11

CHAPTER 2

LITERATURE REVIEW

2.1 GENERAL

In this chapter types of landslides, general landslide causes, and

previous landslide studies in India and around the world are briefly explained.

Landslide definition from literature can be organized into three stages:

Detection and classification of landslides, monitoring activity of existing

landslides and analysis and prediction of the slope failures in space (spatial

distribution) and time (temporal distribution) (Mantovani et al 1996). In

general way, the results of an international research projects dealing with the

application of Remote Sensing and GIS in Landslide analysis and prediction

of slope failures are briefly reviewed in this chapter.

Landslides are recognized as the third type of natural disaster in

terms of worldwide importance. Due to natural conditions or man- made

actions, landslides have produced multiple human and economic losses

(Fleming 1980, Guzzetti 1999). Individual slope failures are generally not so

spectacular or so costly as earthquakes, major floods, hurricanes or some

other natural catastrophes. Slope failures are more widespread, and over the

years they may cause more damage to properties than any other geological

hazards (Varnes 1984). Most of the damages and a considerable proportion of

the human losses associated with earthquakes and meteorological events are

caused by landslides, although these damages are attributed to the main event

12

which leads to a substantial underestimation of the available statistical data on

landslide impact.

This is illustrated in Table 2.1, which shows the statistics of

landslides disasters per continent from April 1903 till January 2007 from the

Emergency Disaster Database, EM-DAT, (OFDA/CRED 2007). In this period

landslides have caused 57,028 deaths and affected more than 10 million

people around the world. The quantification of damage is more than US $5

billion. These losses have driven the scientific community to produce disaster

risk reduction plans for landslides, which imply first of all landslide risk

assessment.

Landslides are the damaging natural hazards in the mountainous

terrain such as Nilgiris. The study of landslides has drawn worldwide

attention mainly due to increasing awareness of the socio- economic impact

of landslides, as well as the increasing pressure of urbanization on the

mountain environment (Aleotti and Chowdhury 1999). Although it is yet

difficult to predict a landslide event in space and time, an area may be divided

into near-homogeneous domains and ranked according to degrees of potential

hazard due to mass movements (Varnes 1984). Such maps are called

Landslide Hazard Zonation (LHZ) or Landslide Susceptibility Zonation (LSZ)

maps.

Many examples can illustrate the catastrophic nature of landslides

in the world (Brabb and Harrod 1989, Brabb 1993). Schuster and Fleming

(1986) estimated annual losses in United States, Japan, India and Italy at one

billion or more each. Subsequently, Schuster and Highland (2001) analyzed

the socioeconomic impact of landslides in Western Hemisphere highlighting

extreme events such as a debris avalanche in 1970 in Huascaran, Peru with a

death toll of 20,000 people, a debris flow in 1985 in Nevado del Ruiz,

13

Colombia killing 25,000 people and the 30,000 that were killed or are missing

as result of the 1999 landslides and floods in northern Venezuela. The

mismatch between these data and the ones from table 2.1 is due to the manner

in which events were recorded, and the minimum threshold for deaths and

economic impact, which is used to include an event in the official EM-DAT

database.

Statistical data about landslide impacts varies considerably when

comparing the reports of different scientific organizations. This reflects that

the comparisons are imprecise and that there is reason to assume higher losses

(both economic and human) by landslides than reported, due to the following

causes:

• Landslides occur frequently, and per event they do not cause

such levels of damage as other types of events. Since many of

the disaster databases apply a minimum threshold of victims or

economic losses for disaster impact, most landslide disasters are

not recorded.

• Landslide impacts in the past (historic events) are frequently not

recorded.

• The records of other countries under similar natural conditions

show larger variations.

• The registration of landslides in mountainous areas with low

risk but high hazard is cumbersome as not many people are

affected.

14

Table 2.1 World statistics for landslides

Continents Events Killed Injured Homeless Affected Total

affected

Damage

US (000'S)

23 745 56 7,936 13,748 21,740 No data Africa

Average

per event 32 2 345 598 945 No data

145 20,684 4,809 186,752 4,485,037 4,676,598 1,226,927 Americas

Average

per event 143 33 1,288 30,931 32,252 8,462

255 18,299 3,776 3,825,311 1,647,683 5,476,770 1,534,893 Asia

Average

per event 72 15 15,001 6,462 21,478 6,019

72 16,758 523 8,625 39,376 48,524 2,487,389 Europe

Average

per event 23 7 120 547 674 34,547

16 542 52 18,000 2,963 21,015 2,466 Oceania

Average

Per event 34 3 1,125 185 1,313 154

Total 511 57,028 9,216 4,046,624 6,188,807 10,244,647 5,251,675

Source: EM-DAT database for the period 1903-2007 (OFDA/CRED 2007)

15

Landslides occur as a consequence of various triggering factors.

Rainfall is one such factor. But the human intervention like deforestation may

cause the soil to lose its capacity and ultimately leads to landslides during

heavy rainfall. The Nilgiris in the Western Ghats entered an anxious era of

landslides since the calamitous landslides of 1978. The frequency of

landslides has been increased in recent years with major slides occurring in

1993, 1995, 2002, and 2007 and very recently in November 2009. The

Nilgiris landslides have been demonstrated to be the reflection of pore

pressure increase during the rainy seasons (Ramasamy et al 2006). The major

problem in Nilgiris district is deforestation. Between 1849 and 1992, the

shoals were decreased from 8,600 ha to 4,225 ha (Newspaper article

reference). Previous studies on deforestation and land use changes in Western

Ghats (Jha et al 2000) showed a loss of 25.6% in forest cover between 1973

and 1995 in the southern part. The present study aims to find the extent of

deforestation in Nilgiris district and the increase of landslides due to

deforestation.

