Saito et al. Progress in Earth and Planetary Science (2018) 5:15
https://doi.org/10.1186/s40645-018-0169-6

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Landslides triggered by an earthquake andheavy rainfalls at Aso volcano, Japan,
detected by UAS and SfM-MVS
photogrammetry
Hitoshi Saito1,2*, Shoichiro Uchiyama3, Yuichi S. Hayakawa2 and Hiroyuki Obanawa4

Abstract
Unmanned aerial systems (UASs) and structure-from-motion multi-view stereo (SfM-MVS) photogrammetry haveattracted a tremendous amount of interest for use in the creation of high-definition topographic data for geoscientificstudies. By using these techniques, this study examined the topographic characteristics of coseismic landslides
triggered by the 2016 Kumamoto earthquake (Mw 7.1) in the Sensuikyo area (1.0 km2) at Aso volcano, Japan. The
study area has frequently experienced rainfall-induced landslide events, such as those in 1990, 2001, and 2012.
We obtained orthorectified images and digital surface models (DSMs) with a spatial resolution of 0.06 m before
and after the 2016 Kumamoto earthquake. By using these high-definition images and DSMs, we detected a totalof 54 coseismic landslides with volumes of 9.1–3994.6 m3. These landslides, many of which initiated near topographic
ridges, were typically located on upside hillslopes of previous rainfall-induced landslide scars that formed in 2012. This
result suggests that the topographic effect on seismic waves, i.e., amplification of ground acceleration, was importantfor coseismic landslide initiation in the study area. The average depth of the coseismic landslides was 1.5 m, which is
deeper than the depth of the rainfall-induced landslides prior to these. The total sediment production of the coseismic
landslides reached 2.5 × 104 m3/km2, which is of the same order as the sediment production triggered by the previous
single heavy rainfall event. This result indicates that the effects of the 2016 Kumamoto earthquake in terms of sedimentproduction and topographic changes were similar to those of the rainfall-induced landslide event in the study area.
Keywords: Coseismic landslides, Rainfall-induced landslides, 2016 Kumamoto earthquake, UAS, SfM-MVS
photogrammetry, Aso volcano

Introduction
Landslides are major natural hazards that contribute tolandscape evolution and erosion in mountainous regions(Keefer and Larsen 2007). Most landslides are triggeredby episodic events such as heavy rainfall events, earthquakes, or combinations of both. Differences in locationand topographic effects between rainfall-induced landslides and coseismic landslides have been analyzed on thebasis of past landslide events (Chang et al. 2007; Meunier* Correspondence: hsaito@kanto-gakuin.ac.jp
1
College of Economics, Kanto Gakuin University, 1-50-1, Mutsuura-higashi,
Kanazawa-ku, Yokohama, Kanagawa 236-8501, Japan
2
Center for Spatial Information Science, The University of Tokyo, 5-1-5,
Kashiwanoha, Kashiwa, Chiba 277-8568, Japan
Full list of author information is available at the end of the article

et al. 2008). Previous studies have compared rainfallinduced landslides and coseismic landslides in different
regions (e.g., Meunier et al. 2008) or rainfall-induced landslides following earthquakes (e.g., Lin et al. 2006).
Japan is characterized by high-relief topography, frequent earthquakes, and rainfall events; thus, rainfall andearthquakes are the dominant triggers of landslides inJapan (e.g., Sato et al. 2005; Saito et al. 2014). In April
2016, the Kumamoto earthquake, which was a series ofearthquakes, occurred on Kyushu Island, Japan (Furumura
2016; Lin et al. 2016). The main shock of Mw 7.1 on April
16, which was Mj 7.3 on the Japan Meteorological Agency
intensity scale (Fig. 1), caused many landslides near the
hypocenter area, including landslides in the Aso volcano
region (e.g., Miyabuchi 2016). Among these, several large© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Saito et al. Progress in Earth and Planetary Science (2018) 5:15

