- Open Access
Coseismic landslide susceptibility assessment using geographic information system
© The Author(s). 2016
- Received: 31 August 2016
- Accepted: 16 November 2016
- Published: 1 December 2016
Indonesia is one of the most earthquake prone countries in the world. More than 14,000 earthquakes of magnitude greater than 5 occurred in Indonesia between 1897 and 2009. Earthquakes are a major cause of slope instability eventually triggering coseismic landslides, which cost 1.5 million US$/ year in Java: one of the most densely islands in Indonesia. This paper aims to assess coseismic landslide susceptibility using Geographic Information System (GIS) on the western flank of Baturagung Escarpment, 8 km southeast of the Yogyakarta City, a data sparse area. Therefore, we have used a probabilistic seismic hazard analysis to calculate the peak ground accelerations, while the coseismic landslide susceptibility analysis was done by the scoring method in the GIS adopted from Mora and Vahrson model (Costa Rica), which is well adopted for data sparse areas.
The west flank of Baturagung Escarpment is dominated by moderate level of coseismic landslide with an average Coseismic Landslide Susceptibility Level (CLSL) of 33–162. The upper slope of Baturagung Escarpment, which consists of Semilir Formation has the CLSL of 163–512, corresponding to medium level CLSL (Mora and Vahrson model). The low level CLSL is mainly located on the foot slopes of Baturagung Escarpment, while the alluvial and colluvial plains located along the Opak River have very low CLSL (0–6).
Based on the mapped landslide occurrence, the landslides tend to occur in the zone of moderate CLSL and they are distributed along the border between moderate and low coseismic landslide zone, meaning that the change on local condition could be playing an important role in triggering coseismic landslide.
- Coseismic landslide hazard
- Geographic Information Systems (SIG)
Indonesia is located at the junction among three active tectonic plates: the Eurasian Plate; the Indo-Australian Plate, and the Pacific Plate. The Indo-Australian Plate and the Pacific Plate are moving northward about 7.23 cm/year. (Demets et al. 1994) and westward about 11–12.5 cm/year. (Irsyam et al. 2010). Meanwhile, the Eurasian Plate is relatively constant. As a result, a giant fault known as Sunda megathrust was formed and extends approximately 5500 km from the north, running along the western side of Sumatra to the south of Java and Bali. This seismogenic structure is responsible for many great earthquakes in Indonesia. More than 14,000 earthquakes of magnitude greater than 5 (M > 5) had occurred over 1900–2009. Some of them had big impacts to the community such as Aceh earthquake in 2004 (Mw = 9.2); Nias earthquake in 2005 (Mw = 8.7); Yogyakarta earthquake in 2006 (Mw = 6.3); Tasikmalaya earthquake in 2009 (Mw = 7.4); Padang earthquake in 2009 (Mw = 7.6); and Kebumen earthquake in 2014 (Mw = 6.1) (Badan Geologi 2014).
Earthquakes can trigger secondary natural hazards, including landslides, rock falls, debris flows, barrier lakes and floods and tsunamis. Among all of those secondary hazards, coseismic landslides are the most widespread (Keefer 1994). In Java, one of the most densely islands in Indonesia, a thousand landslides were reported from 1990 to 2005 and caused damages that exceeded tens of thousands dollars (Hadmoko et al. 2010). The high intensity of rainfalls and the high seismic activities made Java as one of the most susceptible regions for coseismic landslides, especially in the southern mountainous areas.
Both seismic and landslide hazards assessment have been well developed in Southern Yogyakarta. Some studies have identified the characteristics of the seismic and landslide hazards in Yogyakarta such as Walter et al. 2007; Wagner et al. 2007; Burton and Cole 2006; Burton et al. 2008; Haifani 2008; Sulaeman et al. 2008; Abidin et al. 2009a, b; Daryono 2011; Hartantyo and Brotopuspito 2012; Cahyaningtyas 2012; Karnawati et al. 2005; Hadmoko et al. 2010; Priyono 2012; Wacano and Hadmoko 2012; Nugroho et al. 2012. However, no studies about coseismic landslide susceptibility assessment have been conducted in this area.
Summary of deterministic and statistical coseismic landslide models
- calculates the coseismic landslide likelihood based on the dynamic stability of the slope and the earthquake ground motion.
- appropriate for site specific coseismic landslide assessment and suitable for fairly stiff materials.
- highly simplistic and contains many assumptions
- Newmark’s method treats a landslide as a rigid-plastic body
- based on the mathematical methods.
- uses the finite-element model to estimate the strain potential at each node based on cyclic laboratory shear test of soil samples.
- gives the most accurate explanation of slope behaviour during an earthquake
- require high quality and sophisticated soil constitutive models
- requires high quality and quantity of data
- requires undisturbed soil samples and extensive laboratory analysis
- regression equations were generated using the data derived from the Newmark displacement model.
