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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 1, 2011
© Copyright 2010 All rights reserved Integrated Publishing services
Research article ISSN 0976 – 4380
Submitted on September 2011 published on November 2011 121
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains A Study from Middle Himalayan Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel1, Karanjot Kaur Brar
2, Yashwant Chauhan
3
1- Map Curator, Centre of Advanced Study in Geography, Department of Geography, Panjab
University, Chandigarh
2- Associate Professor, Centre of Advanced Study in Geography, Department of Geography,
Panjab University, Chandigarh
3- Product Specialist (Remote Sensing), ESRI Muscat, Oman
ABSTRACT
Landslides are short lived and suddenly occurring natural phenomena; it is just a hazard
when it occurs in an uninhabited place, however it turns into a disaster causing extraordinary
landscape changes and destruction of life and property when it occurs in the vicinity of
human habitation. Landslides are particularly common and cause massive damage in
tectonically active Himalayas. This work conducts a landslide hazard zonation in western
Himalayan district of Kullu in Himachal Pradesh using remote sensing and GIS. The western
Himalayan district of Kullu with a location on the southern side of Pirpanjal mountain range,
an established history and inherent susceptibility to massive landslides has been chosen for
landslide hazard zonation. The satellite imageries of LANDSAT ETM+, IRS P6, ASTER
along with Survey of India (SOI) topographical sheets formed the basis for deriving baseline
information on various parameters like slope, aspect, relative relief, drainage density,
geology/lithology and land use/land cover. The weighted parametric approach was applied to
determine degree of susceptibility to landslides. The landslide probability values thus
obtained were classified into no risk, very low to moderate, high, and very high to severe
landslide hazard risk zones. The results show that over 80 per cent area is liable to high-
severe landslide risk and within this about 32 per cent has very high to severe risk.
Keywords: Landslide, Hazard Zonation, Remote Sensing & GIS.
1. Introduction
Landslide activities are intimately associated with the tectonically active Himalayan
Mountains (Sarkar et al. 1995; Rautela & Thakur, 1999; Anbalagan et al. 2008; Chauhan et al.
2010). Landslide is one of the most common natural hazards in Kullu district; it can be
disastrous with massive destruction to life and property and may also lead to large scale
landscape transformations. There are records of several massive landslides (Punjab
Government, 1926; The Tribune, 12 September 1995; Gardner, 2002; The Tribune, 18 March
2008) occurring in the past that caused massive damage to property and infrastructure along
with human casualties in the study area. The district is also experiencing large scale
developmental activities related to hydro-power, tourism and transport networks which are
leading to terrain alteration and other negative impacts on environment. These facts make it
essential to develop a Landslide Hazard Zonation (LHZ) delineating the threat area to reduce
the risk from potential landslides. Landslide hazard zonation (LHZ) demarcates an area into a
number of subclasses according to their susceptibility to landslide activities based on certain
selected parameters (Varnes, 1984; Hansen, 1984; Anbalagan 1992). Physiographic
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 122
characteristics such as slope, aspect, relative relief, geological character, drainage and
landuse/land cover play a major role in deciding the potential sites for slope failure. Such
analysis is a complex task involving numerous factors affecting slope failure and requires
inclusion of several parameters and analytical techniques. Several scholars have proposed
different schemes for landslide hazard zonation using qualitative approaches or quantitative
approaches (Carrara et aI., 1977, 1978; Yin and Yan 1988; Choubey & Litoria 1990; Gupta &
Joshi, 1990; Pachauri & Pant 1992; Anbalagan & Singh, 1996; Soeters and van Westen,
1996; van Westen et al. 1997; Aleotti and Chowdhury 1999; van Westen, 2000; Dai & Lee,
2002; Lin and Tung, 2003; Mathew et al., 2007; Sharma and Kumar, 2008; Chauhan et al.,
2010, Das et al., 2010). These approaches to landslide hazard zonation range from
qualitative/semi quantitative techniques involving parameter-weighting method, weighted
landslide hazard mapping, geomorphological methods to quantitative techniques such as
multivariate statistical methods including linear regression, discriminant analysis and logistic
regression as well as bivariate statistical method for LHZ.