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Fig. 1 Study area. a Distribution of the peak ground acceleration (PGA; cm/s/s) of the Mw 7.1 main shock of the 2016 Kumamoto earthquake on
April 16, derived from records from the K-NET and KiK-net strong motion network (triangles) of the National Research Institute for Earth Scienceand Disaster Resilience (NIED). The star shows the hypocenter. b Aso caldera and location of the Sensuikyo area (black polygon). KMM004 represents
the Ichinomiya station of K-NET and KiK-net of NIED

landslides near the hypocenter were detected immediately
following the earthquake, and their characteristics andmechanism were analyzed (e.g., Song et al. 2017). However, the 2016 Kumamoto earthquake also caused manysmaller landslides with widths of less than a few tens ofmeters and depths of less than a few meters around thecentral cones of Aso volcano (Miyabuchi 2016). It is necessary to evaluate the effect of the 2016 Kumamoto
earthquake on the overall landslide occurrence, includingthese smaller landslides. Because landslide size and frequency generally have a nonlinear relationship (Korup
et al. 2012), the impact of such smaller landslides in the
Aso volcano area remains underestimated.
A limitation on landslide detection from remote sensing images is that landslides due to multiple different
events before the time of image shooting cannot be
identified from a single image shot at a single point intime (Saito et al. 2009). The Aso volcano region experienced severe rainfall-induced landslide events in 1990,
2001, and 2012 (Miyabuchi and Daimaru 2004; Saito etal. 2016), and the landslide scars from the 2012 eventare still obvious in the area. Thus, separating therainfall-induced landslides from the coseismic landslides
caused by the 2016 Kumamoto earthquake in a single
image acquired after the earthquake is difficult (Geospatial
Information Authority of Japan 2017). Multi-temporal images are required to detect the spatial distributions andtopographical characteristics of coseismic and rainfallinduced landslides in the area. Moreover, landslides withsmaller volumes are not fully detected in traditional remote sensing images, such as satellite and aerial images,
because of the limited spatial resolution. Although lightdetection and ranging (lidar) data have digital elevation
models and aerial images with submeter resolutions (Oguchi et al. 2011), repeating observations by lidar is not costeffective. Recently, unmanned aerial systems (UASs) andstructure-from-motion multi-view stereo (SfM-MVS)
photogrammetry have become popular methods and havebeen applied widely in various geomorphological studies(e.g., Westoby et al. 2012). The data processing of SfMMVS photogrammetry is more straightforward and allows
instantaneous acquisition of high-resolution, accurate
topographic data from various platforms, including UASs.
Unlike satellite images and traditional aerial photographs,
the combination of UAS and SfM-MVS photogrammetry
provides high-definition orthorectified images and digitalsurface models (DSMs) for detailed landslide analysis witha spatial resolution of < 1.0 m (Niethammer et al. 2012;
Westoby et al. 2012, Hayakawa et al. 2016; James et al.
2017; Peternel et al. 2017). These techniques should enable detection of coseismic and rainfall-induced landslides
in the Aso volcano area.
The objective of this study is to examine the spatial distribution and the topographic characteristics of the coseismic
landslides triggered by the 2016 Kumamoto earthquake
and compare these characteristics with those of the rainfallinduced landslides that occurred in 2012 by using highdefinition images and DSMs from UAS and SfM-MVS

Saito et al. Progress in Earth and Planetary Science (2018) 5:15

photogrammetry. We have conducted field monitoring
using UAS at the northern part of the central cones of Aso
volcano since 2012 to measure rainfall-induced landslides(Saito et al. 2016). The study area is suitable for comparing
coseismic landslides with rainfall-induced landslides.
Additionally, landslide susceptibility may have increased
because of a decrease in slope stability caused by earthquakes (Jan and Chen 2005; Lin et al. 2006; Chang et al.
2007). Detailed geomorphological and spatial-statisticalstudies on the characteristics of the landslides triggered by
the 2016 Kumamoto earthquake are therefore needed toassess future landslide hazards.
Study area

Aso volcano is composed of Aso caldera and postcaldera central cones (Fig. 1). The Aso caldera, running
25 km north–south and 18 km east–west, was formedby four gigantic pyroclastic-flow eruptions of andesitic
to rhyolitic magma from about 270 to 90 ka (Ono et al.
1977; Miyabuchi and Daimaru 2004). The post-caldera
central cones were formed soon after the last calderaforming eruption at 90 ka and have produced large volumes of fallout tephra and lava flows. At least 17 cones
are visible on the surface (Miyabuchi 2009).

