Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya

ORIGINAL ARTICLE

Regional-scale landslide activity and landslide susceptibilityzonation in the Nepal Himalaya

Ranjan Kumar Dahal

Received: 1 March 2013 / Accepted: 1 November 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Landslide susceptibility zonation mapping is a

fundamental procedure for geo-disaster management in

tropical and sub-tropical regions. Recently, various land-

slide susceptibility zonation models have been introduced

in Nepal with diverse approaches of assessment. However,

validation is still a problem. Additionally, the role of var-

ious predisposing causative parameters for landslide

activity is still not well understood in the Nepal Himalaya.

To address these issues of susceptibility zonation and

landslide activity, about 4,000 km2 area of central Nepal

was selected for regional-scale assessment of landslide

activity and susceptibility zonation mapping. In total, 655

new landslides and 9,229 old landslides were identified

with the study area with the help of satellite images, aerial

photographs, field data and available reports. The old

landslide inventory was ‘‘blind landslide database’’ and

could not explain the particular rainfall event responsible

for the particular landslide. But considering size of the

landslide, blind landslide inventory was reclassified into

two databases: short-duration high-intensity rainfall-

induced landslide inventory and long-duration low-inten-

sity rainfall-induced landslide inventory. These landslide

inventory maps were considered as proxy maps of multiple

rainfall event-based landslide inventories. Similarly, all

9,884 landslides were considered for the activity assess-

ment of predisposing causative parameters. For the Nepal

Himalaya, slope, slope aspect, geology and road con-

struction activity (anthropogenic cause) were identified as

most affective predisposing causative parameters for

landslide activity. For susceptibility zonation, multivariate

approach was considered and two proxy rainfall event-

based landslide databases were used for the logistic

regression modelling, while a relatively recent landslide

database was used in validation. Two event-based suscep-

tibility zonation maps were merged and rectified to prepare

the final susceptibility zonation map and its prediction rate

was found to be more than 82 %. From this work, it is

concluded that rectification of susceptibility zonation map

is very appropriate and reliable. The results of this research

contribute to a significant improvement in landslide

inventory preparation procedure, susceptibility zonation

mapping approaches as well as role of various predisposing

causative parameters for the landslide activity.

Keywords Event-based landslide � Geo-disasters �Susceptibility zonation maps � The Nepal Himalaya �Blind landslide inventory � Landslide activity � Validation

of susceptibility maps

Introduction

Movement of a mass of rock, debris or earth down a slope is

simply called landslide (Cruden 1991). Many terrains in

mountainous region have been subjected to landslides at least

once under the influence of a variety of intrinsic and extrinsic

causative factors. The incidences of slope instability are

significantly increased due to improper land use and infra-

structure development planning in the landslide-prone areas.

Hence, landslides continue to be one of the most threatening

and widespread geo-disasters in the mountainous area of

tropical and subtropical regions (Dahal et al. 2011).

To reduce and manage landslide-related geo-disasters, it

is necessary to assess areas that are susceptible to land-

slides during extreme events of rainfall or earthquake.

R. K. Dahal (&)

Department of Geology, Tribhuvan University,

Tri-Chandra Campus, Ghantaghar, Kathmandu, Nepal

e-mail: [email protected]

123

Environ Earth Sci

DOI 10.1007/s12665-013-2917-7

In the past three decades, diverse methodologies for

landslide susceptibility and hazard assessment have been

introduced by several ‘‘school of thoughts’’. Significant

progress has been made by establishing the basic definition

of terms related to landslide hazard and risk assessment

(Varnes 1984). Works of Guzzetti et al. (1999) and van

Westen (2000) have addressed available methods of land-

slide susceptibility and hazard analysis and suggested for

improving the quality as well as uniform approach to

landslide hazard mapping on an international level. In

2008, JTC-1 (Joint International Society of Soil Mechanics

and Geotechnical Engineering, International Society of

Rock Mechanics and International Association of Engi-

neering Geology Technical Committee on Landslides and

Engineered Slopes) prepared the guidelines and defined

landslide susceptibility and hazard in the prospective of

interaction between intrinsic and extrinsic variables as well

as frequency of occurrence of the events (Fell et al. 2008).

In the landslide susceptibility and hazard science, basically,

intrinsic (e.g., geology, geomorphology, soil depth, soil

type, slope gradient, slope aspect, slope curvature, eleva-

tion, land use pattern, drainage patterns, and so on) and

extrinsic variables (heavy rainfall, earthquakes and volca-

noes) are used to describe landslide susceptibility and

hazard assessment (Dai et al. 2001; Cevik and Topal 2003;

van Westen et al. 2003; Suzen and Doyuran 2004; Ruff and

Czurda 2008; Dahal et al. 2008a, b, 2012; Regmi et al.

2010; Ghimire 2011; Sharma et al. 2011; Ramani et al.

2011; Nefeslioglu and Gokceoglu 2011; Bednarik et al.

2012; Kayastha et al. 2012; Schicker and Moon 2012;

Ghosh et al. 2012; Pourghasemi et al. 2012; Magliulo

2012; Poudyal et al. 2010; Huang et al. 2013; Ercanoglu

and Temiz 2011; Park et al. 2013; Erener and Duzgun

2012; Bai et al. 2011; Constantin et al. 2011).

According to JCT-1 definition, landslide susceptibility is

a quantitative or qualitative assessment of the classifica-

tion, volume (or area) and spatial distribution of landslides

which exist or may potentially occur in an area. Landslide

susceptibility zoning requires an inventory map of land-

slides that occurred in the past together with assessment of

the areas with the potential to occurrence of landslides in

future, but with no assessment of frequency (annual prob-

ability) of occurrence (Cascini 2008). Landslide suscepti-

bility map includes landslides which have their source in

the area, or may have their source outside the area but may

travel through the area or return into the area (Fell et al.

2008; Cascini 2008; Frattini et al. 2010).

In general, landslide susceptibility can be depicted as the

physical potential of the process to produce landslides and

associated damage. Fundamentally, landslide susceptibility

can be characterized by statements of ‘what’, ‘where’ and

‘when’. The susceptibility predictions are generally made

in terms of likelihoods and probabilities. In many landslide

susceptibility zonation techniques, probabilities of failure

are classified as per the distribution of susceptibility index.

In general, a landslide susceptibility zonation map is able to

demonstrate spatial (what will occur? and where it will

occur?) and temporal occurrence (when it will occur?) of

future landslides in terms of probability. Numerous studies

have shown that the spatial distribution of landslides can

be better understood through GIS-based susceptibility

assessment. In practice, heuristic and statistical methods are

well practiced for regional-scale susceptibility mapping.

Deterministic methods are considered as more pragmatic

white box model and are applied in large-scale land use

planning purpose (Mihalic 1998; van Westen 2000). Event-

based landslide inventory is a compulsory parameter in

landslide susceptibility and hazard assessments. In many

cases, only single event landslide database has been con-

sidered to assess landslide susceptibility. Many different

statistical or quantitative methods have been applied so far

in various studies of landslide susceptibility zonation. Such

studies can be identified on the basis of the technique used,

such as bivariate probabilistic methods (Lee and Choi 2004;

Guzzetti 2005; Lee and Touch 2006; Dahal et al. 2008a, b;

Regmi et al. 2010; Ozdemir 2011), logistic regression

methods (Atkinson and Massari 1998; Dai et al. 2001;

Nefeslioglu et al. 2008; Dahal et al. 2012) and artificial

neural network methods (Ermini et al. 2005; Lee et al. 2003;

Gomez and Kavzoglu 2005; Melchiorre et al. 2008; Ne-

feslioglu et al. 2008; Poudyal et al. 2010; Yilmaz 2010).

