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GIS-based Landslide Hazard Zonation in the Bhagirathi (Ganga) Valley,HimalayasA. K. Saha; R. P. Gupta; M. K. Arora

To cite this Article Saha, A. K. , Gupta, R. P. and Arora, M. K.(2002) 'GIS-based Landslide Hazard Zonation in theBhagirathi (Ganga) Valley, Himalayas', International Journal of Remote Sensing, 23: 2, 357 — 369To link to this Article: DOI: 10.1080/01431160010014260URL: http://dx.doi.org/10.1080/01431160010014260

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int. j. remote sensing, 2002, vol. 23, no. 2, 357–369

GIS-based Landslide Hazard Zonation in the Bhagirathi (Ganga)Valley, Himalayas

A. K. SAHA, R. P. GUPTA*

Department of Earth Sciences, University of Roorkee, Roorkee, 247667, India

and M. K. ARORA

Department of Civil Engineering, University of Roorkee, Roorkee, 247667,India

(Received 5 October 1999, in � nal form 2 August 2000 )

Abstract. Landslides cause widespread damage in the Himalayas. LandslideHazard Zonation is important to take quick and safe mitigation measures andmake strategic planning for the future. A part of the Bhagirathi Valley in theGarhwal Himalaya was selected for landslide hazard zonation. The study utilizeddiVerent types of data including Survey of India topographic maps, geological( lithological and structural ) maps, IRS-1B and -1D multispectral and PAN satel-lite sensor data and � eld observations. The processing of multi-geodatasets wascarried out in a raster GIS environment. The various data layers generated andco-registered were: landuse/landcover, buVer map of thrusts, buVer map of photo-lineaments, lithology, buVer map of drainage, slope angle and relative relief. Dataintegration was carried out using the ordinal scale (qualitative) relative weightingrating technique to give a Landslide Hazard Index (LHI) value. The breaks inthe LHI frequency diagram were used to delineate various landslide hazard zones,namely, very low, low, moderate, high and very high. Field data on landslideswere employed to evaluate and validate landslide hazard zonation map. It isinterpreted that the distribution of landslides is largely governed by a combinationof geoenvironmental conditions like proximity (>500 m) to the thrust zone, pres-ence of Munsiari Formation (Higher Himalayan Crystalline) and barren orless-vegetated areas.

1. IntroductionAmong the various natural hazards, landslides are the most widespread and

damaging hazard. They cause loss of life and property, damage to natural resources(e.g. vegetation, land and soil ) and hamper developmental projects like roads, bridgesand communication lines, etc. It has been estimated that, on average, the damagecaused by landslides in the Himalayan range costs more than US$ 1 billion besidescausing more than 200 deaths every year, which overall is considered as 30% ofsuch types of losses occurring world-wide (Naithani 1999). The high susceptibilityto landslides of the Himalayan terrain is mainly due to a complex geological setting

*Corresponding author; e-mail: [email protected]

Internationa l Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2002 Taylor & Francis Ltd

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A. K. Saha et al.358

combined with contemporary crustal movements, varying slopes and relief, heavyrainfall, along with ever-increasing human interference in the ecosystem. Mitigationof disasters caused by landslides can be taken up only when detailed knowledgeabout the expected frequency, character and magnitude of mass movements in anarea is available. To take a quick and safe mitigation measure and future strategicplanning, identi� cation of landslide-prone areas and Landslide Hazard Zonation(LHZ) are carried out.

Landslide Hazard Zonation is a process of ranking diVerent parts of an areaaccording to the degrees of actual or potential hazard from landslides (e.g. Varnes1984). It is useful for the following applications (Anbalagan 1992).

First, the LHZ maps assist planners in choosing favourable sites for developmentschemes, such as buildings, dams and road construction. Even if the hazardous areascannot be avoided altogether, their recognition in the initial stages of planning mayhelp adopt suitable precautionary measures.

Secondly, the maps help identify and delineate hazard-prone areas, so thatenvironmental regeneration programmes can be initiated adopting suitablemitigation strategies.

