Landslide hazard assessment in the Three Gorges area, China, using ASTER imagery: Wushan–Badong

Geomorphology 84 (2007) 126 – 144 www.elsevier.com/locate/geomorph

Landslide hazard assessment in the Three Gorges area, China, using ASTER imagery: Wushan–Badong I.G. Fourniadis a,⁎, J.G. Liu a , P.J. Mason b a

Department of Earth Science and Engineering, Imperial College of Science, Technology and Medicine, Prince Consort Road, London SW7 2AZ, UK b HME Partnership, P.O. Box 3036, Romford RM3 0EY, UK Received 23 May 2006; received in revised form 27 July 2006; accepted 27 July 2006 Available online 26 September 2006

Abstract The objectives of this study are to develop a methodology for regional scale assessment of landslide hazard using remotely sensed data, and to produce a landslide hazard map of the Wushan–Badong area in the Three Gorges, China, from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The area is undergoing rapid urban development, with slope instability causing a widespread natural hazard. Landslide-related parameters were largely estimated through image processing and visual interpretation of ASTER multi-spectral and topographic data. We used field measurements to define the quantification for three parameters: (a) slope angle in relation to lithology; (b) distance to drainage network in relation to stream order; and (c) distance to tectonic lineament in relation to lineament length. We employed a multi-parameter elimination and characterization model, based on estimation of the geometric mean, to remove areas where landslides are not expected to occur, and classify the remaining areas into landslide hazard categories. Our results show increased landslide hazard in and around Wushan and Badong Towns, as well as other populated areas along the Yangtze and its tributaries. We used field data on landslide distribution to identify typical geomorphological settings for different landslide types, and to provide ground control for the hazard assessment. The results indicate good correlation between classified high-hazard areas and field-confirmed slope failures, and show the usefulness of ASTER imagery for landslide hazard assessment at a regional scale. © 2006 Elsevier B.V. All rights reserved. Keywords: Advanced Spaceborne Thermal Emission Radiometer (ASTER); Slope instability; Landslides; Three Gorges; Hazard; China

1. Introduction The Three Gorges Project (TGP) is located on the Yangtze River in Hubei Province, China. Despite the benefits of the 181 m tall dam in terms of power generation and flood control, the TGP has attracted attention for its potential impact on ecosystems and ⁎ Corresponding author. E-mail address: [email protected] (I.G. Fourniadis). 0169-555X/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2006.07.020

socio-economic stability. Slope instability is responsible for the most widespread TGP-associated natural hazard, with hundreds of known landslides in the TGP region— some in a state of active deformation (Chen and Cai, 1994; Liu et al., 2004). Slope instability links the geological and socio-economic environments: after reservoir impoundment and the associated resettlement to higher ground, the frequency and magnitude of landslides are expected to increase through the reactivation of old landslides and triggering of new ones. The terrain in the TGB region is predominantly rugged, with

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only small patches of land suitable for settlement and cultivation. The hazardous areas tend to occur on outcrops of less resistant material; it is on those more problematic areas that this study is concentrated. Slope instability threatens numerous towns along the Yangtze and its tributaries. The main focus for this study is landslide hazard in and around Wushan and Badong Towns. These regions have been experiencing intensive resettlement of residents due to the dam's operation that has led to increased instances of slope failure. We have used ASTER imagery to estimate a number of environmental parameters which we consider related to slope instability. We have used a geometric meanbased model to integrate the spatial datasets and derive a landslide hazard index. Field investigation indicates a good correlation between high-hazard areas as predicted by the model and locations where slope failures have occurred. This work demonstrates the uses of ASTER imagery for landslide hazard assessment at a regional scale, especially in areas where geological and topographical data are not readily available.

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2. The study area in the Three Gorges region and the data The Three Gorges lie along the middle reaches of the Yangtze River, in the mountains separating the Sichuan and Jianghan Basins. The gorges are thought to have been formed by river incision of massive limestone mountains of Lower Palaeozoic–Mesozoic age in response to episodic tectonic uplift during the Quaternary (Li et al., 2001). Elevation ranges from 800 m to 2000 m a.s.l., and the terrain comprises a succession of limestone ridges and gorges, with inter-gorge valleys where interbedded mudstones, shales, and thinlybedded limestones predominate. Failure-prone lithological formations where landslides tend to occur are concentrated in the inter-gorge valleys. The study area is between Wushan County of Chongqing Metropolitan City, and Badong County of Hubei Province (Fig. 1). Wushan County Town (Lat: 31°05′, Long: 109°48′) lies at the start of the Wu Gorge, and the confluence of the Yangtze and Daning Rivers.

