Incorporating ground cracks in the estimation of postseismic landslide susceptibility
Shui Yamaguchi, Mio Kasai§,
Research Faculty of Agriculture
Hokkaido University
Kit-9, Nishi-9, Kita-ku, Sapporo, Japan§
kasaim@for.agr.hokudai.ac.jp
Abstract—An intense earthquake not only induces numerous
landslides over a broad area, but can also trigger landslides; this isbecause the ground loses strength when strong tremors occur.
Following an earthquake, landslide susceptibility should beappraised immediately to avoid further disasters and enable safe andprompt restoration work to proceed in the affected region. This
study developed a topographic index to represent a soil mass with
dense seismic ground cracks (DCI), to allow the degree of ground
disturbance to be expressed on a local scale. The index was then used
as a conditioning factor in a statistical model based on the weight ofevidence (WoE) approach, to assess the correlation between DCI andlandslides and estimate landslide susceptibility. An analysis of 38
post-seismic slides was conducted in a 2 km2 area of the Aso regionin Kyushu, Japan, where a powerful earthquake (Mw 7.0) struck in
April 2016. Tectonic, seismic, lithological, climatic, and vegetational
conditions were assumed to be similar over the area due to its small
size, and only topographic conditioning factors were considered. The
results demonstrated a correlation between DCI and post-seismic
landslide occurrence, and the area under the receiver operating
characteristic curve (AUC) indicated slightly greater model
accuracy when DCI was included. Further studies using larger
datasets are required to develop an appropriate model to express therelationships between the index and controlling factors, to improve
the accuracy of post-seismic slide susceptibility assessments.

I.

INTRODUCTION

An intense earthquake can trigger numerous landslides over abroad area, causing damage to human lives, property, andinfrastructure. Following an earthquake, an area will remain prone
to landslides because ground that is affected by strong tremors
requires months to years to recover its strength. Under thesecircumstances, landslide susceptibility should be appraised as soon
as possible to avoid further disasters and enable safe and prompt
restoration work to begin in the area. Appraisals are often made by
the “direct method” [1], i.e., identifying topographic featuresindicative of slope instability, such as ground cracks, slope knicklines, and steep slopes, via field survey and/or examining aerial
photographs or topographic maps derived from a light detection
and ranging (LiDAR) survey. However, this is time-consuming
and labor-intensive work, and the outcome can vary according to
the skill of the operators. As an alternative, landslide susceptibility
can also be estimated indirectly using a statistical model
incorporating tectonic, lithological, climatic, hydrologic,
topographic, and vegetation conditioning factors [2]. This method
has been widely applied to co-seismic slides [3,4,5], while it has
rarely been used for post-seismic slides, possibly because of ashortage of information regarding landslides after an intense
earthquake. An appropriate and accurate model for post-seismic
slides would facilitate assessments of landslide susceptibility overa broad area.
In this study, we attempted to develop a statistical model toappraise post-seismic landslide susceptibility by considering
seismic ground cracks. The location of the ground cracks is a key
issue when using the direct method, but is rarely considered instatistical models, due to the coarse resolution of elevation data
and the difficulty of digital expression of the features. A 2 km2 areaof the Aso region in Kyushu, Japan, where a powerful earthquake(Mw 7.0) occurred in April 2016, was selected for the analysis(Figure 1). As the first step, an index to represent soil mass with
dense seismic ground cracks (DCI) was proposed by employing asurface roughness filter. The DCI, together with topographic
factors selected following a field survey [6], was then assessedusing the weight of evidence (WoE) approach [7], to examine thecorrelation between the indices and the occurrence of landslides,and estimate landslide susceptibility. Finally, landslide
susceptibility estimations were compared with versus without useof the DCI, to determine the improvement in the model achievedby incorporating seismic ground cracks into the set of conditioning
factors.

Shui Yamaguchi and Mio Kasai (2020) Incorporating ground cracks in the estimation of post-seismic landslide susceptibility:
in Massimiliano Alvioli, Ivan Marchesini, Laura Melelli & Peter Guth, eds., Proceedings of the Geomorphometry 2020 Conference, doi:10.30437/GEOMORPHOMETRY2020_45.

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Geomorphometry 2020

Yamaguchi and KasaiII.

