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Sample records for landslide susceptibility mapping

  1. Landslide susceptibility map: from research to application

    NASA Astrophysics Data System (ADS)

    Fiorucci, Federica; Reichenbach, Paola; Ardizzone, Francesca; Rossi, Mauro; Felicioni, Giulia; Antonini, Guendalina

    2014-05-01

    Susceptibility map is an important and essential tool in environmental planning, to evaluate landslide hazard and risk and for a correct and responsible management of the territory. Landslide susceptibility is the likelihood of a landslide occurring in an area on the basis of local terrain conditions. Can be expressed as the probability that any given region will be affected by landslides, i.e. an estimate of "where" landslides are likely to occur. In this work we present two examples of landslide susceptibility map prepared for the Umbria Region and for the Perugia Municipality. These two maps were realized following official request from the Regional and Municipal government to the Research Institute for the Hydrogeological Protection (CNR-IRPI). The susceptibility map prepared for the Umbria Region represents the development of previous agreements focused to prepare: i) a landslide inventory map that was included in the Urban Territorial Planning (PUT) and ii) a series of maps for the Regional Plan for Multi-risk Prevention. The activities carried out for the Umbria Region were focused to define and apply methods and techniques for landslide susceptibility zonation. Susceptibility maps were prepared exploiting a multivariate statistical model (linear discriminant analysis) for the five Civil Protection Alert Zones defined in the regional territory. The five resulting maps were tested and validated using the spatial distribution of recent landslide events that occurred in the region. The susceptibility map for the Perugia Municipality was prepared to be integrated as one of the cartographic product in the Municipal development plan (PRG - Piano Regolatore Generale) as required by the existing legislation. At strategic level, one of the main objectives of the PRG, is to establish a framework of knowledge and legal aspects for the management of geo-hydrological risk. At national level most of the susceptibility maps prepared for the PRG, were and still are obtained

  2. The European landslide susceptibility map ELSUS 1000 Version 1

    NASA Astrophysics Data System (ADS)

    Günther, Andreas; Van Den Eeckhaut, Miet; Malet, Jean-Philippe; Reichenbach, Paola; Hervás, Javier

    2013-04-01

    With the increase in availability of environmental data sets at global and continental scale and the adoption of the Thematic Strategy for Soil Protection in 2006, small scale risk assessments of soil threats received increasing attention in Europe. We focus on landslides and present an approach for landslide susceptibility evaluation at the continental scale (1 km resolution) over the European territory covered by the EU member states and adjacent countries. Different to previous continental and global scale landslide susceptibility studies, we start with collecting more than 102,000 landslides in 22 European countries. These landslides are heterogeneously distributed over Europe, but are indispensable for the evaluation and classification of Pan-European datasets that can be used as spatial predictors for landslide susceptibility, and the validation of respective assessments. We further attempted a subdivision of the European territory into seven different climato-physiographic zones by combining morphometric and climatic data sets for terrain differentiation, and additionally defining coastal areas as a 1km inland from the coastline. Landslide susceptibility modelling was performed for the individual zones involving heuristic spatial multicriteria evaluations, and validated with the inventory data using receiver operating characteristics. The reliability of the resulting susceptibility map ELSUS 1000 Version 1 was examined on an administrative terrain unit level in areas with landslide information. ELSUS 1000 was further evaluated through comparisons with available national and regional landslide susceptibility maps. These evaluations suggest that although the first version of ELSUS 1000 is capable for a correct synoptic assessment of landslide susceptibility in the majority of the area, it needs further improvement in terms of data used. These should also consider differentiated susceptibility evaluations with respect to different landslide types. ELSUS 1000

  3. Landslide susceptibility mapping using a neuro-fuzzy

    NASA Astrophysics Data System (ADS)

    Lee, S.; Choi, J.; Oh, H.

    2009-12-01

    This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide

  4. Landslide susceptibility mapping in three selected target zones in Afghanistan

    NASA Astrophysics Data System (ADS)

    Schwanghart, Wolfgang; Seegers, Joe; Zeilinger, Gerold

    2015-04-01

    In May 2014, a large and mobile landslide destroyed the village Ab Barek, a village in Badakshan Province, Afghanistan. The landslide caused several hundred fatalities and once again demonstrated the vulnerability of Afghanistan's population to extreme natural events following more than 30 years of civil war and violent conflict. Increasing the capacity of Afghanistan's population by strengthening the disaster preparedness and management of responsible government authorities and institutions is thus a major component of international cooperation and development strategies. Afghanistan is characterized by high relief and widely varying rock types that largely determine the spatial distribution as well as emplacement modes of mass movements. The major aim of our study is to characterize this variability by conducting a landslide susceptibility analysis in three selected target zones: Greater Kabul Area, Badakhshan Province and Takhar Province. We expand on an existing landslide database by mapping landforms diagnostic for landslides (e.g. head scarps, normal faults and tension cracks), and historical landslide scars and landslide deposits by visual interpretation of high-resolution satellite imagery. We conduct magnitude frequency analysis within subregional physiogeographic classes based on geological maps, climatological and topographic data to identify regional parameters influencing landslide magnitude and frequency. In addition, we prepare a landslide susceptibility map for each area using the Weight-of-Evidence model. Preliminary results show that the three selected target zones vastly differ in modes of landsliding. Low magnitude but frequent rockfall events are a major hazard in the Greater Kabul Area threatening buildings and infrastructure encroaching steep terrain in the city's outskirts. Mass movements in loess covered areas of Badakshan are characterized by medium to large magnitudes. This spatial variability of characteristic landslide magnitudes and