Fig. 1. Geographical setting of the study area. The Yangtze River traverses the area, flowing over the county boundary from Wushan (west) to Badong (east). A: location of Fig. 3. B: location of Fig. 6.

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The new county town of Badong (Lat: 31°03′, Long: 110°18′) lies at the Xirangpo location of the Badong Block, 8 km west of the old Badong County Town that was flooded when the dam reservoir's water level reached 135 m in June 2003 (Liu et al., 2004). 2.1. Geology and tectonic setting The geology of the area consists of two major components: a pre-Sinian crystalline basement, and a Sinian–Jurassic sedimentary cover (Wu et al., 2001). The former is composed of magmatic and metamorphic rocks, and outcrops only sporadically in the area. The latter is widespread and comprises interbedded carbonate, sandstone and shale formations (Fig. 2). Regional geological structures dominantly trend NE–SW, and are associated with major anticline–syncline fold systems. The numerous fault zones that follow the NE–SW orientation of these fold systems tend to form weaker

zones of tectonically-stressed rock with high slope instability. 2.2. Slope failure Slope failure is a recurrent problem in the Wushan– Badong region, with numerous large scale landslides occurring in recent years; examples include the Huanglashi and Huangtupo landslides (Deng et al., 2000; Wang et al., 2000). Shallow debris slides, rotational slides and translational slides are identified as the three main types of slope failure in the area. The massive urban development, required for relocating towns and villages to higher locations following the dam's operation, has been related to the triggering of several large landslides. Adequate geotechnical investigation has not always preceded the relocation process, however, often leading to the reactivation of landslides and the inevitable evacuation of newly built towns. Reactivation of the

Fig. 2. Regional geological and tectonic map. After Liu et al. (2004).

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Huangtupo landslide in the eastern study area, for example, forced the new county town of Badong to be relocated for a second time to Xirangpo. 2.3. Data 2.3.1. ASTER multi-spectral data ASTER is now an established tool for geological studies (Abrams, 2000) that represents an improvement over other commonly used image types like Landsat ETM+. Its high spatial resolution of 15 m in the visible and near infrared (VNIR) is superior to the 30 m provided by ETM+. Similarly, ASTER has a higher spectral resolution in the shortwave infrared (SWIR), with five bands covering the range 2.145 to 2.430 μm in comparison to two ETM+ bands in the range 1.55 to 2.35 μm. ASTER's imaging capabilities allowed us to estimate the majority of landslide-related parameters for our investigation through image processing and visual interpretation. For this study, we used an ASTER Level1B scene covering a 60 × 60 km swath around Wushan Town, acquired in February 2001. 2.3.2. ASTER topographic data ASTER's along-track stereo capability provides topographic data that are useful for geomorphological studies, mainly because of their high spatial resolution (30 m) and vertical accuracy (15–30 m), as well as their co-registration with ASTER's 14 multi-spectral bands. ASTER elevation data, however, have been known to give too low elevation values (Kamp et al., 2003), particularly in high-altitude areas like mountain peaks.

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To take account of this elevation bias, we have used elevation data from the Shuttle Radar Topography Mission (SRTM) to enforce vertical control on the ASTER DEM. SRTM, is considered the most complete topographic dataset of the globe (Rabus et al., 2003); it has a spatial resolution of 90 m, and a slightly higher vertical accuracy (± 16 m) compared to ASTER. We fused the two datasets through a simple algorithm that extracted the minimum and maximum elevation values from the SRTM data and calculated the remaining elevation values through an averaging of the ASTER and SRTM elevation data. The elevation profiles in Fig. 3 suggest that the ASTER-SRTM fused DEM thus produced have elevation values largely controlled by SRTM, while maintaining ASTER's higher spatial resolution of 30 m. This treatment removes the known ASTER DEM elevation bias for high peaks and deep valleys. 2.3.3. Geological data A 1:200 000 geological map (Hubei Province Geological Survey, 1965), covering the western part of the ASTER scene, provided information on the regional lithologic, stratigraphic, and tectonic setting. 3. Data processing and calculation of landsliderelated parameters 3.1. Model configuration Depending on data availability and scale of observation, numerous approaches exist for the generation of

Fig. 3. Example of elevation profiles for the ASTER DEM, SRTM DEM, and the fused ASTER-SRTM DEM. Elevation values from the fused ASTER-SRTM DEM have acquired the vertical accuracy of SRTM, while retaining ASTER's higher spatial resolution. The location of this section is shown in Fig. 1.

