International Journal of Geosciences, 2012, 3, 1010-1018
http://dx.doi.org/10.4236/ijg.2012.325101 Published Online October 2012 (http://www.SciRP.org/journal/ijg)
Geology and Geomorphology of the Manipur Valley Using
Digitally Enhanced Satellite Image and SRTM DEM in the
Eastern Himalaya, India
Abdullah Khan1*, Sarfaraz Ahmad2, Shadab Khurshid2
1Department of Earth Sciences, Sikkim University, Gangtok, India
2Department of Geology, Aligarh Muslim University, Aligarh, India
Received January 22, 2012; revised June 9, 2012; accepted September 2, 2012
ABSTRACT
Landsat ETM+ data and SRTM DEM data were used to delineate the geological, structural and geomorphological fea-
tures in the intermontane Imphal Valley in Manipur, India. This area has simple geology, structural features and there-
fore provides an ideal site to test the utility of remote sensing and GIS techniques in geological studies. The various
techniques such as band ratioing, Principal Component Analysis (PCA) and generation of FCC (False Colour Compos-
ite) were employed on ETM+ data. The SRTM DEM data is used in generating the west-east transects of altitude pro-
files in the valley for characterization of altitude levels of the litho-units. DEM derived drainage network and relative
drainage density in the basin were used in interpreting the location of fault plane in the valley. The slope and lineament
maps were prepared using SRTM DEM. It suggests that entire valley is covered by very low slope (0˚ - 9˚). Lineaments
are oriented N-S, 180˚ while in south-east of valley the direction is largely NW-SE. The change in lineament direction
suggests that the eastern side of the valley is controlled by Indonesian Island arc strike direction.
Keywords: Imphal Valley; Remote Sensing and Geographical Information Systems; SRTM DEM; Landsat ETM+
1. Introduction
In geological studies of covering large areas, recognizing
the discontinuities and determining the relationship be-
tween them is very important for regional planning and
resource managements. Remote Sensing (RS) and Geo-
graphical Information System (GIS) techniques are used
for this purpose in various studies. Remote sensing is a
technique of obtaining information about objects through
the analysis of the data collected by special instruments
that are not in physical contact with the objects of inves-
tigation [1,2]. On the other hand, GIS is a powerful set of
tools for collecting, storing, retrieving, analyzing, inte-
grating and displaying spatial data from the real world
for a particular set of purposes [3-5]. It is possible to ob-
tain data about an area at faster rate by using Remote
Sensing and then storing the data and analyzing those
using statistical and mathematical criteria with the help
of GIS. Thus, close link between the two has been util-
ized for various studies in earth sciences. In contrast to
the conventional methods of geological fieldwork which
is time consuming, expensive and complex logistics,
Remote Sensing techniques offer efficient, faster and low
cost applications to supplement the preliminary geo-
logic-geomorphologic investigations. As a result of the
technological developments there have been some radical
changes in the technology for preparation of geological
and geomorphological maps through time and particu-
larly in the last twenty years.
The satellite images are unique resources for geologi-
cal, geomorphological, global change research and util-
ized in agriculture, forestry, regional planning, education
and national security [6,7]. Geological mapping involves
the identification of landforms, rock types, and geologi-
cal structures (folds, faults, fractures) and the portrayal of
geologic units and structures on a map or other display
media in their correct spatial relationship with one an-
other. Landsat images covering large areas with multis-
pectral data have been effectively used in geosciences.
Lithological mapping has been successfully carried using
multispectral optical Remote Sensing data in arid and
semiarid areas [8]. Different spatial informations such as
land cover, hydrology and Digital Elevation Model
(DEM) integrated in a GIS allows interpretation and
analysis of geomorphologic features more precisely and
conveniently.
In the present work, the geological, topographic and
*Corresponding author.
C
opyright © 2012 SciRes. IJG

A. KHAN ET AL. 1011
structural characteristics have been evaluated for Ma-
nipur, a state of India. It lies in the north-eastern part of
the country (Figure 1). It is surrounded by Myanmar
(neighboring country) in the east and south-east, and by
other adjacent states of India, namely Assam in west,
Nagaland in the North, and Mizoram in the south-west.
The study area (Manipur) extends from 92˚58'E to 94˚45'
longitudes and 23˚50' to 25˚42'N latitudes and covers an
area of 22,327 sq km. The elevation of the study area
ranges from 790 m - 2020 m above the mean sea level
(msl). The climatic condition of the state is sub- tropical
with a normal rainfall of 1969.5 mm. The rainfall is as-
sociated with the SW monsoon.
