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Remote-sensing and GIS-basedlandslide-susceptibility zonation usingthe landslide index method in Igo RiverBasin, Eastern Himalaya, IndiaJawan Singh Rawat a & Ramesh Chandra Joshi ba Department of Geography, Government Degree College,Chaukhutia, Uttarakhand, 263656, Indiab Department of Geography, Kumaun University, Nainital,Uttarakhand, 263001, India

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To cite this article: Jawan Singh Rawat & Ramesh Chandra Joshi (2012): Remote-sensing and GIS-based landslide-susceptibility zonation using the landslide index method in Igo River Basin, EasternHimalaya, India, International Journal of Remote Sensing, 33:12, 3751-3767

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International Journal of Remote SensingVol. 33, No. 12, 20 June 2012, 3751–3767

Remote-sensing and GIS-based landslide-susceptibility zonation usingthe landslide index method in Igo River Basin, Eastern Himalaya, India

JAWAN SINGH RAWAT*† and RAMESH CHANDRA JOSHI‡†Department of Geography, Government Degree College, Chaukhutia, Uttarakhand

263656, India‡Department of Geography, Kumaun University, Nainital, Uttarakhand 263001, India

(Received 14 December 2009; in final form 20 July 2011)

A remote-sensing and geographical information sysytem (GIS)-based quantita-tive methodology for landslide-susceptibility zonation is described in a stepwisemanner with its application in the Igo River Basin in the West Siang Districtof Arunachal Pradesh, Eastern Himalaya, India. Parameters such as geology,physiography, slope angle, slope length, slope aspect, slope type, generic land-forms, lineament distance, road distance, drainage distance, altitudinal zones andland cover are used for landslide-susceptibility zonation. The quantitative relationbetween landslides and the selected parameters is established through the land-slide index method of the International Institute for Geo-Information Science andEarth Observation (ITC), The Netherlands, by assigning weights. A weight valuefor a certain parameter class is defined as the natural logarithm of the landslidedensity in the class divided by the landslide density in the entire map. The finallayer containing the composite index is divided into seven landslide-susceptibil-ity categories. The maximum portion of the study area experiences moderatelylow to moderate landslide susceptibility, and each portion occupies an area of91 km2, representing 30% of the total area. High concentrations of very high andextremely high-susceptibility landslide areas are noticed in the steep slope areas,especially in the Sub-Himalayas. The settlements are found in the safe areas ofvery low, low and moderately low landslide-susceptibility categories. About 9% and1.99% of the roads are exposed to high and very high landslide-susceptibility areas,respectively. About 15% of the slash-and-burn cultivation (jhum) is found along thehigh-susceptibility areas, 3.89% is found in very high-susceptibility areas and 0.19%is prone to extremely high susceptibility. The high-susceptibility zones are alsofound under dense and moderately dense forests.

1. Introduction

Movement of a mass of rock, debris or earth down a slope results in a geomor-phic makeover of the Earth’s surface, and this active process contributes to erosionand landscape evolution, which is often referred to as a landslide (Cruden 1991).Landslides are also referred to as mass movement, slope failure, slope instability andterrain instability (Nagarajan 2004). It covers almost all varieties of mass movementson slopes, including some, such as rock falls, topples and debris flows, that involvelittle or no true sliding. ‘Zonation’ refers to the division of the land surface into areas

*Corresponding author. Email: jsr_06@rediffmail.com

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis

http://www.tandf.co.uk/journalshttp://dx.doi.org/10.1080/01431161.2011.633121

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3752 J. S. Rawat and R. C. Joshi

and the ranking of these areas according to the degree of actual or potential hazardof landslides or other mass movements on the slope (UNESCO 1984).

India is vulnerable to different natural hazards due to its geodynamics and uniqueclimate. Estimates show that about 60% of its landmass is vulnerable to earthquakesof different magnitudes, about 8% to cyclone hazard, about 68% to drought, 12% tofloods and approximately 15% of the total area is susceptible to landslides. Landslidesare one of the most common natural phenomena in the Himalayan terrain, caus-ing widespread damage to property and infrastructure, besides loss of human lives(Mathew et al. 2007). Himalaya witnesses waves of landslides of different size andextent each year, especially in the monsoon and post-monsoon periods (Pachauri2004). Lack of proper geological and geotechnical investigation at the planning stagehas adversely affected existing geo-environmental conditions in the Himalayas lead-ing to increased incidences of slope instability (Anbalagan et al. 2008). Tectonically,the convergence of the Indian plate with Eurasia is accommodated by active thrustsand faults that are expressed in associated geomorphic features such as landslides(Sati et al. 2007). The presence of deep gorges, debris avalanches and uplifted terraces(active and stabilized) in the Himalayas are attributed to recent movement (Valdiya2001). On the other hand, climate change at variable timescales exerts profound con-trol on hill slope and fluvial transport processes (Sundriyal et al. 2007). Therefore,mapping and monitoring of the areas affected by or susceptible to landslides wouldhelp to mitigate the related risk. To prioritize an area for hazard-mitigation efforts, it isbeneficial to have a landslide-susceptibility zonation (Anbalagan 1992). In general, thecommon factors considered for landslide-susceptibility zonation are lithology, weath-ering, slope types, slope angle, aspect, geomorphology, slope morphology, drainagedensity, lineaments, proximity to faults, land cover, rainfall, seismicity and anthro-pogenic activity (Mathur and Sarkar 2004). This article is an attempt towardslandslide-susceptibility zonation, using the landslide index method in the Igo RiverBasin on the basis of selected remote-sensing-based parameters.

