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Remote Sensing Techniques Using ArcView 3.2 and ERDAS Imagine 8.4

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Page 1
Remote Sensing Techniques Using
ArcView 3.2 and ERDAS Imagine 8.4
CEE 4984/5984
GPS/RS Applications in CEE
May 1, 2001
Mark Dougherty
Colin Kraucunas
Jen Verwest



Download file Pdf


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Table of Contents
Page
Table of Contents……………………………………………………………...
1
Project Statement…………………...………………...…………….………..
2
Tutorial 1: Mosaicking images in ERDAS Imagine 8.4………..……….…….
Tutorial 2: Creating image subsets in ERDAS Imagine 8.4….……...…..……
3
6
Tutorial 3: Stacking images in ERDAS Imagine 8.4…......………………….
Tutorial 4: Supervised classification in ERDAS Imagine 8.4….……………..
11
15
Tutorial 5: Unsupervised classification in ERDAS Imagine 8.4……………...
Tutorial 6: Vegetative mapping in ArcView 3.2………......………………….
22
30
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Project statement
The purpose of this document is to present the techniques needed for basic analysis of
remotely sensed images, including mosaicking, subsetting, stacking, unsupervised and
supervised classification, and vegetative mapping. The document is written as a set of six
tutorials with step-by-step instructions. It is assumed that the student has access to a
working version of both ERDAS Imagine 8.4 and ArcView 3.2, and has a basic
familiarity with both software packages.
It is recommended that the two data sets provided with this document be used to
complete the tutorials. The data sets include both SPOT and Landsat images for the New
River Valley, located in southwestern Virginia. SPOT image files 37080SE.BIL and
37080SW.BIL are located in folders of the same name, as shown in the figure below.
These two SPOT images, when mosaicked together, provide remotely sensed coverage
for the entire New River Valley. The remaining files, shown below as nrvband1.img
through nrvband5.img, represent the five bands of light reflectance (visible through mid-
infrared) captured by Landsat instrumentation. The Landsat images, when stacked
together, provide remotely sensed data coverage for the same geographic area as the
SPOT images.
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Tutorial 1: Mosaicking images in ERDAS Imagine 8.4
This tutorial will help you join two images together in a process called mosaicking. It is
often helpful to mosaic two images together, especially when the two joined images
result in a complete picture of an area being analyzed. Please note that the images must
be in *.img format to complete the steps outlined in this tutorial. If the images are in
another format, open them in the viewer and save them as *.img files.
Complete the following steps to convert images to *.img files:
1. In ERDAS Imagine, click the Viewer button to open a new viewer.
2. Click the Open Layer button or choose File->Open->AOI Layer
3. Select the correct file type, select the file, and hit OK
4. Choose File->Save->Top Layer As…
5. Select a path and filename to save the image and hit OK
6. When the copying image process is finished, hit OK
7. Close the viewer
Complete the following steps to mosaic two IMAGINE images (*.img):
1. Click on the DataPrep button on the ERDAS Imagine
Toolbar
2. Click on Mosaic Images…
3. The Mosaic Tool Window will open. Choose Edit-
>Image List…
4. The Mosaic Image List will open. Click on Add…
5. The Add Images for Mosaic window will open, as shown below. Click Add… to add
images. Find and select the first image file and click Add. Find and select the second
image and click Add. When finished, click Close, then click Close on the Mosaic
Image List window.
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6. The outlines of your two files
should be shown in the Mosaic
Tool window, as shown at
right. To edit the mosaicking
options, choose Edit->Image
Matching…
7. The Matching Options window
shown at right will open.
Choose “For All Images” as
the Matching Method and click
OK.
8. To edit the overlap area
options, choose Edit->Set
Overlap Function…
9. The Set Overlap Function
window shown at far right will
open. Choose the “Feather”
function and click Close
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10. You are now ready to mosaic the images. Choose Process->Run Mosaic…
11. Specify an output path and filename for the combined image, then click OK
12. ERDAS Imagine will run the Mosaic procedure, which may take a few minutes.
When it is complete, click OK in the progress dialog box as shown below.
