Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong
Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong
Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong
www.elsevier.com/locate/geomorph
Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, Peoples Republic of China
b
Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, Peoples Republic of China
Received 22 September 2000; received in revised form 14 March 2001; accepted 20 March 2001
Abstract
Steep terrain and high a frequency of tropical rainstorms make landslide occurrence on natural terrain a common
phenomenon in Hong Kong. This paper reports on the use of a Geographical Information Systems (GIS) database, compiled
primarily from existing digital maps and aerial photographs, to describe the physical characteristics of landslides and the
statistical relations of landslide frequency with the physical parameters contributing to the initiation of landslides on Lantau
Island in Hong Kong. The horizontal travel length and the angle of reach, defined as the angle of the line connecting the head of
the landslide source to the distal margin of the displaced mass, are used to describe runout behavior of landslide mass. For all
landslides studied, the horizontal travel length of landslide mass ranges from 5 to 785 m, with a mean value of 43 m, and the
average angle of reach is 27.7. This GIS database is then used to obtain a logistic multiple regression model for predicting
slope instability. It is indicated that slope gradient, lithology, elevation, slope aspect, and land-use are statistically significant in
predicting slope instability, while slope morphology and proximity to drainage lines are not important and thus excluded from
the model. This model is then imported back into the GIS to produce a map of predicted slope instability. The results of this
study demonstrate that slope instability can be effectively modeled by using GIS technology and logistic multiple regression
analysis. D 2002 Elsevier Science B.V. All rights reserved.
Keywords: Landslides; Runout; Logistic multiple regression; Geographical Information Systems (GIS)
1. Introduction
Landslides in mountainous terrain often occur
during or after heavy rainfall, resulting in the loss of
life and damage to the natural and/or built environment. Mapping or delineating areas susceptible to
landslides is essential for land-use activities and
management decision-making in mountainous areas.
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0169-555X/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 9 - 5 5 5 X ( 0 1 ) 0 0 0 8 7 - 3
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The limitations in this approach are in the reproducibility of results and in the subjectivity in decision
rules. Statistical models involve the statistical determination of the combinations of physical parameters
that have led to past landslides. Quantitative or semiquantitative estimates are then made for areas currently free of landslides, but where similar conditions
exist. Both multiple regression analysis and discriminant analysis have been used to explore relations
between landslide occurrence and the terrain variables (e.g. Yin and Yan, 1988; Carrara et al., 1991,
1995; Brunori et al., 1996; Dhakal et al., 1999). A
major deterrent to such techniques has undoubtedly
been the logistics of collecting and calculating quantitative data (Rowbotham and Dudycha, 1998).
Another problem is that the probability values computed from such techniques can often fall outside the
0 to 1 range of the probability values, which makes
it difficult to relate the output to a systematic probability surface. Recently, logistic regression, one of
a family of generalized linear models that are well
suited to analyzing a presence absence dependent
variable, has been used to predict slope instability
(Carrara et al., 1991; Mark and Ellen, 1995; Rowbotham and Dudycha, 1998). Geotechnical process
approaches are based on slope stability analyses, and
are applicable only when the ground conditions are
fairly uniform across the study area and the landslide
types are known and relatively easy to analyze (e.g.
Terlien et al., 1995; Wu and Sidle, 1995). The
advantage of the geotechnical process models is that
they permit quantitative factors of safety to be calculated, while the main problem is the high degree
of simplification that is usually necessary for the use
of such models.
An assessment of landslide hazard requires knowledge of the landslide characteristics and runout behavior of landslide mass. This research was undertaken
with a view to characterizing landslides on natural
terrain of Lantau Island, Hong Kong, and then developing a Geographical Information Systems (GIS)
approach to modeling slope instability. This study
area is prone to landslides when subjected to heavy
rainstorms. For example, widespread landslides
occurred in Lantau Island, following heavy rainfall
on 18 July 1992 and 5 November 1993 (Franks,
1999). There are four objectives in this research: (1)
to characterize landslides on natural terrain of Lantau
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3. Data sources
The study area was examined using the ArcView
GIS software. The data available for this study include
topography, land-use classification, a terrain morphological map, superficial and bedrock geology, and the
locations and trails of landslides. All locational, geological, and geomorphological features provided by
the different thematic maps mentioned above were
imported into the ArcView GIS, or digitized using the
GIS software PC Arc/Info, and then transferred to
ArcView for subsequent analyses.
Contour lines and drainage lines are obtained from
the 1:20,000 scale topographic maps with a contour
interval of 20 m. Elevation data were obtained from
the digital elevation model (DEM) with a resolution of
20 20 m derived from the 1:20,000 scale digital
contour lines of the area. Two data layers are derived
from these elevation data, namely slope aspect and
slope gradient. Proximity to drainage line is calculated
using GIS functions.
Superficial and bedrock geological data are
obtained from 1:20,000 scale solid and superficial
geological maps developed by the Hong Kong Geological Survey of the Geotechnical Engineering
Office (GEO), previously known as the Geotechnical
Control Office (GCO). The maps covering the study
area describe the geological groups, each comprising
geological units of broadly similar lithology. For ease
of analysis, the groups were further reclassified into
nine categories: alluvial, terrace and beach deposits
(ATB), debris flow deposits and talus (DF), sedimentary rock (SR), metasedimentary rock (MSR), intrusive rock (IR), minor intrusive rock (MIR), ash tuff,
tuffite and tuff breccia (BCT), trachydacite, dacite
and rhyolite lava (TDR), and volcaniclastic sedimentary rock (VSR), based on their stratigraphy and
genesis.
