lab4

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Lab Instructions Lab 4, due 5:30 pm October 1, 2007 EES5053/ES4093: Remote Sensing, UTSA Student Name: ___________________ Digital Image Processing I: Atmospheric Correction, Radiance, Reflectance, NDVI from Landsat Image Objective: In this lab, you will learn the basic procedure of digital image processing from converting the digital number to radiance, and then to reflectance by using band math. Based on reflectance, you will learn how to calculate the NDVI, and classify the NDVI using Density slice. Part I: Concepts and short questions: 1. Give a four bands image of 4 x 3 as the following, please replace them into BSQ, BIP, and BIL formats. 1, 4, 5, 6 2, 5, 8, 9 105, 79, 103, 114 20, 25, 31, 26 3, 4, 4, 5 10, 3, 2, 1 114, 100, 124, 134 29, 19, 29, 50 2, 3, 9, 10 9, 8, 7, 6 130, 125, 109, 150 25, 30, 33, 50 2. Calculate the two multivariate parameters (covariance and correlation) of the above 4 bands. Identify which two bands have the best correlation. 1

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Lab4

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Page 1: Lab4

Lab InstructionsLab 4, due 5:30 pm October 1, 2007EES5053/ES4093: Remote Sensing, UTSA

Student Name: ___________________

Digital Image Processing I: Atmospheric Correction, Radiance, Reflectance, NDVI from Landsat Image

Objective: In this lab, you will learn the basic procedure of digital image processing from converting the digital number to radiance, and then to reflectance by using band math. Based on reflectance, you will learn how to calculate the NDVI, and classify the NDVI using Density slice.

Part I: Concepts and short questions:

1. Give a four bands image of 4 x 3 as the following, please replace them into BSQ, BIP, and BIL formats.

1, 4, 5, 6 2, 5, 8, 9 105, 79, 103, 114 20, 25, 31, 26 3, 4, 4, 5 10, 3, 2, 1 114, 100, 124, 134 29, 19, 29, 50 2, 3, 9, 10 9, 8, 7, 6 130, 125, 109, 150 25, 30, 33, 50

2. Calculate the two multivariate parameters (covariance and correlation) of the above 4 bands. Identify which two bands have the best correlation.

3. Summarize the differences and similarities of supervised and unsupervised classifications. You may use examples to illustrate them.

4. Assume that two road intersections shown on a photograph can be located on a 1:25,000 scale topographic map. The measured distance between the intersections is 47.2 mm on the map and 94.3 mm on the photograph. (a) What is the scale of the photograph? (b) at that scale, what is the length of a fence line that measures 42.9 mm on the photograph?

5. The area of a lake is 52.2 cm2 on a 1:7500 vertical photograph. find the ground area of the lake in m2, ha, and acres

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Part II

1. Preparation: (1). Create a Lab4 directory under c:\Fall2007-RS\YourName\. Today we will use the atmospherically corrected image (with mask applied) that you processed in Lab3: Mask_DOS_020708.tif. Save your results to Lab4 directory.

2. Spectra radiance calculation

 Equation 1 is the basic equation for calculating spectral radiance from the Digital Number (DN) of Landsat 4, 5 and 7:

 LMINQCALMINDN

QCALMINQCALMAXLMINLMAXL

)(*

                  (1)

where, DN is the Digital Number of each pixel in the image (in this lab, it will be Mask_DOS_020708.tif, although a simple DOS atmospheric correction has been performed, it is still DN, not radiance yet), LMAX and LMIN are the calibration constants, and QCALMAX and QCALMIN are the highest and the lowest points of the range of rescaled radiance in DN.

For Landsat 7, However, there is a more simple way for calculating L (Landsat 7 Science User Data Handbook Chap.11, 2002). This is what we will use in this lab.

offsetDNgainL *                      (2)

 In Equation 2, the “gain” corresponds to the “Gain” in the header file, and the “offset” corresponds to the “Bias” in the header file. The unit of radiance is W m-2 sr-1 um-1

The image NE_p27r40_020708_12347.tif you worked on in Lab 3 was just a portion (or a subscene) of the original full image of LE70270400000218950. This image can be downloaded free from the TexasView Remote Sensing Consortium that UTSA (LRSG) is the member of the Consortium. From the LRSG website (www.utsa.edu/LRGS/), there is a link to the website. Now I would like you to explore this website (there are many cool stuff for you to explore) and to know how you may download data from there for your own research. Also you can see the header file information you need for this lab. When are in the TexasView website, click Landsat Data under Data Archives on the left hand column. Now you should a figure like this:

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This is the available Landsat 7 images for the Texas and its adjacent states. You can see the Path (east to west) from 24-33 and Row (north to south) from 35-42. Path tells you each satellite orbit, row tells the descending or ascending of the orbits.

Question1, from this figure, is Landsat 7 a descending or ascending satellite for the day time passing Texas (usually 10:00 am). What is descending or ascending?

