Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33
https://doi.org/10.1186/s40645-018-0191-8

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Variations in volumetric erosion rates ofbedrock cliffs on a small inaccessible
coastal island determined using
measurements by an unmanned aerial
vehicle with structure-from-motion andterrestrial laser scanning
Hiroyuki Obanawa1,2*

and Yuichi S. Hayakawa3

Abstract
We conducted topographic measurements using unmanned aerial vehicles and by terrestrial laser scanning at
inaccessible sea cliffs with an overhanging complex shape in eastern Japan. From the repeated measurements, weconstructed multitemporal three-dimensional point clouds of the sea cliffs to quantitatively evaluate the volumetricchanges in the cliffs with a high spatial resolution (in centimeter). As potential main triggers of rockfalls and slope
failures, high sea waves and strong earthquakes were examined with the time series of eroded volumes. It issuggested that sea waves have a greater impact than earthquakes on sea-cliff erosion. The approaches provided inthis study have great potential for a wide range of applications including high spatial monitoring of otherinaccessible, complex-shaped sea cliffs with high data accuracy and low acquisition and operational costs.
Keywords: Unmanned aerial vehicle, Terrestrial laser scanning, Point cloud, Sea cliffs, Coastal erosion

Introduction
Monitoring rapid erosion along a bedrock coast is crucial for the management of infrastructure and conservation of natural landscapes (Sunamura 1992; Hutchinson
2002). In particular, if the bedrock is not sufficiently erosion resistant to preserve the coastline, the loss of land
and landscapes can be accelerated with the increasing
frequency and potential power of storm waves as a response to rapid climate change (Naylor et al. 2010).
Long-term, decadal changes in the bedrock coastlines
can be detected by time series of aerial photographs if
coastal erosion is sufficiently rapid (Horikawa and* Correspondence: obanawah924@affrc.go.jp
1Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho,Inage-ku, Chiba-shi, Chiba 263-8522, Japan
2
Present address: Hokkaido Agricultural Research Center, National Agriculture
and Food Research Organization, 1 Hitsujigaoka, Toyohira, Sapporo, Hokkaido
062-8555, Japan
Full list of author information is available at the end of the article

Sunamura 1967), but this only allows for the detectionof two-dimensional (2D) changes. Three-dimensional(3D) changes, which are necessary to show geomorphological processes acting, can hardly be assessed withsuch conventional aerial photographs. Recent developments in measurement methodologies including terrestrial laser scanning (TLS) has enabled monitoring of
short-term, detailed changes in coastal cliffs (e.g., Lim etal. 2005; Rosser et al. 2005; Kuhn and Prüfer 2014).
However, this approach is only possible when there is anappropriate space to establish a measurement device on
the seaside such as a breakwater. In many cases, such aplace is limited along a bedrock coast.
For assessment of inaccessible areas, recent developments in small unmanned aerial vehicles (UAVs) or system (UAS) combined with structure-from-motion
multiview stereo (SfM-MVS) photogrammetry has enabled detailed shape measurements and change detections (e.g., Westoby et al. 2012; Fonstad et al. 2013;© 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.

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

Obanawa et al. 2014a). The authors reported on themethod of operating a small-quadcopter survey system(UAV-SfM) and accuracy of the generated data (e.g.,
Obanawa et al. 2014b, 2015). Among the advantages ofthe small-quadcopter survey, it can shoot and measuresteep slopes including overhanging portions. Such complex portions can hardly be measured via conventional
survey with manned airplanes because the flight heightis relatively high and far from the object. In addition, because the running cost of a small UAV is far less and its portability is very high, repetitive measurements over different
times are much easier than those of the existing methods.
With these advantages, here, we apply the UAV-SfM
approach, combined with TLS, to monitor erosion in inaccessible bedrock sea cliffs with overhanging slopes. In
general, erosion in such areas of complex morphology
with overhangs is difficult to measure in a 2D space. To
overcome the difficulties in the evaluation of short-termchanges in areas with complex morphology, 3D data repeatedly obtained using UAV-SfM and TLS were used toevaluate 3D volumetric changes in the sea cliffs. The

A

140°E

Page 2 of 10

quantified topographic changes of the cliffs were thencompared to the potential factors affecting short-termcoastal erosion, including the occurrence of significant
earthquakes and storm waves.

