Page 1

Landslides (2011) 8:293–308

DOI 10.1007/s10346-011-0257-9

Received: 12 August 2010

Accepted: 11 February 2011

Published online: 5 March 2011

© The Author(s) 2011. This article is

published with open access at

Springerlink.com

Iswar Das I Alfred Stein I Norman Kerle I V. K. Dadhwal

Probabilistic landslide hazard assessment using

homogeneous susceptible units (HSU) along a national

highway corridor in the northern Himalayas, India

Abstract The increased socio-economic significance of landslides

has resulted in the application of statistical methods to assess

their hazard, particularly at medium scales. These models

evaluate where, when and what size landslides are expected. The

method presented in this study evaluates the landslide hazard on

the basis of homogenous susceptible units (HSU). HSU are

derived from a landslide susceptibility map that is a combination

of landslide occurrences and geo-environmental factors, using an

automated segmentation procedure. To divide the landslide

susceptibility map into HSU, we apply a region-growing segmen-

tation algorithm that results in segments with statistically

independent spatial probability values. Independence is tested

using Moran’s I and a weighted variance method. For each HSU,

we obtain the landslide frequency from the multi-temporal data.

Temporal and size probabilities are calculated using a Poisson

model and an inverse-gamma model, respectively. The methodology

is tested in a landslide-prone national highway corridor in the

northern Himalayas, India. Our study demonstrates that HSU can

replace the commonly used terrain mapping units for combining

three probabilities for landslide hazard assessment. A quantitative

estimate of landslide hazard is obtained as a joint probability of

landslide size, of landslide temporal occurrence for each HSU for

different time periods and for different sizes.

Keywords Landslides.Hazard.HSU.Segmentation.

Himalayas.India

Introduction

Landslides are events that pose a major hazard for human

activities and that often cause substantial economic losses and

property damages (Hong et al. 2007; Nadim et al. 2006).

Landslides, in a strict sense, are the movement of a mass of rock,

debris or soil along a downward slope, due to gravitational pull. A

variety of movements is associated with landslides, such as

flowing, sliding (translational and rotational), toppling or falling.

Many landslides exhibit a combination of two or more types of

movements, resulting in a complex type (Varnes 1984). They are

triggered by a number of external factors, such as intense rainfall,

earthquake shaking, water level change, storm waves, rapid

stream erosion etc. (Dai et al. 2002). In addition, extensive

human interference in hill slope areas for construction of roads,

urban expansion along the hill slopes, deforestation, and rapid

change in land use contribute to instability. This makes it difficult,

if not impossible, to define a single methodology to identify and

map landslides, to ascertain landslide hazards, and to evaluate the

associated risk (Guzzetti et al. 2005). It thus necessitates a detailed

understanding of the physical process of landslides, including

historical information on their occurrence. Growing environ-

mental concern in recent years has resulted in a range of

quantitative landslide hazard and risk assessment studies

(Alexander 2008; Carrara and Pike 2008). The assessment of

landslide hazard has become an important assignment for various

interest groups comprising technocrats, planners and others,

mainly due to an increased awareness of the socio-economic

significance of landslides (Devoli et al. 2007). So far, a number of

methods has been proposed for quantitative landslide spatial

probability mapping, e.g. discriminant analysis (Baeza and

Corominas 2001), likelihood ratio (Chung and Fabbri 2003),

ANN (Kanungo et al. 2006) and logistic regression (Das et al.

2010; Lee and Pradhan 2007). However, actual methodological

developments for quantitative hazard analysis have been scarce,

particularly in medium scales (Guzzetti et al. 1999; Van Westen et

al. 2006). Except for a limited number of studies (Guzzetti et al.

2005; Hong et al. 2007; Zezere et al. 2004), most of the methods

proposed as landslide hazard modelling can best be classified as

susceptibility models, as they only provide the estimate of where

landslides are expected (Guzzetti et al. 2005).

Varnes (1984) was the first to propose the definition of

landslide hazard as ‘the probability of occurrence within a

specified period of time and within a given area of a potentially

damaging phenomenon’. This definition includes two parameters:

the geographical locations (where) and the recurrence between

events (when) of the landslides. Later the magnitude of the event

was added to the definition of landslide hazard by Aleotti and

Chowdhury (1999) and Guzzetti et al. (1999). Quantifying land-

slide hazard thus necessitates the determination of magnitude

probability, along with spatial probability (susceptibility) and

temporal probability. We notice that estimating where and when

landslides will occur is comparatively straightforward, whereas

estimating magnitude is difficult. This is because, unlike other

natural hazards such as floods, cyclones and earthquakes, which

are controlled by rainfall, wind speed and ground motion

respectively, landslides lack a spatially continuous magnitude

measurement parameter. It may be argued that landslide

magnitude is a function of the momentum, which includes both

mass (volume and density) of the landslide material and the

expected velocity. However, volume and velocity are difficult to

evaluate in a medium scale for large areas. Nevertheless, landslide

area can be precisely determined from a multi-temporal inventory

map (Guzzetti et al. 2005). Therefore, landslide area can act as a

good approximation for landslide magnitude.

Landslide susceptibility mapping aims to differentiate a land

surface into homogeneous areas according to their probability of

failure caused by mass movements (Varnes 1984). To achieve this

objective at medium scales, terrain mapping units (TMU) are

generated to evaluate the suitability of landslide occurrence in an

area on the basis of such homogeneous conditions (Carrara et al.

1995; Pasuto and Soldati 1999; Soeters and van Westen 1996;

Carrara et al. 1991). Furthermore, the use of mapping units is also

Landslides 8 & (2011)

293

Original Paper

Page 2

common in landslide hazard assessment purposes. These are, in

principle, homogeneous internally and heterogeneous externally.

With increasing sophistication of GIS, TMU are either derived

automatically from a combination of geo-environmental factors

or semi-automatically using expert knowledge (van Westen et al.

1997). As they are generated independently, i.e. without incorpo-

ration of landslide occurrences, TMU fall short of representing

actual homogenous susceptible areas. Instead, they can at best

represent homogenous terrain conditions with respect to certain

geo-environmental factors that control landslides. Furthermore,

combining multiple geo-environmental factors to generate TMU

can result in an uncontrolled number of units. Therefore,

segmentation-based homogenous susceptible units (HSU), which

can be generated automatically from a susceptibility map using a

region-growing algorithm, may be considered as an alternative to

TMU. The HSU can address the inherent homogeneity conditions

of factors with respect to landslides, and can suitably replace the

TMU for hazard assessment.