JTC-1 has also well described the methodology for

landslide susceptibility and hazard (Cascini 2008). How-

ever, it still lacks information about standardization and

regionalization of landslide susceptibility map. Following

the JTC1 definition of landslide hazard, it is very difficult

to prepare an extreme weather-based landslide hazard

zonation map in regional scale, particularly because annual

probability cannot be interpreted for wide area because

topographic settings and non-uniformity in rainfall distri-

bution do not permit to explore annual probability. For the

wide mountainous region, the temporal distribution of

landslide is always sparse and too complicated to be sep-

arated event-wise. The same problem arises in landslide

susceptibility zonation for extreme monsoon events.

Most available scientific works have evaluated landslide

susceptibility in terms of good accuracy of landslide pre-

diction in their study area. However, even in the same

region and similar geological and geomorphological set-

tings, there is remarkable lack of uniformity in methodol-

ogy and accuracy of the landslide susceptibility zonation

maps. An overview of landslide susceptibility zoning

practices in the past few years reveals that susceptibility

maps are not consistent regarding their accuracy and

reliability. Selection procedure of predisposing causative

parameters for landslide susceptibility assessment is also

Environ Earth Sci

123

not uniform. In many cases, priority was given to DEM-

based parameters only. The role of selected predisposing

causative parameter for landslide activity and process is

still not well understood for the Nepal Himalaya. To

address theses issues of susceptibility and landside activity,

this work focuses on susceptibility zonation mapping on

the basis of multi rainfall event-based landslide database

and evaluation of landslide activity with respect to pre-

disposing causative parameters. In this study, the rainfall-

induced landslides were evaluated qualitatively to under-

stand the effect of intrinsic parameters for landslide activity

and process. The major objectives of the research were to

understand the landslide activity in the Nepal Himalaya

and to develop a regional-scale landslide susceptibility

zonation mapping techniques.

Study area

The study area is located in the central Nepal (Fig. 1).

Geographically, the area is extended between latitude

27�3800000N–28�0000000N and longitude 84�2203000E–

85�2200500E of central Nepal. The major highways of central

Nepal, connecting the capital city Kathmandu to other parts of

the country, run through middle part of the study area. The

study area covers a total of 3,844 km2; however, the water

body and river courses are excluded from the analysis.

The Trishuli River, a major drainage system of central

Nepal, also drains through central part of the study area.

The Trishuli River is also called as the Gandaki River in

south and its catchment has fragile geological setting and

steep topography, predisposing the landscape to have sig-

nificant problems of geology- and geomorphology-related

disasters. Mugling, Narayanghat and Gorkha are major

towns in the study area including few part of Kathmandu

City and the whole study area is highly populated. Some of

the major national-level hydropower projects also lie in this

area. The area is highly populated hilly region of Nepal.

The study area is equally important from spiritual point of

view as two major Hindu temples, namely Manakamana

and Gorkha Kali are also situated in this region. Thousands

of Nepalese and Indian Hindu pilgrims visit these temples

every year.

Geological and geomorphological settings

Geology of the Nepal Himalaya has been divided into east–

west trending five major tectonic units (Fig. 1). From south

to north, these units are Terai Zone, Sub-Himalayan Zone

(Siwalik), Lesser Himalayan Zone, Higher Himalayan

Zone and Tibetan-Tethys Zone. Geologically, the study

area falls into the Lesser Himalayan Zone, Higher Hima-

layan Zone and part of Siwalik Zone. Physiographically,

83 % of Nepal falls within the mountainous terrain and the

Fig. 1 Study area and geological map of Nepal. The major geological zones are shown in map. Details are given in text

Environ Earth Sci

123

remaining 17 % in the northern edge of the alluvial plains

of the great Gangetic Basin. Nepal is well defined into

eight physiographic provinces from south to north, namely

(1) Terai (the northern edge of the Indo-Gangetic plain),

(2) Siwalik (Churia) Range, (3) Dun Valleys, (4) Ma-

habharat Range, (5) Midlands, (6) Fore Himalaya, (7)

Higher Himalaya and (8) Trans Himalaya. Physiographi-

cally, the study area partly falls into Midlands and partly

into Mahabharata Range in south.

The geological zones of study area are mainly composed

of meta-sedimentary and metamorphic rocks. Details of

geological formations and major lithology in the study area

are provided in Table 1 and Fig. 2. Nearly half of the study

area in northern part is composed of the Kuncha Formation

(gritty phyllite and quartzite). The black slate (Benighat

Slate) and grey schist (Kalitar Formation) as well as

quartzite and calcareous formations are also found. The

area is extensively dissected by many faults, folds and

thrusts. Major Himalayan thrusts, MBT and MCT also pass

through the study area.

Factors such as the excessive rainfall and human inter-

vention are the main triggering agents for landslides in the

study area. Factors like groundwater condition, river toe

cutting and deforestation on slopes along with geomor-

phologic setting facilitate the occurrence of landslides. The

study area has maximum elevation of 3,020 m in the north-

east corner and minimum elevation of 195 m in the south-

west corner. The average slope and relief of terrain are

Table 1 Geology and

stratigraphy of the study area

(modified after Stocklin and

Bhattarai 1978)

Name of the unit Lithology Average thickness (m) Age

Lesser Himalayan Zone

Kathmandu Complex

Phulchoki Group

Godavari Limestone Limestone 300 Devonian

Chitlang Formation Slate 1,000 Silurian

Chandragiri Limestone Limestone 2,000 Cambro-Ordovician

Sopyang Formation Slate, calc. phyllite 200 Cambrian

Tistung Formation Meta-sandstone, Phyllite 3,000 Early Cambrian

Unconformity

Bhimphedi Group

Markhu Formation Marble, schist 1,000 Late Cambrian

Kulekhani Formation Quartzite, schist 2,000 Precambrian

Chisapani Quartzite White quartzite 1,000 Precambrian

Kalitar Formation Schist, quartzite 2,000 Precambrian

Bhainsedoban Marble Marble 800 Precambrian

Raduwa Formation Garnet-schist 1,000 Precambrian

Mahabharat Thrust/Main Central Thrust (MCT)

Nawakot Complex

Upper Nawakot Group

Robang Formation Phyllite, quartzite 2,000–1,000 Paleozoic

Malekhu Limestone Dolomitic limestone 800 Paleozoic

Benighat Slate Slate 3,000–5,000 Paleozoic

Erosional Unconformity

Lower Nawakot Group

Dhading Dolomite Dolomite 500–1,000 Late Precambrian

Nourpul Formation Phyllite, quartzite 800 Late Precambrian

Dandagaon Phyllite Phyllite 1,000 Late Precambrian

Fog–Fog Quartzite White quartzite 400 Late Precambrian

Kuncha Formation Phyllite, quartzite 3,000 Precambrian

Main Boundary Thrust

Siwalik Zone

Upper Siwalik Mudstone, siltstone 2,000–2,500 Pleistocene

Middle Siwalik Sandstone 1,400–2,000 Pliocene

Lower Siwalik Gravel conglomerate 1,500–2,000 Miocene

Environ Earth Sci

123

higher (above 1,500 m from mean sea level) in the

southern part. Many parts of the study area are inhabited by

relatively large human population with agriculture and

livestock as the mainstays of livelihood. River valleys,

comprised of narrow tracts of flatland, are extensively used

for cultivation and settlement. The infrastructures, popu-

lation and livelihood activities are highly vulnerable to

landslides, debris torrents, debris slides and debris flows

from elevated slopes. Fast population growth, development

and economic activities are also creating enormous

pressure in the environment. Similarly, throughout the

study area, the rainfall is normally in the range of

1,500–2,000 mm year-1. As a result, geological conditions

and the climate render the area highly susceptible to

landslides.