Several qualitative and quantitative methods, discussed below, have been devisedfor landslide-susceptibility analysis. Qualitative methods depend on an expert’s opin-ion and are often useful for regional assessment (Aleotti and Chowdhury 1999, VanWesten et al. 2003). Quantitative methods rely on observed relationships between con-trolling factors and landslides (Guzzetti et al. 1999). Among the numerous papers onthe subject, Rupke et al. (1988) use geomorphological and qualitative methods forassessing landslide susceptibility; Pachauri and Pant (1992) demonstrate a weightedlandslide-susceptibility mapping procedure; Gupta et al. (1999) and Saha et al. (2002)weigh parameters for landslide-susceptibility mapping; NRSA (2001) adopts the ana-lytical hierarchy process methodology for preparing susceptibility maps; Mathewet al. (2007) use a weights of evidence method to find the probability of occur-rence of landslides; Sarkar and Kanungo (2004) suggest an integrated approach forlandslide-susceptibility mapping using remote sensing and a geographical informa-tion sysytem (GIS); Kanungo et al. (2006) advocate GIS-based landslide-susceptibilityzonation using neuro-fuzzy weighing; Deva and Srivastava (2006) suggest a grid-basedapproach for classifying the terrain using factors such as lithology, ruggedness numberand land cover; Pachauri (2007) concludes that facet-based zonation is a very effectivetool for landslide mapping in high relief areas of the Himalayas and is cost-effective;Champatiray et al. (2007) also suggest a fuzzy-based method for landslide susceptibil-ity in active seismic zones of Himalaya; Pachauri et al. (2006) attempt risk zonationand conclude that rock-fall velocity modelling can be useful in landslide-risk zonationof an area; Thigale and Umrikar (2007) study the Konkan landslide with the help of

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Remote-sensing and GIS-based landslide-susceptibility zonation 3753

a physical frame (climate, geology, geomorphology and seismology) along with struc-tured interviews; and Vijith et al. (2009) make use of the information value (InfoVal)method and GIS techniques for modelling landslide susceptibility.

2. Study area

For this study, a landslide-affected watershed of the Igo River in the West SiangDistrict of Arunachal Pradesh, India, which falls into Eastern Himalaya, has beenselected (figure 1). Bounded by 27◦ 46′ 36′′ N–27◦ 57′ 17′′ N latitude and 94◦ 35′ 35′′E–94◦ 54′ 39′′ E longitude, the Igo basin covers an area of about 306 km2. The southernpart of the study area comprises the Kimin and Dafla formations (loose conglomer-ate, shale and sandstone) of Outer Himalaya corresponding to the Miocene to thePleistocene. A tectonic belt, that is a main boundary thrust (MBT), passes throughthe middle of the study area delineating Lesser Himalaya from Outer Himalaya. Themaximum area contains rocks of Lesser Himalaya belonging to Palaeoproterozoic and

94° 50′ E

27° 5

0′ N

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

27° 0

0′ N

27° 0

0′ N

28° 3

0′ N

28° 3

0′ N

94° 45′ E94° 40′ E

91° 30′ E 93° 00′ E 94° 30′ E 96° 00′ E 96° 30′ E

Figure 1. Igo River Basin: location map.

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3754 J. S. Rawat and R. C. Joshi

Lower Permian periods. The Dolimestone of the Bomdila group occurs in the north-eastern part, The Chilliepam formation of the Lower Gondwana group is found inthe western tip whereas the remaining parts of Lesser Himalaya consist of the Miriformation. Physiographically, the undulating hills of low to moderate altitude formmaximum parts of the area with scattered patches of plain lands along the river cor-ridors. On average, the area receives 2370 mm of annual rainfall with mean minimumtemperature between 7.9◦C in the month of January and 22.4◦C in July and the meanmaximum temperature fluctuating from 16.4◦C to 28.5◦C in January and August,respectively.

There are 14 settlements in the study area, which are mainly villages. They are Garu,Garu Camp, Rilu, Tapo, Igo Camp, New Dari, Old Dari, Dali, Dali Hydel, DaliCamp, Ichi, Chisi, Padi and Rimi. The distant villages, such as Tapo, New Dari, OldDari and Ichi, are connected by village road or footpath tracks. Other settlements arefound along the road passing through the middle of the study area. The inhabitants,being tribal communities, predominantly practice traditional slash-and-burn meth-ods of cultivation along the hillslopes, known as jhum cultivation, with a few isolatedsettled wet-rice cultivation patches along the plains of the river corridors.

