Past Landslide Research Articles

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Abstract. There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce. The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available. The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions. The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

Estimation of seismic quality factor: Artificial neural networks and current approaches

The aims of this study are to estimate soil attenuation using alternatives to traditional methods, to compare results of using these methods, and to examine soil properties using the estimated results. The performances of all methods, amplitude decay, spectral ratio, Wiener filter, and artificial neural network (ANN) methods, are examined on field and synthetic data with noise and without noise. High-resolution seismic reflection field data from Yeniköy (Arnavutköy, İstanbul) was used as field data, and 424 estimations of Q values were made for each method (1,696 total). While statistical tests on synthetic and field data are quite close to the Q value estimation results of ANN, Wiener filter, and spectral ratio methods, the amplitude decay methods showed a higher estimation error. According to previous geological and geophysical studies in this area, the soil is water-saturated, quite weak, consisting of clay and sandy units, and, because of current and past landslides in the study area and its vicinity, researchers reported heterogeneity in the soil. Under the same physical conditions, Q value calculated on field data can be expected to be 7.9 and 13.6. ANN models with various structures, training algorithm, input, and number of neurons are investigated. A total of 480 ANN models were generated consisting of 60 models for noise-free synthetic data, 360 models for different noise content synthetic data and 60 models to apply to the data collected in the field. The models were tested to determine the most appropriate structure and training algorithm. In the final ANN, the input vectors consisted of the difference of the width, energy, and distance of seismic traces, and the output was Q value. Success rate of both ANN methods with noise-free and noisy synthetic data were higher than the other three methods. Also according to the statistical tests on estimated Q value from field data, the method showed results that are more suitable. The Q value can be estimated practically and quickly by processing the traces with the recommended ANN model. Consequently, the ANN method could be used for estimating Q value from seismic data.

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

The purposes of this study is to create a landslide susceptibility map (LSM) for Lompobattang Mountain area in Indonesia. The foot of the Lompobattang Mountain area suffered flash flood and landslides in 2006, which led to significant adverse impact on the nearby settlements. There were 158 identified landslides covering a total area of 3.44 km2. Landslide inventory data were collected using google earth image interpretations. The landslide inventories were prepared out of the past landslide events, and future landslide occurrence was predicted by correlating landslide causal factors. In this study landslide inventories were divided into landslide data for training and landslide data for validation. The LSM was prepared by Frequency Ratio (FR) and Logistic Regression (LR) statistical methods. Lithology, distance from the road, distance from the river, distance from the fault, land use, curvature, aspect, and slope degree were used as conditioning parameters. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) was used to check the performance of the models. In the analysis, the FR model results in 85.8 % accuracy in the AUC success rate while the LR model was found to have 86.9 % accuracy. However, the accuracy of both these models in AUC predictive rate is the same at around 85.1 %. The LR model is 6.34 % higher than the FR model in comparison to its accuracy for ratio of landslide validation. The landslide susceptibility map consist of the predicted landslide area, hence it can be used to reduce the potential hazard associated with the landslides in this study area.

Back monitoring of the San Leo (northern Italy) rock cliff by means of SqueeSAR technique

San Leo (Emilia-Romagna Region, northern Italy) is a medieval town built on top of an isolated rock massif, located within the Marecchia river valley. The area is historically affected by rock fall events occurred during the last 400 years. As a consequence of a huge rock fall, occurred on February 27th 2014, minor structural damages were reported and a few buildings of the town were promptly evacuated as a precautionary measure. Today the San Leo cultural heritage is in safety but it is still threatened by the instability of the cliff newly formed by the rock fall. In this framework, satellite remote sensing tools (Synthetic Aperture Radar) were used in order to evaluate the presence of precursory phenomena with respect to the 2014 rock fall. For this purpose 74 COSMO-SkyMed archival images were processed by using the SqueeSAR approach. Results highlighted the presence of: i) ground deformations which still affect the past landslide deposits surrounding the rock massif: ii) displacements which can be considered as possible precursory phenomena.

Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China

The main objective of the present study was to produce a landslide susceptibility map by implementing a novel methodology that combines Information Theory and GIS-based methods for the Nancheng County, China, an area with numerous reported landslide events. Specifically, the information coefficient that is estimated from Shannon’s entropy index was used to determine the number of classes of each landslide-related variable that maximizes the information coefficient, while three methods, logistic regression, weight of evidence, and random forest algorithm, were implemented to produce the landslide susceptibility map. The comparison of the various models was based on the assessment of a database of 112 past landslide events, which were divided randomly into a training dataset (70 %) and a validation dataset (30 %). The identification of the areas affected was established by analyzing airborne imagery, extensive field investigation, and the examination of previous research studies, while the morphometric variables were derived using remote sensing technology. The geo-environmental conditions in those locations were analyzed regarding their susceptibility to slide. In particular, 11 variables were analyzed: lithology, altitude, slope, aspect, topographic wetness index, sediment transport index, profile curvature, plan curvature, distance to rivers, distance to faults, and distance to roads. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic, and the area under the success and predictive rate curves. Each model gave similar outcomes; however, the random forest model had a slightly higher predictive performance in terms of area under the curve (0.9220) against the ones estimated for the weight of evidence (0.9090) and the logistic regression model (0.8940). The same pattern of performance was reported when the success power of the models was calculated. Random forest was slightly better than the other two models in terms of area under the curve (0.9350) in comparison with the weight of evidence (0.9255) and logistic regression (0.9097). The predictive performance was estimated by using the validation dataset, while the success power of the models was estimated by using the training dataset. From the visual inspection of the produced landslide susceptibility maps, the most susceptible areas are located at the west and east mountainous areas, while moderate to low susceptibility values characterize the central area.

A method predicting the earthquake-induced landslide risk by back analyses of past landslides and its application in the region of the Wenchuan 12/5/2008 earthquake

The work considers critical slopes located at regions with high danger and proposes a method which approximately predicts the risk of earthquake-induced excessive movement of these slopes, in the case where relevant soil strength data along their slip surface do not exist. The method utilizes (1) simplified constitutive equations predicting soil response along slip surfaces and (2) a multi-block sliding system model, both recently proposed. It involves the following steps: (a) collect relevant topographic information and laboratory test results of past landslides at the region of interest, (b) (i) analyze the relevant laboratory tests to estimate the soil constitutive parameters and (ii) estimate the mobilized residual soil strength by back analysis of relevant past landslides, (c) select constitutive model parameters for future applications at the region based on step (b) and validate them based on analysis of the triggering of the past landslides and (d) apply the selected constitutive model parameters in critical slopes in the region under consideration to predict not only whether triggering occurs, but also the post-triggering slide deformation. The paper, after describing in detail the proposed method, applies it at the Wenchuan region, where the 12th May 2008 earthquake triggered many landslides. During the application, four ring shear test results and twelve landslides of the 12th May 2008 earthquake were considered. The application was successful and the following were observed: (i) in the back analyses, the multi-block model predicted reasonably well the final configuration of all slides, (ii) apart from two significantly larger back-estimated values of the residual soil strength, small scatter existed in the other back-estimated values and (iii) the selected model parameters were duly validated under step (c). Furthermore, as the back-estimated friction angle of most landslides was less than 18°, and the materials along the slip surface have a Liquid Limit value less than 25 %, it is inferred that some, or all of the slip surface during these slides, was sheared in an undrained manner.

Spatial patterns of landslide dimension: A tool for magnitude mapping

The magnitude of mass movements, which may be expressed by their dimension in terms of area or volume, is an important component of intensity together with velocity. In the case of slow-moving deep-seated landslides, the expected magnitude is the prevalent parameter for defining intensity when assessed as a spatially distributed variable in a given area. In particular, the frequency–volume statistics of past landslides may be used to understand and predict the magnitude of new landslides and reactivations. In this paper we study the spatial properties of volume frequency distributions in the Arno river basin (Central Italy, about 9100km2). The overall landslide inventory taken into account (around 27,500 events) shows a power-law scaling of volumes for values greater than a cutoff value of about 2×104m3. We explore the variability of the power-law exponent in the geographic space by setting up local subsets of the inventory based on neighbourhoods with radii between 5 and 50km. We found that the power-law exponent α varies according to geographic position and that the exponent itself can be treated as a random space variable with autocorrelation properties both at local and regional scale. We use this finding to devise a simple method to map the magnitude frequency distribution in space and to create maps of exceeding probability of landslide volume for risk analysis. We also study the causes of spatial variation of α by analysing the dependence of power-law properties on geological and geomorphological factors, and we find that structural settings and valley density exert a strong influence on mass movement dimensions.

