Original Article

Landslides (2006) 3: 3950DOI: 10.1007/s10346-005-0005-0Received: 21 September 2004Accepted: 24 May 2005Published online: 17 January 2006 Springer-Verlag 2005

Hans-Balder Havenith Alexander Strom Fernando Caceres Eric Pirard

Analysis of landslide susceptibility in the Suusamyr region, TienShan: statistical and geotechnical approach

Abstract The Suusamyr region is located in the northern part ofthe Tien Shan Range in Central Asia. In 1992, this region was hit bythe Ms = 7.3 Suusamyr earthquake triggering several large landslidesalong the Suusamyr Valley and on the southern slopes of the adjacentSuusamyr Range. One of these landslides had been investigatedby geophysical and geotechnical methods in order to determinelocal trigger factors. The present paper focuses on the influence ofgeological and morphological factors upon landslide occurrence ona regional scale. The analysis is based on a digital data set includinglandslides triggered in 1992 and several older landslides as well asvarious types of digital elevation models (DEMs), ASTER image data,and geological and active fault maps. These data were combined tocompute landslide susceptibility (LS) maps using statistical methods,Landslide Factor and Conditional Analyses (LFA, CA), as well as ageotechnical one, the Newmarks Method (NM). The landslide dataset was also analyzed with respect to the sizefrequency relationship.

Keywords Landslides . Susceptibility . GIS . ASTER image .

Suusamyr . Kyrgyzstan

Abbreviations LS: Landslide Susceptibility . LFA,(M)CA: Landslide Factor Analysis, (Modified) ConditionalAnalysis . NM, ND: Newmarks Method, Newmark Displacement .

PP: Predictive Power . Sc (Lb): map-scaled Scarp (Landslide body)area density . UCU: Unique Condition Unit . FS: Factor of Safety

IntroductionIn the frame of the European project (19972000), Landslide Risktriggered by Earthquakes in Kyrgyzstan, Tien Shan, some landslidesin northern Kyrgyzstan had been investigated by geophysical andgeotechnical methods to determine local landslide trigger. The entirestudy also included dynamic modeling and slope stability computa-tions to analyze the dependency of slope failure initiation on ground-water pressures, material shear strength, and seismic factors, such asground motion amplification (Havenith et al. 2003).

The present work was carried out to study the spatial influence ofgeological and morphological factors upon landslide occurrence ona regional scale: the mapping of landslide susceptibility (or poten-tial as in Gritzner et al. 2001). The basic principle of this approachis that under similar environmental conditions, the spatial distribu-tion of past and recent slope-failures is the key for predicting slopemovements in the future (Kuchai 1975; Carrara et al. 1995).

LS mapping has become very popular since the development ofefficient spatial analysis tools available with any GIS software. An im-portant milestone in the application history of statistical LS analysesis the work published by Carrara et al. (1995). It provides a criticalreview of almost all relevant existing statistical methods. Anotherprominent study is the one by Guzzetti et al. (1999), which evaluatesthe efficiency of various methods by comparing the results with each

other. Among the various available statistical methods, we have cho-sen one generally referred to as Conditional Analysis (CA). We alsotested a modified version of this method based on a simple LandslideFactor Analysis, called here Modified Conditional analysis (MCA).An example of successful application of the CA is the assessment ofLS in the Northern Apennines carried out by Clerici et al. (2002).

Statistical-probabilistic methods are probably the most commonlyused techniques, but other also proved to be valuable tools, suchas the application of neural networks (Fernandez-Steeger 2002; Leeet al. 2004), fuzzy sets (Ercanoglu and Gokceoglu 2002), and phys-ical or process-based models to LS mapping. The latter, also calledgeotechnical methods, are generally applied if a particular trigger fac-tor is taken into consideration, such as soil wetness (Vanacker et al.2003) and seismic effects (Khazai and Sitar 2000). In addition to theConditional Analysis, we will apply a process-based technique, theNewmarks Method (NM), to LS mapping in the Suusamyr area andcompare the produced maps with each other on the basis of theirrespective Predictive Power (PP). One goal of this study is to evaluateon the basis of this PP which method is the most efficient in providingpredictions for large areas, such as the entire Tien Shan Range.

