at-satellite radiance and at-satellite temperatures
for the different surface features.
3. You can use Basic Tools Statistics Compute Statistics to compute the statistics for an
image.
4. To do pseudocolor mapping of temperature on the Image menu select Tools Color
Mapping ENVI Color Tables to set a pseudo-color for displaying a temperature image.
Select an appropriate color table that makes sense to correctly display temperatures.
5. To smooth the temperature image suing a low-pass filter, which can be found in Filter
Convolutions and morphology on the ENVI main menu bar. You should select the
Convolutions and Morphology Tool window, and use a Low Pass with a 3×3 Kernel Size. Click
Quick Apply; select the appropriate input band Notice that the temperature image is smoothed.
Make a hard copy of this image and include it in your lab report.
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Task 3. Under the DAEDALUS subdirectory, there are two image files aerial.jpg and thermal.jpg.
These two images were acquired in November 1976 over Brookhaven National Laboratory on Long
Island, New York. The panchromatic aerial photograph aerial.jpg includes an overlay of buried
steam and condensate lines and manholes that was provided by the Brookhaven maintenance staff.
The night thermal image thermal.jpg was acquired in the spectral band 8-14 μm by Daedalus
Enterprises, Inc. Visually interpret the thermal images and answer the following questions:
Question 12. What features are located in the regions H and F? Explain why these regions appear
bright in the nighttime thermal image.
Question 13. Some light spots can be seen on the rooftops of buildings D and E. What are these
features? Explain why they appear light on the thermal image.
Question 14. What do the pavement (G) and the lawn grass look like in the nighttime thermal
image?
Question 15. Can you observe the buried heating lines and manholes on the nighttime thermal
image?
Question 16. The Brookhaven maintenance staff suspects that the steam lines were leaking the heat
because pipe insulation had deteriorated. Visually interpret the thermal image and identify the
possible locations for major leaks. Please print out a hardcopy map and identify possible leaks using
the annotation tool
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Task 4. Under the ATLAS subdirectory, there are two thermal atlanta0.jpg and atlanta1.jpg. They
are ATLAS channel 13 imagery (9.6-10.2 μm) with 10m spatial resolution (original images) over
Atlanta central business district (CBD), one during the day and one at night. Both images are
oriented with north at the top. Visually interpret the images and answer the following questions:
Question 17. Which image is the daytime thermal image and which is the nighttime image? Explain
why?
Question 18. Why do the lakes appear dark in the daytime thermal infrared image and light in the
nighttime thermal infrared image?
Question 19. Can you observe the shadows from tall building on the daytime thermal image?
Explain why the Georgia Dome (round shaped object) appears dark in both images?
Question 20. Several objects appear very dark in the nighttime image while are relatively bright in
the daytime image. What are these objects?
II. Lab Report
The lab report must be typed, as hand-written work will not be accepted. You should put your name
and lab session number on the cover page. The lab report should contain:
1) Short answers to each question in your own words; and
2) The required maps.
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