landslide hazard mapping using gis and weight of evidence model in qingshui river watershed of 2008...

Journal of Earth Science, Vol. 23, No. 1, p. 97–120, February 2012 ISSN 1674-487X Printed in China DOI: 10.1007/s12583-012-0236-7

Landslide Hazard Mapping Using GIS and Weight of Evidence Model in Qingshui River Watershed of 2008 Wenchuan Earthquake

Struck Region

Chong Xu (许冲)

Key Laboratory of Active Tectenics and Volcano, Institute of Geology, China Earthquake Administration, Beijing

100029, China; Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China

Xiwei Xu* (徐锡伟)

Key Laboratory of Active Tectenics and Volcano, Institute of Geology, China Earthquake Administration,

Beijing 100029, China

Fuchu Dai (戴福初), Jianzhang Xiao (肖建章)

Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China

Xibin Tan (谭锡斌), Renmao Yuan (袁仁茂)

Key Laboratory of Active Tectenics and Volcano, Institute of Geology, China Earthquake Administration,

Beijing 100029, China

ABSTRACT: Tens of thousands of landslides were triggered by May 12, 2008 earthquake over a broad

area. The main purpose of this article is to apply and verify earthquake-triggered landslide hazard

analysis techniques by using weight of evidence modeling in Qingshui (清水) River watershed, Deyang

(德阳) City, Sichuan (四川) Province, China. Two thousand three hundred and twenty-one landslides

were interpreted in the study area from aerial photographs and multi-source remote sensing imageries

post-earthquake, verified by field surveys. The landslide inventory in the study area was established. A

spatial database, including landslides and associated controlling parameters that may have influence on

the occurrence of landslides, was constructed from topographic maps, geological maps, and enhanced

thematic mapper (ETM+) remote sensing imageries. The factors that influence landslide occurrence,

This study was supported by the International Scientific Joint

Project of China (No. 2009DFA21280), the National Natural

Science Foundation of China (No. 40821160550), and the Doc-

toral Candidate Innovation Research Support Program by Sci-

ence & Technology Review (No. kjdb200902-5).

*Corresponding author: [email protected]

© China University of Geosciences and Springer-Verlag Berlin

Heidelberg 2012

Manuscript received March 25, 2011.

Manuscript accepted June 20, 2011.

such as slope angle, aspect, curvature, elevation,

flow accumulation, distance from drainages, and

distance from roads were calculated from the

topographic maps. Lithology, distance from

seismogenic fault, distance from all faults, and

distance from stratigraphic boundaries were de-

rived from the geological maps. Normalized dif-

ference vegetation index (NDVI) was extracted

from ETM+ images. Seismic intensity zoning

was collected from Wenchuan (汶川) Ms8.0

Earthquake Intensity Distribution Map pub-

lished by the China Earthquake Administration.

Chong Xu, Xiwei Xu, Fuchu Dai, Jianzhang Xiao, Xibin Tan and Renmao Yuan

98

Landslide hazard indices were calculated using the weight of evidence model, and landslide hazard

maps were calculated from using different controlling parameters cases. The hazard map was com-

pared with known landslide locations and verified. The success accuracy percentage of using all 13 con-

trolling parameters was 71.82%. The resulting landslide hazard map showed five classes of landslide

hazard, i.e., very high, high, moderate, low, and very low. The validation results showed satisfactory

agreement between the hazard map and the existing landslides distribution data. The landslide hazard

map can be used to identify and delineate unstable hazard-prone areas. It can also help planners to

choose favorable locations for development schemes, such as infrastructural, buildings, road construc-

tions, and environmental protection.

KEY WORDS: Wenchuan earthquake, landslides, weight of evidence, Geographic Information Systems

(GIS), landslide hazard mapping.

INTRODUCTION

Earthquake-triggered landslides always caused

tragic deadly events and serious economic losses

(Keefer, 1984). The May 12, 2008 Wenchuan earth-

quake and the extensive landslide triggered by the

earthquake caused extensive damage to property and

loss of life during the strong shaking. The identifica-

tion of landslide hazard regions is important for car-

rying out quicker and safer mitigation programs after

an earthquake as well as future planning of this area

(Pradhan and Lee, 2010b).

To achieve a scientific landslide hazard mapping

of an area susceptibility to landsliding, several differ-

ent approaches have been developed and used in cur-

rent literature. Reviews of landslide hazard mapping

approaches are given by many researchers, such as

Sassa et al. (2009), Alexander (2008), Corominas and

Moya (2008), Carrara and Pike (2008), van Westen et

al. (2008, 2006), Keefer and Larsen (2007), Chacon et

al. (2006), Brenning (2005), Saha et al. (2005), Wang

et al. (2005), van Westen (2004), Guzzetti (2003),

Begueria and Lorente (2002), Dai and Lee (2002a),

Guzzetti et al. (1999), Aleotti and Chowdhury (1999),

Dikau et al. (1996), and Carrara et al. (1999, 1995).

Using Geographic Information System (GIS) as the

basic analysis tool for landslide hazard mapping can

be effective for spatial data management and manipu-

lation. Landslide hazard may be assessed by using

heuristic, deterministic, and statistical methods. A

heuristic approach is a direct or semi-direct mapping

methodology in which a relationship is established

between the occurrence of slope failures and the

causative factors. In heuristic approaches, expert

opinions are used to estimate landslide potential from

intrinsic variables. They are based on the assumption

that the relationship between landslide hazard and the

intrinsic variables are known and are specified in the

models. A set of variables is entered into the model to

estimate landslide hazard. The weighting factors of

variables were assigned by experts’ experiments. Re-

cently, there have been studies on landslide hazard

mapping applied heuristic approaches (Kouli et al.,

2010; Patwary et al., 2009; Kamp et al., 2008; Pandey

et al., 2008; Shaban et al., 2001; Temesgen et al., 2001;

Mora and Vahrson, 1994; Anbalagan, 1992). In this

approach, the opinions of experts are very important

in estimating landslide potential from the data for in-

trinsic variables. Therefore, assigning weight values

and ratings on the variables is very subjective, and the

results are often not reproducible. There are several

statistical models have also been applied to landslide

hazard mapping and include probabilistic models (Oh

and Lee, 2011; Pareek et al., 2010; Pradhan and Lee,

2010b; Bai et al., 2009; Jadda et al., 2009; Magliulo et

al., 2009, 2008; Dahal et al., 2008a, b; He and Beigh-

ley, 2008; Lee et al., 2008; Lee and Sambath, 2006;

Pradhan et al., 2006; Saha et al., 2005; Singh et al.,

2005; Lee and Choi, 2004; Wu et al., 2004; Lin and

Tung, 2003; Dai et al., 2001; Chung and Fabbri, 1999;

Pachauri et al., 1998) and logistic regression models

(Pradhan and Lee, 2010b; Garcia-Rodriguez et al.,

2008; Lee and Sambath, 2006; Dai et al., 2004, 2002;

Lee, 2004; Dai and Lee, 2003, 2002b, 2001). Among

the recent methods to landslide hazard mapping, some

studies adopted artificial neural network models

(Chauhan et al., 2010; Choi et al., 2010; Pradhan and

Lee, 2010a, b, c, 2008; Pradhan et al., 2010; Yilmaz,

2010, 2009a, b; Caniani et al., 2008; Lee, 2006; Lee

Landslide Hazard Mapping Using GIS and Weight of Evidence Model in Qingshui River Watershed

99

and Evangelista, 2006; Wang and Sassa, 2006; Erca-

noglu, 2005; Arora et al., 2004) and support vector

machine models (Yilmaz, 2010; Bai et al., 2008; Gal-

lus et al., 2008; Yao et al., 2008; Yao and Dai, 2006;

Brenning, 2005). Deterministic methods for landslide

hazard mapping also exist in the recent studies (Gun-

ther and Thiel, 2009; Hasegawa et al., 2009; Mavrouli

et al., 2009; Godt et al., 2008; Havenith et al., 2006;

Luzi and Pergalani, 1999; Miles and Ho, 1999). The

deterministic methods are based on slope stability

analyses and are only applicable when the ground

conditions are relatively homogeneous across the

study area and the landslide types are known. Such

models need a high degree of simplification of the in-

trinsic variables.