The evaluation of landslide hazard is a complex task as the occurrence oflandslides is dependent on many factors. In the last few decades, several � eld-basedhazard zonation studies with manual integration of data have been carried out inthe Himalayas (e.g. Anbalagan 1992, Pachauri and Pant 1992, Gupta et al. 1993,Virdi et al. 1997, etc.). However, these approaches have several drawbacks, such asthe extent of the area covered is generally small and manual overlay of thematicmaps is tedious and has poor integration capability.

The use of aerial photographs has removed some of these problems to someextent. With the advent of remote sensing and GIS technology, it has become possibleto eYciently collect, manipulate and integrate a variety of spatial data such asgeology, structure, surface cover, slope characteristics, etc. of an area which can beused for landslide hazard zonation (van Westen 1994, Gupta and Joshi 1990, Gupta1991, Nagarajan et al. 1998, Sabins 1999). The present study is an attempt to preparea landslide hazard zonation map of a part of the Bhagirathi Valley in the Himalayasusing remote sensing and GIS technology.

2. The study areaLandslides pose a serious problem in the Bhagirathi Valley, Himalayas, where

roads often get closed due to landslides, induced by heavy monsoonal rains. A stretchof about 60 km in the Bhagirathi Valley was selected for this study (� gure 1). Thearea has a highly rugged mountainous terrain with altitudes varying from 1080 to4690 m above mean sea level (m.s.l.). The river Bhagirathi has formed a deep con-stricted V-shaped valley and indicates a youthful geomorphological stage of theregion. Generally, the tributary streams are at a higher elevation (about 40–100 m)than the main river (Bhagirathi). The Bhagirathi is an antecedent river and � owstransverse to the structural axis of the Himalayas. It is fed mainly by smallsubsequent streams.

3. Geological settingThe Bhagirathi River passes through diVerent lithotectonic units in its course

from north-east to south-west of the study area. Geographically, the area falls underthe Garhwal Lesser Himalayas and Higher Himalayas. The Main Central Thrust


GIS-based L andslide Hazard Zonation 359

Figure 1. Location map of the study area.

(MCT) passes across the central part of the study area separating the Lesser andHigher Himalayan rocks. The geological setting of this area has been studied byseveral workers (e.g. Valdiya 1980, Manickavasagam et al. 1999). Structurally, thestudy area is highly complex with several thrust-faults and folds. The MCT separatesthe Lesser Himalayan Formations from the overlying Higher Himalayan Munsiarigroup of rocks and Vaikrita Thrust separates the Munsiari Formation from theoverlying Joshimath Formation. The thrust zones are highly crushed and showunstable slopes along the strike. Several minor irregularly oriented faults are alsopresent. A generalized geological map is given in � gure 2.

4. Data used and methodologyThe diVerent types of datasets used were as follows:

1. Topographic maps of Survey of India at 1:50 000 scale to form the base map.2. Geological map representing lithological and structural units (compiled after

Valdiya 1980 and Manickavasagam et al. 1999 ).3. Satellite sensor data. (a) IRS-1B LISS II multispectral digital data (dated

31 May 1998) in four bands (blue, green, red, near-infrared) with 36.25mground resolution. (b) IRS-1D PAN digital data (dated 28 November 1998)in panchromatic broad band with 5.8 m ground resolution.



Figure 2. Geological map of the study area.

4. Field data involving observations on landslides, geology, structure andlanduse/landcover.

The GIS and image processing were carried out using PC-based software ILWIS 2.1and EASI/PACE 6.2. Broadly, the data manipulation can be considered in twostages: pre-processing and processing.

Pre-processing involved digitization of Survey of India toposheets at 1:50 000scale to serve as the base map. The digital multispectral data were corrected foratmospheric path radiance by the dark-object subtraction technique (Chavez 1988)and registered over the topographic map to form an input into the GIS database.