STUDY AREA

The study area was located on a flank of the caldera wall of the
Aso volcano, underlain by Middle Pleistocene pyroxene, andesite,and pyroclastic rock. The 2016 earthquake caused 76 slides andnumerous cracks over an area of 2 km2. A further 38 landslides
were later induced by rainfall; these were of the shallow
translational type, and some of them yielded a debris flow.
Landslides increased in size continuously following theearthquake; this phenomenon was not accounted for in this study.
A landslide inventory was compiled based on aerial photographs
and LiDAR survey data acquired simultaneously in January 2015,
and in April and August 2016. Slopes where landslides were
initiated were targeted for the analysis; therefore, sediment transfer
and deposition zones were not included in the inventory. Tectonic,
seismic (e.g. peak ground acceleration), lithological, and climaticconditions were presumed to be equal throughout the study area
due to its small size. The entire area was covered with aged
Cyptomeria japonica.

Cm. Then, cells with σs chg ≥ Cm were converted into points tocalculate the point density using a kernel density function with abandwidth of 10 m for each 1-m cell in the ArcGIS softwareenvironment. The density was defined as DCI.
The WoE method [7] was applied to estimate thesusceptibility to post-seismic landslides. A bivariate model wasused to examine the correlation between DCI and the occurrenceof post-seismic landslides. We randomly selected 68% of thelandslides (26) to train the model; the remaining 12 were used fortesting. The correlation between a certain conditioning factor
class and landslides is given by the contrast, C, as follows:(2)
𝐶 =𝑊 −𝑊
where 𝑊 is the positive weight of evidence for a certain
conditioning factor class, and 𝑊 is the negative weight ofevidence for the class for a 1-m cell. They are given by:
𝑊 = lnIII.

METHODSThe DCI was derived from a surface roughness filter, basedon the assumption that crack formation is associated with anincrease in roughness. In this study, we applied a standard
deviation of a slope angle for 3 × 3 cells, σs, proposed by [8], tothe filter, using a 1-m digital elevation model (DEM). We
assumed that cracks appeared in cells where the change instandard deviation from pre- (January 2013) to post- (April 2016)
earthquake conditions (σs chg) was greater than or equal to a
threshold value (Cm). The change was calculated as follows:
𝜎

=𝜎

−𝜎

(1)

where the subscript indicates the timing of the LiDAR survey
used to produce two DEMs. We compared the spatial distributionsof σs chg and seismic ground cracks identified in the field, or based
on topographic maps generated after the earthquake, to determine

167

𝑃(𝐹|𝐿)
𝑃(𝐹|𝐿)(3)(4)
𝑃(𝐹|𝐿)
where L indicates the presence of a landslide, F indicates thepresence of a value within a certain conditioning factor class, 𝐿
indicates the absence of a landslide, 𝐹 indicates the absence of avalue within a certain conditioning factor class, 𝑃(𝐹|𝐿) is theprobability of a 1-m landslide cell conditioning a value within acertain conditioning factor class, 𝑃 𝐹 𝐿 is the probability of acell outside of a landslide containing a value within a certain
conditioning factor class, 𝑃 𝐹 𝐿 is the probability of a 1-m
landslide cell not containing a value within a certain conditioning
factor class, and 𝑃 𝐹 𝐿 is the probability of a cell outside of alandslide not containing a value within a certain conditioning
factor class. The combination of positive 𝑊 and negative 𝑊 ,or high 𝐶 , suggests a positive relationship between landslide
occurrence and that conditioning factor class. In contrast, a
combination of negative 𝑊 and positive 𝑊 indicates anegative relationship. If landslide occurrence is independent of thefactor class, then 𝑊 and 𝑊 both equal 0. The classes of DCI
and other conditioning factors, slope angle, plan and profile
curvature, and topographic wetness index (TWI) obtained for each
1-m cell are shown in Table 1. The plan and profile curvature
values for each cell provided excessively detailed topographic
information, and appeared not to show the effects of slope profiles
on landslide occurrence appropriately. Therefore, we averagedthese values within a 10-m radius of each cell, as described
previously by [9], and incorporated the results into the model. The
variance inflation factor (VIF) of the included factors ranged from𝑊 = lnFigure 1. Study area (outlined in red)𝑃(𝐹|𝐿)

Geomorphometry 2020

Yamaguchi and Kasai

Figure 4. ROC curves calculated for (a) training and (b) testing slides.