Among the recent approaches for landslide haz-

ard mapping, the statistical approach is considered to

be more suitable for landslide hazard mapping over

large and complex areas. All possible intrinsic vari-

ables are entered into a GIS and integrated with a

landslide inventory map. The aim of this article is to

apply and verify models of a bivariate probabilistic

statistical model, named weight of evidence, and to

carry out landslide hazard mapping work, using GIS

techniques, in a study area of Qingshui River water-

shed, Deyang City, Sichuan Province of China, struck

by Wenchuan earthquake on May 12, 2008. The land-

slide hazard map can identify and delineate unstable

hazard-prone areas, so that environmental regenera-

tion programmes can be initiated adopting suitable

mitigation measures; it can also help planners to

choose favorable locations for development schemes,

such as buildings and road constructions.

THE STUDY AREA

The study area is located in the Longmenshan

Mountain range, Qingshui River watershed, Deyang

City, Sichuan Province of China, approximately be-

tween 103°54′33″E and 104°11′13″E and 31°26′31″N

and 31°42′03″N (Figs. 1 and 2). The Longmenshan

Mountains are located at the margin of the Tibetan

plateau in Sichuan, China, an area that is deforming as

a result of the collision between the Indian plate and

the Eurasian plate. The Indian plate has been moving

northward resulting in the uplift of the Tibetan pla-

teau.

Figure 1. Location of the study area in Sichuan

Province, China.

Figure 2. Wenchuan earthquake-triggered land-

slides distribution map of the study area.

The study area ranges from 680 to 4 400 m in

elevation with an area of about 410.87 km2. The natu-

ral slopes are generally steep, with an average slope

gradient of 36.5°, and the proportion of area with

slope angle exceeding 30° accounts for 73.1%. On

May 12, 2008 at 14:28 (Beijing time), the study area


100

experienced a catastrophic earthquake with a surface

wave magnitude of 8.0 on the Richter scale and suf-

fered huge losses of life and property. The Wenchuan

earthquake ruptured two large thrust faults along the

Longmenshan thrust belt at the eastern margin of the

Tibetan plateau, one is a 240-km-long surface rupture

zone along the Beichuan fault and an additional

72-km-long surface rupture zone along the Pengguan

fault (Xu et al., 2009b). Addition, there is a

NW-striking left-lateral reverse rupture about 7 km

long between the Beichuan and Pengguan faults (Xu

et al., 2009a).

WEIGHT OF EVIDENCE MODELING

In this study, the weight of evidence modeling

was used for the landslide hazard mapping. The

method uses the Bayesian probability model that has

been applied to landslide hazard mapping for some

cases (Oh and Lee, 2011; Dahal et al., 2008a, b; Singh

et al., 2005; Lee and Choi, 2004). A detailed descrip-

tion of the mathematical formulation of the method is

available in Bonham-Carter (2002) and described in

detail in Dahal et al. (2008a).

For landslide hazard modeling, the method cal-

culates the weight for each landslide causative factor

(F) based on the presence or absence of the landslides

(L) within the area. Therefore, historical landslide data

are essential for weighting factors. The modeling pro-

cedure also relies on the fundamental assumption that

future landslides will occur under conditions similar to

those contributing to past landslides. It also assumes

that causative factors for the mapped landslides re-

main constant over time (Dahal et al., 2008a), as

shown in Bonham-Carter (2002) as follows

Wi+=ln(P{F|L}/P{F| L }) (1)

Wi-=ln(P{ F |L}/P{ F | L }) (2)

where P is the probability and ln is the natural log.

Similarly, F is the presence of potential landslide

causative factor, F is the absence of a potential

landslide causative factor, L is the presence of land-

slide, and L is the absence of a landslide. Wi+ and

Wi- are the weights of evidence when a binary predic-

tor pattern is present and absent, respectively. A posi-

tive weight (Wi+) indicates that the causative factor is

present at the landslide location, and the magnitude of

this weight is an indication of the positive correlation

between presence of the causative factor and land-

slides. A negative weight (Wi-) indicates an absence of

the causative factor and shows the level of negative

correlation. The difference between the two weights is

known as the weight contrast or the final weight (Da-

hal et al., 2008a), which is expressed as follows

Wf=Wi+－Wi

- (3)

The magnitude of contrast reflects the overall

spatial association between the causative factor and

landslides. In weight of evidence modeling, the com-

bination of causative factors assumes that the factors

are conditionally independent of one another with re-

spect to the landslides (Dahal et al., 2008a; Lee and

Choi, 2004; Bonham-Carter, 2002). In this research,

using bivariate statistics, the assumption is made that

all landslides in a given study area occur under the

same combination of parameters and that all sets of

parameters are conditionally independent.

Although weight of evidence model has not been

previously applied in landslide hazard mapping of the

Wenchuan earthquake-affected region, the suitability

of the technique for this purpose is evident in its suc-

cessful use in other studies for examining the distribu-

tion and spatial relationships of particular features.

The study area selected includes landslides triggered

by Wenchuan earthquake, and the intrinsic variables

are quantifiable. Therefore, accurate landslide condi-

tioning factor maps can be produced.

LANDSLIDE CHARACTERISTICS AND

INVENTORY MAP

On May 12, 2008 at 14:28 (Beijing time), a

catastrophic earthquake with a surface wave magni-

tude of 8.0 on the Richter scale, struck the Sichuan

Province, China, the study area that experienced a se-

vere shaking during the earthquake. Tens of thousands

of landslides were triggered by this earthquake over a

broad area. A key feature of this method is that the

possibility of landslide occurrence will be comparable

with Wenchuan earthquake-induced landslides inter-

preted from aerial photographs and multi-source re-

mote sensing imageries post-earthquake, verified by

field check.

A landslide inventory map is the simplest output

of direct landslide mapping. It shows the location and

boundary of discernible landslides. It is a key factor


101

used in landslide hazard mapping by weight of evi-

dence modeling because the overlay analysis requires

an inventory map (Dahal et al., 2008a, b). For prepar-

ing the Wenchuan earthquake-triggered landslide in-

ventory map, landslides occurring during the 2008

Wenchuan earthquake were interpreted from aerial

photographs and multi-source remote sensing image-

ries, verified by field check immediately after the

event. As a consequence of the Wenchuan earthquake,

a total of more than 60 000 landslides were interpreted

in the approximately landslide limited area (supple-

ment on the basis of Xu et al. (2009c) and Dai et al.

(2011)). The Wenchuan earthquake-induced landslide

inventory map was prepared in GIS. Extracted and

checked from the Wenchuan earthquake-triggered

landslide inventory map, 2 321 landslides in the study

area of the Qingshui River watershed were obtained.

The landslide inventory map in the study area is

shown in Fig. 2. In the study area, the LAP, which is

expressed as a percentage of the area affected by land-

slide activity, is LAP=(44.85 km2/410.87 km2)×

100%=10.92%, and the LND, which is calculated as

the number of landslides per square kilometer,

is LND=2 321 landslides/410.87 km2=5.65 landslides/

km2.

LANDSLIDES CONTROLLING PARAMETERS

In the initial part of this stage, for the landslide

hazard mapping, a number of thematic data of causa-

tive factors have been constructed, including topog-

raphic factors such as slope elevation, slope angle,

slope aspect, slope curvature, flow accumulation, and

distance from drainages; geological factors such as

lithology, distance from all faults, and distance from

stratigraphic boundaries; seismic factors such as seis-

mic intensity and distance from seismogenic fault;

vegetation factors such as normalized difference

vegetation index (NDVI) thematic map; and human

activity factors such as distance from roads. Topog-

raphic maps, geological maps, and enhanced thematic

mapper (ETM+) imageries were considered as basic

data sources for generating these layers. A landslide

inventory map after the 2008 Wenchuan earthquake

was prepared from aerial photographs and multi-

source remote sensing imageries post-earthquake in-

terpretation, verified by field check. These data

sources were used to generate various thematic layers

using the GIS software ArcGIS 9.2. Brief descriptions

of the preparation procedure of each data layer are

provided here.

For the landslide hazard mapping, the main steps

were data collection and construction of a spatial da-

tabase. This is followed by assessment of the landslide

hazard using the relationship between landslides and

controlling parameters or causative factors and the

subsequent validation of results. For the landslide

hazard mapping objectives in this study, the following

13 data layers were prepared as landslide controlling

parameter layers for landslide hazard analysis (Figs.

3–5): digital elevation model (DEM) map; slope angle

map; slope aspect map; slope curvature map; flow ac-

cumulation map; buffer map of drainages; lithology

map; buffer map from all faults; buffer map from

stratigraphic boundaries; buffer map from seismogenic

fault; seismic intensity map; buffer map from roads

and NDVI map.