3. Objectives of the study

This article attempts to use the landslide index method of the International Institutefor Geo-Information Science and Earth Observation (ITC), The Netherlands (VanWesten 1997), for landslide-susceptibility zonation in the Igo River Basin, EasternHimalaya, Arunachal Pradesh. To carry out susceptibility zonation, various parame-ters such as geology, physiography, slope angle, slope length, slope aspect, slope type,generic landforms, lineament distance, road distance, drainage distance, altitudinalzones and land cover have been used. These parameters are mainly remote-sensing-based using the Indian Remote Sensing (IRS) 1D LISS III and Shuttle RadarTopographic Mission (SRTM) C and X bands Interferometric Synthetic ApertureRadar (InSAR) Digital Elevation Model (DEM) supported by other ancillary datasources. The ILWIS 3.4 (developed by ITC, Enschede, The Netherlands) and PCIGeomatica 10 GIS (developed by PCI Geomatics, Inc. Richmond Hill, ON, Canada)platform have been used to carry out data analysis. To derive geomorphological andhydrological parameters, the 3 arc-second SRTM elevation data are converted into1 arc-second data, and the undefined values as well as abnormal values have beenreplaced using suitable filters. Using ILWIS 3.4, landslide-susceptibility zones (LSZs)have been delineated in the study area, which may be used for prioritizing the risk mit-igation efforts and landslide management. Finally, the LSZs have been correlated withthe various existing land-cover categories.

4. Data, tools and methodology

In this study, the landslide index method of ITC, The Netherlands (Van Westen 1997),was used for landslide-susceptibility zonation. This method involves the summation ofweights of each parameter class into a composite index known as the landslide index.A weight value for certain parameter class, such as a slope class (table 1), is definedas the natural logarithm of the landslide density in the class divided by the landslidedensity in the entire map. The IRS 1D multi-spectral LISS III data, in 8 bits, path/row113/052 and spatial resolution 23.5 m taken on 16 November 2002, are used for mark-ing a detailed location of landslides in the study area. The landslides are digitized by

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Remote-sensing and GIS-based landslide-susceptibility zonation 3755

Table 1. Steps for the calculation of parameter weight (slope).

Slope (◦) N P L∑

P∑

L L/P∑

L/∑

P W

0–10 2808 269 879 2808 3 055 428 51 547 0.01040 0.01687 −0.483310–20 8222 582 452 8222 3 055 428 51 547 0.01412 0.01687 −0.178320–30 16 556 1 006 762 16 556 3 055 428 51 547 0.01644 0.01687 −0.025630–40 14 602 777 584 14 602 3 055 428 51 547 0.01878 0.01687 0.107140–50 6633 316 011 6633 3 055 428 51 547 0.02099 0.01687 0.218450–60 2250 86 201 2250 3 055 428 51 547 0.02610 0.01687 0.436460–70 456 15 794 456 3 055 428 51 547 0.02887 0.01687 0.536370–80 20 743 20 3 055 428 51 547 0.02612 0.01687 0.467280–90 2 2 2 3 055 428 51 547 0.00000 0.01687 −0.7334

Note: N is the default column of cross function of landslide raster map and slope raster map(parameter map) indicating the number of pixels in the slope map having landslides, P is thenumber of pixels in the parameter class, L is similar to software-generated N redefined by asymbol of the user’s convenience, �P is the total number of pixels of the parameter map, �L isthe total number of pixels with landslides and W is the weight of the parameter.

Table 2. Spectral characteristics of IRS 1D LISS III data.

Spectralbands

Nominal spectrallocation

Wavelength(µm)

Spatialresolution (m) Swath (km)

Band 2 Green 0.52 – 0.59 23.5 141Band 3 Red 0.62 – 0.68 23.5 141Band 4 Near infrared 0.77 – 0.86 23.5 141Band 5 Shortwave infrared 1.55 – 1.70 70.5 148

the interactive visual digitization method using Band 2, Band 3 and Band 4 (table 2),and their false colour compositions (FCCs) were ascertained by sufficient field checks(figure 2(a)). Since the field survey was carried out during November 2007, only thoselandslides of satellite data (November 2002) active as on November 2007 (i.e. active for5 years) were taken into consideration for this study. The raster map containing activelandslides is crossed, one by one, with the raster maps of selected parameters such asgeology, physiography, slope angle, slope length, slope aspect, slope type, generic land-forms, lineament distance, road distance, drainage distance, altitudinal zones and landcover. This results in a cross table, which is used for the calculation of landslide densityper class, landslide density in the entire map and ratio of the density per class to mapdensity (table 1: example for slope angle map). The weights, which are a natural log-arithm of the ratio, have been assigned to each parameter class. Finally, a compositeweight is calculated by summing all parameter weights. For easy understanding, thelandslide index method may be represented with the help of symbols by the followingequations:

LSI =n∑

i=1

Wi, (1)

where LSI is the landslide-susceptibility index (composite index), n is the number ofparameters used, i is the index number of the parameter and W is the parameterweight, as expressed in equation (2) below:

W = ln(

L/P∑L/

∑P

), (2)

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3756 J. S. Rawat and R. C. Joshi

27° 5

0′ N

94° 40′ E 94° 45′ E 94° 50′ E

94° 40′ E 94° 45′ E 94° 50′ E

94° 40′ E 94° 45′ E 94° 50′ E

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

0 10 km

10 km0

N

E

S

W

N

Active landslide

(b)

(a)

Figure 2. Igo River Basin: (a) FCC of IRS 1D LISS III with landslides; (b) hybrid colourcomposite (HCC).

where W is the parameter weight, P is the number of pixels in each parameter class, Lis the number of pixels with a landslide in each parameter class, �P is the total numberof pixels in the parameter map and �L is the total number of pixels with landslides.