IV.
Figure 2. Box plots of σs chg for the locations with cracks distinguished andundistinguished. The interquartile range is represented by the box. Upper
whiskers: 1.5 times the interquartile range above the third quartile. Lower
whiskers: 1.5 times the interquartile range below the first quartile. Outliers areplotted as dots. The horizontal line represents the median value of σs chg.

Figure 3. A part of susceptibility map for post-seismic slides. Areas of coseismic slides were omitted from the analysis. Susceptibility is categorized into
5 classes, very high (10%), high (10%), moderate (20%), medium (20%), low(20%) and very low (20%).

1.03 to 1.13, confirming their independence. C values obtained foreach conditioning factor were summed for each 1-m cell and used
as a landslide susceptibility index for the cell. The accuracy of themodels with/without use of the DCI was compared based on thearea under the receiver operating characteristic curve (AUC) fortrue and false positive rates.

168

RESULTS AND DISCUSSION

The standard deviation of slope angle increased by more than
2° after the earthquake in 75% of the locations where groundcracks were identified; therefore, we set Cm as 2° (Figure 2). Table
1 shows a negative contrast for DCI values < 0.2, which increased
in classes with high DCI values. Overall, the factor that had thegreatest influence on post-seismic slides was a DCI value over 0.6.
The results suggest that integration of the DCI was favorable for
estimating susceptibility to post-seismic landslides. Profile
curvatures from -4 to 0, which corresponded to knick lines, andslope angles from 35° to 50° were other topographic features
closely associated with landslide occurrence; this finding wasconsistent with a previous field investigation of post-seismic
landslides in the same region [6]. A profile curvature < -4
corresponded to abrupt profile changes, such as cliff tops. Positive
contrasts for the 0–4 plan curvature classes demonstrated concave
slopes where landslides were likely to occur, whereas those > 4
mainly indicated narrow ridges.
Part of a post-seismic-slide susceptibility map is presented as an
example in Figure 3. When using the DCI, the AUC values fortraining and testing slides were 0.79 and 0.72, respectively(Figure 4), which were within the range considered acceptable by
the model [10]. In contrast, the AUC values for cases without DCI
were 0.75 for training slides and 0.73 for testing slides (Figure 4).
We obtained a slight improvement in the training data, but noimprovement in the testing data upon incorporating DCI, despite
the close association of the index with landslide occurrence(Table 1). This result can be partly explained by the manner inwhich the topographic features were represented by conditioning
factors, and also by deficits in the bivariate model, which did notconsider associations among controlling factors. In addition, asmall number of landslide data, particularly in the testing dataset,
disproportionately affected the modelling results. Further study

Geomorphometry 2020

Yamaguchi and Kasai

using larger datasets is required to find the most appropriate
combination of topographic factors and statistical model toincrease the accuracy of post-seismic slide susceptibility
assessments. Tectonic, seismic, lithological, climatic, andvegetation conditions should also be considered to extend theapplicability of the model and help to prevent disasters caused bypost-seismic landslides during restoration work undertaken after
powerful earthquakes.
Table 1. Contrast for conditioning factors.
Conditioning
factorDCI

Slope angle(degree)Plancurvature

Profilecurvature

TWI

class

W+

W-

contrast－0.2
0.2－0.4
0.4－0.6
0.6－-0.440
0.499
0.968
1.899

0.673
-0.193-0.090-0.041-1.113
0.692
1.057
1.940

－20

-2.1840.098-2.283

20－25
25－30
30－35
35－40
40－45
45－50
50－

-1.503-0.696-0.075
0.329
0.568
0.371
-0.005

0.067
0.066
0.013
-0.091-0.146-0.0470.001-1.570-0.762-0.089
0.420
0.714
0.418
-0.005－-4-1.7600.074-1.835

-4－-2
-2－0
0－2
2－4
4－

0.032
-0.2610.320
0.326-0.520-0.0040.082-0.193-0.0560.041

0.036-0.3420.513
0.382-0.561－-4-1.2520.036-1.289

-4－-2
-2－0
0－2
2－
－2
2－4
4－6
6－

0.469
0.449
-0.424-1.4220.050-0.0590.236-1.378-0.060-0.3870.193
0.098-0.0420.034-0.042
0.038

0.528
0.835
-0.617-1.5200.091-0.0920.277-1.415

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