Materials and methods

Landslide susceptibility analysis is carried out with heu-

ristic, statistic and deterministic approaches (Ives and

Messerli 1981; Kienholz et al. 1984; Rupke et al. 1988;

Dahal et al. 2008c; Regmi et al. 2010; Xie et al. 2004;

Oztekin and Topal 2005). In the recent years, statistical

method has been used widely as it is suitable for evaluating

landslide hazard and susceptibility for the regional- and

medium-scale work (van Westen 2000). Both bivariate and

multivariate approaches of statistical methods are in prac-

tice. In this research, multivariate approach (logistic

regression modelling) is used for evaluating rainfall-

induced landslide susceptibility in central Nepal. For the

rainfall-induced landslide hazard mapping, intrinsic and

extrinsic parameters along with landslide inventory having

spatial and temporal distribution information are necessary

for overlay analysis. Slope, aspect, curvature, relief, wet-

ness index, lithology, land use, geomorphological units,

soil depth, road proximity, stream proximity and thrust-

fault proximity are selected as intrinsic parameters for

susceptibility modeling. GIS software ILWIS 3.3 was used

to calculate intrinsic and extrinsic data layers. Similarly,

statistical software was used for the logistic regression

modeling. Details of the methodology are described under

the following sub-headings.

Landslide inventory

A reliable map that predicts the landslide susceptibility in a

certain area always needs information regarding the spatial

Fig. 2 Geological map study area with major drainage system and highways

Environ Earth Sci

123

and temporal frequency of landslides. Thus, in landslide

susceptibility study, a landslide inventory is the main data

that should be as complete as possible in both space and

time for the selected area of interest (Glade et al. 2001).

Without event-based landslide inventory maps, validation

of susceptibility zonation is not possible. The availability

of geographical information systems (GIS) technology

solved several problems related to the production, update

and visualization of landslide inventory maps (Guzzetti

et al. 2012). A GIS provides the freedom to separate the

landslide information in multiple layers, maintaining the

geometrical consistency between the layers. A GIS is also

able to handle the landslide information in combination

with environmental information (e.g., on morphometry,

geology, land use, land cover), which is very useful for

susceptibility and activity assessment. Landslide invento-

ries can be carried out using a variety of techniques,

namely direct geomorphic mapping in field and interpre-

tation of air photo and satellite images.

In this study, landslide inventory map is prepared from

the available reports, field visits and time series satellite

images. Technical reports prepared by various organiza-

tions such as Department of Roads and Department of

Water Induced Disaster Prevention about past landslide

events were also used to prepare the inventory maps. Var-

ious dissertation works submitted to Tribhuvan University,

Nepal, were extensively referred. These reports provide

detailed description of rainfall-induced landslides such as

location, slope, detail maps, field photographs, geology, soil

type, land use and probable cause of slide. Topographic

maps and aerial photographs provided by the Department of

Survey, Government of Nepal, are considered as the basic

data sources for generating landslide database. Various field

surveys were also carried out for data collection since

2001–2010 and field notes of the author from past 10 years

were extensively referred. Correction of geological maps,

verification of landslides and collection of new landslide

data were the main purpose of the field survey.

The study area was affected by various extreme

weathers, but not totally. For example, heavy rainfall had

affected the south-central part of the study area in 1993, the

south-western part in 2003, the area around Kathmandu in

both 1993 and 2002 (Dahal et al. 2006, 2010) and the

northern part in 2006.

Depending on the related disaster history of the partic-

ular location, landslides are identified as old landslide (OL)

and new landslide (NL). Comparatively recent landslides in

the specific part of the study area were considered as new

landslides. For example, for eastern part of the study area,

extreme rainfall events were noticed in 2008 and 2009;

therefore, landslides that occurred between 2008 and 2009

in that area were considered as new landslides. Similarly,

depending on various extreme monsoon rainfall events in

the western part of the study area, landslides that occurred

between 2009 and 2010 were considered as new landslides

for the western part of the study area. Similarly, depending

on latest extreme rainfall events in the south-western hills

of Kathmandu, landslides that occurred before 2002 were

considered as old landslides and those after 2002 were

mapped as new landslides. These specific landslide events

are identified on the basis of the known extreme rainfall

events of the region. Finally, two sets (old landslide and

new landslide) of landslide inventory maps were prepared

for this work.

Thus generated old landslide inventory map was a

‘‘Blind Landslide Inventory’’ database and it could not

explain typical rainfall event responsible for particular

landslide. To overcome this issue, considering the size of

the landslide, the blind inventory map was reclassified into

two maps as short-duration high-intensity rainfall-induced

landslide inventory and long-duration low-intensity rain-

fall-induced landslide inventory. These two landslide

inventory maps were considered as proxy of multiple

rainfall event-based landslide inventories and used in sus-

ceptibility zonation.

The following are some of the main characteristics of

the landslides that were included in the database:

• In general, landslides of the study area can be identified

as translational slides, rotational slides and a combina-

tion of both on the basis of the shape of the failure

surface. The translational slides to semi-translational

slides were found to be the most dominant failure mode

of landslide in the study area.

• The volume of landslide was generally found to range

from a few tens to a few thousands of cubic metres.

• In most cases, majority of the debris slides were found

to be shallow with failure depth \3 m.

• In many landslides, bedrock was well exposed after the

slide, which clearly suggested that the upper part of the

fractured rock mass was also involved in the landslid-

ing process.

• Most of the landslides were identified as translational

debris slides occurring first on a steep zero-order valley

or topographic hollow and then flowing through a first-

order stream channel.

• In addition, field observations revealed that most

landslides occurred in natural as well as man-made

slopes with thick colluvial deposits and highly weath-

ered bedrock.

Landslide predisposing parameters

For any landslide susceptibility assessment, selection of

relevant predisposing parameter is one of the most

important steps. The best way to select predisposing

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parameter is usually based on scale of analysis, landslide

type, the failure mechanism and prior knowledge of the

main causes of landslides (Guzzetti et al. 1999; Glade and

Crozier 2005; van Westen et al. 2008; Jaiswal et al. 2010).

In this study, landslide predisposing parameters are selec-

ted on the basis of (1) landslide causative factors men-

tioned in the reports and previous landslide-related studies

in the study area and (2) field observations. Twelve rele-

vant intrinsic parameters such as slope, aspect, relief,

curvature, drainage density, wetness index, land cover,

lithology, rainfall, lineament proximity, stream proximity

and road proximity are selected. A brief description of

each predisposing intrinsic parameter, its significance

and mapping techniques are given under the following

headings.

DEM derived parameters

Two digital elevation models (DEM) were created from

digital contours (20 m interval) and spot heights with res-

olution of 20 and 50 m. The DEM having 20 m resolution

was used to investigate landslide activity with predisposing

parameters and 50 m DEM was used for regional-scale

susceptibility mapping. Slope, aspect, curvature, wetness

index and relief maps were derived as DEM-derived

parameters. All these maps were prepared in both 20 and

50 m resolutions for landslide activity analysis and regio-

nal-scale susceptibility assessment, respectively.

