Computers & Geosciences 61 (2013) 50–63

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Computers & Geosciences

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Study of effect of seismic displacements on landslide susceptibilityzonation (LSZ) in Garhwal Himalayan region of India using GISand remote sensing techniques

Naveen Pareek a,n, Shilpa Pal b, Mukat L. Sharma a, Manoj K. Arora c

a Department of Earthquake Engineering, Indian Institute of technology, Roorkee, 247667 Uttarakhand, Indiab School of Engineering, Gautam Budha University, Greater Noida, 201308 Uttar Pradesh, Indiac Department of Civil Engineering, Indian Institute of Technology, Roorkee, 247667 Uttarakhand, India

a r t i c l e i n f o

Article history:Received 19 January 2013Received in revised form8 June 2013Accepted 18 July 2013Available online 3 August 2013

Keywords:Chamoli earthquakeLandslideSeismic displacement

04/$ - see front matter & 2013 Elsevier Ltd. Ax.doi.org/10.1016/j.cageo.2013.07.018

esponding author. Tel.: +91 880 032 5860.ail addresses: naveeneq@gmail.com (N. Pareeka@gmail.com (S. Pal), sharmamukat@gmail.crora@gmail.com (M.K. Arora).

a b s t r a c t

Landslides are the most damaging and threatening aftereffect of seismic events in Garhwal Himalayas.It is evident from past seismic events in Uttarakhand, India that no other phenomena can producelandslides of so great in size and number as a single seismic event can produce.

Landslide inventories are produced for the study area before and after the occurrence of ChamoliEarthquake using Panchromatic (PAN) sharpened Linear Imaging Self Scanning-III (LISS-III) images.A sudden increase in number of landslides after the earthquake is observed. Further, two LandslideSusceptibility Zonation (LSZ) maps have been derived using pre- and post-Chamoli Earthquake landslideinventories. The difference of two LSZ indicates that landslides are very complex phenomenon and areaffected by static factors in seismic conditions also.

An attempt has been made to estimate the seismic displacements using Differential SyntheticAperture Radar Interferometry (DIn SAR). European Remote Sensing Satellite-1/2 (ERS-1/ 2) SAR imageshave been used for preparing differential interferogram. Geometric and temporal decorrelation in SARimages is very high in the study area, which limits the use of DInSAR for displacement estimation.Theoretical displacement has been estimated using fault displacement modeling parameters for Chamoliearthquake. Post-Chamoli earthquake landslide inventory is overlaid over displacement map for under-standing the impact of seismic displacement pattern with other static factors on the occurrence oflandslides. It is observed that distribution and size of landslides is affected by displacement patterncontrolled by other static factors also.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Garhwal Himalayas are seismically very active and fall underseismic zone IV. Here, landslides are one of the major and mostfrequent natural hazards that cause damages worth more than onebillion US$ and around 200 deaths every year. In the GarhwalHimalayan region, massive landslides in Ukhimath in 1998 and theMalpa landslide reportedly caused deaths of 109 and 210 people,respectively. Phata landslide of 2001, Budhakedar landslide of2002, Uttarkashi landslide of 2003, and Ramolsari landslide of2005 are some of the other major landslides that took place inGarhwal Himalayas causing large-scale socio-economic damages,human losses, and other associated geo-environmental hazards.These data show the paramount importance of identifying and

ll rights reserved.

),om (M.L. Sharma),

understanding the landslide causative factors in the study area andranking them as per their influence on the occurrence of land-slides, which is necessary for taking proper mitigation measuresby planners. It is important to understand the seismic displace-ment pattern in the seismically active Garhwal Himalayas becauseafter the seismic events the distribution pattern of landslidesusceptibility zones changes abruptly.

In the present study, an attempt has been made for integratingthe observed and theoretical displacement maps. The resultingdisplacement map is further used to correlate the displace-ment patterns with the occurrence of landslides. DifferentialSynthetic Aperture Radar Interferometry (DInSAR) has been foundto be a useful technique for surface deformation studies byproviding dense spatial information in two dimensions. DInSARhas been utilized for detecting seismic displacements elsewhere(Massonnet et al., 1993; Zebker et al., 1994; Ozawa et al., 1997;Peltzer et al., 1999; Pedersen et al., 2001; Wright et al., 2004;Funning et al., 2005; Satyabala and Bilham, 2006; Saraf et al.,2012). It has been observed that in the study area, geometric and

Fig. 1. Location map of the study area and distribution of major river network.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–63 51

temporal decorrelations are the major limiting factors for prepar-ing differential interferograms and its further use for displacementestimation. Therefore, theoretical displacements are estimatedusing available fault models proposed by Okada (1985). DIMOT isGIS-based software for modeling surface displacement due to faultdislocations and has been used in this work (DIMOT, 2006).