For the sake of validation or success rate of this remote-sensing technique oflandslide-susceptibility zonation, a few randomly selected points in high, very highand extremely high LSZs have been visited and verified.

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Remote-sensing and GIS-based landslide-susceptibility zonation 3757

IRS 1D multi-spectral LISS III data were used for preparing a database on landcover and extracting lineaments. The normalized difference vegetation index (NDVI)is obtained using Band 3 and Band 4. With the help of NDVI, vegetative and non-vegetative areas are masked. The vegetative layer is converted into fraction vegetationcover in percentage (V ) by the relationship of Zhang et al. (2002) in equation (3):

V = 93.075 (NDVI) + 8.798. (3)

On the basis of V (canopy density), four classes of the forest are obtained using theclassification scheme of the Forest Survey of India. These are dense forest (density> 70%), moderately dense forest (40–70%), open forest (10–40%) and scrub forest(density < 10%). The non-forest layer is further classified into settlement areas, roads,water bodies and cultivated areas, using supervised classification and on-screen dis-criminations from the hybrid colour composite (HCC) and FCC of LISS III. Themaximum lengths of roads in imagery are obscured by vegetation; therefore, theyis delineated by on-screen digitization. Cultivated areas are divided into settled cul-tivation and jhum cultivation with the help of slope and elevation data. HCC isobtained by PCA1, Ratio 1 and Ratio 2 passed through red, green and blue chan-nels, respectively. PCA1 is the first principal component of Band 2, Band 3, Band 4and Band 5; Ratio 1 is the ratio between Band 4 and Band 3; and Ratio 2 is the ratiobetween Band 4 and Band 2, converted into 8 bits (figure 2(b)). With adequate groundtruthing, at every stage the area is classified into different land-cover categories with anoverall accuracy of 73.81% (table 3). A lineament map is derived using the LIN algo-rithm in the PCI Geomatica 10, and with the help of rose diagram, lineament trendsand lengths are shown. The lineament distance is calculated using the map distancealgorithm.

SRTM DEM is used to derive geomorphological and hydrological parameters.SRTM, a joint operation of NASA and the National Geospatial-IntelligenceAgency (NGA), flown on 11–22 February 2002, mapped 80% of the Earth’s landsurface (between 60◦ N and 56◦ S latitudes) in three dimensions using C band(wavelength = 5.6 cm) and X band (wavelength = 3 cm) InSAR techniques with±16 m absolute vertical, ±10 m relative vertical and ±20 m absolute horizontal

Table 3. Igo River Basin: confusion matrix.

DF Jhum MDF OF Road SF SC S WB UC Accuracy

DF 5 0 1 0 0 0 0 0 0 0 0.83Jhum 0 4 0 0 0 0 0 0 0 1 0.80MDF 2 0 2 0 0 0 0 0 0 0 0.50OF 0 0 0 4 0 0 1 0 0 0 0.80Road 0 0 0 0 5 1 0 0 0 0 0.83SF 0 0 1 1 0 2 0 0 0 0 0.50SC 0 0 0 0 0 0 2 0 0 1 0.67S 0 0 0 0 0 0 1 3 0 0 0.75WB 0 0 0 0 0 0 1 0 4 0 0.80Reliability 0.71 1 0.5 0.8 1 0.67 0.4 1 1

Note: DF, dense forest; MDF, moderately dense forest; OF, open forest; SF, scrub forest;SC, settled cultivation; S, settlement; WB, water body; and UC, unclassified. Average accu-racy = 72.04%, average reliability = 78.68% and overall accuracy = 73.81%.

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3758 J. S. Rawat and R. C. Joshi

accuracies. The data are distributed through the United States Geological Survey(USGS) seamless website at 1 arc-second (∼30 m) resolution for areas within theUSA and at 3 arc-second (∼90 m) resolution for the rest of the world. The SRTM tilesrtm_55_07.zip in GeoTIFF format is downloaded from the USGS seamless serverand sub-mapped for the study area. The voids and abnormal values in the SRTMDEM are removed following Castellanos (2005). The abnormal values (negative valueand values above maximum height) are converted into undefined values and removedusing an iterative average filter. The 1 arc-second DEM with 30 m spatial resolutionis generated from the 3 arc-second SRTM DEM through the bicubic polynomialinterpolation method as suggested by Keeratikasikorn and Trisirisatayawong (2008).The effect of the vegetation is removed using vegetation height derived from the NDVIlayer on the basis of ground observation for the samples. The final fine-resolutionSRTM DEM and DEM generated by contour interpolation correlates well withthe correlation coefficient of 0.99, root mean square error (RMSE) of 12.16 m anddifference of means of 0.22 m. The contour DEM is generated using the contourvector layer, which was digitized from Survey of India topographical maps at 20 mcontour intervals and rasterized through the linear interpolation method with a pixelsize of 30 m. Figure 3 shows the minimum, maximum, standard deviation and meanvalues of the corrected SRTM DEM and contour DEM. The corrected SRTM DEMhas 735 lines, 1311 columns and 30 m spatial resolution. The parameters, derivatesof DEM, by default inherit these properties. Parameters from satellite data and othersources are re-sampled into 30 m resolution to perform cross functions and calculateparameter indices and composite index.