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a landslide hazard map (Hutchinson, 1995; Guzzetti et al., 1999; Mason and Rosenbaum, 2002; Ohlmacher and Davis, 2003). A study in the Badong area (Liu et al., 2004) indicated the merits of the geometric mean function in integrating landslide-related parameters for the regional assessment of landslide hazard. In the present study, we have used field evidence to evaluate the relation between landslide distribution and the spatial variation of different environmental parameters. This allowed us to (a) identify new parameters related to slope instability in the Three Gorges, and (b) adopt an improved quantification for the definition of parameter class threshold values and the assignment of weights. Table 1 presents the classification of landslide-related parameters, and the quantification of parameter classes. The improved model eliminates regions where landslides are not expected to happen, while classifying the remaining areas in categories of increasing landslide hazard. The geometric mean is defined as: GM ¼

n Y

!1=n ð1Þ

Pi

categories. Level-2 parameters likewise perform a hazard characterization for the entire study area, while Level-3 parameters, which were estimated through field investigation of high-hazard areas as identified in previous stages of the model, provide a more accurate estimation of hazard within high-hazard areas. The quantification of parameter classes in the geometric mean is equivalent to weighting in the arithmetic mean, with a high value increasing a variable's influence in the final landslide hazard index. A value of 3 is assigned to the less competent lithologies and steeper slopes; this is consistent with the importance of these two parameters with respect to instability, according to field observations and published sources (Wu et al., 2004). The maximum weight assigned to Level-2 parameters is equal to 2, and reflects their relatively lower significance in causing slope instability. Avalue of 1 leaves the geometric mean unchanged, while a value of 0 (reserved for Level-1 parameters) eliminates that area from further consideration. Our work shows the geometric mean-based model to be powerful in its simplicity and transparency of decision rules, and particularly well suited to multivariable hazard assessment at a regional scale.

i¼1

3.2. Level-1 parameters where Pi is the value for parameter class i, and n the total number of parameters. As seen from Table 1, Level-1 parameters have values from zero to three that enables them to perform both an elimination and a classification function. The multiplication-based geometric mean uses zero values to eliminate stable areas–those with gentle slopes or underlain by massive limestone–from further consideration, while the values from one to three characterize the remaining area into landslide hazard

3.2.1. Lithological stability Lithology is one of the most important parameters that control landslide activity (Hutchinson, 1995; Guzzetti et al., 1996; Matsushi et al., 2006; Margielewski, 2006). We have included lithology as a Level-1 parameter, where it eliminates areas underlain by competent formations while classifying the remaining area in lithological stability categories. A lithological

Table 1 Landslide-related parameters: classification and quantification Level-1: elimination of more stable areas Lithology

Massive limestones

Slope angle

Flat

0 Sandstones, shales, thin-bedded 1.5 Mixed layersa, Quaternary deposits 3 limestones 0 Gentle 1.5 Steep 3

Level-2: characterization of areas vulnerable to landsliding Dissection density Distance to drainage network Distance to lineaments Litho-stratigraphical relationship

Low Distant Distant Limestone over mixed layers

1 1 1 2

Intermediate Near Near Mixed layers over limestone

1.5 High 2 2 2 Otherwise

2

1

1.5 Same 2 Otherwise

2 1

Level-3: discrimination of hazard classes within high-hazard areas Bedding/slope: aspect Bedding/slope: angle a

Opposite Slope b Bedding

1 Normal 1 Slope N Bedding

“Mixed layers” refers to mudstone, sandstone and shale interbeddings.

