Content uploaded by Hiromitsu Yamagishi
Author content
All content in this area was uploaded by Hiromitsu Yamagishi on Oct 05, 2020
Content may be subject to copyright.
Original Paper
Landslides 1 · 2004
Landslides (2004) 1:73–81
DOI 10.1007/s10346-003-0006-9
Received: 11 September 2003
Accepted: 7 November 2003
Published online: 21 February 2004
Springer-Verlag 2004
Lulseged Ayalew · Hiromitsu Yamagishi · Norimitsu Ugawa
Landslide susceptibility mapping using GIS-based
weighted linear combination, the case in Tsugawa area
of Agano River, Niigata Prefecture, Japan
Abstract A spatial database of 791 landslides is analyzed using
GIS to map landslide susceptibility in Tsugawa area of Agano
River. Data from six landslide-controlling parameters namely
lithology, slope gradient, aspect, elevation, and plan and profile
curvatures are coded and inserted into the GIS. Later, an index-
based approach is adopted both to put the various classes of the
six parameters in order of their significance to the process of
landsliding and weigh the impact of one parameter against
another. Applying primary and secondary-level weights, a con-
tinuous scale of numerical indices is obtained with which the
study area is divided into five classes of landslide susceptibility.
Slope gradient and elevation are found to be important to
delineate flatlands that will in no way be subjected to slope failure.
The area which is at high scale of susceptibility lies on mid-slope
mountains where relatively weak rocks such as sandstone,
mudstone and tuff are outcropping as one unit.
Keywords Landslide · Susceptibility · GIS · Agano River · Japan
Introduction
Landslides are common along Agano River, in Niigata Prefecture
of Japan. Despite the presence of dense vegetation and little human
interference, some areas are currently too sensitive for high
precipitation and become sites of active landsliding. Every year, a
significant amount of land near or away from the river is changed
into unstable ground. As part of the solution to the problem,
localized studies are repeatedly conducted by engineering com-
panies, and a variety of remedial measures are installed along
roads, river banks, and ledges of mountains. In addition, there are
efforts for a regional landslide “hazard” mapping and analyses in
various sections of Agano River by a group of experts from the
Landslide Society of Japan (Higaki 2003; Chigira 2003). This study
is an extension of not only these efforts but also another GIS-aided
susceptibility mapping carried out in Kakuda-Yahiko Mountains
of the same Niigata Prefecture (Ayalew and Yamagishi 2003).
The Agano River has a channel length of 210 km and a
catchment area of about 7,710 km2. It flows from east to west and
constitutes a large drainage system in Fukushima and Niigata
Prefectures of Japan. The part of Agano River selected for this
study is known as Tsugawa area. It is located about 50 km east of
Niigata City (Fig. 1), and covers four 1:25000 sheets of the
National Geographic Institute of Japan with a total area of around
410.18 km2. Precipitation is high in Tsugawa, and comes in the
form of snow and rainfall. A 20-year record up to the year 2000
yields an annual mean precipitation of 2,293 mm and an average
snow depth of 111 cm. A direct result of this high precipitation is
thick vegetation available throughout the region.
It is known that a landslide susceptibility map relies on a
rather complex knowledge of slope movements and their
controlling factors. Mapping of areas, which are not currently
subjected to landsliding, is based on the assumption that
forthcoming landslides occur under similar conditions of those
observed in the past (Guzzetti et al. 1999). The process of GIS-
aided landslide susceptibility mapping at present involves several
methods that can be considered as either qualitative or quanti-
tative. Qualitative methods depend on expert opinions, and are
often useful for regional assessments (Soeters and van Westen
1996; Aleotti and Chowdhury 1999). Quantitative methods rely on
observed relationships between controlling factors and landslides
(Guzzetti et al. 1999).
In this study, we used a method known as “Weighted Linear
Combination (WLC)”, which can be taken as a hybrid between
qualitative and quantitative methods. Like quantitative approach-
es, such as bivariable statistical methods, it starts with compar-
ison of data-layers corresponding to landslide controlling
parameters and the landslide inventory map and involves the
computation of landslide density to assign primary-level weights
for each class of a particular parameter. Then, it turns to
procedures common in qualitative methods for an application of
secondary-level weights to the parameters themselves using a
pair-wise comparison matrix. The final steps of this method are
the combination of all weighted layers into a single map and the
classification of the scores of this map into landslide suscepti-
bility categories which are neither new nor unfamiliar to both
qualitative and quantitative approaches.