The aim of this study is to develop and apply a quantitative

methodology for landslide hazard assessment using HSU. We

derive the HSU automatically from a grid-based susceptibility

map using a region-growing algorithm and an optimal size factor.

The temporal and size probabilities are multiplied with spatial

probability to obtain a quantitative estimate of landslide hazard

for each HSU. We test the methodology using a multi-temporal

landslide inventory in a national highway corridor in the

Himalayan region.

Methods

A probabilistic landslide hazard assessment procedure demands

the determination of three distinct components of hazard assess-

ment, namely spatial, temporal and size probability (Guzzetti et

al. 2005). The spatial probability is generally calculated by

considering landslide locations and their spatial interaction with

geo-environmental factors. This is a measure of spatial locations

where landslides may occur in the future. Similarly, temporal

probability expresses the frequency of occurrence of landslides in

a given period. The size probability of landslides is determined

from a sufficiently complete landslide record, and indicates the

probability of particular size of landslide to occur. Mathemati-

cally, if the probability of the size of a landslide is denoted by

PALand the probability of occurrence of a landslide in a period t by

Pt, in a HSU at a spatial location i with probability Pi, then the

joint probability of landslide hazard (H) can be represented as

H ¼ Pi? Pt? PAL

ð1Þ

Spatial probability (susceptibility) modelling and generation of HSU

The spatial probability of landslide occurrence can be modelled as

the probability that a particular area will be affected by landslides,

given a set of environmental conditions. In statistical techniques,

such as logistic regression models, the occurrence of landslides is

considered as a discrete and dichotomous response variable, and

the geo-environmental factors that influence it as explanatory

variables; for more details see Das et al. (2010). A logistic

regression in a Bayesian framework uses three key components,

the prior distribution, the likelihood function and the posterior

distribution, for estimating the regression parameters. The

Bayesian logistic regression model used for the calculation of

regression coefficients takes the following form:

Pi¼ Pðyi¼ 1Þ

yi? Bernoulliðlogit?1ð?iÞÞ

?i¼ b0þPk

j¼1bjxij

bj? Nð0;0:00001Þ;j ¼ 0;...;k

ð2Þ

where yirepresents the response variable, the βj’s are coefficients

having independent normal prior distributions with a very high

variance, xijrepresents the value of the jth variable at ith location

and ηiis the linear predictor.

Using the Bayes formula, the posterior distribution of the

parameters β under this model is given by:

p bjy;X

ð Þ /

Y

k

j¼0

Pr bj

? ?

?

Y

n

i¼1

Pr yij?i

ðÞð3Þ

where, β=(β0,β1,…,βk), y=(y1,y2,…,yn), and X ¼ xij

i ¼ 1;2;...;n;j ¼ 1;2;...;k.

This is an extension of the Bayesian formula f ?jy

L yj?

productofthepriordistributionandthelikelihoodfunction.Theβj’s

are the mean of the parameter posterior estimates representing the

regressioncoefficientsasincaseofanordinarylogisticregressionfor

each variable. The analysis was carried out in WinBUGS programme

3.0.3. The GLM programme with the logit link function in WinBUGS

was used for the Bayesian logistic regression analysis of the data. To

assess the prediction rate of the model a receiver operator character-

istic (ROC) curve analysis is carried out. This is a representation of

the trade-off between sensitivity and specificity (Gorsevski et al.

2000). Sensitivity is the probability that a landslide cell is correctly

classified, a true positive rate, whereas 1-specificity is the false-

positive rate.

? ?;

ðÞ agð?Þ?

ðÞ, which relates the posterior distribution as proportional to the

Generation of HSU through segmentation

Segmentation is a process of dividing a raster image/map into objects

orregionsbasedonthehomogeneityconditionsoftheadjacentpixels.

It can be done in different ways, using various techniques such as

density slicing, region growing and split and merge (Kerle and de

Leeuw 2009). We carried out multiresolution segmentation using

region-growing algorithm in Definiens Developer, which is guided

through the use of scale and shape parameters (Definiens 2009). To

divide a susceptibility map into an optimal number of HSU is a

challenge, since such optimisation should satisfy homogeneity

conditions that are in practice highly variable. In a strict sense, true

homogeneity in nature is almost impossible. Multiresolution segmen-

tationisanoptionthatgeneratessegmentsofdifferentsize,andwhere

the user has the option to choose optimal segment size (Baatz and

Schape 2000). To choose such an optimal segment size objectively,

Espindola et al. (2006) proposed an objective function for

measuring the quality of the resulting segments. Therefore, we

created segments/objects of different scale parameters, with

thresholds ranging from 10 to 50. The scale parameter is a

function used to control the maximum allowed heterogeneity

of the objects in generating segments, resulting in a higher

number of segments for lower scale. To assess the quality of

segments and decide upon the optimal segment size, an

Original Paper

Landslides 8 & (2011) 294

Page 3

independency test can be carried out using Moran’s I

autocorrelation matrix and intrasegment variance analysis.

The function aims at maximizing intrasegment homogeneity

and intersegment heterogeneity.

The intrasegment homogeneity is calculated by a weighted

average variance formula:

v ¼

P

P

n

i¼1

ai:vi

n

i¼1

vi

ð4Þ

where viis the variance and aiis the area of the region i. The

intrasegment variance v is a weighted average, where the weights

are the areas of each region.