Characterizing hydrological processes on loess slopes using electrical resistivity tomography – A case study of the Heifangtai Terrace, Northwest China

From the perspective of engineering geology, loess has long been considered as a homogeneous and porous material. It is commonly believed that water penetrates loess via pores and in some cases causing mass movements. However, several researchers have expressed doubts about this mechanism as a cause of slope failures in loess, and moreover the actual hydrological processes operating in loess deposits and their effect on slope failures have not been fully investigated. Here we present the results of an electrical resistivity survey of the Heifangtai loess terrace in northwestern China, designed to characterize the hydrological processes in loess slopes and their relationship with slope failures. The Heifangtai loess terrace is located on the fourth terrace of the Yellow River and consists of 57-m-thickness of aeolian loess. 2D and 3D electrical resistivity tomography (ERT) was used to monitor the movement of ground water before and after irrigation and rainfall events and the evolution of a sink hole in the toe of the landslide deposits. Our main findings are as follows: (1) Based on the 2D ERT results, the depth of infiltration into the thick unsaturated loess is not more than 5m in the profile at the top of the landslide. (2) Electrical resistivity decreased as a result of water infiltration through sinkholes, and this process can increase the soil water content and induce soil liquefaction which can eventually result in land sliding. (3) Landslide deposits block the groundwater drainage channels through the loess, which results in the concentration of water in the toe of the landslide. Consequently, groundwater together with rainfall, triggers the failure of sinkholes or cracks, which may induce a continuing process of new slope failures at the sites of past landslide.

Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study

Abstract: Forests are increasingly valued for services beyond timber and non-timber products including land protection with respect to events such as landslides, soil erosion, floods and avalanches. The most important properties of a protective forest are its ecological and mechanical stability. Planning and implementing multifunctional forest management in protective forests is challenging because of the trade-offs and synergies among the many functions of the forest. In this study, a multifactor empirical method is presented for assessing the protective role of forests on a stand scale with respect to landslide occurrence. Multifactor methodologies typically estimate landslide susceptibility exploiting the relationship between past landslide patterns and site characteristics. Two statistical approaches were here applied to assess the probability of landslide occurrence: the weight-of-evidence technique and the logistic regression technique. Statistical analysis was performed on the basis of landslide detachment zone only. The question of how to estimate protective forest function was answered through the comparison of models established with different sets of predicting factors. This study ultimately aims to provide a decision-support tool focused on mapping the potential role of forests in landslide-prone areas. A case study from the Italian Alps was considered. The density of landslide detachment outside forest areas proves to be more than twice than that within forest areas.

GIS based landslide hazard evaluation and zonation – A case from Jeldu District, Central Ethiopia

In the present study landslide hazard evaluation and zonation (LHZ) was carried out in Jeldu District in Central Ethiopia, about 156km West of Addis Ababa, the capital city of Ethiopia. Through field based inventory mapping and image interpretation 34 past landslides were mapped in the study area. The governing factors; aspect, slope and elevation, were determined from the digital elevation model (DEM), which was obtained from the ASTER satellite. Lithology was adopted from the geological map of Ethiopia (1:2,000,000), while the soil and land use/land cover were processed from Landsat+ETM satellite data. All governing factors were statistically analyzed to find the correlation with the past landslides. In this study, statistical – probabilistic approach was used to rate the governing factors and then customized raster calculation was made in GIS environment to develop the LHZ map.The results revealed that 12% (5.64km2) of the study area falls under no hazard, 27% (12.69km2) as low hazard, 32% (15.04km2) as moderate hazard, 21% (9.87km2) as high hazard and the rest 8% (3.76km2) as very high hazard. The validation of LHZ map shows that, 92% of past landslides fall in high or very high hazard zones, while 6% fall in medium and only 2% in low landslide hazard zones. The validation of LHZ map thus, reasonably showed that the adopted methodology produced satisfactory results and the delineated hazard zones may practically be applied for the regional planning and development of infrastructures in the area.

