2024 年 3 月 21 日

令和 5 年度 国際研究交流推進事業 報告書
報告者氏名
事業名
期間

岡村 秀雄
国際研究交流推進事業
2023 年 4 月～2026 年 3 月

研究課題名

インドネシアと日本における水環境に残留する化学物質の生態リスク研究および気候変動がおこす水力学の変化の影響

課題① インドネシアと日本における水環境に残留する化学物質の生態リスク研究（野村美帆、岡村秀雄、堀江好文）
研究概要
プラスチック汚染は最も深刻な環境問題の一つである。プラスチック製品には用途に合わせて様々な添加剤が含まれているが、低分子量である添加剤は高分子のプラスチックと化学的に結合していないため環境中で溶出する可能性があり、溶出した化学物質は水生生物の生育・生長に影響を及ぼすことが懸念される。添加剤の中でも可塑剤は最も使用量の多い添加剤の一つである。主な可塑剤としてフタル酸エステ
ル類が広く使用されてきたが、生物に対して内分泌かく乱作用を持つ可能性があるため、日本、欧州、米国では現在 6 種類のフタル酸エステル類の使用が規制されている。フタル酸エステル類の規制に伴い、代替可塑剤として非フタル酸可塑剤が使用されているが、非フタル酸可塑剤の生態リスクに関する知見は少ない。そこで、本研究ではインドネシアと日本の海水や河川水、底泥中の非フタル酸可塑剤の生態リスクを評価することを目的として、5 種の非フタル酸可塑剤として Diisobutyl adipate (DIBA), Acetyl tributyl citrate(ATBC), Di-(2-ethylhexyl) adipate (DEHA), Di-(2-ethylhexyl) sebacate (DEHS), Trioctyl trimellitate(TOTM)を対象として環境中での動態の把握及び生態毒性を評価する。具体的な実験項目を以下に示す。
本事業では主にインドネシア、ジョグジャカルタ特別州における河川水、河川底泥中の非フタル酸可塑剤の残留分析を行う。
(1) 水生生物に対する非フタル酸可塑剤の影響評価(2) 非フタル酸可塑剤の生分解性・光分解性評価(3) 大阪湾・ジョグジャカルタ（インドネシア）における非フタル酸可塑剤の環境残留の把握(4) 土壌粒子への非フタル酸可塑剤の吸着性評価
研究成果と今後の計画
初年度は海水中の非フタル酸可塑剤の分析方法を確立し、大阪湾における海水・底泥中の非フタル酸
可塑剤の環境残留分析を実施した。その結果、非フタル酸可塑剤はろ過海水よりも底泥中に高濃度で残留していることが明らかになった[1]。
2023 年 11 月 14 日～11 月 27 日にジョグジャカルタに滞在し、ガジャマダ大学(UGM)を訪問した。滞在期間中は UGM の指導教員と研究計画について議論し、UGM の設備を確認し、採取した河川水・河川底泥試料を UGM の既存の設備を用いて処理・分析できるかどうかを検討した。また、現地の河川の様子を
下流から上流にかけて調査し、水・泥試料をサンプリング可能であるかを確認した。
来年度は河川水・河川底泥を採取し、処理後の試料を日本に持ち帰った後、試料中の非フタル酸可塑
剤濃度を測定する。異なる季節に試料を採取することで環境中に残留する非フタル酸可塑剤濃度の季節変動を把握するとともに、大阪湾における非フタル酸可塑剤残留濃度と比較することで日本とインドネシア
での残留状況の違いを明らかにする。来年度は計 3 回の渡航を計画しており、1 回目は 2024 年 4 月 25
日～7 月 2 日の予定で先方と調整している。
研究成果[1] M. Nomura, H. Okamura, Y. Horie, M. P. Hadi, A. P. Nugroho, B. R. Ramaswamy, H. Harino, T. Nakano,2024. Residues of non-phthalate plasticizers in seawater and sediments from Osaka Bay, Japan, Marine
Pollution Bulletin, 199, DOI: 10.1016/j.marpolbul.2023.115947

課題② Volcanic Dome Hazards and subsequent disaster risks (comparison of Unzen and Merapi Volcanoes)（大海陸人、クリストファーゴメス）
研究概要
R. Daikai begun his research career at the SABO laboratory in collaboration with two researchers fromUGM (Universitas Gadja Madah) and UMS (Universitas Muhammadiyah Surakarta) in Indonesia [1].
After publishing his first international paper, he continued to Master, which he is finishing in March 2024,
obtaining the department best presentation award. From April, he will begin his PhD in co-tutelle with UGM
University, and the present collaboration program allowed him to go and meet his future supervision team.
He also went to UMS (Universitas Muhammadiyah Surakarta), where he gave a lecture on the new bulksediment density method based on SfM-MVS photogrammetry, which he is presently perfecting (paper in
preparation for the journal of the Sabou-gakkai).
Both Unzen and Merapi have been considered sister volcanoes, because of the manner the domes collapse into
block and ash pyroclastic density-currents (previously named pyroclastic flows).
Although Unzen Volcano is presenting a dome that is moving as a landslide by a few mm a year (the Masterthesis of Daikai Rikuto, with a submission in preparation for the Journal of Volcanology and Geothermal
Research), the Merapi Volcano, which still has an active feeding column is displaying further explosive activity,
such as phreatomagmatic explosion.
Leveraging the MOU we signed between the faculty of Oceanology and UGM, and the University of Paris
1 Sorbonne in France, the team studied the explosions of the dome of Mt. Merapi [2], and we were able to
make further observations during the field trip that lasted from the 14th to the 17th November 2023.
研究成果[1] R. Daikai, C. Gomez, B. Bradak, Saputra, A., D. Sri Hadmoko. 2022. Predisposition Factor of Safety ofLandslide Dams from Typhoon Talas, Kii peninsula, Japan. Forum Geografi 36, 2,
doi: 10.23917/forgeo.v36i2.20668[2] C. Gomez, M.A., Setiawan, N. Listyaningrum, S. B. Wibowo, D.S. Hadmoko, W. Suryanto, H. Darmawan,
B. Bradak, R. Daikai, S. Sunardi, Y. Prasetyo, A. J.Astari, L. Lukman, I.W., Nurani, M. Dede, I., Suhendro,
F. Lavigne, M.N. Malawani. 2022. LiDAR and UAV SfM-MVS of Merapi Volcanic Dome and Crater Rim
Change from 2012 to 2014, Remote Sensing 14, 5193, https://doi.org/10.3390/rs14205193
添付資料
上記論文３編の pdf ファイル

HTTPS://JOURNALS.UMS.AC.ID/INDEX.PHP/FG/
ISSN: 0852-0682 | E-ISSN: 2460-3945

Predisposition Factor of Safety of Landslide Dams from Typhoon Talas, Kii
Peninsula, Japan
Rikuto Daikai 1, *, Christopher Gomez 1, Balazs Bradak 1, Aditya Saputra 2, Danang Sri Hadmoko 3
1

Graduate School of Oceanology, Sediment Hazards and Disaster Risk Laboratory/ Kobe University, Kobe, Japan
Department of Geography, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia
3
Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, Indonesia
2*)

Correspondance : rikuto.d221@gmail.com

Citation:

Abstract

Daikai, R., Gomez, C., Bradak, B., Sapu-

Landslide dams are less frequent than other landslides, and their very existence is often very-much shortlived, because the temporary dam tends to collapse rapidly. Because of the resulting lack of evidences, there
has been less research done on this topic, although the potential catastrophe they can be at the origin of needs
assessment. For this purpose, the present contribution aims at differentiating landslides that trigger dams
against those that do not inside a group of valleys of the Kii peninsula in Japan, where landslides occurred
after the typhoon Talas in 2011. Using topographic map before the event and LiDAR data in its aftermath,the authors have calculated the factor of safety (FS) of different landslides in the same valleys, comparing
the data of landslides that created dams against those that did not. The results show that landslides thattriggered dams seemed to have a higher FS than those that did not. The authors suggest that it is because
larger landslides are needed and thus appear more stable, but also because at the location where the slopes
are stable, the riverbed can incise further instead of growing horizontally, and thus the sediments damming
the channel have more chances to block it (as it is narrow) and stop the river from flowing.

tra, A., & Hadmoko, D.S. (2022) Predisposition Factor of Safety of Landslide
Dams from Typhoon Talas, Kii Peninsula,
Japan. Forum Geografi. Vol. 36, No. 2.

Article history:
Received: 4 July 2022
Accepted: 29 December 2022
Published: 28 December 2022

Keywords: landslide predisposition facts, landslide dams.