Due to the lack of a landslide inventory, the knowledge about

geological, geomorphological, tectonic and hydrological conditions under

which these events happen is limited or even unknown in Nilgiris. The

interpretative criteria to identify and recognize these phenomena in aerial

photos or satellite images for the case of Nilgiris have been studied.

Likewise, limited work has been done so far where landslides

correlate with environmental variables like soils, slope, etc. to produce

susceptibility and hazard maps. Even fewer studies have been carried out in

areas where the landslide hazards are correlated to elements at risk to generate

risk maps.

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2.2 TYPES OF LANDSLIDES IN GENERAL

The term "landslide" describes a wide variety of processes that

result in the downward and outward movement of slope-forming materials

including rock, soil, artificial fill, or a combination of these. The materials

may move by falling, toppling, sliding, spreading, or flowing. Figure 2.1

shows a graphic illustration of a landslide, with the commonly accepted

terminology describing its features.

Figure 2.1 An idealized slump-earth flow showing commonly used

nomenclature for labeling the factors of a landslide

The various types of landslides can be differentiated by the kinds of

material involved and the mode of movement. A classification system based

on these parameters is shown in Table 2.2. Other classification systems

Crown Cracks

Crown

Minor scarp

Transverse Cracks

Transverse ridges

Radial Cracks

Toe

Foot

Surface of separation

Toe of surface of rupture

Main Body

Surface of rupture

17

incorporate additional variables, such as the rate of movement and the water,

air, or ice content of the landslide material.

Table 2.2 Types of landslides and the Abbreviated version of Varnes’

classification of slope movements (Varnes 1978)

Type of materials

Engineering soils Type of movement

Bedrock Predominantly

coarse

Predominantly

Fine

Falls Rock Fall Debris fall Earth fall

Topples Rock Topple Debris topple Earth topple

Slides

Rotational

Translational Rock slide Debris slide Earth slide

Lateral spreads Rock spread Debris spread Earth spread

Debris flows Earth flow Flows Rock flows

(deep creep) (soil creep)

Complex Combination of two or more principal types of movement

Although landslides are primarily associated with mountainous

regions, they can also occur in areas of generally low relief. In low-relief

areas, landslides occur as cut-and-fill failures (roadway and building

excavations), river bluff failures, lateral spreading landslides, collapse of

mine-waste piles (especially coal), and a wide variety of slope failures

associated with quarries and open-pit mines. The most common types of

landslides are described as follows and are illustrated in Figure 2.2.

18

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Block Slide

Source area

Main track

Depositional area

Tilted Pole

Firm clay

Soft clay with water-bearing

silt and sand layers

Lateral spread

Soil ripples

Fence out of alignment

Bedrock

Debris avalanche Earthflow Creep

Rockfall Topple Debris flow

I

Rotational landslide Translational landslide

Surface

rupture

Surface of

rupture

Curved tree trunks

A B C

D E F

G H

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Figure 2.2 Illustration of major types of landslide movement

2.2.1 Slides

Although many types of mass movements are included in the

general term "landslide," the more restrictive use of the term refers only to

mass movements, where there is a distinct zone of weakness that separates the

slide material from more stable underlying material.

19

The two major types of slides are rotational slides and translational

slides.

Rotational slide: This is a slide in which the surface of rupture is

curved concavely upward and the slide movement is roughly rotational about

an axis that is parallel to the ground surface and transverse across the slide

(Figure 2.2A).

Translational slide: In this type of slide, the landslide mass

moves along a roughly planar surface with little rotation or backward tilting

(Figure 2.2B). A block slide is a translational slide in which the moving mass

consists of a single unit or a few closely related units that move down slope as

a relatively coherent mass (Figure 2.2C).

2.2.2 Falls

Falls are abrupt movements of masses of geologic materials, such

as rocks and boulders, which become detached from steep slopes or cliffs

(Figure 2.2D).

Separation occurs along discontinuities such as fractures, joints,

and bedding planes and movement occurs by free-fall, bouncing, and rolling.

Falls are strongly influenced by gravity, mechanical weathering, and the

presence of interstitial water.

2.2.3 Topples

Toppling failures are distinguished by the forward rotation of a unit

or units about some pivotal point, below or low in the unit, under the actions

of gravity and forces exerted by adjacent units or by fluids in cracks

(Figure 2.2E).

20

2.2.4 Flows

There are five basic categories of flows that differ from one another

in fundamental ways.

(i) Debris flow: A debris flow is a form of rapid mass movement in

which a combination of loose soil, rock, organic matter, air, and water

mobilize as slurry that flows down slope (Figure 2.2F). Debris flows include

<50% fines. Debris flows are commonly caused by intense surface-water

flow, due to heavy precipitation or rapid snowmelt that erodes and mobilizes

loose soil or rock on steep slopes. Debris flows also commonly mobilize from

other types of landslides that occur on steep slopes, are nearly saturated, and

consist of a large proportion of silt- and sand-sized material. Debris-flow

source areas are often associated with steep gullies, and debris-flow deposits

are usually indicated by the presence of debris fans at the mouths of gullies.