To calculate the intersegment heterogeneity, Moran’s I

autocorrelation index was used to calculate the spatial autocorre-

lation of a segment with adjacent segments. For each region, the

algorithm calculates its mean grey value and the relationship with

adjacent regions. Moran’s I can be expressed as:

I ¼

N

P

P

ij

wij

P

i

P

j

wijðXi? XÞðXj? XÞ

P

i

ðXi? XÞ2

ð5Þ

where N is the total number of regions, wijis the measure of

spatial adjacency between segments i and j, Xi, Xj are the

index values of the segments i and j. Therefore, Moran’s I

represent how, on average, the mean value of each region

differs from the mean values of its neighbours. The objective

function thus combines the variance measure and autocorre-

lation measure using a normalisation procedure (Espindola et

al. 2006):

Fðv;IÞ ¼ FðvÞ þ FðIÞð6Þ

Temporal probability of landslides

Provided no significant changes occurred to a natural system,

the past is the key to the present. Historical landslide

inventories in a time series can give insight into hidden trends

in the probability scale for the occurrence of a hazardous event

in a particular time frame. Landslides, being highly discrete,

can be considered as independent random point events that

occur in time. A Poisson model is commonly used to

investigate the occurrence of naturally occurring random point

events in time (Corner and Hill 1995; Crovelli 2000; Coe et al.

2004). Considering landslides as such, this model has been

used to determine the exceedance probability of landslides in

time (Coe et al. 2004). Assuming landslide frequency to follow

a Poisson model, the probability of experiencing N landslides

during time t is given by:

P NðtÞ ¼ n

½ ? ¼ eð?ltÞðltÞn

n!

n ¼ 0;1;2;...:

ð7Þ

Where

N

λ

is the total number of landslides that occur during a time t

average rate of occurrence of landslides

Here, time t is specified, whereas the rate λ is to be estimated

from empirical records. In fact, λ can be estimated from a

historical catalogue of landslide events, or from a multi-temporal

landslide inventory.

Hence, Guzzetti et al. (2005) derived the probability of

experiencing one or more landslides during time t (i.e. the

exceedance probability) as:

Pt¼ NðtÞQ1

½ ? ¼ 1 ? expð?ltÞð8Þ

In our study, the temporal probability calculation was done

for each individual HSU on the basis of the frequency of

occurrences of landslides for the 28 years period (1982–2009)

from the landslide inventory. For each HSU, we obtained land-

slide recurrence values, i.e. the expected time between successive

failures, based on past events.

Size probability of landslides

The probability that a landslide of a given size occurs can be

estimated using frequency–area relationships. Recently, several

studies have been carried out to determine the probability of

landslide magnitude (area or volume) using frequency–area or

frequency–volumestatisticsoflandslides(Malamudetal.2004).The

probability of the landslide size, in terms of it affecting an area

greater or equal than a given size, can be modelled using probability

density functions, as suggested by Malamud et al. (2004). They

showed that the mean area of landslides triggered by an event is

approximately independent of the event size. Guzzetti et al. (2005)

used the same method for a multi-temporal inventory map covering

45 years of landslide data to calculate the probability of landslide

size. For this study, a similar method was used for estimating the

probabilityoflandslidesareaineachclass,byconsidering28yearsof

historical landslides record. This is expressed as:

pðALÞ ¼

1

NLT

dNL

dAL

ð9Þ

where p(AL) is the probability density of landslide area, δNLis the

number of landslides, with area ranging between ALand δAL, and

NLTis the total number of landslides. A scatter plot with landslide

area in square kilometres on the x-axis and probability density on

the y-axis gives an empirical estimate for a probability distribu-

tion on the basis of an existing dataset. The probability density

function of the landslide area has a strong correlation with a

power-law distribution of type:

pðALÞ ¼ kðALÞ?b

where k and β are constant and β is the power-law scaling

exponent. Using Eq. 10, the probability that a landslide has an

area exceeding aL, i.e. PAL¼ P ALQaL

ð10Þ

½?, is given by:

PAL¼

Z1

AL

pðALÞdAL¼

Z1

AL

kðALÞ?b

hi

dAL

ð11Þ

Use of Eq. 9 requires the catalogue of landslide areas from which

the distributions are derived to be statistically substantially

complete.

Landslides 8 & (2011) 295

Page 4

Study area and landslide characterisation

The study area lies between 30°47′29″ and 30°54′45″ N latitude and

78°37′41″ and 78°44′03″ E longitude in the northern Himalayas,

India in the catchment of the river Bhagirathi, a tributary of the

river Ganges (Fig. 1). The area is traversed by a national highway

corridor leading to the famous Gangotri shrine of India in the

interior Himalaya (Agarwal and Kumar 1973). The study area of a

12-km long road corridor with a total area of 8.88 km2is selected

judiciously with corroboration that any landslide that occurs in

the area affects the road. The area experiences a subtropical

temperate climate throughout the year because of its high

altitude. Average temperature ranges between 300C in summers

and below 5°C in winters with December and January being the

coldest months with occasional snow fall. Elevation in the area

ranges between 1,550 and 2,100 m with a high relative relief,

average elevation of the area is around 1,900 m.

The last three decades of rainfall information between 1982

and 2009 show that the highest (1,900 mm) and lowest (600 mm)

annual rainfall occurred in years 2003 and 1991, respectively, with

an annual average of approximately 1,200 mm.(Vinod Kumar et

al. 2008). The area receives heavy precipitation during the

summer months starting from mid of June to mid of October

and moderate rainfall during the winter months from January to

March (Fig. 2). In the Himalayan region, landslides are recurring

annually and are prominent during the summer months between

June and October when the seasonal monsoon occurs. Landslides

in this area are the result of a combination of geotectonics,

adverse natural topography, such as steep slopes, weathered rocks

and soils, human influences on the topography and high rainfall

(Choubey and Ramola 1997; Saha et al. 2005).

Site characteristics

Detailed mapping of the study area was carried out using satellite

images and multiple field surveys, to ascertain the nature of

terrain and the factors influencing landsliding, that vary strongly

throughout the world (Ayalew and Yamagishi 2005; Karsli et al.

2009). In the Himalayan terrain, rock strength and geological

structures play a major role in the landslide activity. The rock

types in the study area include low to high grade metamorphics

(green-schist to upper amphibolite facies) which have been

deformed repeatedly (Naithani et al. 2009). The dominant rock

types in the area include low grade metamorphic rock such as

chlorite schist, schistose quartzite and quartz mica schist along

with high grade migmatites and gneisses. Geotechnical inves-

tigations were carried out in the area using the slope stability

probability classification (SSPC) method (Das et al. 2010). The

entire road stretch was divided into 32 uniform slope sections

based on the attitude of bedding, slope angle and rock types, and

the geotechnical data were collected quantitatively for determin-

ing the rock mass parameters required in the SSPC system (Das et

al. 2010). Rock mass properties, such as intact rock strength (IRS),

discontinuity spacing and condition, were tabulated in the field.