2. Study area

A part of Garhwal Himalayas has been selected as the studyarea extending around 1300 km2 and bounded by 30115′N and30130′N latitudes and 791E and 79130′E longitudes (Fig. 1). Thearea is characterized by various thrusts and faults with igneousand metamorphic rocks types mainly granites, granotoids, schist,gneisses and phyllites (Fig. 2). A total of 8247 small and 5 largerivers drain the study area.

The study area falls in seismic zone IV as per the IS Code 1893–2001 (IS, 1893, 2002). In the Garhwal seismo-tectonic block, a totalof 83 seismic events of magnitudeZ4 have been recorded fromyear 1842 to 1996 (Geological Survey of India (GSI, 2001), BulletinSeries-B no. 53). Chamoli earthquake is taken as an example in thepresent study, which triggered number of landslides and reacti-vated older slides.

3. Methodology

The methodology adopted in the present study includes inputdata collection, thematic data layer preparation, pre- and post-Chamoli earthquake landslide susceptibility zonation, surface

displacement estimation and estimation of effect of seismicallyproduced surface displacements on the pattern of occurrence oflandslides (Fig. 3). Data from a variety of sources has beencollected and used in this study. These include remote sensingoptical and radar images, Google Earth images, ancillary data (fielddata, Survey of India (SOI) toposheets at 1:50,000 scale, faultmodeling parameters for Chamoli earthquake, and publishedgeological structure and tectonic maps).

4. Spatial data layer preparation for LSZ mapping

In the present study, a number of landslide causative factors(slope, aspect, relative relief, tectonic structures, lithology, landcover and drainage) have been obtained from various sources(Fig. 3) and projected to a common geographic reference systemusing Arc-GIS software. Landslide inventories have also been pre-pared for the area representing pre- and post-Chamoli Earthquakescenarios. Field data and Google earth images have been used asreference data for the preparation of thematic layers of landslidedistribution and validation of land cover in the study area.

The land use map is prepared at 23.5 m�23.5 m grid size,therefore, all the thematic layers are resampled at 23.5 m�23.5 mgrid size for all the statistical operations in GIS environment.The details of the data layer preparation and their use in LSZ mappreparation are as follows:

4.1. Landslide inventories

The identification and mapping of existing landslides is aprerequisite to establish relationship between the distribution of

Fig. 2. Geological structures and litho units in the study area.

Difference Im

age of Post and Pre C

hamoli E

Q L

SZ M

aps

Post Cham

oli E

Q L

SZ M

apPre C

hamoli

EQ

LSZ

Map

Data Input ERS - 1/ 2 SAR images, (IRS-1C/ D LISS-III and PAN images, SOI Toposheets, Geological and Tectonic Maps, Google Earth Images, Field Data and Chamoli Earthquake (EQ) modeling parameters given in table …

Ist Slave image(ERS – 2 (30/04/96))

Master image(ERS – 1 (29/04/96))

IInd Slave image(ERS – 2 (07/09/99))

Master to Slave image registration

Master to Slave image registration

Interferogram

Coherence image

Phase Unwrapping

Unwrapped phase image with holes

Phase to Heigth image (only partially developed)

Interferogram

Coherence image

Phase Unwrapping

Unwrapped phase image with holes

Differemtial Interferogram

Highly Decorrelated image

Displacement image not generated

Differential SAR image processing

PreC

hamoli E

QL

andslide Extraction on

PAN

Sharpened LISS –

III image

Pictorial representation of displacem

ent along Munsiari fault

Analysis of im

pact of seismic

displacement on landslide activity

Estimation of theoretical displacement

Them

atic Data

Layers Preparation

Weight and rating assignm

ent (Info V

al Method)

PostCham

oli EQ

Landslide E

xtraction on PA

N Sharpened L

ISS –III im

age

DE

MSlope

Aspect

Relative R

elief

Land use/ land

cover map

Geological structure m

apStructural B

uffer

Lithology

River N

etwork

Drainage B

ufferD

rainage Basin

Pre Cham

oli E

Q L

SI Map

Post Cham

oli E

Q L

SI Map

Fig. 3. Work flow of methodology adopted for the study.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–6352

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–63 53

landslides and influencing factors. The landslide influencingfactors can broadly be categorized as static and dynamic factors.Static factors include lithology, slope, relative relief, aspect,tectonic and structural features, river and land cover, while thedynamic factors are rainfall and seismicity. In present study, theimpact of seismic displacement pattern on the occurrence oflandslides with other static factors has been considered. Rainfall

Fig. 4. PAN sharpened LISS-III image showing som

Fig. 5. Field photographs of few landslides and their appearance

is an important factor but it was not possible to include it becauseof data scarcity.

Existing landslides in the study area are identified on a fusedremote sensing product, a Panchromatic (PAN) sharpened multi-spectral image, generated spatial enhancement of IRS LinearImaging Self Scanning-III (LISS-III) multi-spectral image. In thefused product, the landslides appear in distinct bright-white

e of the landslides encircled on the image.

on Google Earth image and merged image in the study area.