Topographic Controlling Factors

A DEM representing spatial variation in altitude

for the study area is shown in Fig. 3b. The terrain was

used to generate various geomorphic parameters that

influence landslide activity in an area (Dahal et al.,

2008a). A DEM of the study area with 10×10 m cell

size was prepared using digital contour data derived

from the topographic map in a scale of 1 : 5 000. The

digitized contours were interpolated and resampled to

10×10 m pixel size. From this DEM, geomorphic

thematic data layers such as slope elevation, slope an-

gle, slope aspect, slope curvature, and flow accumula-

tion were prepared. Distance to drainages is derived

from the topographic map directly. The hillshade map

and rivers of the study area are shown in Fig. 3a.

DEM map

According to Dai et al. (2001), landslide occur-

rence perhaps is affected by slope elevation. At very

high elevations, there are mountain summits that are

usually characterized by weathered rocks, and the

shear strength of these is much higher. At intermediate

elevations, however, slopes tend to be covered by a

thin colluvium, which is more prone to landslides.

Therefore, we chose the slope elevation as an


102

Figure 3. Thematic maps of the study area. (a) Hillshade map and drainages; (b) DEM and drainages; (c)

slope angle map; (d) slope aspect map; (e) slope curvature map; (f) flow accumulation map.


103

Figure 4. Thematic maps of the study area. (a) Drainages buffer map; (b) geological map, 1. Q4al; 2. T3xj; 3.

T2lk+t; 4. T1f+tj; 5. P2; 6. P1; 7. C; 8. D3tw; 9. D2gw; 10. Dyl; 11. Smx; 12. ∈; 13. Zbq; 14. βμ4; 15. γ2, δ2, γδ2; (c)

buffer map of all faults; (d) stratigraphic boundaries buffer map; (e) seismogenic fault buffer map; (f)

seismic intensity map.


104

Figure 5. Thematic maps of the study area. (a) Roads buffer map; (b) NDVI map.

earthquake-triggered landslide controlling parameter.

The slope elevation map is shown in Fig. 3c. As

shown in Table 1, the slope elevation data (DEM)

were comprised 17 classes (<1 000, 1 000–1 200,

1 200–1 400, 1 400–1 600, 1 600–1 800, 1 800–2 000,

2 000–2 200, 2 200–2 400, 2 400–2 600, 2 600–2 800,

2 800–3 000, 3 000–3 200, 3 200–3 400, 3 400–3 600,

3 600–3 800, 3 800–4 000, and >4 000 m). The rela-

tion of the Wf values of landslides with elevation is

shown in Fig. 6a. It shows the Wf values in relation to

the elevation. It can be seen that hill slopes less than

2 000 m in elevation had positive Wf values.

Slope angle map Slope angle thematic data layer is an essential

parameter in slope stability assessment. As slope angle

increases, the level of gravitation-induced shear stress

in the colluviums or residual soils increases as well.

Gentle hill slopes are expected to have a flow fre-

quency of landslides because of generally lower shear

stresses associated with low gradients (Dai et al.,

2001). It is the first derivative of elevation with each

pixel denoting the angle of slope at a particular loca-

tion. The slope angle map of the study area is shown

in Fig. 3c. It was observed that the slope angle calcu-

lated from the DEM had a range of 0° to 82°. As

shown in Table 1, the slope angle data comprised 16

classes (<5°, 5°–10°, 10°–15°, 15°–20°, 20°–25°,

25°–30°, 30°–35°, 35°–40°, 40°–45°, 45°–50°,

50°–55°, 55°–60°, 60°–65°, 65°–70°, 70°–75°, and

>75°). The correlations between this classification and

Wf values of landslides were determined after inte-

grating the slope thematic map and the landslide in-

ventory map. The statistical result is shown in Fig. 6b.

Slope aspect map

Slope aspect is another DEM-based derivative

and is defined as the direction of maximum slope of

the terrain surface and divided into nine classes for the

study area, namely, flat, N, NE, E, SE, S, SW, W, and

NW (Table 1). The slope aspect map of the study area

is shown in Fig. 3d. The relation of the Wf values of

landslides with slope aspect is shown in Fig. 6c.

Slope curvature map

Figure 3e shows the slope curvature distribution

map in the study area. Slope angle, slope aspect, and

slope curvature map were all computed and mapped

using GIS software of ArcGIS 9.2. As shown in Table

1, the slope curvature data comprised 12 classes (<-1,

-1 to -0.1, -0.1 to -0.05, -0.05 to -0.02, -0.02 to -0.01,

-0.01 to 0, 0–0.01, 0.01–0.02, 0.02–0.05, 0.05–0.1,

0.1–1, and >1). The correlation between this classifi-

cation and Wf values of landslides is shown in Fig. 6d.

It shows a rough trend that the slope curvature value

close to zero, the less landslide occurrence.


105

Flow accumulation map

The process of water flow from convex areas and

accumulation in concave areas is represented by the

flow accumulation parameter, which is equivalent to

the upstream area. Flow accumulation is a measure of

the land area that contributes surface water to an area

where surface water can accumulate. It can be ex-

plained as the number of pixels, or area, which con-

tributes to runoff of a particular pixel (Dahal et al.,

2008a, b). This parameter was considered relevant to

slope instability because it defines the locations of

water concentration after rainfall and hence possible

landslides during earthquake. As shown in Fig. 3f and

Table 1, the flow accumulation indexes were classified

into 12 classes: 1, 2, 3–5, 6–10, 11–20, 21–50, 51–100,

101–200, 201–500, 501–1 000, 1 001–10 000, and

>10 000 cells. The correlation between this classifica-

tion and Wf values of landslides is shown in Fig. 6e. It

shows a trend that the moderate flow accumulation

value was susceptibility to landslide.

Buffer map of drainages

The under-cutting action of the river may trigger

instability of slopes. In the study area, Wenchuan

earthquake-triggered landslides often occur frequently

along drainages. Thus, distance of a landslide from

drainages was considered as a controlling factor of

earthquake-triggered landslides. Segments were ex-

tracted to include the effect of this causative factor and

buffered. Buffer zones for drainages were set to 50 m,

and the distance from drainages comprised 11 classes:

0–50, 50–100, 100–150, 150–200, 200–250, 250–300,

300–350, 350–400, 400–450, 450–500, and >500 m.

Then, the buffer map of distance from drainages was

converted into raster format (Fig. 4a). The correlation

between the distance from drainages classification and

Wf values of landslides is shown in Fig. 6f.

Geology Controlling Parameters

The geological map in a scale of 1 : 20 000 pro-

vides information on lithology, faults, and strati-

graphic boundaries of the study area. The lithology,

distance from faults, and distance from stratigraphic

boundaries maps were extracted from the geological

map for the next work. The regional geological map of

the study area is shown in Fig. 4b.

Lithology map

It is widely recognized that lithology exerts a

fundamental control on the geomorphology of a land-

scape (Dai et al., 2001). The erodibility or the re-

sponse of rocks to the processes of weathering and

erosion has been the main criteria in awarding the rat-

ings for subcategories of lithology (Anbalagan, 1992).

It also plays an important role in determining landslide

hazard because different geological units have differ-

ent susceptibilities to slope landsliding processes,

even when the slope failure is not triggered by an

earthquake, because lithological and structural varia-

tions often lead to a difference in strength and perme-

ability of rocks and soils.

In the study area, the lithology was divided into

15 categories are shown as follows.

(1) Holocene, Quaternary alluvium (Q4al): Recent

river Holocene alluvial sediment of Quaternary, such

as sand, gravel, and clay strata.

(2) Late Triassic, Xujiahe Foramtion (T3xj): feld-

spar, siltstone of the shale intercalated layer in the Xu-

jiahe Formation of the Upper Triassic strata.

(3) Middle Permian, Leikoupo and Tianjingshan

formations (T2lk+t): limestone in the Leikoupo and

Tianjingshan formations of the Middle Triassic strata.

(4) Early Triassic, Feixianguan and Tongjiezi

Formation (T1f+tj): shale, mudstone, and siltstone in

the Feixianguan and Tongjiezi formations of the

Lower Triassic strata.

(5) Late Permian (P2): limestone and shale in

Longmenshan Mountain area of the Upper Permian

strata.

(6) Early Permian (P1): limestone in Longmen-

shan Mountain area of the Lower Permian strata.

(7) Carboniferous (C): limestone in Longmen-

shan Mountain area of the Carboniferous strata.

(8) Late Devonian, Tangwangzhai Formation

(D3tw): dolomite, dolomitic limestone, and phosphorite

of the Upper Devonian strata.