Description of landslides
It is now becoming universal that susceptibility mapping starts
with the inventory of landslides. In this study, landslides were
mapped by interpreting the 1:20000 scale aerial photographs
taken in 1971 and 1976. As a supplement for this, a red relief image
(RRI) of the area was obtained from Asia Air Survey Company
(Tokyo), and was subjected to a variety of remote sensing
analytical techniques. Band 2 and 3 of this image were especially
useful to confirm the boundary of landslides using image
enhancement methodologies such as contrast stretch and digital
filtering. Band 1 allowed marking out ridges and stream lines.
In total, we were able to make an inventory of 791 landslides
and later described them using the system introduced by Cruden
and Varnes (1996). The total area covered by landslides is about
53 km2, nearly 13% of the area under study. For reasons linked to
geomorphology and geology, many of the landslides are located in
the eastern half of the study area (Fig. 2). The smallest landslide is
0.13 km2in extent, while the largest one has an area of 0.9 km2.
A series of field surveys have been conducted with an aim of
studying the characteristics of landslides at different times.
Accordingly, as far as the type of movement is concerned, it was
observed that slides are the dominant forms of slope failure. In
terms of depth, many landslides are shallow, although some of
those occurred on hillsides are relatively deep-seated. The state or
73
Fig. 1 Location map of the study area
Landslides 1 · 2004
74
Original Paper
activity of landslides was in such a way that most of the mapped
landslides are relict and stabilized. This was determined on the
basis of signs of old mass movements such as crescent-shaped
scarps, abnormal bulges on inclined slopes and hummocky
surfaces. However, a number of landslides in the central part of
the study area near Agano River, which contain degraded
channels and blocks of bedrocks whose only source appears to
be upslope, are active. Figure 3 presents the effect of one of these
active landslides on a road that is located in the southeastern part
of the study area, close to Agano River channel.
Event-controlling parameters
The occurrence of landslides in general is largely a function of the
interaction of natural phenomena such as unfavorable lithology,
stratigraphic sequence, structural makeup, geomorphological
setting, earthquake, rainfall, etc. In GIS-based analyses, these
phenomena which are directly or indirectly related with the
formation of landslides (the event) are commonly known as
event-controlling parameters. Although, it is believed that the
accuracy of susceptibility mapping increases when all event-
controlling parameters are included in the analytical process, it is
usually difficult to get so, because detailed data is hard to find. For
this reason, analyses in this study depend only on lithology and
the topographic attributes of the region such as elevation, slope
gradient, aspect and curvature. A discussion on these parameters
with regard to their effect on the process of landsliding is given
below.
The lithological makeup of the study area
According to a 1:50000 geological map compiled by Hasegawa
(1983), more than 20 rock units are present in the study area. The
southwestern part is composed of a Pre-Cretaceous complex of
sedimentary rocks including mudstone, sandstone, chert, lime-
stone and greentuffes. To the north of this complex, a massive
Fig. 2 Landslide distribution in Tsugawa area of Agano River
Fig. 3 A landslide occurred at a locality called Iwatsu on October, 2001 in the
southeastern part of the study area, close to Agano River channel
Landslides 1 · 2004 75
granite of Cretaceous age occupies more than half of the western
part of the study area. The eastern limit of both the Pre-
Cretaceous complex and the massive granite is marked by two
major faults that run in the NNE-SSW direction, almost parallel to
each other. Further east, the area is covered mainly by Neogene
formations (Uemura and Yamada 1988), composed of sedimen-
tary rocks such as conglomerates and sandstones and a mixture
of rhyolite lavas, pyroclastics deposits and perlitic hyaloclastic
breccias intruded by rhyolitic, andesitic, or basaltic dykes.
Ignoring stratigraphic content and focusing on lithological
similarity, the 20 rock units shown on the geological map
compiled by Hasegawa (1983) were in this study simplified into 11
as shown in Fig. 4. Our study makes a distinction between debris
flow deposits and other types of mass movements. Hence,
although there are some places in the study area where old
debris flow deposits are present, as it is shown in Fig. 4, these
materials are not included in the landslide distribution map in
Fig. 2. The reason is that these deposits are mixed with in-situ
Fig. 4 The simplified form of the lithological map of the study area modified from Hasegawa (1983). Symbols such as AL, AN, etc, are useful to read the corresponding
landslide densities in Fig. 5
Landslides 1 · 2004
76
Original Paper
rock materials, and are highly stabilized and significantly
lithified, and we found it not reasonable to associate them with
other types of mass movements.