Target areaThe Tien Shan is a Cenozoic orogenic belt in Central Asia with aneastwest extension of about 2500 km and a maximum width ofmore than 500 km. The structure of the Tien Shan is characterizedby alternating, roughly eastwest trending mountain ranges and in-termountain basins often bounded by neotectonic faults. This is alsothe case for the northern part of our study area, the Suusamyr Basin(20002500 m a.s.l.) filled with Neogene (siltstone and claystone) andQuaternary (alluvial and glacial deposits) sediments (Fig. 1). Activefault zones delimit the basin from the high-mountain ranges (35004800 m a.s.l.) made of Paleozoic granitic, sedimentary, or volcanicrocks (Fig. 1c). The entire target region also includes a part of theNaryn valley (Figs. 1c and 2a).

In 1992, the Aramsu fault located in the west of the basin rupturedand produced a Ms = 7.3 event (main fault scarp location shownin Fig. 1c). Relatively few slope failures were triggered by this earth-quake, such as the Belady rock avalanche and associated debris flowshown in Fig. 2a (location in Fig. 1c). During the same event, severallandslides were triggered or re-activated on the southern slope of theChetKorumdy ridge and partly destroyed the BishkekOsh highway(Fig. 2c).

Input data and preprocessingThe first data set mainly included information that could be ob-tained free of charge, such as the NOAA (National Oceanic andAtmospheric Administration) and the SRTM (Shuttle Radar To-pographic Mission) DEMs as well as LANDSAT 5 and 7 ETM+

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images. The low resolution of the NOAA DEM (about 1 km)clearly revealed to be insufficient to analyze surface morphologyat regional scale. Hence, for this study we only used the 90 mSRTM DEM. The LANDSAT 5 and 7 ETM+ images were used asbasis for georeferencing all collected data. Landslide bodies and scarpswere outlined separately on KFA-1000 and KFA-3000 images, somelocation controls were made with CORONA images (example ofzoomed CORONA image in Fig. 2b). Fault zones were mapped onthe basis of the same images and field observations. The recordedlandslides include all kinds of coherent failures in rock (rock slides)and earth slopes (debris and earth slides, slumps) as well as severalrock avalanches (e.g., Belady and Seit rock avalanches developed ingranitic rocks, in Fig. 2a and 2b). Rock falls and debris flows, such as

the one that developed from the Belady rock avalanche mass (Fig. 2a),were not mapped.

Recently, two ASTER images (sequential images of the June 9, 2001;Fig. 3) were acquired in order to get a better topographic and imageinformation. Indeed, ASTER images include 14 spectral bands, 3+1(1,2,3N+3B) in the VNIR (visiblenear infrared, 15 m resolution)domain, 6 in the SWIR (short-wave infrared, 30 m), and 5 in theTIR (thermal infrared, 90 m) domains, while LANDSAT images onlyinclude 6 bands, at lower spatial resolution in the VNIR and SWIRdomain. The 15 m 3N (nadir-looking) and 3B (backward looking)ASTER images in the VNIR domain provide a stereo-pair that can beused to construct reliable 30 m DEMs. The processing of the ASTERimages for DEM extraction and orthorectification of the images has

Fig. 1 a Map of Central Asia. b NOAA DEM of the Northern and Central Tien Shan Mountains.c UTM zone 43N-projected SRTM (light shaded colors) and ASTER DEM (dark shaded colors,outlined) and digital geological map available for the target area, overlay of earthquakes (Ms

> 4, location of 1992 main shock is indicated), fault lines (brown), landslide body (white),and landslide scarps (black); locations of the Chet Korumdy ridge, the Belady and Seit rockavalanches, and the main 1992 fault rupture are shown