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This study analyzed the Sensuikyo area (1.0 km2,
Fig. 1b) located at the northern slopes of the postcaldera central cones, where many landslides were triggered by both the 2016 Kumamoto earthquake and theheavy rainfall events of 1990, 2001, and 2012. These
slopes steepen with elevation. The elevation in the study
area ranges from about 620 to 860 m, the angle of thehillslopes ranges from about 25° to 45°, and the angle ofthe channel slopes is < 15°. The study area is characterized by grass vegetation used for grazing, which is the
typical landscape of the Aso volcano region (Fig. 2). Several deciduous trees are distributed along the channels.
The study area is located downwind of the prevailingdirection of tephra dispersal from the active post-caldera
central cones. Volcanic ash and lapilli with a thicknessof 10 m or more have accumulated there during the
Holocene (Miyabuchi and Watanabe 1997). The thicktephra layers are composed mostly of sand- to silt-size
ash-fall deposits and several scoria-fall deposits, which
are interbedded with humic paleosols (Miyabuchi 2009).
The distribution of the peak ground acceleration (PGA)of the Mw 7.1 main shock of the 2016 Kumamoto earthquake is shown in Fig. 1a. Strong ground motion spreadthroughout Kyushu Island. In particular, large PGA

Fig. 2 Sensuikyo area following the 2016 Kumamoto earthquake. a Overview of the study area, b coseismic landslide scar and rainfall-induced
landslide scarps (black arrow) described in Fig. 4, and c the investigation using Trimble UX5

Saito et al. Progress in Earth and Planetary Science (2018) 5:15

contours of > 200 cm/s/s extend from the hypocenter toward the northeast region along the fault zone (Furumura
2016). The study area is 31 km northeast of the hypocenter. Ichinomiya station (KMM004 in Fig. 1b) observed a PGA of 403 cm/s/s. The effect of thesignificant shaking of the main shock on the surface
soils caused landslides characterized by shallow slides
with a depth of a few meters (Fig. 2b).
The study area experienced frequent severe landslide
events triggered by heavy rainfalls in July 1990, June
2001, and July 2012 (Miyabuchi and Daimaru 2004; Saito
et al. 2016). The stratified composition of the thicktephra affected the hydrological characteristics of theslopes of the study area and controlled the rainfallinduced landslides (Miyabuchi and Daimaru 2004;
Shimizu and Ono 2016). Most of these landslides wereshallow in unconsolidated tephra deposits at depths of <1.0 m. In July 2012, a heavy rainfall event of 85.0 and
427.6 mm for maximums of 1 and 24 h rainfalls, respectively, triggered many shallow landslides. We conducted a field survey with a UAS to detect the locationof these landslides in 2014. The heavy rainfall in 2012
caused 378 shallow landslides with an average depth of
0.7 m in the study area (Saito et al. 2016).

Methods/experimental
We conducted field surveys on March 17 and May 11,
2016, before and after the 2016 Kumamoto earthquake,
respectively. Aerial imageries of the study area were obtained by using the UAS (Trimble UX5) with a digital
camera (SONY NEX-5T, 16.10 megapixels) (Table 1,

Table 1 General information related to the unmanned aerial
system (UAS), camera, and global navigation satellite system(GNSS)UAS
Airplane model