13. In the Mosaic Tool window, choose File->Save to save the mosaic options as a *.mos
file. Choose the path and filename and click OK
14. Close the Mosaic Tool window. Your mosaicked image is ready for viewing.
15. Click on Viewer in the ERDAS Imagine Toolbar to open a new viewer
16. In the viewer, choose File->Open->AOI Layer… or hit the Open Layer button
17. Change the file type to IMAGINE Image (*.img), select your image file and click OK
18. Your mosaicked image is displayed in the viewer.
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Tutorial 2: Creating image subsets in ERDAS Imagine 8.4
Subsetting an image can be useful when working with large images. Subsetting is the
process of “cropping” or cutting out a portion of an image for further processing. In this
tutorial, you will create an image subset using the following steps.
1. Open the image in a viewer (any one of the nrvband*.img files can be used).
2. Using the rectangle selection tool, create an AOI (area of interest) rectangle
around the desired subset area. This can be done from the viewer menu by
selecting AOI -> Tools.
3. Choose Utility ->
Inquire Box->Fit to
AOI. This will
automatically set the
extents of the Inquire
Box to match those of
the AOI. Hit Apply.
Rectangle tool
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4. Next, select Data Prep-> Subset Image. This will give you the following dialog
box:
5. As shown at right,
select the input file
using the browse
button, then specify
the output file name
and location.
6. Hit the “From Inquire
Box” button. This
sets the extents of the
subset to match those
of the inquire box
selected in step 3.
7. Hit OK to create a
new subset image.
8. A progress bar, shown below, will display when the new subset image is created.
Hit OK to accept the new subset image, which will be saved to the location
specified in step 6.
9. Repeat steps 1 – 8 for each of the four remaining band files (nrvband*.img).
[Note: For each image, first load, then select the rectangular AOI from step 2.
When you repeat step 3, you will automatically fit the coordinates of the pre-
selected AOI to the current image.]
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10. The next step in the subsetting process is to combine three band images into one
multispectral image. Begin by selecting Data Prep-> Create New Image. The
following dialog box appears.
11. From the Create File dialog box, select the output file browse button and specify a
path and output file for the combined image. Hit “From Inquire Box.” This will
set the coordinates of the new image to match those of the subsets previously
created.
12. The cell size
fields should
be changed to
28.50 to
match those of
the subset
images.
[Note: you
can find this
information
under
Utilities->
Layer Info, as
shown at
right.]
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13. When all values in
the Create File dialog
box for Date Type
and Output Options
are as shown above
(step 10), hit OK. A
progress bar will
appear. When the
process has
completed, hit OK.
14. The next step is to
open the new image
created in step 10.
The image will
appear all white
because it does not
yet have raster
information in it. To
do this, select Raster
-> Band
Combinations. The
dialog box, below,
will appear.
15. Use the
browse
buttons to
select the
appropriate
band subset
files for
each band.
Note: The image combination shown
above represents the default band
combinations representing visible
blue, green, and red bandwidths.
16. Each subset image only has one
layer, therefore layer settings can be
left at one. Hit apply and you should
have a color image similar to the one
shown at right.
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17. In order to save your image with these settings, select File -> View to Image File,
then select a name and output location.
18. Depending on the types of analysis you are doing, it may be necessary to re-order
the band
combinations
to obtain the
correct
appearance.
This is done
by once again
selecting
Raster ->
Band Combinations.
Note: The image combination
shown above represents the
default band combinations
representing visible green and
red bandwidths, as well as near
infra-red. The resulting image,
shown below, is widely used in
the classification of vegetation
vs. bare surfaces because the
healthy vegetation has a very
high infra-red reflectance,
while bare earth or asphalt has
very little to zero infra-red or
red reflectance.
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Tutorial 3: Stacking images in ERDAS Imagine 8.4
In order to analyze remotely sensed images, the different images representing different
bands must be stacked. This will allow for different combinations of RGB to be shown
in the view. The following steps show how to stack images.
19. In ERDAS, click on the Interpreter button on the ERDAS Imagine Toolbar
20. When the Image Interpreter dialog box appears, select the Utilities.
21. I n the Utilities menu, select Layer Stack. The Layer Selection and Stacking dialog
box will appear.
22. In the Layer Selection and Stacking dialog box, select the first layer for the Input File
by selecting the browse button. Navigate to the desired folder, and select the image
that will be Layer 1 in the new image.
23. Click the Add button to create this file as Layer 1.
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24. Continue to select the input files in order and click Add. The files will become the
layers of the new image.
25. Once all the files are added, create an Output File by selecting the browse button and
navigate to the desired folder. Name the file and hit Ok.