- Needs extensive data on strong-motion and coseismic landslide occurrences
- suitable only for large number of earthquake strong motion data and for rapid preliminary screening of sites.
Integrated frequency ratio (FR) and logistic regression (LR)
- analyses various factors that might affect coseismic landslide
- provides better explanation of relationship among the factors that might affect coseismic landslide
- needs an extensive field survey and observation.
- the results are sensitive to the data quality
Umar et al. 2014
- derives from the Newmark displacement model
- needs extensive data on strong-motion and coseismic landslide occurrences.
- Needs a data set of Newmark displacement
- analyses various factors that might affect coseismic landslides
- provides graphically and probabilistically of correlative and causal relationship among variables.
- provides a natural way of handling missing data
- can be easily combined with other analytic tools to aid management
- difficult to treat continuous data
- needs the accurate data on landslide occurrences
Song et al. 2012
Several statistical methods have also been proposed to evaluate and improve the Newmark’s model. Some statistical analysis based on the Newmark’s model result in a new equation and attenuation to assess coseismic landslide susceptibility (Romeo 2000; Jibson 2007; Jian et al. 2010; Rajabi et al. 2011). Other statistical methods based on the actual coseismic landslide occurrences have also been developed by Song et al. 2012 and Umar et al. 2014. Song et al. 2012 used the Bayesian network to describe the coseismic landslide, while Umar et al. 2014 used an integrated method of frequency ratio (FR) and logistic regression (LR) to define the most important factor in coseismic landslide occurrences. Since then, statistical method has become an alternative to model the coseismic landslide assessment, partly because of its flexibility of input data determination. The statistical method can describe the relationship among different combinations of instability factors. However, the statistical method gives indistinct results of spatial distribution of seismic characteristics (Huang et al. 2012) . In addition, the statistical method needs high accuracy data of coseismic landslide occurrences, which are not often available in Indonesia
The enhancement of remote sensing (RS) and geographic information system (GIS) technology have successfully answered this problem. With rapid computation capacities and relatively low cost, RS and GIS provide good platforms to model earthquake induced landslides (Wang et al. 2010). Several methods ranging from the simplest scoring and weighting calculation to a complex GIS model, either qualitative or quantitative analyses, have been developed to assess the coseismic landslide susceptibility. For instance, Khanzai and Sitar (2003) found that the highest abundance of the coseismic landslides occurred less than 40 km from the epicentre of the Chi-Chi earthquake. They found also that the ground motion was the most significant triggering factor of the coseismic landslides. Further improvements were brought by Miles and Keefer (2009) who successfully explained how to combine the Newmark displacement with fuzzy logic systems and GIS, while M.W. Huang, et al. (2012) gave a convincing explanation on how to integrate the geomorphic characteristics and ground motion attenuation. The coseismic model, which was produced by Wang et al. (2010), also successfully provided the basis information for the risk management and regional planning in Dujiangyan City, China. The model succeeded in generating a coseismic weight model that can be effectively used for coseismic landslide hazard and susceptibility assessment. In Indonesia (West Sumatra) Umar et al. (2014) analysed earthquake induced a landslide by combining the statistical methods and GIS analysis to produce a rapid and accurate assessment for coseismic landslide disaster management and decision making. The results indicated that the prediction rates of the models made by peak ground acceleration (PGA) of 7.5, 8.6 and 9.0 were 79%, 78% and 81% respectively.
The methods of Umar et al. (2014) needs a large database of landslide occurrence to be effective but it provides the best results. For sparse data areas, the choice of methods knows a wider array of constraints. For such area, the Japanese Society of Soil Mechanics and Foundation Engineering created a manual in 1993. According to the manual, coseismic landslide assessment is classified into three categories based on their scale, namely; Grade 1, Grade 2 and Grade 3. Grade 1 refers to the lowest cost and most general level of zonation. It is based on the earthquake magnitude and seismic intensity. The rainfall pattern and geological condition are often used as an additional input data in this grade. Grade 1 is suitable for 1:1,000,000-1: 50,000 scale of mapping and suitable for preliminary analysis at the region or province level (Ishihara and Nakamura 1987; Keefer and Wilson 1989). In several cases, the zoning maps based on the Grade 1 category do not provide precise information for site-specific evaluation. Therefore, the zonation based on a Grade 2 assessment is required. Grade 2 zonation is based on the historical data of earthquakes, rainfall patterns, geological and topographical characteristics. It is suitable for a 1:100,000-1: 10,000 scale of mapping and often requires additional field investigations, remote sensing and aerial photo analysis (The Technical Committee for Earthquake Geotechnical Engineering, TC4 1993; Mora and Vahrson 1999). A detailed level of zonation with large accuracy can be achieved by applying Grade 3. It combines the methods of Grades 1 and 2 with site specific investigation information to produce coseismic landslide zonation. Grade 3 is based on geotechnical investigation and suitable for a 1:25,000-1: 5000 scale of mapping (Newmark 1965; Wilson et al. 1979; Tanaka 1982; The Technical Committee for Earthquake Geotechnical Engineering, TC4 1993; Siyahi and Ansal 1999).