2. Study Area
Kullu district situated in the lesser Himalayas between 31º20' - 32º26' north latitudes and
76º59' - 77º50’ east longitudes possesses an intricate system of mountain ranges which are
the result of successive compression movements of the earth’s crust (Burrard and Hayden,
1933). The district is bounded by Pir-Panjal range in the north; Bara Bhangal in the
northwest; the Greater Himalayas in the eastern boundary and Dhauladhar range in the
southwest while River Satluj marks the southern boundary of the district (map 1). The district
has very high absolute relief ranging from 750-6200 meters. The geomorphological character
of Kullu is influenced by both glacial and fluvial processes (Sah & Mazari, 2007); the area is
broadly divided into glaciers & permanent snow fields, rocky/barren slopes, valley slopes &
ridges, and main valley floor. The glaciers & permanent snow fields are found in most of the
eastern parts above an elevation of 4500 meters. The barren/rocky surfaces occupy the lower
parts of glaciers and permanent snow fields while valley slopes occupy a large part in the
district and consist of steep to moderately steep slopes, ridges and narrow valleys where
slopes usually have an inclination of 30-40 degrees. The main valley floor of River Beas is
dominated by outwash fan, alluvial fans and river terraces.
The district has a total population of 381571 persons housed in 76902 households located in
172 villages and 4 towns. The average density of population in the district is 69 persons per
km². Kullu district is the most rapidly growing district in the state in terms of population; the
rural population grew by 24.89 per cent which was the highest rural growth in the state while
urban population growth was 43.22 per cent during 1991-2001. This growth is attributed to
the development of tourism industry, horticulture development and initiation of hydro-power
generation ventures on a large scale which has attracted large number of people from the
other areas.
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 123
Map 1: Kullu District: general physiography
2.1 Data Source, Methodology and Input Parameters
The analysis is based on maps from Survey of India and Geological Survey of India and
satellite imageries (table 1). A landslide occurrence database was generated from newspaper
archives for 1971-2009 and GPS measurements were taken during field survey. Various
thematic maps pertaining to slope, aspect, relative relief, drainage, geological structure and
landuse/land cover were generated with the help of ArcGIS 9.3 and ERDAS 9.3 software for
Kullu district. The slope, aspect and relative relief layers were derived from ASTER DEM
using ‘Modeler’ tools in ERDAS IMAGINE, while drainage density analysis was performed
using ‘Hydrology’ tools and ‘Fishnet’ analysis in ArcGIS. The NDVI analysis was
undertaken to enhance the spectral variation in LANDSAT ETM+ (2005) and IRS P6 (2005)
satellite imageries in order to derive meaningful land use/land cover classification
Table 1: Data type/source for landslide hazard zonation
DATA TYPE DATA DESCRIPTION USE/ PURPOSE
1 Topographical Sheet Scale 1:50,000
Satellite Data ASTER
Spatial Resolution 30m
DEM: Slope (degree), Aspect,
Relative Relief, Drainage
network and Density
2 LANDSAT ETM+
2005 and IRS-P6 LISS III 2005
Spatial Resolution 15m and
23.5m Land use/land cover
3 Geological Map Scale 1:250,000 Geology: Lithology and
structure 4 Field Data GPS Locations Landslide Locations
N
N
Vertical Exaggeration- 1:3
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 124
5 Secondary Data Source
Newspaper Archives 1971-
2009 Landslide Locations
Figure 1: Methodology for Landslide Hazard Zonation
The approach followed (figure 1) in this analysis is based on the empirical relationship
between landslide activity and causative factors. Inherent causative factors depending upon
their influence in causing slope instability were given rates/weights. Different classes within
each causative factor were also given weights according to their significance in causing
instability. The information on existing landslide sites collected during the field visits and
interpreted from the satellite data was also incorporated to arrive at a more accurate weighted
score for each causative factor and their respective sub classes. This formed the basis for
giving weight to each parameter and defining their relative significance in inducing landslides.
These weighted factor maps were overlaid using multivariate criteria analysis to prepare a
landslide hazard zonation (LHZ) map for Kullu district.
3 Analysis & Discussion
3.1 Slope and Aspect
Slope and aspect are important triggering factors that determine the hazardousness of an area.
The slope degree refers to the rate of change in elevation over distance with lower the slope
value representing flatter terrain and higher values representing steeper terrain (figure 2,
equation-I). Aspect defines the down slope direction of the maximum rate of change or the
direction of steepest slope in x-y plane (figure 2, equation-II).
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 125
In Kullu district gentle slopes (below 20°) form nearly 1/3 (34.25%) of total area of the
district and such slopes are found either along the river’s course or on ridge tops. The
moderately steep and steep slopes account for 35.35% and 24.55% area (map 2) respectively;
about 6 percent of the total area possesses very steep to precipitous (above 40°) slopes. The
aspect distribution in the district has an even distribution as all eight directions have 10-15
per cent of total area (map 3). The aspect has significance in understanding the slope stability.
Usually southeast (SE) to south (S) and southwest (SW) slopes are comparatively more prone
to slope failure and sliding activities.