(9) Middle Devonian, Guanwushan Formation

(D2gw): limestone, shale, and sandstone in the Guan-

wushan Formation of the Middle Devonian strata.

(10) Devonian, Yuelizhai Group (Dyl): limestone,

quartz sandstone, and phyllite intercalated with quartz

sandstone and limestone in the Yuelizhai Formation of

the Devonian strata.


106

Table 1 Computed weights for classes of various data layers based on landslide occurrences

Category Pixel in

domain

% of

domain

Landslide

pixel number

% of land-

slide

Ratio %* W+ W- Wf

A: Elevation

1: 680–1 000 m 252 485 6.145 2 35 315 7.873 2 1.281 2 0.282 9 -0.020 8 0.303 7

2: 1 000–1 200 m 322 102 7.839 6 59 066 13.168 3 1.679 7 0.605 6 -0.066 6 0.672 2

3: 1 200–2 400 m 385 752 9.388 8 76 409 17.034 7 1.814 4 0.700 9 -0.098 4 0.799 3

4: 1 400–1 600 m 392 971 9.564 5 63 723 14.206 5 1.485 3 0.457 0 -0.059 0 0.515 9

5: 1 600–1 800 m 344 299 8.379 9 51 436 11.467 2 1.368 4 0.359 9 -0.038 4 0.398 3

6: 1 800–2 000 m 324 617 7.900 8 40 184 8.958 7 1.133 9 0.142 2 -0.013 0 0.155 2

7: 2 000–2 200 m 334 773 8.148 0 32 235 7.186 5 0.882 0 -0.139 9 0.011 7 -0.151 6

8: 2 200–2 400 m 336 904 8.199 9 22 666 5.053 2 0.616 3 -0.530 1 0.037 9 -0.568 0

9: 2 400–2 600 m 312 307 7.601 2 18 364 4.094 1 0.538 6 -0.673 8 0.041 9 -0.715 7

10: 2 600–2 800 m 275 398 6.702 9 14 929 3.328 3 0.496 5 -0.759 9 0.040 0 -0.799 9

11: 2 800–3 000 m 211 525 5.148 3 11 708 2.610 2 0.507 0 -0.737 9 0.029 7 -0.767 6

12: 3 000–3 200 m 181 909 4.427 5 7 643 1.703 9 0.384 9 -1.027 6 0.031 6 -1.059 2

13: 3 200–3 400 m 149 983 3.650 4 3 298 0.735 3 0.201 4 -1.695 7 0.033 5 -1.729 3

14: 3 400–3 600 m 111 608 2.716 4 4 229 0.942 8 0.347 1 -1.135 2 0.020 3 -1.155 5

15: 3 600–3 800 m 80 917 1.969 4 3 787 0.844 3 0.428 7 -0.914 7 0.012 8 -0.927 5

16: 3 800–4 000 m 56 896 1.384 8 2 431 0.542 0 0.391 4 -1.010 0 0.009 6 -1.019 6

17: 4 000–4 400 m 34 206 0.832 5 1 125 0.250 8 0.301 3 -1.281 9 0.006 6 -1.288 5

B: Slope angle

1: <5° 147 026 3.578 4 7 680 1.712 2 0.478 5 -0.799 1 0.021 5 -0.820 7

2: 5°–10° 51 462 1.252 5 2 546 0.567 6 0.453 2 -0.856 3 0.007 8 -0.864 1

3: 10°–15° 82 966 2.019 3 3 821 0.851 9 0.421 9 -0.931 5 0.013 3 -0.944 8

4: 15°–20° 150 200 3.655 7 7 276 1.622 1 0.443 7 -0.878 5 0.023 5 -0.902 0

5: 20°–25° 263 444 6.411 9 16 198 3.611 2 0.563 2 -0.626 3 0.033 2 -0.659 4

6: 25°–30° 410 063 9.980 5 29 646 6.609 3 0.662 2 -0.452 7 0.041 4 -0.494 1

7: 30°–35° 584 107 14.216 5 50 666 11.295 6 0.794 5 -0.254 9 0.037 7 -0.292 5

8: 35°–40° 708 005 17.232 1 73 104 16.297 9 0.945 8 -0.062 4 0.012 6 -0.075 0

9: 40°–45° 668 741 16.276 4 81 896 18.258 0 1.121 7 0.129 9 -0.026 8 0.156 8

10: 45°–50° 477 665 11.625 8 70 174 15.644 7 1.345 7 0.340 2 -0.052 1 0.392 3

11: 50°–55° 280 692 6.831 7 48 652 10.846 6 1.587 7 0.537 0 -0.049 3 0.586 3

12: 55°–60° 155 637 3.788 0 29 776 6.638 3 1.752 4 0.657 8 -0.033 7 0.691 5

13: 60°–65° 82 384 2.005 1 17 203 3.835 3 1.912 7 0.767 1 -0.021 1 0.788 3

14: 65°–70° 35 193 0.856 6 7 372 1.643 5 1.918 8 0.771 1 -0.008 9 0.780 1

15: 70°–75° 9 711 0.236 4 2 175 0.484 9 2.051 6 0.856 6 -0.002 8 0.859 4

16: >75° 1 356 0.033 0 363 0.080 9 2.452 1 1.092 9 -0.000 5 1.093 4

C: Slope aspect

Flat 80 704 1.964 2 4 969 1.107 8 0.564 0 -0.624 8 0.009 8 -0.634 6

North 498 662 12.136 9 47 270 10.538 4 0.868 3 -0.157 2 0.020 3 -0.177 5

North-East 467 403 11.376 1 50 625 11.286 4 0.992 1 -0.008 9 0.001 1 -0.010 0

East 646 378 15.732 1 62 706 13.979 8 0.888 6 -0.131 7 0.023 1 -0.154 8

South-East 662 081 16.114 3 71 512 15.943 0 0.989 4 -0.012 0 0.002 3 -0.014 3

South 469 663 11.431 1 53 432 11.912 2 1.042 1 0.046 4 -0.006 1 0.052 5


107

Continued

Category Pixel in do-

main

% of do-

main

Landslide

pixel number

% of land-

slide

Ratio % * W+ W- Wf

South-West 363 394 8.844 6 42 142 9.395 2 1.062 3 0.068 1 -0.006 8 0.074 8

West 398 746 9.705 0 58 455 13.032 1 1.342 8 0.337 7 -0.042 0 0.379 7

North-West 521 621 12.695 7 57 437 12.805 1 1.008 6 0.009 6 -0.001 4 0.011 0

D: Slope curvature

1: <-1 1 156 003 28.135 8 144 078 32.121 0 1.141 6 0.150 0 -0.063 8 0.213 8

2: -1 to -0.1 602 287 14.659 0 63 566 14.171 5 0.966 7 -0.037 9 0.006 4 -0.044 3

3: -0.1 to -0.05 45 132 1.098 5 4 449 0.991 9 0.903 0 -0.113 9 0.001 2 -0.115 1

4: -0.05 to -0.02 29 098 0.708 2 2 751 0.613 3 0.866 0 -0.160 2 0.001 1 -0.161 2

5: -0.02 to -0.01 10 057 0.244 8 931 0.207 6 0.848 0 -0.183 4 0.000 4 -0.183 8

6: -0.01 to 0 229 912 5.595 8 19 862 4.428 1 0.791 3 -0.259 3 0.013 8 -0.273 1

7: 0–0.01 68 657 1.671 0 7 153 1.594 7 0.954 3 -0.052 3 0.000 9 -0.053 2

8: 0.01–0.02 10 321 0.251 2 954 0.212 7 0.846 7 -0.185 1 0.000 4 -0.185 5

9: 0.02–0.05 29 657 0.721 8 2 780 0.619 8 0.858 6 -0.169 6 0.001 2 -0.170 7

10: 0.05–0.1 46 649 1.135 4 4 554 1.015 3 0.894 2 -0.124 7 0.001 4 -0.126 1

11: 0.1–1 641 129 15.604 4 63 964 14.260 2 0.913 9 -0.100 6 0.017 8 -0.118 3

12: >1 1 239 750 30.174 1 133 506 29.764 0 0.986 4 -0.015 3 0.006 6 -0.021 9

E: Flow accumulation

1: 1 cells 802 223 19.525 2 71 247 15.883 9 0.813 5 -0.229 0 0.049 8 -0.278 8

2: 2 cells 304 776 7.417 9 28 487 6.350 9 0.856 2 -0.172 8 0.012 9 -0.185 6

3: 3–5 cells 703 375 17.119 4 70 428 15.701 3 0.917 2 -0.096 6 0.019 1 -0.115 6

4: 6–10 cells 725 935 17.668 4 82 106 18.304 8 1.036 0 0.039 8 -0.008 7 0.048 5

5: 11–20 cells 678 087 16.503 9 83 734 18.667 8 1.131 1 0.139 4 -0.029 4 0.168 8

6: 21–50 cells 506 734 12.333 3 64 756 14.436 8 1.170 6 0.178 6 -0.027 2 0.205 8

7: 51–100 cells 159 742 3.887 9 20 335 4.533 5 1.166 0 0.174 2 -0.007 6 0.181 7

8: 101–200 cells 76 478 1.861 4 9 690 2.160 3 1.160 6 0.168 8 -0.003 4 0.172 2

9: 201–500 cells 53 711 1.307 3 6 836 1.524 0 1.165 8 0.173 9 -0.002 5 0.176 4

10: 501–1 000 cells 26 297 0.640 0 3 397 0.757 3 1.183 3 0.191 0 -0.001 3 0.192 3