The IRS computed for the entire slope section varies between 50

and 200 MPa and corresponding cohesion of rock mass varies

Fig. 1 Location and extent of the

study area lies between 30°47′29″ N

and 30°54′45″ N latitude and 78°37′

41″ E and 78°44′03″ E longitude as

depicted on a hill shade image

generated using a DTM derived from a

Cartosat-1 satellite image. The highway

runs along the Bhagirathi River in the

Himalayas, India. Heights of the places

are measured in metres above mean

sea level using DGPS survey

Fig. 2 Mean monthly rainfall values (left, y-axis) and percentages (right, y-axis)

for the period between 1982 and 2009 for the Bhatwari rain gauge station 1,550

m above mean sea level

Original Paper

Landslides 8 & (2011)296

Page 5

between 9 and 29 KPa (Table 1). Gneisses of different kinds

constitute 87% of the total study area. Twenty field measurements

taken in the gneissic areas showed that the IRS varies between 50

and 200 MPa, and the cohesion of rock mass for the same

locations varies between 18 and 29 KPa. Detailed assessment

showed that the IRS varies due to compositional changes and is

higher in migmatite and biotite gneisses compared with the calc-

silicate and augen gneisses. This may be because of the spacing

and orientation of the joints present in these rocks and the degree

of weathering in each rock type. Rocks are jointed and four sets of

joints are present in the gneisses with dominant dip directions in

30°, 120°, 140° and 210° from north. Six measurements each were

taken in the schists and quartzite areas. The quartzites, white to

buff grey/green in colour, are dominantly thinly bedded and

contain three to five sets of joints (Das et al. 2010). IRS varies

between 50 and 150 MPa and the cohesion is between 15 and

27 KPa. Similarly in schists, IRS varies between 10 and 100 MPa,

with cohesion between 10 and 20 KPa. Based on the intact rock

strength and other geotechnical parameters rocks in the area were

divided into eight sub-types, namely augen gneiss, biotite gneiss,

calc-silicate gneiss, migmatites gneiss, quartzite, chlorite schist,

quartz mica schist and schistose quartzite (Fig. 3). Table 2

Table 1 Showing the intact rock strength measured for 32 slope sections in the field and the corresponding rock mass cohesion derived using SSPC method

Slope sections Slope intact rock strength (Mpa)Rock mass cohesion (Mpa)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

100

200

100

100

150

100

50

150

100

150

75

100

100

50

100

100

150

100

150

100

100

100

150

100

150

100

100

150

100

100

150

50

19.668

29.176

22.405

18.01

23.972

19.73

11.923

24.912

19.808

16.049

16.875

22.78

22.208

18.771

22.226

21.382

21.992

16.03

18.442

23.711

21.202

9.826

19.915

27.365

21.302

26.343

21.716

21.217

27.256

19.438

26.555

18.318

Landslides 8 & (2011)297

Page 6

presents the landslide densities computed for all the geo-environ-

mental variables. For the factor lithology, the class representing

calc-silicate gneiss has the highest landslide density. Landslide

density is also higher in the classes like quartz mica schist and

schistose quartzites. This is one of the indications that the rock

mass parameters of these lithologies may be favourable for

landsliding.

Terrain derivatives, such as slope gradient and slope aspect,

are frequently calculated from elevation information contained in

digital elevation models, which has been well documented

(Ohlmacher and Davis 2003; Ayalew and Yamagishi 2005; Moore

et al. 1991; Guzzetti et al. 2005). We used the photogrammetric

software SAT-PP (Zhang and Gruen 2006) to extract for the study

area a digital surface model, i.e. a model that includes also above-

ground features, from Cartosat-1 stereo data, which has recently

been shown to be an accurate source of elevation information

(Martha et al. 2010b). This was converted into a digital terrain

model (DTM), which in our case only meant removal of

vegetation clusters, using the procedure described by Martha et

al. (2010a). We derived slope and aspect maps from a topo-

graphically corrected 10×10 m DTM, using standard ArcGIS

functions. Slope angles and aspect values were divided into six

and eight classes, respectively, following slope classifications used

in other studies (Anbalagan 1992; Kanungo et al. 2006; Das et al.

2010). The slope class (>35–45°) has the highest landslide density

in the study area (Table 2). The highest landslide density was

observed on slopes with southern aspect, followed by south-west

aspect (Table 2).

Road construction severely alters the slope stability in hilly

areas, increasing the susceptibility to slope instability and

landslides (Chakraborty and Anbalagan 2008). The best way to

include the effect of a road section in a slope stability study is

to make a buffer around them (Ayalew and Yamagishi 2005;

Larsen and Parks 1997). Extent of slope cuttings due to road

construction was mapped using field investigation. Landslide

frequency in the study area was observed to be highest within

about 100 m around the narrow road (average width of 7 m).

Thus a road buffer of 50 m was placed on either side of the

road centre, marking the area likely influenced by cutting-

related slope instability. Other landslide influencing topo-

graphic parameters in the area, such as soil depth, terrain

geomorphic units, land cover, drainage density and weathering

conditions, were derived using ground surveys and interpreta-

tion of multi-temporal satellite images detailed in Das et al.

(2010).

Landslide identification and mapping

A correct landslide database is the pre-requisite for any kind

of landslide study (Varnes 1984). A combination of various

sources, means and methods has been suggested for landslide

inventory mapping, as no single best method for landslide

inventorization exists (Galli et al. 2008; van Westen et al.

2008). A detailed landslide inventory for susceptibility assess-

ment requires mainly the following data input: the location of

a landslide, its frequency, potential causes of a landslide and

the type of landslide. For the precise landslide identification,

accurate landslide mapping and the collection of landslide

data from reliable sources plays an important role. The major

organizations which keep the updated record of landslides in

the Indian Himalayan terrain are the Border Road Organiza-

tion (BRO) and the Geological Survey of India. The landslide

records in the form of digital catalogues of the BRO compiled

between 1982 and 2009 were used in this study for preparing

the inventory. The BRO catalogue consists of three types of

records: (1) registry of landslides, a decadal report on each

landslide hitting the road, (2) history of landslides, a quarterly

report on significant landslides and (3) daily road stirrup, a

report on the reasons of road blockage. All these three types

of records were checked simultaneously to compile the

landslide database for last 28 years which consists of 380 records of

landslide occurrences. The technical records of BRO provided a

detaileddescriptionoflandslidelocation,morphometry,volumeand

date of occurrence of landslides. This helped us in identifying the

morphological imprints left by the landslide scars on the road

corridor leading to detection of landslides. Compilation of records

also helped us in identifying the reactivated landslides with little

difficulty as well as the frequency of landslides occurring in a

particular location.