40 Landslides 3 . 2006

Fig. 2 a In 1992 earthquake-triggeredBelady rock avalanche and associated debrisflow (aerial photograph 1996). b PrehistoricSeit rock avalanche with scarp andaccumulation outlined (CORONA image1968). c In 1992 earthquake-triggered orre-activated landslides on the southern slopeof the Chet Korumdy ridge (Photograph of1998). Locations are indicated in Fig. 1c

Fig. 3 Mosaic of two ASTER images (limitmarked by dashed line), 3-2-1 VNIR bands,with overlay of scarp (red) and landslide body(yellow) outlines as vertical view (a) andperspective views from the lower Suusamyrvalley towards the NW (b) and from thewestern Suusamyr Range towards the E (c)

been carried out with the PCI Geomatica 8.0 Orthoengine software. Adetailed description of the DEM generation within this PCI softwareis given by Al-Rousan et al. (1997).

After extraction of the original DEMs, the latter had to be cleaned:first, artifacts such as unrealistic peaks or holes (generally conic inshape) were manually removed; second, a median filter with 55 cellwindows was applied.

Finally, the processed ASTER DEMs and the images were mergedto produce a mosaic. The ASTER DEM mosaic and the 90 m SRTMDEM of the Suusamyr region are shown in Fig. 1, overlaid is also thegeological map (with SRTM hill-shading) that had been digitized fora rectangular area including the Suusamyr Basin and adjacent ranges.

Spectral information included in the various bands ASTER imagesthat can be extracted by different processing techniques, e.g., by

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Original Article

Fig. 4 Frequency density function forlandslide areas in the Suusamyr region

computation of ratios between the spectral bands. We principallyapplied the Principal Component (PC) transformation. For theLS analysis we used the three first Principal Component imagesextracted from the combination of the 3 VNIR and 6 SWIR bands ofthe ASTER image mosaic.

Landslide sizefrequency relationshipLandslides were first analyzed with regard to the size(cumulated)frequency distribution, considering the landslide body area as sizeunit since information about landslide volumes is generally notprovided. It is certain that the use of areas instead of volumes willaffect all the following analyses. In particular, the size of rock slideswith a relatively large volume will be underestimated compared tothe size of landslides in soft sediments characterized by volumes thatmay be significantly smaller (for a similar area). This aspect will beconsidered in the final discussion.

The sizefrequency relationship of landslides (including also rock-slides) were computed using the method suggested by Malamud et al.(2004) and analyzed in terms of the Frequency Density Function (f)of the landslide areas (AL Eq. (1)):

f (AL) = NLAL

(1)

where NL is the number of landslides with areas between AL andAL+AL.

In a loglog graph (Fig. 4), this function shows the typical behaviorof natural event records (earthquakes, landslides, etc.) characterizedby a more or less linear tail (in loglog graph) for the large sizeswhich can be fit by a power-law and a rollover for smaller eventswith decreasing frequency density for very small landslide areas. Therollover can partly be explained by undercounting of the small events(less accurate mapping) which can be taken into consideration byparticular distribution fits proposed by Stark and Hovius (2001).On the other hand, Malamud et al. (2004) noticed that several datasets verified as complete exhibit a rollover with a maximum fre-quency density for an area of about 400500 m2. Our data set revealsa maximum frequency density for about 7500 m2, which may in-dicate that the inventory is incomplete for landslides smaller than70008000 m2. Beyond an area of 100,000 m2 the sizefrequency

relationship can be fit by a power-law with an exponent of about1.94. This value is significant lower than those obtained for varioustypes of landslide distributions Stark and Hovius (2001) and Mala-mud et al. (2004), which are larger than 2.2. Till now we cannotexplain why our data set reveals a power law tail with such a lowexponent.

Creation of GIS platformAs GIS tool we mainly used the Arcview 3.2 software and in particularits Spatial Analysis extension. The main part of the processing wascarried out with data in grid (raster) format; hence, the mappingunits are the pixels with a size depending on the involved data (90 mif SRTM DEM, 30 m if the ASTER DEM, 15 m if the PC images wereused). Vectors, such as scarp, landslide body, and fault outlines weretransformed into grids according to the following procedure. First,grids of distances to each feature were computed; in the case of thescarp outlines, the values of pixels within 100 m around the lineswere set to 1, the others to 0 (i.e., 100 m buffer around lines); inthe case of the landslide body polygons, a 50 m buffer (in additionto the landslide body area) was used. In the case of the fault zones,the computed Distance-to-Fault map was simply reclassified in 5distance-units (see Tables 1 and 2).