Slope in degrees is derived from the DEM by calculating height differences in xand y directions and converting them into degree values in the ILWIS platform (ITC2001). The relationship between the slope steepness in percentages (S) and slope lengthin metres (L) presented in equation (4), as suggested by Toxopeus (1997), is used togenerate slope length in raster format:

L = 0.4 S + 40. (4)

00

500

500

1000

SR

M D

EM

(m

)

1500

y = 1.009x – 7.315R2

= 0.999RMSE = 12.16

2000

1000

Contour DEM (m)

1500 2000

1789Maximum

987Mean

184Minimum

463.5Standard deviation

Figure 3. Comparison between contour DEM and corrected SRTM DEM.

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Remote-sensing and GIS-based landslide-susceptibility zonation 3759

The slope aspect is obtained by running the RADDEG (ATAN2 (DX, DY) + PI) algo-rithm in ILWIS (ITC 2001). Similarly, running the D2FDXDY filter, which calculatesthe second derivative in all diagonals, over DEM results in the slope type or slopeshape differences (ITC 2001). The generic landforms map (with information such asslope, channel, ridge, plain and pit) is generated using the ILWIS script. The inputsare slope map, plan curvature, profile curvature, mean curvature, shape complexityindex (SCI), fuzzy exponent and a table with a definition of class centres. Plan cur-vature is the curvature of the corresponding normal section, which is tangential toa contour. Vertical or profile curvature is the curvature of the corresponding normalsection, which is tangential to a flow line. Mean curvature is the average of normalsection curvature. Negative values describe mean concave landforms, whereas positivevalues refer to mean convex ones.

By using the ‘DEM hydro-processing’ operation, drainage network is extracted fromDEM in vector format. To reduce computational robustness, drainages are extractedwith a stream threshold value of 500 pixels. The drainage distance layer is computedfrom drainage vector data using the map distance function. Similarly, road distancelayer is calculated using the road vector map digitized from satellite data. The geologi-cal information is based on the secondary sources in consultation with satellite imagesand duly cross-checked during field survey. Similarly, the physiographic parameter isdigitized from the Natural Resource Atlas of Arunachal Pradesh, prepared by the StateRemote Sensing Application Centre, Itanagar.

5. Result and discussion

The slope in the study area ranges from 0◦ to 80◦ with a mean 27◦, standard deviation12.13◦ and coefficient of variation 45.01% (table 4). The distribution is more asym-metrical towards the upper value with a skewness of –0.55. Slope lengths range from40 to 269 m with an average length of 107 m. The standard deviation of the slopelength is 42.79 m, which is 39.82% to its mean. The positive value of the skew showssymmetrical distribution. Similarly, slope aspect values range from 0 to 360◦, bothof which indicate the northern aspect of the slope. The mean and median values ofthe slope aspect represent the southern aspect; however, the modal value indicatesthe northern aspect. The slope type data reveal a minimum value of –20.8, whichis a convex slope, and a maximum of 21.30, which is a concave slope. The meanand medial values represent the rectilinear slope. With a range of 184–1789 m, the

Table 4. Morpho-statistics.

Slope angle(◦)

Slope length(m)

Slope aspect(◦) Slope shape Elevation (m)

Minimum 0.00 40.00 0.00 −20.800 184.00Maximum 80.09 268.92 360.00 21.300 1789.10Mean 26.95 107.46 175.35 0.010 987.60Median 26.79 104.86 165.30 0.000 773.10Mode 33.62 40.00 0.00 −20.800 380.00SD 12.13 42.79 105.94 9.790 463.50CV (%) 45.01 39.82 60.42 – 39.00Skew −0.55 0.58 1.66 0.002 1.34

Note: SD, standard deviation; CV, coefficient of variation.

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3760 J. S. Rawat and R. C. Joshi

maximum occurring elevations are of 380 m and mean elevation is 798.6 m. The posi-tive value of the skewness indicates symmetrical distribution of the elevation. Analysisof the generic landforms shows that river channels and pits occur in close associa-tion. River channel covers 13.61 km2, whereas the pits are found in an area of 2.511km2. Plain lands in the valleys, mainly the river terraces, account for 8.87 km2 of thearea. The ridges are spotted by peaks in many places. The ridges form an area of11.69 km2, whereas the peaks are found covering an area of only 2.74 km2 of the IgoRiver Basin. About 267 km2 or 87.15% of the total area is under slopes of differentmagnitudes.

