Pamela Sifuentes
GEOG 4233
Dr. de Beurs
Spring 2011
Lab 11 & 12 Report: Southern Brazil
For the assignment, Classification Methods: Lab 11 and 12, classifying images were
made using the Supervised Classification Method. As part of the assignment for training and
validation, ROIs (regions of interest) had to be selected. The assignment also required six
classes to be defined using a minimum of ten polygons, for the ROIs that were selected for each
class. The samples used for classification that were being collected followed the method
presented in Lillesand and Kiefer (1994), where the one percent for the image pixels were being
met to identify for the image classifications.
I initiated the assignment by subset-ting my Atmospherically Corrected anniversary
images, of May 29, 2005 and May 24, 2009, to 700x700 pixels. These images do overlap, so they
both show the exact same area. This is of course needed to determine change in the area over
time.
I then proceeded with classifying the image from May 29, 2005, Figures 1a)-7. As you
can see the confusion matrix reveals that the overall accuracy of this classification is 99.6 %
accurate. The lowest class for accuracy percentage was Forest at 98.54 %. Forestry and
Cropland was the most difficult to classify in my images, for both years. What seemed to be
forest did not really follow a pattern that a forest would. In fact, what I perhaps should have
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classified as forestry followed more a cropland pattern. So I resorted to Google Earth under the
SPEAR tool to help me determine what type of land cover it was. It turned out to be quite clear
that it was croplands, the reason it seemed so condense was because of the agricultural
practices that occurred in the area. Images through Google Earth also confirm this. From what I
found the crop that area grown and cultivated are in fact from trees, which is why they seem to
look like a gridded forest, if you will. However, I had to classify this class as dense croplands,
because in fact it is a cropland. The trees in the area are clearly are not in grid form. This was
also supported by Google Earth. So, I had to classify them as forest because in fact they were
compact and not at all regulated by some sort of grid. This on the other hand is not evident on
the classified image, except for some specific areas. Another issue I ran into while trying to
classify my image was with the fallow land in the area and the urban class. For the reason that
fallow land and urban land have a high reflectance, ENVI could not make the distinction, I even
tried changing the bands to generate some sort of different reflection or color and nothing
seemed to do the trick. This was evident with my separability report as well. So I had to make a
conscious decision to join both classes under Urban as its classification. As you can tell from the
original subset image, there is something in the water. Not sure of what it was I classified water
as Deep water, where the depth of the water is greater, and as regular water to see if the water
was hollow or just simply contaminated. In retrospect, I should have classified water as
contaminated water and deep water as simply water. The image is from May which is fall for
Brazil, but because of its location temperatures are still very high, just as their summers.
For the supervised classification of the image of May 24, 2009, I had the same issues as I
did for that of 2005. This resulted in me having no problem using the same ROIs, figures 8-12.
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The accuracy percentage for this image was 98.8%, once again forestry at the lowest with
98.39%. This time it was more difficult to make the distinct in between what I had classified as
grass other than dense croplands. As you can see from the original subset images what was
once fallow land no longer is true. It is really more cropland but the reflection seem to be the
same as what I classified as forest. Once again I resorted to Google Earth under SPEAR tool but
that seemed to be not as useful. Despite the change in land cover, 98.39% is still a percentage
acceptable to continue with the classification.
The Change Detection Statistics (CDS), figures 13-16, displays that there has been a large
increasing in urban land cover because of the 70% in class change, as well as a grass increase
with a 68 % in image change. This is evident on the images. From 2005 to 2009 there has been
in increase in urban and grass land with a loss in forest and dense croplands, as supported by
the CDS with a Cropland loss at 13.8% change and a forest loss at 52.3% change. Water
contamination is also supported by a loss of deep water at 44% change; which is also supported
by the image in 2009, an increase in contamination ( what I should have labeled contaminated
water and not as water). All further supported by the loss in area, in squared kilometers, as you
can see for yourself in figure 15.
Over all the two classified images are far from being similar. It is evident that there has
been a loss of dense croplands and forestry, replaced by a gain in grasslands and urban
development from 2005 to 2009. However, this is really far from the truth. The classifications
images show deforestation and loss in croplands perhaps because of urban development; yet
the original subset images show that there has not been a full lost in deforestation and
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croplands. Focusing on the original subset images you can see that there has been a gain in
croplands as well as a loss in 2009 than in 2005. The areas that should have been classified as
fallow land show up as areas that should have been classified as croplands, not dense but
subtle. In addition, areas that were classified as dense croplands are now as fallow land. If one
plays close attention, it is evident that these changes should not be considered deforestation
but as a re-growth or as an agricultural cycle. This could perhaps be traced or supported to the
agricultural practices of the area, for further study.
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Figure 1 a) & b): Original Subset Images at 700x700 pixels
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Figure 2: Training Set for May 29, 2005
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Figure 3: Validation Set for May 29, 2005
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Figure 4: ROIs 2005
Figure 5 and 6: Classified Image for 2005 and zoom
Figure 7: Confusion Matrix 2005
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Figure 8: Training Set for May 24, 2009
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Figure 9: Validation Set May 24, 2009
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Figure 10: ROIs 2009
Figure 11: Classification Image and Zoom for 2009
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Figure 12: Confusion Matrix 2009
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Figures 13-16: Change Detections Statistics
Pixel Count
Percentage
Area (Square Km)
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Reference
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