The GIS work was started by rasterizing Fig. 4 and the
landslide distribution map (Fig. 2). The pixel size of these raster
maps was determined by the dimension of the digital elevation
model used in this study. Next, the two maps were overlapped and
landslide pixels lying on each of the rock units were counted, and
the areas that they cover were calculated. Then, the ratios between
each of these areas and the total areas covered by the
corresponding rock units were computed and changed into
percentages to form what is known as landslide densities in this
study. The landslide densities were used as a means of rating rock
units against susceptibility.
In the study area, about 40% of the slopes affected by
landslides are covered by Tertiary pumiceous tuffs. But, in terms
of landslide density, the sandstone layers present mainly in the
eastern part account for 23% (Fig. 5). With a landslide density of
20%, the Tertiary pumiceous tuffs immediately follows the
sandstones and the rock unit corresponding to rhyolite lavas
and dykes was third by having a density of 18%. On the lower side
of the spectrum, the alluvial deposits and the massive granite
have a negligible amount of landslide densities, and are believed
to play a little role in the process of landsliding.
Topographical constraints
Thanks to the advance in technology, digital elevation models
(DEMs) are now standard tools for landslide analyses. This study
took the full advantage of this, and significant terrain attributes
were produced from a 10mX10m DEM obtained from GISMAP of
Hokkaido Chizu Co. Ltd. GIS technology permits patterns of
instability to be resolved and mapped at the scale of the DEM.
This means, with the DEM employed in this study, it was possible
to conduct a relatively fine scale analyses which can include slope
failures as small as 100 m2in extent.
Primary topographic attributes which can be produced from a
DEM are generally first and second derivatives of elevation data,
and include parameters such as slope, aspect, profile and plan
curvatures (Moore et al. 1991). The effect of slope and aspect on
landslides is widely documented by Lee and Min (2001) and Dai
and Lee (2002). But, little is said about the influence of profile and
plan curvatures in the literature. The profile curvature represents
the rate of change of slope for each cell in the direction of dipping.
The plan curvature shows the bending of the surface perpendic-
ular to the slope direction. Together with other factors, plan and
profile curvatures control the flow of water in and out of slopes
and are, therefore, important in the study of landslides.
In the study area, highlands with altitudes greater than
1,000 m correspond to the summits of granite in the northeastern
end and some ridges in the central part. In between and away
from such peaks, the topography is rough and consists of twisting
valley walls and cliffs. Undoubtedly, the main features of the study
area are landslides which occupy ample sectors of these places. In
order to assess the effect of altitude on landslide distribution, we
classified the elevation map of the project site into 22 categories
on a 50-m basis. Then, we calculated landslide densities for each
class of elevation adopting the system discussed in the lithology
section above. It became apparent that landslide density is greater
at ranges of altitudes that correspond to a little higher than the
foot-hills of mountains (201–250 m). Density decreases both
upward and downward from these elevation marks and becomes
0 above the height of 800 m (Fig. 5).
As far as gradient is concerned, five classes of slope angles
were established and the corresponding landslide densities were
calculated. It is found that landslide density is high (42%) on
slopes with gradients in the range of 2.5–15 (Fig. 5), and decreases
with an increase or decrease in slope angle. The aspect of the
topography is also an essential component of stability analyses,
and it was observed that landslide density is higher in east-facing
slopes than in their west oriented counterparts, most probably
due to the action of erosion by westward flowing streams.
In addition, both plan and profile curvatures were inputs in
this study. The type of landslide occurring in a certain landform
is a function of plan curvature (Ayalew and Yamagishi 2004). This
is true because debris and mudflows usually occur when the
lateral profile is concave. Profile curvature governs the run-out
distance of disturbed materials. Some other properties like the
amount of material involved, the frequency of occurrence, the
shape of rupture surfaces, etc, are in addition to other factors,
functions of both plan and profile curvatures.