Slope

Slope is an important topographic attribute affecting both

the surface and subsurface hydrological condition and

terrain stability. Therefore, it is a main predisposing

parameter for the landslide susceptibility assessments

(Guzzetti et al. 1999; Dai and Lee 2002; Dahal et al. 2008a,

b; Regmi et al. 2010). For the landslide activity analysis

with slope, 79 classes such as \1�, 1�–2�, 2�–3�……75�–

76�, 76�–77� and 77�–78� are selected and landslide

activity with respect to slope is evaluated. For the sus-

ceptibility analysis, a total of ten slope classes such as

B5�,[5�–10�,[10�–15�,[15�–20�,[20�–25�,[25�–30�,

[30�–35�, [35�–40�, [40�–45� and [45� are used to

prepare slope map.

Aspect

Slope aspect is another factor influencing the stability of a

terrain because it controls the moisture content in soil and

vegetation growth based on the exposure to the sunlight.

For the susceptibility analysis, aspect map is classified into

nine classes such as N, NE, E, SE, S, SW, W, NW and Flat.

For the landslide activity analysis, 360 classes such

as\1�, 1�–2�, 2�–3�……345�–350�, 350�–355� and 355�–

360� were selected and landslide activity with respect to

each aspect class evaluated.

Curvature

Slope curvature is the curvilinear shape of the slope. It

greatly influences the movement direction and deposition

of landslides. Concave slope gives a positive curvature

value and convex slope gives a negative curvature value.

Following rainfall events, water flows from areas of

convex curvature and accumulates in areas of concave

curvature. This process is known as flow accumulation

and in fact curvature is a measure of the land area that

contributes surface water to an area where water can

accumulate. In raster GIS, the average curvature, e, can

be derived from the following relationship (Uchida et al.

2004):

e ¼o2fox2 1þ of

oy

� �2� �

þ o2foy2 1þ of

ox

� �2� �

� 2 ofox

ofoy

o2foxoy

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2 1þ of

ox

� �2

þ ofoy

� �2� �3

s ;

ð1Þ

where of=ox and of=oy are first derivatives of DEM and

o2f=ox2 and o2f=oy2 are second derivatives of DEM. When

curvature is calculated from the 20 m resolution DEM of

the study area, 49.5 and -50.0 are obtained as maximum

positive curvature value and minimum negative curvature

value, respectively. So landslide activity between curvature

values of 49.5 and -50.0 is evaluated to understand dis-

tribution pattern of landslides in the convex and concave

slopes. For susceptibility assessment, curvature map is

classified into five classes: convex, semi convex, planar,

semi concave and concave forms.

Relief

Relief is another important parameter controlling the

landslides, especially in the tectonically active regions. In

such regions landslides are the main landform degrada-

tion process that helps to maintain the continuously

uplifting mountains relief to threshold level (Carson and

Petley 1970). 50 m DEM map is used to prepare the

relief map. The relief map is divided into six clas-

ses: B500 m, [500–700 m, [700–900 m, [900–1,100 m,

[1,100–1,500 m and [1,500 m and used for suscepti-

bility analysis. DEM having 20 m pixel resolution is used

to analyse landslide activity with elevation.

Environ Earth Sci

123

Geology-related parameters

Geology and geomorphology play important roles in

landslide activity and susceptibility because different

geological and geomorphological units have different

strengths and susceptibilities to landslide processes

(Pachauri and Pant 1992; Roering et al. 2005). The study

area lies in the Midland of the Lesser Himalayan Zone;

rocks are highly folded, faulted and fractured. Complex

network of joints and fractures on rock mass make the

terrain highly susceptible to landslide by any triggering

agent such as extreme rainfall, seismic events, snow melt

and so on. All the geological formations observed in the

study area (Stocklin and Bhattarai 1978) are used to pre-

pare the geological map and finally converted to major

lithologic domain. For this purpose, geological formations

are normalized into lithologic domain according to major

rock types within the formation. Landslide activity is also

evaluated on the basis of lithologic domains rather than

geological formations.

Thrust and fault are weak planes through which the

tectonic movements occur. Due to the tectonic movements,

the surrounding areas are highly fractured, faulted and

weathered. On such heavily crushed and weathered terrain,

extreme rainfall can easily induce landslides. Major thrusts,

folds and local faults are included for the preparation of

lineament proximity map. The lineament proximity

map is classified into 12 classes: B100 m, [100–300 m,

[300–500 m, [500–700 m, [700–900 m, [900–1,100 m,

[1,100–1,300 m, [1,300–1,500 m, [1,500–1,700 m,

[1,700–1,900 m, [1,900–2,100 m and [2,100 m. Land-

slide activities in the various distances from lineament

were also evaluated.

Environment-related parameters

Increase in population and haphazard development activi-

ties gradually deteriorate natural environment. Disturbance

in environmental balance in mountainous regions always

results landslides and associated hazard. When change in

slope morphology is accelerated in a haphazard manner,

such as cultivation on extreme slopes, marginal land

exploitation without proper protection and deforestation,

landslide incidence always increase. In this study, land

cover, stream proximity, road proximity, wetness index,

rainfall and drainage density are considered as major

environmental-related parameters that contribute to land-

slide process in the study area.

Land cover

Land cover always controls landslide process. Well-for-

ested slopes are always less prone to landslide occurrence

whereas barren slopes always have the potential for slope

failures. To interpret land cover pattern of the study area,

Normalized Difference Vegetation Index (NDVI) was

considered and the NDVI map was prepared from ALOS

satellite image. The NDVI value was calculated using the

NDVI = (IR - R)/(IR ? R) formula, where IR is near-

infrared band image and R is red band image of ALOS.

The NDVI value denotes areas of vegetation in an image.

The presence of dense green vegetation implies high

NDVI values due to higher concentration of chlorophyll

and high stacking of leaves. Sparse vegetation, on the

other hand, gives low NDVI values. Likewise, water and

clouds have larger visible reflectance than near-infrared

reflectance. Thus, these features yield negative index

values. Rock and bare soil areas have similar reflectance

in the two bands and give vegetation indices near zero.

For susceptibility zonation mapping, NDVI is categorised

into six classes: B0, [0–0.1, [0.1–0.2, [0.2–0.3,

[0.3–0.4 and [0.4. All NDVI values are used to evaluate

landslide activity in the area with the land cover causative

parameter.

Stream proximity map

In the study area, appearance of landslide was very fre-

quent along the stream. Thus, location of the landslide from

stream was considered as another geomorphology-related

causative factor. Field observations indicated that slope

failure was more frequent along stream due to groundwater

movement towards stream and toe undercutting. Thus, a

distance to drainage map was generated for the landslide

activity analysis and susceptibility assessment. In order to

produce the stream proximity map and to use in suscepti-

bility assessment, the drainage segment map was rasterised

and the distance to the drainage was calculated in metres.

The resultant map was then sliced into seven classes:

B25 m, [25–50 m, [50–75 m, [75–100 m, [100–125 m,

[125–150 m and [150 m.

For landslide activity analysis, stream proximity map

was generated and classified into 10 m interval class and

used to evaluate landslide activity.

Wetness index

Wetness index is commonly used to quantify topographic

control on hydrological processes and helps to understand

the relationships between topography and rainfall vari-

ability (Beven and Kirkby 1979; Wilson and Gallant 2000;

Schmidt and Persson 2003). In this work, wetness index

was calculated using the equation described by Beven and

Kirkby (1979).