(ii) Debris avalanche: This is a variety of very rapid to extremely

rapid debris flow (Figure 2.2G).

(iii) Earth flow: Earth flows have a characteristic "hourglass"

shape (Figure 2.2H). The slope material liquefies and runs out, forming a

bowl or depression at the head. The flow itself is elongate and usually occurs

in fine-grained materials or clay-bearing rocks on moderate slopes and under

saturated conditions. However, dry flows of granular material are also

possible.

(iv) Mudflow: A mudflow is an earth flow consisting of material

that is wet enough to flow rapidly and that contains at least 50 percent sand-,

silt-, and clay-sized particles. In some instances, for example in many

newspaper reports, the mudflows and debris flows are commonly referred to

as "mudslides."

21

(v) Creep: Creep is the imperceptibly slow, steady, downward

movement of slope-forming soil or rock. Movement is caused by shear stress

sufficient to produce permanent deformation, but too small to produce shear

failure.

There are generally three types of creep: (1) Seasonal, where

movement is within the depth of soil affected by seasonal changes in soil

moisture and soil temperature. (2) Continuous, where shear stress

continuously exceeds the strength of the material, and (3) progressive, where

slopes are reaching the point of failure as other types of mass movements.

Creep is indicated by curved tree trunks, bent fences or retaining walls, tilted

poles or fences, and small soil ripples or ridges (Figure 2.2I).

2.2.5 Lateral Spreads

Lateral spreads are distinctive because they usually occur on very

gentle slopes or flat terrain (Figure 2.2J). The dominant mode of movement is

lateral extension accompanied by shear or tensile fractures. The failure is

caused by liquefaction, the process whereby saturated, loose, cohesion less

sediments (usually sands and silts) are transformed from a solid into a

liquefied state. Failure is usually triggered by rapid ground motion, such as

that of experienced during an earthquake, but can also be artificially induced.

When coherent material, either bedrock or soil, rests on materials that liquefy,

the upper units may undergo fracturing, extension and may then subside,

translate, rotate, disintegrate, or liquefy and flow off. Lateral spreading in

fine-grained materials on shallow slopes is usually progressive.

The failure starts suddenly in a small area and spreads rapidly.

Often the initial failure is a slump, but in some materials movement occurs for

no apparent reason. Combination of two or more of the above types is known

as a complex landslide.

22

In our study area, numerous numbers of landslides have occurred in

last two decades. Nilgiris experienced debris avalanche, debris flow, Rock

fall, creep and translational types of landslides have occurred.

2.3 LANDSLIDE CAUSES IN GENERAL

There are three major causes for landslides in general, i.e.

geological causes, morphological causes and human causes. They are

describes below.

(i) Geological causes

a) Weak or sensitive materials.

b) Weathered materials.

c) Sheared, jointed, or fissured materials.

d) Adversely oriented discontinuity (bedding, schistosity,

fault, unconformity, contact, and so forth).

e) Contrast in permeability and/or stiffness of materials.

(ii) Morphological causes

a) Tectonic or volcanic uplift

b) Glacial rebound

c) Fluvial, wave, or glacial erosion of slope toe or lateral

margins

d) Subterranean erosion (solution, piping)

e) Deposition loading slope or its crest

f) Vegetation removal (by fire, drought)

g) Thawing

h) Freeze-and-thaw weathering

i) Shrink-and-swell weathering

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(iii) Human causes

a) Excavation of slope or its toe

b) Loading of slope or its crest

c) Drawdown (of reservoirs)

d) Deforestation

e) Irrigation

f) Mining

g) Artificial vibration

h) Water leakage from utilities

Although there are multiple types of causes of landslides,

specifically the three that cause most of the damaging landslides around the

world are these:

2.3.1 Landslides and Water

In Nilgiris, slope saturation by water is a primary cause of

landslides. This effect can occur in the form of intense rainfall, snowmelt,

changes in ground-water levels, and water- level changes along coastlines,

earth dams, and the banks of lakes, reservoirs, canals, and rivers.

Landsliding and flooding are closely allied because both are related

to precipitation, runoff, and the saturation of ground by water. In addition,

debris flows and mudflows usually occur in small, steep stream channels and

often are mistaken for floods; in fact, these two events often occur

simultaneously in the same area.

Landslides can cause flooding by forming landslide dams that block

valleys and stream channels, allowing large amounts of water to back up. This

causes backwater flooding and, if the dam fails, subsequent downstream

flooding. Also, solid landslide debris can "bulk" or add volume and density to

24

otherwise normal stream flow or cause channel blockages and diversions

creating flood conditions or localized erosion. Landslides can also cause

overtopping of reservoirs and/or reduced capacity of reservoirs to store water.

2.3.2 Landslides and Seismic Activity

Many mountainous areas that are vulnerable to landslides have also

experienced at least moderate rates of earthquake occurrence in recorded

times. The occurrence of earthquakes in steep landslide-prone areas greatly

increases the likelihood that landslides will occur, due to ground shaking

alone or shaking-caused dilation of soil materials, which allows rapid

infiltration of water.

2.3.3 Landslides and Volcanic Activity

Landslides due to volcanic activity is melting of volcanic lava at a

rapid rate, causing a deluge of rock, soil, ash, and water that accelerates

rapidly on the steep slopes of volcanoes. These volcanic debris flows (also

known as lahars) reach great distances, once they leave the flanks of the

volcano, and can damage structures in flat areas surrounding the volcanoes.

