1-1: Subjective. BACK
1-2: The histogram is bimodal, i.e., two peaks. One
narrow peak near a DN value of 12 corresponds to the dark tones associated with
the ocean; the second broader peak near 28 relates to tones found on the land.
Very few pixel DNs occur beyond 50. As plotted in this diagram, most of the
values assigned as gray levels (which for Landsat can extend from 0 to 255 DNs)
would be dark and the resulting image would not be pleasing to the eye nor very
informative. Yet the Band 3 image as shown above has a considerable amount of
light gray tones and looks "good". The histogram here is "raw", i.e., results
from the actual DN values recorded by the sensor system. This DN distribution
has been stretched to cover a wider range of gray levels, as will be discussed
later in this section, giving rise to the image seen that has a suitable contrast
of gray tones. BACK
1-3: Morro Bay itself is just below the central town.
The large rock in the foreground (Morro Rock) is easily spotted. In the image,
he breakers along the beach are evident as is the sandy beach beyond it (on
its right, part of the beach is covered with dark vegetation which appears in
the Landsat image as well. The range of hills in the center, running roughly
perpendicular to the photo can also be readily seen in Band 3 as can the taller
hills (the main Coast Range) in the upper left. The barren brown area with a
green field can be picked out in the photo. Note the five oil storage tanks
just left of Morro Rock; look carefully, as these too are visible. You probably
found still other features in common. The second photo is located near the bottom
left corner of the Landsat image. BACK
1-4: The silty water (a in map) is medium to dark
gray in TM bands 1, 2, 3 and blackish in 4, 5, 7. It cannot be distinguished
in band 6 (thermal) from its enclosing water, as all have a black tone. The
breakers (h) are whitish in bands 1, 2, 3, mottled white-gray in 4, and dark
gray in 5 and 7. They aren't visible in band 6. The beach sand (non-vegetated
part at c) is whitish in bands 1, 2, 3, and 7, light gray in bands 4 and 5,
and medium gray in 6. Los Osos is marked by a criss-cross pattern (the streets)
and is a mottled medium gray in bands 1, 2, 3, a somewhat more uniform medium
gray in 3, 5, 7, and uniform medium gray with large whitish patches (business
section?) in 6. The marshy delta (o) is medium to medium-dark in all bands except
4, which is a mottled light and medium gray. Many sunlit slopes (d) tend to
be whitish in all bands (most so in band 6) but somewhat more gray in 4. Shadows
(e) are black to very dark gray in all bands; but in band 4 some shadowed slopes
are medium to dark gray. BACK
1-5: This Landsat image was acquired in mid-November.
At that time of year, the Sun is rather low in the sky at this latitude, so
solar rays come in at lower angles. The amount of irradiation is controlled
by this angle (mathematically, it varies with the trigonometeric cosine of the
angle of incidence [measured relative to the vertical or normal with respect
to the surface]); a low angle reduces the amount or intensity of radiation reaching
the surface (in part, because for a small solid angle which would project as
a square for vertically incident radiation, that solid angle for low incidence
spreads out into an ellipse). Also, at low angles the pathway of solar radiation
through the atmosphere is longer (hence, more absorption). So, scenes in winter
time should be darker overall, and relative contrasts may be less compared with
brighter summer images. Shadows will be wider in winter images (ellipse effect),
so in hilly terrane the sense of relief (term which applies to relative height
differences) will be accentuated. And, of course, vegetation may be largely
dormant in winter scenes, reducing contrast in Bands such as TM 1-3, darkening
Band 4, and having some effect on 5-7. The effect of changing the time of day
is twofold: first the Sun elevation (angle) changes and second Sun Azimuth (compass
direction of incidence) also changes. This can have a profound influence on
the appearance of mountains. A morning azimuth from the southeast causes a significant
bias in illumination of slopes - those whose ridges run NE-SW will have their
SE slopes illuminated and NW slopes darkened. An afternoon azimuth emphasizes
mountain groups with crests running NW-SE, with corresponding SW slopes bright
and NE slopes darker. In general, all Landsat images (and those from other spacecraft
sensors operating with morning overpasses) have this prevailing bias in shadowing;
radar imagery also has selective relief bias (see Section 8). BACK
1-6: This is a good example
of the role of image size (scale) and resolution.
For most viewers, with 17 inch or less screens, it
is very difficult to see the storage tanks in the
small scale images that occupy only part of the screen.