Among the land-cover categories of the Igo River Basin, settlements and roadsoccupy areas of only 0.82 and 0.59 km2, respectively (figure 4(a)). The wet-rice settledcultivation concentrated in the plains cover an area of 5 km2, and shifting cultivationalong the slopes or ridges cover an area of 9 km2. The maximum area of the basin isfound under the dense forest spreading over 130 km2; moderately dense forest coversan area of 68 km2, open forest covers 84 km2 and scrub forest covers 6 km2. The waterbodies form an area of 4 km2 comprising 1.39% of the Igo River Basin.

The stream density in 100 m × 100 m grids ranges from 0 to 891 m with an averagedensity of 89 m and standard deviation of 150.29 m. The drainage distance rangesfrom 0 to 12 744 m with a mean distance of 6382 m. Out of 988 lineaments, maximumnumbers of lineaments (34.51%) are trending towards 30◦–60◦ directions parallel toMBT (figure 4(b)). The least numbers of the lineaments are in 150◦–180◦ and 120◦–150◦ directions. The distance from the lineaments varies from 0 to 12 660 m and themean lineament distance is 6335 m. Due to the sparse road network in the study,distance from the road varies greatly, from 0 to 15 261 m, with a mean of 7640 m.

Kimin and Dafla formations of Outer Himalaya form an area of 54 km2 and 30km2 of the Igo River Basin, respectively. The Miri formation of the Lower Gondwanaconstitutes 136 km2, whereas the Chilliepan formation covers an area of only 19 km2.The Dolimestone of the Bomdila group comprises an area of 67 km2 of the Igo RiverBasin. A gently sloping physiographic unit covers an area of 10 km2 with deep soil andclay loam to silty loam texture. Moderately dissected moderate slope covers 0.79 km2,and soils are deep to very deep with texture loam to gravelly sandy loams. Moderatelydissected steep slope moderate erosion accounts for an area of 243 km2, having fineclayey loam to dominantly loam soil texture. The moderately dissected steep slopewith severe erosion category consists of an area of 16 km2 where soils are moderatedeep and texture loam to sandy. A highly dissected area is found in 15 km2, which isunder severe and moderate erosion, whereas an area of 14 km2 covering 4.69% of thebasin is under severely dissected hills.

The landslide index in the Igo River Basin is sliced into seven groups (figure 5). Theindex ranges from –11.61 to 4.68 (table 5). Since the numbers of pixels with values lessthan –6 are low, the area below this index value is considered to be under a very lowlandslide-susceptibility category. In this way, the whole range has been divided intoseven categories, each representing an LSZ of particular order or degree. The areaunder very low landslide susceptibility (below –6) is 4 km2, which accounts for 1.37%of the area. As shown in figure 5, the very low LSZ is concentrated mainly in plainareas along the valley bottom of the study area. The low LSZ with landslide indexvalues ranging from –6 to –3 is found covering an area of 21 km2, which forms 7% ofthe total area. Low LSZ is found around the very low LSZ category and other areas ofgentle elements of slope, especially on piedmonts. Some isolated patches also occur onthe flat hill summits. The maximum portions of the study area experience moderately

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94° 40′ E 94° 45′ E 94° 50′ E

94° 40′ E

(a)

(b)

94° 45′ E 94° 50′ E

27° 5

0′ N

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

27° 5

0′ N

27° 5

5′ N

94° 40′ E93° 35′ E

Dense forestJhumModerately dense forestOpen forestRoadScrub forestSettled cultivationSettlementWater body

94° 45′ E 94° 50′ E

N

E

S

W

N

E

S

W

10 km0

10 km

Lineaments

015 00015 000

180°

135°

90°

45°

0°10 00010 000 5000

Length (m)5000 0

Figure 4. Igo River Basin: (a) land-cover map; (b) lineament map.

low (–3 to –1) to moderate (–1 to 0) LSZs, each of which occupies an area of about91 km2, which is around 30% of the total. These categories spread in all parts ofthe study area. The moderately low LSZ is mainly found along with low LSZ. Thiscategory is scattered in large patches in the western margin and along the middle ofthe study area with moderately low altitude, relief and slope associated with Dafla,

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94° 50′ E

27° 5

0′ N

27° 5

0′ N

27° 5

5′ N

27° 5

5′ N

94° 40′ E 94° 45′ E

94° 50′ E94° 40′ E 94° 45′ E

Landslide susceptibilityVery lowLowModerately lowModerateHighVery highExtremely high V1, V2 & v3 = Field check points

for validation 10 km

N

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Figure 5. Igo River Basin: landslide-susceptibility map.

Table 5. Igo River Basin: landslide index.

Landslideindex Category Area (km2) Area (%)

Cumulativepercentage

<–6 Very low 4.21 1.37 1.37−6 to –3 Low 21.48 7.00 8.37−3 to –1 Moderately low 91.19 29.72 38.09−1 to 0 Moderate 91.68 29.88 67.97

0 to 1 High 68.17 22.22 90.191 to 2 Very high 24.89 8.11 98.302 to 5 Extremely high 5.22 1.70 100.00

Total 306.83 100.00

Chilliepam and Dolimestones. The moderate LSZ occurs in conjunction with themoderately low LSZ along the ridges, moderate slopes, reliefs and altitudes. However,the major concentrations associated with high LSZs are seen in the Kimin, Miri andDolimestone formations.