The integration of RS and GIS based on Grade 2 can generate a coseismic landslide model, which is well adapted for sparse data area, where field data acquisition is difficult. The application of this integrated method is very suitable for developing countries especially Indonesia due to their poor landslide data inventory and its physical condition.
Profile of study area
Like the other areas on Java, Baturagung’s is characterized as a humid tropical climate with seasonal monsoonal rainfall. The maximum rainfall of monsoonal type occurs during September-February (Hamada et al. 2002). The highest average of annual rainfall between 1983 and 2003 was 1986 mm and the minimum average of annual rainfall (1983–2003) was 1081 mm. These high frequency and intensity of rainfall caused the western flank of Baturagung Escarpment susceptible to a high intensity of erosion and mass movement. Additionally, the foot slope and the upper slope of Baturagung area consist of relatively soft ancient volcanic rock.
Situated along the Baturagung Escarpment, the research area is dominated by three major groups of landforms originated from structural, fluvial, and denudation processes (Nurwihastuti et al. 2014) . The structural landform can be recognized from the topographical difference between the escarpment in the east and the sub horizontal area in the west of the research area. The intensive denudation processes occur on the middle slope and upper slope of the escarpment and also on the hilly areas of the Semilir Formation, which have less vegetation and utilized as dry land farming and traditional mining of breccia pumice. The fluvial landform containing alluvium is located along the Opak River in the western part of research area. The fluvial processes also take place in the narrow plains between the hilly areas in the east part of research area.
Description of the input data for the Mora and Vahrson (1999) model (Grade 2)
Raster shape file (shp.)
▪ Derived from the slope analysis and contour data
▪ Contour interval: 12.5 m
▪ Scale 1:25,000
▪ Published by Geospatial Information Agency of Indonesia
Vector shape file (shp.)
▪ Derived from the 1:100,000 geology map of Yogyakarta (Rahardjo et al., 1995) and double-checked by topographic map, geomorphology map (Nurwihastuti, 2008), and visual interpretation result of ASTER imagery using 3,4, PCA56789 colour composite.
Natural humidity of Soil
Vector shape file (shp.)
▪ Derived from the rainfall data (1983–2013) of 10 rainfall stations in study area (Barongan, Dogongan, Jatingarang, Karangploso, Piring, Tanjungtirto, Terong, Umbulharjo, Wates and DPU Yogyakarta rainfall station)
▪ spatialized by Thiessen polygons
Raster shape file (shp.)
▪ Calculated from all of earthquakes occurrence with the magnitude greater than 5 between 1973 and 2014 obtained from USGS earthquake catalogue
▪ Attenuation used: Kanai attenuation model (Douglas 2011).
▪ Interpolation used: IDW method
▪ Using predominant frequency of soil from field measurement conducted by (Daryono, 2011).
Vector shape file (shp.)
▪ Derived from the rainfall data (1983–2013) of 10 rainfall stations in study area (Barongan, Dogongan, Jatingarang, Karangploso, Piring, Tanjungtirto, Terong, Umbulharjo, Wates and DPU Yogyakarta rainfall station)
▪ represented in Thiessen polygons
This study used the coseismic landslide susceptibility factors such as relief, lithology, and soil moisture. Additionally, the seismicity and rainfall intensity was used also as the triggering factors because the study site has a high intensity of rainfall and is prone area to the earthquake. When the heavy rainfall occurs, the potential erosion is higher and the slope instability increases. As a results, the probability of coseismic landslide occurrence also increases. Therefore, by considering both seismic and rainfall intensity, the accurate susceptibility map can be produced. The detailed information of parameters used in this study are explained below.
The correlation between slope and relief (van Zuidam, 1986)
The lithological map was generated based on the 1:100,000 geological map of Yogyakarta (Rahardjo et al. 1995), the geomorphological map produced by Nurwihastuti (2008), and the visual interpretation results of ASTER imagery. The geomorphological map and the result of geological interpretation were used to control and improve the lithological information that was obtained from the geological map of Yogyakarta. The visual interpretation technique of ASTER colour composite of 3,4,PCA56789 was also used to obtain the information about the lithological feature in the research area. Three elements of visual interpretation namely colour or tone, texture, and pattern that was used to distinguish the lithological units. The lithology index (Ts) was obtained through the scoring process of the lithological classification in Table 5. Soft sediment or rock and highly eroded rock is classified as a very high (5) susceptibility score, while the compact rock is scored as a low susceptibility score. In this case, the Semilir formation was scored higher than the other lithological units.