Wetness index is higher in flat and converging terrains

and lower in steep and diverging terrains. In this work, its

Environ Earth Sci

123

values are classified into five different classes: B8 m,

[8–9 m,[9–10 m,[10–11 m,[11–12 m and[12 m. All

ranges of wetness index values were used to evaluate

landslide activity with the wetness index landslide causa-

tive parameter.

Road proximity map

One of the controlling factors for the stability of slopes is

road construction activity. However, in the study area, road

access was prime factor of forest degradation and slope

failure occurrence. Road construction without any mitiga-

tion measures adversely affects slopes and many landslides

were noticed along the newly constructed road. Haphazard

tree cuttings are also increasing after road accessibility in

the villages. Thus, road proximity map was generated as

per the hypothesis that slope failure may be more frequent

along the roads. In order to produce the map showing road

proximity, the road segment map was rasterised and

the distance to road was calculated in metres. The resul-

tant map was then categorised into 11 classes: B100 m,

[100–200 m, [200–300 m, [300–400 m, [400–500 m,

[500–600 m, [600–700 m, [700–800 m, [800–900 m,

[900–1,000 m and [1,000 m. Landslide activity with the

road proximity causative parameter was also evaluated for

the study area.

Rainfall

Previously, as rainfall is extrinsic parameter it had been used

with susceptibility map for producing landslide hazard map

(Dahal et al. 2008a). But after appreciating guidelines pre-

pared by JCT-1 (Fell et al. 2008), rainfall distribution is also

considered as one of the causative parameters for landslide

susceptibility analysis. To prepare rainfall distribution map

of the area, annual rainfall data of all rainfall gauging sta-

tions in and around the study area were selected and contour

map of annual average rainfall was prepared which was

finally converted into raster map of annual average rain-

fall with the help of contour interpolation technique in

GIS. In total, 11 classes (B1,900 mm,[1,900–2,000 mm,

[2,000–2,100 mm,[2,100–2,200 mm,[2,200–2,300 mm,

[2,300–2,400 mm,[2,400–2,500 mm,[2,500–2,600 mm,

[2,600–2,700 mm, [2,700–2,800 mm, [2,800 mm) were

considered to prepare in rainfall distribution map and it was

used for susceptibility modelling. Landslide activity was

also evaluated with annual average rainfall of the study area.

Drainage density

Drainage density is a ratio of total length of streams in a

drainage basin to the total projected area of the basin. In

this study, however, the drainage density was not computed

in the basin scale; it was rather calculated in 1-km grid

scale of the study area, and classified into five categories:

very low drainage density (\0.006 km-1), low drainage

density ([0.006–0.0075 km-1), medium drainage density

([0.0075–0.009 km-1), high drainage density ([0.009–

0.0115 km-1) and very high drainage density

([0.0115 km-1). The classified drainage density map was

then used for landslide susceptibility zonation mapping.

With DEM of 20 m resolution, landslide activity per square

km of drainage density was also evaluated.

Susceptibility modelling

In this study, logistic regression model was used to assess

landslide hazard indexes. Logistic regression can be used to

determine the relation of landslide occurrence and the

related factors (Guzzetti et al. 1999; Dai and Lee 2002;

Ohlmacher and Davis 2003; Lee 2005; Ayalew and Ya-

magishi 2005; Zhu and Huang 2006; Chen and Wang 2007;

Akgun and Bulut 2007; Das et al. 2010; Chauhan et al.

2010; Dahal et al. 2012). It is useful when the outcome

variable or dependent variable is binary or dichotomous.

The dependent variable for this analysis is the absence or

presence of a landslide. Considering n independent vari-

ables x1, x2, x3,…, xn, affecting landslide occurrences, the

vector X = (x1, x2, x3,…, xn) has been defined. In logistic

regression analysis, the logit y is assumed as a linear

combination of independent variables and is given as

follows:

y ¼ b0 þ b1x1 þ b2x2 þ b3x3 þ � � � þ bnxn; ð2Þ

where b0 is the constant of the equation, and b1, b2,…, bn

are the coefficients of independent variables x1, x2, x3,

……, xn. For landslide hazard assessment, the dependent

variable is a binary variable, with values of 1 or 0

representing the presence or absence of landslides.

Quantitatively, the relationship between the occurrence

and its dependency on several variables can be expressed

as follows (Hosmer and Lemeshow 2000):

P ¼ 1=1þ e�y; ð3Þ

where P is the estimated conditional probability of

landslide occurrence and e is the constant 2.718. From

Eqs. (2) and (3), a relationship can be obtained in which the

natural logarithm of the odds, log(P/1 - P), is linearly

related to the independent variables as follows:

log P=1� P

� �¼ b0 þ b1x1 þ b2x2 þ b3x3 þ � � � þ bnxn:

ð4Þ

The goodness of fit of the model was tested with the

Wald statistics and Hosmer–Lemeshow Test (Hosmer and

Lemeshow 2000). By examining the sign of a dependent

Environ Earth Sci

123

variable’s coefficient estimate, the effect of that variable on

the probability of landslide occurrence can be determined.

These values were utilized to decide landslide

susceptibility index (LSI) of the study area.

Analysis

Landslide activity

Landslide scars recognized in satellite images (ASTER and

ALOS) and aerial photographs are also validated with the

Field data, available maps and reports and cross checked

with Google Earth images also. All together, 655 new

landslides and 9,226 old landslides were mapped from

1993 to 2010 and landslide inventory map was prepared

(Fig. 3). Basically, whole landslide area is marked for

rotational slide whereas in the case of translational slide

and debris flow, deposition part is not included in mapping.

All landslides more than 20 m in size either in length or

breadth are considered for mapping. Finally, combined

raster maps (20 m 9 20 m grid size) of both old and new

landslides are prepared and all together 21.785 km2 of old

landslides and 0.795 km2 of new landslides are noticed in

3,769.786 km2 of study area (excluding water bodies and

rivers). To understand the role of intrinsic factors in

landslide activity, all 12 causative factors such as slopes,

aspect, curvature, relief, geology, lineament proximity,

land cover, stream proximity, wetness index, road prox-

imity, rainfall and drainage density were compared with

landslide inventory map (database map) in the form of

raster calculations.

The concept of normalized landslide density was used to

evaluate causative factors of landslide. Normalized density

gives relative landslide distribution in various classes or

categories (such as in causative factor geology, lithology

class phyllite). Equation (5) was used to calculate the

normalized densities of landslide in various classes of

causative factors:

NLD ¼ li=ciPni¼1

li=ci

; ð5Þ

where NLD is normalized landslide density, ci is the area of

ith class of a factor (such as, in causative factor geology

lithology class phyllite), li is landslide area within ith class of

a factor (such as lithology phyllite) and n is the total number

of class in causative factors. Then the parameter maps are

overlaid or crossed with the landslide inventory map to

calculate the normalized landslide densities based on

Eq. (5). Details of analysis are given in the following section.

A DEM representing the terrain is a key to generate

various geomorphological parameters, which influence the

landslide activity in an area. Hence, DEM is prepared with

digital contour data (1:25,000 scale) obtained from the

Department of Survey, Government of Nepal. The DEM of

study area is prepared in 20 m 9 20 m pixel size.

Slope angle, an important parameter in slope stability

analysis, comprises of minimum 0�–73� in the study area.