  5. Update of the European Landslide Susceptibility Map (ELSUS Version 2)

    NASA Astrophysics Data System (ADS)

    Wilde, Martina; Günther, Andreas; Malet, Jean-Philippe; Reichenbach, Paola; Hervás, Javier

    2015-04-01

    We present an update of the initial version of the European Landslide Susceptibility Map (ELSUS Version 1) that was released in 2012 through the EU Joint Research Centre (JRC) European Soil Data Centre (ESDAC). The susceptibility evaluation methodology employed for the updated map ELSUS Version 2 presented in this paper is identical to the previous approach, and comprises the differentiation of the analyzed European area into seven climate-physiographical model zones, the use of a reduced set of spatial susceptibility predictors (shallow subsurface lithology, slope angle, and land cover), and model zone-specific heuristic spatial multicriteria evaluations (SMCE) for susceptibility mapping. The most important improvement for ELSUS version 2 is the replacement of the original "lithology" data set consisting of soil parent material information derived from the European Soil Database (ESDB) by new information derived from the digital version of the International Hydrogeological Map of Europe at scale 1 : 1.5 Million (IHME 1500). IHME lithology describes both consolidated and unconsolidated shallow geological materials over Europe and can be shown to have a higher significance for landslide susceptibility evaluation than the soil parent material derived from ESDB. Other improvements consist in the change of the mapping unit from 1 km to 200 m grid size and the incorporation of terrains not covered by ELSUS version 1 (e.g., Iceland, the Faroers, the Shetlands, and Cyprus). Additionally, the new ELSUS version 2 was calibrated and validated with an updated pan-European landslide inventory now containing more than 155,000 landslides (30% more than used for ELSUS version 1). The enhanced and updated landslide inventory and the higher quality of the "lithology" data enabled us to establish more consistent SMCE-schemes for the individual model zones. The enhancements of ELSUS Version 2 result in an overall increase of the predictive power of the map for about 10%, as indicated

  6. Map showing landslide susceptibility in Prince Georges County, Maryland

    SciTech Connect

    Pomeroy, J.S.

    1989-01-01

    Prince Georges County was identified during a statewide investigation of landslide susceptibility (MF-2048) as the county with the most serious slope-stability problems. This map uses a ranking system ranging from 1 (nil to very low susceptibility) to 4 (moderate to severe susceptibility). Geologic factors and precipitation are major elements in the initiation of landslides in the county. The Potomac Group and the Marlboro Clay are the most slideprone units. This map should enable users to make a rapid, generalized evaluation of the potential for mass movement. Planners, engineers, soil scientists, geologist, university faculty, and elected officials should find it useful in the assessment of slope hazards for county-wide analyses.

  7. Spatially explicit shallow landslide susceptibility mapping over large areas

    USGS Publications Warehouse

    Bellugi, Dino; Dietrich, William E.; Stock, Jonathan D.; McKean, Jim; Kazian, Brian; Hargrove, Paul

    2011-01-01

    Recent advances in downscaling climate model precipitation predictions now yield spatially explicit patterns of rainfall that could be used to estimate shallow landslide susceptibility over large areas. In California, the United States Geological Survey is exploring community emergency response to the possible effects of a very large simulated storm event and to do so it has generated downscaled precipitation maps for the storm. To predict the corresponding pattern of shallow landslide susceptibility across the state, we have used the model Shalstab (a coupled steady state runoff and infinite slope stability model) which susceptibility spatially explicit estimates of relative potential instability. Such slope stability models that include the effects of subsurface runoff on potentially destabilizing pore pressure evolution require water routing and hence the definition of upslope drainage area to each potential cell. To calculate drainage area efficiently over a large area we developed a parallel framework to scale-up Shalstab and specifically introduce a new efficient parallel drainage area algorithm which produces seamless results. The single seamless shallow landslide susceptibility map for all of California was accomplished in a short run time, and indicates that much larger areas can be efficiently modelled. As landslide maps generally over predict the extent of instability for any given storm. Local empirical data on the fraction of predicted unstable cells that failed for observed rainfall intensity can be used to specify the likely extent of hazard for a given storm. This suggests that campaigns to collect local precipitation data and detailed shallow landslide location maps after major storms could be used to calibrate models and improve their use in hazard assessment for individual storms.