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Fig. 4. Lithological formations classified on the basis of stability.

stability map was produced through visual interpretation of ASTER imagery coupled with field observations. We found the ASTER 3-2-1, 4-6-9, and 4-6-12 RGB band combinations to offer the most information for lithological discrimination and mapping, especially after they had been enhanced using the Direct Decorrelation Stretch (DDS) method (Liu and Moore, 1996). The 15 m spatial resolution of ASTER VNIR bands 1, 2, and 3 provided good geomorphological and structural detail, which allowed us to map lithological formations in greater detail than the published 1:200 000 geological

Table 2 Slope angle class intervals, defined on the basis of underlying lithology and field evidence Slope angle range (deg.) Class

Sandstones, Quaternary deposits, shales, thinmudstone/sandstone/shale bedded limestones interbeddings

Gentle 0–15 Intermediate 15–30 Steep N30

0–10 10–20 N20

Value

0 1.5 3

map. Lithological formations were grouped according to landslide susceptibility and classified into three stability-related broad categories (Table 1, Fig. 4). Quaternary deposits–including fluvial clay and gravel on stream terraces, as well as slope deposits– and mixed layers (mudstones, sandstones, and shales) are those lithologies most susceptible to landslides in the study area, with shallow debris slides being the dominant landslide type. These lithologies often form gentle slopes in the rather rigid mountainous region of the Three Gorges, which are favourable locations for settlement and cultivation. We classified these formations in the low stability category, and assigned them the value of 3, which gave them significant influence in the overall hazard classification (Table 1). Sandstones, shales and thinly-bedded limestones are the region's intermediately competent lithologies. These units exhibit a smaller relative landslide frequency than the mudstone group, and they tend to give rise to beddingcontrolled translational slides. We classified them as intermediately stable, and assigned them the value of 1.5. Massive limestone is the most stable formation in the area. Although limestone ridges may experience

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Fig. 5. Map of slope angle classified on the basis of underlying lithology.

Fig. 6. Water erosion at slope toe leads to retrogressive slumping. The landslide back-scarp (dashed line) is clearly visible, as the slide cuts backwards and threatens the property. The location of this photo is shown in Fig. 1.

I.G. Fourniadis et al. / Geomorphology 84 (2007) 126–144 Table 3 Distance to drainage network Drainage network buffer distance (m) Class/stream type

Yangtze River

Tributaries Minor streams

Value

Near Distant

0–500 N500

0–200 N200

2 1

0–100 N100

rock falls and topples, field evidence suggests that limestone slopes are not prone to the modes of failure under consideration by our model (i.e. shallow debris slides, rotational and translational sliding). Limestones were thus assigned a value of 0, and areas where they outcrop were eliminated by the multiplication-based geometric mean from further consideration. 3.2.2. Slope angle A slope angle map of the study area was created from the corrected ASTER DEM. We used field measurements to estimate critical slope angles for different lithologies like the preceding lithological mapping (see

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Section 3.2.1), and to divide the slope map into lithology-controlled stability classes (Table 2). This led to a classification whereby for the same slope class in terms of stability (i.e. gentle, intermediate or steep), threshold slope angles would vary depending on underlying lithology. Quaternary deposits and mixed layers are most susceptible to landslides: slopes with an angle below 10° are largely stable, whereas an increase to 20°+ could prove unstable. Slopes composed of sandstones, shales and thinly-bedded limestones are largely stable below 15° slope angle, and unstable above 30°. Massive limestones were eliminated from the slope angle classification, since they are the most stable formations and least prone to failure. The slope angle was classified into ‘Gentle’, ‘Intermediate’, and ‘Steep’ slope categories (Fig. 5). Instability phenomena were mainly concentrated in the Steep slope category, which was assigned the value of 3 (Table 2); this weight indicates the importance of slope angle in landslide hazard calculations, and is only matched by that assigned to the least competent lithologies in the area. Moderately steep slopes did

Fig. 7. Buffer map of drainage network.

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resolution of the ASTER-SRTM DEM, however, is not sufficient for the delineation of these features. Unlike the uniform classification of slope angle by Liu et al. (2004), in this study we defined different critical slope angles for the initiation of instability phenomena in slopes of different lithologies. This lithology-controlled classification goes some way towards redressing the issue of increased landslide susceptibility in gullies and smaller valleys formed on slopes of softer material (see Table 2), although a DEM of higher resolution would still be required for the direct detection of these features. Fig. 8. Histogram of dissection density. Classification performed via the Natural Breaks method.

experience failure when other geomorphological factors converged, and thus were assigned the value of 1.5. Failures did not occur at gentle slope angles, and, by virtue of the value of 0, these were eliminated from further processing. Field observations indicate that gullies and smaller valleys–mainly encountered on outcrops of softer lithologies–are often steeper and more susceptible to slope failure than the main valleys and slopes; the 30 m

3.3. Level-2 parameters 3.3.1. Distance from drainage network Many of the larger landslides in the Three Gorges occur in close proximity to water courses, especially the Yangtze River and its tributaries (Wu et al., 2004), mainly due to erosion along river banks that causes undercutting and can lead to slope failure (Fig. 6). Examples include the currently active Huanglashi landslide, on the Yangtze's northern bank east of Badong Town, and the Qianjiangping landslide that

Fig. 9. Thresholded dissection density map.