Natural humidity of soil
Average monthly of rainfall intensity classification in order to generate the natural humidity of soil
Average rainfall intensity
<125 mm per month
125–250 mm per month
>250 mm per month
Parameters used in coseismic landslide susceptibility assessment
Relief (m/km2) (Sr)
Permeable limestone, slightly fissured intrusions, basalt, andesite, granites, ignimbrite, gneiss, hornfels, low degree of weathering. Low water table, clean regose fractures, high shear strength rocks.
High degree of weathering of above mentioned lithologies and of hard massive clastic sedimentary rocks, low shear strength, shareable fractures
Considerably weathered sedimentary, intrusive, metamorphic, volcanic rocks, compacted sandy regolithic soils, considerable fracturing, fluctuating water tables, compacted colluvium and alluvium
Considerably weathered, hydrothermally altered rocks of any kind, strongly fractured and fissured, clay filled, poorly compacted pyroclastic and fluvio-lacustrine soils, shallow water table.
Extremely altered rocks, low shear resistance alluvial, colluvial and residual soils, shallow water tables.
(natural humidity index of soil) (Sh)
summation of average monthly rainfall
Intensities (MM) (Ts)
Influence of rainfall intensity as a triggering factor for landslide
(rainfall n < 10 Years; average (mm) (Tp)
The resulted intensity value from the Eq. 4 was scored by using the Mora and Vahrson (1999) model (Table 5) to generate the Ts index. The maximum Ts index of 10 was given to the intensity XII and the minimum Ts index of 1 was given to the intensity III. The higher the intensity of particular area was, the higher the ground motion occurred and consequently, it might affect the slope stability.
Annual rainfall intensity
Similar to the natural humidity index analysis, the annual rainfall intensity (Tp value) was generated by calculating the average annual rainfall in each Thiessen polygon for at least 10 years. There were five classes of average annual rainfall intensity (Tp) with the lowest class was very low (<50 mm) and the highest class was very high (>175 mm) (Table 5). The higher the value of Tp index was, the higher the susceptibility level of landslide occurrence.
Coseismic landslide susceptibility assessment
where, Sr is the relative relief index, Sllis the lithological susceptibility, Sh is the natural humidity of the soil, Ts is the seismic intensity, and Tp is the rainfall intensity. All the indexes of the input parameters were calculated by using the raster calculator with the output cell size was 67.08 m. Based on the Eq. 5, six categories of areas, i.e., negligible, low, moderate, medium, high, and very high was resulted. The minimum value of the coseismic landslide susceptibility index using the Eq. (4) was 0, while the maximum value was 1250 (Table 6).
Based on the monthly average rainfall intensity, the study area was divided into two main zones of natural soil humidity. The area was divided between low (class 2) and medium (class 3) natural humidity. The ‘low class’ of natural humidity is mainly located in the mountainous areas in the east part of study area, while the medium class is located in only a small part of the plain in the west part of study area. Regarding to the seismic intensity level, the maximum PGA (950 gal) is located southeast part of study area near the epicentre of the last Yogyakarta earthquake. The PGA value gradually declined in the west part of research areas. The lowest PGA was about 654 gal. Based on the intensity calculation by using the Eq. 4, the study site was defined into the intensity IX. The resulted map of scored parameters is shown in Fig. 7.
The low CLSL or class II is associated with the border areas between mountainous and flat areas in the eastern part. Most of them are located on sloping areas (8–15%) and very close to rivers. These areas include the lower slopes of strong eroded denudation hills of the Semilir Formation and residual hills of the Nglanggaran Formation. Although these zones are relatively stable and safe, building constructions should be avoided, because the landslide body is often deposited in the lower slopes areas. The total area of the low susceptibility zone was 4.02 km2 (3.38%).
About 41.32 km2 or 34.71% of the area was categorized as the moderate level of CLSL. This area is associated with the moderate to steep slope areas (15–30%). The moderate zones are mainly located on the middle slope of the strong and weak eroded denudation hills of Semilir Formation, which contains of interbedded tuff-breccia, pumice breccia, dacite tuff and andesite tuffs and tuffaceous claystone. The Semilir Formation is characterized as fractured weathered rocks with thin soil thickness due to the advance denudation process. The traditional mining of breccia pumice has created an extensive open area and caused the excessive erosion. According to the landslide occurrence map, most of landslides occurred in this zone (Fig. 8).
The medium of CLSL was about 2.25 km2 (2.14%), mainly located on the steep or very steep slopes regions (>30%). This zone is often associated with the upper slope of the Baturagung Escarpment member of Semilir Formation. Human activities still can be found in these areas, although the areas have more than 30% of slope steepness. These zones are mainly used as dry fields or “tegalan”. Additionally, there are several fresh water springs located in the medium zones. Landslides will eventually bring the worst impact to the local people, because they really depend on the fresh water springs to meet their daily water needs.