3.2 Physiography and Relief
The area possesses high relative or local relief which refers to the difference between the
highest and the lowest altitude in an area. The higher values indicate rapid rise in altitude and
presence of faults, lower relief signifies mature topography. A determinant of morphological
character of an area, relative relief has noteworthy alliance with landslide by acting as a
triggering factor. As a risk agent, relative relief plays a decisive role in the vulnerability of
settlements, transport network and land. In Kullu district, there is wide variation in relative
relief (map 4) ranging from low to very high. About 13.39 %, 60.13% and 26.48 % area has
low (below 200m), moderate (200-400m) and high (above 400m) relative relief respectively.
INPUT FACTOR/PARAMETRIC LAYERS
Map 2
Map 3
Figure 2 SLOPE DEGREE AND ASPECT CALCULATION
SLOPE (Degrees) = ATAN (√([dz/dx]2 + [dz/dy]2)) * 57.29578 ------ equation I Where: dz/dx = The rate of change in the x direction, and dz/dy = The rate of change in the y direction
ASPECT = 57.29578 * atan2 ([dz/dy] - [dz/dx]) ------------------ equation II Where [dz/dx] = The rate of change in the x direction and [dz/dy] = The rate of change in
the y direction.
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 126
Map 4
Map 5
INPUT FACTOR/PARAMETRIC LAYERS
Map 6
Map 7
3.3 Geological Structure
In Kullu district, a broad central zone of crystalline unfossilliferous rocks consisting of
granite, gneisses, schist and other metamorphic rocks forms the axis of the Himalayas
(Kayastha, 1964). Five major litho-tectonic units (map 5) express the geology of the area and
these are referred to as (1) Vaikrita Group (2) Jutog Group (3) Kullu Group (4) Larji Group,
and (5) Rampur Group. The area is dissected by several major thrusts, namely Jutogh Thrust,
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 127
Kullu thrust and Vaikrita thrust along with several local faults/lineaments. These thrusts are
still active and play a major role in the neo-tectonics of the area (Choubey et al., 2007). The
Jutogh thrust separates rocks belonging to Kullu group and Jutogh group while Kullu thrust
or Chail thrust (Bhargava and Bassi, 1994) defines boundary between the rocks of Rampur-
Larji group and Kullu group. Structurally, the main Kullu Valley is a synclinorium/gently
folded antiform having River Beas following its axial plane along a fault running NNW-SSE
from the upper catchment to near Aut where it is intersected by a cross fault almost at right
angles (Sah & Mazari, 2007). This fault is a dextral tear fault with a dislocation of nearly 1.5
km (Shankar & Dua, 1978). The rivers follow these fault traces which are well reflected in
the trellis like drainage pattern (Das et al. 1979). The rivers Beas, Parbati, Hurla Nala, Sainj
Khad, Tirthan Khad etc. follow such fault traces. The area west of river Beas from Bhuntar
and south of Parbati River to Rampur along the course of Satluj River is very unique.
Structurally the area forms 'window in a window' structure also known as Kullu-Larji-
Rampur Window (KLRW). Here, rocks of Kullu formation thrust over rocks of Larji group as
well as Banjar group thrust over Larji group.
3.4 Drainage Character
The drainage patterns in the area are an outcome of long time interaction between the
geological structure, topography and slope. The overall drainage reflects early stage of
dendritic pattern with visible traces of parallel dendritic and trellis patterns in between. A
mathematical expression of drainage morphometry of an area is drainage density which is a
measure of the length of stream channel per unit area of drainage basin (figure 3). The
measurement of drainage density is useful in determining landscape dissection and runoff
potential. Higher values denote higher degree of dissection of land, as well as indicate the
higher probability of slope failure.
The drainage density in the study area (map 6) can be divided into low (below 1.0 km²) to
very high (above 3.0 km²). About 2/3 area has low density mostly comprising of mountain
tops, major ranges and ridges. The valley floors of all the major streams have high to very
high drainage density. Such areas account for only 8 per cent of total area of the district.
3.5 Land use/land cover
Landuse/land cover analysis reflects relationships between land use, disaster risk and
vulnerability to disaster events. The landuse/ land cover analysis for this study is based upon
LANDSAT ETM+ (2005) and (2000), IRS-P6 LISS-III (2005) and ASTER DEM. The
landuse classification of mountainous terrains suffer from certain drawbacks as high relief
results in shadow areas and confusion between land use classes like barren rocky surfaces,
water bodies and settlements. To reduce the error in classification, Normalized Difference
Vegetation Index (NDVI) was calculated to enhance the spectral difference between different
objects. NDVI is based on the formula:
Figure 3 DRAINAGE DENSITY CALCULATION
Drainage Density (Dd) = Stream Length (L)/ Basin Area (A)
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 128
NDVI= (NIR-R)/(NIR+R)
NIR represents spectral reflectance of objects in near infrared (NIR) band while R represents
the same in the red band. In addition, ASTER digital elevation model (DEM) was used to
eliminate the possibility of land use classes being wrongly categorized by adding some
criteria. The maximum likelihood classification (MLC) algorithm which is the most accurate
classifier (Foody et al., 1992; Richards and Jia, 1999; Saha et al. 2005) has been used. A
large part of the district which includes glaciers (17.61%), rocky barren surfaces (22.07%),
forests (32.86%), and open pasture lands (8.87%) is beyond the direct use by population. The
agriculture/horticulture activities are spread over 10 per cent of the total area while about
4.48 per cent is occupied by settlements/built up area. This implies that land use in study area
under direct human occupation has very high intensity particularly in the valley floor region.