On the basis of the DEMs, three morphological factor maps werecomputed on the basis of a moving 33 cell window: the slope angle(SLOPE), slope aspect (ASPECT), and surface curvature (CURVA-TURE) maps. This processing combined with the previous medianfiltering introduces significant smoothing of the DEM characteristics,which needs to be taken into consideration for the following analyses.

While only one method is available for the computation ofslope angles and aspects with the standard Spatial Analysis tool for

Table 1 Geological-tectonic factors and classifications used for the LS analysis (GEOLOGYmap extent)

Geology lithology Sc Lb Fault distance (m) Sc Lb

Quaternary 1.02 1.27 0500 4.37 4.20Neogene 4.76 4.88 5002000 2.16 2.57Pal. granite 0.78 0.62 20005000 1.15 0.99Pal. sediments 0.51 0.35 500010000 0.56 0.46Pal. volcanic 0.00 0.00 1000030000 0.17 0.27

42 Landslides 3 . 2006

Table 2 Morphological factors and spectral information used for the LS analysis (ASTER mosaic extent)

Slope () Sc Lb Aspect () Sc Lb CURVa (1/100m) Sc Lb PC 1 Sc Lb PC2 Sc Lb PC3 Sc Lb04 0.38 0.54 030 1.42 1.52 38 0.32 0.13 270300 0.56 0.67 3040 1.39 0.67 10 0.24 0.08 10 0.26 0.07 10 0.61 0.66

300330 0.65 0.83 4050 1.24 0.41 11 0.12 0.06 11 0.19 0.07 11 0.37 0.20330360 1.02 1.15 5060 1.06 0.26 12 0.08 0.19 12 0.08 0.19 12 0.29 0.14

>60 0.92 0.22aCurv is the tangent curvature (1000 to avoid decimals), i.e., in inverse proportion to the radius of the curve adjusted to the earth surface at the pixel location ( is concave and + is convex),with values multiplied by 100 to reduce the number of decimals; Note, density values are enhanced by grayscale (light gray to black: 2.4), per unit the largestdensity is underlined; PC is Principal ComponentbSc (Lb) is the map-scaled scarp (landslide body) area density

Arcview, the CURVATURE can be calculated in terms of plan, profile,or tangent curvature. We considered the latter type as most usefulfor our study since it combines the characteristics of the two firstones which are both relevant to landslide susceptibility analyses, i.e.,predisposition to flow convergence or divergence and to erosion ordeposition. We found only a few studies including the CURVATUREas potential landslide susceptibility factor, generally referring to theaforementioned hydrological, hydrogeological, or geomorphologicalcharacteristics of the curvature (Gritzner et al. 2001; Ayalew et al.2004; Lee et al. 2004). In this paper, also other possible effects of thismorphological factor (often neglected) will be discussed.

The images of the three first Principal Components computed fromthe merged ASTER data were added to the GIS platform as 15 m grids.All factor classes are summarized in Tables 1 and 2.

Landslide factor analysisThe Landslide Factor Analysis, the direct correlation between factorclasses and landslide distribution, is commonly applied as firstapproach to evaluate the environmental dependencies of landslideoccurrence. For this study, landslide occurrence was divided intolandslide scarp location and landslide body position in order todistinguish between the effects of the factors on the detachmentfailure and on the mass movement (and accumulation). It shouldbe noticed, that most LS studies do not consider such a separation(Dai and Lee 2002; Ayalew et al. 2004; Lee et al. 2004 among others)or do not explain their use (Clerici et al. 2002). Another possibledistinction can be made between the area within the landslidescar and the area surrounding it, which is more representative forprefailure conditions. This approach has been applied by Suzen andDoyuran (2004) to the LS mapping in Turkey by introducing the useof Seed-cells around the landslide. In this first general study, it wasdecided that not only prelandslide conditions should be consideredfor the LS analysis but also those of developing and ancient landslidessince they contribute to the overall future landslide potential (e.g., byre-activations). Thus, by buffering the features, both prelandslide (of-ten more than 50% of landslide scarp area) and landslide conditionsare taken into consideration by our analysis. A more detailed work isplanned to evaluate LS only on the basis of prelandslide conditions.