26. Verify the remaining options, and click Ok.
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27. When the Modeler dialog box is complete, click Ok.
28. Open a new Viewer and open the newly created raster image.
29. To change the layer being displayed, choose Raster then Band Combinations in the
Viewer dialog box.
30. Change the layers that are displayed for the respective colors in order to get the
desired bands visible. The selection shown below displays a combination commonly
used for land use and vegetative mapping. The Red layer is near infrared, the Green
layer is red, and the Blue layer is green.
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31. Hit Ok to view the image, below.
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Tutorial 4: Supervised classification in ERDAS Imagine 8.4
Image interpretation is the most important skill to be learned before producing accurate
land use maps from remotely sensed data. Supervised classification allows the user to
define the training data (or signature) that tells the software what types of pixels to select
for certain land use. Facts about the area, knowledge about aerial photography, and
experience in image interpretation permit pixels with specific characteristics to be
selected for a better classification of the image. Through experience, supervised
classification becomes easier and more accurate.
19. Open the raster image with the different bands stacked in layers as created in
tutorial 3.
20. On the Viewer menu, choose Raster and select Band Combinations from the list.
21. When the Band Combinations dialog box appears, change the layers so that the
Red Layer is Near Infrared (Layer 4), the Green Layer is Red (Layer 3), and the
Blue Layer is Green (Layer 2).
22. Hit Ok. The image that appears, shown on following page, is a common band
combination used to evaluate land use and vegetation. In this image, green (band
2), red (band 3), and near infra-red (4) band are represented by blue, green, and
red, respectively. This band combination and color selection make identification
of bare surfaces easily distinguishable from healthy vegetation.
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23. On the main ERDAS menu, select the Classifier button.
24. In the Classification menu, select Signature Editor. The signature dialog box will
appear with a new file opened to begin defining training data. The signature
editor allows the user to select areas of interest (AOI) to be used as training
samples to categorize the photograph.
25. In the Viewer menu, select AOI and then choose Tools.
26. In the AOI Tools dialog box, select the polygon tool to create an AOI.
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27. Zoom into an area to be classified, and draw a polygon around a specific region to
be used for training data.
28. With the AOI still selected, hit the Select New Signature(s) from AOI button.
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29. Change the signature name by clicking in the field and entering a more
descriptive name. Change the color to be displayed that is defined by this
signature by clicking in the color field and selecting a new color.
30. Continue to select more signatures until all desired land uses or areas are selected.
31. In the Signature Editor dialog box, select File then Save As. Browse to the
desired folder. Name the file and click Ok. The resulting image classifications
will distinguish deciduous from coniferous trees using different shades of green.
Urbanized bare areas will be represented by red, water by blue, and agriculture
by yellow. With careful pixel selection, bare soil can be distinguished from
urbanized bare areas. In this example, based on knowledge of the area, bare soil
is considered to be bare soil fields (as opposed to urbanized impervious areas such
as asphalt).
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32. Close the Signature Editor dialog box by selecting File then Close.
Now that the Signature File has been created to select the different classifications, a
supervised classification can be performed.
33. In the Classification dialog box, select the Supervised Classification button. The
Supervised Classification dialog box will appear.
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34. To select the Input Raster File, hit the browse button and navigate to the desired
folder. Select the file to be classified that is open in the viewer.
35. To select the Input Signature File, hit the browse button and move to the preferred
folder. Select the Signature File previously created.
36. To create a Classified File, hit the browse button and navigate to the folder where
files are being saved. Name the file and hit Ok.
37. Verify the other settings below and click OK.
38. When the Status dialog box is complete, click Ok.
39. Open the newly classified file to observe the classifications and verify the
signature file. Most of the land use is deciduous trees (light green), followed by
agriculture (yellow), bare soil (pink), and urban (red). Water is shown in blue.
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40. If the signature file needs to be edited, open the Signature Editor by clicking on it
in the Classification dialog box.
41. In the Signature Editor dialog box, select File then Open. Navigate to the desired
file and open the previously created signature file.
42. Edit the signature file by using the add , replace , merge , and delete (in
the edit menu) options.
43. Save the redefined signature file and repeat steps 15 through 21.
Through experience, supervised classification becomes easier and more accurate. Image
interpretation is the most important skill to be learned before producing accurate land use
maps.