Methods/Experimental
Study site

The study site is in the central part of the sea cliffs
around Cape Taitosaki in eastern Chiba prefecture, Japan(Fig. 1). A peninsular-rock, named Suzumejima Island,
has a cone-like, circular shape with a diameter of 50 m
and a height of 30 m. Although one side of the island isconnected to land during low tide, it is difficult andquite dangerous to climb to it on foot because thesurrounding slopes of Suzumejima are very steep. The
seaside of the island is therefore invisible and cannot bemeasured from the land side. The bedrock is composedof alternating Neogene sandstones and mudstones.
With a high retreat rate often exceeding 1 m/year,
sea cliffs around Cape Taitosaki are among the major
sources of sediment supply toward the Kujukuri

B

140.5°E

36°N

35.5°N
×35°N

C

Fig. 1 Location map of the study area (a, b) and aerial photograph (c) taken by the UAV camera from the north. In a, the white circle is thelocation of the seismic observation point and the x-mark is the location of the wave observational point

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

Coast, on the northern side of the Taitosaki area
along the downstream direction of a coastal current(Horikawa and Sunamura 1967). However, artificial
embankments have been constructed since the1960s, which have caused a significant decrease in
sea-cliff retreat (Machida 1990; Hoshigami et al.
2006). The drastic decrease in sediment supply has
become a major cause of coastal erosion at the
Kujukuri sandy beach. The assessment of both
long-term and short-term rates of sea-cliff erosion istherefore crucial not only for cliff preservation but
also for the management of the sand beach. Because
Suzumejima Island is outside of the artificial embankment (Fig. 1c), the island is an unusual place
where one can still observe the natural conditions of
sea-cliff erosion regardless of the nearby artificial
protections.
Data acquisition and analysis

Field surveys were conducted seven times over 2 years
from June 2014 to June 2016 with an interval of approximately 4 months. The inter-measurement periods aredefined as period I to VI. During each field campaign,
we measured the topography of Suzumejima Islandusing UAV-SfM and TLS, whose details are described in
the following sections. The resultant point cloud data
were then post-processed to detect volumetric changes
during each period through the timeline of the topographic data.
UAV-SfM was used to capture the entire shape of
the island including the seaside, whereas TLS wasused to measure the land-side morphology of the island at a higher accuracy. As noted, TLS measurements of coastal cliffs have often been applied toprovide an accurate measurement of coastal erosion(Rosser et al. 2005; Kuhn and Prüfer 2014). The
photogrammetric UAV-SfM data, on the other hand,
can often show greater uncertainties largely dependingon the positional accuracies and distribution ofground control points (GCPs) (James and Robson2014). In this study, we could only establish GCPs on
the land near the island, not on the target island itself. The uncertainties of the 3D point cloud using
UAV-SfM can therefore be large on the island. To reduce such uncertainties, we also conducted TLS measurements from the land side at the same time as
those recorded using a UAV. Although the TLS data
could be obtained only for the land side of the island(i.e., not the entire island), it enabled robust measurements of the relative coordinates of this area with
better accuracies than the UAV-SfM clouds. These
data were used to rectify the UAV-SfM point cloud
which included the sea side of the island. In addition,
as the reference for the UAV-SfM point clouds, thePage 3 of 10

TLS-derived point clouds at different times of measurement were aligned to one another based on a reference period whose coordinates are provided withthe highest georeferenced accuracy. This was completed to avoid misalignments because of the decrease
in reliability of the control points for a longer periodof measurement (time interval between two datasets).
The volumetric changes were calculated using theregistered UAV-SfM data.
Topographic measurements using UAV-SfM