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(Fig. 4). This assisted in improving the overall interpretability ofthe image and hence in the detection of smaller landslides (Welchand Ehlers, 1987; Sabins, 1996; Sharma et al., 1996; Saraf, 2000;Prakash et al., 2001; Sanjeevi et al., 2001; Shanmugam andSanjeevi, 2001; Gupta, 2003, Champati Ray et al., 2007, Pareeket al., 2009, Pareek et al., 2013). The interpreted landslides wereverified on Google Earth images and also through field checks(Fig. 5).

Two landslide inventories one each for pre- and post-ChamoliEarthquake scenarios were prepared from PAN sharpened LISS-IIIimage obtained before and after the earthquake. A total of248 landslides were identified in pre-earthquake image (Fig. 7)whereas a sudden increase in number of landslides (i.e. 530) wasobserved in the post-earthquake image (Fig. 8).

Fig. 6. Thrust buffer zone and post-

Fig. 7. Pre-earthquake (Chamoli) land

4.2. Lithology

Different litho units produce different resistance againstweathering and erosion due to their varied inherent characteris-tics, such as composition, structure, and compactness. The mainrock types in the study area are phyllites, slate, gneiss, granite togranodiorie, limestone, and dolomite (Fig. 2).

4.3. Digital elevation model (DEM)

Slope, aspect and relative relief thematic data layers have beenextracted from DEM. The slope map shows variation of slopes from01 to 901, which have been further categorized into five categoriesas per slope classification scheme given by Anabalagan (1992).

earthquake landslide inventory.

slide inventory of the study area.

Fig. 8. Post-earthquake (Chamoli) landslide inventory of the study area.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–63 55

Relative relief data layer has been obtained from the difference ofminimum and maximum elevations and has been sliced into fiveclasses at 30 m elevation difference. Aspect, defined as the direc-tion of maximum slope of the terrain surface, has been dividedinto nine classes, namely, N, NE, E, SE, S, SW, W, NW, and Flat.

4.4. Tectonic and structure features

The study area is characterized by the presence of a number ofthrusts. Geological and seismic evidences show that these areactive thrusts that make the sloping terrain more vulnerable tolandslide occurrences. Frequency or incidence of occurrence oflandslides decreases with the increase in distance from structuralfeatures (Pareek et al. 2009). In the present study, tectonic andstructural map of Garhwal–Kumaon Himalayas by Valdiya (1980)has been used for preparing structural feature map. In the GarhwalHimalayas, the effect of structural features over the landslideincidences has been found to be varying from 250 to 500 m(Saha et al. 2002). From previous studies and thorough investiga-tion of landslide distribution layer, a buffer zone map of thestructural features has been derived, which shows six equaldistance classes at 500 m interval. It has been observed from thestudy of landslide distribution layer that the occurrence of land-slides is more on hanging wall of thrusts. Therefore, a buffer layerhas also been created in hanging wall direction of thrusts at 5-kmdistance (Fig. 6).

4.5. River network

Headward erosion and toe erosion due to rivers are the mainmechanisms that contribute to occurrence of landslides in moun-tainous regions. Hence, drainage has also been considered as a keycausative factor. The drainage network for the study area has beenobtained by digitized stream lines from Survey of India toposheetsat 1:50,000 scale. Erosion and weathering processes of rivers areaffected by flow rate and lithology of the area. Therefore, thedrainage network has been classified into five major river basins.It was observed that density of landslides is more in Alaknandariver basin (Table 1), which flows over quartzites and phyllites rocktypes that are more susceptible to weathering and erosion.

4.6. Land cover

Land cover is also one of the key factors responsible for theoccurrence of landslides,. Based on the variation in the spectralresponse of various land cover classes, as depicted on LISS image,nine classes have been considered that may have an impact onlandslide activity in the region. These classes are dense forest,sparse vegetation, agricultural land, fallow-barren land, landslidedebris, settlements, river sediments, water bodies, and snow cover(Table 1).

5. Pre- and post-Chamoli earthquake LSZ map

A number of statistical techniques have been used in the pastfor determining the weights to be assigned to each of the causativefactors. In the present study, bivariate Information Value method,proposed by Yin and Yan (1988), has been adopted for assessingthe weights to different factors affecting the occurrence of land-slides. It produces reasonably acceptable weights and has beensuccessfully applied in a number of studies on LSZ (Van Westen CJ,1997; Süzen and Doyuran, 2004; Saha et al., 2005, Pareek et al.,2009).