In the study area, data on profile and plan curvatures allowed
dividing slopes into classes of concave, planar and convex
topographic surfaces. Concave slope facets are characteristics of
landscapes affected by old mass movements. Topographic
surfaces made up of convex plan curvatures are present in places
where existing cliffs portray strong lithological variations in the
horizontal direction. They are also common in locations where
terrains are dissected by sub-parallel, deeply incised ravines.
Planar topographic surfaces exist in localities where the under-
lying geology is relatively homogeneous allowing the slope to dip
in one direction.
There is a general consensus that high probability of failure
exists when at least one of the slope curvatures is concave because
of the possibility for the concentration of groundwater in a deep
soil stratum. Besides, many researchers agree that landslides on
convex topographic surfaces need long time to develop since the
slope geometry forces water to drain away from the site. But in
the study area, it was observed that landslides occur both on
convex and concave slopes. In fact, there was no difference in
landslide densities between concave and convex profile curva-
tures, although a significant discrepancy was observed in the case
of concave and convex plan curvatures (Fig. 5).
Weighted linear combination (WLC)
As it is stated earlier, weighted linear combination (WLC) is a
concept where event-controlling parameters can be combined by
applying primary- and secondary-level weights. The primary-
level weights are rule-based in that ratings are given to each class
of a parameter on the basis of a certain criterion. In this study,
this criterion is the landslide density, a ratio between the area
occupied by landslide pixels on a class of a certain parameter and
the total area of that class, changed into percentage. The
secondary-level (factor) weights are, however, opinion-based
scores, which determine the degree of tradeoff of one parameter
against another. The WLC adopted in this study shares some
similarities with the type of AHP (analytic hierarchy process)
used by Yagi (2003). The difference is that the latter considers
only one-level weighting system developed by collecting expert
opinions, the ratings of which might correspond to secondary-
level (factor) weights of this study.
Landslides 1 · 2004 77
Fig. 5 Bar graphs showing landslide
densities. Sub-divisions of the X-axis
represent classes of the six landslide-
controlling parameters
Landslides 1 · 2004
78
Original Paper
A variety of techniques are available to develop factor weights.
When the numbers of parameters are more than two like the case
in this study, however, breaking the information down into simple
pair-wise comparisons in which only two factors can be
considered at a time can greatly facilitate the weighting process.
This technique has been described by Saaty (1988, 1994) and Saaty
and Vargas (2001) in the context of decision making processes.
The idea behind is that weights that converge to one are derived
through a principal eigenvector of a square reciprocal matrix of
pair-wise comparisons between event-controlling parameters. A
pair-wise comparison matrix has also been used by Juang et al.
(1992) to map slope failure potential using fuzzy sets.
In our case, secondary-level or factor weights that can capture
the relative importance of one parameter relative to another were
established on the basis of a 9-point recording scale, which
represents nine linguistic expressions and their corresponding
numerical values. The linguistic expressions explain the fact that
the state of knowledge is imperfect, while the numerical values are
quantified translations useful for calculating factor weights.
Science still lacks a direct way of evaluating intuition or
expressions, and the validity of the numerical values may best
be judged by the factor weights and the consistency of the
calculation process.
The complete lists of expressions and numerical grades
adopted in this study are given in Table 1. To find the appropriate
linguistic expressions, one may use a pixel by pixel investigation,
divide the area into small partitions of significant extent or
consider the project site as a single entity and perform analyses.
In all cases, the assessments are to a large extent subjective, and
since the knowledge source varies from person to person, it is
always true that the best judgment comes from an individual with
good expertise.
In this study, we divided the area into around 41 partitions of
100,000 pixels. Then, we investigated how landslide pixels are
distributed in each partition, and attempted to determine the
effect of a certain parameter on slope failures compared to
another. Next, we assigned appropriate linguistic expressions for
each assessment and put the corresponding numerical grades.
Afterwards, we summed up these grades to find a total value, and
divided this value by the number of partitions to determine the
average. For example, after assessing each partition for the role of
elevation and aspect, we came to the conclusion that the former is
on average “moderately more important” than the later in
forming landslides. Hence, we put three in the pair-wise
comparison matrix box that correlates the two parameters
(Table 1). If the inverse were the case, we would consider 1/3 as
a rating value.