The Landslide Factor Analysis is based on the computation of scarp(and landslide body) area densities within a certain factor class (orunit). The analyses were carried out over two different map extents(Geological map and ASTER mosaic, shown in Fig. 1c) and, in order tobe comparable, the densities were scaled to the map extent accordingto the following Eq. (2):

Sc(Lb) = buffer class countsclass counts

map countsbuffer counts (2)

where Sc (Lb) is the map-scaled scarp (landslide body) area den-sity, buffer-class-counts the number of pixels of the same class (=Unique Condition Unit, see next paragraph) within all scarp (land-slide body) buffers, class-counts the number of all class (UCU) pixels,map-counts the number pixels within the map extent, and buffer-counts the number of pixels within all scarp (landslide body) buffers.Thus, the first factor is the simple density of class pixels within alllandslide scarp (body) buffers and the second, the inversed total den-sity of landslide scarp (body) buffers within the map. The densityof all buffers of one type within a certain map extent is a constant;for the scarps it is 0.78 and 0.88% within the GEOLOGY map andASTER mosaic extent, respectively; for the landslide body, it is 1.04and 1.07% within the GEOLOGY map and ASTER mosaic extent,respectively.

The results of the LFA are presented in Tables 1 and 2. Five factorshave been included, one geological, one tectonic (Table 1), and threemorphological (Table 2). Further, landslide distributions were spa-tially correlated with the PC images (Table 2). The latter are not con-sidered as factors (direct influence upon slope stability), but rather asadditional information about particular earth surface characteristics.Indeed, it can be supposed that landslide prone areas are marked by aparticular reflectance related to the morphology and type of surfacematerial (e.g., weathered rocks), to hydrogeological conditions (in-creased wetnesspresence of vegetation), etc. These conditions maypresent a spatial variation, which is not characterized by the formerfactor types (e.g., general geological map without any structural orhydrogeological or geotechnical information).

From the Tables 1 and 2, it can be observed that landslide occurrenceis most strongly depending upon geological and tectonic factors.

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Indeed, the densities of scarps and landslide bodies are particularlylarge within the Neogene unit and in close vicinity to the faults. ThePaleozoic rocks are clearly less prone to landslide occurrence thanboth the Neogene and Quaternary sediments.

Factor-to-factor correlations and the map-fault zone overlay inFig. 1c show that the presence of Neogene sediments at the sur-face and the location of faults are spatially connected. Thus, at thisstage of the analysis, it cannot be defined, which of the two fac-tors has the strongest influence on landslide occurrence. On onehand, the clayey material with lower shear strength may explain thelarger LS within the Neogene; on the other hand, reduced shearstrength and enhanced seismic hazard (rupture, seismic source ar-eas, seismic wave trapping) close to the faults also favor landslideoccurrence.

The morphological factors generally have a minor effect on thelandslide and body location. Among them, the SLOPE is generallyconsidered as the most important factor since larger slopes inducelower slope stability. However, here, even though the SLOPE ap-pears as the most important morphological factor (largest densityvalues), landslide body and scarp occurrence is not correlated withlarge but with relatively small slope angles. A similar paradox cor-relation has already been pointed out by Ayalew et al. (2004) andClerici et al. (2002). As those authors, we think that the preferentiallocation of landslides and their scarps within zones marked by smallslope angles is likely to be related to a factor-to-factor spatial rela-tionship, the interdependency between slope angle and geological-tectonic factors. Indeed, numerous landslides and scarps formedwithin Neogene sediments (or close to faults), which are markedby milder slopes than areas within Paleozoic rocks (less prone toslope failure). Thus, the milder slopes can certainly be not consid-ered as cause for enhanced landslide potential but result themselvesfrom the presence of weaker geological materials prone to failure.ASPECT and CURVATURE are (almost) not spatially correlated withany of the two geological-tectonic factors. Their respective influ-ence has to be explained by other interactions with environmentalconditions.