11: 1 001–10 000 cells 47 394 1.153 5 5 299 1.181 4 1.024 1 0.026 8 -0.000 3 0.027 1

12: >10 000 cells 23 900 0.581 7 2 233 0.497 8 0.855 8 -0.173 2 0.000 9 -0.174 2

F: Distance from drainages

1: 0–50 m 358 401 8.723 1 36 544 8.147 2 0.934 0 -0.076 4 0.007 1 -0.083 4

2: 50–100 m 334 132 8.132 4 41 665 9.288 9 1.142 2 0.150 5 -0.014 2 0.164 8

3: 100–150 m 319 551 7.777 5 39 931 8.902 3 1.144 6 0.153 0 -0.013 8 0.166 7

4: 150–200 m 303 779 7.393 6 37 383 8.334 2 1.127 2 0.135 5 -0.011 5 0.146 9

5: 200–250 m 288 006 7.009 7 34 029 7.586 5 1.082 3 0.089 2 -0.007 0 0.096 2

6: 250–300 m 272 138 6.623 5 29 542 6.586 1 0.994 4 -0.006 4 0.000 4 -0.006 8

7: 300–350 m 253 626 6.173 0 26 171 5.834 6 0.945 2 -0.063 1 0.004 0 -0.067 1

8: 350–400 m 234 258 5.701 6 24 016 5.354 2 0.939 1 -0.070 3 0.004 1 -0.074 4

9: 400–450 m 215 694 5.249 8 23 981 5.346 4 1.018 4 0.020 5 -0.001 1 0.021 6

10: 450–500 m 197 018 4.795 2 21 611 4.818 0 1.004 8 0.005 3 -0.000 3 0.005 6

11: >500 m 1 332 049 32.420 6 133 675 29.801 7 0.919 2 -0.094 1 0.042 8 -0.136 9

G: Lithology


108

Continued

Category Pixel in do-

main

% of do-

main

Landslide

pixel number

% of land-

slide

Ratio % * W+ W- Wf

1: (Q4al) 64 761 1.576 2 1 146 0.255 5 0.162 1 -1.917 3 0.015 0 -1.932 3

2: (T3xj) 123 603 3.008 4 15 650 3.489 0 1.159 8 0.168 0 -0.005 6 0.173 6

3: (T2lk+t) 109 058 2.654 3 14 592 3.253 2 1.225 6 0.231 5 -0.006 9 0.238 4

4: (T1f+tj) 88 845 2.162 4 7 512 1.674 7 0.774 5 -0.282 8 0.005 6 -0.288 4

5: (P2) 310 508 7.557 4 27 295 6.085 2 0.805 2 -0.240 3 0.017 8 -0.258 0

6: (P1) 191 274 4.655 4 14 002 3.121 6 0.670 5 -0.439 3 0.017 9 -0.457 2

7: (C) 77 226 1.879 6 5 819 1.297 3 0.690 2 -0.408 0 0.006 6 -0.414 7

8: (D3tw) 92 199 2.244 0 25 612 5.710 0 2.544 5 1.143 8 -0.040 4 1.184 2

9: (D2gw) 313 072 7.619 8 44 705 9.966 6 1.308 0 0.307 0 -0.028 8 0.335 8

10: (Dyl) 130 189 3.168 7 6 833 1.523 4 0.480 8 -0.794 1 0.018 9 -0.813 0

11: (Smx) 105 345 2.564 0 4 961 1.106 0 0.431 4 -0.908 2 0.016 7 -0.924 9

12: (∈) 308 204 7.501 3 52 309 11.661 9 1.554 6 0.511 6 -0.051 5 0.563 2

13: (Zbq) 1 323 322 32.208 2 125 920 28.072 8 0.871 6 -0.153 0 0.066 7 -0.219 7

14: βμ4 69 578 1.693 5 1 797 0.400 6 0.236 6 -1.530 9 0.014 7 -1.545 6

15: γ2, δ2, γδ2 801 468 19.506 8 100 395 22.382 2 1.147 4 0.155 7 -0.040 7 0.196 5