Most of the remote sensing studies for geological and
structural analysis/interpretation were carried out mainly
in the arid and semi arid areas. In these areas the soil,
rock surface etc. have sparse vegetation cover and facili-
tates clear satellite images and analysis using popular
band combinations provides satisfactory results [9-12].
The study area is selected for study because; being a
hilly region with dense forest cover, very few studies are
carried out for this area. The area is also unique with
simple geological variability. Therefore, use of the new
techniques and methods for identifications of geological
variability from remote sensing data for densely forested
region can be employed for this area. Therefore, more
Figure 1. Location showing study area Imphal Valley, Ma-
nipur (www.googlearth.com).
specialized techniques and methods were adopted for
image enhancement for better interpretation and better
decision making in geological studies.
Geological, structural and geomorphologic character-
istics of the area have been evaluated using the digital
image processing techniques such as, generation of False
Color Composites (FCC), Band Ratioing and Principal
Component Analysis (PCA). The aim of image enhan-
cement is to improve the visual interpretability of an im-
age by increasing the apparent distinction between the
features on the land [13,14].
The results of this study were subjected to ground ve-
rification through field visits, wherever possible and their
conformity is also checked against the published geo-
logical/geomorphologic maps of the region.
2. Materials and Methods
Landsat ETM+ (Enhanced Thematic Mapper plus) and
SRTM-DEM (Shuttle Radar Topography Mission-Digi-
tal Elevation Model) data were used for various Geologi-
cal, Structural and Geomorphic analyses. In present study
the Landsat data is used because it is available since
1970s upto the present, free and easy online access
through website. A scene of the study area is taken dur-
ing the month of October, because the sky is clear during
this time in the region. The data source for this study is
the subset of the Imphal Valley region of the Landsat
ETM+ of October, 2000 with path 135 and row 43 ac-
quired from Global Land Cover Facility (GCLF), Uni-
versity of Maryland, USA website, http://glcf.umiacs.umd.
edu/index.shtml. This Landsat data is made available un-
der the National Aeronautics and Space Administration,
NASA sponsored Multi-resolution Seamless Image Da-
tabase (MrSID) maintained by NASA at http://zulu.ssc.
nasa.gov/mrsid and the University of Maryland Global
Land Cover facility web sites. The Landsat ETM+ has 6
reflective wavelength bands of 28.5 meters spatial reso-
lution, 1 thermal band of 60 meters resolution and pan-
chromatic band of 15 meters resolution. The 3-arc
SRTM-DEM data on 90 meter spatial resolution for Ma-
nipur region was also downloaded from website
http://srtm.csi.cgiar.org/. The Shuttle Radar Topography
Mission is an international research effort that obtained
digital elevation models on a near-global scale from 56˚S
to 60˚N, to generate the most complete high-resolution
digital topographic database of Earth to date. SRTM
consisted of a specially modified radar system that flew
onboard the Space Shuttle “Endeavour” during the 11-
day mission in February 2000. The Landsat ETM+ and
SRTM-DEM are already geometrically corrected. The
SRTM-DEM has many voids (data gaps). These voids
were removed using the Preprocessing techniques that
include the sink removal with the help of SAGA 2.0. The

A. KHAN ET AL. 1012
generation of FCC, ratioing pseudo images and principal
component analysis were determined using the Geo-
matica 9.1.0. The various modules like, Geological and
Hydrological modules of Geomatica were used for mak-
ing calculations and cartographic outputs. The enhanced
featured in various images were compared with geologi-
cal, geomorphological, soil, and structural maps of the
region. The SRTM-DEM is used for generating the lon-
gitudinal elevation profiles, streams, and relative drain-
age density and lineaments maps of the valley using Hy-
drological Analysis module of GIS software the Geo-
matica 9.1.0.
In present study, the geological, geomorphological,
structural and pedological characteristics of the Imphal
Valley have been assessed using the digital image proc-
essing techniques such as band ratioing, PCA and gen-
eration of FCC. The FCC image is produced by compo-
siting three band images. Each image is assigned a sepa-
rate primary color (RGB).The advantage of FCC image
is that it is easily obtainable without the need for addi-
tional data and it also allows visual interpretation of the
earth’s surface features. Three FCC images of the stan-
dard band combinations for geological studies, that of
754, 742 and 531 (Figures 2-4) were generated and they
were subjected to visual interpretations. In these images
much contrasts is not developed because the area is
mostly covered by forest and thereby affecting the visual
interpretation for geological, structural and geomor-
phological features. Further, band ratioing is utilized for
delineating geological related information. This is a proc-
ess by which the brightness values of pixels in one band
are divided by the brightness values of their correspond-
ing pixels in another band in order to create a new output
image [1]. These ratios may enhance or subdue certain
Figure 2. FCC of the area with band combination of 745
(RGB).