The 1:20,000-scale digital terrain classification
maps covering the study area, developed by the
GEO, were available to the authors. This dataset
contains terrain classification information that
includes erosion and stability, terrain component and
morphology, and slope gradient, which was derived
from Geotechnical Areas Studies Programme (GASP)
primarily using aerial photography interpretation
(API) technique (Brand, 1988; Geotechnical Control
Office, 1988a,b). Based on the terrain classification
information, terrain morphology which describes the
physical appearance of the slope and the general shape
of the slope profile (straight, concave or convex) is
extracted and then reclassified into 10 categories for
simplicity: hillcrest or ridge (A), straight sideslope
(B), concave sideslope (C), convex sideslope (D),
straight footslope (E), concave footslope (F), convex
footslope (G), drainage plain (H), rock outcrop (M),
and others, such as reclamation and coastal plain, (O).
All the footslope and drainage plain terrain consists of
colluvium, and all the sideslope terrain consists of
insitu geological materials (Geotechnical Control
Office, 1988a,b).
The landslide database used was derived from the
Geotechnical Engineering Office work in which landslide locations and trails were digitized from 23
temporal sets of 1:20,000 to 1:40,000 scale stereoscopic aerial photographs dating from 1945 to 1994
(Evans, 1998; King, 1999). The aerial photographs
used thus cover a period of 50 years and recent
landslides as old as about 10 years were visible before
re-vegetation masked most scars. Recent landslides
(Fig. 3) were observed on aerial photographs as a
217
Fig. 3. Shaded relief map of the study area showing locations of landslides (black dots).
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ogy and structure, slope gradient and slope morphology, slope aspect, elevation, proximity to drainage
line, and land-use type. The digital map of landslide
distribution was overlain on the raster data layers of
physical parameters mentioned above using the GIS,
and landslide frequency, which is the number of
landslides per squared kilometer, was calculated for
each category on the physical parameter maps.
220
Fig. 6. Correlations between landslide frequency (landslides per squared kilometer) and the physical parameters (symbols refer to text).
tion model (DEM). Examination of landslide frequency with the corresponding slope gradient categories shows an increase with slope gradient until the
maximum frequency is reached in the 35 40 category, followed by a decrease in the >40 category
(Fig. 6b).
Slope morphology can probably affect the susceptibility of a slope to landslide in several ways. The
shape of a slope influences the direction of and
amount of surface runoff or subsurface drainage
reaching a site. Concentration of subsurface drainage
within a concave slope, resulting in higher pore water
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222
Z
where Pr(event) is the probability of an event occurring. In the present situation, the Pr(event) is the
estimated probability of landslide occurrence. As Z
varies from 1 to + 1, the probability varies from
0 to 1 on an S-shaped curve. Z is the linear combination:
Z B0 B1 X1 B2 X2 . . . Bn Xn
where Bi (i = 0, 1,. . ., n) is the coefficient estimated
from the sample data, n is the number of independent
variables (i.e. landslide-related physical parameters),
and Xi (i = 1, 2,. . ., n) is the independent variable.
However, in a strict sense, it is not a probability since
the dynamic variables triggering landslides, such as
rainfall, are not accounted for. It may be more
appropriate to term it hereafter slope instability or
landslide susceptibility based on the quasi-static physical parameters. In logistic multiple regression, a
coding scheme should be selected for the categorical
variables by creating a new set of variables that
correspond in some way to the original categories.
The number of new variables required to represent a
categorical variable is one less than that of the
number of categories. The coefficients of the logistic
multiple regression model are estimated using the
maximum-likelihood method. In other words, the
coefficients that make the observed results most
likely are selected. Since the relationship between
the independent variables and the probability is nonlinear in the logistic multiple regression model, an
iterative algorithm is necessary for parameter estimation.
Logistic multiple regression modeling is intended
to describe the likelihood of landslide occurrence on a
regional scale, and is very suitable for the assessment
of slope instability, since the observed data consist of
locations (points) or cells with a value of 0 (absence of
landslide) or 1 (presence of landslide). This method
allows a spatial distribution of probabilities or sus-
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Table 1
Regression coefficients estimated for the model
Variable
Constant term
Slope gradient ()
Elevation (m)
Categories
0 10
10 15
15 20
20 25
25 30
30 35
35 40
R 40
0 50
50 100
100 150
150 200
200 250
250 300
300 350
350 400
400 450
450 500
500 550
550 600
600 650
650 700
>700
Coefficient
Variable
Categories
Coefficient
9.755
10.678
4.369
3.374
2.639
1.153
0.863
0.077
Slope aspect
Flat
N
NE
E
SE
S
SW
W
NW
developed land
forested land
shrub-forested land
densely grassed land
moderately grassed land
sparsely grassed land
alluvial, terrace and beach deposits
debris flow deposits and talus
sedimentary rock
metasedimentary rock
intrusive rock
minor intrusive rock
ash tuff, tuffite and tuff breccia
trachydacite, dacite and rhyolite lava
volcaniclastic sedimentary rock
0.431
0.112
0.468
0.670
0.548
0.882
0.547
0.303
11.214
11.441
11.445
11.181
11.322
11.212
10.959
10.816
10.550
10.434
9.240
9.235
8.629
7.653
Land-use type
Lithology
7.121
0.033
0.257
0.225
0.258
7.298
0.984
0.233
0.716
2.076
2.413
0.990
0.076
224
225
226
227
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