For the acquisition of low-altitude aerial photographs,
we used small UAVs including the DJI Phantom 2 with acompact camera (Nikon Coolpix A) and the DJI
Phantom 3 (Table 1). The camera lens direction was set
to be both orthogonal and oblique to the ground to capture the entire terrain of the island with overhanging
slopes. We took approximately 300 aerial photographs
during each observation. Figure 2 shows an example of
the aerial photographs. Detailed structure of vertical
cliffs including the overhanging slope facing the sea andfallen rocks on the wave-cut platforms are clearly shown inthese photographs. GCPs were placed on the land side of thestudy site using black and white checkerboard-type targets.
These targets are readily identified in the photographs takenusing the UAV. The number of GCPs was from three toseven for each measurement. The geographical coordinatesof the GCPs were obtained using a global navigation satellite
system (GNSS) receiver (Trimble GeoExplorer 6000XH),
which is capable of positioning with carrier-phase correction
to provide a centimeter-scale accuracy. The base station dataof the nearby GNSS stations provided by the Geospatial
Information Authority of Japan were used to perform
post-processing kinematic correction to the rover data.
Using the UAV-derived photographs and GCP coordinates, topographic data were constructed using the
SfM-MVS software (Agisoft PhotoScan Professional). 3D
point clouds with red, green, and blue (RGB) colors werethe main data derived, whereas the other 2D data including digital elevation models and orthorectified photographs were not used in this study because thecomplex shape of the target island cannot be correctly
represented in such 2D data. The outliers in the 3D
point clouds were manually removed but vegetation was
mostly left as is because the existing filtering algorithms
for vegetation are often optimized for 2D-based data
and do not work correctly for this dataset.
Topographic measurements using TLS

We used a long-range scanner (Topcon GLS-1500) anda short-range scanner (Trimble TX5) with measurement
accuracies on the order of millimeters. These scanners
were used together from multiple observation points.
Internal registrations, i.e., the alignments of two or moreObanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

Page 4 of 10

Table 1 Specifications of the UAVs

A

Phantom 2 and COOLPIX A
UAV specifications
Product name

DJI Phantom 2

Aircraft class

Quadcopter

Dimensions

350 × 350 × 190 mm

Weight1000 gMax flight speed

15 m/sMax ascent/descent speed

6 m/s

Communication distance1000 m

Max flight time

25 minPayload

400 g (empirical value)

B

Camera specifications
Product name

Nikon COOLPIX A

Pixels

16.2 million

Image sensor

23.6 × 15.6 mm

Dimensions

111.0 × 64.3 × 40.3 mm

Weight299 g

Interval shooting

More than 1 s

Phantom 3
UAV specifications
Product name

DJI Phantom 3 advanced

Aircraft class

Quadcopter

Dimensions

350 × 350 × 193 mm

Weight1280 gMax flight speed

16 m/sMax ascent/descent speed

5/3 m/sCommunication distance2000 m

Max flight time

23 minPayload0g

Camera specifications
Product name

DJI FC300S

Pixels12.4 million

Image sensor

6.16 × 4.62 mm

Dimensions

Connected to the UAV

WeightPart of the UAVInterval shooting

More than 2 s

point clouds from different scan positions, for eachscanner and among scanners were implemented using
the iterative closest point (ICP) method (Besl and
McKay 1992; Teza et al. 2007). The ICP method uses
points in the same area of two point clouds in which thecharacteristic morphological features common to bothclouds are present. One cloud is slightly moved toanother reference cloud to reduce the mean distance
between the closest points. This procedure is thenFig. 2 Examples of aerial photographs of Suzumejima taken by UAV
on June 17, 2015. a Taken from above the island (west on top) andb ) taken from the northiterated to minimize the distances. The internally registered point cloud is then georeferenced using the GCPs
for each time of measurements. The GCPs were thesame for both the UAV-SfM and TLS data. We performed initial filtering of outliers in the TLS-derived
point clouds but did not remove the vegetation during
this stage.
Because the georeferenced clouds still compriseabsolute positional uncertainties on the order of 100–101 cm
because of the uncertainties in the GNSS measurements, we further refined the external registration
using the ICP method for the time series of TLS
point clouds. Among the georeferenced TLS point
clouds, that with the least uncertainty was selected as
the reference. Point clouds immediately before orafter the reference cloud are registered to the reference, and the other clouds are then successively registered to the previous or following cloud. The ICP
registration requires unchanged features in bothclouds. To exclude changed areas, the overlaps of two
georeferenced point clouds were investigated in detail.
First, areas with large differences, including vegetation, sand deposits, and apparently missing bedrock,were manually excluded from both the moving andObanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