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blocked the Qingan River on 14th July 2003, leading to 24 casualties and heavy economic losses (Wang et al., 2004). We included distance from drainage network as a landslide-related parameter in our hazard assessment. The drainage network was extracted from the DEM through computer-aided hydrological modelling, and the stream line vectors were classified in three broad categories—minor streams, tributaries to the Yangtze, and the Yangtze River itself. Fieldwork indicates that larger drainage channels can have a greater influence upon slope instability compared to smaller ones. Thus, as shown in Table 3, a greater distance was assigned to the Yangtze and its main tributaries for the calculation of buffer zones and the creation of a distance to drainage buffer map (Fig. 7). A simple Boolean classification was applied, whereby buffer polygons in proximity to the channels were assigned a value of 2, and remaining areas were given a background value of 1. 3.3.2. Dissection density Dissection density is a measure of terrain dissection which encompasses erosion in the form of rills and gullies; small-scale lithological discontinuities and

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Table 4 Distance to lineaments Lineament buffer distance (m) Class/lineament length (m)

Smaller than 2500

Between 2500 and 5000

Greater than 5000

Value

Near Distant

0–100 N100

0–200 N200

0–500 N500

2 1

drainage features; and the presence of roads and engineering sites. Increased ground surface complexity can facilitate water infiltration, which in turn can increase pore-water pressures and lower shear strength, leading thus to increased landslide susceptibility. Softer lithologies are more susceptible to natural erosional processes, and are thus associated with increased degrees of terrain dissection. Areas undergoing urban construction can also exhibit increased dissection; this makes dissection density relevant to the Three Gorges, where construction associated with town resettlement on higher ground has been responsible for the destabilization of marginally stable slopes and the triggering of failure.

Fig. 10. Lineaments over ASTER Band 2 image.

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Fig. 11. Translational slides along contacts (solid line) between lithologies of contrasting physical properties, in this case mudstone (M) and karstified limestone (L).

Fig. 12. Example of rotational slumping (light dashed line) and mudflows (solid line) triggered by water springing from the lithological contact (heavy dashed line) between karstified limestone and mudstone.

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Fig. 13. Schematic cross-section of the area shown in Fig. 12. Water flows through karstified limestone and springs from the limestone/mudstone contact where a permeability contrast can form. It then saturates the underlying mudstone, leading to slope failure.

A vector-based approach to identify dissection features is computationally inefficient, and–since it would rely mainly on digitization and manual extraction of features–one that would necessitate high costs in terms of time. In our study, we have applied a raster approach that is based on the textural measure of the intensity of surface dissection in the ASTER image. We

estimated a measure of dissection density through a sequence of edge-enhancement, thresholding and smoothing operations. The ASTER Band 2 was chosen for its high spatial resolution and minimum effect of vegetation at its wavelength (0.63–0.69 μm). The Laplacian high pass filter was applied, and the positive value edges were retained. The texture image was then

Fig. 14. Digitized geological map with structural features and 200 m elevation contours. Symbols indicate location of bedding measurements that were used to interpolate dip direction and angle across the study area.

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Fig. 15. Topographic/bedding relationship. Slopes where bedding is opposite to slope aspect are relatively stable, while those with same-direction bedding and aspect, and slope angle greater than bedding angle, are in greater risk of failure.

smoothed using a 9 × 9 averaging kernel, with pixel values in the final image giving a measure of the density of dissection (Liu et al., 2004). The output value at each image pixel position was decided by the number of edge pixels in a calculation window centred at the pixel. This value is therefore equivalent to length over area, and its unit can be expressed as km km− 2. Finally, we divided the image (Fig. 8) into three broad classes of increasing dissection density (Fig. 9). Higher values of dissection were generally associated with terrain more susceptible to failure, like outcrops of softer lithologies and areas of urban construction. 3.3.3. Distance from lineaments Lineaments are linear ground surface features that are often of tectonic origin. Tectonic lineaments are often associated with highly fractured, and therefore weakened, zones that can promote landsliding through the creation of over-steepened terrain, and the provision of unconsolidated material in the form of broken rock (Wen et al., 2004). Where reliable published structural