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Tutorial 5: Unsupervised classification in ERDAS Imagine 8.4
Performing an unsupervised classification, covered in this tutorial, is simpler than a
supervised classification. Unfortunately, simplicity comes at a cost. In unsupervised
classification, the signatures are automatically generated by an algorithm named
ISODATA. The resulting classification has less discerning abilty than a supervised
classificationdue to the lack of training data supplied to the clustering algorithm. In this
tutorial, you will perform an unsupervised classification using the following steps.
44. On the main ERDAS menu, select the Classifier button, which will open the
Classificationmenu.
45. On the Classification menu, shown at
right, select Unsupervised Classification.
46. The Unsupervised Classification dialog box, shown on the next page, will appear.
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47. Under Input Raster File,
place the name of the file and
file location to classify.
Under Output Cluster Layer,
give the target destination
and filename of the output
file. Click the Output
Signature Set to disable the
Output Signature Set
filename box (you will not be
creating a signature set as
you did with supervised
classification).
48. Next set the clustering
options as follows;
Maximum Iterations = 24
and Convergence
Threshold = 0.950, as
shown below.
49. Put 5 for number of
classes. Click OK to
begin the classification
process.
50. The job status dialog box,
shown below, will alert
you as to progress. Hit
OK when the process is
100% complete.
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51. After the classification process is complete (yes, that is all there is to unsupervised
classification processing), you will want to evaluate and test the accuracy of the
classification. You may also want to reclassify your image using a different
number of classes with a different number of iterations. Let’s see what the
classified image looks like.
52. A good way to evaluate the
results of the unsupervised
classification is to overlay
the original image data
with your *_isodata.img
file. To do this, from the
Viewer menu bar open the
original image with File
Open
Raster Layer.
The image at right should
appear in your viewer.
53. Change the colors displayed to match those used in tutorial 4 by selecting from
the Viewer menu bar Raster Band Combinations. Change the Layers to Colors
4,3, and 2. The following familiar image should appear.
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54. The next step is to overlay the classified image over the original. Open a new file
dialog box as described in step 9, above. Select the directory where you saved the
*_isodata.img file previously. In order to add the new image without clearing the
original image, click the Raster Options tab at the top of the Select Layer to Add
dialog, as shown below, and clear the Clear Display box.
55. After loading the *_isodata.img image, select Raster Attributes then Edit
Column Properties. You will rearrange the columns of the following editor box
as follows;
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56. Select the column headings one at a time as shown and hit the Up key to rearrange
the headings, as shown. When complete, hit OK.
57. The Raster Attribute Editor box, below will appear with columns as below.
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58. Next, select the Opacity column to highlight all the values in blue (shown above).
Select Edit Formula and place a zero in the Formula box function above as a
value for all cells in the column. This makes the newly classified image
effectively transparent, until you are ready to add colors, one at a time.
59. Change the color and opacity
in Class 1 to red and 1,
respectivel, and note the
change in the raster image
displayed.
60. Continue editing the five classes one at a time, adding colors of your choice to
represent what you think to be specific features. The appearance of the final
drawing will depend on the color combination and number of classifications you
choose. For example,
the first color chosen
above, red, does not
seem appropriate for
water, which is
obviously displayed
in the image. This
color and heading can
easily be edited to
blue by clicking on
the cell. The
resulting drawing and
legend may look like
the following, at
right. Note that the
Classes have not been
given names, yet.
With unsupervised
classification, it is
often necessary to do
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ground truthing after the classification is complete. As stated previously, several
attempts at the unsupervised classification may be desirable to achieve a land
classification that is understandable.
61. A somewhat more intuitive image display is presented below. The color scheme
is the same, but the image has been re-classified using only four classes.
Although the map below may be easier to interpret than the one above, it likely
will have somewhat less discriminatory detail. Be aware that there are bound to
be trade-offs in the selection of classes that will depend upon the use being made
of the data and the land use being categorized.
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62. A useful aid to evaluating unsupervised classifications is through the use of the
Utility menu on the viewer menu, specifically the Blend, Swipe, and Flicker
commands. Each of these commands will bring a control box that will either
blend, or swipe, or flicker the upper-most image alternately with the lower image
within the View. Recall from step 11 that you overlaid the classified image on
top of the original, which you can now view in periodic “swipes” or “flickers” or
“blends” to help evaluate the types of land cover beneath your classified image.
63. The following are the three control boxes that are activated by the Blend, Swipe,
and Flicker commands. We leave it to you to experiment with these handy tools
as you gain more experience in your classification skills. Enjoy!
