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Remote sensing for resources development and environmental management
Damen, M. C. J.

thresholds within the 67 spectral classes in order to
reclassify the data into more definitive land cover
categories. After working with the thermal data it
was decided to add two more land cover categories to
the classification. The light vegetation class was
divided into a class of sparse vegetation with soil
showing through, and another class of mostly soil
with a little green vegetation remaining. The mixed
pixels were divided into three groups instead of two.
These categories include mixed pixels with a low
percentage of vegetation (10-40 percent), medium
vegetation response (around 0.50) , and mixed pixels
with a dominant vegetation component (over 0.75).
These two additional categories raised the number of
land cover types that were identified to 14.
Fran a thermal channel 6 histogram it was often
possible to observe "breaks," or places where bimodal
distribution curves crossed. It was mentioned, for
example, that class 54 of the classification map
represented both open water and coal. The mean
multispectral signatures for these two cover types
were nearly identical yet the thermal band
demonstrated a clear distinction between the two
categories. Other class separations were not as
clear as in class 54. A more typical situation is
class 56, where stubble fields were classed the same
as the mixed cover in several trailer courts. The
thermal channel indicted that agricultural fields
were slightly cooler than their urban residential
counterparts. After changes were made the map was
again reclassified and grouped into the 14 cover
types. The reclassified map with 122 classes is
referred to as THRM67.
In order to compare the utility of the two land cover
maps (THRM67 and CLUS67) an accuracy assessment was
made on each. The first step was to geometrically
correct the maps so that each classified pixel
corresponded to a particular Universal Transverse
Mercator (UTM) coordinate on the ground.
A random sample stratified by map categories was
selected as the procedure for assessing map accuracy.
An ELAS module named "RANS" (uniform random sample)
created a new data file from the geometrically
corrected THRM67 classification map. This file
contained the original classification data minus 350
sample locations (25 samples each from the 14 land
cover categories). A cursor was trained on each
sample pixel to determine the precise UTM coordinates
for each sample. These coordinates were then
plotted onto USGS 1:24,000 topographic maps. When
all 350 samples were plotted, an investigation
ccnmenced to determine the "ground truth" category
for each of the sample sites. In an attempt to
eliminate the bias, the map categories were not
included on the maps taken to the field for ground
When the "ground truth" determination was
canpleted, the observed cover categories were
compared to the map categories and a confusion matrix
was constructed. Percentages were calcuated for the
number of pixels correctly classified, pixels that
were classified in the wrong category (errors of
catmission), and pixels that were not placed in the
correct categories (errors of omission). The map
category marginal proportions were calculated in
order to remove the over-represenation of small
categories from the stratified randan sample (Card
1982). These marginal proportions were multiplied by
the percentages of correctly classified pixels and
then surtmed to provide the overall map classification
accuracy (estimated probability correct).
The same 350 samples derived from the THRM67
classification were also used to build a confusion
matrix for the CLUS67 map. The CLUS67 map
demonstrated an overall map accuracy of 80.2 percent
for the fourteen land cover categories, with a 0.05
confidence interval between 0.7581 - 0.8459. The
THRM67 map, on the other hand, showed an accuracy of
91.6 percent with a 0.05 confidence interval of
0.8866 - 0.9434. The overall accuracy assessments
fran each map were compared using a "difference of
proportions" test to determine if there was a
significant difference between them. There
definately was a significant improvement in
classification accuracy using thermal channel 6 data
The mapping accuracies for each class were also
compared and tested for significant differences at
0.05 level. In these particular cases a "t"
statistic was computed rather than a "z" value since
the number of sample sites per class was lower than
50. The results of these tests indicate that there
is a significant improvement in detecting asphault,
coal, water, and sparse vegetation by using thermal
thresholds. The most dramatic improvement was
observed in the water and inert classes. CLUS67
maintained a high degree of classification error
between water and coal. Water was also carrmonly
classified as dark inert material and asphault.
These descrepancies were almost entirely eliminated
by use of the thermal data.
Another significant advantage of using thermal
thresholds was in discriminating between cool
agricultural areas and the warmer urban and natural
grass cover types. Even though a minor confusion of
classes is still evident after using a combination of
multispectral and thermal classification techniques,
the land cover map of the Salt Lake study area
derived from this study is a substantial improvement
over previous TM, IMS, and MSS classifications of
urban areas.
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