Trimble UX5Airplane type

Fixed-wing aircraft

Weight2500 gMax. duration of flight

50 minCamerasCamera model

SONY NEX-5 T

Number of pixels

16.10 M

Sensor size

23.6 × 15.6 mm

Focal length

15 mm

GNSS
ReceiverTrimble GeoExplorer 6000XH

Antenna

Trimble Zephyr Geodetic 2

Frequency band

GPS L1/L2, GLONASS L1/L2, SBAS

Page 4 of 10

Fig. 2c), which enabled autonomous flight and image acquisition. Images were captured at an altitude of about
140 m above ground level with 80% overlap and 80%
sidelap. In total, more than 1000 images were acquiredin March and May 2016. The theoretical spatial resolution of the images was 0.04 m. We measured groundcontrol points (GCPs) using a global navigation satellite
system (GNSS; Trimble GeoExplorer 6000XH) by thekinematic method (Table 1).
These aerial images were processed with 10 GCPs byusing SfM-MVS photogrammetry software (Agisoft
PhotoScan, professional version). Following the standard
SfM-MVS photogrammetry workflow, we obtained
orthorectified images and DSMs. Because the study area
was covered by grass vegetation in May 2016, the DSMs
include grass height, except at landslide scars (Fig. 2b).
However, the grass is burned off for grazing in theearly spring, from late February to early March ofeach year. We acquired the first set of aerial images
just after the burning in March 2016. Therefore, theDSM acquired in March can be regarded as a digitalterrain model (DTM) of the area. These processes
used to create the orthorectified images and DSMs
were verified by Saito et al. (2016). The spatial accuracy as the root mean square error (RMSE) of thesedata was 0.08 and 0.09 m in the horizontal and vertical directions, respectively.
This study compared the orthorectified images andDSMs acquired in March and May 2016 to detect landslides. To avoid the effect of the crustal deformation
triggered by the 2016 earthquake, the orthorectified
image and DSM in May 2016 were co-registered to thegeographic coordinates of the orthorectified image andDSM in March 2016. Sequential DSMs with the same
spatial resolution were used to create a difference of
DSMs based on cell-by-cell subtraction. Such sequential
DSMs are particularly relevant to geomorphic studies
because a difference of DSMs provides a high-resolution,
spatially distributed surface model of the topographic andvolumetric changes through time (e.g., James et al. 2012).
The DSM that was acquired in May 2016 included
vegetation height, but the landslide scars had novegetation. We confirmed that no new landslide
events, other than those triggered by the earthquake,were observed between March and May 2016. The
difference of DSMs in landslide scars therefore
showed topographic changes caused by landslides triggered by the 2016 Kumamoto earthquake, allowing us
to obtain landslides volumes.
We then detected initiation areas of coseismic landslides larger than 10.0 m2 by using the orthorectified images and the difference of DSMs. Although separation oflandslide initiation, transport, and deposition areas fromorthorectified images alone was difficult, we detectedSaito et al. Progress in Earth and Planetary Science (2018) 5:15

landslide initiation areas from the decreased areas in the
difference of DSMs. We calculated the area and volumeof each landslide from the orthorectified images and thedifference of DSMs. The total sediment production of
the coseismic landslides was estimated from the netchanges in topography.
The distribution of rainfall-induced landslides in 2012
was provided by Saito et al. (2016), who also detected
landslide initiation areas using orthorectified images anda difference of DSMs obtained by UAS and SfM-MVS
photogrammetry. We compared the distribution andtopographic characteristics of the landslides triggered by
the heavy rainfall in 2012 with those of the landslides
triggered by the 2016 Kumamoto earthquake.
This study examined the topographic locations ofthese coseismic and rainfall-induced landslides in termsof distances from ridge and stream networks, whichwere derived from the DSMs. We calculated the distancefrom the center cell of a landslide initiation area to thenearest ridge (dridge) and the distance to the nearest
stream (dstream). The distance to the nearest ridge wasPage 5 of 10

normalized by the total length of the measured landslide
hillslope using
d ridge ¼

d ridge:d ridge þ d stream

ð1Þ

|dridge| varies from 0 for a cell located in a ridge to 1 fora stream cell. This normalized distance can be used to
examine the relative positions of coseismic and rainfallinduced landslides on the hillslopes.

Results
We obtained orthorectified images and DSMs with spatial
resolutions of 0.06 m in March and May 2016 (Fig. 3). A
comparison of these images and DSMs between the
2 months revealed that 54 landslides and many fissuresoccurred near topographic ridges in the study area as a result of the 2016 Kumamoto earthquake (Figs. 3, 4, and 5).
The area of the coseismic landslides ranged from 11.3 to1461.2 m2, and the total area of the landslides reached1.2 × 104 m2, which corresponds to 1.2% of the study area.