nA

Jun. 24, 2014

B

Page 5 of 10

Whole period
10 m

w

e

20 m

N

e

w

s

C

n

s

n

s

n

s

n

s

n

s

n

s

n

s

n

s

D
Jun. 24, 2014

w
w

e
n

10 m

e
e

s

w

Oct. 31, 2014

w
w

e
n

e
e

s

w

Feb. 11, 2015

w
w

e
n

e
e
s

w

Jun. 17, 2015

w
w

e
n

e
e
s

w

Oct. 15, 2015

w
w

e
n

e

e

s

w

Feb. 23, 2016

w
w

e
n

e

e

w

s
Jun. 18, 2016

w
w

e
n

e
s

e

w

Fig. 3 3D models (a, c) and cross sections (b, d) of Suzumejima from June 24, 2014, to June 18, 2016. a Viewed from the top and cviewed obliquely from above with traverse lines (red lines) of cross sections. Lowercase letters (n, s, e, and w) indicate the directions oftraverse lines. The left figures of c are viewed from the north, and the right figures are viewed from the south. b Superimposed crosssections of all periods (d). The left figures of d show cross sections of an east-west orientation, and the right figures show cross sectionsof a north-south orientationreference clouds. Second, ICP registration was performed once on the moving cloud. Third, the overlaps
were checked automatically and points in overlapping
areas were left. The second and third procedures
were then repeated from one to several times tominimize the mean closest point-to-point distance between the point clouds.

The TLS-derived point clouds registered in the timeline were then used as the reference for the UAV-SfM
clouds. A point cloud using UAV-SfM was aligned tothat using TLS for the same measurement time, following the same procedure of ICP as previously described
type. The externally registered UAV-SfM point clouds
were used for the following analyses.

–Period length (days)

33.5
18,319,013
9,761,070

Total number of measured points used

Number of points used

13.9

UAS registration errors (mm)21.8External registration errors (mm)16.8

Horizontal errors in GCP (mm)Vertical errors in GCP (mm)UAV

410,926,651
5,172,098

34,949,713

63,738,045

39.7

21.2

–
18.8*

14.9
29.4

–

7

–
–

990,882–

294,888

Number of TLS points used

Number of GCPs

5.3

750,045

138,546,188–

Internal registration errors (mm)Total number of TLS points measured

–

10

–
–

103

129

Sunny

Feb. 11, 2015

3

1

Number of TLS scan position

ICP

GNSS

TLS

CloudyCloudyWeather

Oct. 31, 2014

Jun. 24, 2014

Data of measurement2

1

Number of surveys

25,753,386

46,470,682

24.6

8.1

29.2

13.5

6

14,062,296

67,488,458

3.5

6

126

Sunny

Jun. 18, 2015

4

7,181,782

18,468,025

30.1

6.4

21.3

14.3

3

2,624,403

36,013,373

8.9

4

120

Sunny

Oct. 23, 2015

5

7,032,236

24,070,025

25.1[BASE]

24.6

13.4

6

4,508,131

56,232,226

15.9

6

131

Sunny

Feb. 23, 2016

6

7,042,244

16,751,012

30.2

10.6

22.8

13.6

5

5,895,657

65,453,912

11.7

8

116

sunny

Jun. 18, 2016

7

Table 2 Properties of measurement data. Asterisk (*) indicates that, only for this case, the UAS point cloud was registered to the UAS point cloud of number 3 (Feb. 11, 2015)
because of the lack of TLS data

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33
Page 6 of 10

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

decimeters) remained, the total volume of missing rockblocks due to erosional changes was obtained from thepoint clouds before and after the change during eachperiod. Although very small changes that did not exceed
the level of detection could not be counted, the majorchanges that largely contributed to the total volume of
the changes were captured.