1. Introduction
Landslides (Hadmoko and di Mauro, 2012) and landslides’ dams are a major threat across mountainous regions of the world, with the potential of sudden dam-breach, which in turn have thepotential to lead to downstream floods. Moreover, recreational mountaineering and climatechange have increased the disaster risk due to mass movements (Purdie et al., 2015).
The largest contemporary landslide dam on Earth is the Usoi dam, produced in 1911 in Tajikistan,with a lake as deep as 505 m. Other smaller-size landslides can be found in most countries withmountainous areas: in Turkey, the Tortum dam is 180 million m3 dam, impounding a 6.77 km2
surface lake (Duman, 2009). Across the world, Costa and Schuster (1991) recorded 463 landslidedams, and more recently Korup (2002) recorded more than 130 for New Zealand only and Yan(2006) found 388 for China alone, suggesting that the data first provided are very conservative.
More recently, Peng and Zhang (2012) compiled a database with 1,239 landslide dams, demonstrating the difficulty of working with landslide dams, because they are often temporary features,
which are often drained when possible. Peng and Zhang (2012) have demonstrated that 87% oflandslide dam fails within one year, while 71% within 1 month and a bit more than half within aweek, and that the failure occurs by either overtopping (90%), piping (8%), and slope failure (1%).
Interestingly, they also demonstrated that this distribution does not compare well with the manmade earth and rockfill dams. Consequently, morphological indices of valley dams (Stefanelli etal., 2016) and statistical approaches (Shen et al., 2020) are being employed to improve scientificunderstanding of the longevity of landslide dams and when they are most likely to break.

Copyright: © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of
the Creative Commons Attribution (CCBY) license (https://creativecommons.org/licenses/by/4.0/).

Daikai et al.

Because of the difficulties, Science still faces with fully physical simulations for prediction of
frequency and runout (e.g. Huang et al., 2015; Cama et al., 2015), remote sensing (Gomez andPurdie, 2016; Lissak et al., 2020) combined with statistical and geostatistical methods has been
widely used (Saputra et al., 2015). These methods have especially led to empirical predictors
relying on geometric factors (e.g., volume, area, runout, and distance to river, H/L ratio, type, andlithology) and the statistical relations between these parameters (Fan et al., 2014). The relation
between the translated volume of material and the deposition extend has been found to be a powerlaw relating the area of the landslide A [m2] to the volume V [m3], so that: V = 0.316A1.361,
with a R2 of 0.906 (Chen et al., 2014). In Hokkaido (North Japan), the relation between the width(W) and the length of the deposit (L) also showed that for earthquake-triggered landslides in theimmediate aftermath of a typhoon. The relation was almost scale-invariant with
L=2.2492W1.0296 (R2=0.89). Moreover, in Hokkaido the runout was exceptionally long (Gomez
and Hotta, 2021), with a H/L ratio between 0.15 and 0.5 for relatively small landslides. This H/LPage 91

Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668

ratio - or Fahrboschung - was calculated to be F=0.3521-9*10EXP-06D (Gomez and Hotta, 2021).
By comparison, the Naga landslide (Catane et al., 2019), and the Zhaotong rock avalanche of1991 (Xing et al., 2020) show Fahrboschung of 0.17 to 0.25, which at equal volume in Hokkaido
would lead to a Fahrboschung of 0.028. It shows that in between locations and events, there are
great disparities, but at the same time the research corpus also suggests that for a single type of
events in homogeneous valleys and lithologies, comparable geometric relations can also be expected.
To these inherent difficulties, landslide dams add further challenges to their predicting, as it is thecase for secondary- and multi-hazards (Saputra et al., 2021). Indeed, in a geologically “homogeneous region”, only a small proportion will block valleys and generate impoundments. For instance, out of the approximately 30,000 landslides generated by the 12 May 2008 Sichuan Earthquake, only 257 blocked rivers to form impounded lakes (Cui et al., 2009; Peng and Zhang, 2012).
Moreover, landslide dams generated by rainfall-induced landslides tend to collapse in 82% ofcases, while earthquake-induced landslides tend to be more stable (57% collapse) under the pressure of the impounded water (Kuo et al., 2011), further emphasizing the difficulty in predicting
potential for impoundments and downstream floods.
In the light of this research body, which suggests that in homogeneous terrain, and for a homogeneous triggering event, the variability is site specific and eventually linked to the morphology ofthe slope, the objective of this work has been to test whether the factor of safety (Fs) could be
used as a factor to determine whether a landslide could turn into a dam or not. Such work would
then allow for pre-event assessment of the slopes from high-resolution topographic data, prior tofurther engineering invasive investigation.

2. Research Method
To reach the present objective, the research was conducted at the Kii peninsula, located to thesouth-west of Honshu Island, Japan (Figure 1), where a set of landslides have occurred following
the landfall of typhoon 12 or Typhoon Talas, on September 3rd, 2011 (Figure 2). The local topography is characterized by elevation not exceeding 1,500 meters, but with slopes commanding
topographic gradients of 500 m to 800 m locally, with gravel-bed rivers resulting for the tectonically active area. Erosion is an important shaping factor of these reliefs as they are cut from shalewith blocks of chert and greenstones alternating with sandstones, dating back to 80~90 million
years. This material was generated as part of the Ryujin accretionary prism, created by the subduction of the tectonic plates, and dated to be Middle Miocene in igneous rocks with signs ofremobilization during the late Maastrichtian based on U-Pb dating (Hoshi et al., 2022).
This typhoon was the second catastrophic event after the 1889 typhoon, which brought 1000 mmof rainfall to the Kii Peninsula (Kharismalatri et al., 2017). The geology belongs to the Miyama
Formation of the Hidakagawa Group, which is part of the Chalk Shimanto Belt and is composed
mainly of chert, greenstones, and alternating sandstone and shale (Kinoshita et al., 2013).
Using 8 landslides, triggered by the typhoon 12 (or Typhoon Talas), four of which became landslide dams (Figure 3), the authors reconstructed the topography prior to the landslide using the1989 topographic maps 1/25,000 and the new topographic profiles in the center of the landslide
scar from LiDAR data, in order to measure the depth of the slide by subtracting one from theother. The alignment of both dataset was performed using the open-source software Cloud-Compare and QGIS, based on 86 and 99 control points for the two map tiles used in the present study.
In both cases, the alignment error was 3.24-11 and 9.14-12 and deemed to have no significant
effect on the results. The 1989 map were used as a confirmation that the local slope was in continuation to the surrounding ones that did not collapse. This way the edge of the landslide measured from the LiDAR data were interpolated to the missing areas from which the pre-landslide
topographic profile was generated (the data are provided in appendix as a csv file).

Daikai et al.

Page 92

Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668

Figure 1. Regional Map of the research area (a) map of Japan with the survey region squared in red, (b) Kii
Peninsula located in between Osaka City and Nagoya City.

In order to link the landslide to predisposition factors of stability, the authors have used the infinite
slope model, for which the unit density of material was tested at 20 kN/m3 (γ) was used as asimplification of the local material. The stability of slopes using the infinite slope method is basedon the shear strength equation:𝜏𝑐 = 𝐶 ′ + 𝜎 ′ tan 𝜑′(1)

Where τ_c is the shear strength, C^' is is the cohesion, σ^' is the normal stress on the potential
failure surface and φ' is the angle of friction. If we simplify the concept by omitting the porepressure and water intake to compare slopes using the factor of safety (F_s), the equation can be
reduced to:

𝐹𝑠 =

𝐶′
𝛾𝐻 𝑐𝑜𝑠 2 𝛽 tan 𝛽+

tan 𝜑′tan 𝛽(2)Where H is the depth at which the failure occurs, and β is the slope angle of the failure plane. Thisequation was computed in the open-source software Hyrcan, using the method of slice(http://www.geowizard.org/download_hyrcan.html). Iteratively, Hyrcan tests the potential failure
planes at different depths. The highest probability of failure corresponds to the “lowest” factor ofsafety, which once calculated was compared to the landslide failure plane, so that the authors

Daikai et al.

Page 93

Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668

could define whether – or not – the location of the simulated failure plane corresponded to theactual failure plane following the September 2011 rainfall landslides.

Figure 2. Rainfall Record of the 2011 Talas Typhoon (Typhoon 12) over the Kii Peninsula.

Figure 3. Aerial oblique photographs of the four studied landslides triggered by typhoon 12 in Nara prefecture, on the Kii Peninsula (source: MLIT).

Daikai et al.

Page 94

Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668

3. Results and Discussion
3.1. Results
The 8 simulated landslides show depths (H) varying between 0.017 m and 83.160 m. The deepest
simulated landslide found a depth of 83.160 m at AKATANI② location. The actual landslide
depths varied between -0.073 m and 76.900 m, and the local discrepancy vary from 0 m to 44.696
m locally, with an underestimation of the sliding plane at AKATANI① landslide (Figure 4). At
AKATANI and NAGATONO landslide, the lower part of the sliding plane does not correspondto the simulation, because accumulation occurred in the valley, and such accumulation could not
be taken into account.
For the landslides that did not trigger a landslide dam, the depths varying between 0.009 m and45.120 m. The deepest simulated landslide found a depth of 45.120 m at NIGOTANI① location.
The actual landslide depths varied between -4.748 m and 58.500 m, and the local discrepancy
vary from 0 m to 35.398 m locally, with an underestimation of the sliding plane at NIGOTANI②
landslide (Figure 5).