mapping does not exist, large scale lineaments are commonly extracted through visual interpretation of digital satellite imagery. We used edge-enhanced ASTER imagery to identify tectonic discontinuities of varying extent; the majority of these lineaments were found to follow the general NE–SW tectonic trend of the area (Fig. 10). We then defined buffer zones using a distance function that depended on lineament length (Table 4), and defined two classes, ‘Near’ and ‘Distant’, that were assigned the values of 2 and 1 respectively. Table 5 Landslide hazard classes, their hazard index threshold values, and coverage percentage of the study area Hazard values

Hazard classes

Coverage (%)

0 1–1.20 1.20–1.37 1.37–1.62 1.62–2.29

Stable Low Intermediate High Very high

66 16 8 7 2

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Fig. 16. Histogram of landslide hazard index values. Classification performed via the Natural Breaks method.

3.3.4. Distance from litho-stratigraphical contacts The inter-layering of formations of contrasting physical properties such as strength and permeability can lead to differential reaction to changes in the environment that include increases in pore-water pressure and reduction of shear strength. This behaviour has been found to promote slope instability, particularly of the translational sliding type (Guzzetti et al., 1996). In

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the Three Gorges, we observed increased translational sliding activity where limestone and mixed layers (mudstone, sandstone and shale) were in stratigraphical contact (Fig. 11). Where mixed layers overlaid limestone, the latter would provide sliding surfaces for overlying strata to slide upon. Where limestone overlaid mixed layers, a permeability contrast would form that could allow for localised high pore-water pressures to develop; springs would form along the contact, saturating the underlying softer layers and promoting instability (Figs. 12 and 13). A simple method to take account of the effect of the litho-stratigraphical relationship on slope stability was to delineate the contacts between lithologies of contrasting competence using the geological map. Mudstones, sandstones, and shales made up the “mixed layers” category, whereas the “limestone” category comprised both thinly-bedded and massive formations. With the contacts between groups of contrasting competence identified, we then calculated a 100 m buffer zone around them so as to indicate the extent of the litho-

Fig. 17. Landslide hazard map of the study area: the left-side inset in an enlargement of the area around Wushan Town, while the right-side inset in an enlargement of the area that includes the Badong Block. Black polygons represent field-mapped landslides, while pseudo-colours refer to landslide hazard classes. The area outlined to the north–east of Wushan Town marks the location of Fig. 11.

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Fig. 18. Landslide hazard map and field-mapped landslides of the area southwest of Wushan Town. Map legend as in Fig. 17.

stratigraphical contact's influence in promoting slope instability. 3.4. Level-3 parameters 3.4.1. Topography/bedding relationship Geological structure can have a strong influence on slope stability, with different degrees of landsliding

related to the angular relationship between bedding attitude, slope aspect and slope angle (Meentemeyer and Moody, 2000; Wen et al., 2004; Wu et al., 2004). Where dip angle is greater than slope angle, a relatively stable condition can be presumed; on the contrary, if the dip angle is less than the slope angle, the bedding plane daylights on the slope's free face and an unstable setting is formed. Geological structure provides the primary

Fig. 19. Landslide hazard map of the Badong Block, with field-mapped landslides (after Liu et al., 2004). Map legend as in Fig. 17.