Fig. 3 Distributions of landslide initiation areas triggered by the 2016 Kumamoto earthquake (yellow polygons) and the heavy rainfalls of 2012
(white polygons) with the a orthorectified images and b digital surface models (DSMs), with a spatial resolution of 0.06 m, acquired in May 2016.
The black polygon shows the study area. Details in the black rectangles are shown in Figs. 4 and 5, respectively

Saito et al. Progress in Earth and Planetary Science (2018) 5:15

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Fig. 4 Examples of landslides triggered by the heavy rainfalls of 2012 (black polygons) and the 2016 Kumamoto earthquake (shown in b) on
orthorectified images acquired in a March 2016 and b May 2016. c Difference of digital surface models (DSMs) between March and May 2016
(blue represents a decrease in DSM). d Profile of the slope on which the landslide was located in April 2004 (gray dashed line), March 2016 (blackline), and May 2016 (blue line), shown by the black profile line (P1–P2). The arrow in d shows the scarp of the rainfall-induced landslide in 2012.
The slope profile in April 2004 was derived from lidar-based DSM with a spatial resolution of 2.0 m (Saito et al. 2016)

Figure 6 shows the difference of DSMs between May
and March. Most of the area is characterized by increases in DSM (higher in May than in March) that represent sedimentation of landslides along channels,
growth of grass, and leafing of deciduous trees between
March and May. Areas of decrease represent erosion
areas caused by landslides. The depth of coseismic landslides ranged from 0.8 to 4.6 m, with an average of
1.5 m. Individual landslide volume, estimated from thedifference of DSMs, ranged from 9.1 to 3994.6 m3 withan average of 458.3 m3. The total sediment production
reached 2.5 × 104 m3/km2.
The distribution of the coseismic landslides was not uniform (Figs. 3 and 6). The landslides initiated near topographic ridges of steep and high-relief slopes (Fig. 7).
Because the slopes become steeper with elevation in thesouthern part of the study area, the landslides were clustered there. Figure 3 also shows the distribution of therainfall-induced landslides of 2012, which had a total of 378

sites and an average depth of 0.7 m. The rainfall-induced
landslides were distributed over all slope segments but wereclustered in the lower parts of the hillslopes (Fig. 7).
A comparison of the initiation areas of the landslides
triggered by rainfall and by the earthquake revealed thatthe coseismic landslides initiated in the upper parts ofhillslopes of rainfall-induced landslide scars in the study
area (Figs. 3 and 7). Eighteen coseismic landslides withvolumes < 100.0 m3 around rainfall-induced landslide
scarps were shallow failures with depths < 1.0 m. However,
36 landslides had depths > 1.0 m, which is larger than thedepth of the rainfall-induced landslides (Fig. 4). The
coseismic landslides in the area were characterized bydeep slip surfaces, and they occurred in the upside slopeof the previous rainfall-induced landslide scar in the area.

Discussion
Earthquakes trigger landslides because the combination ofgravitational and seismic acceleration causes the short-Saito et al. Progress in Earth and Planetary Science (2018) 5:15

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Fig. 5 Example of fissures triggered by the 2016 Kumamoto earthquake shown by orthorectified images acquired in a March and b May 2016

lived stress to be greater than the combined cohesive andfrictional strength of the underlying rocks and soils (e.g.,
Newmark 1965). The topographic characteristics of the locations of coseismic landslides have been discussed forpast earthquake events in mountainous regions. For example, observation of coseismic landslides and modelingof seismic waves have shown that these landslides tend tocluster near ridge crests, where landslide susceptibility ishighest (Chang et al. 2007; Meunier et al. 2008). The
present study also showed that the 2016 Kumamoto
earthquake triggered landslides near topographic ridges in
the area (Fig. 7).
In contrast, the rainfall-induced landslides of 2012were distributed throughout the slope segment, but wereclustered in the lower hillslopes (Fig. 7). Fifty-four percent of the landslides initiated in the lower half ofhillslopes and at the convex-up knicklines of the hillslopes (Figs. 4 and 7). Our results showed that thecoseismic landslides initiated at upside hillslopes of previous rainfall-induced landslide scars in the study area.
During an earthquake, ground accelerations are amplified in both topographic ridges and convex-up knicklines
within ridge flanks (Meunier et al. 2008). The landslide