Figure 4. Profiles of the landslides that triggered landslide dams, with the simulated landslide and the actual
sliding plane. The orange-coloured slices reveal the slicing method used to solve the factor of safety on theslope.

Daikai et al.

Page 95

Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668

Figure 5. Landslide profiles for which landslide dams did not occur, comparing the simulated landslide
against the actual landslide.

The differentiation in typical depth and sheer size of the landslides that triggered landslide dams
compared to the normal landslide also exists when comparing the factor of safety for all of them
(Table 1). Indeed, the factor of safety of the landslides that triggered landslide dams are comprised
between 0.896 and 1.1, while those for landslides that did not trigger landslides, the FS is lower
and comprised between 0.692 and 0.877. Consequently, predicting the potential of landslide dams
based on soil mechanics and equilibrium approach can be challenging, as the method is not best
suited to predict those.

Table 1. Factor of safety of the 8 different landslides. The landslides that triggered landslide dams have ahigher safety factor than those that did not trigger landslide dams.
LocationFS
Location
Fs

AKATANI①
0.859
KAWARABI①
0.811

AKATANI②
0.896
KAWARABI②
1.1

NAGATONO①
0.877
NIGOTANI①
0.692

NAGATONO②
0.852
NIGOTANI②
1.02

3.2. Discussion
The present investigation of the predisposition factors for a selection of 8 landslides for whichdata were accessible, shows that the landslides that trigger temporary dams in river are eventuallylarger than their other counterparts, but also that the factor of safety is actually higher. The interpretation of the results suggests that landslides that occur less often are then eventually able toremobilize a greater amount of material, but also that the valley floor might play an important
control role as well. Indeed, if sediments are continuously supplied to the valley floor, a widening
and an adaptation to this situation occurs in the river network. To the contrary, a zone with no orless sediment supply will result in more vertical erosion and a potentially narrower channel, which
in turn, during rare events will tend to be more prone to be filled up and to block the valley. Suchcharacteristics, as well as road cuts are both well-known triggering factors of landslides (Ling and
Chigira, 2020).

Daikai et al.

Page 96Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668

As Cui et al. (2009) expressed, only a few percent of the triggered landslides generate landslide
dams. The present finding is in line with these existing findings, because – based on this limited
study – their Fs is slightly higher. In other words, slopes that are less stable and prone to progressive erosion and small-scale mass movements are despite of their appearance not the most dangerous, they are most likely to not trigger a large-scale event. This is however not always the caseas series of large-scale landslides can also occur and temporarily block rivers (Gomez et al., 2020).
Hadmoko et al. (2010) discusses that qualitative, semi-quantitative and quantitative methods havebeen developed to measure hazards and disaster risks, and so using a broad range of datasets thatcan characterize the landslides. This logic is based on the concept that previous landslides can beused as a base to characterize future events. This broad concept is verified in the present case as
well for landslides that are “dam-genic”, because the FS – which is an expression of geometricparameters of the slope in absence of rainfalls - have shown to be of a higher Fs compared tolandslides that are not dam-genic. This in turn signifies that, within a certain level, there are geometric parameters - besides the influence of rainfalls - that are sufficient to differentiate large vssmall more regular landslides for the Kii peninsula.

Acknowledgements
The present research was part of
the Graduate Thesis of Mr. Daikai
Rikuto, obtained at Kobe University, Department of Oceanology,
Laboratory of Sediment Hazard
and Disaster Risk. Our thanks go to
the Sabo Office of Wakayama Prefecture, which supported thepresentation of the preliminary dataat the Sabo-gakkai Taikai in Miyazaki Prefecture in 2022.

The comparison provided in the present contribution however, is based on one single rainfall
events, which, if one disregards the spatial variability of rainfall, and so in one single set of geological units. But this realm may not hold in complex and very differentiated geological settings
or in the case when rainfalls or earthquake acceleration vary spatially. At present, understanding
how one factor play over another one is often difficult, and scientists have often to resort using
the simple events in the dataset (e.g., extracting the mass movements with deposits that did not
mix with other deposits: Gomez and Hotta, 2021), like it is the case here, but there is still further
work to be done on the role of multiple factors together.

4. Conclusion
In conclusion, we have proven that, before a rainfall event occurs, it was possible for a homogeneous catchment to predict landslides that will be more prone to trigger natural dams against thosethat have less probability to trigger one. Landslides that can be the source of landslide dams areof larger amplitude and are more difficult to predict, because of their higher Fs values. The investigation found that landslides triggering temporary dams in rivers tend to be larger but have ahigher factor of safety, while slopes that are less stable and prone to small-scale movements are
not necessarily the most dangerous. The study suggests that previous landslides can be used tocharacterize future events, particularly for "dam-genic" landslides, but further work is needed tounderstand the role of multiple factors together in complex geological settings.

Author Contributions
Conceptualization: Rikuto Daikai,
Christopher Gomez, Balazs Bradak, Aditya Saputra, Danang Sri Hadmoko;
methodology: Rikuto Daikai, Christopher Gomez, Balazs Bradak, Aditya Saputra, Danang Sri Hadmoko; investigation: Rikuto Daikai, Christopher
Gomez, Balazs Bradak, Aditya Saputra,
Danang Sri Hadmoko; writing—original draft preparation: Rikuto Daikai,
Christopher Gomez, Balazs Bradak, Aditya Saputra, Danang Sri Hadmoko;
writing—review and editing: Rikuto
Daikai, Christopher Gomez, Balazs
Bradak, Aditya Saputra, Danang Sri
Hadmoko; visualization: Rikuto
Daikai, Christopher Gomez, Balazs
Bradak, Aditya Saputra, Danang Sri
Hadmoko. All authors have read andagreed to the published version of themanuscript.

Daikai et al.

ReferencesCama, M., Lombardo, L., Conoscenti, C., Agnesi, V., Rotigliano, E. (2015) Predicting storm-triggered debris flow events:
application to the 2009 Ionian Peloritan disaster (Sicily, Italy). Naturall Hazards Earth System Sciences 15, pp.
1789-1806.
Catane, S., Vercruz, N., Flora, J., Go, C., Enrera, R., Santos, E. (2019) Mechanism of a low-angle translational block slide:
Evidence from the September 2018 Naga Landslide, Philippines. Landslides 16, pp. 1709-1719.
Chen, K.-T., Kuo, Y.-S., Shieh, C.-L. (2014) Rapid Geometry Analysis for Earthquake-induced and Rainfall-induced
Landslide Dams in Taiwan. Journal of Mountain Sciences Vol. 11, pp. 360-370.
Costa, J.E., Schuster, R.L. (1991) Documented historical landslide dam found around the world. US Geological Survey
Open-File Report Vol. 91, 486p.
Cui, P., Zhu, Y.U., Han, Y.S., Chen, X.Q., Zhuang, J.Q. (2009) The 12 May Wenchuan earthquake-induced landslide
lakes: distribution and preliminary risk evaluation. Landslides Vol.6, pp.209-223.
Duman, (2009) The largest landslide dam in Turkey: Tortum landslide. Engineering Geology Vol. 104, pp. 66-79.
Fan, X., Rossiter, D.G., van Westen, C.J. van, Xu, Q., Gorum, T. (2014) Empirical prediction of coseismic landslide dam
formation. Earth Surface Processes and Landforms Vol. 39, pp. 1913-1926.
Gomez, C., Purdie, H. (2016) UAV-based photogrammetry and geocomputing for hazards and disaster risk monitoring –a review. Geoenvironmental Disasters 3, 1-11.
Gomez, C., Hotta, N. (2021) Deposits’ Morphology of the 2018 Hokkaido Iburi-Tobu Earthquake Mass Movements from
LiDAR and Aerial Photographs. Remote Sensing Vol. 13-3421, https://doi.org/10.3390/rs13173421
Gomez, C., Allouis, T., Lissak, C., Hotta, N., Shinohara, Y., Hadmoko, D.S., Vilimek, V., Wassmer, P., Lavigne, F.,
Setiawan, A., Sartohadi, J., Saputra, A., Rahardianto, T. (2020) High-Resolution Point-Cloud for Landslides in
the 21st Century: From Data Acquisition to New Processing Concepts. In World Landslide Forum: Understanding
and Reducing Landslide Disaster Risk, pp. 199-213.
Hadmoko, D.S., Lavigne, F., Sartohadi, J., Hadi, P., Winaryo. (2010) Landslide hazard and risk assessment and theirapplication in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia. Natural Hazards Vol. 54, pp. 623-642.
Hadmoko, D.S., Di Mauro S.E. (2012) Landslide and other Mass Movements. In The Routledge Handbook of Hazards
and Disaster Risk Reduction (Eds. Wisner et al.). 13p.