Extensive field verification was carried out in consultation

with the BRO to map the landslides in the study area. A total

of 178 active landslides were mapped at the 1:10,000 scale.

These were correlated with the BRO records in their digital

catalogue of landslides for the road corridor occurring along

the cut slopes, as well as in the natural slopes of the road

corridor (Fig. 4). Slide events along these active sites were

reported 332 times in the last 28 years, with a maximum of 60

occurrences in 1994 (Fig. 5). Landslides in the study area are

Fig. 3 Geological map of the study area showing the lineaments and the eight

categories of litho-types identified through rock mass characterization

Original Paper

Landslides 8 & (2011)298

Page 7

Table 2 Landslide densities computed for the geo-environmental factor maps used for landslide susceptibility assessment

FactorsVariablesLandslide density (%)

Lithology Biotite–gneiss

Migmatite gneiss

Calc-silicate gneiss

Chlorite schist

Quartzite

Quartz mica schist

Augen gneiss

Schistose quartzite

Intermontane valley wide

Intermontane valley narrow

Massive type poorly dissected denudational hills

Ridge type highly dissected hills

Cuesta type mod. dissected denudational hills

Hog back type highly dissected structural hills

Dome type moderately dissected denudation hills

Low

Moderate

High

Scrub land

Dense forest

Agricultural land

Barren land

Sand area

Built-up land

Degraded forest

Open forest

River channel

Very shallow

Shallow

Moderate

Deep

Low

Moderate

High

North

North East

East

South East

South

South West

West

North West

14.87

5.15

26.21

10.16

1.91

20.97

2.03

18.70

6.21

4.34

14.23

18.05

24.12

21.92

11.13

12.34

43.23

44.43

28.23

11.32

9.24

22.35

0

2.54

19.87

6.45

0

0

37.32

33.73

28.95

29.27

34.36

36.37

3.76

7.25

14.28

15.71

21.33

20.45

11.10

6.10

Terrain unit

Lineament density

Land cover

Soil depth

Weathering

Aspect

Landslides 8 & (2011) 299

Page 8

mostly triggered by monsoon rainfall during July to October

every year. The landslides were characterized according to their

types of movements, the materials involved and the states or

activities of failed slopes (Cruden and Varnes 1996). This was

done to understand different geo-environmental factors that

control different slope movement types. Field observations

revealed that the area is dominated by rock and debris slides

(Fig. 6). Accordingly, the landslides considered in this study

are mainly translational rock slides and debris slides that are

prominent in this area (Das et al. 2010). The materials involved

in majority of landslides are a mixture of rocks, pebbles,

gravels and cobbles. Landslide bodies were mapped from

crown to toe of rupture, as the detachment zones (zone of

depletion) are the true susceptible areas, leaving aside the

runout zones. We described landslide types according to

Cruden and Varnes (1996). The annual summer monsoon in

the area during June to October triggers both fresh as well as

reactivated landslides. Changes in the water level of the main

stream, the Bhagirathi river, also influences toe cutting,

resulting in few landslides in the road corridor. Landslides on

the cut slopes of the road corridor are smaller in size but occur

frequently. A record of every landslide affecting the road

corridor is logged by BRO. The mapped landslides cover an

area of 0.45 km2, corresponding to 5.6% of the total area (min,

125 m2; max, 40,500 m2; and mean, 3,967 m2). As the overall

landslide density was low in the area, we considered all

landslide types together for the susceptibility modelling.

Hazard assessment

Landslide spatial probability (susceptibility) assessment

The landslide susceptibility map was created using a Bayesian

logistic regression model, using a grid-cell-based method. The geo-

environmental variable and landslide maps were first rasterized into

10×10 m grids, and converted to ASCII format for inputting into the

WinBUGS programme to create dummy variables. The landslide

map was binarized (1=‘landslide’ and 0=‘non-landslide’) for model

development. Prior to the implementation of the model, the

landslide dataweredividedequally intotraining andtesting samples

by adopting a rationalized selection method manually. The regres-

sion model was carried out with landslides as response variable and

the geo-environmental factors as explanatory variables. The model

resulted in mean parameter posterior estimates, standard deviations

and quantiles for intercept and coefficients (Table 3).

Analysis of the results indicated that several but not all of the

categories of explanatory variables were significant contributors to

the model. Out of the total of 53 categorical variable classes

considered in the model (slope, 6; terrain units, 7; land cover, 9;

soil, 4;aspect, 8; lithology, 8; lineamentdensity, 3;weathering, 3; road

buffer, 2; and drainage density, 3), 17 variables were found to be

contributing significantly (Table 3). Using the intercepts and

coefficients obtained from the Bayesian logistic regression model, a

logitformulawas created for thelinearpredictor ηiasdetailed below

to calculate the landslide probability for each pixel, resulting in a

landslide spatial probability map.