H: Distance from all faults

1: 0–50 m 152 301 3.706 8 13 830 3.083 3 0.831 8 -0.204 6 0.007 2 -0.211 8

2: 50–100 m 152 272 3.706 1 14 044 3.131 0 0.844 8 -0.187 5 0.006 7 -0.194 2

3: 100–150 m 149 553 3.640 0 14 273 3.182 0 0.874 2 -0.149 7 0.005 3 -0.155 1

4: 150–200 m 147 459 3.589 0 14 637 3.263 2 0.909 2 -0.106 2 0.003 8 -0.110 0

5: 200–250 m 145 111 3.531 8 15 087 3.363 5 0.952 3 -0.054 7 0.002 0 -0.056 6

6: 250–300 m 141 567 3.4456 15 293 3.409 4 0.989 5 -0.011 8 0.000 4 -0.012 2

7: 300–350 m 137 572 3.348 3 15 611 3.480 3 1.039 4 0.043 5 -0.001 5 0.045 0

8: 350–400 m 132 856 3.233 6 16 175 3.606 1 1.115 2 0.123 3 -0.004 3 0.127 6

9: 400–450 m 127 065 3.092 6 16 332 3.641 1 1.177 3 0.185 2 -0.006 4 0.191 6

10: 450–500 m 121 737 2.962 9 16 284 3.630 4 1.225 3 0.231 1 -0.007 7 0.238 9

11: >500 m 2 701 159 65.743 2 296 982 66.209 6 1.007 1 0.007 9 -0.015 4 0.023 3

I: Distance from stratigraphic boundaries

1: 0–50 m 469 856 11.435 8 51 351 11.448 3 1.001 1 0.001 2 -0.000 2 0.001 4

2: 50–100 m 433 852 10.559 5 46 856 10.446 2 0.989 3 -0.012 1 0.001 4 -0.013 5

3: 100–150 m 363 356 8.843 7 38 978 8.689 8 0.982 6 -0.019 7 0.001 9 -0.021 6

4: 150–200 m 302 489 7.362 2 30 227 6.738 9 0.915 3 -0.098 8 0.007 5 -0.106 3

5: 200–250 m 259 927 6.326 3 25 154 5.607 9 0.8864 -0.134 4 0.008 6 -0.143 0

6: 250–300 m 222 776 5.422 1 20 342 4.535 1 0.836 4 -0.198 5 0.010 5 -0.209 0

7: 300–350 m 193 577 4.711 4 17 668 3.938 9 0.836 0 -0.199 0 0.009 1 -0.208 0

8: 350–400 m 170 525 4.150 4 17 235 3.842 4 0.925 8 -0.086 2 0.003 6 -0.089 8

9: 400–450 m 150 376 3.660 0 15 617 3.481 7 0.951 3 -0.055 9 0.002 1 -0.058 0

10: 450–500 m 135 016 3.286 1 13 627 3.038 0 0.924 5 -0.087 7 0.002 9 -0.090 6

11: >500 m 1 406 902 34.242 4 171 493 38.232 9 1.116 5 0.124 6 -0.070 0 0.194 6

1: HW 1 km 157 861 3.842 2 39 671 8.844 3 2.301 9 1.007 6 -0.059 8 1.067 3

2: HW 2 km 156 714 3.814 2 29 847 6.654 1 1.744 5 0.652 2 -0.033 6 0.685 8

3: HW 3 km 147 318 3.585 6 23 160 5.163 3 1.440 0 0.420 1 -0.018 5 0.438 6


109

Continued

Category Pixel in

domain

% of domain Landslide

pixel number

% of

landslide

Ratio % * W+ W- Wf

J: Distance from seismogenic fault

4: HW 4 km 143 174 3.484 7 12 046 2.685 6 0.770 7 -0.288 2 0.009 3 -0.297 5

5: HW 5 km 145 766 3.547 8 12 829 2.860 1 0.806 2 -0.238 9 0.008 0 -0.246 9

6: HW 6 km 151 435 3.685 8 20 070 4.474 4 1.214 0 0.220 5 -0.009 2 0.229 7

7: HW 7 km 165 401 4.025 7 9 554 2.130 0 0.529 1 -0.692 7 0.022 0 -0.714 7

8: HW 8 km 183 825 4.474 1 5 799 1.292 8 0.289 0 -1.325 0 0.036 8 -1.361 9

9: HW 9 km 197 796 4.814 1 7 905 1.762 4 0.366 1 -1.079 7 0.035 5 -1.115 2

10: HW 10 km 196 633 4.785 8 13 400 2.987 4 0.624 2 -0.516 3 0.021 0 -0.537 3

11: HW >10 km 902 172 21.957 9 31 008 6.913 0 0.314 8 -1.236 4 0.200 2 -1.436 5

a: FW 1 km 158 127 3.848 6 33 839 7.544 1 1.960 2 0.798 2 -0.043 9 0.842 1

b: FW 2 km 151 864 3.696 2 29 172 6.503 7 1.759 6 0.662 8 -0.033 2 0.695 9

c: FW 3 km 150 252 3.657 0 13 473 3.003 7 0.821 4 -0.218 4 0.007 6 -0.226 0

d: FW 4 km 159 998 3.894 2 22 268 4.964 5 1.274 8 0.277 1 -0.012 6 0.289 6

e: FW 5 km 150 239 3.656 6 31 271 6.971 6 1.906 6 0.763 1 -0.039 2 0.802 3

f: FW 6 km 147 912 3.600 0 26 724 5.957 9 1.655 0 0.587 5 -0.027 8 0.615 2

g: FW 7 km 135 060 3.287 2 20 767 4.629 8 1.408 4 0.393 8 -0.015 7 0.409 5

h: FW 8 km 107 147 2.607 8 15 761 3.513 8 1.347 4 0.341 7 -0.010 5 0.352 2

j: FW 9 km 89 185 2.170 7 12 873 2.869 9 1.322 1 0.319 5 -0.008 0 0.327 6

k: FW 10 km 81 780 1.990 4 16 820 3.749 9 1.883 9 0.748 0 -0.020 3 0.768 3

k: FW >10 km 228 993 5.573 4 20 291 4.523 7 0.811 7 -0.231 5 0.012 4 -0.243 9

Seismic intensity

IX 287 524 6.998 0 31 093 6.931 9 0.990 6 -0.010 6 0.000 8 -0.011 4

X 3 821 128 93.002 0 417 455 93.068 1 1.000 7 0.000 8 -0.010 6 0.011 4

Distance from roads

1: 0–20 m 119 954 2.919 5 12 134 2.705 2 0.926 6 -0.085 2 0.002 5 -0.087 7

2: 20–40 m 114 933 2.797 3 11 992 2.673 5 0.955 7 -0.050 7 0.001 4 -0.052 1

3: 40–60 m 108 894 2.650 4 11 937 2.661 3 1.004 1 0.004 6 -0.000 1 0.004 7

4: 60–80 m 103 493 2.518 9 12 438 2.772 9 1.100 9 0.108 5 -0.002 9 0.111 5

5: 80–100 m 98 161 2.389 1 12 751 2.842 7 1.189 9 0.197 4 -0.005 2 0.202 6

6: 100–120 m 93 653 2.279 4 12 793 2.852 1 1.251 2 0.255 4 -0.006 6 0.262 0

7: 120–140 m 89 552 2.179 6 12 509 2.788 8 1.279 5 0.281 3 -0.007 0 0.288 3

8: 140–160 m 85 694 2.085 7 12 124 2.702 9 1.295 9 0.296 2 -0.007 1 0.303 3

9: 160–180 m 81 767 1.990 1 11 716 2.612 0 1.312 5 0.311 0 -0.007 1 0.318 1

10: 180–200 m 77 952 1.897 3 11 302 2.519 7 1.328 1 0.324 8 -0.007 1 0.331 9

11: >200 m 3 134 599 76.292 6 326 852 72.868 9 0.955 1 -0.051 4 0.152 8 -0.204 2

NDVI

1: <0 754 893 18.373 3 97 084 21.644 1 1.178 0 0.185 9 -0.045 8 0.231 7

2: 0–0.1 681 392 16.584 3 73 440 16.372 8 0.987 2 -0.014 4 0.002 8 -0.017 2

3: 0.1–0.2 857 681 20.875 0 93 876 20.928 9 1.002 6 0.002 9 -0.000 8 0.003 7

4: 0.2–0.3 1 056 590 25.716 2 122 403 27.288 7 1.061 1 0.066 9 -0.024 0 0.090 9

5: 0.3–0.4 652 315 15.876 6 57 915 12.911 7 0.813 3 -0.229 3 0.039 0 -0.268 3

6: 0.4–0.5 100 028 2.434 6 3 716 0.828 5 0.340 3 -1.155 7 0.018 3 -1.174 1

7: >0.5 5 753 0.140 0 114 0.025 4 0.181 5 -1.802 0 0.001 3 -1.803 3

HW means hanging wall and FW means footwall. *. Ratio % of landslide/% of domain


110

Figure 6. Weight of evidence of 13 factors (a) elevation; (b) slope angle; (c) slope aspect; (d) slope curvature;

(e) flow accumulation; (f) distance from drainages; (g) lithology; (h) distance from all faults; (i) distance

from stratigraphic boundaries; (j1) distance from seismogenic fault (hanging wall); (j2): distance from

seismogenic fault (footwall); (k) seismic intensity; (l) distance from roads; (m) NDVI. The categories of each

factor were shown in Table 1.


111

(11) Silurian, Maoxian Group (Smx): phyllite,

schist, slate, and sandstone and limestone intercalated

layers in Maoxian Group of the Silurian strata.

(12) Cambrian (∈): Sandstone, siltstone, chert,

and slate intercalated layer of the Cambrian strata.

(13) Sinian, Qiujiahe Formation (Zbq): sandstone

and siltstone of the Upper Sinian strata.

(14) Magmatic rock of Hercynian orogeny period

(βμ4): bedrock and ultrabasic rock of the Hercynian

period.

(15) Magmatic rocks of Jinning orogeny period

(γ2, δ2, and γδ2): granite and diorite intrusive rock of

the Jinning period.

The correlations between lithology and area

(km2), area percentage (%), landslide area (km2), and

landslide area percentage (%) are shown in Table 2.

The relation of the weight of evidence of landslides

with lithology is shown in Fig. 6g. It can be seen that

the formations of (T3xj), (T2lk+t), (D3tw), (D2gw), (∈),

and (γ2, δ2, γδ2) had the positive weight of evidence. It

means that these formations had high landslide hazard

index (LSI).

Table 2 Landslides and lithology within the study area of the 2008 Wenchuan earthquake

Age Geological

unit

Description of lithology Area

(km2)

Area

(%)

LS area

(km2)

LS (%) LS area in

fm. area (%)

Q 1: Q4al Recent river alluviums, such as sand and gravel 6.48 1.58 0.11 0.26 1.77

T 2: T3xj Sandstone, shale, siltstone, limestone, and shale

containing coal layers, oil shale

12.36 3.01 1.57 3.49 12.66

3: T2lk+t Dolomite, limestone, dolomitic limestone, chert

limestone, argillaceous dolomite, breccia, con-

taining gypsum

10.91 2.65 1.46 3.25 13.38

4: T1f+tj Shale, mudstone, siltstone, argillaceous lime-

stone, limestone

8.88 2.16 0.75 1.67 8.46

P 5: P2 Chert limestone, shale, aluminum-iron rock in-

tercalcated with coal layers, and pyrite

31.05 7.56 2.73 6.09 8.79

6: P1 Limestone, argillaceous limestone, and shale 19.13 4.66 1.40 3.12 7.32

C 7: C Limestone, crystalline limestone, shale, sand-

stone intercalcated with hematite, kaolin

7.72 1.88 0.58 1.30 7.54

D 8: D3tw Dolomite, dolomitic limestone, phosphorite 9.22 2.24 2.56 5.71 27.78

9: D2gw Limestone, sandstone and shale, argillaceous

limestone, quartz sandstone, hematite

31.31 7.62 4.47 9.97 14.28

10: Dyl Limestone, quartz sandstone, phyllite intercal-

cated with quartz sandstone and limestone

13.02 3.17 0.68 1.52 5.25

S 11: Smx Phyllite folder crystalline limestone, metamor-

phic sandstone

10.53 2.56 0.50 1.11 4.71

∈ 12: ∈ Siltstone, sandstone, chert, and calcareous

phosphorite

30.82 7.50 5.23 11.66 16.97

Zb 13: Zbq Shale, chert, dolomite, limestone, containing

manganese

132.33 32.21 12.59 28.07 9.52

HX 14: βμ4 Diabase 6.96 1.69 0.18 0.40 2.58

JN 15: γ2, δ2, γδ2 Magmatic rocks, granite, diorite, granodiorite 80.15 19.51 10.04 22.38 12.53

All 410.87 100 44.85 100 10.92

HX. Period of Hercynian orogeny; JN. period of Jinning orogeny; fm. formation.