rate decreases systematically with distance from theearthquake hypocenter as the seismic waves spread andattenuate, and the topographic site effects on the seismic
waves dominate the landslide pattern on the scale of anindividual mountain ridge (Meunier et al. 2007). The
study area is located about 31 km from the hypocenter.
However, the area is characterized by high relief andsteep hillslopes. Hence, the topographic site effects on
the seismic waves were important for amplification ofground acceleration and for landslide initiation. Our results suggest that erosion caused by past rainfall-induced
landslides in the lower hillslopes decreased the slope stability at the upper hillslopes, which made these hillslopes
susceptible to coseismic landsliding.
The depth of coseismic landslides was larger than thedepth of the rainfall-induced landslides. The stratified
composition of the thick tephra in the hillslopes affected
the hydrological characteristics of the slopes in the study
area and controlled the rainfall-induced landslides(Miyabuchi and Daimaru 2004; Shimizu and Ono 2016).
The slip surface of the rainfall-induced landslides wasformed near the boundary between upper blackish andlower brownish tephra layers; the horizon is dated asSaito et al. Progress in Earth and Planetary Science (2018) 5:15

Fig. 6 Difference of digital surface models (DSMs) between May andMarch 2016. Blue represents a decrease in DSM caused by coseismic
landslides, whereas green represents sedimentation of landslides alongchannels, increased grass growth, or leafing of deciduous trees. Details
in the black rectangle are shown in Fig. 4. The black polygon shows
the study area

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3000 years old (Miyabuchi and Daimaru 2004). Thesefeatures were detected for the rainfall-induced landslides
in 2012, when the landslide depth was generally < 1.0 m,
with an average of 0.7 m in the study area (Saito et al.
2016). However, the present study showed that thecoseismic landslides had depths reaching > 3.0 m, withan average of 1.5 m. These results indicate that the potential slip surfaces of the coseismic landslides were different from those of the rainfall-induced landslides.
Our results show that the total sediment production
owing to the coseismic landslides reached 2.5 ×
104 m3/km2. The study area has frequently experienced
severe landslide events triggered by heavy rainfalls in
the past three decades (Miyabuchi and Daimaru 2004;
Saito et al. 2016). A statistical analysis of the probablerainfall showed that these landslide events were likely
triggered by rainfall events with return periods shorter
than about 20 years (Table 2). The sediment production and discharge triggered by a single heavy rainfall
event was estimated to be from 103 to 105 m3/km2,
which corresponds to the sediment production triggered by the 2016 Kumamoto earthquake (Table 2).
Although the total number of coseismic landslides
was smaller than that of rainfall-induced landslides, thecoseismic landslide depth was larger than the rainfallinduced landslide depth. The total effect of the 2016 Kumamoto earthquake on the sediment production andtopographic change had a similar impact as a single heavy
rainfall-induced landslide event in the study area.
Previous studies have shown that earthquakes strongly
influenced the locations and susceptibility of subsequent
rainfall-induced landslides (Lin et al. 2006; Chang et al.
2007; Tang et al. 2011). The 2005 Kashmir earthquake
caused extensive fissures which could potentially cause
additional landslides (Owen et al. 2008). Our orthorectified images and DSMs also showed many fissures nearFig. 7 Frequency density distributions of normalized distances to ridges from coseismic (black line) and rainfall-induced (gray dashed line) landslides.
Normalized distances to ridges vary from 0 for a cell located on the ridge to 1 for a stream cell

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Table 2 Comparison of sediment discharge and production triggered by the heavy rainfall events in 1990, 2001, and 2012 (after
Miyabuchi and Daimaru, 2004 and Saito et al. 2016), and the 2016 Kumamoto earthquake. The rainfalls and return periods were
calculated from radar/rain-gauge analyzed precipitation provided by the Japan Meteorological Agency (Saito and Matsuyama, 2015)
Event