2.4 LITERATURE REVIEW ON LANDSLIDE RISK ANALYSIS

Risk is the result of the product of probability (of occurrence of a

landslide with a given magnitude), costs (of the elements at risk) and

vulnerability (the degree of damage of the elements at risk due to the

occurrence of a landslide with a given magnitude). A complete risk

assessment involves the quantification of a number of different types of losses

(FEMA, 2004), such as:

Losses associated with general building stock: structural and

nonstructural cost of repair or replacement, loss of contents.

25

Social losses: number of displaced households, number of people

requiring temporary shelter, casualties in four categories of severity (based on

different times of day).

Transportation and utility lifelines: for components of the lifeline

systems: damage probabilities, cost of repair or replacement and expected

functionality for various times following the disaster;

Essential facilities: damage probabilities, probability of

functionality, insufficiency of beds in hospitals;

Indirect economic impact: business inventory loss, relocation

costs, business income loss, employee wage loss, loss of rental income, long-

term economic effects on the region

In many areas hazard and risk assessment procedures have been

implemented, for example in California (Blake et al 2002), Hong Kong

(Hardingham et al 1998), New Zealand (Glassey et al 2003), Australia

(AGSO 2001, Michael-Leiba et al 2003), France (Flageollet 1989) and

Switzerland (Lateltin 1997). In Australia, the National Geohazards

Vulnerability of Urban Communities Project (or Cities project) was a program

of applied research and technique development designed to analyze and assess

the risks posed by a range of geo-hazards to urban communities (AGSO

2001). The Cities Project initiated a series of case studies in Australian cities,

e.g. Southeast Queensland, Cairns, and Mackay.

The quantification of landslide risk is often a difficult task, as both

the landslide intensity and frequency will be difficult to estimate for an entire

area, even with sophisticated methods in GIS. In practice, often simplified

qualitative procedures are used, such as the one developed in Switzerland

(Lateltin 1997).

26

2.5 PREVIOUS STUDIES ON LANDSLIDE AROUND THE

WORLD

Landslides have been occurring on the mountainous areas since

time immemorial. But they have been studied, with some scientific curiosity,

only since the 19th century. The need to overcome this geohazard has been felt

since long. In fact the earliest records of regional landslide maps date back to

1783 when numerous huge landslides in parts of Calabria in Italy had affected

many settlements and blocked rivers and streams creating 215 lakes as a co-

seismic effect of a major earthquake (Cotecchia and Melidaro 1974).

Surprisingly such important geohazard, whose menacing power had been

recognized since long, received the attention of specialist, towards correlation

of various parameters in varied geoenvironments to establish their slidability

only in the early sixties of the twentieth century, though a singular attempt of

field data based landslide map on 1:5,00,000 scale was prepared by Almagia

in 1910.

A few of such studies dealing with application of remote sensing

and GIS technology in landslides are briefly discussed in the following

paragraphs.

Carrara et al (1999), in an interesting overview paper on the use of

GIS technology for the prediction and monitoring of landslide hazards,

indicated some of the negative aspects of the extensive use of GIS in the

process, such as:

• Computer-generated results are considered to be more objective

and accurate than products derived by experts in the

conventional way through extensive field mapping;

27

• The use of GIS and the production of less accurate hazard maps

by users that are not experts in earth sciences;

• The increased focus on the use of new computational techniques

for landslide hazard assessment, and less interest on the

collection of reliable data;

For the average earth scientist it is difficult to keep up with the

rapid developments in the field of Geo-information Science and Earth

Observation. The number of new sensors and platforms, and the amount of

acronyms is overwhelming. Also the change of GIS software from one

version to the next, in which the methods that had been developed earlier on

do no longer function, because of changes in file structure or interface, can be

frustrating to many earth scientists. Nevertheless, GIS has become an almost

compulsory tool in landslide hazard and risk assessment, and it is the

challenge to keep on using it as a tool, and not as an objective in itself. When

using GIS, the following components of a landslide risk project can be

differentiated: data collection, data entry, data management, and data

modeling.

Powers et al (1996) developed a digital method for visual

comparison between two sets of multi temporal aerial photographs, of the

active portion of the Slumgullion earth flow in Colorado, to determine

horizontal displacement vectors from the movements of visually identifiable

objects, such as trees and large rocks. Baum et al (1998) report on the result

of displacement gradients obtained through photogrammetrical work of multi-

temporal aerial photos in Honolulu, Hawaii. Maas and Kersten (1997) present

two practical studies on the helicopter-based use of a high-resolution digital

still-video camera for digital aero-triangulation and the automatic generation

of digital elevation models and ortho-photos. Test regions were an alpine

village and a landslide area in Switzerland.

28

Moon and Blackstock (2003) used an entirely different approach in

their study on deterministic landslide hazard assessment for the city of

Hamilton in New Zealand. They selected representative slope profiles from a

DEM within the various geomorphological units. For the slope stability

analysis both circular (using the Bishop Simplified method) and noncircular

(using the Spencer- Wright method of analysis) failure surfaces were used,

taking into account variations in water table and seismic accelerations.

Miller and Sias (1998) worked with a two dimensional finite-

element model (MODFE) to simulate unconfined groundwater flux and to

calculate water table elevations and factors of safety for large landslides using

Bishop's simplified method of slices along individual slope transects.