112

Buffer map from all faults and stratigraphic

boundaries

Faults and stratigraphic boundaries lines were

extracted from the geological map. The distance from

all faults buffer map and distance from stratigraphic

boundaries buffer map was prepared using polygon

mode under ArcGIS environment, as shown in Figs.

4c and 4d, respectively. The lineaments (faults and

stratigraphic boundaries) were enclosed by 50 m

buffer zones. The buffer maps were rasterised and

correlation between distance from all faults, strati-

graphic boundaries, and the Wf values of landslides

are shown in Figs. 6h, 6i and Table 1.

Earthquake Parameters

Buffer map from seismogenic fault The spatial distribution of Wenchuan earthquake-

triggered landslides indicates a strong correlation be-

tween distance from the seismogenic fault and the

earthquake-triggered landslides. The landslides oc-

curred mainly along the seismogenic fault and de-

creased sharply with distance from the fault. The

buffer map from seismogenic fault for the study area

is shown in Fig. 4e. Buffer zones for seismogenic fault

were set to 1 km. The relations between the distance

from seismogenic fault and weight of evidence of

landslides were calculated from footwall and hanging

wall directions, respectively. The statistical results of

the study area are shown in Figs. 6j1 and 6j2, sepa-

rately. On the hanging wall, it shows a general trend

that weight of evidence value of landslide increases

close to the seismogenic fault. On the footwall, the re-

lation between landslide hazard and distance from

seismogenic fault is relative irregular.

Seismic intensity map

The seismic intensity map of earthquake struck

area was collected from Wenchuan Ms8.0

Earthquake Intensity Distribution Map published

by the China Earthquake Administration

(http://news.xinhuanet.com/politics/2008-09/02/conte

nt_9752627.htm). The seismic intensity information

(Fig. 4f) of the study was extracted from the map. In

the study area, there are only two seismic intensity

categories named IX and X. The correlation between

seismic intensity and weight of evidence value of

landslide shown in Fig. 6k and Tables 1 and 2 indi-

cated that the landslide hazard in X seismic intensity

zone is higher than IX seismic intensity zone.

Other Controlling Parameters

Buffer map from roads

One of the controlling factors for the stability of

slope is human activity, such as road construction. In

the study area, many earthquake-induced landslides

occurred along roads and foot trails due to inappropri-

ately cut slopes and drainage from the roads and trails.

In order to produce the map showing the distance to

roads, the road segment map was rasterised and the

distance to these roads was calculated in meters. The

buffer map from roads for the study area is shown in

Fig. 5a. Buffer zones for roads were set to 20 m. Thus,

distance to road was calculated and mapped using the

road and trail segment maps, and the obtained values

were sliced into 11 classes as shown in Table 1: 0–20,

20–40, 40–60, 60–80, 80–100, 100–120, 120–140,

140–160, 160–180, 180–200, and >200 m. The corre-

lation between the weight of evidence value of land-

slide and distance from roads is obvious as shown in

Fig. 6l.

NDVI map

The presence or absence of thick vegetation may

affect landslide hazard, but there is much conflicting

evidence in the literature concerning this issue (Dai et

al., 2001; Collison and Anderson, 1996). The NDVI

map was obtained from ETM+ remote sensing image-

ries. The NDVI value was calculated by using the

common formula: NDVI=(IR－R)/(IR+R). The NDVI

value denotes areas of vegetation in an image. The

presence of dense green vegetation implies high

NDVI values due to high concentration of chlorophyll

resulting in a low reflectance in the red band as well

as due to the high stacking of leaves. Sparse vegeta-

tion, on the other hand, implies low NDVI values due

to less or even no chlorophyll and leaves (Pradhan and

Lee, 2010b). The NDVI map of the study area is

shown in Fig. 5b. As shown in Table 1, the NDVI data

comprised seven classes (<0, 0–0.1, 0.1–0.2, 0.2–0.3,

0.3–0.4, 0.4–0.5, and >0.5). To assess the effect of

vegetation cover on the occurrence of Wenchuan

earthquake-triggered landslides, the correlation be-


113

tween NDVI and weight of evidence value of land-

slides is shown in Fig. 6m. It clearly shows that the

higher NDVI value, the lower weight of evidence

value of landslide, in other words, the lower landslide

hazard.

GENERATION OF LSI

All Factors Used Case

To evaluate the contribution of each factor to-

wards landslide hazard, Wenchuan earthquake-

induced landslide distribution data layers were com-

pared with various thematic data layers. For this pur-

pose, equations (1) and (2) were rewritten according

to numbers of cells as follows (Dahal et al., 2008a, b)

1 1 2

3 3 4

ln(( /( )) /

( /( )))iW Npix Npix Npix

Npix Npix Npix

(4)

2 1 2

4 3 4

ln(( /( )) /

( /( )))iW Npix Npix Npix

Npix Npix Npix

(5)

where Npix1 is the number of cells representing the

presence of both a potential landslide causative factor

and landslides; Npix2 represents the presence of land-

slides and absence of a potential landslide causative

factor; Npix3 represents the presence of a potential

landslide causative factor and absence of landslides;

and Npix4 represents the absence of both a potential

landslide causative factor and landslides.

All thematic maps were stored in raster data for-

mat (2 879 rows and 2 622 columns) with a cell size

of 10×10 m and were combined with Wenchuan

earthquake-induced landslide inventory maps for the

calculation of the positive weights, the negative

weights, and the final weights. The calculation result

was obtained in Microsoft Excel using equations (4)

and (5). All of the thematic maps contain several

classes. Therefore, in order to obtain the final weight

of each factor, the positive weight of the factor itself

was added to the negative weight of the other factors

(Dahal et al., 2008a, b; van Westen et al., 2003). The

final calculated weights for landslides are given in Ta-

ble 1.

The resulting total weights, as shown in Table 1,

directly indicate the importance of each factor. If the

total weight is positive, the factor is favorable for the

occurrence of landslides, whereas if it is negative, it is

unfavorable. Some of the factors show little relation to

the occurrence of landslides, as evidenced by weights

close to zero (Dahal et al., 2008a, b). For example, the

Wf values of some classes of distance to drainage os-

cillate around zero without any extreme positive or

negative values, indicating that distance to drainage is

less important. However, it does not mean that the role

of distance to drainage must be exempted absolutely

in the modeling, because its class domain has some

weights. The frequency ratio (% landslide/% area) as-

sists in assessing the relationship between the factors

and landslide occurrences (Dahal et al., 2008a, b; Lee

and Sambath, 2006). For example, slope gradients of

less than 40° show low probabilities of landslide oc-

currences, whereas slope gradients of higher than 40°

are highly vulnerable.

The weights were assigned respectively to the

classes of each thematic layer to produce weighted

thematic maps, which were overlaid and numerically

added to produce a LSI map

LSI=WfSlope+WfAspect+WfFA+WfElevation+

WfCurvature+WfDisDrainage+WfLithology+

WfDisStraBoun+WfDisFaults+WfIntensity+

WfDisSeiFault+WfNDVI+WfDisRoad

(6)

where WfSlope, WfAspect, WfFA, WfElevation,

WfCurvature, WfDisDrainage, WfLithology,

WfDisStraBoun, WfDisFaults, WfIntensity, WfDisSei-

Fault, WfNDVI, and WfDisRoad are distribution-

derived weights of slope angle, slope aspect, flow ac-

cumulation, elevation, curvature, distance from drain-

ages, lithology, distance from stratigraphic boundaries,

distance from all faults, seismic intensity, distance

from seismogenic fault, NDVI, and distance from

roads, respectively. An attribute map was prepared

from LSI values, which were in the range from -7.699

to 4.502.