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control on slip surface position, and in the majority of landsliding in the Three Gorges area failure surfaces are closely associated with a pre-existing surface of weakness (Wu et al., 2001). Dip direction and angle measurements were taken during field visits: dip direction was used to broadly interpret geological structure in high-hazard areas (Fig. 14), while interpolated surfaces of dip angle were produced using ordinary kriging (Meentemeyer and Moody, 2000). Slope aspect and angle were calculated from the corrected ASTER DEM (see Section 2.3). Bedding direction and slope aspect datasets were overlaid, and their relationship expressed in the form of a polygon vector shapefile. Relative weights of 2, 1.5 and 1 were assigned to slope aspect/bedding direction categories, based on their influence on slope stability. A second polygon layer was created through a comparison of slope and dip angle; slopes with dip angle smaller than slope were assigned a value of 2, while a value of 1 was assigned in the opposite case (Table 1). We then compared the two datasets, and arrived at the final estimate of the influence that the topographic/bedding relationship can exert on slope stability (Fig. 15). 4. Landslide hazard mapping and field investigation 4.1. The landslide hazard map The geometric mean model eliminated stable areas where landslides are not expected to occur, while characterizing the remaining region in landslide hazard categories. Level-1 parameters (see Table 1) had the greatest influence in the overall hazard classification due to the elimination function of the zero value, and the increased quantification level in comparison to other variables. The spatial distribution of hazard categories largely depended on Level-2 parameters, while Level-3 variables provided detailed hazard mapping in highhazard areas. Although the parameters were categorized in three levels–to improve understanding of their function in the model–the geometric mean function was applied using all the raster image layers in a single operation to produce the landslide hazard index map. The value range of the landslide hazard index was from 1 to 2.29 (Table 5); a number of discrete peaks were identified in the histogram of the hazard index and used to classify the area in five classes of relative landslide hazard (Fig. 16). The landslide hazard map indicates large parts of the study area as stable, with concentrated areas of varying landslide hazard (Fig. 17). The outcropping of massive limestone and gentle slopes with other lithologies are considered essentially free of landsliding and are

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classified as stable. As expected, higher hazard areas are associated with steep slopes formed in less competent lithologies and in proximity to drainage streams and lineaments. High values of dissection density, coupled with unfavourable structural—and litho-stratigraphical settings, often led to locally increased hazard. In the rugged landscape of the Three Gorges, the outcropping of softer lithologies–Quaternary deposits and mudstone–in proximity to the Yangtze and its tributaries offers the best locations for settlement and cultivation; as this work has shown, however, these areas are characterized by increased landslide hazard. The main focus of this study is landslide hazard around Wushan and Badong Towns. Our attention was drawn to these locations owing to the population potentially at risk, and the urban development that encroaches on marginally stable ground. 4.1.1. Wushan Town The area extending to the southwest of Wushan Town indicates high values of landslide hazard (Fig. 18). The area's proximity to Wushan makes it a natural candidate for urban development, although the unavailability of gently-sloping land means that potentially unstable areas are being settled and developed. Field observations indicate that slope instability is widespread in and around Wushan Town, where the model has correctly classified as being an area of intermediate to high-hazard. Extensive landslide mitigation engineering works have been implemented throughout the city, testifying to the seriousness of the hazard; the geotechnical details of these landslides, however, are beyond the scope of this regional scale study. 4.1.2. Badong Block The eastern reaches of the hazard map (Fig. 17) extend to Badong Block, a hilly block of sandstone and shale that holds the main concentration of population in the area. This is an area of intense urban development, accelerated by the relocation of Old Badong Town to higher ground due to the reservoir's rising water levels. Urban construction associated with the initial relocation upset the natural slope equilibrium, and contributed towards the reactivation of the Huangtupo landslide. This led to a second relocation of Badong Town to the present location, 8 km west from the old town's original position. The hazard map suggests that slope instability is widespread within the Badong Block owing to soft lithologies and intense surface dissection related to urban development (Fig. 19). The results concur with Liu et al. (2004), who had classified the area as being largely of intermediate to high-hazard, and with the

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documented presence of landslides at several locations on the Badong Block and around the New Badong Town (Deng et al., 2000). The present study offers a more accurate assessment of landslide hazard than previous work, owing to the field evidence that provided improved quantification of landslide-related parameters (see Section 4.2). 4.2. Field investigation Field observations in Wushan and Badong led to the compilation of a landslide inventory of the study area which allowed us to: (1) evaluate the relation of different types of slope failure to the spatial variation of landsliderelated parameters, and (2) explore the accuracy and limitations of our hazard assessment model. Walk-over surveys were carried out at high-hazard areas, and data were collected on landslide type, failure mechanism and materials involved. These observations suggest a strong influence of physical setting on the occurrence of different landslide types, and support our assumptions regarding the classification and quantification of landslide-related parameters. 4.2.1. Geomorphological setting for different landslide types Our fieldwork identified shallow debris slides as the most frequent landslide type in the Three Gorges. Shallow debris slides occurred mostly in Quaternary deposits and mudstones, and were usually of moderate size (on the order of a few tenths of square meters). These failures were closely associated with minor valleys and steep gullies, and often occurred in proximity to low-order streams. Despite their frequency, their small size means that they represent little if any threat to life and property. Rotational slides tended to occur in mid-gradient slopes (about 15–30°) of mudstone/shale interbeddings, and mostly produced slip zones not directly dependent on structural surfaces. Their relative frequency seems lower than that of shallow debris slides, although their larger size means they can potentially threaten roads or infrastructure such as pipelines and electricity cables. Translational sliding was mostly concentrated where interbedded lithologies of contrasting physical properties occur in proximity to fault zones. Sliding surfaces are usually controlled by planes of weakness, and the relationship between bedding and topography (see Section 3.4.1). Translational slides exhibit the lowest relative frequency of the commonly observed landslide types in the Three Gorges, but are often associated with the more catastrophic slope failures in the wider region; examples include the Xintan (in June 1985, 9 dead,

