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					                                             Remote Sensing Lab

Before you begin:

If your computer does not have MicroMSI, go to http://www.nima.mil/micromsi (cut and paste
this link into your web browser if necessary). Click on Downloads:

Installation v1.55 (5MB)
Extract or Unzip the files and save them to the C drive. Double-click on SETUP.EXE and install the software.

Next, download the data from “Remote Sensing Lab” and unzip into a new folder.

You are then ready to begin the lab…

________________________________________________________________________________

                            MicroMSI Exercise: Monitoring Tropical Deforestation
                                                    by
                                           Joseph W. Bencloski
                              Department of Geography and Regional Planning
                                             10 Leonard Hall
                                    Indiana University of Pennsylvania
                                         Indiana, PA 15705-1087
                                             joeben@iup.edu

Introduction. The purpose of this exercise is to explore the use of remotely sensed imagery in assessing "real
world" environmental problems. One of the major environmental issues facing Earth today is the loss of
biodiversity associated with tropical deforestation (The major causes of deforestation include logging, cattle
ranching, agricultural colonization, plantation agriculture, mining, hydroelectric power development, and illegal
drug plantations.). Good estimates of the extent of deforestation are difficult to obtain from ground level
observations alone, but satellite imagery can be a valuable tool in assessing the extent of forest loss in remote
areas. We are assuming, of course, that the imagery is of good quality in which surface features are not
obscured by cloud cover.

1. Start MicroMSI, and open the Rondonia Brazil Landsat TM July 20 1996.idw (From the main menu bar,
select File|Open Scene|Rondonia Brazil Landsat TM July 20 1996.idw|Open).

2.     Create a 234 (BGR) false color composite of the Rondonia subscene as follows:

        Select Display|Multiband from the main menu. When the Multiband Selection dialog box opens, make
the following band assignments:

               Display Color                        Image Band to Select

       Display this Band as Blue                    Band 2 (520-600 nanometers)
       Display this Band as Green                   Band 3 (630-690 nanometers)
       Display this Band as Red                     Band 4 (760-900 nanometers)
Click OK.
Display the false color composite at the 2:1 scale. Use the magnifying glass tool at the top of the false color
image window.

       Image description:

The scene is a 19 x 19 mile (1024 rows x 1024 columns) area in eastern Rondonia, Brazil extracted from the
full-size Landsat 5 Thematic Mapper (TM) image (July 20, 1996; path 231, row 68).

The scene shows the orderly clearing of rain forest land along the main and branching side roads, which were
built by the government to encourage agricultural settlement.

The false colors on the image represent the following features (Note: View the following examples at the 2:1
scale):

       Smooth pink or bright red rectangular areas (e.g., row 3949, col 2982): newly clearcut areas where
              undergrowth has not yet been cleared. To view this area, select View|Go to. Type 3949 at the
              Row <tab> Type 2982 as the col <tab> to Go to. Press <Enter> on the keyboard. To highlight
              the cursor, select the F2 key.
       Smooth bluish, greenish and whitish rectangular areas along roads (e.g., row 3885, col 3438): older
              clearcuts.
       Dark red (or brownish) textured or mottled areas: (e.g., around row 3765, col 2964): undisturbed rain
              forest.


3. The problem. Using the MicroMSI unsupervised classification operation and the false color composite,
complete the attached table to determine the area of natural forest cover, and the area of cleared land. Since
there is great biodiversity in rain forest areas, you might want to create a few general classes such as Forest 1,
Forest 2, etc.) when interpreting your unsupervised classification image. Use a similar technique for the cleared
areas (e.g., cleared 1, Cleared 2, etc.). The table on page 3 contains more blank spaces than are actually needed
for the exercise.

5. Perform an unsupervised classification.

       Select Display|Classified|Unsupervised
       When the Unsupervised Classification dialog box opens, select bands 1, 2, 3, 4, 5, and 7.
       Set the number of iterations at 20.
       Select 6 as the number of classes.
       Leave everything else at default settings.
       Select “OK”

       When Unsupervised Classification box appears, select ALL, then STOP when classifier is complete. (It
       may not run through all 20 iterations)

       Change the scale back to 2:1.

       With your mouse pointer over the image, click left mouse button and select Edit|Class Definition.
     When the Class Definitions dialog box opens, change class name from Class 1 to “Forest 1." (Make
     sure the Class Name is set to “Class 1” when you start).

     On the Class Definitions dialog box click on Class Color, and select bright green. Click OK to select the
     color. Finally, select Change and Close.

     Note: The natural vegetation cover has great variation, but it isn’t possible to identify different species
     areas without training site information. Thus, you should create general classes such as Forest 1,Forest
     2, etc. Identify the clearcut areas as New Clearcut, Old Clearcut 1, and Old Clearcut 2. This strategy is
     fine, because our ultimate purpose is to determine how much of the area has been deforested.

     Rename the other five classes as you did with class 1. Change the other classes as follows:

     Class 2:   Forest 2 olive green
     Class 3:   Forest 3 dark green
     Class 4:   New Clearcut (e.g., row 3949, col 2982) red
     Class 5:   Old Clearcut 1 (e.g., row 3897, col 3156) orange
     Class 6:   Old Clearcut 2 (e.g., row 3885, col 3438) yellow

     Click the “Change” button then click “Close.” Your maps should redraw with the new colors.

     When your classification is complete, click the right mouse button to observe the number of pixels and
     percentages for each class on the Classification Results dialog box.

6.   Complete the attached table by recording the information from the Classification Results dialog box.

     A.     Calculate the total acreage of natural forest and deforested land (i.e., old and new clearcuts) by
            dividing the total number of pixels for each class by 4.5. (Note: You can access a calculator by
            selecting Start|Programs|Accessories|Calculator.
                          Deforestation Classification Results Table
                           (You will not need all of the blank spaces)

Class Name              # of Pixels     Percent of Area       Acres*

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____

__________              ___________     _______________       _____



                                      Summary Statistics

Total natural forest:    percent_______ acres________

Total area deforested: percent_______ acres________



*Acres are calculated by dividing the number of pixels in each category by 4.5.

				
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