Figure 3. FCC of the area with band combination of 742
(RGB).
Figure 4. FCC of the area with band combination of 531
(RGB).
attributes found in the image, depending on the spectral
characteristics in each of the two bands chosen. Band
ratioing reduces shadow and brightness from slopes and
aspects of topography or seasonal changes of solar illu-
mination intensity [15]. In addition, spectral band ratio-
ing is a proven technique in lithological discrimination,
especially in hydro-thermally altered areas and in rocks
containing limonite or hydroxyl minerals and also for
separating soil and vegetation [1,16]. FCC of ratio im-
ages of band 1/band 7 (red), band 1/band 5 (blue) and
band 1/band 4 (green) (Figure 5) is prepared and de-
tailed visual interpretation of this image was able to de-
lineating the sand and shale dominated alluvium in the
valley part. However, the detailed mapping of the differ-
ent litho-units in the Imphal Valley could not be made

A. KHAN ET AL. 1013
Figure 5. FCC of the area with band combination of 145
(RGB).
possible through this method. Therefore, the FCC is gen-
erated based on the OIF (Optical Index Factor) method.
The optical index factor (OIF) techniques have been
used to determine the most suitable band combination of
FCC [17]. The OIF technique ranks all possible three-
band combinations on the basis of the strength of the
correlation and the variance between the bands being
combined [18]. The OIF is based on the digital number
(DN) correlation (representing the uncorrelated informa-
tion) and the standard deviations (representing the unex-
pected DN contrast.
3. Results and Discussions
Table 1 shows the important statistical parameters of all
the reflective ETM+ bands used in present study. From
the table, it is learned that spectral variance is widest
between ETM+ 1 and 7 and narrowest between band 3
and 7. Table 2 shows the correlation between the Land-
sat ETM+ bands. Six reflective bands of Landsat ETM +
show 20 possible combinations (Table 3).
Higher values of OIF of the band combination means
more spectral information is transform into contrast-rich
FCC. These techniques have been used in enhancing the
visual interpretability of image by increasing the appar-
ent distinctiveness among various features on the land. It
is widely recognized in various research works related to
geological mapping and structural analysis. The 20 pos-
sible band combinations of six reflective bands of the
Landsat ETM+, the band combination of the bands, 145
has the highest OIF value for the study area. FCC of this
band combination is generated (Figure 6). This FCC
gives better contrast for visual interpretation than com-
pare to standard FCC and FCC generated through differ-
ent ratioing channels. In this FCC, the combination of
145 highlighted the contrast not only rock type but also
the vegetation.
Table 1. Statistical Parameters of the Reflective ETM+
data.
Band Mean Standard Deviation
1 45.1 36.1
2 34.9 29.2
3 30.6 27.5
4 36.3 33.1
5 49.6 42.3
7 28.8 26.7
Table 2. Correlation of (R2) of the Reflective ETM+ data
bands.
B1 B2 B3 B4 B5 B7
B1 0 0.99 0.96 0.83 0.93 0.93
B2 0 0.98 0.83 0.94 0.93
B3 0 0.77 0.93 0.96
B4 0 0.82 0.75
B5 0 0.97
B7 0
Table 3. OIF values and ranking for 20 ranks possible band
combinations of Reflective ETM+ data.
RankBand Combination ∑σ ∑I r I OIF
1 145 111.06 2.58 43.04651
2 345 102.4 2.52 40.63492
3 245 104.1 2.59 40.19305
4 457 101.1 2.56 39.49219
5 147 94.96 2.51 37.83267
6 125 107.66 2.86 37.64336
7 134 96.26 2.56 37.60156
8 157 104.66 2.83 36.98233
9 124 97.96 2.65 36.96604
10 247 88 2.51 35.05976
11 347 86.3 2.48 34.79839
12 235 99 2.85 34.73684
13 234 89.3 2.58 34.6124
14 257 97.7 2.84 34.40141
15 357 96 2.86 33.56643
16 127 91.56 2.85 32.12632
17 135 89.96 2.82 31.90071
18 123 92.86 2.93 31.69283
19 137 89.86 2.85 31.52982
20 237 82.9 2.87 28.88502

A. KHAN ET AL. 1014
Figure 6. FCC image of band 1/band 7 (red), band 1/band 5
(blue) and band 1/band 4 (green) of showing contrasts be-
tween different litho-units in the Imphal Valley.
Most of the area is covered with forest, in this condi-
tion the textural elements of vegetation can be efficiently
utilize for geological delineation. The spectral reflec-
tance of all the litho-units is indistinguishable in this case.