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Tutorial 6: Vegetative mapping in ArcView 3.2
Vegetative mapping finds areas of healthy vegetation as well as stressed vegetation from
a remotely sensed image. In order to do vegetative mapping, two bands are needed,
visible red and near infrared. These bands are chosen because vegetation, especially
healthy vegetation, is very reflective in the near infrared range and it provides good
contrast with water. In order to distinguish healthy vegetation from other reflective
sources, the visible red is chosen, which contrasts vegetation from bare soil, rocky
surface, and man-made features. From the visible red and near infrared layer, a
Normalized Difference Vegetation Index (NDVI) is calculated in ArcView using the
formula NDVI = (IR-R) / (IR+R), where IR is infrared and R is visible red. A single
band theme in grayscale is created that highlights vegetation. The following tutorial
shows how to quickly build an NDVI vegetative mapping image in ArcView 3.2.
64. In ArcView, add the Image Analysis extension by selecting File then Extensions.
65. Check Image Analysis and click Ok.
66. Open a new view to add the raster image for the vegetation mapping with the
green, visible red, and near infrared bands created in tutorial 2.
67. Open the image by clicking the Add Theme button.
68. Change the Data Source to Image Analysis Data Source and Navigate to the
desired folder. Click Ok.
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69. Click the check box next to the theme to draw it.
70. Double-click the theme to bring up the Legend Editor. Change the Red Band to
the Red Layer, the Green Band to the Green Layer, and the Blue Band to the Blue
Layer. Click Apply and close the Legend Editor dialog box.
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71. The image is displayed in the commonly used form for vegetative mapping.
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72. In the Main menu, select Image Analysis the Vegetative Index. The Vegetative
Index dialog box appears.
73. Change the Near Infrared Layer and Visible Red Layer to Layer_Red and
Layer_Green, respectively, or according to how the image was generated in
ERDAS. Click Ok.
74. Click the check box next to the NDVI theme to draw it.
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75. The bright areas represent areas of vegetation and the dark areas represent water,
urban, and bare soil.
76. Save the image by selecting the NDVI theme. In the View menu, select Theme
then Save Image As.
77. Navigate to the desired folder, and name the image. Verify that the file type is the
desired format, and click Ok. Choose IMAGINE Image as the file type to be able
to use in ERDAS.
78. Select No to add the image as a theme to the view.

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Inkonsistensi Perizinan Pada PT Gunung Mas Raya terhadap RTRWN, RTRWP, RTRWK, maupun TGHK
From Pelanggaran Terhadap Kawasan Bergambut
Dari 12627.9 Ha perkebunan eksisting terdapat, 1962 ha pada kawasan gambut dengan kedalaman 2-4 meter, 1843 ha pada kawasan gambut dengan kedalaman lebih dari 4 meter.
From PT Gunung Mas Raya 1
Google earth
From PT Gunung Mas Raya 1
From PT Gunung Mas Raya 1
Citra Landat Tahun 1990
From PT Gunung Mas Raya 1
Citra Landsat 2000
From PT Gunung Mas Raya 1
Citra Landsat 2005
From PT Gunung Mas Raya 1
Citra Landsat 2007
From PT Gunung Mas Raya 1
Pelanggaran Terhadap Perda No 10 tahun 1994 Tentang Rencana Tata Ruang Wilayah Provinsi Riau.
From PT Gunung Mas Raya 1
Pelanggaran Terhadap PP No 10 Tahun 2008 tentang Rencana Tata Ruang Wilayah Nasional
From PT Gunung Mas Raya 1
Pelanggaran Terhadap Rencana Tata Ruang Wilayah Kabupaten Rokan Hilir
From PT Gunung Mas Raya 1
Pelanggaran Terhadap Tata Guna Hutan Kesepakatan Pemukiman Dalam Kawasan Suaka Margasatwa Bukit Rimbang Bukit Baling Muara Bio
From Pemukiman Dalam SM Rimbang Baling
Tarusan
From Pemukiman Dalam SM Rimbang Baling
Miring
From Pemukiman Dalam SM Rimbang Baling
Gajah Bataluik
From Pemukiman Dalam SM Rimbang Baling
Batu Sanggan
From Pemukiman Dalam SM Rimbang Baling
Aur Kuning
From Pemukiman Dalam SM Rimbang Baling

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