Page 97Forum Geografi, 36(2), 20668; DOI: 10.23917/forgeo.v36i2.20668
Hoshi, H., Kawakami, Y., Iwano, H., Danhara, T. (2022) Zircon U-Pb and fission-track ages of felsic tuff from the Ryujin
Complex in the Shimanto Accretionary Prism on the eastern part of the Kii Peninsula, Japan. Journal of the Geological Society of Japan 128, pp. 229-237 (in Japanese).
Huang, J., Ju, N.P., Liao, Y.J., Liu, D.D. (2015) Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method. Natural Hazards Earth System Sciences 15, pp. 2715-2723.
Kharismalatri, H.S., Ishikawa, Y., Gomi, T., Shiraki, K., Wakahara, T. (2017) Collapsed material movement of deepseated landslides caused by Typhoon Talas 2011 on the Kii Peninsula, Japan. International Journal of Erosion
Control Engineering, Vol. 10, pp. 108–119.
Kinoshita, A., Ogawauchi Y., Mayumi, T., Shibasaki, T. (2013) Geological factors of deep-seated catastrophic landslideand physical and mechanical properties of its sliding layer triggered by the heavy rainfall associated with Typhoon
Talas（T 1112）in Kii Peninsula. Journal of the Sand Prevention Society Vol. 66-3, pp.3-12.
Korup, O. (2002) Recent research on landslide dams – a literature review with special attention to New Zealand. Progress
in Physical Geography Vol. 26, pp. 206-235.
Kuo, Y.-Y., Tsang, Y.-C., Chen, K.T., Shieh, C.-L. (2011) Analysis of Landslide Dam Geometries. Journal of Mountain
Sciences Vol. 8, pp. 544-550.
Ling, S., Chigira, M. (2020) Characteristics and triggers of earthquake-induced landslides of pyroclastic fall deposits: An
example from Hachinohe during the 1968 M7.9 tokachi-Oki earthquake, Japan. Engineering Geology 264,
105301, pp. 1-11.
Lissak, C., Bartsch, A., de Michele M., Gomez, C., Maquaire, O., Raucoules, D., Roulland, T. (2020) Remote Sensing for
Assessing Landslides and Associated Hazards. Surveys in Geophysics 41, pp. 1-45.
Peng, M., Zhang, L.M. (2012) Breaching parameters of landslide dams. Landslides Vol. 9, pp. 13-31.
Purdie, H., Gomez, C., Espiner, S. (2015) Glacier recession and the changing rockfall hazard: Implications for glacier
tourism. New Zealand Geographer 71, pp. 189-202.
Shen, D., Shi, Z., Peng, M., Zhang, L., Jiang, M. (2020) Longevity analysis of landslide dams. Landslides Vol. 17, pp.
1797-1821.
Stefanelli, C.T., Segoni, S., Casagli, N., Catani, F. (2016) Geomorphic indexing of landslide dams evolution. Engineering
Geology Vol. 208, pp. 1-10.
Saputra, A., Sartohadi, J., Sri Hadmoko, D., Gomez, C. (2015) Geospatial Assessment of Coseismic Landslides in Baturagung Area. Forum Geografi 29, 99-113.
Saputra, A., Gomez, C., Delikostidis, I., Zawar-Reza, P., Hadmoko, D.S., Sartohadi, J. (2021) Preliminary identificationof earthquake triggered multi-hazard and risk in Pleret sub-district (Yogyakarta, Indonesia). Geo-spatial Information Science 24, 256-278.
Xing, A., Wang, G., Yin, Y., Tang, C., Xu, Z., Li, W. (2016) Investigation and dynamic analysis of a catastrophic rockavalanche on September 23, 1991, Zhaotong, China. Landslides 13, pp. 1035-1047.
Yan, R. (2006) Secondary disaster and environmental effect of landsllides and collapsed dams in the upper reaches ofMinjiang River. Master Thesis, Sichuan University, 143 p. (in Chinese).

Daikai et al.

Page 98

remote sensing
ArticleLiDAR and UAV SfM-MVS of Merapi Volcanic Dome andCrater Rim Change from 2012 to 2014
Christopher Gomez 1,2, * , Muhammad Anggri Setiawan 2,3 , Noviyanti Listyaningrum 2,3 , Sandy Budi Wibowo 4 ,Danang Sri Hadmoko 2,3 , Wiwit Suryanto 5 , Herlan Darmawan 5 , Balazs Bradak 1 , Rikuto Daikai 1 ,Sunardi Sunardi 6,7 , Yudo Prasetyo 8 , Annisa Joviani Astari 9 , Lukman Lukman 10 , Idea Wening Nurani 11,12 ,Moh. Dede 7 , Indranova Suhendro 2,3 , Franck Lavigne 12 and Mukhamad Ngainul Malawani 3,12
1

2
3

4

5
6
7

8

9

10
11

Citation: Gomez, C.; Setiawan, M.A.;
Listyaningrum, N.; Wibowo, S.B.;
Hadmoko, D.S.; Suryanto, W.;

12*

Laboratory of Sediment Hazards and Disaster Risks, Faculty of Oceanology, Kobe University,
Kobe 657-8501, Japan
Center for Disaster Study, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia
Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada,
Yogyakarta 55281, Indonesia
Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada,
Yogyakarta 55281, Indonesia
Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 40133, Indonesia
Doctoral Program on Environmental Science, Postgraduate School (SPs), Universitas Padjadjaran,
Bandung 40132, Indonesia
Department of Geodetic Engineering, Faculty of Engineering, Universitas Diponegoro,
Semarang 50277, Indonesia
Geographic Information Science Study Program, Faculty of Social Science Education, Universitas Pendidikan
Indonesia, Bandung 40154, Indonesia
National Research and Innovation Agency (BRIN), Cibinong 16911, Indonesia
Department of Development Geography, Faculty of Geography, Universitas Gadjah Mada,
Yogyakarta 55281, Indonesia
Laboratory of Physical Geography UMR 8591, Université Paris 1 Panthéon-Sorbonne, 94320 Thiais, France
Correspondence: christophergomez@bear.kobe-u.ac.jp

Darmawan, H.; Bradak, B.; Daikai, R.;
Sunardi, S.; et al. LiDAR and UAV
SfM-MVS of Merapi Volcanic Domeand Crater Rim Change from 2012 to2014. Remote Sens. 2022, 14, 5193.
https://doi.org/10.3390/rs14205193
Academic Editor: Xuan Zhu
Received: 29 August 2022
Accepted: 11 October 2022
Published: 17 October 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Abstract: Spatial approaches, based on the deformation measurement of volcanic domes and crater
rims, is key in evaluating the activity of a volcano, such as Merapi Volcano, where associated disasterrisk regularly takes lives. Within this framework, this study aims to detect localized topographic
change in the summit area that has occurred concomitantly with the dome growth and explosion
reported. The methodology was focused on two sets of data, one LiDAR-based dataset from 2012 andone UAV dataset from 2014. The results show that during the period 2012–2014, the crater walls were100–120 m above the crater floor at its maximum (from the north to the east–southeast sector), while
the west and north sectors present a topographic range of 40–80 m. During the period 2012–2014, theevolution of the crater rim around the dome was generally stable (no large collapse). The openingof a new vent on the surface of the dome has displaced an equivalent volume of 2.04 × 104 m3 ,corresponding to a maximum −9 m (+/−0.9 m) vertically. The exploded material has partly fallen
within the crater, increasing the accumulated loose material while leaving “hollows” where thevents are located, although the potential presence of debris inside these vents made it difficult todetermine the exact size of these openings. Despite a measure of the error from the two DEMs, adding
a previously published dataset shows further discrepancies, suggesting that there is also a technicalneed to develop point-cloud technologies for active volcanic craters.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.

Keywords: Merapi Volcano; Indonesia; natural hazards; disaster risk; point-cloud technology