Blue: June 24, 2014
Red: June 17, 2015

Cliff erosion

Results and discussion

Removal of rockfallsnPage 7 of 10

Time series of 3D point clouds
5ms

Fig. 4 Cliff erosion between June 24, 2014, and June 17, 2015, on
the cross section in Fig. 3

Detection of changed areas

We calculated the volume of eroded bedrock during aperiod by constructing 3D polygonal models of changed
areas from paired point clouds before and after thechange. The procedure of volume estimation is described in the following paragraph.
First, normals of the surface in point cloud for eachmeasurement time were calculated to explicitly define
the surface orientation of the target bedrock. The distances between the nearest points in the two, before
and after point clouds, during a period were computed for both clouds using the cloud-to-cloud (C2C)
distance function in the CloudCompare software.
Points having more than a certain C2C distance (exceeding the level of change detection, which isregarded to be spatially uniform and sufficiently larger
than the registration errors) were extracted from boththe before and after clouds. The normals of the cloudafter the change were then set inversely to represent
the surface orientation of missing volume blocks. The
volumetric polygonal models of the missing areas
could then be generated from the extracted points
both before and after clouds with surface normal forthe blocks, using the function of Poisson surface reconstruction in CloudCompare (Kazhdan et al. 2006).
Although the unchanged areas were effectively removed even if the original data was unfiltered, the resultant volumetric polygonal models often comprisebulky meshes showing errors including vegetation anduncertain polygons on the rock surfaces. Therefore,
we visually inspected the polygons, as well as the original point clouds before and after the change, tocheck whether the polygons show actual changes ornot. The remaining erroneous polygons were thenmanually removed. The total volume of the remaining
polygons was finally obtained for each period.
As a result, although the uncertainty derived from theregistration errors (typically several centimeters to

We successfully obtained detailed topographic data of
Suzumejima Island with the integrated approach using
UAV-SfM and TLS (Fig. 3, Table 2). The 3D topographic data have characteristics of significantly
higher spatial and temporal resolution than that of
the existing data such as aerial photographs from amanned airplane. The uncertainty in the registrationof the UAV-derived point clouds (mean point-to-point
distances between two overlapping clouds) ranged
from 20 to 40 mm (Table 2).
The 3D data shows detailed topographic features, including a ceiling of the sea cave, a vertical cliff on theeast side, and flat areas of the wave-cut bench and floorof the sea cave. In addition, we could obviously
recognize bedrock erosion via rockfalls, as well as theremoval of accumulated rock blocks beneath the cliff.
By creating a topographic profile using the 3D data, wefound that the base of the island has thinned becauseof the intense erosion at the sea cave and that the island has been destabilized as a whole (Fig. 4). These results demonstrate the high potential of this type ofsurvey method mainly composed of UAV measurements for 3D data acquisition of higher spatial resolution in such an inaccessible coastal area.
Figure 5c shows the eroded volumes for each period
derived from the difference in the 3D point clouds.
Assuming that we can detect changes more than
twice the value of the registration errors (up to
40 mm as previously noted), the level of detection for
the volume calculation was set as 100 mm. Eroded
volumes varied from a minimum of 10.6 m3 during
period II to a maximum of 178.5 m3 during period
IV (an average of 96.9 m3), resulting in a total loss of
581.5 m3. The erosion rate for the whole island varied
from 2.5 to 36.5 cm/year, an approximately 15 times
difference. The total volume of the island, measured after
removing vegetation, was approximately 13,000 m3 inJune 2014, and the volumetric percentage of erosion foreach period was calculated to be from 0.1 to 1.4%. The
total loss of volume over the 2 years was 4.5%, suggestingthat the island will disappear in approximately 45 years if
erosion continues at the same pace. This is plausible
because the island has been intensively eroded during recent decades as shown in historical aerial photographs.