?i¼ ?1:531 þ 0:239 slope ð> 35?? 45?Þ þ 0:454 calc ? silicate gneiss þ ð?1:182Þ biotite gneiss þ ð?0:453Þ

migmatite gneiss þ 0:436schistose quartzite þ 1:205 hogback type highly dissected structural hillsþ

1:215 cuesta type moderately dissected denudational hills þ ð0:894Þ scrubland þ ð?0:763Þ river channelþ

ð?0:762Þ deep soil þ ð?2:546Þ shallow soil ? 2:053 high weathering þ 2:83 aspect ðSEÞ þ 2:013 aspect ðSÞþ

0:7 high drainage density þ 0:529 high lineament density þ 0:445 road buffer

ð12Þ

Accuracy assessment of the model used and validation

The ROC curve (Fig. 7) shows that the area under the curve

(AUC) is 0.86, which corresponds to an accuracy of 86% for the

model developed using Bayesian logistic regression. The BLR

model was validated by using 50% of the landslide cells kept

separately for validation. The 2,254 landslide cells that were not

used in developing the model, along with an equal number of

randomly selected non-landslide cells, were used for the ROC

Table 2 (continued)

FactorsVariablesLandslide density (%)

Slope1–15°

15–25°

25–35°

35–45°

45–60°

>60°

Low

Moderate

High

Road buffer

Outside road buffer

03.38

11.25

16.22

42.67

15.23

11.25

19.36

31.25

49.39

63.2

36.8

Drainage density

Road buffer

Original Paper

Landslides 8 & (2011) 300

Page 9

curve analysis. It shows an AUC of 0.839, i.e. an accuracy of 83.9%

(Fig. 7). The standard error in the ROC curve in all cases is less

than 0.005. The close association of AUC of both training and

testing data confirms the correctness of sampling procedure.

Generation of HSU

The grid-cell-based susceptibility map indicating probability

values for each cell was considered for the generation of HSU

using multiresolution segmentation in eCognition software (Def-

iniens 2009). In eCognition, segmentation is controlled by scale

(size), colour and shape, with shape being further classified into

compactness and smoothness (Definiens 2009). Our primary aim

was to generate segments that are internally homogeneous and

should be distinguishable from its neighbourhood. In our study,

scale parameters 10 and 50 resulted in 1191 and 56 segments,

respectively. The optimal scale parameter determination was

based on Moran’s I autocorrelation index and variance analysis,

which as calculated for each of the scale parameters. The

normalised values of the objective function were plotted to

identify the optimal scale factor that controls segments size

(Fig. 8). The highest objective function value (1.24) corresponded

to segments with scale parameter 21. This is an indication of the

optimal intrasegment homogeneity and intersegment heteroge-

neity (Espindola et al. 2006). Using this optimal size parameter

the susceptibility map was divided into 315 statistically independ-

ent HSU (Fig. 9).

Temporal probability of landslides

Knowing the mean recurrence interval of landslides in each HSU

from 1982 to 2007 and assuming that the rate of future slope

failures will remain unchanged, and by adopting a Poisson

probability model (Eq. 8), we computed the exceedance proba-

Fig. 4 Landslide inventory map of the

study area showing dominance of rock

slides. The 178 landslide bodies

experienced a total of 322 landslide

events between 1982 and 2009

Fig. 5 Histogram showing the

frequency of landslide occurrence

(left, y-axis) and percentages (right,

y-axis) for the period between 1982

and 2009

Landslides 8 & (2011)301

Page 10

bility of having one or more landslides in each mapping unit for

1-, 5- and 10-year return periods (Fig. 10A–B). Similar maps can be

prepared for any period. The highest values of temporal

probability for 1-, 5- and 10-year return periods are 0.826, 0.998

and 0.9999, respectively. As expected, the probability of having

one or more slope failures increases with time.

Table 3 Posterior distribution summaries of parameter estimates of Bayesian logistic regression model for landslide occurrence with reference to significant geo-

environmental variables

Variables Mean of parameter

posterior estimate

Standard deviation of

posterior distribution

Quantiles of posterior distribution

2.5% Median 97.5%

Intercept

Slope gradient, >35–45°

Lithology, Calc-silicate gneiss

Lithology, biotite gneiss

Lithology, migmatite gneiss

Lithology, schistose quartzite

Terrain units, HTHDSH (Hogback type highly

dissected structural hills)

Terrain units, CTMDDH (Cuesta type

moderately dissected denudational hills)

Land cover, scrub land

Land cover, river channel

Soil depth, deep

Soil depth, shallow

Weathering, high

Aspect, South-east

Aspect, South

Drainage density, high

Lineament density, high

Road buffer

−1.531

0.239

0.4548

−1.182

−0.4531

0.4362

1.205

0.483

0.127

0.187

0.342

0.227

0.3141

0.405

−3.392

0.002

−0.253

−2.123

−1.654

−0.1792

0.616

−1.433

0.288

0.467

−1.098

−0.483

0.4318

1.352

−0.481

0.582

0.925

0.254

−0.050

1.057

2.207

1.215 0.4040.8121.2552.4

0.894

−0.763

−0.762

−2.546

2.053

2.830

2.013

0.700

0.529

0.445

0.210

0.178

0.347

0.240

0.304

0.305

0.320

0.150

0.1284

0.150

0.297

−1.106

−2.63

−3.889

1.511

1.228

1.464

0.154

−0.166

0.154

0.826

−0.7604

−0.755

−2.422

2.110

2.824

2.107

0.654

0.608

0.4454

1.899

−0.424

−0.457

−1.946

3.911

3.478

2.707

0.940

1.252

0.740

Fig. 6 Landslides occurring along the road corridor: a a large rock-cum-debris

slide damaging the houses and the road; b a rock slide blocking the road partially

Fig. 7 ROC curves representing true positive rates (sensitivity) and false-positive

rates(1-specificity)fortheBayesianLogisticregression(BLR)model.Theareaunder

the curve (AUC) is 0.860 and 0.839 for training and testing data, respectively

Original Paper

Landslides 8 & (2011) 302

Page 11

Poisson model validation

The temporal model was checked for its consistency in predicting

landslide occurrence. To validate the results the probability values

were checked with the field data of landslide occurrence for the year

2008 and 2009. The model tested for its accuracy of the 1 year

scenario of 2008 showed that 81% of the failures occurred in the

higher probability zones between 0.6 and 1.0, whereas 60% of the

failures of the year 2009 occurred in these units. However, for a

5 yearsscenario, the model prediction showed91.5% slopefailures to

take place in the high to very high temporal probability zones.