Digitization of all the relevant data maps, namely lithological, structural and drainage

maps was carried out and all the datasets were georeferenced.Processing involved various advanced digital image processing and GIS tech-

niques, such as contrast manipulation, edge enhancement, colour compositing,

pseudo-colour, majority � ltering, gradient � ltering, rank-order � ltering, distancebuVering, overlay and logical operations. Interpretations were made from these

processed images.

5. Data layer preparationFor the landslide hazard zonation objectives in this study, the following data

layers were prepared:

d buVer map of thrust-faultsd buVer map of photo-lineamentd lithology mapd landuse/landcover mapd buVer map of drainaged slope angle mapd relative relief mapd landslide map along the road

An overview of methodology of data layer preparation is given in � gure 3. Details

of data layer preparation are described in Gupta et al. (1999 ).

5.1. Structural and lithological featuresStructure and lithology are among the most important parameters for landslide

hazard zonation. There is a wide range of rock units exposed in the study area. In

this work, the various litho-stratigraphic units were grouped into four categories: (i)Joshimath Formation lithologically comprising high grade metamorphic rocks

(schists and gneiss); (ii ) Munsiari Formation comprising diVerent types of schists and

gneisses lying in intrathrust zone; (iii) quartzite of diVerent Groups/Formationsincluding Ramgarh, Nagthat and Berinag; (iv) limestone and greywacke of Deoban

and Rautgara Formation. A lithological map was compiled (after Valdiya 1980 andManickavasagam et al. 1999) (� gure 2) and the boundaries of diVerent rock groups

digitized.

Structurally the rocks are folded, faulted and sheared to varying degrees. There

are two major thrusts, the Main Central Thrust (MCT) and Vaikrita Thrust, passing

through the area. The thrusts have a greater importance in LHZ as these are marked

by large shear zones. The thrust traces were digitized from the regional geologicalmap of the study area.

Besides the above major thrusts, there are other faults and structural dislocations

seen which appear as photo-lineaments . These were identi� ed from the IRS-1B

LISS-II image edge-enhancement (Laplacian) � ltering and digitized on-screen.

Distance function was applied to create buVer zones around each thrust and photo-

lineament and classi� ed. This takes into account the distance from these structural

features as likelihood of occurrence of landslides may increase with the proximity tothese features.



Figure 3. Methodology of thematic data layer preparation.

5.2. L anduse/landcoverLanduse/landcover is an important parameter for landslide hazard zonation. The

classes of landuse/landcover identi� ed in this area are: snow, water body, barren,built-up area, agriculture, sparse vegetation (with pine trees) and dense (mixed) forest.



The IRS-1B LISS II multispectral image data were classi� ed using the supervisedMaximum Likelihood Classi� er (MLC). The IRS-1D PAN data, topographic mapsand � eld information were used as reference data. About 330 well-distributed trainingpixels for each class were selected for classi� cation. A set of 100 separate pixels foreach class was selected on the basis of ground check and PAN image for testing ofclassi� cation accuracy. The overall accuracy achieved was 79%. Post-classi� cation� ltering was carried out to remove the very high frequency variations in the landuse/landcover classi� ed map.

5.3. DEM-based derivativesA Digital Elevation Model (DEM) represents spatial variation in altitude. The

DEM (prepared from Survey of India toposheets 1:50 000 scale) was used to generateslope map and relative relief map.

Slope is an important parameter for stability consideration. It is the � rst derivativeof elevation with each pixel denoting the angle of slope at a particular location. Itwas observed that the slope angle calculated from the DEM had a range of 0°–77°.

Relative relief is de� ned as the diVerence in maximum and minimum altitudewithin an area or facet and is calculated in a 3×3 matrix. The range of relative reliefwas found to be 0 to 334 m.

5.4. DrainageThe under-cutting action of the river may induce instability of slopes. Hence,

some of the major drainage segments were digitized to include the eVect of thiscausative factor and converted into raster format and buVered.

5.5. L andslidesA systematic � eld study of the landslides was carried out along the road section

running parallel to the Bhagirathi Valley. There are diVerent types of landslideoccurring in this area, namely rockslide, rockfall, debris slide, debris fall and debris� ow, totalling 53 occurrences. The � eld data on distribution of landslides was usedas a ground check in the analysis of landslide hazard zonation, as discussed later.