To explore most influencing range of slope angle, slope

map is classified into 74 classes, from 0� to 73�. The

Fig. 3 Landslide distribution in the study area

Environ Earth Sci

123

classified slope map having slope class from 0� to 73� is

overlaid and crossed with landslide raster map. The scatter

plot of normalized landslide density in each slope class is

shown in Fig. 4a, and it is found that slope angle from 1� to

45� has linear relationship with landslide density. High

angle slope has higher landslide density. Similarly, when

the 360 classes of aspect of the slope are crossed with

landslide maps, it is noticed that north-east to west facing

slopes have higher landslide density (Fig. 4b) in the study

area. The distribution curve of normalize landslide density

with aspect also shows that the peak landslide occurrence is

in south to south west facing slopes and aspect between 60�and 270� are the most critical zone for the study area.

Slope convexity or concavity also influences landslide

activity. In the study area, however, the slope curvature

shows more or less same normalized landslide density in

concave slope and convex slope (Fig. 5a). Landslide den-

sity in various curvature classes shows ‘‘Rabbit Ear’’ type

of distribution pattern and a typical mirror image rela-

tionship between concave and convex curvature of slope

with landslide activity was notice.

Relative relief always affects landslide activity. In the

study area, the relative relief between 1,000 and 2,200 m

is corresponding to higher normalized landslide density

(Fig. 5b).

In this study, NDVI was used to interpret land cover

and landslide relationship. NDVI map is generated from

two same-season ALOS images and they are classified

into 0.01 interval class. When classified NDVI map is

crossed with landslide, a curvilinear relationship of NDVI

is found with normalized landslide density. This rela-

tionship is very distinct with the NDVI values between

-0.4 and 0.5. Higher normalize density value is also

noticed in lower NDVI value area and it is well repre-

sentation of relationship of land cover with landslide

incidences (Fig. 6a). Relationship between wetness index

and normalized landslide density is polynomial and dis-

tribution curve is similar to power law curve. Plot shows

that area of higher wetness index, which are usually

stream or rivers, usually has low normalized density in

comparison with lower (\9) wetness index (Fig. 6b).

To compare geology with landslide activity, the

geological map is normalized into lithology map as per

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 10 20 30 40 50 60 70 80 90

Slope (degrees)

No

rmal

ize

lan

dsl

ide

den

sity

a

0.0

0.4

0.8

1.2

1.6

2.0

2.4

0 30 60 90 120 150 180 210 240 270 300 330 360

Aspect (degrees)

No

rmal

ize

lan

dsl

ide

den

sity

Critical zoneb

Fig. 4 Normalized landslide density a in various slope angles and

b in various aspects of slope

0.0

10.0

20.0

30.0

40.0

-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5

Curvature

No

rmal

ize

lan

dsl

ide

den

sity

Convex slopeConcave slope

a

0.0

0.5

1.0

1.5

2.0

200 600 1000 1400 1800 2200 2600

Relief (m)

No

rmal

ize

lan

dsl

ide

den

sity

Critical zoneb

Fig. 5 Variation in normalized landslide density in a different slope

curvature and b relative relief

Environ Earth Sci

123

the major lithology of the formation and crossed with

landslide map. The results show that geological formations

having gabbro, quartzite schist and calcareous rocks show

higher normalized landslide density (Fig. 7a). In quartzite

and calcareous rocks, mainly rock falls and rock slides are

common and normalized landslide densities area also high.

Two distinct clusterings have been noticed when proximity

to lineament map is crossed with landslide inventory

including all old and new landslides (Fig. 7b). Lineament

proximities between 2,200 m and 3,400 m have shown

dual landslide activities.

A stream proximity map was generated in 20 m cell size

and classified into 10 m interval class. When this map is

crossed with landslide, the normalized landslide density is

found to be maximum for 50–100 m distance range and

decreased gradually for higher value of distance to drain-

age as shown in Fig. 8a. Drainage density (length of stream

per unit square km) is measured in unit square km grid and

crossed with landslide map. It is noticed that area of low

drainage density has low normalized landslide density

(Fig. 8b).

In the study area, distribution of landslide is also fre-

quent along the transportation routes (rural roads and

highways). A road proximity map is generated and classi-

fied into 40 m interval classes. When classified distance to

transportation routes map is crossed with landslide map,

the normalized landslide density is found to be increasing

in trend within the range value of 200–700 m distance and

landslide activity is decreased in the area far from the roads

as shown in Fig. 9a. The relationship of normalized land-

slide density with rainfall also confirmed that landslide

activity is high in the higher rainfall area (Fig. 9b).

Landslide susceptibility zonation

Treatment in landslide inventory map

In Nepal, short-duration high-intensity rainfall triggers

relatively small landslide whereas long-duration low-

intensity rainfall triggers relatively huge landslides (Dahal

and Hasegawa 2008; Dahal et al. 2009). As a result,

landslide disaster history of the whole study area is not

0.0

1.0

2.0

3.0

4.0

5.0

-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Land cover (NDVI value)

No

rmal

ize

lan

dsl

ide

den

sity

a

Landslide area

Curvilinear relationship

0

1

2

3

4

5

6

5 6 7 8 9 10 11 12 13 14 15 16 17 18

Wetness index

No

rmal

ize

lan

dsl

ide

den

sity

b

Fig. 6 Variation in normalized landslide density in a NDVI and

b wetness index

0.0

0.4

0.8

1.2

1.6

2.0

Calc R

ock

Conglom

erate

Gabbro

Gneiss

Granite

Mudstone

Phyllite

Quartzite

Quatenary

Sandstone

Schist

Siw

alik Sst

Slate

Lithology

No

rmal

ized

lan

dsl

ide

den

sity

a

0.0

2.0

4.0

6.0

8.0

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Lineament distance (m)

No

rmal

ize

lan

dsl

ide

den

sity

b

Critical zone 2

Critical zone 1

Fig. 7 Variation in normalized landslide density a rock type and b in

distance to the lineament

Environ Earth Sci

123

similar. In the initial stage of susceptibility modeling, out

of 9,229 old landslides, 62 landslides were very small in

size (\100 m2) and they were noticed in quaternary

deposit. Meantime, the DEM used in this analysis was

generated from digital contour map having 20 m contour

interval. The DEM hardly shows the cliffs and scars within

flat area of quaternary deposit in 50 m cell size. Similarly,

those 62 landslides were noticed in less than 5� slopes

which, in fact, cannot be represented in DEM. To over-

come this issue of DEM, only 9,167 landslides were

selected for susceptibility analysis. In the initial stage of

modelling, all 9,167 landslides were used for logistic

regression analysis. But, various significance test of mod-

elling suggested that the performance of model was not in

acceptable range. It clearly suggested that using multiple

rainfall event-related landslide data to prepare single sus-

ceptibility modelling usually does not give accurate model.

This issue was also mentioned by Guzzetti et al. (2006),

Fell et al. (2008) and Bhandary et al. (2013). To overcome

this issue, it was assumed that the landslides in the study

area were triggered by two types of rainfall events. Short-

duration high-intensity rainfall was considered as the first

type of rainfall events which triggered relatively small

landslides, and long-duration low-intensity rainfall event

was considered as the second event rainfall which was

responsible to trigger relatively big landslides. Dates

of rainfall events were not taken into account and only

short-duration high-intensity rainfall and long-duration

low-intensity rainfall were considered as proxy of multiple

rainfall events. Depending on working scale (50 m cell

size), maximum two pixel sized landsides (area\5,000 m2)

were considered as relatively small landslides triggered by

short-duration high-intensity rainfall and landslides having

area more than 5,000 m2 were considered as relatively big

landslides triggered by long-duration low-intensity rainfall.