This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Remote Sens. 2022, 14, 5193. https://doi.org/10.3390/rs14205193

https://www.mdpi.com/journal/remotesensing

Remote Sens. 2022, 14, 5193

2 of 10

1. Introduction
On stratovolcanoes, domes are a major source of hazards as they often collapse under
the actions of both internal gas pressure [1] and gravity [2], creating hazardous pyroclasticdensity currents [3]. Without gravitational collapses, internal gas pressure can generate
explosive eruptions, eventually propelling ash and other volcanic material into the upper
atmosphere. In addition, chemically stable domes can still explode as a result of phreaticand phreatomagmatic processes [4]. Even during the more quiescent phase, volcanic domes
are still evolving, eventually sliding away from the top of the volcano [5] and breaking
apart into “smaller pieces”, generating long-runout rockfalls [6]. Environmental factors ofthe dome also contribute to the dome instability: for instance, precipitation contributing
to hydrothermal alteration; the general movement of the volcanic structure (uplift andsubsidence); the local and regional seismic activity (from Vazquez et al. after the workof McGuire [7]). However, as it was recently pointed out, despite a variety of explaining
factors, the contribution of one over another remains scientifically unclear [8]. For disasterrisk management and to avoid catastrophe, dome monitoring is thus essential, even during
more quiescent periods.
In Central Java, Indonesia, a series of dome-collapse pyroclastic-density currents havebeen sweeping the flanks of Merapi Volcano during the Holocene period, although larger
eruptions and sector collapses were also found in the earlier Quaternary period, known
since ~360,000 BP [9,10]. The pyroclastic-density currents of the last major eruption in2010 (VEI4: [11]) covered an area of 22.3 km2 [12,13], in turn turning into lahars that haveflooded the valleys towards Yogyakarta City [14,15]; in its aftermath, the growth of thedome has also generated rockfall hazards [6].
Consequently, the evolution of the dome has attracted the attention of scientists
in different fields of research, e.g., rock geochemistry [16], gas analysis [17], numericalmodeling [18], petrology [19], surface deformation [20,21], and seismology [22]. During thehistorical period, the dome of Merapi Volcano has been lodged into a horseshoe crater-rim,
opened towards the south, directing most of the gravity collapse pyroclastic flows androckfalls [6], locally called ”guguran”, in this same direction (Figure 1).
The present dome was born from the millennial eruption of 2010 [13], which topographically resembles a 150 m-diameter tabletop, which fractured due to phreatic explosions
between 2012 and 2014 [4]. Monitoring of volcanoes and dome evolution represents significant technical challenges and risks for the personnel. Therefore, scientists have been
striving to create models and simulations of dome growth and collapse [8], but field dataand evidence are still essential to support those models. Focusing on the period 2012–2014,
the present contribution proposes the use of high-resolution geodetic measurements fromairborne LiDAR and UAV photographs for photogrammetric purposes, in order to detect
topographic changes that would have occurred in the crater rim during this period of
relative quiescence.

Remote Sens. 2022, 14, 5193
Remote Sens. 2022, 14, x FOR PEER REVIEW

3 of 103 of 10Figure 1. High-resolution (3 m) image from the PlanetScope satellite for the area of Mount Merapi
Figure 1. High-resolution (3 m) image from the PlanetScope satellite for the area of Mount Merapi
and its surroundings displayed with a true color composite. Dwellings and agricultural land as closeand its surroundings displayed with a true color composite. Dwellings and agricultural land as close
as 5 km from the dome translate into high-level disaster risk.
as 5 km from the dome translate into high-level disaster risk.

The present
was born from the millennial eruption of 2010 [13], which topo2. Materials
and dome
Methods
graphically resembles a 150 m-diameter tabletop, which fractured due to phreatic exploThe present contribution includes two sets of data: one LiDAR-based dataset fromsions between 2012 and 2014 [4]. Monitoring of volcanoes and dome evolution represents
2012 and one UAV dataset from 2014. The 2012 dataset was derived from airborne LiDAR
significant technical challenges
and risks for the personnel. Therefore, scientists have beenwith a density of >5 point/m2 . The LiteMapper 5600 System was installed on a Cessna 402B
striving
tocreatemodelsandsimulationsof domegrowth andand thecollapse[8], but fielddata
Aircraft flying at an altitude of 820 m above
the summit,
photograph’s
airspeedandstillessential
supportthoseoverlap
models.were,
Focusing
on the period
2012–
was evidence
259 km/h.areThesideoverlaptoandthe frontal
respectively,
40% and
60%.
2014,
the
present
contribution
proposestheuseofhigh-resolution
geodetic
measurements
Concomitantly, GPS surveying for LiDAR base station was conducted at Badan Informasi
fromairborne
LiDAR
UAV photographs
photogrammetricin order toGeospatial(BIG)
fromandreferencepoints usingfor
RTK
GPS Trimble R9purposes,with a minimumofdetecttopographic
changes
that
would
have
occurredinthecraterrim
duringthisperiod6 satellites. From this dataset, the 2012 data were gridded at a 1 m horizontal resolution,ofrelativequiescence.
using
the minimum
vertical value (elevation) in each square-meter grid.
The 2014 dataset was built using structure from motion from 328 photographs of2.
Materials
Methods
3000 × 4000 and
pixels.
The photographs were acquired on 16 October 2014 between 12:30 andpresent
contribution
includeswith
two asetsof data:
one LiDAR-based
datasetfrom
12:50Theusing
a fix-wing
UAV mounted
Canon
PowerShot
S100 at a focal
lengthof2012
and
UAV dataset
2014.
The an2012
datasetderivedThefromairborne LiDAR
5 mm,
anone
exposuretime offrom
1/1250s, andISO
speedwasof ISO-80.
photographs
wereintegrated
intoofthe(structure-from-motion
multiple-view
stereophotogrammewitha density>5 SfM–MVS
point/m2. The
LiteMapper 5600 System
was installed
on a Cessna 402B
try) software
Pro, ofcommercialized
by Agisoft.
includes point-cloud
AircraftflyingMetashapeat an altitude
820 m above thesummit,The
andprocess
the photograph’s
airspeedreconstruction
andsideits densification.
The
denseoverlap
point cloudthen exported
to Cloudwas
259 km/h. The
overlap and thefrontalwere,wasrespectively,
40% and60%.
Compare,
and
the
point
cloud
was
subsampled
at
1
point
per
squaremeter
toexactlymatch
Concomitantly, GPS surveying for LiDAR base station was conducted at Badan Informasi
the point locationof theLiDAR points
data. Using
the next
comparingofthe
Geospatial(BIG) fromreferenceusing this
RTKprocess,
GPS Trimble
R9 step
withofa minimum
6
two datasets
limitsimportanceof thelinked toatvariable
point density
and thesatellites.
From
thisthedataset,the 2012
dataartifacts
were gridded
a 1 m horizontal
resolution,
horizontal
distance between
The construction
the modalities
using
the minimum
vertical points.
value (elevation)in eachandsquare-meter
grid.of the combinationof theseare asfollows:
The two
2014datasets
dataset was
built
using structure from motion from 328 photographs of 3000
twoThepoint
clouds are thenCloud
Comparesoftware)to× 4000The
pixels.
photographs
werealigned
acquiredinon
16 October
2014(open-source
between 12:30
and 12:50
scale
the
SfM-MVS
data
and
match
it
to
the
LiDAR
point
cloud
using
the
C2C
(Cloudtousing a fix-wing UAV mounted with a Canon PowerShot S100 at a focal length of 5 mm,
Cloud)
algorithm,
from
which
the distancesin theofx,ISO-80.
y, and zThe
directions
are separated.
anexposuretime of
1/1250s, andan ISO speed
photographs
were integrated into the SfM–MVS (structure-from-motion multiple-view stereophotogrammetry)

Remote Sens. 2022, 14, 5193

the two datasets limits the importance of the artifacts linked to variable point density andthe horizontal distance between points. The construction and the modalities of the combination of these two datasets are as follows:
The two point clouds are then aligned in Cloud Compare (open-source software) toscale the SfM-MVS data and match it to the LiDAR point cloud using the C2C (Cloud
4 of to10
Cloud) algorithm, from which the distances in the x, y, and z directions are separated.
The LiDAR was thus used as the “true elevation” to calibrate the UAV photogrammetry, although errors varying based on the type of surface have been reported to vary
The LiDAR was thus used as the “true elevation” to calibrate the UAV photogrambetween 18.9 cm for pavement and 25.9 cm for deciduous tree elevation [23]. Moreover,metry, although errors varying based on the type of surface have been reported to vary
LiDAR error increases with the slope, and at Mt. Erebus, for instance, for a slope of 20
between 18.9 cm for pavement and 25.9 cm for deciduous tree elevation [23]. Moreover,degrees, where an additional vertical error of 16 cm had to be added, locally reaching 21
LiDAR error increases with the slope, and at Mt. Erebus, for instance, for a slope ofcm[24]. In thepresent
case, the authors
to16
doublethose
error
valuesand reaching
estimate
20 degrees,
where
an additionalvertical chose
error ofcm hadto be
added,
locally
that
the
LiDAR
was
accurate
to
about
50
cm
and
that
any
change
below
this
value
may
21 cm [24]. In the present case, the authors chose to double those error values and estimate
notberepresentative(althoughallvaluesarereportedinthearticle).
The
choicetodoublethat the LiDAR was accurate to about 50 cm and that any change below this value may
this
value
was also motivatedpossibilityof sandinandgrainsbouncingthenot berepresentative(althoughby
allthevaluesare reported
the asharticle).
The
choice tonear
doublesurface,increasing
thepotentialforerror.
this value was also motivated by the possibility of sand and ash grains bouncing near theIt resulted
in an
(root
surface,increasing
the RMSE
potential
formeanerror. square error) of 90 cm between the two point
cloudswhen
nottakingintoaccount
the crater
area
that
known
have
changed.
It resulted in an RMSE (root mean squareerror)
of 90
cmwas
betweenthetotwopoint
cloudsAdding
both
the
error
from
the
aligned
2014
point
cloud
to
the
error
assignedtothe Liwhen not taking into account the crater area that was known to have changed. Adding
DAR
data,
the from
elevation
error forthepoint
measurements
comparing
the twois about
both theerror
the aligned
2014
cloud to theerror assigned
tosurfaces
the LiDAR
data,
1.4
m.
the elevation error for the measurements comparing the two surfaces is about 1.4 m.
The
The dataset
dataset fromfrom which
which thethe authors
authors have
have worked
worked isis thusthus mademade up
up ofof an
an orthophotoorthophotographofthesummitin2014(Figure
2a),
and
two
datasets
of
the
dome
in
2012
graph of the summit in 2014 (Figure 2a), and two datasets of the dome in 2012 (Figure(Figure 2b)2b)
and 2014 (Figure 2c). From
From a visual inspection
inspection of the hill-shaded representation of theDEM, one can see the changes that have occurred on the dome (most notably the openingof vents [4]).