The influence of the ASPECT upon LS is generally explained bystructural (rock foliation, orientation of bedding) and climatic (com-mon wind direction) factors. For our region, such information is notprovided. Thus, it cannot be quantitatively explained why landslidesare preferentially located on slopes oriented to the NE. Consideringthe climatic factor, it can be supposed, however, that the dependencymay be related to increased wetness on NE slopes due to larger snowaccumulations in winter (preferred westerly winds) and slower melt-ing in the shadows in spring time.

The CURVATURE has apparently the weakest effect on slope sta-bility but it is interesting to notice that, contrary to the former cases,preferred scarp and landslide body locations do not spatially correlatewith the same class of this factor. Landslide bodies are preferentiallylocated within slightly concave and scarps within slightly convexareas. Since the distinction between scarps and accumulations is gen-erally not made, this difference has not yet been outlined accordingto our knowledge (Clerici et al. 2002 did not include the CURVA-TURE in their analysis). The discrepancy needs to be explained bytaking into account various environmental factors. Those generallyput forward and mentioned above are related to geomorphologicaland hydro(geo)logical aspects (Ayalew et al. 2004; Lee et al. 2004).These can explain why landslide bodies accumulate in concave zonesclassified as depositional areas (for sediments and convergence ofwater) but not why scarps form on convex slopes characterized by flow

divergence and deeper ground water table. We think that there arevarious physical reasons for this observation based on geomechanicand seismic aspects. First, under similar hydrogeological conditions,convex slopes are less stable (lower Factor of Safety) since a larger body(larger driving force) acts on the same sliding surface (equal resistantforces). Second, convex morphologies may indicate the presence ofaccumulation material (colluvium) characterized by lower shear re-sistance. Third, convex morphologies as ridge crests and hilltops areaffected by ground motion amplification effects, which may have animpact on slope stability (Havenith et al. 2002). The morphology hasa direct effect on the amplification (surface convexity induces seismicwave convergence) or indirect effects due to the presence of weath-ered material on hilltops. Hence, it is not surprising that landslidesare commonly observed on convex slopes within areas affected byseismic shocks (Harp et al. 1981; Durville and Meneroud 1982 amongothers). Now, it still needs to be explained why zones marked by verystrong convexity (very narrow ridge crests) are not prone to slopefailure. One possible reason (which, however, cannot be proved) isthat these morphologies mark the presence of very strong materialamong zones characterized by weaker materials. A similar correla-tion was put forward by Ayalew et al. (2004). This study and theone by Gritzner et al. (2001) reveal a bimodal relationship betweenlandslide occurrence and CURVATURE. The doubled maximum, forslightly concave and for slightly convex zones, could be explained bythe combined effect of the concave and convex morphology on thelandslide body deposition and detachment not distinguished in thecited works.

The interpretation of the previous statistical analysis cannot begeneralized for all types of landslides, such as rock falls, which arenot included in our study. Further, it should be reemphasized that theslope angle and curvature maps are affected by significant smoothing;hence the slope angles and curvature prone to failure or depositionare likely to be somewhat larger in reality. From field surveys we knowfor example that the upper part of the southern Chet Korumdy ridgeslope has a 3034 slope angle where the ASTER and SRTM DEMspresent a 2028 slope.