Table 6 Relationship between landslide hazard class coverage and recorded landslides Landslide hazard classes

Landslide Recorded Cumulative hazard class landslides landslide coverage (%) hazard class (%) coverage (%)

Very high 2 High 7 Intermediate 8 Low 16 Stable 66

30 42 17 9 2

2 9 17 34 100

Cumulative recorded landslides (%) 30 72 89 98 100

generated a 54 m-high wave that propagated towards the TGP) and Qianjiangping (in July 2003, 24 dead, 350 houses and four factories destroyed) landslides. 4.2.2. Field verification of results Field visits in and around Wushan and Badong Towns highlighted numerous landslides whose distribution was largely controlled by outcrops of soft lithologies and steep slopes. Within those areas, the interaction of landslide-related parameters gave rise to instability patterns that largely conformed to our hazard mapping, supporting our parameter classification and quantification. These observations highlight the merit of a geometric mean model for regional hazard studies, and its elimination function in particular: the latter removes areas essentially free of the phenomenon under study (i.e. landsliding), focusing on the more relevant areas. Despite the success of the model, there were a few instances where it over-estimated the hazard. A case in example is the Liushui area, northeast of Wushan Town. Although our hazard map designated Liushui as unstable, with an increased probability of failure, field examination indicated the area as being stable, with very limited signs of past or potential landsliding. This case serves as a reminder of the limitations facing any landslide hazard mapping that relies mainly on remote sensing data with only limited geotechnical information. Landslide locations were digitized and used to evaluate the accuracy of the hazard mapping. Tabulation of landslide locations over hazard classes indicated good correlation between high-hazard areas and field-confirmed locations where landsliding had occurred; more than 72% of the recorded landslides were contained within the classes of high and very high-hazard which together constitute 9% of the study area (Table 6). The results indicate our model as being able to identify those areas in greater risk of slope failure, and to differentiate between stable and landslide-prone ground.

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5. Conclusions The main contribution of this study is the development of an improved version of the geometric mean model proposed by Liu et al. (2004) for regional landslide hazard assessments. In this model, we have included two new parameters related to slope instability in the Three Gorges, i.e. the distance from lithostratigraphical contacts, and the angular relationship between bedding and topography. We have refined the model through field observations, mainly in the form of measurements of bedding attitude, and slope angle measurements for the identification of critical slope angles for different lithologies. We have used field evidence to establish an improved quantification system of (a) slope angle in relation to lithology; (b) distance to drainage network in relation to stream order; and (c) distance to tectonic lineament in relation to lineament length. We have adopted different quantification ranges for different parameters, to reflect their relative influence on slope instability. We have used field observations to (a) identify relations between geomorphological setting and the occurrence of the more common landslide types in the Three Gorges, i.e. shallow debris slides, rotational and translational sliding; and (b) verify the hazard assessment map. Field evidence suggests good correlation between high-hazard areas and locations of field-confirmed landslides, thus confirming the accuracy of our hazard assessment. A few landslides that were recorded in areas classified by the model as stable highlight the limitations of our approach, due largely to a dearth of geotechnical data. We have extended the previous study to a larger area, and we have achieved a more realistic hazard assessment that largely conforms to field distribution of landslide hazard. It is observed that slope instability threatens numerous towns in the Three Gorges, particularly along the Yangtze and its tributaries. The landslide hazard maps produced in this study can increase awareness of landslide hazard and assist future planning decisions pertaining to urban development in the area. The proposed methodology can be used for areas with similar physiographic conditions to the Three Gorges where published geological and topographical information is not readily available. Acknowledgements Fieldwork was partly funded by an Imperial College Trust grant. Miss Lee Ting Hsuan assisted with the fieldwork, for which we are grateful. We would like to thank two anonymous referees and Dr. Takashi Oguchi

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