Therefore, the main landslide inventory map was again

divided into two inventory maps: Map A (inventory

map having landslide size more than 5,000 m2) and Map B

(inventory map having landslide size \5,000 m2).

Details of inventory preparation procedures are shown in

Fig. 10.

0.0

0.5

1.0

1.5

2.0

0 100 200 300 400 500 600 700 800

Drainage distance (m)

No

rmal

ize

lan

dsl

ide

den

sity

a

0

1

2

3

4

5

6

0 0.01 0.02 0.03 0.04

Drainage density (per sq km)

No

rmal

ize

lan

dsl

ide

den

sity

Main concentration

b

Fig. 8 Effect of a distance to drainage and b drainage density for

landslide occurrence

0

1

1

2

2

0 200 400 600 800 1000 1200 1400 1600 1800

Road proximity (m)

No

rmal

ize

lan

dsl

ide

den

sity

a

0.0

0.5

1.0

1.5

2.0

2.5

1500 2000 2500 3000 3500

Average annual rainfall (mm)

No

rmal

ize

lan

dsl

ide

den

sity

b

Fig. 9 a Effective road proximity and b average annual rainfall to

trigger landslides in the study area as shown by higher normalized

landslide density

Environ Earth Sci

123

Susceptibility modelling

As mentioned in the earlier section, logistic regression

modelling was done for landslide susceptibility assessment.

In this study the causative parameters were categorised into

various classes and each parameter was a nominal variable.

To avoid the creation of an excessively high number of

dummy variables, these nominal variables were converted

to numeric variables by coding. For this purpose, the rel-

ative landslide density in each class was used to transform

nominal variable to numeric variable. Equation (6) was

used to calculate the relative landslide density in each class

of causative factors:

LD ¼ li

Ln

; ð6Þ

where LD is relative landslide density, Ln is total landslide

area in the selected class and li is landslide area within ith

class of a factor (such as slope 10�–15�). Then the

parameter maps were overlaid with the landslide inventory

maps (Map A and Map B) to calculate landslide densities

based on Eq. (3). Thus, all the classes of causative factors

were converted to numerical variable. The domain of

landslide inventory maps was changed from landslide

present and landslide absent to numerical variables 1 and 0,

respectively. All spatial databases of causative parameters

Old landslide map Blind landslide inventory map

New landslide map (Recent event only)

Inventory mapping

Rough susceptibility modeling

Validation of rectified map (LSZ) with new

landslides

Map-A

Prediction rate and best model selection

Merging of class maps Rectification

1% class

LSZ-B

LSZ

Good prediction rate

Final susceptibility map

Yes

No

Map-B

Regression modeling

100 maps of each class

100 maps of each class

LSZ-A

LSI-B LSI-A

1% class

Final landslide susceptibility zonation map

Landslide size >5000 sq m

Landslide size >5000 sq m

Fig. 10 Procedures for event

based landslide inventory

preparation and rectification of

landslide susceptibility zonation

maps

Environ Earth Sci

123

and landslide inventory were exported to statistical soft-

ware (SPSS) and logistic regression analysis was per-

formed for old landslide inventory.

When using logistic regression model, the number of

samples to create dependent variables is always an issue.

The current literatures have been establishing mainly three

kinds of practices (Zhu and Huang 2006). The first one uses

data from all over the study area, which leads to unequal

proportions of landslide and non-landslide pixels (Guzzetti

et al. 1999; Ohlmacher and Davis 2003). Usually, large

volume of data are taken in this method. The second practice

is to use all the landslide pixels and equal non-landslide

pixels, which leads to decrease in data number. The third

practice is to use all landslide pixels and equal number of

randomly selected pixels from areas free of landslides

(Yesilnacar and Topal 2005). In this study, the third category

was used for logistic regression modelling and two LSI maps

LSI-A and LSI-B were prepared after the analysis with

landslide inventory Map A and Map B. When LSI-A and

LSI-B were compared with the new landslide inventory, the

ROC value is obtained to be \0.70, which indicates weak

prediction rate for the susceptibility maps. So, these two

maps cannot be considered as the most suitable suscepti-

bility index maps for the study area. According to the defi-

nition, after the landslide susceptibility assessment, a

zonation map should accurately indicate potential zone of

occurrence of future landslides. Previous studies on the

statistical landslide hazard assessment, in most cases, indi-

cate that the accuracy of susceptibility maps ranges from 65

to 85 %. Many comparative studies also suggest that the

accuracy of a landslide susceptibility map relies more on

good data rather than the model approaches, and in general,

the multivariate approaches yield better prediction rates.

When LSI-A and LSI-B are visually analysed for spatial

distribution of the susceptibility zones, it is understood that

many pixels of lesser susceptibility indexes of LSI-A were

higher susceptibility indexes of LSI-B. So, LSI-A needed

to combine with LSI-B in such a way that the higher sus-

ceptible indexes are accommodated and preserved well in

the zonation map. For this purpose, a rectification process

was done, in which both LSI maps of the two events (short-

duration high-intensity rainfall and long-duration low-

intensity rainfall) were first divided into 100 classes of low

to high LSI values as per the cumulative distribution per-

centage of the values, and two zonation maps, LSZ-A and

LSZ-B, having 100 susceptibility zonation were prepared.

Using ‘‘raster mask’’ operation in ILWIS, each class was

again extracted from LSZ-A and LSZ-B maps. As a result,

a total of 200 maps having susceptibility zonation class of

1, 2, 3…100 % were prepared. LSZ-A-1, LSZ-A-2, LSZ-

A-3,…, LSZ-A-100 % were referred for zonation class

maps generated from LSZ-A and LSZ-B-1, LSZ-B-2, LSZ-

B-3, …, LSZ-B-99, LSZ-B-100 % were applied to refer

zonation class maps generated. Then, the maps of the same

classes, such as LSZ-A-1 and LSZ-B-1 % were merged to

obtain a combined map of only 1 % class of low to high

LSI values in LSZ-A and LSZ-B maps. Following this

procedure, a total of 100 merged maps were generated out

of the 200 maps. To obtain a single zonation map having

zonation from 1 to 100 % classes of low to high LSI values

of LSZ-A and LSZ-B maps, all 100 maps were again

merged and a single raster map was prepared. When

joining 100 maps, many overlapping in higher zonation

class to lower zonation class were noticed. To overcome

this issue, priority was given to higher zonation class. For

example, if 1 % zonation class of LSZ-A map and 2 %

zonation class of LSZ-B map represent same pixels having

same coordinate, the priority was given to the 2 % zonation

class (i.e., higher zonation class) of LSZ-B map during

merging. Following the same rule of prioritizing, a single

raster map, LSZ having 1–100 % classes of low to high

LSI values of LSZ-A and LSZ-B maps, was prepared. A

diagrammatical flow of rectification procedures are given

in Fig. 10. The whole merging procedure was written as a

script consisting of a number of commands in Ilwis 3.8.