Initialising landslide-generated tsunamis for probabilistic tsunami hazard assessment in Cook Strait

The Cook Strait Canyon is a submarine canyon which lies within 10 km of Wellington, the capital city of New Zealand. The canyon flanks are scarred with the evidence of past landslides that may have caused large local tsunamis. City planning and civil defence management require information on the magnitude and frequency of these tsunamis to adequately plan for them. Submarine-landslide-generated tsunamis are by nature local features. While they may be catastrophic in the near field, they are generally far smaller scales than co-seismic tsunamis and their energy does not travel very far. Including them within a comprehensive tsunami hazard assessment requires accounting for a large number of potential landslide sources. Unless we only use simple rules of thumb to approximate tsunami height, this requires considerable computing power. This article describes a technique for expanding two-dimensional vertical-slice tsunami generation by landslide modelling into a two-dimensional horizontal surface which can be used for tsunami propagation and inundation modelling. As such, it spans the gap between full three-dimensional modelling of the landslide and simple initialisation.

Analysis and evaluation of stability for the reactivated landslide along deep-seated weakness zones

In areas where landslides have occurred in the past, it is prone to additional landslides because of similar geological and geographical characteristics. A slope sliding by a reactivated landslide can result in accidents, extra cost and delayed construction schedule. Hence, it is important to identify evidence of past landslides during the design and construction phases in these areas. In the study area presented in this paper, tension cracks were found during clearing and grubbing work prior to the construction of cut slopes at a tunnel portal, implying that landslides had occurred in the past. To mitigate risk, slope gradient was decreased to 1:1.3 with removal of tension cracks, however, 90 m-long tension cracks occurred subsequently. Therefore, detailed geotechnical investigations and analysis were carried out. This paper aims to present procedures and results of analysis to clarify causes of landslides, including selection of the optimum countermeasures for stabilization of the cut slope. We hope that this case study will provide useful information to other engineers and contribute to the prevention of disaster caused by landslide.

Evaluation of Shallow Landslides in the Northern Walgau (Austria) Using Morphometric Analysis Techniques

Landslides play a key role in landscape evolution in the Eastern Alps. These geomorphic phenomena are influenced by multiple interdependent and interacting natural and anthropogenic factors. An in-depth evaluation of the spatial distribution of existing landslides enables to gain first insights into potentially hazardous areas. Morphometric analysis techniques of mapped landslides as well as their date of occurrence allow to infer their activity and also potential impacts on affected areas. The prevalent slow moving landslides and inactive slipping areas were mapped and analysed via digital terrain models (DTM), shaded relief images of highly resolved airborne laserscanning (ALS) data and in-field observations. Orthophotos from aerial surveys and ALS data allowed a deferred-time analyses of past landslide occurrences including a record of recent slope movements. All mapped landslides were classified and analysed with geomorphometric indices. Pedological processes, the lithological setting and anthropogenic landscape transformationwere taken into account when interpreting the results. The geomorphometrical evaluation of the sliding areas determine the creation of a multitemporal landslide inventory in the Northern Walgau.

How different are the results acquired from mathematical and subjective methods in dendrogeomorphology? Insights from landslide movements

Knowledge of past landslide activity is crucial for understanding landslide behaviour and for modelling potential future landslide occurrence. Dendrogeomorphic approaches represent the most precise methods of landslide dating (where trees annually create tree-rings in the timescale of up to several hundred years). Despite the advantages of these methods, many open questions remain. One of the less researched uncertainties, and the focus of this study, is the impact of two common methods of geomorphic signal extraction on the spatial and temporal results of landslide reconstruction. In total, 93 Norway spruce (Picea abies (L.) Karst.) trees were sampled at one landslide location dominated by block-type movements in the forefield of the Orlické hory Mts., Bohemian Massif. Landslide signals were examined by the classical subjective method based on reaction (compression) wood analysis and by a numerical method based on eccentric growth analysis. The chronology of landslide movements obtained by the mathematical method resulted in twice the number of events detected compared to the subjective method. This finding indicates that eccentric growth is a more accurate indicator for landslide movements than the classical analysis of reaction wood. The reconstructed spatial activity of landslide movements shows a similar distribution of recurrence intervals (Ri) for both methods. The differences (maximally 30% of the total Ri ranges) in results obtained by both methods may be caused by differences in the ability of trees to react to tilting of their stems by a specific growth response (reaction wood formation or eccentric growth). Finally, the ability of trees to record tilting events (by both growth responses) in their tree-ring series was analysed for different decades of tree life. The highest sensitivity to external tilting events occurred at tree ages from 70 to 80years for reaction wood formation and from 80 to 90years for eccentric growth response. This means that the ability of P. abies to record geomorphic signals varies with not only eccentric growth responses but also with age.

Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7 and 3 Normalised Difference Index: A case study of central region of Kenya

Landsat series multispectral remote sensing imagery has gained increasing attention in providing solutions to environmental problems such as land degradation which exacerbate soil erosion and landslide disasters in the case of rainfall events. Multispectral data has facilitated the mapping of soils, land-cover and structural geology, all of which are factors affecting landslide occurrence. The main aim of this research was to develop a methodology to visualize and map past landslides as well as identify land degradation effects through soil erosion and land-use using remote sensing techniques in the central region of Kenya. The study area has rugged terrain and rainfall has been the main source of landslide trigger. The methodology comprised visualizing landslide scars using a False Colour Composite (FCC) and mapping soil erodibility using FCC components applying expert based classification. The components of the FCC were: the first independent component (IC1), Principal Component (PC) with most geological information, and a Normalised Difference Index (NDI) involving Landsat TM/ETM+ band 7 and 3.The FCC components formed the inputs for knowledge-based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, exposed volcanic rocks, agriculture, green forest, new forest regrowth areas, clear, turbid and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% of the points coincided well with landslide areas mapped in the year 2000. The classification maps showed landslide areas on the steep ridge faces, other erosions in agricultural areas, highly erodible zones being already weathered rocks, while runoff were mainly fluvial deposits. Thus, landuse and rainfall processes play a major role in inducing landslides in the study area.

GIS based Grid overlay method versus modeling approach – A comparative study for landslide hazard zonation (LHZ) in Meta Robi District of West Showa Zone in Ethiopia

The present study area is located in Meta Robi District of West Showa Zone in Oromiya Regional State in Ethiopia. The main objective of the present study was to evaluate landslide hazard zonation (LHZ) by utilizing ‘Grid overlay’ and ‘GIS modeling’ approaches. Also, it was attempted to know the effectiveness of the two methods. The methodology followed was based on the analysis of past landslides in the area. For the present study six causative factors namely; slope material, slope, aspect, elevation, land use and land cover and groundwater surface traces were considered. Later, Landslide Susceptibility Index (LSI) was computed based on the relative influence of causative factors on past landslides. For the ‘Grid overlay’ method a grid with cells 10m by 10m was overlaid over the study area and later it was geo-processed to delineate various sub-classes of each causative factor. LSI values were assigned to each sub-causative factor within each grid cell and a ‘Total Landslide Susceptibility Index’ was calculated to produce the LHZ map. For ‘GIS modeling’ the same causative factors and similar LSI values were utilized. In the case of LHZ map prepared by the ‘Grid overlay’ method about 82% of past landslides fall within ‘very high hazard’ or ‘high hazard’ zones whereas in the case of ‘GIS modeling’ about 95% of past landslides fall within ‘very high hazard’ or ‘high hazard’ zones. Finally, the validation showed that ‘GIS modeling’ produced better LHZ map. Also, ‘Grid overlay’ method is more tedious and time consuming as compared to GIS modeling.

Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models

Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. This study aimed to identify the optimal quantitative method for landslide susceptibility mapping in Mizunami City, Japan. Three mathematical methods, logistic regression (LR), bivariate statistical analysis (BS), and multivariate adaptive regression spline models (MARSplines), were used to create landslide-susceptibility maps by comparing the past landslide distribution and the conditioning factor thematic maps. A landslide inventory map with a total of 222 landslide locations was extracted from aerial photographs provided by NIED (National Research Institute for Earth Science and Disaster Prevention, Japan). Then, the landslide inventory was randomly divided into two datasets: 50% was used for training the models and the remaining 50% for validation purposes. The landslide inventory map provided by NIED and an area under the ROC curve were used to evaluate model performance. We found that the MARSpline method resulted in a better prediction rate (79%) when compared to LR (75%) and BS (77%). In addition, a higher percentage of landslide polygons were found in the high to very high classes using the MARSpline method. Therefore, we concluded that the MARSpline method was the most efficient method for landslide susceptibility mapping in this study area.