The method is based on the determination of probabilityof landslide occurrence within each class of thematic data layerand the rating of a particular class of a thematic data layer isdetermined using Van Westen (1997) Information Value methodgiven as

Wi ¼DensclasDensmap

� �¼ ln

NpixðSiÞ=NpixðNiÞ∑n

i ¼ 1NpixðSiÞ=∑ni ¼ 1NpixðNiÞ

ð1Þ

where Wi denotes the weight given to the ith class of a particularthematic data layer (e.g., dense forest or barren land in thethematic layer Land cover); Densclas denotes the landslide densitywithin the particular class of thematic data layer; Densmapdenotes the landslide density within the thematic data layer; Npix(Si) denotes the number of pixels, which contain landslides in aparticular class of the thematic layer; Npix(Ni) denotes the totalnumber or pixels in a particular thematic class and n is the numberor total classes in a thematic data layer. Weights are calculated fordifferent classes in a thematic data layer.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–6356

The weights are calculated for various classes in each thematicdata layer using Eq. (1). Table 1 shows the weights calculatedusing Information Value method (Eq. (1)) for both pre- and post-earthquake conditions. The weights computed for each class ofparticular thematic layer indicate the relative importance ofthat class towards the occurrence of landslides. Comparing theweights of pre- and post-earthquake LSZ, it is observed fromTable 1 a drastic increase in south and south-east facing slopesin the areas where slope is moderate to steep (Z351). Alaknandais a major river basin in the study area where the numbers of

Table 1Weights and ratings for causative factors and their respective attribute classes of LSZ m

Classes Pre-Chamoli earthquake

Landslide (counts) Area (counts)

Slope (deg)r15 845 28687715–25 1208 53886925–35 1545 71527535–45 1385 516885445 1559 253140

Relative relief (m)r30 2346 96817430–60 2606 109719460–90 1051 21056490–120 240 279704120 299 7144

AspectFlat 283 260700N 393 249232NE 780 227182E 1541 210892SE 1203 217140S 1089 227975SW 713 234451W 380 249886NW 160 217656

LithologyGranite and quartz porphyry 1467 586036Granite, granodiorite and gneiss 1888 723554Dolomite and limestone 548 255969Phyllite and quartzite 2542 682791Greywake, siltstone and slate 97 62696

LandcoverBuilt�up area 83 28885Fallow land 1168 589907Dense forest 885 978146Sparse vegetation 472 250767Fresh sediments 57 2516Barren land with debris 3209 214068Exposed rocks 483 77741Water 33 27281Agriculture 152 141735

Structural feature buffer (m)r500 3971 1120830500–1000 1337 6921441000–1500 139 1160181500–2000 267 3204432000–2500 390 4094242500 438 20669

River basinsAlaknanda river basin 4426 1207037Others 13 25417Mandakini river basin 802 441442Nagaland river basin 338 232704Birhaiganga river basin 213 122854Nandakini river basin 750 281592

Buffer in hanging wall direction (km)Hanging wall 4544 1601815Foot wall 1998 709231

landslide counts have almost doubled after the Chamoli Earth-quake. Structure buffer map shows that most affected landslideprone areas are within 1 km circumference from tectonicfeatures.

Various thematic data layers (slope, aspect, relative relief,tectonic structures, Lithology, land cover and drainage) are inte-grated in GIS environment to produce the Landslide SusceptibleIndex (LSI) (Pareek et al., 2009; Pareek et al., 2013). The LSI valuesare falling in range from �2.302 to +2.693 and �2.953 to +2.587for pre- and post-earthquake landslide conditions.

ap derived using bivariate Information Value method.

Post-Chamoli earthquake

Weights Landslide (counts) Area (counts) Weights

�0.693 945 266877 �0.38426�0.234 1698 538869 �0.50092�0.270 2217 715275 �0.51741�0.055 2878 516885 0.068370.777 4269 253140 1.17653

�0.155 3077 968174 �0.49236�0.175 4527 1097194 �0.231350.567 3117 210564 1.046171.108 792 27970 1.694772.693 494 7144 2.58760

�2.302 481 260700 �1.03616�1.221 706 465164 �1.231430.192 813 227182 �0.373670.700 2368 217140 0.740600.919 2706 210892 0.903220.523 2603 227975 0.286525

�0.071 1534 234451 0.229728�0.62 619 249886 �0.74155�1.35 177 217656 �1.85542

�0.053 1971 586036 �0.435740.118 2964 723554 �0.23585

�0.468 3765 255969 1.039788�0.357 3171 682791 �0.113040.26 136 62696 �0.87430

0.015 109 28885 �0.32063�0.358 1350 589907 �0.82076�1.143 990 978146 �1.63661�0.409 441 250767 �1.084132.080 166 2516 2.540651.667 8235 214068 2.001190.786 537 77741 0.28395

�0.851 21 27281 �1.91032�0.973 122 141735 �1.78985

�0.224 6826 1120830 0.15801�0.382 3279 692144 �0.09135�0.860 1241 320443 �0.29469�1.22 157 116018 �1.34615�1.897 220 40942 0.032812�1.897 284 20669 0.97168

0.255 7649 1207037 0.191446�1.715 7 25417 �0.96758�0.445 1101 441442 �0.73396�0.667 841 232704 �0.36816�0.491 1596 122854 0.91221�0.0618 813 281592 �0.58519

0.00199 9651 1601815 0.139761�0.0050 2356 709231 �0.45476

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–63 57

The LSI values are categorized into five zones: Very LowSusceptibility (VLS), Low Susceptibility (LS), Moderate Suscept-ibility (MS), High Susceptibility (HS) and Very High Susceptibility(VHS) representing near equal distribution of cumulative

Fig. 9. Pre-earthquake (Chamoli)

Fig. 10. Post-earthquake (Chamol

Table 2Distribution pattern of landslide counts in different LSZ.