Path and Row together define the one Landsat image. For San Antonio, it is Path27, Row40 or 2740 in the image. Click the 2740 in the image, you will see there are 4 years of images available for San Antonio: y1999, y2001, y2002, y2003. The image we are using is in July 8, 2002. Click y2002, you will see three images in different days (Julie day): d189, d317, d365). The Julie Day for July 8, 2002 is the day 189. so click 189. you will see two different types of images. What we want to use is the ‘nlaps’. Click the ‘nlaps’, you will see the many image files there: the same file name with different file extension. H1, H2, H3, HI are header files; I1, I2, …, I9 are real images. To understand what they mean, please click the README.TXT file. I would encourage you to understand all of them using your spare time, since it is the first step for you to really get familiar with a satellite sensor and what many parameters mean. If you have any question about them, you are always welcomed to ask me.

You should be able to find the gain and offset information from those files for the Lab. But to save your time, I list them for you to use.

  Band  |  Ref          DN to Radiance          Default

      |  Detector    gain       offset       Abs Calib?------------------------------------------------------- 1    |   15        0.775686   -6.20000       FALSE 2    |   12        0.795686   -6.39999       FALSE 3    |    8        0.619216   -5.00000       FALSE 4    |    7        0.965490   -5.10001       FALSE 5    |   14        0.125725   -0.99999       FALSE 6    |    8        0.066823   0.000000       FALSE

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7    |   10        0.043726   -0.35000       FALSE 8    |   27        0.971765   -4.70000       FALSE 9    |    8        0.037059   3.200000       FALSE  

      In this lab, we only do band 3 and band 4. You can use the Band Math tool to do so. From the main ENVI menu, click Basic Tools -> Band Math, type the equation for band 3 as the figure 1 below, and click OK. A new window will popup, select the atmospheric corrected band 3 (Mask_DOS_020708.tif) as b3, then save this image to your directory. In the similar way, you can do the band 4. Output as Radn_189_b3.tif and Radn_189_b4.tif

Figure 1. Band math expressions

Question 2, calculate and show the basic statistics of the two radiance images, and give a brief analysis of the statistics.

3. Spectra reflectance calculation

     The reflectance for band is computed by the following equation (Markham and Barker,1986 and Landsat 7 Science User Data Handbook Chap.11, 2002):

 

cos

2

ESUN

dL

              (3)

 where L is at satellite spectral radiance which is the outgoing radiation energy of the band observed at the top of atmosphere by the satellite (in this lab, we use the results calculated from step 3), d is the Earth-Sun distance in astronomical units (), ESUN is mean solar exoatmospheric irradiances for the band , and cos  is the cosine of the

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solar incident angle. Supposing a horizontal land surface is flat, the cosine of solar incident angle (cos) can be calculated from the Sun Elevation cos(90-SunElevation). The Sun elevation angle for the image is 65.26º (you can get this from the head file of  LE7027040000218950.H1 mentioned above)

 Since the inverse of d2 (which is 1/d2) in Equation 3 is equivalent to “inverse squared relative distance Earth-Sun, dr“, the Equation 3 can be rewritten as:

  rdESUNL

cos                     (4)

 The annual averaged value of dr is 1.0, and it ranges from about 0.97 to 1.03.  You can find a real number for a special date (such as the189 day: July 8 for this image used is 1.0167) from Table 11.4 of this link here at: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter11/chapter11.html

 The values for ESUN in Equation 4 are given in Table 3. The value of ESUN for band 6 is not available.

 Table 3. ESUN for Landsat 4 and 5 TM in mW/cm2/μm (Markham and Barker, 1986), and for Landsat 7 ETM+ in W/m2/μm (Landsat 7 Science User Data Handbook Chap.11, 2002)

Band1 Band2 Band3 Band4 Band5 Band6 Band7Landsat-4 TM 195.8 182.8 155.9 104.5 21.91 - 7.457Landsat-5 TM 195.7 182.9 155.7 104.7 21.93 - 7.452

Landsat 7 ETM+ 1969 1840 1551 1044 225.7 - 82.07

      In this lab, we only calculate the reflectance of band 3 and band 4, using the Band Math tool as in step 2, output as Reflt_189_b3 and Reflt_189_b4.

  Question 3. Calculate and compare the basic statistics of reflectance Band 3 and 4 .

4. Calculate NDVI

NDVI stands normalized difference of vegetation index: the difference between the near infrared band (~0.83 µm) and the red band (0.66 µm). For Landsat image (TM or ETM+ image), they are band 4 and band 3, respectively. Thus, the NDVI can be calculated based on this equation: (b4-b3)/(b4+b3), using the reflectance band 3 and band 4 as input, name as NDVI_02189.  

Question 4. Show the basic statistics of NDVI.

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5. Density Slice

In the image window, click Overlay->Density Slice. In the density slice window, select one of the item, then edit the data range and color, click apply. Then click file->Save Range to your folder as NDVI_class.

   Question 5. Upload an image as RGB742, link it with NDVI Density Slice (gray). Please make a simple discussion/comparison about the spatial distribution of the NDVI values (you can edit the range of NDVI to match the RGB) and vegetation coverage.

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