Additionally, the results of this study are in line with other landslide research in the west of Yogyakarta (Priyono et al. 2011; Samodra et al. 2016). They also found that the level of landslide occurrence was high on the middle slopes. Priyono, et al. (2011) found that the high level of landslide vulnerability was characterised as middle to upper slope area with steep to very steep slopes (>60%), which displayed a high level of weathering and erosion. These results were also corroborated by the trajectory analysis of rock falls data inventory conducted by Samodra et al. (2016). They found that the most of potentially rock falls were triggered from the middle slope. Moreover, the middle slope also produced the most dangerous rock falls for homes located nearby, because it generateed some of the highest velocity of rock falls (Samodra, et al., 2016)
The CLSL result provideed important information in coseismic landslide susceptibility zoning. The unavailability of coseismic landslide, landslide data and the difficulties to investigate the coseismic source directly were the main problems in this study area. Therefore, this information can be used as a basic information for local government to protect the residential house and important asset against the coseismic landslide. For instance, the royal cemetery complex in Imogiri. The Imogiri Sub-District is special sub-districts for Yogyakarta Royal circles, because the royal cemetery is located in this sub-district. This area is very valuable for the cultural heritage of Yogyakarta. Until today, many pilgrims from both local and international locations visit the royal cemetery during holidays or particular religious’ events. In 2011, at least 635 of international tourists and 20,290 local tourists visited the royal cemetery (Dinas Pariwisata DIY, 2011). The complex is located in Girirejo, Bantul regency. it was built in 1632 by Sultan Agung, the King of Mataram Kingdom. Situated on the slope of Baturagung Escarpment, this area is prone to landslide and coseismic landslide (Fig. 9). The complex is located in moderate zones of CLSL which is very close to the nearest medium CLSL about 1.5 km downslope. It will likely become very high risk area, if an earthquake occurred.
The results prove that the proposed method can provide a better description of coseismic landslides spatial distribution. Based on the model, there are four distinctive CLSL: negligible, low, moderate, and medium zones. The negligible zone of CLSL are defined as the most stable and safe areas, which have value range between 0 and 6. These zones are usually located on flat—gentle slope areas (0–8%). Most of them are associated with an alluvial plain, colluvium-alluvium foot slopes and natural levees. The low zones of CLSL are associated with the border areas between mountainous and flat areas in the eastern research area. Most of them are located on sloping areas (8–15%) and very close to rivers. The moderate zones of CLSL are mainly located on the middle slope of the strong and weak eroded denudation hills of the Semilir Formation, which consists of interbedded tuff-breccia, pumice breccia, dacite tuff and andesite tuffs and tuffaceous claystone. The medium zones of CLSL is defined as the most unstable and susceptible to coseismic landslides in the study area. These zones are often associated with the upper slope of the Baturagung Escarpment, which are mainly located on the steep to very steep slopes (>30%). There is still a need improvement for further, which focus on the coseismic landslide data inventory and statistical analysis of coseismic landslide in order to obtain the better results of coseismic landslides hazard zonation.
This paper is part of the PhD research at University of Canterbury, New Zealand. The program is funded under The Indonesia Endowment Fund for Education. Authors would like to thank Department of Geography, University of Canterbury, New Zealand for providing an adequate reference to support the project, Geography Faculty at Universitas Gadjah Mada and Muhammadiyah Surakarta for providing the spatial data related to the project.
AS and CG collected the data, AS carried out the analysis of coseismic landslide, CG support on interpretation of the results. AS drafted the manuscript, CG, DSH and JS revised the manuscript. All the authors drafted, read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Abidin, H.Z., H. Andreas, T. Kato, T. Ito, I. Meilano, F. Kimata, D.H. Natawidjaja, and H. Harjono. 2009a. Crystal deformation studies in Java (Indonesia) using GPS. Journal of Earthquake and Tsunami 3(2): 77–88.View ArticleGoogle Scholar
- Abidin, H.Z., H. Andreas, I. Meilano, M. Gamal, I. Gumilar, and C. Abdullah. 2009b. Deformasi koseismik dan pascaseismik gempa Yogyakarta 2006 dari hasil survei GPS. Jurnal Geologi Indonesia 4: 275–284.Google Scholar
- Badan Geologi (2014) Tanggapan Gempa Bumi Kebumen 27 Januari 2014. In: Kementrian Energi dan Sumber Daya Mineral, Badan Geologi. Available via http://www.vsi.esdm.go.id/index.php/gempabumi-a-tsunami/kejadian-gempabumi-a-tsunami/306-tanggapan-gempa-bumi-kebumen-27-januari-2014. Accessed 3 Feb 2014.
