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Water was expected to have a low spectral response 
in all four channels, especially in band 5, the 
water-absorption band. There was difficulty, 
however, in distinguishing between water and other 
dark cover categories by four channel mutlispectral 
classification alone. Class 37 displayed a typical 
pattern for open water and was found in lakes, sewage 
treatment ponds, and in other deep ponds. Class 54, 
though, was comprised of equal proportions of open 
water and coal piles. By training on individual 
pixels in both water and coal cover categories it was 
noted that the spectral responses frcm the visible 
and near infrared bands were essentially identical, 
while the middle infrared channel (band 5) showed a 
slightly lower response for the water. This, 
however, did not present enough variability to keep 
the two classes from being merged in the hierarchical 
clustering process. Classes 64, 28, and 27 also 
showed a high propensity for water within their 
spectral classes. The spectral curve for class 43 
displayed a rather odd shape for water, yet it 
characterized the Jordan surplus canal with it silty 
water and its mix with embankment materials. 
The very dark materials in Figure 2(b) were 
primarily considered to be open coal or slag piles, 
with occasional sites of tar or very black asphalt. 
As mentioned earlier, there was considerable 
difficulty in clearly distinguishing these cover 
types from water surfaces by spectral characteristics 
alone. Other methods were later tested to better 
differentiate these cover types. Class 54 
represented the darkest coal and slag piles in a 
smelter location. Lighter colored materials were 
represented by classes 64 and 28 and were found in 
scattered coal piles throughout the industrial areas 
of Salt Lake. 
Asphalt surfaces cover much of the urban Salt Lake 
area and are good indicators of cannercial and 
transportation land uses. A distinction was made 
between light and dark asphalt surfaces since they 
often represent different land use activities or have 
different effects on the urban environment. The dark 
inert materials cover type mostly included blacktop 
areas and dark material mixed with soil (such as 
railroad yards). Classes 12, 38, and 27 represented 
this cover category although class 27 also 
characterized some water bodies. The other classes 
were also occasionally confused with water in the 
surplus canal. The light asphalt surfaces were 
generally asphalt mixed with gravel to form roads and 
parking lots. Several of the pixels in these classes 
also represented roofing materials in the commercial 
areas composed of tar and gravel. Classes 58 and 10 
especially characterized these areas devoid of 
vegetation. Classes 59 and 5, on the other hand, had 
a higher proportion of vegetation cover mixed in, as 
evidenced by the flatter slopes between channels 4 
and 5 in their spectral curves shown in Figure 2(d). 
Classes 15 and 35 primarily represented parking lots 
and road networks. Although class 63 was also mostly 
transportation, there was a little mix with natural 
grass areas. 
There were large portions of the study area with 
mixed pixel responses. This generally occurred in 
residential areas where a large variety of 
heterogeneous surface materials were spaced very 
closely together, usually into an area smaller than 
the spatial resolution of a pixel. The mixed 
responses from surfaces such as lawns, concrete, 
asphalt shingles, trees, metals, etc. made the 
hybridized signature curves displayed in Figure 2 (e- 
f). While these classes occupied virtually the same 
region on the discriminant function scatter diagram 
as other non-mixed land cover categories, they 
reflected distinct differences in the shapes of their 
spectral curves. This was observed by comparing 
Figure 2 (e-f) with Figure 2 (g-i). Class 56 had a 
very bright response and was found in many trailer 
courts. The major contributor to this response was 
the shiny roofs of trailers with sparse lawns and 
asphalt mixed in. This class also expressed some 
confusion with agricultural fields having bare ground 
and stubble remaining. The shape of the spectral 
curve for class 36 was almost identical to class 56 
yet it was slightly darker in reflectance. Class 36 
was also primarily shingle roofs with small mixes of 
lawns and trees. This class was found among light 
roofed condominium complexes and was also 
occasionally confused with stubble fields. Classes 8 
and 34 were found in condominium complexes and other 
high density residential areas fringing the central 
business district (CBD). There were three distinct 
cropped fields, however, that were also identified as 
class 8. Class 45 was not as commonly associated 
with residential areas. This class more often 
represented the mixed pixels where asphalt borders on 
natural grass areas (e.g., along freeways, railroad 
tracks, and in cortmercial or industrial areas) . 
Other mixed pixel locations showed a higher 
percentage of vegetation contributing to the 
spectral response. Classes 65 and 4 represented 
surface materials that were approximately 50 percent 
covered by vegetation (usually lawns and trees). 
Class 1 contained about 60-70 percent vegetation 
cover, while classes 14 and were generally over 75 
percent healthy vegetation. These mixed pixels with 
high vegetation components were again found primarily 
in residential locations where surface cover was very 
heterogeneous within a small area. These mixed 
classes also showed a very large within-class 
spectral variance (over 6.5 times the mean variance 
for all remaining land cover classes). 
A large portion of the study area was not developed 
and was covered by senesced annual grasses and weeds. 
A wide variety of species and cover densities were 
grouped within a few signatures for this category. 
Figure 2(g-h) illustrates two distinct patterns in 
these natural grasslands. Classes 44, 47, 13, and 
30 displayed a pattern of healthier vegetation, as 
indicated by the steepness in curves between channels 
3 and 4. These were usually weedy fields or vacant 
lots where the plants were not completely senesced. 
In classes 13 and 30 the soil was often wet, 
contributing to the darkness of the response. There 
was seme confusion between this cover category and 
fields in areas where crops were newly planted and 
the soil was still contributing a major portion of 
the response. Classes 33 and 22 still had a 
considerable amount of vegetative response and were 
quite reflective, containing a high proportion of 
light soil. Classes 51 and 57 represented darker 
colored soils in natural, undeveloped areas, while 
classes 25, 7, and 11 were often found as idle fields 
in dry farm or irrigated areas. 
Cropped or sparsely watered agricultural fields 
represented in Figure 2(i), were typified by mixed 
soil and vegetation responses. The surface materials 
contributing to these spectral curves mostly included 
cropped fields with seme stubble and sane new growth 
showing through; newly planted or young crops; 
pasture areas, irrigated or subirrigated, with weeds 
or bare patches; and occasionally short cropped 
grasses with same soil showing through. Since this 
category was a blend of the vegetation and soil 
responses there was some confusion with other similar 
cover categories. Pixels representing these spectral 
classes were often found in areas classed as lawns, 
natural grasses, or in residential areas. Classes 
23, 55, and 67 were usually correctly identified as 
pastures or cropped fields, while classes 24 and 9 
were frequently confused with sparse lawns. 
Distinguishing lawns from other vegetative surface 
materials was also rather difficult. Various lawns 
were quite different from each other in terms of 
moisture content, amount of thatch, shortness of the 
grass, and grass vigor. Fairways of golf courses and 
school playfields were most characteristic of the 
lawn cover type. Classes 66 and 17 from Figure 2(j) 
showed the short cropped, but healthy grass found at 
parks, playgrounds, and many golf courses. Class 39 
identified lawns that often had trees or shrubs 
nearby. Several other spectral classes were