Conditional analysis

Original conditional analysis (CA)The principles of the Conditional Analysis (CA) are well outlined inCarrara et al. (1995) and graphically represented in Clerici et al. (2002).Basically, it consists in subdividing the entire area in units character-ized by a specific combination of environmental conditionstheUnique Condition Units (UCUs). In practice, we applied the com-bine function to several factor-grids to create one single grid includ-ing all the information of the former grids. Thereby, each pixel of theresulting grid belongs to a specific combination of the involved factor-classes (units), a UCU referred to by a new index. In order to evaluatethe landslide susceptibility of the UCU, we computed the map-scaledscarp (landslide body) area densities for each UCU according toEq. (1). Since the combination of even a few factor-grids may result ina grid with thousands of UCUs, some studies (Carrara et al. 1995) sug-gest applying neighborhood majority filtering to exclude small mean-ingless UCUs. This approach was also tested, but it was found thatfiltering computations strongly reduce the efficiency of this methodby increased processing-time and by requiring additional classifi-cation (introducing subjectivity). Hence, the results shown in thefollowing were obtained without intermediate filtering. The disad-vantage of not including filtering is that the number of UCU produced

44 Landslides 3 . 2006

by combinations of many factor classes is limited by computationalconstraints.

Modified conditional analysis (MCA)In addition to the original CA, we also applied a modified typeof the CA (called here MCA). It is based on the results of previouslandslide factor analysis. The map-scaled densities obtained for theinvolved factor-classes are summed up and averaged for each pixel.The resulting map is reclassified (using Natural Breaks or StandardDeviation options) and per class the map-scaled density of scarps(landslide bodies) is computed to obtain the final LS map.

With each method, more than 10 grid-combinations were com-puted, at least five for each map extent (GEOLOGY map and ASTERmosaic). The most relevant results are in Table 4, summarized afterthe next paragraph, in terms of maximum map-scaled densities andthe respective count (number of pixels of the UCU) obtained for eachmap, and in terms of predictive power of the LS map. We defined thePredictive Power (PP) by the following Eq. (3):

PP = averageofthe10%ofthemostsusceptibleUCUaverageofthe90%oftheleastsusceptibleUCU

(3)

Note that this comparative study was only carried out for the scarps;hence the susceptibility of a UCU is here defined as the map-scaleddensity of UCU within scarps (Sc).

Geotechncial approach

Newmarks methodAs geotechnical or process-based model we applied the simplifiedNewmark approach to the Suusamyr region within the GEOLOGYmap extent. This method is commonly used to evaluate LS in seis-mically active regions since, in some cases, it was shown that suchgeotechnical models can successfully predict failure potential (Jibsonet al. 1998; Miles and Ho 1999); however, in others, a clear mismatchbetween computation results and landslide occurrence was observed(Khazai and Sitar 2000).

The original Newmarks Method (Newmark 1965) is based on asimple model of a block sliding on an inclined plane. It aims atcomputing the distance of sliding during the seismic shaking (i.e.,coseismic sliding), the Newmark Displacement (ND). The NewmarkDisplacement is computed from an acceleration time history by in-tegrating twice the values larger than the critical acceleration (thethreshold acceleration required to initiate sliding). For a detailed de-scription of the procedure applied to GIS we refer to the works ofJibson et al. (1998) and Miles and Ho (1999). Here, the method willonly roughly be outlined. It consists of a simplified computationscheme based on Eq. (4) (Miles and Ho 1999) by replacing the labo-rious integration over an accelerogram. By this equation, NewmarkDisplacements are directly calculated from Arias Intensities (Ia, Arias1970) and critical accelerations (ac).

log(ND) = 1.46 log(Ia) 6.642 ac + 1.546 (4)

with ND in (cm), Ia in (m/s), and ac in (m/s2).Arias Intensity maps were created in several ways. In most cases

we used the map of probabilistic seismic hazard (see Abdrakhmatovet al. 2003) computed for this region both in terms of peak-groundacceleration and Arias Intensity values (here, a period of 50 years

with a probability of nonexceedance of 90% was considered). For thecomputation of the second type of map, we apply the (relatively free)assumption that all faults within the GEOLOGY map extent wereat least once seismically activated (but not necessarily ruptured).Arias Intensity values produced over the area by the shocks and theattenuation of the seismic ground motion were computed using theempirical Arias Intensity attenuation relationship (Eq. (5)) proposedby Wilson and Keefer (1985) and valid for earthquake magnitudes lessthan 7:

logIa = 4.1 + M 2logR + 0.5P (5)

where M is the earthquake magnitude (here Ms), R the hypocentraldistance (here, the distance to the mean fault rupture depth), P is aprobability term including possible variations and uncertainty, hereset to 0. The computations were carried out for various magnitudes(5.47) and rupture depths (515).