Hence, in this condition, the vegetation texture/erosion
features were studied and the results show that the identi-
fication of the various lithological units can made on the
basis of vegetation/erosion textures. The arrangement of
vegetation is haphazard in the softer, Dishang Shales
while the vegetation pattern is linear and follows the
structure of the underlying rock in the harder Barail
Sandstone. The extreme-north east region is occupied by
ophioltie and metamorphic rocks, which is reflected in
vegetation texture of the region. The study also reveals
the limitations in the use of the above mentioned spectral
bands for geological studies in the tropical humid regions
where there is ample vegetation cover, even though these
bands are proven to be very useful and effective in the
geological studies in the arid and semi arid regions of the
world. The FCC (145) generated by OIF technique is
quite similar (745), but there are some better contrast in
145 FCC in the area in Barak valley, north-east region
(ophiolites and metamorphic) and extreme South east
(molasses). It suggests that in forest cover region the
FCC generated based on OIF can be utilize for more ac-
curate delineation of geological units in the region.
Since the area is covered with forest, the textural ele-
ments of the vegetation are more useful in delineating
different rock type. Further, PCA technique is used in
gathering the contrast or de-correlated information from
the image. To obtain the maximum benefit from the mul-
ti-spectral bands, PCA was applied to data from ETM+
bands. The original set of images was transformed into a
new set of output images that are least correlated. The
PCA utility of the Geomatica 9.1.0 is performed using 6
ETM+ Landsat optical bands excluding the thermal band
and 4 images were produced as resultant components.
The examination of eigen values and ei- genvectors de-
rived from the variance-covariance matrix indicates that
these components explain the enormous amount of the
variation in data (92%) shown in Table 4 and relation-
ship between PCA components and bands are given in
Table 5.
Eigenvectors are used as weighing factors for redistri-
bution of variance in the original dataset to the output
principal components. PC-1 eigenvectors are positively
correlated for all the bands of the image data. Positive
correlation across all bands of the image data shows that
lighting geometry is the main source of variability in the
image [19].
Table 4. PCA landsat ETM+ eigen values.
Eigen channel Eigen value Deviation %Variance
1 6002.5 77.4758 92.07%
2 317.64 17.8224 4.87%
3 148.93 12.2041 2.28%
4 41.18 6.4171 0.63%
5 6.79 2.6052 0.10%
6 2.78 1.6658 0.04%
Table 5. PCA landsat ETM+ eigenvector matrix.
PCA1 PCA2 PCA3 PCA4
Band 1 0.98 0.01 0.16 0.04
Band 2 0.99 0.05 0.14 –0.04
Band 3 0.97 0.17 0.1 –0.17
Band 4 0.88 –0.47 –0.07 –0.09
Band 5 0.98 0.09 –0.2 0.03
Band 7 0.96 0.22 –17 –0.14

A. KHAN ET AL. 1015
PCA-1 shows topography of the study area, PCA-2
represents alluvial plain; PCA-3 indicates soil moisture
and PCA-4 shows reflective surfaces. Three PCA images
(PCA-1, PCA-2, and PCA-3) were combined to form an
FCC assigning PCA-1 as red, PCA-2 as green and
PCA-3 as blue. This FCC combination show the spatial
variation in soil type occurring in the in the region,
Hence, this type FCC can be use in differentiation of
various soil type (Figure 7).
4. Elevation Profile of the Study Area
The elevation map generated from the SRTM data of the
Imphal Valley and its catchment’s area shows that the
maximum and minimum elevation is 760 m and 2357 m
Figure 7. FCC image of PCA-1 (Red), PCA-2 (Blue) and
PCA-3 (Green) of the Imphal Valley showing different soil
types.
above msl. However, the mean elevation and the relief of
the catchment area is 984 m and 1560 m above msl re-
spectively (Figure 8). The low elevation areas are repre-
sented by the valley where as the high elevation areas are
represented by the hillocks within the valley and the hills
surrounding the valley. The Imphal Valley is almost flat
and does not show much variation in the elevation. The
elevation range in the valley varies from 760 m to 937 m
above msl. However there are many hillocks present in
the valley which show elevation as much as 1800 m
above the msl and these hillocks are exposed mostly in
the northern and southern sides of the valley. These small
hills follow NE-SW trend and they are called as struc-
tural hills in the literature. The mountain ranges that run
parallel with the valley on both western and eastern sides
have the maximum elevation of 2357 m above the msl.
Two longitudinal profiles were generated across the
valley along profile sections A & B in the DEM of the
Imphal Valley and parts of its catchment (Figure 8) and
are shown in Figures 9(a) and (b). These longitudinal
profiles revealed three main altitude levels in the valley.