In this study, the pair-wise comparison matrix contained 36
boxes. Since pair-wise comparison matrices are symmetrical in
nature, only 21 values were needed to fill in the diagonal and the
lower triangular half of the matrix. Then, in order to compute the
principal eigenvector of the matrix and obtain a best-fit set of
factor weights automatically in the way Saaty (1994) and Saaty
and Vargas (2001) have described, raster maps produced by
combining the parameters with landslide distribution were
necessary.
The final result consists of the factor weights and a calculated
consistency ratio (CR), as it is shown in Table 1. The CR is a ratio
between the matrixs consistency index and random index, and in
general ranges from 0 to 1. The random index is the average
consistence index obtained by generating large numbers of
random matrices. A CR close to 0 indicates the high probability
that the weights were generated randomly (Saaty 1988, 1994).
Values less than 0.1 are often acceptable, although this depends on
the objective of the study. A CR of 0.07 in this study is good
enough to recognize the factor weights as reasonable values.
The landslide susceptibility map
In seeking a landslide susceptibility map, the primary-level
weights corresponding to classes of parameters were multiplied
by secondary-level or factor weights to produce a continuous
scale of numerical values. Dividing these values into susceptibility
classes was, however, not easy as there are no statistical rules
which can guide categorize continuous data automatically. There
are some mathematical methods proposed by Scott (1979) and
Friedman and Diaconis (1981), which rely on the optimum bin
width classification of the histogram, but they are ineffective to
multimodal distributions (Szen and Doyuran 2004). The prob-
lem of changing continuous data into two or more categories
remains always unclear in landslide susceptibility mapping,
because most authors use their expert opinion to develop class
boundaries.
In this study, we took into consideration four systems of
classifiers that use the so-called natural breaks, quantiles, equal
intervals, and standard deviations, and attempted to choose the
one that best suits the objectives of our study. While these
classifiers are well established in statistics, they often lead to
different results, as they make very different statements about
how values should be divided. The classification scheme that
relies on natural breaks for example identifies break points by
Table 1 A pair-wise comparison matrix for calculating factor weights
Pair-wise comparison 9 point continuous rating scale
Extremely V. Strongly Strongly Moderately Equally Moderately Strongly V. Strongly Extremely
Less important Important More Important
1/9 1/7 1/5 1/3 1 3 5 7 9
Aspect Elevation Lithology Plan curvature Profile curvature Slope gradient Factor weights Consistency ratio (CR)
Aspect 1 0.0657 0.07
Elevation 3 1 0.1929
Lithology 3 3 1 0.2569
Plan curvature 1 1/5 1/3 1 0.0715
Profile curvature 3 1 1 1 1 0.1478
Slope gradient 3 1 1 5 3 1 0.2651
Landslides 1 · 2004 79
looking for patterns inherent in the data. In quantile classifica-
tion, features are grouped by equal numbers in each class. The
equal interval scheme divides the range of values into equal-sized
subdivisions. When a standard deviation is used, data is classified
based on the amount a value varies from the mean.
The quantile classification has a disadvantage in that it places
widely different values into the same class. Hence, it was not used
in this study. Using equal intervals was also found to be not
practical since it emphasizes one class of susceptibility relative to
others. In natural breaks, boundaries are set where there are
relatively big jumps in data values. The histogram of the
numerical values obtained in this study (Fig. 6) is multimodal
and has a number of peaks but shows no empty class intervals or
jumps. So, natural breaks were also not appropriate. The last
alternative was the classification scheme that uses standard
deviations. This method has a certain merit in that it uses the
mean value to generate class breaks and allowed us to divide the
result of this study into five categories by adding or subtracting 1
standard deviation at a time (Fig. 6). As is shown in Fig. 7, the five
categories correspond to five relative scales of landslide suscep-
Fig. 6 The histogram of the numerical
indices (result) obtained
Fig. 7 The landslide susceptibility map
Landslides 1 · 2004
80
Original Paper
tibility, namely extremely low (7.04%), very low (26.54%), low
(29.04%), medium (33.75%) and high (3.63%).
Most highlands together with the foothills of mountains fall in
either very low or low susceptible zones indicating that elevation
and slope gradient played an important role in the classification
process. High susceptibility is found to be characteristics of weak
rocks like mudstones and tuffs. As expected, flatlands such as
river channels and interfluves are designated to be extremely low
susceptible. In general, by honoring what was observed in some
sectors of the project site, the map given in Fig. 7 is believed to
reflect the potential for sliding of slope materials in Tsugawa area
of Agano River.