The high LSZ with its index range of 0–1 and very high landslide susceptibility rang-ing from 1 to 2 cover areas of approximately 68 and 25 km2, representing 22% and8.11% of total area, respectively. These categories occur along the steep slopes, highrelief areas, dense lineaments and near the jhum fields or in abandoned jhums. Highconcentrations of these categories are seen associated with Kimin and Dafla forma-tions of Sub-Himalaya and the Miri formation of the Lesser Himalaya. The extremelyhigh landslide susceptibility with index value ranging from 2 to 5 is found in a 5 km2

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area constituting only 1.70% of total area. This susceptibility category has a maxi-mum concentration in the Kimin formation of the Sub-Himalaya. It is worth notingthat apart from the jhum activities, the geology and slope emerge as the main factorsof landslides in the Igo River Basin. The incidences of landslide are quite conspicuousin the moderately dense and dense forests. The sandstones and other unconsolidatedmaterials, especially in Sub-Himalaya, are more susceptible to landslide, especially inhigh elevations and steep slopes.

The results of correlation between land cover and LSZs reveal that 67% of thesettlements are found in the safe areas of very low landslide susceptibility (table 6).Another 30% and 3% are found in low and moderately low-LSZs, respectively.About 44% of the road lengths pass through moderately low LSZ followed by33% lying in the low-susceptibility category. About 9% of the roads are exposedto high and 1.99% to very high landslide susceptibilities. The settled cultivation ismainly found in plain areas obviously concentrated along the very low, low andmoderately low-susceptibility zones. Only 2.63% of settled cultivations are found inmoderate-landslide areas and 0.05% in the high-landslide areas. On the other hand,jhum cultivation has maximum occurrences along the moderately low-susceptibilityzone followed by low and moderate categories each consisting of about 20%.Another 15% of the jhums are exposed to high-susceptibility areas and about 4%to very high and extremely high-susceptibility areas. The scrub forest has maximumconcentration along the moderate and moderately low-susceptibility categoriesconstituting 34.51% and 32.60%, respectively. About 20% of the scrub forests are

Table 6. Landslide susceptibility in each land-cover types.

Landslide susceptibility category

Land use/land cover Very low Low

Moderatelylow Moderate High

Veryhigh

Extremelyhigh

Settlement 0.48 0.21 0.02(67.32) (29.81) (2.88)

Road 0.02 0.20 0.26 0.06 0.05 0.01(2.73) (33.15) (43.58) (9.72) (8.83) (1.99)

Settledcultivation

2.50 1.72 0.62 0.13 0.00

(50.33) (34.61) (12.39) (2.63) (0.05)Jhum

cultivation0.08 1.81 3.52 1.83 1.35 0.35 0.02

(0.93) (20.20) (39.25) (20.45) (15.09) (3.89) (0.19)Water body 0.21 1.35 2.04 0.50 0.14 0.03 0.00

(4.89) (31.76) (47.76) (11.64) (3.17) (0.73) (0.05)Scrub forest 0.03 0.25 1.99 2.11 1.21 0.40 0.11

(0.51) (4.16) (32.60) (34.51) (19.78) (6.60) (1.83)Open forest 0.47 4.37 25.49 26.33 18.71 7.09 1.37

(0.56) (5.21) (30.41) (31.42) (22.32) (8.46) (1.63)Moderately 0.22 3.56 15.49 18.23 19.22 9.02 2.08

dense forest (0.32) (5.25) (22.84) (26.88) (28.34) (13.31) (3.06)Dense forest 0.20 8.01 41.75 42.48 27.49 7.99 1.64

(0.15) (6.18) (32.23) (32.79) (21.22) (6.16) (1.27)

Note: All values in bold letters are in square kilometres and the values in parentheses arepercentages of the total of respective land-cover category.

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found in the high-susceptibility area and approximately 8% in very high and extremelyhigh-susceptibility zones. A similar trend is observed in open forest with 31.42%occurrence under moderate susceptibility, 30.41% in moderately low, 22.32% in highand about 10% in very high and extremely high-susceptibility zones. About 28% ofthe moderately dense forest is found in high, 26.88% in moderate and 22.84% inmoderately low LSZs. On the other hand, dense forest has maximum area, that is32.79% and 32.23% under moderate susceptibility and moderately low-susceptibilityareas, respectively. About 21% of the dense forest lies in high and approximately 15%in low, very high, extremely high and very low LSZs.

Although high and very high LSZs are observed associated with jhum and aban-doned jhum cultivation, the high concentration of the LSZ of extreme magnitudes inforested areas suggests that the geological structure, slope and relief are the majorcontrolling factors of landslides in the area, over anthropogenic activities. The highconcentration of high, very high and extremely high magnitude landslides in the Sub-Himalayas indicates that the recent Sub-Himalayan rocks are more susceptible toslope failures than high-grade Lesser Himalayan rocks. Since MBT passes throughthe middle of the study area, the pressure of continuous movement of the Indian platetowards the northeast is obviously most felt along this weak tectonic corridor. Themaximum number as well as the length of lineaments trending parallel to MBT sub-stitute this reason. This factor may be playing a crucial role in triggering recurrentlandslides in the area. Since settlements and cultivated areas are found in the lowLSZs, there is a low magnitude of risk associated, although the area as a whole isadversely affected by landslides. The low risk is also attributed to the current scantypopulation density of the study area. However, for future land-use planning and landmanagement, the LSZs provide an insight into risk mitigation.