Figure 2. Dome of Merapi Volcano in 2012 and 2014: (a) orthophotograph constructed from UAV
imagery in October 2014; (b) surface map of the dome in 2012 from LiDAR and (c) in 2014 from
UAV-SfM-MVS. The captions are (d) dome in transparent red; (v) vent in transparent yellow; (tw) top
of the wall and it corresponds to the blue line; (w) wall and it corresponds to the transparent blue.

3. Results
The comparison of the two topographic data is presented with first the modifications
to the crater rim and the talus, and secondly, a quantification of the volume removed either
from the volcanic explosions or gravitational collapses.

blue.

3. Results
Remote Sens. 2022, 14, 5193

The comparison of the two topographic data is presented with first the modifications
5 ofei10to the crater rim and the talus, and secondly, a quantification of the volume removed
ther from the volcanic explosions or gravitational collapses.
3.1. TheThe Crater
Crater Rim
Rim andand Its
Its Talus
Talus
3.1.
At thethe summit
rim
traps
thethedome.
DuringtheAtsummit ofofMerapi
MerapiVolcano,
Volcano,aahorseshoe
horseshoecrater
crater
rim
traps
dome.
Duringperiod
2012–2014,
thecrater
walls
were
100
–
120
m
high
above
the
craterfloor
atits
maxthe period 2012–2014, the crater walls were 100–120 m high above the crater floor at its
imum (fromthe northto thesector),while
the west
and andnorthsectorspremaximum(fromthe northto east–southeast
the east–southeast
sector),while
the west
northsectors
sent a topographic
range
of 40
80 m (Figure
3). 3).
TheThecrateris opento thesouth.
Duringthepresent
a topographic
range
of–40–80m (Figurecrateris opento thesouth.
Duringperiod
2012–2014,
thetheevolutionof ofthecraterstabletheperiod
2012–2014,
evolutionthecraterrim
rimaroundaroundthe
thedomedome waswas generally
generally stable(no large
large collapse),collapse), althoughalthough thethe data
data shows
shows variation
variation inin thethe geometrygeometry ofof thethe subverticalsubvertical(nowalls aroundaround thethe crater
crater rimrim (b–h,(b–h, andand kk inin Figure
Figure 3).
3). The
The walls
walls thatthat spreadspread outside
outside ofof thethewallscrater
rim
seem
to
be
more
stable
(a,l,
and
m
in
Figure
3).
At
the
foot
of
the
craterwalls,
crater rim seem to be more stable (a,l, and m in Figure 3). At the foot of the crater walls, thethe topography
to was
2014stable
was stable
overall,showingno wallmajor
wall collapses,
topography
fromfrom
2012 2012
to 2014overall,showingno majorcollapses,
exceptine, h, and(Figurewhere3),thefloor thelevel
increasedby >2 m locally.
the walls
exceptin e,Ih,and I 3),(Figurewhere
floor
level increasedby >2 mAlthough
locally. Althoughseem
to showlarge
discrepancies
between 2012
and 20143b),(Figure
the absenceof
the walls
seemlocallyto show
locallylarge discrepancies
between2012(Figureand 20143b), thesignificant
at their
toes suggests
thatsuggests
those arethat
potentialthat areexaminedabsence ofdeposits
significant
deposits
at their toes
those artifacts
are potential
artifacts
thatin
discussion.
aretheexaminedin the discussion.

Figure 3. Cross-sections of the crater rim. The black line is the topography in 2012 as extracted fromthe LiDAR-DEM at 1 m horizontal resolution, and the blue line is the topography in 2014, extracted
from the UAV-based photogrammetric model.

3.2. Dome’s Topographic and Volumic Change with the Localized Explosions
As already reported in the literature, during the period 2012–2014, the dome locally
exploded and split into a set of aligned new vents along a north–west–southeast axis.
Around the dome, surface changes also occurred with an increase in elevation (Figure 4).
The opening of a new vent on the surface of the dome has displaced an equivalent volumeof 2.04 × 104 m3 , corresponding to a maximum −9 m (+/−0.9 m) vertically (location 4
in Figure 4). At the two vents generated to the north and south of the dome, the volume
changes are 8.56 × 104 m3 at location 1, 2.61 × 105 m3 (location 2), and 4.82 × 103 m3
at location 5 (Figure 4). The variability is mostly controlled by the visual depth of theRemote Sens. 2022, 14, 5193

As already reported in the literature, during the period 2012–2014, the dome locally
exploded and split into a set of aligned new vents along a north–west–southeast axis.
Around the dome, surface changes also occurred with an increase in elevation (Figure 4).
The opening of a new vent on the surface of the dome has displaced an equivalent volumeof 2.04 × 104 m3, corresponding to a maximum −9 m (+/−0.9 m) vertically (location 4 in
6 of 10Figure 4). At the two vents generated to the north and south of the dome, the volume
changes are 8.56 × 104 m3 at location 1, 2.61 × 105 m3 (location 2), and 4.82 × 103 m3 at location
5 (Figure 4). The variability is mostly controlled by the visual depth of the openings (ifopenings
obstructedby appeardebris, shallower).
they appearTherefore,shallower).
Therefore,
thosetovaluesneedobstructed(ifbydebris, they
thosevalues need
be considto
be asconsideredminimalvalues. Surrounding
plateaucreatedby loosethe dome,
loose
ered
minimal as
values.
Surrounding
the plateau thecreatedby thedome,materialismaterialis
also
displaying
topographic
change.
It
hasmostlyincreased
this
time.
For
also displaying topographic change. It has mostly increased this time. For the period 2012–
theperiod
2012–2014,
the corresponds
rise of material
to a respective
2014,
the riseof material
to acorresponds
respective volume
change volumeof 1.88 ×change
10 4 m3 ofat
4 m3 at location 3 and 6.22 × 104 m3 at location 8. These changes correspond to
1.88
×
10
4
3
location 3 and 6.22 × 10 m at location 8. These changes correspond to changes nearing 18
changesnearing
18 m (+/1−and
0.9 m)
at location
11 (+/−8.0.9
m)top
m of
at location
8. On
top
m (+/− 0.9m) at location
11 (+/−
0.9 m) 1mand
at location
Onthese major
topoofthese major
topographic
changes,gravitational
collapses
have also
observed
thegraphic
changes,gravitational
collapses
have also
been observed
onbeenthe craterrimon(locacraterrim(location
10)
and
a
small
portion
to
the
south
of
the
dome
seems
to
have
also
tion 10) and a small portion to the south of the dome seems to have also collapsed (location
collapsed
6 in Figure 4).
6 in Figure(location
4).

Figure 4. Topographic change from 2012 to 2014. These data show the vents opening and the “split
Figure 4. Topographic change from 2012 to 2014. These data show the vents opening and the “split
in two” of the dome, as well as the areas between the crater rim and the dome, where the topography
in two” of the dome, as well as the areas between the crater rim and the dome, where the topography
increased, arguably due to exploded material deposition. These changes emphasize the changes that
increased,
due onto exploded
deposition.
These RMSE
changesemphasize
theofchanges
thatare >2.5 marguably(i.e., in blue
the map),materialvalued above
the overall
andthe RMSE
the outside
are
>2.5
m
(i.e.,
in
blue
on
the
map),
valuedabove
theoverall
RMSE
and
the
RMSE
of
the
outsidestructure. This procedure may erase some of the minor variations, but it is aimed at eliminating thestructure.
This fromprocedure
may erase some of the minor variations, but it is aimed at eliminating thefalse positives
the dataset.
false positives from the dataset.