But, in the map view, the image occupies the full
screen and the tanks stand out, aided by the increase
in size as well as the contrast (tanks white surrounded
by medium gray background). BACK
1-7: No revelation here. You may be right, but we'll
keep you guessing til later in the section. The question just makes sure you
accepted the challenge put forth in the text. BACK
1-8: Two reasons likely account for this darkness.
First, in the right corner, the land has risen to near the crest of the Coast
Ranges. Being higher, it is somewhat cooler. Second, pines, redwoods, and more
deciduous vegetation occur at these higher altitudes, and these tend to further
cool the air by evapotranspiration. BACK
1-9: In Bands 2 and 3 note the two small, very bright
patches, rather rectangular in shape. These are presumably sand pits. They appear
darkest in Bands 5 and 7. Band 5 is somewhat lighter than 7 although the feature
contrasts are about the same. This could be related to one or more of three
things: The radiance level is actually brighter in 5; the Band 5 detector has
a slightly higher gain; or, when the image is projected on the screen, or printed
on paper, there is a bar scale of progressively darker gray level steps (shown
in the New Jersey images in Section 1) that serves to calibrate the (photo)density
levels - if these are not exactly the same in two images being compared, then
the effect will be an overall difference in tonal levels (all features being
raised or lowered in gray level for one band scene relative to the other). BACK
1-10: First off, in the early days of Landsat, people
(mostly geologists) were finding linear features or "linears" all over Landsat
images and (too) many called them geological in nature. Some images had hundreds
of such features but field checking often failed to find them. Dr. Yngvar Isachsen
of the New York Geological Survey studied this phenomenon (if all were indeed
valid as faults/fractures, the oil and mineral corporation companies would have
a new, ultra-powerful for exploration) and found in one image of the Adirondack
Mountains that less than half were valid geologically. These fracture or fault
systems were discernible mainly because they controlled erosion along the features
creating straightish valleys (highlighted by shading) or were elongate depressions
that were filled with water. Another common expression of faults was vegetation
aligned along them because of moisture concentration. Faults also usually juxtapose
rocks of different character/composition so that there were tonal discontinuities
that produced straight lines. Linear boundaries are also produced at the boundary
(contact) between two geologic units (formations) of differing color/composition.
Among common non-geological linears are: roads, railroads, fence lines; crop
field boundaries; telegraph and power lines; hedge rows. BACK
1-11: The red and pink areas of the standard fcc
now become deep to light blue. The light sun-facing slopes now are more yellowish
but with some hints of purple here and there. Not much actual red will be in
the scene. The sand and breakers continue whitish but with associated greenish
tints. The ocean water will continue dark but with an overall weak green tone
except the silt will be more green. BACK
1-12: Again, this is somewhat subjective. Clearly,
the simple linear stretch didn't do much good in improving the image, until
new maximum-minimum limits were set. But that new image still didn't have strong
contrast. The linear with saturation and histogram-equalization stretches were
better from the start. The saturation stretch made the hillslopes perhap a bit
too bright. By a slight edge, I judge the histogram-equalization stretch best
because it had a nicer balance in contrast. These stretches were done with the
Idrisi processing program. My experience with other images is that usually -
not always - the histogram-equalization stretch yields superior end products.
BACK
1-13: The low bandpass filter image is similar to
TM Bands 1-3, but a bit darker. It also seems somewhat more fuzzy. But it could
pass for an aerial photo. The edge enhanced image is definitely sharper, as
though the image resolution has been improved. The high bandpass image is dramatically
different from any others that we have seen, and seems rather "noisy". The roads
in the towns are sharply defined. But, every slope and gully in the hills also
show linear patterns brought about by highlighting hill crests and stream channels.