Zone category Pre-earthquake (counts) Post-earthquake (counts)

VLS 507811 360012LS 482082 424812MS 439004 484826HS 442018 569894VHS 440131 471502

frequency of LSI. The LSZ map for pre-earthquake case is shownin Fig. 11.

Seismic acceleration causes landslides, collapses and othergravitational phenomena on sloping terrain (Solonenko, 1977).Many studies have been carried out in the past to map thelandslides triggered after the earthquake (e.g., Saraf, 2000;Ravindran and Philip, 2002; Sato et al., 2007; Owen et al., 2008;Pareek et al., 2009) and show that earthquakes not only reactivateold landslides but also trigger new ones. In present study,contribution of each thematic data layer has been found to bechanged due to increase in number of landslides after theoccurrence of earthquake.

It can be observed from Table 1 that the weights in classes ofstructure buffer layer increased after the occurrence of Chamoli

LSZ map of the study area.

i) LSZ map of the study area.

Fig. 11. Changes in distribution pattern of various landslide susceptibility zones in the study area after the occurrence of Chamoli Earthquake.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–6358

earthquake. The pre- and post-earthquake LSZ maps (Figs. 9 and 10,Table. 2, respectively) clearly depict that after the occurrence of theearthquake, the areas of moderate to very high susceptible zonehave increased. Quantitative changes in different zones due to theoccurrence of the earthquake were determined by subtracting pre-earthquake LSZ map from post-earthquake LSZ map to perceive theshifting from lower hazard to higher hazard zones (Fig. 11).

6. Chamoli earthquake generated surface displacementand landslides

The ongoing northwards drift of Indian plate makes theHimalaya geo – dynamically active where the seismic activities isconcentrated along the major thrusts Main Boundary Thrust (MBT)and Main Central Thrust (MCT). Seismic events of moderatemagnitudes are common in Garhwal Himalaya, where earthquakeoccurrence results in the disturbance of the stability of the slopingterrain and in turn reactivate existing landslides and creating newones. The Chamoli Earthquake has been taken as an exampleearthquake to estimate the seismically generated displacementson the occurrence of landslides. The two different approaches i.e.observed displacement estimation using DInSAR and theoreticaldisplacement estimation using fault modeling parameters havebeen tested for surface displacement estimation.

6.1. Observed displacement

Observational data i.e., the remote sensing data (ERS-1 and 2 SARimages) have been utilized for ground displacement estimation usingDInSAR technique. Graham (1974) and Zebker and Goldstein (1986)did pioneer work to demonstrate the use of InSAR for topographymapping. Later Massonnet et al. (1993) revolutionized the techniqueby capturing movements produced by the 1992 Landers earthquakein California. Table 3 summarizes the works carried out by variousscientists for capturing ground displacements in different parts of the

world. It is observed that in most of the studies ERS-1/2 data havebeen used.

6.1.1. Surface displacement and SAR interferometry in the study areaThree pass DInSAR technique has been adopted in the present

study and the software EV-InSAR-2.1 a product of Atlantis ScientificInc. has been used for SAR data processing. One interferometric datapair before the Chamoli earthquake has been selected to generate aDEM and another SAR image acquired after the Chamoli earthquakehas been used for differential interferogram generation (Table 4).During the data selection care has been taken to minimize thedecorrelations due to the temporal gap between two images andthe geometrical changes in the orbital path of the images. Tosupport the selection of suitable InSAR image pairs, various combi-nation of the temporal and spatial baselines have been studied thatare available on descw image catalog (Descw for ERS, 2005).

A major problem in Himalayan region for interferometric SARdata processing is temporal and geometric decorrelations due tothick vegetation cover and undulating terrain conditions respec-tively (Pareek and Sharma, 2008). Steep sloping terrain anduneven topographic conditions in Himalaya are responsible forshadows, layovers and foreshortening. Another problem in dataprocessing is presence of a lot of phase discontinuities in the formof residues which restrict the phase unwrapping. Fig. 12 depictsthe large number of phase discontinuities that could not beremoved due to software limitations. Further, the area is char-acterized by dense vegetation cover, which has resulted in verylow coherence. Most of the area appears in black in the coherenceimage (Fig. 12), which shows that the changes in the relativeposition of scatterers with in a pixel are greater than the radarwavelength used. Phase unwrapping was not possible due to holes(no phase information) present in unwrapped phase image(Fig. 12). A large number of discontinuities avoiding fringe forma-tion and stretching in fringes has also occurred due to nonavail-ability of phase values (Fig. 12). The DEM obtained (Fig. 12) fromthe interferogram also shows errors in the elevation information.