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

Page 8 of 10

A

B

150
100

40

178.5 36.5

C30

127.5 24.9

Volume Rate

92.3 17.3

84.8 16.1

87.9 18.6

50

20
10

10.6 2.50Period I

Period II

Period III Period IV Period V Period VI

from Jun.
24, 2014
to Oct.
30, 2014

from Oct.
31, 2014
to Feb.
10, 2015

from Feb.
11, 2015
to Jun.
16, 2015

Erosion volume (m3)from Oct.
15, 2015
to Feb.
22, 2016

Number of occurrences of the earthquakes
with a seismic intensity over 2
1
2
3
4

0
200

from Jun.
17, 2015
to Oct.
14, 2015

Cliff erosion rate
(cm/year)

Eroded volume
(m3)

200

0

from Feb.
23, 2016
to Jun.
17, 2016

5

D

150
100
y = -9.8x + 116.4
R = 0.25

50

y = 4.3x – 31.5
R = 0.64

0
0

10
20
30
40
Number of occurrences of the waves
over 3.0 m wave height50

Fig. 5 Changes in the occurrences of potential triggering factors and cliff erosion between survey periods. a Wave height at Bosohanto-oki. b
Earthquake at Choja, Misaki-machi, Ishumi-shi. c Erosion rate of the cliff. d Relationships between potential triggering factors and eroded volume.
Blue-colored circles are wave datasets and brown-colored x-marks are earthquake datasets

Triggering factors affecting erosion

Glancing the temporal changes in erosion volume, seasonal variations in erosion rates were not clear (Fig. 5c).
Here, as potential main triggers of rockfall and slope
failure, we focus on sea wave conditions and earthquake
occurrences (e.g., Young et al. 2009, 2013; Brain et al.
2014; Vann Jones et al. 2015; Earlie et al. 2015).
Estimated daily (9 a.m. and 9 p.m.) values of significant
wave height at the observation point nearest to the studysite (Bosohanto-oki, 35°20′ N, 140°45′ E) are shown in

Fig. 5a. Observational records were obtained from the climate and ocean database of the Japan Meteorological
Agency (2017). The average wave height was approximately
2.0 m during the entire observational period, and the maximum height was 5.9 m on October 6, 2014. Average wave
heights during each observational period varied from 1.8 to
2.2 m and did not show clear annual trends in its variation.
The number of occurrences of a wave height greater than
3.0 m during each period was a maximum of 44 times during period IV and a minimum of 19 times during period VI,

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

whose difference was more than two times. If compared toerosion volume (Fig. 5c), both values of erosion volume andhigh-wave occurrence have a peak during period IV andshow relatively good correspondence to one another, withan exceptional case during period II (Fig. 5d). Although athreshold value of wave height affecting cliff erosion should
vary depending on the observational period and/or study
site, we preliminarily presumed it to be 3.0 m, 1.5 times theaverage and one-half of the maximum wave height in thiscase. Further studies are needed to decide a threshold valueof effective wave height.
Figure 5b shows the number of occurrences of large
earthquakes of a seismic intensity scale from the earthquake database of the Japan Meteorological
Agency (2017) greater than two during each period at
the nearest seismic observational point (Choja,
Misaki-machi, Ishumi-shi, 35°18′ N, 140°23′ E, 3.1 km
southwest of the study site). The Japanese Seismic Scalecannot be converted to the Modified Mercalli Intensity
Scale because of the difference in the calculation
method. Observational records were obtained from theearthquake database of the JMA (2017). During theperiod of June 24, 2014, to June 17, 2016, the maximum
intensity was four (May 30, 2015). The number of occurrences was a maximum of four times during period III
and a minimum of zero times during period V. When
compared to the temporal change in the eroded volumes(Fig. 5c), a correspondence between these values was unclear. For example, although a maximum erosion volume(178.5 m3) was recorded during period IV, the numberof occurrences of an earthquake with a seismic intensity
greater than two on the Japanese Scale was relatively few(two times). In conclusion, it is suggested that sea waves
have a greater impact than earthquakes in regard to triggering rockfalls.