Geomorphological investigations on landslide dams

The study of past landslide dams and their consequences has gained a considerable significance for forecasting induced hydraulic risk on people and property. Landslide dams are rather frequent in Italy, where a broad climatic, geological and morphological variability characterize different part of the peninsula, and have already been studied in literature, focusing different geographical regions with different levels of detail. In order to develop specific tools to assess the landslide dam formation and stability, the first step is to realize a large data archive including a big number of data, collected with a consistent methodology to standardize the quality. For this reason, this paper reports the results of an extensive bibliographic work and geomorphologic investigation on landslide dams that lead to the development of the wider systematic inventory in Italy. Through the revision and the update of scientific works and historical reports, three hundreds of landslide dams from the Alps to the Southern Apennine and Sicily were identified. During investigations and through cartographic and aerial photos interpretation, several geomorphic parameters of the landslide, the dam body, the valley and the lake, if any, have been determined, or estimated using historical and bibliographical documents analysis. The collected data were resumed in a database, formed by 57 information fields easy to collect and measure to privilege intuitive usability and future implementation. In order to describe the characteristics of landslide dams in Italy some specific analysis on the different types of landslide movements and their volume, the dam longevity, the main triggers and their geographical distribution were carried out.

Mass movements in small canyons in the northeast of Baiyun deepwater area, north of the South China Sea

The process of mass movements and their consequent turbidity currents in large submarine canyons has been widely reported, however, little attention was paid to that in small canyons. In this paper, we document mass movements in small submarine canyons in the northeast of Baiyun deepwater area, north of the South China Sea (SCS), and their strong effects on the evolution of the canyons based on geophysical data. Submarine canyons in the study area arrange closely below the shelf break zone which was at the depth of–500 m. Within submarine canyons, seabed surface was covered with amounts of failure scars resulted from past small-sized landslides. A complex process of mass transportation in the canyons is indicated by three directions of mass movements. Recent mass movement deposits in the canyons exhibit translucent reflections or parallel reflections which represent the brittle deformation and the plastic deformation, respectively. The area of most landslides in the canyons is less than 3 km2. The trigger mechanisms for mass movements in the study area are gravitational overloading, slope angle and weak properties of soil. Geophysical data indicate that the genesis of submarine canyons is the erosion of mass movements and consequent turbidity currents. The significant effects of mass movements on canyon are incision and sediment transportation at the erosion phases and fillings supply at the fill phases. This research will be helpful for the geological risk assessments and understanding the sediment transportation in the northern margin of the SCS.

Generating landslide inventory by participatory mapping: an example in Purwosari Area, Yogyakarta, Java

This paper proposes an approach for landslide inventory mapping considering actual conditions in Indonesia. No satisfactory landslide database exists. What exists is inadequate, focusing, on data response, rather than on pre-disaster preparedness and planning. The humid tropical climate also leads a rapid vegetation growth so past landslides signatures are covered by vegetation or dismantled by erosion process. Generating landslide inventory using standard techniques still seems difficult. A catalog of disasters from local government (village level) was used as a basis of participatory landslide inventory mapping. Eyewitnesses or landslide disaster victims were asked to participate in the reconstruction of past landslides. Field investigation focusing on active participation from communities with the use of an innovative technology was used to verify the landslide events recorded in the disaster catalog. Statistical analysis was also used to obtain the necessary relationships between geometric measurements, including the height of the slope and length of run out, area and volume of displaced materials, the probability distributions of landslide area and volume, and mobilization rate. The result shows that run out distance is proportional to the height of the slope. The frequency distribution calculated by using non-cumulative distribution empirically exhibits a power law (fractal statistic) even though rollover can also be found in the dataset. This cannot be the result of the censoring effect or incompleteness of the data because the landslide inventory dataset can be classified as having complete data or nearly complete data. The so-called participatory landslide inventory mapping method is expected to solve the difficulties of landslide inventory mapping and can be applied to support pre-disaster planning and preparedness action to reduce the landslide disaster risk in Indonesia. It may also supplement the usually incomplete data in a typical landslide inventory.