Table 3A glance of main seismic deformation studies carried out using InSAR.

Event Satellite

Coseismic deformation:Massonnet et al. (1993) 1992 Landers, California ERS-1Zebker et al. (1994) 1992 Landers, California ERS-1Feigl et al. (1995) 1992 Landers aftershock, California ERS-1Massonnet and Feigl,1995 1993 Eureka Valley, California ERS-1Peltzer and Rosen (1995) 1993 Eureka Valley, California ERS-1Massonnet et al. (1996) 1992 Landers, California ERS-1Meyer et al. (1996) 1995 Grevena, Greece ERS-1Murakami et al. (1996) 1994 Northridge, California JERS-1Ozawa et al. (1997) 1995 Kobe, Japan JERS-1Fujiwara et al. (1998) 1997 Kagoshima-ken-hokuseibu, Japan JERS-1Tobita et al. (1998) 1995 North Sakhalin, Russia JERS-1Hernandez et al. (1999) 1992 Landers, California ERS-1Peltzer et al. (1999) 1997 Manyi earthquake, Tibet ERS-2Wright et al. (1999) 1995 Dinar earthquake, Turkey ERS-1/2Reilinger et al. (2000) 1999, Izmit earthquake, Turkey ERS-2Pedersen et al. (2001) 2000 South Iceland, earthquakes ERS-2Burgmann et al. (2002) 1999 Duzce, Turkey ESR-1/2Jonsson et al. (2002) 1999 Hector Mine, California ERS-2Wright et al. (2003) 1999 Nenana Mt. earthquake, Alaska ERS-2, Radarsat-1Pathier et al. (2003) 1999, Chi-Chi earthquake, Taiwan ERS-2Wright et al. (2004) 2002, Denali earthquake, Alaska Radarsat-1Funning et al. (2005) 2003 Bam earthquake Iran Envisat

Postseismic deformation:Massonnet et al. (1994) 1992 Landers, California ERS-1Pollitz et al. (2001) 1999 Hector Mine, California ERS-2Jonsson et al. (2002) 2000 South Iceland earthquakes ERS-2Funning et al. (2005) 1998, Bolivia earthquake ERS-1/2Satyabala and Bilham (2006) 1999, Chamoli earthquake ERS-1/2Saraf et al. (2012) 2001, Bhuj earthquake Envisat

Aseismic deformation:Rosen et al. (1998) San Andreas fault, California ERS-1Burgmann et al. (2000) Hayward fault, California ERS-1/2Fruneau et al. (2001) Southwest, Taiwan ERS-1/2Wright et al. (2001) North Anatolian Fault, Turkey ERS-1/2

Table 4Details of ERS-1/2 SAR data used for the study.

Master image Slave image

Orbit Satellite Acquisition date Product Orbit Satellite Acquisition date Product

25050 ERS-1 29/4/1996 SLCI 5377 ERS-2 30/4/1996 SLCI22912 ERS-2 7/9/1999 SLCI

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–63 59

Further, the differential interferogram could not be generated dueto very low coherence (Fig. 12). Therefore; displacement mapusing DInSAR technique could not be generated for the study area.An alternative approach, theoretical displacement estimation, hasbeen adopted to estimate surface displacements due to theChamoli Earthquake.

6.2. Theoretical displacement estimation

If an earthquake is produced by a fracture of the Earth's crust, amechanical representation of its source can be given in terms offractures or dislocations in an elastic medium (Lee et al., 2002;Singh et al., 2002; Yoseph and Ramana, 2008). The theory assumesthat an earthquake fault is formed by the superposition of a largenumber of incremental shear dislocations whose sudden releaseproduces the shock. It is also assumed that the incrementaldislocations are released in such a way that the average slip isproportional to the square root of the area of slip.

In the present study, DIMOT software has been used to estimatesurface displacements produced due to movement along Munsiari

thrust (DIMOT, 2006). The fault model in DIMOT is calculated usingthe analytical formulation proposed by Okada, 1985. The formulationcalculates the dislocation in a semi-infinite medium such that theboundary surface of the medium (the surface of the earth's crust) isfree from tractions. In any rock unit, dislocation takes place when thestress exceeds the elastic limit (Reid, 1911). Okada (1985) has given acomprehensive closed analytical expression to calculate displace-ment, strain, and tilt fields due to inclined shear and tensile faults.Input parameters required for fault modeling are fault geometry(latitude, longitude and aerial extension, depth, strike and dip angle)and the dislocation components (strike slip, dip slip and tensileopening).