4. Discussion
4. Discussion
This discussion is organized into two sections, with the first being an interpretation
This discussion is organized into two sections, with the first being an interpretation ofoftheresults
and
what
they
mean
termsofofvolcanic
volcanicprocesses.
processes.Then,
Then,ititfollows
followsaa section
sectionthe results
and
what
they
mean
inin
terms
that
compares
the
data
from
the
present
contribution
withadataset
from
2015,
and
that compares the data from the present contribution with a dataset from 2015, and finally
finally
we discuss
discuss thethe implications
implications ofof thethe present
present work
work forfor hazards
hazards andand disasterdisaster risk
risk monitoring.
monitoring.
we
4.1. Surface Deformation and Interpretation
The dome of Merapi Volcano locally exploded during the period 2012–2014, creating a
set of aligned vents [4], which represent an estimated volume of material of 2.01 × 104 m3
for the central elongated vent at the top of the dome, 2.61 × 105 m3 to the north (high-value
determined by good visibility at a depth of almost 30 m below the pre-explosive surface),and one vent to the south, just shy of 1 × 104 m3 . The central elongated vent represents
a topographic drop of about 9 m (Figure 5), although it is most likely that the opening
could have been deeper (filled by either ejecta going back to the vent or the full depth being
invisible to the SfM-MVS method due to the narrowness of the vent). The origin of these
explosions is the result of pressure increase from the contact with rainwater [4] as well asprobable material weakening due to hydrothermal alteration, a process that was observedat the dome summit and its surroundings at a later date [25]. Around the dome, the material
in at least two locations rose topographically (Figure 5), even without having had major

Remote Sens. 2022, 14, 5193

7 of 10wall collapses that would match this rise. If we work by analogy, the dome of 2011, which
is visible between 2012 and 2014, may have grown over other extruded material in thesame way that the 2017 dome climbed over the 2011 dome [25]. The exploded material may
have partly escaped the space of the crater rim, but it is most likely that a large portion of it
remained inside the crater, with the largest blocks traveling a shorter distance. Despite ameasure of the visible volume change (Figure 4), it is difficult to link both the post-phreatic
explosion holes to the deposits for the following reasons: (1) Due to decompression anddeposit bulk density changing from that of the dome, the measured volume change does
not reflect a material volume change; (2) Part of the material escaped the crater rim; (3) The
post-explosion holes will have certainly collapsed; (3) For the deepest part of the holes,
ALS or SfM-MVS may not record the real “bottom”. This shows the necessity to develop
Remote Sens. 2022, 14, x FOR PEER REVIEW
8 ofa methodological framework that is more adapted to volcanic vent surveying due tothe10
numerous complexities it holds.

Figure5.5.Interpretation
Interpretationfigure
figureofofthe
thedeformation
deformationatatthe
thedomedomeandandnearnearthethedome.
dome.
Figure4.2.
4.2.Hazards
Hazardsin
inQuiescent
QuiescentTime,Time,When
WhenthetheVolcano
VolcanoActs
ActsasasaaMountain
MountainEveninperiodswhen
thevolcanoisnotmagmaticallyEven in periods when the volcano is not magmaticallyactive
active[25],[25],minor
minorexplosions
explosions
and
other
erosionprocesses
canalso
occur.
In
thepresent
case,
there
is
an
and other erosion processes can also occur. In the present case, there is anaccumulation
accumulationofofvolcanic
volcanicclasts
clastswithin
withinthe
thecrater
craterand
andsurrounding
surroundingthe
thevolcanic
volcanicdome.
dome.Such
Suchlow-energy
low-energy
events
can
still
be
a
hazardtomountainclimbers,
as
has
been
seen
at
Mt.
Ontakeevents can still be a hazard to mountain climbers, as has been seen at Mt. Ontake [26],[26],

where, in 2014, around 340 individuals were located in the surrounding of the summit
when it exploded, creating ballistic for which hazard maps are essential [27]. Based on the
topographic change at Merapi Volcano, a large portion of the large ballistic seems to havebeen contained within the crater, as the topographic change from 2012 to 2014 does notRemote Sens. 2022, 14, 5193

8 of 10where, in 2014, around 340 individuals were located in the surrounding of the summit
when it exploded, creating ballistic for which hazard maps are essential [27]. Based on
the topographic change at Merapi Volcano, a large portion of the large ballistic seems tohave been contained within the crater, as the topographic change from 2012 to 2014 does
not show significant accumulation zones outside the crater. However, even if the material
is contained within the crater and the crater is defined as a “no trespassing zone”, thematerial produced usually ranges from fine-grain material to coarser material, and uponrainfall, the material can also be transformed into a slurry of debris and water, eventuallycreating lahars, such as when it did for the Ontake eruption [28]. These “small-scale”
hazards can then be very pervasive because, instead of imposing a “new rhythm” on
the volcano and the valley, they will be highly dependent on other factors, such as a
trigger by an earthquake, or in some cases, the role of seasonal snow cladding [29]. Finally,
unconsolidated material located at the summit of the volcano is a further source of hazard
during long quiescent times, as it has been shown at Unzen Volcano [30], where the gullies
are progressing towards the volcanic dome by regressive erosion, modifying the watershedgeometries [31] and helping the release of material from the summit. In the case of a solid
compact dome, breaking parts may require some “extra-effort”, but if the material is alreadyunconsolidated, it is a further pool of material that can be released on the slopes.
From a hazard perspective, small-scale explosions of a phreatic type only affect asmall area, but the timing of such an explosion is extremely difficult to predict, and thefragmentation of material results in a pool of clastic sediments at the top that can then beremobilized by seasonal events (rain and snow melting) as well as longer-timescale events
such as regressive erosion or eventual seismic acceleration.
5. Conclusions
The main conclusions of the present paper are that (1) during the period 2012–2014
when the dome of Merapi experienced phreatic explosions, the topography around thedome rose; (2) this rise does not seem to be related to large wall collapses, and it is likelythat the exploded material accumulated in a low-topographic area; and (3) from a technicalperspective, there is a need to develop UAV-imagery-acquisition methods that are fit forvolcanic craters, with step walls (e.g., avoiding NADIR images), in order to reduce the error
due to the lack of data; (4) volcanoes, even during quiescent time, are hazardous and forthis reason, inter-eruption monitoring is essential.
Author Contributions: Conceptualization, C.G., F.L., S.B.W., D.S.H., M.N.M.; methodology, M.A.S.,
N.L., B.B., R.D.; resources, M.A.S., W.S., S.S., Y.P., A.J.A., L.L., I.W.N., M.D.; writing—original draft
preparation, C.G.; writ-ing—review and editing, C.G., F.L., H.D., S.B.W., I.S.; funding acquisition,
S.B.W., C.G. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Riset Kolaborasi Indonesia (RKI) number 1545/UN1/DITLIT/DitLit/PT.01.03/2022. The authors also thank two anonymous reviewers and the editor for their valuablecomments to strengthen this paper. Further funding was provided at Kobe University through the
IMARC international program on volcanic hazards, led by C. Gomez.
Data Availability Statement: All the data used in the present contribution can be accessed upon
contacting the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.

References
1.
2.

Voight, B.; Elsworth, D. Instability and collapse of hazardous gas-pressurized lava-domes. Geophys. Res. Lett. 2000, 27, 1–4.
[CrossRef]
Swanson, D.A.; Holcomb, R.T. Regularities in Growth of the Mount St. Helens Dacite Dome, 1980–1986. IAVCEI Proc. Lava Flows
Domes 1990, 2, 3–24.

Remote Sens. 2022, 14, 5193

3.

4.
5.

6.
7.

8.
9.
10.

11.

12.

13.

14.

15.

16.

17.
18.

19.

20.
21.
22.
23.
24.