The lowest altitude is in the valley and second and third
altitude levels are in eastern side of the valley which is
separated by hills and small hillocks in the region.
Figure 8. Digital elevation model of the Imphal Valley with
parts of its catchment area.

A. KHAN ET AL. 1016
(a)
(b)
Figure 9. (a) Longitudinal profile of the Imphal Valley (Pro-
file A). (b) Longitudinal profile of the Imphal Valley (Pro-
file B).
The slope map of the Imphal Valley and its catchment
area is also extracted based on SRTM-DEM (Figure 10),
which shows that the slope of the area varies from 0˚ to
63˚. Further the slope has been subdivided into five equal
classes viz; very gentle (0˚ to 13˚), gentle (13˚ to 25˚),
moderate (25˚ to 38˚), steep (38˚ to 50˚) and very steep
(50˚ to 63˚) slopes.
Major part of the valley has very gentle slope (0˚ to
13˚), whereas the isolated hills present in the valley and
the surrounding mountains has moderate to steep slopes.
These areas show the slope ranging from 25˚ to 50˚. The
area which is very adjacent to the valley scattered in the
whole catchment’s area shows gentle slope (13˚ to 25˚).
However the north-western and south-eastern parts of the
catchment’s area show very steep slope (50˚ to 63˚).
The SRTM-DEM was used for extracting drainage and
watersheds in the valley using Geomatica 9.1.0 hydro-
logical module. The relative drainage densities in the
Figure 10. Slope map of the Imphal Valley and parts of its
catchment area.
watersheds were computed using drainage and watershed
layers. Based on relative drainage density, the watersheds
were divided into three types, high, medium and low.
The spatial variations of these classified watersheds are
shown in Figure 11. In the figure the watersheds along
the Imphal River at entry point in the northern side of the
valley indicate continuous clusters of watersheds with
high drainage density. These points in the PCA based
FCC shows as big piedmont along the Imphal River in
the northern part of the valley. The formation of this big
fan deposit is related to the faults cutting across the Dis-
ang litho-unit at this point.
The SRTM-DEM was also used for extracting major
lineaments in the valley (Figure 12). The direction of the
lineaments ranges from 53˚ to 322˚ to true north with an
average of 183˚ to true North. The slope ranges from 14˚
to 63˚ with an average slope of 43˚. Lineaments show
spatial variation in direction. In the western side they are
NE-SW trending, while in south-eastern side, the linea-
ments are NW-SE trending. The south-eastern part of the
valley indicated a swing in the direction of lineaments
direction and they follow the northern prolongation di-
rection of the Indonesian Island, which has a westward
convexity. The south-eastern part of the valley forms a

A. KHAN ET AL. 1017
Figure 11. Drainage density, fan deposit and faults along
the Imphal River in the northern part of the Imphal Valley.
Figure 12. Pattern of major lineaments in the Imphal Valley
surrounding areas de r ived from SRTM DEM.
part of the lower part of this convex system.
5. Conclusion
The study reveals the limitations in the use of reflectance
of spectral bands for geological studies in the tropical
forest area. However, these bands are proven to be very
useful and effective in the geological studies in the arid
and semi arid regions of the world. The optical index
factor (OIF) is useful to generate the appropriate FCC for
enhancing the maximum contrast, which help in deline-
ating the various vegetation/geological and geo-morpho-
logical units. In heavily forested area, the litho-logical
units can delineated on the basis of vegetation/erosion
textural elements of the spectral reflectance. The PCA
technique is useful for delineating the topographical ele-
ments, alluvium, urban area, soil moisture conditions.
The FCC generated by PCA showed the usefulness for
differentiating various soil types. The altitudinal longitu-
dinal profiles generated across the valley revealed three
main altitude levels in the valley. The SRTM DEM gen-
erated morphometric parameter i.e., drainage density can
be use in delineating the big fan deposit in valley. Li-
neaments based on SRTM show spatial variation in di-
rection, in the western side of the valley they are NE-SW
trending, while in south-eastern side, the linea- ments are
NW-SE trending. The south-eastern part of the valley
indicated a swing in the direction of lineaments direction
and they follow the northern prolongation direction of
the Indonesian Island, which has a westward convexity.
The south-eastern part of the valley forms a part of the
lower part of this convex system.
6. Acknowledgements
The authors would like to express their thanks to Chair-
man, Department of Geology, Aligarh Muslim Univer-
sity for providing the laboratory and library facilities.
They also appreciate the financial assistance provided by
University Grant Commission (UGC), Ministry of Hu-
man Resources and Development, Government of India
to conduct the present research work.
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