In the field of landslide run out modeling also GIS has been used

extensively (Hungr 1995). Dymond et al (1999) developed a GIS-based

computer simulation model of shallow landslides and associated sediment

delivery to the stream network, for different rainstorm events and landuse

scenarios. A high resolution DEM is one of the major components in the

model. Cellular automata have also been used extensively in modeling the

flow velocity and extend of landslides (Aviolo et al 2000).

The use of physical distributed models for landslide hazard

zonation with GIS also has a number of drawbacks. As the input data

normally have a high degree of uncertainty, the values that result from the

calculations should not be taken as absolute values of landslide occurrence,

and therefore cannot directly serve for quantitative landslide risk assessment.

Furthermore, a considerable parameterization is needed, and from sensitivity

analysis the estimated soil depth appears to be a crucial factor, which is also

most difficult to measure. The models are also not suitable in predicting the

development of complex landslides with a complex hydrological system (Van

Asch et al 1999).

29

Van Westen et al (2003) have analyzed the evolution of the Tessina

landslide using sequential aerial photographs and direct field mapping. The

interpretations were converted to large scale multi temporal topographical

maps, resulting in detailed geomorphological maps of the Tessina landslide

for different periods. Further, he emphasizes the need of series of digital

elevation models for different time steps to calculate the total volumes of

material removed and accumulated for the entire Tessina landslide using

quantitative volumetric analysis.

Clerici et al (2002) have discussed landslide susceptibility zonation

by the conditional analysis method which is applied to a sub division of

landscape in Unique Condition Units (UCU), their discussions have evolved

as the conceptual simplicity of this method, however does not necessarily

imply that it is simple to implement, especially at it requires rather complex

operations and a high number of GIS commands. More over, there is the

possibility that, in order to achieve satisfactory results, the procedure has to be

repeated a few times changing the factors or modifying the class sub division.

To solve this problem, a shell program which, by combining the shell

commands, the GIS Geographical Research Analysis Support System

(GRASS) commands and the gawk language commands, carries out the whole

procedure automatically, this makes the construction of a landslide

susceptibility map easy and fast for large areas too, and even when a high

spatial resolution is adopted.

Hervas et al (2003) have proposed a method for mapping new

landslide occurrence and monitoring ground surface changes related to land

activity using optical remote sensing imagery. The method is based on

automatic digital image change reduction and thresholding techniques. They

suggested that the image processing techniques should be used with caution

on digital aerial photographs in the absence of suitable satellite imagery

30

covering a major reactivation episode. Further, they demonstrated the

usefulness of this method on panchromatic images separated by long time

intervals, typically more frequent observations would be needed to more

precisely monitor ground surface dynamics.

Chau et al (2004) have presented landslide inventory based and GIS

based frame work for systematic landslide hazard analysis by employing

historical landslide data in Hong Kong, coupling with slope angles, elevation,

lithology, soil deposit distribution potential run out area of landslide, rainfall

and population. Based on the above parameters, they proposed the landslide

hazard zonation and risk maps using raster calculations.

Wu et al (2002) have focused on the zonation of the landslide

hazards using an integrated information model based on the investigation and

statistics of landslides. It is divided into destructive, disastrous, slightly

disastrous, likely disastrous and essentially non-disastrous areas. They

suggested that the zonation of landslide hazards may serve the purpose of

providing some geological environment data for the constriction of the

project.

Flageollet (1996) has studied the temporal dimensions of mass

movements. He observed that the time terminology was often inaccurate or

incomplete. So he has tried to clarify such as the state and mode of activity,

dormancy, the return time and the age of a movement.

Dikau et al (1996) have discussed the use of database and GIS for

temporal occurrences and forecasting of landslides. They state the temporal

landslide database information is correlated with recent ad historical

triggering factors to calculate temporal probabilities for landslide forecasting

using landslide frequency analysis. Their discussions have evolved that at the

medium and broad scales different combinations of landslide data with factor

31

maps lead to static susceptibility and hazard assessments, which allow

probability evaluations for future landslide occurrences and local scales

process and deterministic slope stability models are in use.

Cheng et al (2004) have discussed the landuse / landcover change

detection for locating landslides using remotely sensed images. Their

discussions have evolved, that a grey level threshold of the band ratio

difference images is determined as the value whose exceeding probability

equals the aerial percentage of landuse change. DTM data were also used to

further restrict landslides areas to steep slope areas.

Wilcke et al (2003) have assessed the impact of landslides on soil

fertility and compared the properties of shallow translational debris slides

with those of adjacent undistributed soils. Their discussions have evolved as

the most obvious change in soil properties caused by the landsliding was

partial of complete removal of the organic layer, which was not restored

during the 20 years covered by the chronosequence. Their decreased the

topsoil fertility of the landslide area.

Dai et al (2002a) have demonstrated that slope instability modeling

by using GIS technology and logistic multiple regression analysis. They state

that the GIS tools have made possible the production of innovative slope

instability maps. In particular, they have facilitated the application of the

logistic multiple regression analysis technique which is applied to training

samples collected from existing data layers considered to be relevant to

landslide occurrences was able to predict slope instability at a rate of about

85% concordance. The predicted susceptibility was used to produce a map of

relative landslide susceptibility.

Mantovani et al (1996) have presented the inventory of researches

concerning the use of remote sensing for landslide studies and hazard

32

zonation as mainly carried out in the countries belonging to the European

Community. They emphasized the applicability of remote sensing in the

following three oases of landslide studies are detection, monitoring and

hazard analysis.