- BPS-Statistic of Bantul Regency (Processing Integrated and Statistical Desimination Section, Ed.) (ed.). 2014. Pleret District in figures. Yogyakarta: BPS-Statistic of Bantul Regency.Google Scholar
- Bray, J.D., and E.M. Rathje. 2000. Nonlinear coupled sesimic sliding analysis of earth structures. Journal of Geotechnical and Geoenvironmental Engineering 126(11): 1002–1014.View ArticleGoogle Scholar
- Bray, J.D., E.M. Rathje, A. Augello, and S. Merry. 1998. Simplified seismic design procedure for geosynthetic-lined, solid-waste landfills. Geosynthetic International 5(1–2): 203–235.View ArticleGoogle Scholar
- Burton, P.W., and S.W. Cole. 2006. Development towards the seismic hazard mapping of Java. In The Yogyakarta Earthquake of May 27, 2006, ed. D. Karnawati, S. Pranumijoyo, R. Anderson, and S. Husein, 10_1–10_13. California: Star Publishing.Google Scholar
- Burton, P.W., G. Weatherill, D. Karnawati, and S. Pramumijoyo. 2008. Seismic hazard assessment and zoning in Java: New and alternative probabilistic assessment models. Jakarta: International Conference on Earthquake Engineering and Disaster Mitigation 2008 (ICEEDM08).Google Scholar
- Cahyaningtyas, D.R. 2012. Studi kerawanan seismik Kecamatan Temon, Wates, dan Panjatan, Kabupaten Kulon Progo. (Unpublished master's thesis). Yogyakarta, Indonesia: Universitas Gadjah Mada.Google Scholar
- Daryono. 2011. Indeks kerentanan seismik berdasarkan mikrotremor pada setiap satuan bentuklahan di zona Graben Bantul Daerah Istimewa Yogyakarta. Dissertation: Universitas Gadjah Mada.Google Scholar
- Demets, C., D. Gordon, Argus, and S. Stein. 1994. Effect of recent revisions to the geomagnetic reversal time scale on esimates of current plate motions. Geo-phys 21: 2191–2194.Google Scholar
- Dinas Pariwisata Provinsi DIY (2011) Dinas Pariwisata Daerah Istimewa Yogyakarta. http://www.visitingjogja.com. Accessed 3 Feb 2014.
- Douglas, J. 2011. Ground-motion prediction equations 1964-2010, 444. Berkeley, CA: Pacific Earthquake Engineering Research Center.Google Scholar
- Elgamal, A.-W.M., R.F. Scott, M.F. Succarieh, and L. Yan. 1990. La Villita dam response during five earthquakes including permanent deformation. Journal of Geotechnical Engineering 116(10): 1443–1462.View ArticleGoogle Scholar
- Hadmoko, D.S., F. Lavigne, J. Sartohadi, P. Hadi, and Winaryo. 2010. Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia. Natural Hazards 54(3): 623–642.View ArticleGoogle Scholar
- Haifani A (2008) GIS application on micro-seismic hazard analysis in Yogyakarta Province. Master Thesis, Universitas Gadjah Mada.Google Scholar
- Hamada, J.-I., M.D. Yamanaka, J. Matsumoto, S. Fukao, P.A. Winarso, and T. Sribimawati. 2002. Spatial and temporal variations of the rainy season over Indonesia and their link to ENSO. Journal of the Meteorological Society of Japan 80(2): 285–310.View ArticleGoogle Scholar
- Hartantyo, E., and K.S. Brotopuspito. 2012. Estimasi nilai PGA, PGV dan PGD area Jogjakarta studi kasus gempa Jogja 2006. In Penginderaan Jauh dan Sistem Informasi Geografis: National conference proceedings , ed. P. Priyono, Y. Priyana, A.A. Sigit, J. Jumadi, A. Saputra, and Andriyani, 602. Central Java, Indonesia: Muhammadiyah University Press.Google Scholar
- Huang, M.-W., C.-Y. Chen, T.-H. Wu, C.-L. Chang, S.-Y. Liu, and C.-Y. Kao. 2012. GIS-based evaluation on the fault motion-induced coseismic landslides. Journal of Mountain Science 9(5): 601–612.View ArticleGoogle Scholar
- International Society of Soil Mechanics and Foundation Engineering. TC4, Earthquake Geotechnical Engineering. 1993. Manual for zonation on seismic geotechnical hazards, 1st ed. Tokyo: Japanese Society of Soil Mechanics and Foundation Engineering.Google Scholar
- Irsyam M, Sengara W, Aldiamar F, Widiyantoro S, Triyoso W, Natawidjaja DH, Kartapati E, Meilano I, Suhardjono, Asrurifak M, Ridwan M (2010) Ringkasan hasil studi tim revisi peta gempabumi. http://www.preventionweb.net/files/14654_AIFDR.pdf. Accessed 1 May 2016.