The parameters of Chamoli earthquake i.e. location, size in terms ofvarious magnitudes, hypo-central depth and proposed fault planesolution have been summarized in Table 5. A rectangular ruptureplane with 19 km�21 km dimensions has been assumed and dividedinto four rectangular elements of equal area. Each element works as asource of an earthquake of ‘M’magnitude that will be smaller than thecumulative shock generated from all four elements (Joshi, 2001). Joshi(2001) reported minimum RMS error considering four elements.

Fig. 12. Output of ERS-1/2 SAR image data processing for DEM generation for the study area.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–6360

The epicenter of Chamoli earthquake is surrounded by anumber of tectonic faults including two major thrusts namelyMunsiari and Vaikrita thrusts. The contact of two thrusts creates aweak zone which acts as source of new rupture (Joshi, 2001).In the study area similar configuration is found where Munsiarithrust meets other local thrusts. The rupture plane has been takenat the junction of these two thrusts, which extends 21 km insoutheast direction. The rupture length and the ruptured area arecomputed using empirical relation of Arya and Kiureghiamm(1988) and Kanamori and Anderson (1975), respectively. The focusof the Chamoli earthquake given by USGS falls within theprojected rupture plane area. The hypo-central depth of 12 kmshows that the earthquake could have originated in the basementof shear zone marked by Metcalfe (1993). The modeling parameterof Chamoli earthquake including length of rupture plane (L), width(D), surface wave magnitude (Ms), dip (δ) and strike (φ) of therupture plane, rupture velocity (Vr), Surface (S) wave velocity inthe medium, and total number of element (n) along rupture planeare given in Table 6. Due to linearity of the displacement comparedto dislocation, it is possible to split a fault plane in differentpatches of constant slip and calculate the total displacement fieldby adding the contribution of each patch. Theoretical estimate ofthe displacement pattern in vertical direction is shown in Fig. 13.

6.2.1. Surface displacement pattern and landslides in the study areaIn the study area maximum vertical displacement is 149.360 mm

(Fig. 13). The displacement map has been divided into five equalclasses and compared with post-earthquake landslide distributionlayer (Table 7). It has been observed that the maximum number oflandslides falls in the area where displacement is less or equal to

60 mm. Landslides are very complex phenomena, which take placewhen other landslide causative factors get mixed-up with triggeringfactors like earthquakes. The occurrence of landslides in low dis-placement regions can be explained in following points:

(1)

No major river network exists in the areas where displacementis greater than 90 mm, except Birahiganga River, which is atributary of River Alaknanda.

(2)

Main rock types in the areas falling in greater than 90 mmdisplacement are Granite and Granodiorite, which are toughand compact rocks.

(3)

In the areas where displacement is less than 90 mm, the mainrock types are graywackes, siltstone, phyllites, slates andlimestones, which are more susceptible to landslides.

(4)

In study area the two main rivers Alaknanda and Mandakinialso flows in the areas where displacement is less than 90 mm.

(5)

It is observed that 164 new landslides of various dimensionsare occurred after the Chamoli earthquake. It is observed that78.65% of total landslides occurred after the earthquake isfalling in the area of 1000 m buffer zone of thrusts.

7. Results and discussion

Post-Chamoli earthquake landslide inventory is overlain overdisplacement map in GIS environment for understanding the impactof seismic displacement pattern with other static factors on theoccurrence of landslides. An increase in number of pixels from442,018 (pre-earthquake) to 569,984 pixels (post-earthquake) in HSzone has been observed in two maps, which indicates that theearthquake generated displacement pattern has significant control

Table 5Parameters of Chamoli Earthquake of March 29, 1999 used for the study.

Date, Origin time and Location Hypo. depth Size Fault plane solution Reference

28.03.99 12 Ms¼6.6 NP1: φ¼2821, δ¼91, λ¼951 Rapid momenttensor solution, USGS19:05:13.59 (GMT) mb¼6.3

Mw¼6.4 NP2: φ¼971, δ¼811, λ¼89130.4921N Mo¼5.2�1018 Nm79.2881E

29.03.99 21 ML¼6.8 IMD00:35:13.4 (IST)30.411N79.421E

19:05:18.1 15 7.8�1018 Nm NP1: φ¼2801, δ¼71, λ¼751 CMT30.381N NP2: φ¼1151, δ¼831, λ¼921 (Har)79.211E

Table 6Modeling parameters of Chamoli Earthquake and respective method of their estimation (Joshi. 2001).