Other Factor Combination Cases

The LSI map was prepared by referencing the 13

factor maps. In the weight of evidence modeling, the

effect of factor maps is very critical (Lee and Choi,

2004) and effect analysis suggests the predictive

power of factor maps (Dahal et al., 2008a). The mod-

eling assumes that the factors are conditionally inde-

pendent of one another with respect to landslides

(Dahal et al., 2008a, b), which is a precondition for the

modeling (Dahal et al., 2008a; Lee and Choi, 2004).


114

We also tested the conditional independency of factors

to acquire a high success rate. Thus, the following

eight combinations were selected for the effect analy-

sis, except all factors used case in previous chapter.

Combination 1: Geomorphology, geology, vege-

tation, and human intervention-related factor maps

(WfSlope, WfAspect, WfFA, WfElevation, WfCurvature,

WfDisDrainage, WfLithology, WfDisStraBoun, WfDis-

Faults, WfNDVI, and WfDisRoad).

Combination 2: Earthquake and geomorphology-

related factor maps (WfSlope, WfAspect, WfFA, WfE-

levation, WfCurvature, WfDisDrainage, WfIntensity,

and WfDisSeiFault).

Combination 3: Geology and earthquake-related

factor maps (WfLithology, WfDisStraBoun, WfDis-

Faults, WfIntensity, and WfDisSeiFault).

Combination 4: Geomorphology, geology, and

earthquake-related factor maps (WfSlope, WfAspect,

WfFA, WfElevation, WfCurvature, WfDisDrainage,

WfLithology, WfDisStraBoun, WfDisFaults, WfInten-

sity, and WfDisSeiFault).

Combination 5: Geomorphology, geology, earth-

quake, and vegetation-related factor maps (WfSlope,

WfAspect, WfFA, WfElevation, WfCurvature, WfDis-

Drainage, WfLithology, WfDisStraBoun, WfDisFaults,

WfIntensity, WfDisSeiFault, and WfNDVI).

Combination 6: Geomorphology, geology, earth-

quake, and human intervention-related factor maps

(WfSlope, WfAspect, WfFA, WfElevation, WfCurvature,

WfDisDrainage, WfLithology, WfDisStraBoun, WfDis-

Faults, WfIntensity, WfDisSeiFault, WfNDVI, and

WfDisRoad).

Combination 7: Factor maps showing conditional

Table 3 AUC values of different factor combinations

Case AUC value (%)

All factors used 71.82

Combination 1 70.08

Combination 2 71.18

Combination 3 68.63

Combination 4 71.91

Combination 5 72.01

Combination 6 71.72

Combination 7 71.45

Combination 8 71.70

independence of geomorphology, geology, earthquake,

vegetation, and human intervention-related factor

maps (WfSlope, WfAspect, WfElevation, WfDisDrain-

age, WfLithology, WfDisFaults, WfDisSeiFault,

WfNDVI, and WfDisRoad).

Combination 8: Factor maps showing conditional

independence of eight impact factors (WfSlope,

WfAspect, WfElevation, WfDisDrainage, WfLithology,

WfDisFaults, WfDisSeiFault, and WfNDVI).

To compare the landslide hazard value (Table 3)

of all eight combinations along with all the factors

map (calculated as equation (6)), both success rates

were calculated from the area under the curve of the

rate graphs to validate the rationality in the following

chapter.

VALIDATION OF THE MODEL

Success Rate

The distribution of Wenchuan earthquake-

triggered landslides was used to evaluate the validity

of the landslide hazard evaluation result. Verification

was performed by comparing the known landslide lo-

cation data with the landslide hazard map. The rate

curves were created and their areas under the curve

were calculated for the containing all factors case and

other eight cases. The rate explains how well the

model and controlling factors predict the landslide.

Therefore, the area under the curve can assess the

model validation qualitatively. To obtain the success

rate curve for LSI map, the calculated index values of

all cells in the map were sorted in descending order.

Then, the ordered cell values were categorized into

100 classes with 1% cumulative intervals, and classi-

fied LSI maps were prepared with the slicing opera-

tion in GIS software of ArcGIS 9.2. This map was

crossed with the landslide inventory map. Then, the

success rate curve was created from the cross-table

values. The rate verification results appear as a line.

Take the case of all 13 factors used as an example.

The success rate curve is shown in Fig. 7. This curve

is measure of goodness-of-fit. The success rate reveals

that 10% of the study area where LSI had a higher

rank could explain about 30% of total landslides.

Likewise, 20% of higher LSI value could explain

about 50% of all landslides and 30% of higher LSI

value could explain about 64% of all landslides. Fig-


115

ure 5 also provides percentage coverage of landslides

in various higher rank percentage of LSI. To compare

the landslide hazard result, area under the curve (Da-

hal et al., 2008a, b; Lee, 2004), a quantitative measure

of the success rate of LSI, was estimated from the suc-

cess rate graph. A total area equal to 1 denotes perfect

prediction accuracy, whereas an area less than 0.5

shows that the model is invalid (Dahal et al., 2008b).

In this study, area under the curve is 0.718 2, meaning

that the success rate is 71.82%; thus, the model is

valid.

Classified Hazard Map

For providing classified hazard maps, reference

to prediction rate curves (see Fig. 7) was made and di-

vided into five categories, i.e., extremely high, high,

moderate, low, and extremely low by using natural

breaks law. Figure 8 shows the classified hazard map

in the study area.

Figure 7. Cumulative landslide area percentage

diagram showing LSI occurring in cumulative per-

centage of landslide occurrence.

Figure 9 is a bar chart showing comparison of

percent area and percent landslide incidences for each

landslides hazard level. Table 4 shows the landslide

statistic result in different landslide hazard ranks. The

“very high hazard” level covers about 79.1 km2,

19.2% of the total area, but has a very high (21.4 km2,

47.6%) landslide-area percentage of landslides. Fur-

thermore, the landslide frequency in the “high hazard”

level occupied about 106.3 km2; 25.9% of the total

area is also a high (30.0%, 13.5 km2) percentage of

total landslide area. The low and very low levels con-

stitute 20.5% and 13.5% of the study area and have a

landslide occurrence of 7.0% (3.2 km2) and 3.7% (1.7

km2), respectively. The landslide-area percentage in

very high, high, moderate, low, and very low level are

27.0%, 12.7%, 6.1%, 3.7%, and 3.0%, respectively.

The sequence of the landslide-area percentage de-

scends expeditiously.

Figure 8. Landslide hazard zonation map of the

study area.

Figure 9. Bar chart showing relative distribution of

various hazard levels and landslide occurrence. CONCLUSIONS

Landslide hazard mapping is essential in deline-

ating landslide-prone areas in Wenchuan earthquake-

struck region. Various methodologies have been pro-

posed for landslide hazard mapping. In this study, a

weight of evidence modeling with bivariate statistical

methods by means of GIS technology of the Qingshui

River watershed, in the Deyang City, Sichuan Prov-

ince, considering 13 factors affecting landslides was

carried out. The result shows considerable promise in

the identification of landslide hazard areas caused by


116

earthquake. In the verification of landslide hazard map,

the weight of evidence model showed a high success

accuracy of 71.82% in the case of all 13 factors used.

The result map can identify and delineate unsta-

ble hazard-prone areas as basic data to assist slope

management and land use planning. It can also help

planners to choose favorable locations for develop-

ment schemes. This study may be less useful at a

site-specific scale, although the result is valid for gen-

eralized assessment purposes.

Table 4 Landslide statistic result in different landslide hazard rank

Landslide hazard rank Area (km2) Area of % Landslide area (km2) Landslide area of % Landslide-area

percentage (%)

Very high 79.1 19.2 21.4 47.6 27.0

High 106.3 25.9 13.5 30.0 12.7

Moderate 85.7 20.8 5.2 11.6 6.1

Low 84.4 20.5 3.2 7.0 3.7

Very low 55.5 13.5 1.7 3.7 3.0

“Area of %” means the percentage of total study area divided of level area; “Landslide area of %” means the per-

centage of total landslide area divided of landslide area in a level; and “Landslide-area percentage (%)” means the

percentage of area in a level divided of landslide area in the level.

ACKNOWLEDGMENTS

This study was supported by the International

Scientific Joint Project of China (No. 2009DFA21280),

the National Natural Science Foundation of China (No.

40821160550), and the Doctoral Candidate Innovation

Research Support Program by Science & Technology

Review (No. kjdb200902-5).

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