To validate the present remote-sensing-based landslide-susceptibility zonation, afew randomly selected areas have been visited and are marked as V1, V2 and V3in figure 5. V1 lies along the high and very high LSZs. At this point, near the Riluvillage, massive landslides have been observed in the field mostly controlled by tec-tonic activities. The tectonically active MBT is almost perpendicularly intersected bya fault along Siki River, a tributary of Igo. A large portion of the area is movingdown towards the east direction along the fault line of river line and the road anddrainage system are completely disrupted by the motion. The upslope steep areas arespotted by recurrent landslides and the outcrops are observed to be highly thrustedand crumbled. Therefore, this area has been named a sinking zone. Further, the inci-sion of the river terraces, overhanging valley of tributary streams, steep free-face slopesegments, steep-sided deep gorges, fault scarps, etc., bear the testimony of dynamictectonic activities in the study area. In many places, the steep free-face slope ele-ments, sometimes in two levels (near Daring village), are observed while the regoliths,colluviums, colluvial fans and cones are very common along the gentle elements.Villages such as Daring, Ichi, Chisi and Padi are situated on piedmonts. Multi-levelterraces are observed on both banks near the confluence of the Sida and Igo riversand presently used for wet-rice cultivation. The opposite banks of the Igo River nearIgo Camp, Chisi and Padi have very steep scarps, apparently resembling fault scarps.At V2, selected at high and very high LSZs, both active and relict landslides areobserved, which are natural as well as anthropogenic in origin. The jhum cultivationand deforestation are main anthropogenic causes to which the landslides are found tobe associated with. There are many instances of closely spaced debris and rock creeps,rock falls, etc. along the steep and precipitous slopes, roadsides and river undercut

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areas. Series of extensive landslides are found at V3, which falls under extremely highLSZ. Here, the Dafla formation corresponding to Upper Siwalik overlies the Kiminformation, the equivalent of Lower Siwalik. The Kimin formation includes alterationsof soft current-bedded sandstone, siltstone, clay stone, marl and gravels. The overlyingDafla formation is crudely bedded with alternative beddings of low-grade sandstone,pebbly sandstone, conglomerates and shale. Sandstones are coarser and texturallyand minerologically immature showing poor rock strength and high slope failures.The landslides, as observed, are natural in origin and mainly associated with coarsesandstones with steep slopes irrespective of dense vegetation cover.

6. Conclusion

This study demonstrates that remote-sensing-based parameters evaluated with thehelp of GIS techniques play a significant role in precise LSZ mapping. The correctedSRTM data adequately substantiate the geomorphological and hydrological param-eters for the study. The landslide index method with a parametric approach is bestsuited for LSZ. With the help of remote-sensing data and GIS techniques, it is aneasy task to handle multiple parameters to calculate the landslide index and to pre-cisely work out susceptibility zonation on a much more broader scale for land-useplanning and risk mitigation. The outcome of the study in Igo River Basin may beuseful for decision-makers to prioritize LSZs for future loss-reduction and risk mit-igation. The study of Igo River Basin using the landslide index method reveals thatthe geological structure, slope and relief have much more bearing on the landslidesthan the much debated anthropogenic activities. The occurrence of high, very highand extremely high LSZs in the undisturbed dense forest and moderately dense forestadequately testifies this fact. The sandstones and other unconsolidated materials ofSub-Himalaya are more susceptible to landslide, especially in high elevations, strongrelief areas and steep slopes. Undoubtedly, the jhum cultivation and deforestationalong the hillslopes, to a certain extent, also assist in triggering landslides. The over-all results of validation through random field checks in high, very high and extremelyhigh-susceptibility zones indicate a positive success rate of this remote-sensing-basedlandslide zonation method with slightly varying causes of origins while the tectonicactivities and geological structures are observed as the predominant causes through-out. Since the method is purely parameter based without any relative importanceto more dominating factors, this study suggests that parameters must be selectedwith adequate care. The causative parameters should necessarily be area specific andshould be based on pre-conceived ground information. This approach of landslide-susceptibility zonation with the help of remote-sensing data is a useful method for therapid assessment of landslide risk especially in inaccessible Himalayan areas that areconsidered seismically and tectonically active and geologically and ecologically fragile.Use of remote-sensing data and DEM with higher spatial resolutions is desirable for amore precise result.

AcknowledgementsThis article is a part of the PhD thesis of Mr J.S. Rawat under the Supervisionof Prof. R.C. Joshi, Dean, Environmental Sciences, Rajiv Gandhi University, RonoHills, Itanagar, Arunachal Pradesh, India. Authors are thankful to the Department ofScience & Technology, Ministry of Science and Technology, Government of India, for

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providing a Senior Research Fellowship to Mr Rawat in a research project entitled ‘AnAssessment of Soil Loss using GIS’, which facilitated the accomplishment this PhD.

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