Carmen Solana and Christropher Kilburn (2003) have demonstrated

the public awareness programme of landslide hazards around populations

existing perception of landslides. They state that it is crucial that vulnerable

communities are aware of the hazards they face and know how to respond in

an emergency, as a case study to gauge the awareness of the landslide

hazards, a survey has been conducted among vulnerable communities in the

Barranco de Tiranjana (Bolt) basin on Canaria, one of the most active zones

of slope movement in the canary Islands. Results from a formal questionnaire,

together with anecdotal evidence, suggest that the communities are generally

aware that landslides occur in the basin and can be dangerous.

Julian et al (1996) have discussed the different aspects of landslide

activity. They stated that the context for landslide development is a

particularly favorable one, both in terms of the geomorphic and structural

setting and of the climatic, hydrologic and seismic factors that triggers such

failures.

Dattilo and Spezzano (2003) have demonstrated parallel simulator

developed by a problem solving environment, called Cellular Automata

Environment for systems modeling Open Technology (CAMELOT) that

handles debris / mud-flows. They stated that CAMELOT is a simulation

environment that uses the cellular automata formalism to model and simulate

dynamic complex phenomena on parallel machines and it combines

simulation, visualization, control and parallel processing into one tool which

allows to interactively explore a simulation, visualize the state of the

33

computation as it progresses and change parameters, resolution or

representation on the fly.

Dai et al (2003) have discussed the characterization of rainfall

induced landslides. The main objective of this study is to characterize the

initiation process and the subsequent travel distance of the landslides resulting

from recent rainstorms by integrating aerial photogrammetry with GIS.

Further, this emphasized this approach could serve as an effective means of

landslide characterization.

Jibson et al (1998) have presented a method for producing digital

probabilistic seismic hazard maps. They have used the data sets are inventory

of triggered landslides, about 200 strong motion records of the main shock,

1:24,000 scale geological mapping of the region, engineering properties of

geologic units and high resolution digital elevation models of the topography.

Combining these data sets in a dynamic model based a Newmarks’a

permanent-deformation (sliding-blocks) analysis yields estimates of co-

seismic landslide displacement in grid cell from the Northrige earthquake.

Then, they compared this modeled displacements with the digital inventory of

landslides triggered by the earthquake. They anticipate that this mapping

procedures will be used construct seismic landslide hazard maps that will

assist in emergency preparedness planning and in making rational decisions

regarding development and construction in areas susceptible to seismic slope

failure.

Jibson (1993) has discussed the Newmarks’s sliding block analysis

method for modeling a landslide as a rigid plastic block sliding on an inclined

plane provides a workable means of predicting earthquake-induced landslide

displacements. He stated that this method yields much more useful

information than pseudostatic analysis and if for more practical then finite

element modeling. Further, he suggested that a simplified Newmark method

34

can be used for approximate results; it can be used, which estimates Newmark

displacements as a function of landslide critical acceleration and earthquake

shaking intensity.

Dai et al (2002b) have reviewed the recent advances in landslide

risk assessment and management, and discussed the availability a variety of

approaches to assessing landslide risk. Firstly, they proposed a framework for

landslide risk assessment and management by which landslide risk can be

reduced. This is followed by a critical review of the current state of research

an assessing the probability of landsliding runout behavior, and vulnerability.

Further described the effective management strategies for reducing economic

and social losses due to landslides followed by problems in landslide risk

assessment and management is also examined. Finally they concluded that the

modern technologies, such as GIS and remote communications, should have

wider application in landslide risk assessment and management.

Parise and Jibson (2000) have analyzed the frequency, distribution

and geometries of triggered landslides near the earthquake epicenter. Their

analyses have evolved the landslide morphologies by computing simple

morphometric parameters (area, length, width, aspect ratio, slope angle). They

calculated two indices: the susceptibility index and the frequency index to

quantify and rank the relative susceptibility of each geologic unit to seismic

landsliding. They divided the susceptibility categories into very high, high,

moderate and low.

Refice et al (2001) have presented the applications of Differential

SAR interferometric techniques to the assessment of the stability of landslide

prone areas. They have shown the technique, known as the Permanent scatters

approach, to give excellent results over areas with high densities of man mode

target. This technique deals with some of the applications over different areas

such as those affected by slope instability phenomena.

35

2.6 PREVIOUS LANDSLIDE HAZARD ZONATION STUDIES

IN INDIA

Landslide Hazard Zonation studies in India were initiated by the

Geological Survey of India (GSI), the GSI has been involved in the site-

specific investigations of a number of landslides particularly those related to

communications routes, urban settlements and River Valley projects. The

pioneering investigation related to the stability of slopes for urban settlement

dates back to 1896 when survey was called upon to study the stability of

slopes around Nainital, an important hill resort. Prior to this, a classical

documentation was carried out by the Sir T.H.Holland of the survey in 1893,

of the catastrophic rock slide in Brihaiganaga valley that lead to creation of

huge reservoir. This was instrumental in obviating loss of life by flooding due

its partial breach that was predicted with uncanny accuracy.