Page 9 of 10

Scale did not show a clear correspondence with theeroded volume. In conclusion, it is suggested that sea
waves have a greater impact than earthquakes in termsof triggering sea-cliff erosion. However, this result isbased on a comparison during a relatively short periodof time of 2 years. Further observational data for longer
time periods should lead to a more concrete discussion.
In addition, not only the external triggering factors but
also conditional factors such as weakening bedrock
strength due to weathering, lithological weakness, andstructural weakness depending on the 3D shape of theisland should be further considered from the viewpointof immunity to mass movement.
Abbreviations
GCPs: Ground control points; GNSS: Global navigation satellite system;
ICP: Iterative closest point; MVS: Multiview stereo; RGB: Red, green, and blue;
SfM: Structure from motion; TLS: Terrestrial laser scanning; UAV: Unmanned
aerial vehicle
Acknowledgements
This work is a part of the Joint Research by the Center for Spatial
Information Science (CSIS), The University of Tokyo.
Funding
This work was supported by JSPS KAKENHI Grant Numbers JP25702014,
JP25750148, JP17H02031 and JP16KK0012.
Availability of data and materials
A part of the data used in this paper is available from the Joint Research
Assist System (JoRAS), The University of Tokyo.
Authors’ contributions
HO conducted field measurements using an UAV and drafted thismanuscript. YSH performed TLS measurements in the field and data analysis
and drafted this manuscript. Both authors read and approved the final
manuscript.
Competing interests
The authors declare that they have no competing interests.

Publisher’s Note
Conclusions
Using repeated UAV-SfM and TLS measurements for
2 years from June 24, 2014, to June 18, 2016, multitemporal 3D models of the sea cliffs were constructed, andthe topographic changes of high spatial resolution wereclarified. Erosional rates were not constant but varied up
to approximately 15 times depending on the period,
from 2.5 to 36.5 cm/year, although seasonal variances
between the summer and winter seasons were unclear.
As the potential main triggers of rockfall and slope failure, we examined the occurrences of high sea waves andsignificant earthquakes. The average wave height was approximately 2.0 m and the wave height maximum value
was 5.9 m. The number of occurrences of a wave heightgreater than 3.0 m showed relatively good correspondence with the time series of eroded volume. On theother hand, the number of occurrences of an earthquakeof a seismic intensity greater than two on the Japanese

Springer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho,Inage-ku, Chiba-shi, Chiba 263-8522, Japan. 2Present address: Hokkaido
Agricultural Research Center, National Agriculture and Food Research
Organization, 1 Hitsujigaoka, Toyohira, Sapporo, Hokkaido 062-8555, Japan.
3
Center for Spatial Information Science, The University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa, Chiba 277-8568, Japan.
Received: 2 July 2017 Accepted: 25 May 2018

References
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans
Pattern Anal Mach Intell 14:239–256. https://doi.org/10.1109/34.121791
Brain MJ, Rosser NJ, Norman EC, Petley DN (2014) Are microseismic grounddisplacements a significant geomorphic agent? Geomorphology 207:161–
173 https://doi.org/10.1016/j.geomorph.2013.11.002
Climate and Ocean Database (2017) Japan Meteorological Agency.
http://www.data.jma.go.jp/gmd/kaiyou/db/wave/chart/wavepoint/wave_
point.html?point=0&year=2017&month=3. Accessed 31 Mar 2018. (inJapanese, title in translation)