This type of image, with so much intersecting linear segments, would be difficult
to interpret in search of fault and fracture patterns. Note how the horizontal
scan lines are emphasized in the ocean and bay waters. BACK
1-14: The key word is "correlation" or perhaps better,
"decorrelation". Several of the TM bands, especially 1, 2, and 2 are strongly
correlated, which means that variations in one band are closely matched in the
others, and thus tonal patterns or gray levels may not show enough differences
to separate features that have similar responses in each band. Principal Components
Analysis gets around this by shifting the axes that show strong correlations
to new spatial positions that cause significant differences (decorrelation)
in gray levels from band to band and thus discrimination. The new images contain
the influence of all bands being considered for cross-correlations. A special
processing method known as decorrelation stretching takes three PCA images (usually
1, 2, 3), manipulates their eigenvectors, and then transforms these into an
RGB (red-green-blue) image that is stretched. Thus, the decorrelation effect
is transferred back into the more conventional image types. BACK
1-15: It is similar to the first three TM bands and
resembles a somewhat dark aerial photo. But, this version probably wasn't contrast-stretched
enough, as the waves and sand are too dark. BACK
1-16: In the Principal Components 2 image, the ocean
is much brighter and shows some suggestions of varying silt patterns over a
wide area. The waves are the brightest feature in the PC 2 image. Part of the
town of Los Osos is quite bright and has a square pattern, similar to its street
layout as observed in the TM image. Morro Bay also is bright. The hills and
mountains north of Highway 1 display a greater number of smaller tonal patterns
that vary in gray level than is evident in the TM image. A large part of the
countryside south of the highway is darker; its pattern doesn't obviously correlate
with any individual TM band images. The delta is brighter but the golf course
is darker. BACK
1-17: Two things: There is some hint of the thermal
effluent near Morro Rock, and second, there is an irregularly shaped light (whitish)
tonal pattern in the lower right quadrant that doesn't seem to correlate with
any particular feature in the TM images and color composites. BACK
1-18: Ratioing TM Band 4 to Band 2 should make the
distinction. Vegetation in Band 4 should be quite bright whereas the copper
stain will be greenish and rather bright. The ratio should be very light in
tone or gray level. But, for the stain, its reflectance will be low in Band
4 and moderate (medium gray) in 2, so the ratio should be a medium-dark but
probably at a higher gray level than if Band 3 had been used instead. BACK
1-19: The ratio image closely resembles PC 3. BACK
1-20: If four or more bands are used to make clusters,
this cannot be shown in a conventional 3-axis diagram but will plot conceptually
in mathematical space (4 or more dimensions). This can only be shown as numbers.
But the resulting unsupervised image is likely to show better defined (unidentified)
classes, so that such multi-band classifications are usually superior and serve
as better aids to the interpreter in defining just what classes are present
and their spatial distribution. Unfortunately, the latest version of Idrisi,
used to make these unsupervised classifications, is restricted currently to
a maximum of 3 classes; it does have a special program (isoclus) that can handle
more than 4 bands but its instructional manual does not specify the steps involved
in doing this. The writer has seen unsupervised programs using all 7 TM bands
run on a different image processing program and can vouch for the superiority
of >3 bands as input. BACK
1-21: At first glance, both the 1,2,3 and 4,7,1 unsupervised
classifications strike one as a hodgepodge that doesn't have any obvious correlations
to the features identified so far from individual bands and color composites.
This is particularly true for most of the hilly to mountainous countryside where
the variegated colors don't usually coincide with anything meaningful. In 1,2,3
the towns are generally blue but the same or similar blue colors are scattered
about the neighboring hillsides, where they don't belong. In 1,2,3, the open
ocean is a uniform black and the main silt patterns a bright red. But, both
breakers and sand are green, as are some vegetated areas. The ocean in 4,7,1
has considerable orange which may be singling out some broader silt patterns.
My impression overall is that the 3-band unsupervised classifications are only
minimally helpful, particularly compared with the supervised classification
to be shortly shown, and to some extent with the 7-band unsupervised classifications
we are unable to picture here. BACK
1-22: All of the water-related classes have nearly
identical signatures except for the waves. That signature is much brighter,
in keeping with its high reflectances in most bands; this foamy water greatly
scatters the light, giving it a whitish appearance. The signatures for the other
named classes will definitely differ from those of the non-wave water. Consider
just band 3: its DNs will be a) water 12; b) town 32-39; c) marsh 27; d) sun
slope 41; shade 14 (note differences in the 7 bands between shadow and open
seawater). For the water signatures plotted in the illustration, all values
tend to be at about 112 for band 6; this thermal band shows most classes as
similarly bright, so that special contrast stretching is needed to show the
small differences as distinctive gray levels. BACK
1-23: The most obvious difference is in the marshy
delta. In the max. like. image, it is more uniformly orange; a few patches of
orange appear in several areas away from the marsh in the min.dist. map The
scrubland class is more broken up in the min.dist. classification and that map
shows more cleared land. Shadows in min. dist. are more broken up, being mixed
with several other classes. BACK
The remainder of the questions presented in Section 1 are associated with the "exam" that is accessed
by the "here" link on page 1-16. Those questions are linked to a separate answer sheet.