This can be particularly seen in the Kullu valley of river Beas (map 7). Another very
important factor that emerges from the analysis with respect to the agriculture/horticulture
and built-up land use is that the officially declared area under revenue is just 10% of the total
geographical area of the district, whereas the combined sum of these two classes as derived
from this analysis is about 15% of the same. This additional area indicates the encroachment
on government land which is at locations vulnerable to disasters - these being largely in the
vicinity of the streams/river and other unsuitable sites.
4. Landslide Hazard Analysis: Conclusion/Findings
The analysis shows that almost entire district is prone to landslide risk of varying magnitude.
Over 80 per cent area is liable to high-severe landslide risk and within this about 32 per cent
has very high to severe risk while about 48 per cent of the total area has high risk of landslide
occurrence (table 3). Such areas include southern slopes of Pir-Panjal range in Rohtang-
Manali area, southern off-shoot of Pir-Panjal forming western border of Kullu valley and
slopes on the northern parts of Parbati river valley particularly in the areas around Malana
valley (map 8). Another section of high-severe risk comprise of Kullu-Larji-Rampur (KLR)
geological window which spread over Hurla, Sainj and Banjar areas of district. The rocks are
not only highly deformed but the area also possesses active faults/thrusts. The northern part
of Nirmand tahsil also falls in this very high landslide risk class.
Table 3: Kullu district: landslide hazard zones
Landslide Risk Category Area (km.²) Area (per cent)
1 No Risk 23.22 0.42
2 Low-Moderate 1068.65 19.42
3 High 2650.19 48.16
4 Very High-Severe 1960.94 32.00
Total 5503 100 Source: ASTER DEM, LANDSAT ETM+ (2005); IRS P6 LISS III (2005)
About 20 per cent area of the district has low to moderate risk of landslides. These include
the valley floors mainly of Kullu valley between Manali and Bajaura, parts of Ani tahsil and
adjoining areas, and high altitude areas of permanent snow and glaciers. Very small area
(0.42%) constituting river channel/streams is devoid of landslide risk.
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 129
The susceptibility to landslides is inherent in the natural characteristics of the landscape and
there is a definite relationship between landslide occurrence and geo-physical setup of the
area. The high slope angles, drainage density, high local relief and geological structure
produce suitable conditions for landslide occurrence; the torrential rainfall in monsoon season
is invariably the immediate trigger. Out of total of 49 landslides during 1971-2009, nearly
63.27 per cent occurred in monsoons; 26.53 per cent were recorded during winter months
(January-March) while pre and post monsoon seasons together recorded less than 10 per cent
landslides. In addition, the past events show that these have close association with the landuse
and were confined to the built-up (roads) and agricultural lands. The intensification of human
activities, encroachment on vulnerable land, uncontrolled settlement and rampant expansion
of roads adds to landslide vulnerability. It is pertinent to note that landslide activity is largely
confined to the inhabited part of the district primarily in the vicinity of the rivers and roads
and this is substantiated by field visits and data. These are the prime locations of all human
activities and this enhances the risk potential of this disaster.
Source: ASTER DEM, LANDSAT ETM+ (2005); IRS P6 LISS III (2005)
Map 8
RS & GIS Based Landslide Hazard Zonation of Mountainous Terrains: A Study from Middle Himalayan
Kullu District, Himachal Pradesh, India
Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 130
The present study demonstrates high degree of hazarduousness of Kullu district of Himachal
Pradesh, India. The higher degree of landslide hazard is associated with geo-physical
elements especially slope, relative relief and lithology of the area. The presence of faults,
particularly in the vicinity of human occupancy enhances vulnerability. Vulnerability is
compounded by mindless and rampant expansion of settlement onto vulnerable land and
ambitious road construction that aids this settlement. In addition, anthropogenic activities
play a significant role in triggering such events.
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Vishwa B. S. Chandel, Karanjot Kaur Brar, Yashwant Chauhan
International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 131
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International Journal of Geomatics and Geosciences
Volume 2 Issue 1, 2011 132
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