Obanawa and Hayakawa Progress in Earth and Planetary Science (2018) 5:33

Earlie CS, Young AP, Masselink G, Russell PE (2015) Coastal cliff groundmotions and response to extreme storm waves. Geophys Res Lett 42:
847–854. https://doi.org/10.1002/2014GL062534
Earthquake Database (2017) Japan Meteorological Agency. http://www.data.jma.
go.jp/svd/eqdb/data/shindo/. Accessed 31 Mar 2018. (in Japanese, title intranslation)
Fonstad MA, Dietrich JT, Courville BC, Jensen JL, Carbonneau PE (2013)
Topographic structure from motion: a new development inphotogrammetric measurement. Earth Surf Process Landf 38:421–430
Horikawa K, Sunamura T (1967) Study on recession of sea cliffs using aerial photographs.
Annu J Coast Eng JSCE 14:315–324. https://doi.org/10.11532/proce1955.14.315(in Japanese)
Hoshigami Y, Uda T, Noshi Y, Ozawa H (2006) Investigation of origin of littoral
forming Kujukuri coast plane. Proc Civ Eng Ocean 22:403–408 (in Japanese,title in translation)
Hutchinson JN (2002) Chalk flows from the coastal cliffs of north-west Europe.
In: Evans SG, JV DG (eds) Catastrophic landslides: effects, occurrence, andmechanisms. Geological Society of America, Boulder, pp 257–312
James MR, Robson S (2014) Mitigating systematic error in topographic models
derived from UAV and ground-based image networks. Earth Surf Process
Landf 39:1413–1420. https://doi.org/10.1002/esp.3609
Kazhdan M, Bolitho M, Hoppe H (2006) Poisson surface reconstruction. Proc
Symp Geom Process:61–70. https://doi.org/10.1145/1364901.1364904.
Kuhn D, Prüfer S (2014) Coastal cliff monitoring and analysis of mass wastingprocesses with the application of terrestrial laser scanning: a case study of
Rügen, Germany. Geomorphology 213:153–165. https://doi.org/10.1016/j.
geomorph.2014.01.005
Lim M, Petley DN, Rosser NJ, Allison RJ, Long AJ, Pybus D (2005) Combined
digital photogrammetry and time-of-flight laser scanning for monitoring cliff
evolution. Photogramm Rec 20:109–129
Machida H (1990) Topographic effects of artificial structures on Kujukuri coast.
Graduation thesis, University of Tsukuba, Tsukuba (in Japanese, title intranslation)
Naylor LA, Stephenson WJ, Trenhaile AS (2010) Rock coast geomorphology:
recent advances and future research directions. Geomorphology 114:3–11
Obanawa H, Hayakawa YS, Gomez C (2014a) 3D modelling of inaccessible areasusing UAV-based aerial photography and structure from motion. Trans Jpn
Geomorphological Union 35:283–294 (in Japanese, title in translation)
Obanawa H, Hayakawa YS, Kato A, Gomez C (2015) Simplified survey method
using small UAV and single-point positioning GNSS equipped digital camera.
Tran Jpn Geomorphological Union 36:87–106 (in Japanese, title intranslation)
Obanawa H, Hayakawa YS, Saito H, Gomez C (2014b) Comparison of DSMs
derived from UAV-SfM method and terrestrial laser scanning. Japan Soc
Photogrammetry Remote Sensing 53:67–74 (in Japanese, title in translation)
Rosser NJ, Petley DN, Lim M, Dunning SA, Allison RJ (2005) Terrestrial laser
scanning for monitoring the process of hard rock coastal cliff erosion. Q J
Eng Geol Hydrogeol 38:363–375. https://doi.org/10.1144/1470-9236/05-008
Sunamura T (1992) Geomorphology of rocky coasts. Wiley, Chichester
Teza G, Galgaro A, Zaltron N, Genevois R (2007) Terrestrial laser scanner to detect
landslide displacement fields: a new approach. Int J Remote Sens 28:3425–
3446. https://doi.org/10.1080/01431160601024234
Vann Jones EC, Rosser NJ, Brain MJ, Petley DN (2015) Quantifying theenvironmental controls on erosion of a hard rock cliff. Mar Geol 363:230–242
https://doi.org/10.1016/j.margeo.2014.12.008
Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012)“Structure-from-motion” photogrammetry: a low-cost, effective tool forgeoscience applications. Geomorphology 179:300–314
Young AP, Guza RT, Dickson ME, O'Reilly WC, Flick RE (2013) Ground motions on
rocky, cliffed, and sandy shorelines generated by ocean waves. J Geophys
Res Oceans 118:6590–6602. https://doi.org/10.1002/2013JC008883
Young AP, Guza RT, Flick RE, O'Reilly WC, Gutierrez R (2009) Rain, waves, andshort-term evolution of composite seacliffs in southern California. Mar Geol
267:1–7 https://doi.org/10.1016/j.margeo.2009.08.008

Page 10 of 10