Since those early days, the officers of the survey have carried out

detailed evaluation of mechanism of failure of specific slides in different

geoenvironments and have evolved treatment measures for communications

routes, natural and curt slopes, engineering projects as well as urban

settlements. These studies, though essential for designing safe slope cuts and

for evolving treatment measures for failing slopes. Even though they do not

answer the queries of environment conscious communities and for planners of

developmental activities to arrive where, when and how much is the hazard in

a particular domain. Though in regional hazard evaluation, all these questions

may not be possible to be replied to. But the most central one is “how much

hazardous” a particular domain for failure. This question has to be addressed

objectively in any zonation exercise.

In most cases, landslide hazard zonation exercise assesses the

relative hazard by comparing the slopes with one another, using the

influencing parameters without calculation of safety factors. The quality as

36

well as, the utility of such maps is dependent on the scale at which these maps

are prepared, because of this choice and treatment of the stability influencing

parameters would be scale dependent.

For example, the first generation, small scale maps (1:1 million)

could take into consideration the parameters like the general physical

characteristics of the slope forming materials (lithology), the general relief

and annual rainfall precipitation. The regional small scale zonation, maps are

the simple thematic representation of terrain evaluation and they serve the

purpose of synoptic representation of areas where this natural phenomenon is

prevalent. Contrary to this, the medium scale second generation maps on

1:50,000 scale would take into consideration the shear characteristics of the

slope forming materials, the slope morphometry, the landuse, geomechanical

behavior of the discontinuity surfaces etc., as the inputs. Local networks of

rainfall measurements as well as, the groundwater conditions are now

available and these could be used for hazard evaluation as well as mapping.

During the last few decades, attempts at landslide hazard zonation

studies have been made in different parts of the country. Since then a large

number of landslides were investigated, but Landslide Hazard Zonation, as it

is commonly understood today, is relatively a new concept. Different

approaches to zonation have been followed by different investigators.

Krishnaswamy (1980) was perhaps the first to attempt landslide

zonation at the national level. He made the three fold geomorphic division of

India into the penisular, the Indo-Gangetic plain and the Extra-Peninsular as

the basis for evaluating the relative incidence of landslides.

The first attempt on regional level landslide hazard zonation studies

in the North Eastern region (Majundar 1980) and in the North West Himalaya

37

(Narula et al 1996) was made by GSI. The next major attempt on regional

zonation was made in 1982 for the Nilgiris district of Tamil Nadu.

These maps were prepared taking into consideration of the

lithology, general physiography, rainfall patterns, seismicity and domains of

crustal adjustments. The basic approach in both these maps has been similar

to the one suggested by Krohn and Slossen (1976) in which the landslide

prone or resistant bed-rock and steepness of the relief were used and the area

categorized as high, moderate and low. The perusal of these maps would

indicate that these maps could serve only the thematic representation of the

lithoilogical, tectonic and physiographic conditions and would be of very

limited use for planning and execution of developmental activities or for

mitigation purposes. As because within the very high domains demarcated in

these maps, these area could again be subdivided into various vulnerability

classes on larger scales when more rigorous analysis of the parameters is

carried out on 1:50,000 scale. To explain this, larger scale map of a window

of high hazard zone of the smaller scale if reproduced.

The first attempts of the second generation landslide hazard

zonation maps on 1: 50000 scale was attempted by the GSI in the Nilgiris

hills, southern part of India, in which more than the overlay, the numerical

method with ratings were given for slope angles, thickness of soils, drainage

and landuse. Five landslide susceptibility zones were identified. (GSI 1982) it

addresses soil and debris slides. Landslide Hazard Zonation maps on 1: 50000

scale have been prepared of an area aggregating about 12,000 sq. km. in the

Chenab, Sutlej, Beas and Ganga basins utilizing the overlay methods. In all

these studies the remotely sensed database was also utilized for making the

landslide incidence maps with representative field checking. The inputs for

these included the detailed morphometry, characterization of slope forming

materials, and the geomechanical behavior of the discontinuity surfaces which

38

contain low strength sheared material, the critical angles of failure of different

materials, identification of type of failure in a particular material and given

natural conditions as derived from landslide incidences (Gupta 1988, Sharan

1992). These inputs give normalized conditions for identifying areas of

different landslide potentials.

One of the early projects on zonation was carried out by Central

Road Research Institute in 1984, in which hazard zonation techniques were

used to choose a most suitable alignment from the possible alternative

alignments on landslide affected stretches in Sikkim area. Subsequent

monitoring has shown that the choices made have been proved to be

successful. During 1989, a hazard zonation map was prepared for a part of

kathgodam- Nainital highway. This map was prepared with the objective of

enabling the department to evolve a suitable maintenance strategy to keep the

hill slopes along the road free of landslide problem (Sharma 1999).

Landslide hazard zonation studies in oarts of Beasvalley, Himachal

Pradesh (Prakash Chandra 1996) and in parts of Bhagirathi valley, Garhwal in

North Western Himalayas (Gupta 1996, Sharma 1996) are mostly confined to

small area and limited number of slides. Studies along NH31A of Sikkim in

Eastern Himalayas (Sengupta and Gohosh 1996) are mostly based on the

Landslide Hazard Evaluation Factors (LHEF) rating scheme, which is mainly

a quantitative way to ascertain relative importance to factors for slope

instability.

Landslide Hazard Zonation along the ilgrim road routes in the

Himalayan regions of Uttranchal and Hiamchal Pradesh was done using

remote sensing and GIS techniques based on the Analytical Hierarchical

Process and Saaty’s principle of pair wise Comparison model by NRSA,

Hydrabad (2001). They modeled landslide hazard zonation based on true

topographic conditions without the effect of triggering factors.