9 of 10

Loughlin, S.C.; Calder, E.S.; Clarke, A.; Cole, P.D.; Luckett, R.; Mangan, M.T.; Pyle, D.M.; Sparks, R.S.J.; Voight, B.; Watts, R.B.
Pyroclastic flows and surges generated by the 25 June 1997 dome collapse, Soufriere Hills Volcano, Montserrat. In The Eruptionof Soufriere Hills Volcano, Montserrat, from 1995 to 1999; Druit, T.H., Kokelaar, B.P., Eds.; Geological Society: London, UK, 1990;
Volume 21, pp. 129–209.
Darmawan, H.; Walter, T.R.; Troll, V.R.; Budi-Santoso, A. Structural weakening of the Merapi dome identified by drone
photogrammetry after the 2010 eruption. Nat. Hazards Earth Syst. Sci. 2018, 18, 3267–3281. [CrossRef]
Gomez, C.; Allouis, C.; Lissak, N.; Hotta, N.; Shinohara, Y.; Hadmokok, D.S.; Vilimek, V.; Wassmer, P.; Lavigne, F.; Setiawan,A.; et al. High-Reolustion Point-Cloud for Landslides in the 21st Century: From Data Acquisition to New Processing Concepts, In
Understanding and Reducing Landslide Disaster Risk; Arbanas, Z., Ed.; Springer Nature: Cham, Switzerland, 2021; Volume 6, pp.
199–213.
Darmawan, H.; Yuliantoro, P.; Rakhman, A.; Santoso, A.B.; Humaida, H.; Suryanto, W. Dynamic velocity and seismic characteristics of gravitational rockfalls at the Merapi lava dome. J. Volcanol. Geotherm. Res. 2020, 404, 107010. [CrossRef]
Vazquez, R.; Macias, J.L.; Alcala-Reygosa, J.; Arce, J.L.; Jimenez-Haro, A.; Fernandez, S.; Carlon, T.; Saucedo, R.; Sanchez-Nunez,
J.M. Numerical modeling and hazard implications of landslides at the Ardillas Volcanic Dome (Tacana Volcanic Complex,
Mexico-Guatemala). Nat. Hazards 2022, 113, 1305–1333. [CrossRef]
Kelfoun, K.; Santoso, A.B.; Latchimy, T.; Bontemps, M.; Nurdienm, I.; Beauducel, F.; Fahmi, A.; Putra, R.; Dahamna, N.; Laurin,A.; et al. Growth and collapse of the 2018–2019 lava dome of Merapi Volcano. Bull. Volcanol. 2021, 83, 8. [CrossRef]
Gomez, C.; Janin, M.; Lavigne, F.; Gertisser, R.; Charbonnier, S.; Lahitte, P.; Hadmoko, D.S.; Fort, M.; Wassmer, P.; Degroot, V.; et al.
Borobudur, a basin under volcanic influence: 361,000 years BP to present. J. Volcanol. Geotherm. Res. 2010, 196, 245–264. [CrossRef]
Newhall, C.G.; Bronto, S.; Alloway, B.; Banks, N.G.; Bahar, I.; del Marmol, M.A.; Hadisantono, R.D.; Holcomb, R.T.; McGeehin, J.;
Miksic, J.N.; et al. 10,000 years of explosive eruptions of Merapi Volcano, Central Java: Archeological and modern implications. J.
Volcnaol. Geotherm. Res. 2000, 100, 9–50. [CrossRef]
Surono; Jousset, P.; Pallister, J.; Boichu, M.; Buongiorno, M.; Budisantoso, A.; Costa, F.; Andreastuti, S.; Prata, F.; Schneider, D.;et al. The 2010 explosive eruption of Java’s Merapi volcano—A ‘100-year’ event. J. Volcanol. Geotherm. Res. 2012, 241–242, 121–135.
[CrossRef]
Charbonnier, S.J.; Germa, A.; Connor, C.B.; Gertisser, R.; Preece, K.; Komorowski, J.-C.; Lavigne, F.; Dixon, T.; Connor, L.
Evaluation of the impact of the 2010 pyroclastic density currents at Merapi volcano from high-resolution satellite imagery, field
investigations and numerical simulations. J. Volcanol. Geotherm. Res. 2013, 261, 295–315. [CrossRef]
De Belizal, E.; Lavigne, F.; Robin, A.K.; Sri Hadmoko, D.; Cholik, N.; Thouret, J.C.; Sawudi, D.S.; Muzani, M.; Sartohadi, J.; Vidal,
C. Rain-triggered lahars following the 2010 eruption of Merapi volcano, Indonesia: A major risk. Volcanol. Geotherm. Res. 2013,
261, 330–347. [CrossRef]
Komorowski, J.-C.; Jenkins, S.; Baxter, P.J.; Picquout, A.; Lavigne, F.; Charbonnier, S.; Gertisser, R.; Preece, K.; Cholik, N.; BudiSanto, A.; et al. Paroxysmal dome explosion during the Merapi 2010 eruption: Processes and facies relationships of associatedhigh-energy pyroclastic density currents. J. Volcanol. Geotherm. Res. 2013, 261, 260–294. [CrossRef]
Sri Hadmoko, D.; de Belizal, E.; Mutaqin, B.W.; Dipayana, G.A.; Marfai, M.A.; Lavigne, F.; Sartohdai, J.; Worosuprojo, S.; Starheim,
C.A.; Gomez, C. Post-eruptive lahars at Kali Putih following the 2010 eruption of Merapi Volcano, Indonesia: Occurrences andimpacts. Nat. Hazards 2018, 94, 419–444. [CrossRef]
Allard, P. Proportions des isotopes 13 C et 12 C du carbone émis à haute température par un dome andésitique en cours decroissance; Le Merapi (Indonésie)—Proportions of C-13 and C-12 isotopes of carbon emitted at high temperature by an andesitic
dome during growth; Merapi, Indonesia. C. R. Acad. Sci. Série D 1980, 291, 613–616.
Le Guern, F.; Gerlach, T.M.; Nohl, A. Field gas chromatograph analyses of gases from a glowing dome at Merapi Volcano, Java,
Indonesia, 1977, 1978, 1979. J. Volcanol. Geotherm. Res. 1982, 14, 223–245. [CrossRef]
Kelfoun, K. Processus de Croissance et de Déstabilisation des Dômes de lave du Volcan Merapi (Java Centrale, Indonésie). Modélisations Numériques des Dômes, Dynamique des Ecoulements Pyroclastiques Associés et Surveillancepar Stéréophotogrammétrie. Ph.D. Thesis, Univ. Blaise Pascal Clermont-Ferrand II, Aubière, France, 1999.
Clocchiatti, R.; Joron, J.-L.; Kerinec, F.; Treuil, M. Quelques données préliminaires sur la lave du dome actuel du volcan Merapi(Java, Indonésie) et sur ses enclaves—Preliminary data on lava from the present dome of the volcano Merapi (Java, Indonesia)
and on its xenoliths. C. R. Acad. Sci. Série 2 1982, 295, 817–822.
Voight, B.; Young, K.D.; Hidayat, D. Deformation and seismic precursors to dome-collapse and fountain-collapse nuées ardentes
at Merapi Volcano, Java, Indonesia, 1994–1998. J. Volcanol. Geotherm. Res. 2000, 100, 261–287. [CrossRef]
Young, K.D.; Voight, B. Ground deformation at Merapi Volcano, Java, Indonesia: Distance changes, June 1988–October 1995. J.
Volcanol. Geotherm. Res. 2000, 100, 233–259. [CrossRef]
Brodscholl, A.; Kirbani, S.B.; Voight, B. Sequential dome-collapse nuées ardentes analyzed from broadband seismic data, Merapi
Volcano, Indonesia. J. Volcanol. Geotherm. Res. 2000, 100, 363–369. [CrossRef]
Hodgson, M.E.; Bresnahan, P. Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget. Photogram.
Eng. Remote Sens. 2004, 70, 331–339. [CrossRef]
Csatho, B.; Schenck, T.; Kyle, P.; Wilson, T.; Krabill, W.B. Airborne laser swath mapping of the summit of Erebus volcano,
Antarctica: Applications to geological mapping of a volcano. J. Volcanol. Geotherm. Res. 2008, 177, 531–548. [CrossRef]

Remote Sens. 2022, 14, 5193

25.

26.
27.

28.
29.

30.

31.

10 of 10

Darmawan, H.; Troll, V.R.; Walter, T.R.; Deegan, F.M.; Geiger, H.; Heap, M.J.; Seraphine, N.; Harris, C.; Humaida, H.; Muller, D.
Hidden mechanical weaknesses within lava domes provided by buried high-porosity hydrothermal alteration zones. Sci. Rep.
2022, 12, 3202. [CrossRef] [PubMed]
Tsunematsu, K.; Ishimine, Y.; Kaneko, T.; Yoshimoto, M.; Fujii, T.; Yamaoka, K. Estimation of ballistic block landing energy during
2014 Mount Ontake eruption. Earth Planets Space 2016, 68, 88. [CrossRef]
Fitzgerald, R.H.; Tsunematsu, K.; Kennedy, B.M.; Breard, E.C.P.; Lube, G.; Wilson, T.M.; Jolly, A.D.; Pawson, J.; Rosenberg,
M.D.; Cronin, S.J. The application of a calibrated 3D ballistic trajectory model to ballistic hazard assessments at Upper Te Maari,Tongariro. J. Volcanol. Geotherm. Res. 2014, 286, 248–262. [CrossRef]
Kataoka, K.S.; Matsumoto, T.; Saito, T.; Nagahashi, Y.; Iyobe, T. Suspended sediment transport diversity in river catchments
following the 2014 phreatic eruption at Ontake Volcano, Japan. Earth Planets Space 2019, 71, 15. [CrossRef]
Kataoka, K.S.; Matusmoto, T.; Saito, T.; Kawashima, K.; Nagahashi, Y.; Iyobe, T.; Sasaki, A.; Suzuki, K. Lahar characteristics as afunction of triggering mechanism at a seasonally snow-clad volcano: Contrasting lahars following the 2014 phreatic eruption of
Ontake Volcano, Japan. Earth Planets Space 2018, 70, 113. [CrossRef]
Tsunetaka, H.; Shinohara, Y.; Hotta, N.; Gomez, C.; Sakai, Y. Multi-decadal changes in the relationships between rainfall
characteristics and debris-flow occurrences in response to gully evolution after the 1990-1995 Mount Unzen eruptions. Earth Surf.
Process. Landf. 2021, 46, 2141–2162. [CrossRef]
Gomez, C.; Shinohara, Y.; Tsunetaka, H.; Hotta, N.; Bradak, B.; Sakai, Y. Twenty-Five Years of Geomorphological Evolution in the
Gokurakudani Gully (Unzen Volcano): Topography, Subsurface Geophysics and Sediment Analysis. Geosciences 2021, 11, 457.
[CrossRef]