Validation of susceptibility map

The final landslide susceptibility zonation map (LSZ) hav-

ing 1–100 % classes of low to high LSI values from both

LSZ-A and LSZ-B was compared with the new landslide

inventory map for the estimation of prediction rate. A

receiver operating characteristic (ROC) curve approach was

used to analyse the prediction accuracy of the proposed

models (Chung and Fabbri 2003). The ROC curve gives the

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

1-Specificity

Sen

siti

vity

Prediction Rate: 82.91

Fig. 11 Prediction curve of final susceptibility zonation map

Environ Earth Sci

123

area under the curve and it is a measure of goodness of fit of

the model. When old landslides were used for zonation and

new landslides for validation, the area under the ROC curve

is called prediction rate. As indicated in Fig. 11, the pre-

diction rate as interpreted from the area under the ROC

curve for LSZ map is 0.8291. It indicates that in the LSZ

map, if 20 % of the classes have high landslide suscepti-

bility value for future landslides, 68 % of the new landslides

can correctly fit. Moreover, if 40 % of the classes have high

landslide susceptibility value for future landslides, 93 % of

the new landslides can correctly fit. Owing to this level of

accuracy, the LSZ map was considered as more suitable

landslide susceptibility map for the study area having

1-100 % (lower to higher susceptibility class) landslide

susceptibility zonation class. To obtain final landslide sus-

ceptibility zonation map, five susceptibility classes; very

low (\20 % class of low to high LSI value in the LSZ map),

low (20–40 % class of low to high LSI value in the LSZ

map), moderate (40–60 % class of low to high LSI value in

the LSZ map), high (60–80 % class of low to high LSI value

in the LSZ map), and very high ([80 % class of low to high

LSI value in the LSZ map) were considered (Fig. 12).

Discussion

Regional-scale landslide activity analysis and susceptibility

assessment are performed in this research. Landslide

susceptibility study in Nepal is still in a preliminary stage

and landslide activity with various intrinsic and extrinsic

factors are still not well understood. In this study nearly

4,000 km2 area of central Nepal is selected for the land-

slide activity and susceptibility assessment in a regional

scale. Landslide activity is evaluated with normalized

landslide density in each predisposing factors and results

exhibit interesting relationship between the predisposing

causative factors and landslide occurrence. From this

study, a linear relationship of normalized landslide density

and slopes of the area was noticed. Slopes aspect between

east and south west are found to be most vulnerable for

landslides in the Nepal Himalaya. This clearly suggests

orographic effect of Mahabharat Range in the Churia

Range and Fore Himalaya in the Midlands. Slope concavity

has shown a typical mirror image type of distribution of

landslides (Rabbit Ear) in concave and convex slopes.

Similarly, wetness index and normalized landslide density

have shown polynomial relationship. Geologically, gabbro,

schist, dolomite, limestone and quartzite are found to be

more active for landslide than other rocks. Likewise, it is

also understood that for the Nepal Himalaya, less drainage

density in the area means profound landslide activity.

Improper road construction activities in Nepal is also

enhancing landslide processes and landslide activities.

Similarly, the region of higher annual rainfall always

manifests higher landslide activity zone and it is well

understood in the study area.

Fig. 12 Final landslide susceptibility zonation map

Environ Earth Sci

123

In this study, a methodology was also prepared for

creating a reliable landslide susceptibility zonation map for

Nepal in a regional scale using statistical approaches and

computer-based techniques. A multivariate approach was

used to generate landslide susceptibility map using a blind

landslide inventory database. The JTC-1 (Fell et al. 2008)

guidelines suggest a tool for landslide susceptibility zona-

tion taking into account the frequency of landslide events.

However, an annual value of landslide frequency is not

always possible, particularly in areas of monsoon rainfall.

This study has addressed this issue with preparation of an

event-based landslide susceptibility zonation map. Land-

slides triggered by short-duration high-intensity and long-

duration low-intensity rainfall events are separated

according to the landslide size and the inventories were

used as proxy database of rainfall event-based landslides

for susceptibility modelling.

This study clearly demonstrates the effect of using

different landslide data to evaluate the LSI in regional

scale. Except in heuristic model of landslide suscepti-

bility analysis and especially in statistical method, the

susceptibility modelling has close relationship with

landslide history of the area. Many researchers have

concluded that both bivariate and multivariate methods

are suitable for landslide susceptibility analysis, but the

accuracy of the susceptibility map is always a question.

In this research, multi-event landslide inventory data

were used to evaluate susceptibility with multivariate

method in different scenarios of rainfall events (large

landslide triggered by low-intensity long-period rainfall

and small landslide triggered by high-intensity short-

period of rainfall). The ancillary data sources, such as

Google Earth images and field visit, also largely helped

to revise the location of landslide. Up until now, most

research studies have considered only a single event-

based landslide database for both modelling and valida-

tion. Some researchers have also used old landslides for

modelling and new landslides for validation. In this

study, however, large landslide database and small

landslide database were assumed as different rainfall

event-based landslide data and they were used to prepare

different zonation maps, which were then rectified to

obtain final susceptibility zonation map for the study area

and validated with new landslide database. This had

drastically enhanced the quality and accuracy of the

zonation map. This study suggests that a single event-

based landslide susceptibility zonation cannot adequately

express the future probability although it often results in

a good success rate. To overcome this drawback with the

statistical methods of landslide susceptibility zonation,

event-based landslide database must be an essential

component of the research. To estimate the probability of

future landslide occurrences, event-based probability

zonation maps need to be combined together to generate

a reasonably accurate landslide susceptibility zonation

map.

In this study, significant improvement on available

method of susceptibility modelling has been also accom-

plished by incorporating treatment of blind landslide

inventory into proxy event-based landslide database.

Concluding remarks

Preparing landslide susceptibility map is an essential step

in landslide hazard mitigation in the developing country

like Nepal. So, an ideal terrain of central Nepal was

selected and regional-scale landslide activity and landslide

susceptibility were assessed with the help of 12 landslide

causative factors. Blind landside inventory database was

converted into proxy database of rainfall event-based

landslides and two event-based landslide databases were

prepared. Two proxy database of rainfall event-based

landslides were used for logistic regression for suscepti-

bility modelling. Two different event-based LSI maps were

combined and rectified to prepare a single zonation map.

Finally, a relatively new landslide database was used for

the validation of zonation map.

The conclusions of this study can be explicitly sum-

marized as follows:

• Landslide activity in Nepal is largely regulated by

slope, aspect, geology, hydrogeology and road con-

struction activities.

• Blind landslide inventory can be used in susceptibility

analysis when it is converted into proxy database of

rainfall event-based landslides.

• The single event landslide database is not enough for

landslide susceptibility zonation. Multi event-based

zonation and rectification of zonation maps is necessary

to produce reasonably accurate susceptibility map.

• The methodology described in this paper for event-

based landslide susceptibility map can be used in

planning and managing hill slopes in the Nepal

Himalaya. Meantime, it is also to be noted that to

determine the exact extent of slope instability, areas

within high and very high landslide susceptibility

categories identified in this research require more

site-specific studies (with deterministic approach) by a

professional geoscientist before commencing and plan-

ning projects both on slopes and river valleys.

Acknowledgments The author is thankful to Prof. Ryuichi Yatabe

for providing opportunity to perform this research in Geo-disaster

Laboratory, Ehime University, under the financial support of Japan

Society for Promotion of Science (JSPS). Dr. Netra Prakash Bhadary,

Dr. Manita Timilsina and Mr. Anjan Kumar Dahal are sincerely

Environ Earth Sci

123

acknowledged for their technical support during preparation of this

paper. This research is partly supported by Japan Society for Pro-

motion of Science (JSPS).

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Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya

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