39

Ramakrishnan et al (2002) have been made the attempt to identify

landslide prone areas using photogrammetry with 3D GIS techniques. The

advantage of the high resolution data helps in deriving 2m contour, which is

ideal to get the elevation and slope values of the terrain.

Prabu et al (A1 2009) have developed a new model for landslide

hazard mapping through the integration of GIS, Remote Sensing and Neural

networks. He compared conventional method of landslide mapping with the

use of neural networks in landslide mapping.

Anbalagan (1992) has been evolved new quantitative approach

based on major causative factors of slope instability. He adopted a landslide

hazard evaluation factor rating scheme for Landslide Hazard Zonation.

Sanjeevi Kumar et al (2004) have developed the web based GIS for

landslide inventory for the Nilgiris district. It includes spatio-temporal

landslide database, different landslide inducing factors and landslide hazard

zonation. This application was developed in ArcIMS to view the landslide

information together with other data layers.

Bureau of Indian Standards has published a code (IS 14496 (part

2):1998) on ‘preparation of Landslide Hazard macro Zonation Maps in

mountainous terrains – Guidelines’ based on LHEF rating scheme for

different causative factors.

The BMPTC (2003) has taken the effort to produce the Landslide

Hazard Zonation Atlas of India on 1: 6 million scale. This small landslide

hazard maps only provide a mega view of landslide hazard distribution across

our country.

40

Since then, a number of landslide mapping programmes have been

carried out in different parts of the country, mostly confined to small scales

and with limited terms of references. These examples have been cited to

explain how the utility of the landslide hazard maps goes on increasing with

the scale of the maps.

The national and regional level Landslide Hazard Zonation maps

which depict the thematic representation of slide prone areas based on general

lithological, tectonic, climatic and physiographic conditions. Thus would be

of limited use and for realistic mitigation efforts for larger scale maps at least

on 1: 10,000 scale will have to be prepared.

There are vast tracks of the Nilgiris, Which are landslide prone and

needs quick survey for zonation would take lot of resources, for the large

scale. From the small scale maps, high susceptibility areas should be

identified as a first priority, and then highest hazard areas should be selected

for large scale analysis. This will help to choose favorable locations for sitting

development schemes such as townships, dams, roads and other development.

2.7 SUMMARY

This literature on landslide risk assessment indicates that a lot of

developments have taken place in the last decade in Nilgiris, and that

quantitative risk assessment for individual locations is feasible (Wu et al

1996, Morgenstern 1997, Einstein 1997, Fell and Hartford 1997, Wong et al

1997). However, the generation of quantitative risk zonation maps, expressing

the expected monetary losses as the product of probability (of occurrence of a

landslide with a given magnitude), costs (of the elements at risk) and

vulnerability (the degree of damage of the elements at risk due to the

occurrence of a landslide with a given magnitude) seems still a step to far.

41

In the meantime, risk maps are produced for many municipalities,

following a pragmatic and qualitative approach (Michael-Leiba et al 2003).

Such risk maps form the basis for development and regulatory planning.

Geo- Information tools have become essential for landslide hazard,

vulnerability and risk assessment. For obtaining landslide probability

information the following approaches are possible:

• At large scales deterministic models are used for determining

factors of safety. Dynamic models are used to model trajectories

of landslides. With probabilistic methods, failure probability can

also be obtained.

• At medium scales landslide data is combined with factor maps

(e.g. slope angle, lithology etc) using heuristic or statistical

methods in landslide susceptibility maps. It is also possible to

obtain landslide probabilities, when combining the landslide

frequency analysis with landslide information from temporal

databases.

Finally, the various components of landslide risk assessment should

be integrated in risk information management systems which should be

developed as spatial decision support systems for local authorities of Nilgiris

district dealing with risk management.

The use of statistical methods has a number of drawbacks. One of

these is the tendency to simplify the factors that cause landslides, by taking

only those that can be relatively easily mapped in an area, or derived from a

DEM. Another problem is related to generalization, assuming that landslides

happen under the same combination of factors throughout the study area. The

third problem is related to the fact that each landslide type will have its own

set of causal factors, and should be analyzed individually.

42

The statistical models generally ignore the temporal aspects of

landslides, and are not able to predict the impact of changes in landslide

controlling conditions (e.g. water table fluctuations, or landuse changes).

Hence by keeping all these drawbacks in our mind, we have chosen

a soft computing technique called neural networks for mapping the landslide

susceptibility. An artificial neural network is trained by the use of a set of

associated input and output values. The method is not available within

existing GIS systems, and can been programmed in systems like MATLAB

(Lee et al 2003).

Based on the above literature survey, the main conclusions are

arrived as follows.

For planning and execution of any environment friendly

developmental activity and mitigation of these hazards, its zonation is a

prerequisite. The zonation of landslide hazard must be the basis for any

landslide mitigation project.

Hence it is necessary to develop landslide hazard zonation model

by the combining remote sensing data, socio economic analysis and GIS

technology to generate the Landslide Hazard Zonation. This will help the

planners and decision makers so as to enable them in making quick decisions

and better planning to execute the landslide mitigation measures.

This integrated study on landslide hazard zonation at micro level

will indicate the methodology can be adopted for assessing the landslide on

large scale and also provide an action plan for landslide management as an

optimal solution to the planners and decision makers for better planning. This

study could be applied to similar problematic areas and terrain conditions.