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

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Figure 1. The Study Site (southcentral Alborz Mountains of northern Iran)
Landsat MSS Band 5 image - scale about 1:1,600,000.
The visual image interpretation was accomplished by
means of a color additive viewer using 70mm black and
white film positives of the four Landsat MSS bands.
The interpretation depended upon the evaluation of
image tone, texture, fabric and relief. Another
visual image interpretation method was the use of
Ronchi rulings for analyzing images to identify
linear features. The Ronchi ruling used in this
study is a diffraction grating having a spacing of 79
line pairs per centimeter. When the ruling is
rotated between the eyes and the Landsat image, lines
on the image that are perpendicular to the direction
of ruling are enhanced (by diffraction), and lines in
other directions become diffuse (Pohn, 1978).
The computer-assisted image interpretation techniques
provided superior results to the visual image inter
pretation techniques.
Spectral band ratioing was attempted, using blue for
the MSS band ratio 5/4, green for MSS 7/6, and red
for MSS 6/5. This technique did not enhance faults
and other lineaments. It did, however, provide a
clearer picture of alluvial deposits of different
The second method of analysis was computer-assisted
image processing of Landsat digital data in which the
lineaments were enhanced for the purpose of inter
pretation. In geological analysis, enhancement
techniques are often performed on band 7 which is the
preferred near-infrared band. The enhancement
routines used here are suitable for diverse topo
graphy and complex structural geology. The enhance
ment is a form of digital image processing and
involves the adjustment of brightness value for each
individual pixel. Potentially useful enhancements
include contrast stretching (linear, non-linear),
band ratioing, high pass filtering and diagonal
derivative processing. The most effective enhanced
product for this study was computer-enhanced high-
pass-filtered, contrast-stretched image of MSS band
7, as shown in Figure 2. This enhancement facil
itated the interpretation and was especially useful
in distinguishing between structural features and
The lineament map (Figure 3) was produced from the
computer-enhanced Landsat data and is based on the
application of the following criteria for lineament
identification (Short & Lowman, 1973): (1) lines of
variable length, straightness and continuity which
are differentiated by tonal contrast in images;
(2) tonal discontinuities; (3) bands of variable
width which contrast in tone to the area immediately
adjacent; (4) alignment of topographic forms;
(5) alignment of drainage patterns; (6) association
of vegetation along linear trends; and (7) co
alignment of cultural features (e.g., farms, roads
pattern, etc.) with underlying structural and/or
surrounding topographical control.
In addition to enhancement algorithms, an attempt was
made to obtain a lithological classification using
computer-based spectral pattern recognition. The
classification routine was applied to the Landsat MSS
data set in order to discriminate rock types by
focusing on both sides of major lineaments and by
attempting to identify whether the structural break
occurs at near, or at some depth beneath, the
surface. This discrimination provides an alternative
Figure 3
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