Probability of landslide size (area)

To calculate the probability of landslide area the same 28-year

inventory database was used. We obtained the area of each

landslide polygon. Care was taken to calculate the exact size of

each landslide, avoiding topological and graphical problems

related to the presence of smaller landslides inside larger mass

movements (Guzzetti et al. 2005). Figure 11a shows the probability

density function of landslide areas in the Himalayan road

corridor. We obtained the estimate using the inverse-gamma

function of Malamud et al. (2004) (Eq. 9), and found the rollover

of the distribution at 800 m2representing the smaller sized

landslides (Fig. 11a) We also found from the analysis of our

landslides database that the number of landslides having an area

more than 5,000 m2is less than 20% of the total number. To

calculate the hazard, we derived exemplary size probabilities for

landslides with 800 m2and 5,000 m2. Figure 11b shows the

probability of a particular range of landslide size to occur in the

study area, i.e. the probability that a landslide will have an area

that exceeds 800 m2and 5,000 m2, which were calculated as 0.78

and 0.21, respectively. We also considered another scenario to

calculate hazard for all sizes of landslides to occur, where the

probability is 1.0. We demonstrated the calculation of hazard for

three different size probabilities. However, it can be carried out

for any particular size.

Hazard assessment

Figure 12 shows examples of the landslide hazard assessment

obtained by multiplying the values for spatial, temporal and size

probabilities for each HSU. The figure portrays landslide hazard

for the Himalayan road corridor for nine different conditions, i.e.

for three different return periods (1, 5 and 10 years), and for three

different landslide sizes, (1) ≥5,000 m2, (2) ≥800 m2and (3) all

sizes. Maps are arranged according to the size probability for

three different return periods of 1, 5 and 10 years, respectively.

Overall, the results showed that the hazard probability of larger

landslides having an area of ≥5,000 m2is very low (0.0–0.2) for

one year as well as the 5 and 10 years recurrence periods, whereas

it can be moderate (0.4–0.6) for a landslides area of ≥800 m2.

Considering all landslide sizes together in the model, the hazard

probability can be higher for the 5 and 10 years recurrence

periods.

Fig. 8 Objective function derived from

Moran’s I autocorrelation index and

weighted average variance method.

The optimal size factor was found to

be 21

Fig. 9 Landslide susceptibility map segmented into 315 homogenous susceptible

units (HSU) depicting the probability values in the range of 0.0–0.2, 0.2–0.4,

0.4–0.6, 0.6–0.8, and 0.8–1.0

Landslides 8 & (2011)303

Page 12

Discussion and conclusions

Landsliding, in general, is a geomorphic slope failure process,

triggered by natural as well as anthropogenic factors and is

controlled by favourable terrain conditions that act as causal

factors (Das et al. 2010). Determining landslide hazard is always a

challenge. The problem lies in the data generation, as well as their

integration in a conceptual framework. With improved sophisti-

cation of GIS programmes, the actual data integration process has

gotten easier. Nevertheless, many methods proposed to evaluate

quantitatively landslide hazard geographically can best be classi-

fied as susceptibility models, because they provide an estimate of

spatial probability only (Chung and Fabbri 1999; Soeters and van

Westen 1996; Chen and Wang 2007). Guzzetti et al. (2005)

proposed a quantitative hazard model using spatial, temporal

and size probability. They used geomorpho-hydrological units as

TMU to characterize the landslides and to facilitate the calcu-

lation of spatio-temporal and size probabilities of landslide

hazard. We argue that, being generated independently.without

integration of landslide occurrences, TMUs fall short of repre-

senting actual homogenous susceptible areas. The HSU, on the

other hand, can address the inherent homogeneity conditions of

geo-environmental factors with respect to landslides. This is

because the HSU can be automatically derived from a suscepti-

bility map generated by combining landslides with geo-environ-

mental variables through data-driven models. For this study, we

prepared a multi-temporal landslide inventory map based on the

landslide records collected from the Border Roads Organisation

for 28 years (1982–2009). We used remote sensing satellite data

through visual interpretation for the generation of geo-environ-

mental factor maps. We obtained the susceptibility map using a

Bayesian logistic regression analysis of ten thematic variables,

including morphological, lithological and structural parameters.

We calculated susceptibility on a grid-cell basis and derived the

homogeneity conditions from the data-driven output of the

susceptibility map. The susceptibility map was divided into 315

HSU using a region-growing algorithm, optimized through an

objective function resulting in segments that are statistically

independent. To assess the intersegment heterogeneity we used

Moran’s I autocorrelation index, and to assess the intrasegment

homogeneity we used a weighted average variance method.

Temporal probability for each HSU was calculated using

historical landslide records. This was done for three periods

(1, 5 and 10 years) with a probability of occurrence of one or

more landslides in that particular HSU based on the landslide

frequency in that particular unit. We obtained minimum and

maximum probability values for different periods. One

limitation of the temporal probability calculation is that it

depends on the frequency of landslide occurrences in each

unit. Therefore, no probabilities can be obtained for those

units that have not experienced landslides in the past 28 years

but are in principle susceptible. In the present study, temporal

landslide records of 28 years gave a trend of annual landslide

recurrences and, more precisely, the multiple landslide

occurrences in the spatio-temporal domain during the rainy

months.

Two landslides of a different size can result in different

types of damages depending on the geo-environmental con-

dition of the area, such as topography and land use of the

area, human activity in the area and their perception of

landslide hazard. Landslide area, therefore, is a good approx-

imation of landslide magnitude (Guzzetti et al. 2005). A

commonly used approach for size probability is based on the

Fig. 10 Temporal probability of landslides in each homogeneous unit for a: a 1, b 5 and c 10 years recurrence period

Original Paper

Landslides 8 & (2011) 304

Page 13

landslide area or volume (Guthrie and Evans 2004; Malamud

et al. 2004; Stark and Hovious 2001). This notion holds true

for our study in the sense that in a road corridor, bigger

landslides would likely damage more length of road stretch as

well it has more probability to cause damage to moving

vehicles on the road in comparison to small landslides. During

the fieldwork, we noticed a small live landslide on the order of

a few m2that was part of the bigger active landslide. Such

landslides, if occurring more than once a day at a particular

location, are aggregated in the BRO records as a single

landslide for that day. Furthermore, the inverse-gamma

distribution of size probability may not properly predict such

small landslides.

For this study, nine landslide hazard maps were generated

by multiplying spatial, temporal and size probabilities. Each

map represents a specific scenario. Scenarios were developed

based on three landslide area classes (all sizes, >800 and

>5,000 m2) and for three recurrence periods (1, 5 and 10 years).