July 1990

June 2001

July 20122016 Kumamoto earthquake

Max. 1 h rainfall/return period

80.0 mm/12 years

90.0 mm/23 years

85.0 mm/16 years

0.0 mmMax. 24 h rainfall/return period

497.8 mm/26 years

297.0 mm/2 years

427.6 mm/11 years

0.0 mmSediment discharge (sediment production, m3/km2)

3.1–3.9 × 104

3.0–4.1 × 103

1.2–4.8 × 105

2.5 × 104

topographic ridges (Fig. 5). These facts imply that the2016 Kumamoto earthquake influenced the stability of
the hillslopes and that future landslide development may
occur with lower-frequency rainfall events. Future studies are necessary to track these fissures and evaluate subsequent rainfall-induced landslides.

Conclusions
By using the advantages of the high-definition data, repeatability, and mobility provided by the UAS and the
SfM-MVS photogrammetry method, this study detected
the distribution of landslides triggered by the 2016
Kumamoto earthquake. We analyzed the Sensuikyo areaat the northern slope of Aso volcano, where rainfallinduced landslide events have been frequent during thepast three decades.
We obtained orthorectified images and DSMs with aspatial resolution of 0.06 m. By determining the differences between the data obtained in March and May
2016, which correspond to times before and after theearthquake, respectively, we detected 54 coseismic
landslides with volumes ranging from 9.1 to 3994.6 m3.
These landslides initiated near topographic ridges characterized by upside hillslopes of past rainfall-induced landslide scars. Topographic site effects on seismic waves, i.e.,
amplification of ground acceleration, were important for
the landslide initiation in the study area. Our results suggest that erosion caused by the previous rainfall-induced
landslides decreased slope stability, which made the hillslopes susceptible to coseismic landsliding.
The depth of the coseismic landslides reached > 3.0 m,
with an average of 1.5 m, which is larger than therainfall-induced landslide depth. The total sediment production owing to coseismic landslides was 2.5 × 104 m3/
km2, which is of the same order as the sediment production triggered by a single heavy rainfall event. These results indicate that the effects of the 2016 Kumamoto
earthquake on the sediment production and topographic
changes had a similar impact as a rainfall-induced landslide event.
Abbreviations
DSM: Digital surface model; DTM: Digital terrain model; GNSS: Global
Navigation Satellite System; PGA: Peak ground acceleration; SfMMVS: Structure-from-motion multi-view stereo; UAS: Unmanned aerial system

Acknowledgements
We thank Dr. Yasufumi Iryu, Dr. Jun Matsumoto, Dr. Shigekazu Kusumoto,and two anonymous reviewers for their helpful comments that improved
the manuscript. This study was supported by KAKENHI Grant Numbers
JP15K16287, JP17915136, JP25702014, and JP17H02031, and joint researchwith CSIS, The University of Tokyo (No. 554). We computed statistics using
the R software environment (http://www.r-project.org/).
Funding
This work was supported by JSPS KAKENHI Grant Numbers JP15K16287,
JP17915136, JP25702014, and JP17H02031.
Availability of data and materials
The data used in this paper are available from the authors on request.
Authors’ contributions
HS proposed the topic, conceived and designed the study. SU, YH, and HO
carried out the field measurements and collaborated with the corresponding
author in the construction of the manuscript. All authors read and approved
the final manuscript.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
College of Economics, Kanto Gakuin University, 1-50-1, Mutsuura-higashi,
Kanazawa-ku, Yokohama, Kanagawa 236-8501, Japan. 2Center for Spatial
Information Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa,
Chiba 277-8568, Japan. 3National Research Institute for Earth Science andDisaster Resilience, 3-1, Ten-nodai, Tsukuba, Ibaraki 305-0006, Japan.
4
VisionTech Inc., 2-1-16 Umezono, Tsukuba, Ibaraki 305-0045, Japan.
Received: 29 June 2017 Accepted: 1 February 2018

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