6. Data integration and analysisLandslides are caused by mutual interaction of various factors. The process and

exact mechanism are still not well understood. This makes prediction quite diYcult.Therefore, there is a substantial degree of uncertainty involved in any hazard evalu-ation process. The various methods of data integration for LHZ have been reviewedby van Westen (1994), giving their advantages and disadvantages . The main type ofmethods are: (a) landslide distribution analysis, which provides information on thedistribution of landslides in an area; (b) the ordinal scale (quantitative) approachusing weighting–rating system of various parameters (based on � eld knowledge) and(c) the statistical method, which is quite suited to small areas with detailed � eldinformation. In this study, the ordinal scale (qualitative) hazard analysis using relativeweighting–rating system has been adopted for landslide hazard zonation as limited� eld data are available.

6.1. Data layers—their weights and ratingsThe weighting–rating system is based on the relative importance of various

causative factors derived from � eld knowledge. The various input data layers, namely



thrust (buVer) map, lithological map, slope map, etc. were arranged in a hierarchicalorder of importance, and a weighting number (from 0 to 9) given to each map layer.Similarly, each class within a layer was given an ordinal rating from 0 to 9. Table 1describes the weights and ratings given to each data layer and their classes, respect-ively. These weights and rating values have been re-adjusted using trial and errormethod.

The thrust-faults constitute the most important direct cause for landslide activity,as they signify the area of sheared rocks. Hence the highest weighting is given tothrusts. As the distance from the thrust (buVer) increases, the hazard of landslidedecreases. Accordingly, the thrust (buVer) classes have been given a rating in decreas-ing order with the distance from the thrust. The photo-lineaments are relatively

Table 1. Various data layers and landslide hazard weighting–rating system adopted in thisstudy.

Sl no. Data layers Classes Weighting Rating

1. Thrust (buVer) A. <500 m 9 9B. 500–1000 m 6C. >1000 m 3

2. Lithology/ A. Higher Himalayan Crystallines 8 9Lithostratigraphic (Munsiari Formation)unit B. Schist and gneiss 8

(Joshimath Formation)C. Quartzite (Rautgara, 5

Nagthat–Berinag Formation)D. Limestone and greywacke 4

(Deoban and RautgaraFormation)

3. Landuse/ A. Barren 7 9landcover B. Sparse vegation (with pine 8

trees)C. Built-up area 5D. Agriculture 4E. Dense (mixed) forest 3F. Snow 2G. Water body 1

4. Photo-lineament A. <500 m 6 5(buVer) B. 500–1000 m 3

C. >1000 m 1

5. Slope angle A. >45° 5 9B. 36–45° 7C. 26–35° 5D. 16–25° 3E. 15° 1

6. Relative A. >120 m 4 9relief B. 91–120 m 7

C. 61–90 m 5D. 31–60 m 3E. 30 m 1

7. Drainage A. In� uence of drainage ( 50 m) 2 4(buVer) B. Rest of the area 1



minor features in comparison to thrusts and hence a relatively lower rating isassigned to the each buVer class in comparison to the thrust buVers. The lithologyplays a signi� cant role in this area for landslide activity. The rocks of the MunsiariFormation and Joshimath Formation are highly jointed in nature and form steepslopes. Hence, these are given a higher rating, whereas the quartzite and limestone–greywacke are less prone to landslides as observed in the � eld. The various landuse/landcover classes considered in this study are snow, water body, agriculture, built-up area, barren, sparse vegetation (with pine trees) and dense (mixed) forest. Thebarren ( least vegetated) areas are the most prone to landslide activity, and hencehighest rating is given to this class. As the vegetation density increases, the stabilityof slope tends to increase, hence ratings were given accordingly. The slope-angle isalso a very important parameter in landslide activity. In this area, the slopes weredivided into � ve classes and accordingly ratings to the slope categories were assigned.The relative relief values were also classi� ed into � ve classes using histogram distribu-tion. Ratings were given in increasing order as the relative relief increases. Thedistance to drainage (buVer) was considered to take into account the river undercut-ting which may induce landslides. The highest rating was given to the buVer zoneup to a distance of 50 m from the river.