Hazard w.r.t. large landslides i.e. >5,000 m2is low in the study

area, with probability rarely exceeding 0.2. However, the

landslide probability of sizes less than 5,000 m2is relatively

higher in the study area. In the 1 year scenario, the probability

of landslides repeating themselves in exactly same place is

generally low. However, in the 5 years scenario the probability

of occurrence of any size of landslide is higher in the northern

stretch of the study area, mainly because of the favourable

rock types and slope conditions. Looking at a 10-year scenario,

the probability of occurrence of landslide is almost certain in

any part of the road section, though the probability is low

away from the road. Our study highlighted the dynamic nature

of landslide hazard mapping and the factors associated with it.

The hazard maps presented in Fig. 12 gave the annual, 5 and

10 years probability of experiencing one or more landslides in

a particular HSU with a given size. Similar maps can be

prepared for any period and any size to provide quantitative

information on future slope failures to planners, decision

makers, road maintenance authorities and hazard mitigating

agencies.

Conditional independence of spatial, temporal and size

probabilities was demonstrated for the final hazard assessment

(Guzzetti et al. 2005). Generally, difficulties arise to demon-

strate the conditional independence of spatial and temporal

probabilities. However, this is not the case in our study. This

is because the temporal model was constructed by calculating

the landslide frequency in each HSU, which is statistically

independent. Hence, it can be considered that the spatial and

temporal probabilities in our study area are independent of

each other. Spatial probability was calculated on a grid-cell-

based model and later upgraded to HSU, on which temporal

probability was calculated. In addition, the temporal proba-

bility calculation is based on the frequency of landslides,

which is mainly dependent on monsoonal rainfall pattern that

is not considered as one of the covariates of susceptibility

mapping. In the present study, it was found that the

frequency–area statistics of the multi-temporal data follow

the three-parameter inverse-gamma distribution of Malamud

et al. (2004), which has been sufficiently demonstrated to be

independent of the physical setting and geo-environmental

conditions. Thus, it can be concluded that the size probability

is independent of susceptibility. In addition the multi-tempo-

ral inventory reveals that the landslides occurred in all sizes

with different frequencies, indicating the independence of rate

of failure from landslide size. Hence, our study sufficiently

demonstrates that the three probabilities are conditionally

independent.

To understand the landslide mechanism in an area and to

identify the unknown factors affecting their occurrence,

several geo-environmental variables are generally included in

the model and significant ones are retained for generating

susceptibility map. Analysis of significant variables revealed

several interesting facts. The positive contribution of the

variable ‘slope gradient >35–45°’ indicates that in the study

area moderate slopes are prone to landslides. Rock types such

as calc-silicate gneiss and schistose quartzite are prone to

landslides, whereas biotite gneiss and migmatites gneiss resist

landsliding in the area. Two significant geomorphology classes,

‘HTHDSH’ and ‘CTMDDH’, contribute positively to the land-

slide occurrence probability. Land cover class ‘scrubland’

contributes positively, whereas ‘river channel’ contributes

negatively, implying their opposite contribution to landsliding

event. Contributions from significant aspect classes (South-

East and South) are positive, indicating their favourability to

landsliding. This is because the sun facing slopes in the

Fig. 11 Probability density (a) and probability (b) of landslide area in the

Himalayan road corridor, using an inverse-gamma function (Malamud et al. 2004).

The probability that a landslide will have an area that exceeds 800 m2and 5,000

m2are 0.78 and 0.21, respectively

Landslides 8 & (2011) 305

Page 14

Himalayas are less vegetated and more prone to landslides.

Lineament density contributes positively to the landslide

occurrence, mainly because the majority of the rockslides is

controlled by lineaments. Soil classes have a negative con-

tribution to the landslide, which may be because of the rocky

nature of the terrain. However, drainage density and road

buffer have a positive contribution, indicating their close

association with the landsliding process. These factors invar-

iably have control on the landslides occurring along natural

slopes. In addition, however, small landslides occurring

exclusively along the cut slopes might be controlled more by

anthropogenic factors rather than the natural terrain factors.

Sensitivity analysis of the landslide controlling geo-environ-

mental factors is important in landslide susceptibility mapping

mainly due to two reasons: (1) landslides are highly discrete

events and (2) the landslide controlling factors are not entirely

independent. A global sensitivity assessment of the suscepti-

bility model was carried out using ROC curve analysis to

ascertain the landslide controlling geo-environmental factors.

However, a local assessment along the cut slopes of the road

corridor through field investigation suggests that the geo-

logical factors, such as rock structures and exposed rock-cut

surfaces, are more crucial to failure. Modification of the slope

along the road section exposes the weak planes of the rocks,

aggravating the slope failure process. In general, the sensitivity

of the output to the chosen input data, such as our choice of a

10-m grid, the types of data used, accuracy of the DTM, or the

choice for the road buffer width, all contain a certain amount

of uncertainty.

The present study is an attempt to generate landslide

hazard maps quantitatively using homogenous susceptible

units. We propose to replace terrain mapping units with more

logical parameter, such as HSU for calculating hazard. With

increasing sophistication of GIS programmes, a high resolution

grid-based landslide susceptibility modelling and further trans-

formation of susceptibility map into HSU is readily possible.

Care needs to be taken to carry out a sufficiently robust data-

driven susceptibility model that strengthens the generation of

HSU.

Acknowledgements

We thank Dr. Oliver Korup, handling editor, and two anonymous

reviewers for helpful and fruitful comments. This research was

carried out under the IIRS-ITC Joint research collaboration. We

Fig. 12 Landslide hazard maps for three different periods (a) 1 year, (b) 5 years and (c) 10 years, and for three probable sizes: 1, more than 5,000 m2; 2, more than

800 m2; and 3, all sizes. Five classes show different joint probabilities of landslide size, of landslide temporal occurrence and of landslide spatial occurrences

Original Paper

Landslides 8 & (2011)306

Page 15

are thankful to Director, NRSC, Hyderabad, for allowing us to

carry out this research. We are also thankful to BRO, Tekla,

Uttarkashi, India, for providing us the detailed landslide inven-

tory data in the national highway corridor.

Open Access This article is distributed under the terms of the

Creative Commons Attribution Noncommercial License which

permits any noncommercial use, distribution, and reproduction

in any medium, provided the original author(s) and source are

credited.

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