Modeling parameters Criteria of selection Value for Chamoli earthquake Reference

L Log (L)¼�2.77+0.619Ms 21 km Arya and Kiureghiamm (1988)D Log (A)¼Ms–4.0 19 km Kanamori and Anderson (1975)

as A¼L�Dfor rectangular rupture planeD¼A/L

δ and φ Fault plane solution δ¼91 and φ¼2821 USGSAmount of slip Along fault plane 744.360 mmVr 0.8�Vs 2.6 km/s Mendoza and Hartzell (1988), Reiter (1990) and Sato (1989)n ¼100.5(M–M′) 2Le ¼L/n 10.5 kmDe ¼D/n 9.5 kmDepth of rupture plane Geological depth section 12 km

L – Length of rupture plane, D –Width of rupture plane, Ms – Surface wave magnitude of an earthquake, δ and φ – Dip and strike of rupture plane, Vr – Rupture velocity, Vs – Swave velocity in the medium, n – Total number of element along or width of rupture plane.

Fig. 13. Theoretically derived displacement map for the study area.

N. Pareek et al. / Computers & Geosciences 61 (2013) 50–63 61

over the distribution of landslide susceptible zones. The results fromthe two LSZ maps have been evaluated to estimate the seismicdisplacement pattern due to Chamoli earthquake in the studyarea.

DInSAR technique and theoretical fault displacement modelinghave been tested for Chamoli earthquake generated seismicdisplacement pattern estimation in the study area. It has beenobserved that despite due care, the SAR images have been badly

Table 7Distribution of landslides and vertical displacementpattern in the study area.

Displacement (mm) No. of slides

4120 3790–120 9660–90 9230–60 168o30 158

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affected by geometric and temporal decorrelation that generatesphase discontinuities which restricts phase unwrapping. Further,suitable SAR data for differential Interferometry is not available forthe region that limits the use of DInSAR for displacement estima-tion in the study area. The results depict that in the Himalayanregion, where thick vegetation cover is present on complexundulating surface; differential Interferometry technique is notas successful for displacement estimation as in plains.

Theoretically observed surface displacement shows that themaximum vertical displacement due to Chamoli Earthquake is149.360 mm in the study area. It has also been observed that 164new landslides of various dimensions have occurred after theChamoli Earthquake. It has been observed that the maximumnumber of landslides fall in areas where displacement is less thanor equal to 60 mm. It has been concluded that the occurrence oflandslides are affected by various static factors (lithology, land usepattern, river network, slope and lineament etc.) under the controlof surface displacement patterns. The area where displacement isless than or equal to 90 mm are mostly falling in litho units ofgraywackes, siltstones, phyllites and slates and in Alaknanda andBirhaiganga River basins. In the Birhaiganga River basin main rocktypes are granite and quartz porphyry with steep to moderateslopes and around 35% area of this basin is occupied by barrenland, which causes higher number of landslides. It is also observedthat 78.65% of total landslides occurring after the earthquake fallin the 5 km buffer zone of thrusts (Fig. 6). Soft rocks such asphyllites, slates, siltstones, greywake, weathered limestones andquartzites cover 58.89% of total number of landslides in study area.These rocks are characterized by foliation planes, thus moresusceptible to landslides under seismic conditions. In general,south facing slopes have lesser vegetation density compared tonorth facing slopes, hence, the erosional activity is relatively morein south facing sloping terrain. In study area after the earthquakenumber of landslides is increased drastically in south and south-east facing sloping terrain (Table 1).

8. Conclusion

The critical conclusions drawn from the study are

(1)

The earthquake generated displacement pattern has controlover the distribution of landslide susceptibility zones.

(2)

It is observed that 164 new landslides of various dimensionsare occurred after the earthquake. These slides are falling inthe areas, where fragile rocks and lineaments are occurred.

(3)

The study for Chamoli region shows the SAR images are badlyaffected by geometric and temporal decorrelation that gener-ates phase discontinuities which restricts phase unwrapping.DInSAR technique is not as suitable for displacement measure-ments in the hilly region like Himalayas as is used in flattopographical regions by many scientists.

(4)

The comparison of distribution of occurrence of landslideswith theoretical estimated displacement pattern of ChamoliEarthquake shows that in same seismic shaking conditions the

area behaves differently. It points out that in the study areaafter the Chamoli Earthquake the occurrence of landslidesaffected by other static factors also.

(5)

The study describes the paramount importance of identifyingand understanding of the landslide causative factors for takingsuitable mitigation measures by planners. The differenceimage of post- and pre-Chamoli Earthquake LSZ images maybe used for recognizing more hazardous areas under seismicshaking conditions in the study area.

Acknowledgment

Naveen Pareek is thankful to the Ministry of Human ResourceDevelopment, Government of India for providing financial supportduring his PhD work and data procurement used in this work.The paper has highly benefited by valuable comments by anon-ymous reviewers on the earlier version of the manuscript. We arealso thankful to the editor for suggestions to improve the paper.Naveen Pareek is also thankful to Dr Ashish Mishra, Geologistin ONGC, Government of India for improving the language ofthe paper. Naveen Pareek is also thankful to Anil Chandla,Scientist, Government of India for his continuous support.

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