Conclusion
The spatial distribution of landslides is a result of the interaction
of many parameters. A reliable and accurate susceptibility map
depends on the inclusion and proper determination of the role of
these parameters. In this study, six landslide-controlling param-
eters, namely lithology, slope gradient, aspect, elevation, and plan
and profile curvatures, were considered. A method called
weighted linear combination was used wherein individual classes
of each parameter are rated and factor weights are assigned in
order to produce a landslide susceptibility map by means of
weighted average values. The results of the entire analyses and
evaluation allowed us to divide the study area into five zones of
susceptibility, namely extremely low (7.04%), very low (26.54%),
low (29.04%), medium (33.75%) and high (3.63%). The landslide
susceptibility map is believed to be useful for identifying slope
sectors liable to landsliding on relative basis.
References
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new
perspectives. Bull Eng Geol Environ 58:21–44
Ayalew L, Yamagishi H (2003) GIS-based landslide susceptibility mapping in Kakuda-
Yahiko Mountains. In: Environmental science of the Mt. Yahiko-Mt. Kakuda and their
surroundings. Report of the Yahiko Project, pp 67–84
Ayalew L, Yamagishi H (2004) Slope movements in the Blue Nile basin, as seen from
landscape evolution perspective. Geomorphology 57:97–116
Chigira M (2003) Landslide hazard assessment by using AHP method in Minowayama
area in the mid watershed of the Agano River. In: Abstract volume of the 42nd
annual meeting of the Japan Landslide Society, Toyama, Japan, pp 221–222
Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL
(eds) Landslides: investigation and mitigation, Transport Res Board Spec Rep 247, pp
36–75
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS,
Lantau Island, Hong Kong. Geomorphology 42:213–238
Friedman D, Diaconis P (1981) On the histograms of a density estimator L2 theory.
Zeitschrift fur Wahrscheinlichkeitstheorie und Verwandte Gebiete 57:453–476
Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a
review of current techniques and their application in a multi-scale study, Central
Italy. Geomorphology 31:181–216
Hasegawa Y (1983) Surficial geological map of Tsugawa area (1:50000) with
explanatory text. Niigata Prefecture, Niigata, 95 pp
Higaki D (2003) Detection of landslide hazardous slopes in the Kanose area, Central
Japan by AHP method. In: Abstract volume of the 42nd annual meeting of the Japan
Landslide Society, pp 217–220
Juang CH, Lee DH, Sheu C (1992) Mapping slope failure potential using fuzzy sets. J
Geotech Eng 118(3):475–494
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea.
Environ Geol 40:1095–1113
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of
hydrological, geomorphological and biological applications. Hydrolog Process 5:3–30
Saaty LT (1988) The analytic hierarchy process: planning, priority setting, resource
allocation, RWS publications, Pittsburgh, 287 pp
Saaty LT (1994) Fundamentals of decision making and priority theory with analytic
hierarchy process, RWS publications, Pittsburgh, 527 pp
Saaty LT, Vargas LG (2001) Models, methods, concepts, and applications of the analytic
hierarchy process. Kluwer Academic, Boston, 333 pp
Scott DW (1979) On optimal and data-based histograms. Biometrika 66(3):605–610
Soeters R, van Westen CJ (1996) Slope instability recognition, analysis, and zonation. In:
Turner AK, Schuster RL (eds) Landslides: investigation and mitigation. Transport Res
Board Spec Rep 247, pp 129–177
Szen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment
using geographical information systems: a method and application to Asarsuyu
Catchment, Turkey. Eng Geol 71:303–321
Uemura T, Yamada T (1988) Regional geology of Japan. Part 4, Chubu, Kyoritu Shuppan,
Tokyo
Yagi H (2003) Development of assessment method for landslide hazardness by AHP. In:
Abstract volume of the 42nd annual meeting of the Japan Landslide Society, pp
209–212
L. Ayalew · H. Yamagishi ()) · N. Ugawa
Department of Environmental Science,
Niigata University,
8050 Ikarashi, 2-no-cho, 950-2181 Niigata City, Japan
e-mail: hiroy@env.sc.niigata-u.ac.jp
Landslides 1 · 2004 81