6.2. Computation of L andslide Hazard IndexThe classes of diVerent data layers are assigned the corresponding rating value

as attribute information in the GIS and an ‘attribute map’ is generated for each datalayer. These attribute maps are then multiplied by the corresponding weights andthen added up to yield the Landslide Hazard Index (LHI) for each cell. The rasteroperation capability of ILWIS has been utilized to compute the LHI. The methodo-logy for this operation is systematically shown in � gure 4. The Landslide HazardIndex is given by

LHI=S weighting×data layer (attribute) (1)

The LHI was found to vary within the range of 83 and 322 in the area.

6.3. L andslide Hazard ZonationThe LHI frequency diagram (� gure 5) was studied and used for landslide hazard

zonation. The following LHI threshold boundaries were used: 134, 154, 184 and 239.Using the ‘slicing’ operation, a Landslide Hazard Zonation (LHZ) map was preparedshowing the � ve zones, namely ‘very low hazard’, ‘low hazard’, ‘moderate hazard’,‘high hazard’ and ‘very high hazard’. In this map, there occur small areas consistingof a few pixels of a class surrounded by another class. Therefore, a 3×3 ‘majority� lter’ has been applied to the map as a post-classi� cation � lter to reduce the highfrequency variation. Figure 6 shows the LHZ map prepared for the study area.

6.4. Comparison of L HZ map with � eld dataThe distribution of existing (active) landslides was used to evaluate the validity

of the landslide hazard zonation map prepared from a GIS-based study. The landslidelocations were digitized and laid over on the landslide hazard zonation map.

Figure 7 is a bar chart showing comparison of percent area and percent landslideincidences for each LHZ class. The ‘very high hazard zone’ covers only about 5.4%of the total area but has a very high (17.1%) frequency of landslide occurrence.Furthermore, the landslide frequency in the ‘high hazard zone’ is also high (50.9%)



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Figure 5. Landslide Hazard Index (LHI) frequency diagram.

Figure 6. Landslide Hazard Zonation map of the study area.

in comparison to the area (30.7%). Together these two zones cover 36.1% of thearea and include 68% of the landslide occurrence. The medium hazard zone covers52.5% of the area and contains only 13 landslides, which constitute 24.5% of the



Figure 7. Bar chart showing relative distribution of various hazard zones and landslideoccurrence within each zone.

total population of the landslide studied. The low and very low hazard zone togetherconstitute 11.7% of the study area and have a landslide occurrence of 7.5%. It isconsidered that the landslides in the moderate hazard and low hazard zones may bemore governed by local eVect, i.e. of the orientation of the discontinuity surfaces,etc. which could not been incorporated in the GIS analysis.

7. Discussion and concluding remarksFrom this study it can be interpreted that the distribution of landslides is largely

governed by a combination of geoenvironmental conditions, such as proximity(<500 m) to the thrust zone, presence of Munsiari Formation (Higher HimalayanCrystalline) and barren or less-vegetated areas, as well as other factors. The role oforientation of local discontinuity surfaces in such catastrophes is well documentedin the literature (e.g. Zaruba and Mencl 1982, Dikau et al. 1996) and is not to beunderestimated, though this type of 3D vector data could not be incorporated inthe present analysis. It is inferred that the presence of a combination of geoenviron-mental conditions lead to landslide susceptibility of the terrain where the localdiscontinuity surfaces provide triggering action and pathways for the landslide activ-ity to take place. Thus, the GIS-based methodology for integration of varioustopographic, geological, structural , landuse/landcover and other datasets seems tobe quite suitable for developing a landslide hazard zonation map.

AcknowledgmentsThis work has been carried out with � nancial support from the Department of

Science and Technology (DST), Government of India, New Delhi. Help providedby Sri Arun Kumar during � eld data collection is appreciated.

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