- Ishihara, K., and S. Nakamura. 1987. Landslide in mountain slopes during the Ecuador earthquake of March 5, 1987. In US-Asia Conference on Engineering for Mitigation Natural Hazards Damages. Bangkok, Thailand: University of Hawaii at Manoa, Asian Institute of Technology.Google Scholar
- Jian, Z., C. Peng, Z. Bingkun, Y. Changwei, and G.H. McVerry. 2010. Earthquake-induced landslide displacement attenuation models and application in probabilistic seismic landslide displacement analysis. Earthquake Engineering and Engineering Vibration 9(2): 117–187.Google Scholar
- Jibson, R.W. 2007. Regression models for estimating coseismic landslide displacement. Engineering Geology 91(2–4): 209–218.View ArticleGoogle Scholar
- Karnawati, D., I. Ibriam, M. Anderson, E. Holcomb, G. Mummery, J.-P. Renaud, and Y. Wang. 2005. An initial approach to identifying slope stability controls in Southern Java and to providing community-based landslide warning information. In Landslide hazard and risk, ed. T. Glade, M. Anderson, and M. Crozier. New York: Wiley.Google Scholar
- Keefer, D.K. 1994. The importance of earthquake-induced landslides to long-term slope erosion and slope-failure hazards in seismically active regions. Geomorphology 10: 265–284.View ArticleGoogle Scholar
- Keefer, D.K., and R.C. Wilson. 1989. Predicting earthquake-induced landslides with emphasis on arid and semi-arid environment. Publication of the Inland Geological Society 2: 118–149.Google Scholar
- Khanzai, B., and N. Sitar. 2003. Evaluation of factors controlling earthquake-induced landslides caused by Chi-Chi earthquake and comparison with the Northridge and Loma Prieta events. Engineering Geology 71: 79–95.View ArticleGoogle Scholar
- Kramer, S.L., and M.W. Smith. 1997. Modified Newmark model for seismic displacement of compliant slopes. Journal of Geotechnical and Geoenvironmental Engineering 123(7): 635–644.View ArticleGoogle Scholar
- Meehan, C.L., and F. Vahedifard. 2013. Evaluation of simplified methods for predicting earthquake-induced slope displacements in earth dams and embankments. Engineering Geology 152: 180.View ArticleGoogle Scholar
- Miles, S.B., and D.K. Keefer. 2009. Evaluation of CAMEL — comprehensive areal model of earthquake-induced landslides. Engineering Geology 104: 1–15.View ArticleGoogle Scholar
- Mora, S., and W. Vahrson. 1999. Microzonation for slope instability in Costa Rica. In Technical Committe for Earthquake Geoctechnical Engineering. Manual for Zonation on Seismic Geotechnical Hazards (Revised Version), 58–61. Tokyo: The Japanese Geotechnical Society.Google Scholar
- Natawidjaja DH (2007) Tectonic setting Indonesia dan pemodelan sumber gempa bumi dan tsunami. In: HRDP Human Resource Development Project. http://www.hrdp-network.com/pirba/content/e5781/e5795/e6459/e13941/eventReport14243/DannyHilman_RISTEK_20Aug2007_c.pdf. Accessed 3 March 2014.
- Newmark, N.M. 1965. Effects of earthquakes on dams and embankments. Geotechnique 15(2): 139–160.View ArticleGoogle Scholar
- Nugroho, E.S., D.S. Hadmoko, C. van Westen, and N. Kingma. 2012. Analyzing and estimating the impact of landslide to road in Samigaluh District, Kulonprogo Regency. Forum Geografi 26(2): 132–145.Google Scholar
- Nurwihastuti, D.W. 2008. Integrasi pemrosesan citra ASTER dan sistem informasi geografis untuk kajian geomorfologi studi kasus di sebagian Daerah Istimewa Yogyakarta. (Unpublished master's thesis). Yogyakarta, Indonesia: Universitas Gadjah Mada.Google Scholar
- Nurwihastuti, D.W., J. Sartohadi, D. Mardiatno, U. Nehren, and Restu. 2014. Understanding of earthquake damage pattern through geomorphological approach: A case study of 2006 earthquake in Bantul, Yogyajarta, Indonesia. World Journal of Engineering and Technology 2: 61–70.View ArticleGoogle Scholar
- Priyono, K.D. 2012. A study of clay mineral in occurrences of landslide disaster area at Kulon Progo Mountains Yogyakarta Special Province. Forum Geografi 26(1): 53–64.Google Scholar
- Priyono, K.D., K.D. Priyono, Sunarto, J. Sartohadi, and Sudibyakto. 2011. Pedogemorphic tipology of landslides in the Menoreh Mountain Kulonprogo District, Yogyakarta Specal Region. Forum Geografi 25(1): 67–84.Google Scholar