The critical acceleration value (ac) in Eq. (4) is evaluated on the ba-sis of the factor of safety (FS), both computed by means of simplifiedequations suggested by Jibson et al. (1998) and Miles and Ho (1999).This calculation involves morphological (slope angle) and geotechni-cal input data (shown in Table 3) and was only carried out for slopeslarger than 4 and if the obtained FS value is larger than 1 (to excludestatic instability leading to negative critical acceleration values). Thegeotechnical input includes shear strength parameters (cohesion, c,and internal friction angle, ), the specific gravity of the material()) and water, the thickness of the potential sliding mass (t) and theproportion of the thickness, which is saturated (m). The respectivevalues are assigned to each unit-pixel of the GEOLOGY map. First es-timates of the shear strength parameter and specific gravity values ofthe Paleozoic rocks are based on data presented from Hoek and Bray(1981), which can be considered as representative for such materials.The parameter values assigned to the Quaternary and Neogene sed-iments have been directly measured on samples taken from trencheson the Chet Korumdy ridge (see Havenith et al. 2000). For the wholemap, the thickness of the potential sliding layer (t) has been fixed at10 m; the proportion of thickness that is saturated (m) was either setconstant for the whole map or varied over some units (see Table 3).The values marked by(1) are the first estimates, those marked by(2)

were determined after the Landslide Factor Analysis: principally, theshear strength of the Neogene sediments which proved to be prone toslope failure and the water table depth in these materials were bothreduced (lower c, and larger m-value); conversely, the shear strengthvalues of the obviously less susceptible Paleozoic rocks (especially thevolcanic rocks where no landslides occurred) and the water tabledepth were increased (larger c, and smaller m-value).

Unlike the maps produced by CA and like the maps produced bythe MCA, the ND maps (Fig. 5f and g) directly provide an indicationof LS. Ideally, larger NDs should indicate a higher LS (Jibson et al.1998). A correlation between ND classes (using the probabilistic Iamap) and the respective map-scaled densities of scarps (Sc) insidethese classes is shown in Fig. 6. This graph reveals that larger NDscalculated on the basis of the probabilistic Ia map and first estimates ofinput data (curve ScND1) do not predict larger Sc-values, while esti-mates taking into consideration the statistical results provide a better(yet not perfect) correlation between increasing NDs and S-values(ScND2).

The same graph also presents the correlation curve betweenSc and ND calculated by using the map of Ia attenuation from(all) faults for a Ms = 6.6 seismic event with a hypocenter depth

Landslides 3 . 452006

Original Article

Table 3 Geotechnical parameters used forFS and ND computations

Geology lithology c1 (Mpa) c2 (Mpa) 1 () 2 () (kg/m2) t (m) m1 m2Quaternary 0.02 0.02 26 24 2100 10 0.5 0.5Neogene 0.07 0.04 24 18 2500 10 0.5 0.9Pal. granite 0.10 0.10 34 42 2600 10 0.5 0.2Pal. sedim 0.05 0.15 32 40 2600 10 0.5 0.2Pal. volcanic 0.05 0.15 27 50 2800 10 0.5 0.2

ac, are the cohesion and internal friction angle values; is the specific gravity of the material, t is the thickness of the potential sliding mass,and m the proportion of the thickness which is saturatedb1,2 denote, respectively, initial parameter values and values modified after Landslide Factor Analysis

Fig. 5 Comparison between eight different maps of LS within the northern Suusamyr Regionin terms of map-scaled scarp densities (yellow-orange-red-violet Sc-scale) and NewmarkDisplacements (yellow-green-blue-violet ND-scale in cm, pink areas represent zones with FS