- Rahardjo, W., Sukandarrumidi, and H. Rosidi. 1995. Geological map of Yogyakarta sheet Jawa. Geological Research and Development Centre. Bandung: Geological Research and Development Centre.Google Scholar
- Rajabi, A.M., M.R. Mahdavifar, M. Khamehchyan, and V. Del Gaudio. 2011. A new empirical estimator of coseismic landslide displacement for Zagros Mountain region (Iran). Natural Hazards 59: 1189–1203.View ArticleGoogle Scholar
- Romeo, R. 2000. Seismically induced landslide displacements: a predictive model. Engineering Geology 58(3–4): 337–351.View ArticleGoogle Scholar
- Samodra, G., G. Chen, J. Sartohadi, D.S. Hadmoko, K. Kasama, and M.A. Setiawan. 2016. Rockfall susceptibility zoning based on back analysis of rockfall deposits inventory in Gunung Kelir, Java. Landslides 13: 805.View ArticleGoogle Scholar
- Sarma, S.K. 1975. Seismic stability of earth dams and embankments. Geotechnique 25(4): 743–761.View ArticleGoogle Scholar
- Saygili, G., and E.M. Rathje. 2008. Empirical predictive models for earthquake-induced sliding displacements of slopes. Journal of Geotechnical and Geoenvironmental Engineering 134(6): 790–803.View ArticleGoogle Scholar
- Seed, H., K. Lee, I. Idriss, and R. Makdisi. 1973. Analysis of the slides in the San Fernando dams during the earthquake of Feb, 9, 1971. EERC 73–2 report. Berkeley: University of California.Google Scholar
- Siyahi, B., and A. Ansal. 1999. Pseudo-static evaluation of slope stability using sesimicity. In Technical Committe for Earthquake Geoctechnical Engineering. Manual for Zonation on Seismic Geotechnical Hazards (Revised Version), 68–70. Tokyo: The Japanese Geotechnical Society.Google Scholar
- Song, Y., J. Gong, S. Gao, D. Wang, T. Cui, Y. Li, and B. Wei. 2012. Susceptibility assessment of earthquake-induced landslides using Bayesian network: A case study in Beichuan, China. Computers & Geosciences 42: 189–199.View ArticleGoogle Scholar
- Sulaeman, C., L.C. Dewi, and W. Triyoso. 2008. Karakterisasi sumber gempa Yogyakarta 2006 berdasarkan data GPS. Jurnal Geologi Indonesia 3(1): 49–56.Google Scholar
- Tanaka, K. 1982. Seismic slope stability map. Journal of Japan Landslide Society 51(8): 662–672.Google Scholar
- Umar, Z., B. Pradhan, A. Ahmad, M.N. Jebur, and M.S. Tehrany. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118: 124–135.View ArticleGoogle Scholar
- van Zuidam. 1986. Aerial photo interpretation in terrain analysis and geomorphologic mapping. The Hague: Smith Publisher.Google Scholar
- Wacano, D., and D.S. Hadmoko. 2012. Pemataan kerawanan longsorlahan di Darah Aliran Sungai (DAS) Tinalah Kulonprogo. In Penginderaan Jauh dan Sistem Informasi Geografis: National conference proceedings, ed. P. Priyono, Y. Priyana, A.A. Sigit, J. Jumadi, A. Saputra, and Andriyani, 602. Central Java, Indonesia: Muhammadiyah University Press.Google Scholar
- Wagner, D., I. Koulakov, W. Rabbel, B.-G. Luehr, A. Wittwer, H. Kopp, M. Bohm, G. Asch, and The MERAMEX scientists. 2007. Joint inversion of active and passive seismic data in Central Java. Geophysical Journal International 170(2): 923–932.View ArticleGoogle Scholar
- Wald, D.J., V. Quitoriano, T.H. Heaton, and H. Kanamori. 1999. Relationships between peak ground acceleration, peak ground velocity, and modified Merchalli intensity in California. Earthquake Spectra 15(3): 557–564.View ArticleGoogle Scholar
- Walter T, Wang R, Zimmer M, Grosser H, Luhr B, Ratdomopurbo A (2007) Volcanic activity influenced by tectonic earthquakes-Static and dynamic stress triggering at Mt Merapi. Geophysical Research Letters, 34 (5).Google Scholar
- Wang, M., J. Qiao, and S. He. 2010. GIS-based earthquake-triggered landslide hazard zoning using contributing weight model. Journal if Mountain Science 7(4): 339–352.View ArticleGoogle Scholar
- Wilson, R., G. Eieczorek, and E. Harp. 1979. Development of criteria for regional mapping of seismic slope stability. In Annual meeting of the Geological Society of America. Colorado USA: American Geological Society.Google Scholar
- Yegian, M.K., E.A. Marciano, and V.G. Ghahraman. 1992. Earthquake-induced permanent deformations: probabilistic approach. Journal of Geotechnical Engineering 118(6): 35–50.View ArticleGoogle Scholar