  8. Sensitivity analysis and scale issues in landslide susceptibility mapping

    NASA Astrophysics Data System (ADS)

    Catani, Filippo; Lagomarsino, Daniela; Segoni, Samuele; Tofani, Veronica

    2013-04-01

    Despite the large number of recent advances and developments in landslide susceptibility mapping (LSM) there is still a lack of studies focusing on specific aspects of LSM model sensitivity. For example, the influence of factors of paramount importance such as the survey scale of the landslide conditioning variables (LCVs), the resolution of the mapping unit (MUR) and the optimal number and ranking of LCVs have never been investigated analytically, especially on large datasets. In this paper we attempt this experimentation concentrating on the impact of model tuning choice on the final result, rather than on the comparison of methodologies. To this end, we adopt a simple implementation of the random forest (RF) classification family to produce an ensamble of landslide susceptibility maps for a set of different model settings, input data types and scales. RF classification and regression methods offer a very flexible environment for testing model parameters and mapping hypotheses, allowing for a direct quantification of variable importance. The model choice is, in itself, quite innovative since it is the first time that such technique, widely used in remote sensing for image classification, is used in this form for the production of a LSM. Random forest is a combination of tree (usually binary) bayesian predictors that permits to relate a set of contributing factors with the actual landslides occurrence. Being it a nonparametric model, it is possible to incorporate a range of numeric or categorical data layers and there is no need to select unimodal training data. Many classical and widely acknowledged landslide predisposing factors have been taken into account as mainly related to: the lithology, the land use, the land surface geometry (derived from DTM), the structural and anthropogenic constrains. In addition, for each factor we also included in the parameter set the standard deviation (for numerical variables) or the variety (for categorical ones). The use of

  9. Landslide susceptibility mapping using a bivariate statistical model in a tropical hilly area of southeastern Brazil

    NASA Astrophysics Data System (ADS)

    Araújo, J. P. C.; DA Silva, L. M.; Dourado, F. A. D.; Fernandes, N.

    2015-12-01

    Landslides are the most damaging natural hazard in the mountainous region of Rio de Janeiro State in Brazil, responsible for thousands of deaths and important financial and environmental losses. However, this region has currently few landslide susceptibility maps implemented on an adequate scale. Identification of landslide susceptibility areas is fundamental in successful land use planning and management practices to reduce risk. This paper applied the Bayes' theorem based on weight of evidence (WoE) using 8 landslide-related factors in a geographic information system (GIS) for landslide susceptibility mapping. 378 landslide locations were identified and mapped on a selected basin in the city of Nova Friburgo, triggered by the January 2011 rainfall event. The landslide scars were divided into two subsets: training and validation subsets. The 8 landslide-related factors weighted by WoE were performed using chi-square test to indicate which variables are conditionally independent of each other to be used in the final map. Finally, the maps of weighted factors were summed up to construct the landslide susceptibility map and validated by the validation landslide subset. According to the results, slope, aspect and contribution area showed the higher positive spatial correlation with landslides. In the landslide susceptibility map, 21% of the area presented very low and low susceptibilities with 3% of the validation scars, 41% presented medium susceptibility with 22% of the validation scars and 38% presented high and very high susceptibilities with 75% of the validation scars. The very high susceptibility class stands for 16% of the basin area and has 54% of the all scars. The approach used in this study can be considered very useful since 75% of the area affected by landslides was included in the high and very high susceptibility classes.

  10. Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS

    NASA Astrophysics Data System (ADS)

    Ozdemir, Adnan

    2009-06-01

    On 19 February 2007, a landslide occurred on the Alaardıç Slope, located 1.6 km south of the town of Yaka (Gelendost, Turkey.) Subsequently, the displaced materials transformed into a mud flow in Eğlence Creek and continued 750 m downstream towards the town of Yaka. The mass poised for motion in the Yaka Landslide source area and its vicinity, which would be triggered to a kinetic state by trigger factors such as heavy or sustained rainfall and/or snowmelt, poise a danger in the form of loss of life and property to Yaka with its population of 3,000. This study was undertaken to construct a susceptibility mapping of the vicinity of the Yaka Landslide’s source area and to relate it to movement of the landslide mass with the goal of prevention or mitigation of loss of life and property. The landslide susceptibility map was formulated by designating the relationship of the effecting factors that cause landslides such as lithology, gradient, slope aspect, elevation, topographical moisture index, and stream power index to the landslide map, as determined by analysis of the terrain, through the implementation of the conditional probability method. It was determined that the surface area of the Goksogut formation, which has attained lithological characteristics of clayey limestone with a broken and separated base and where area landslides occur, possesses an elevation of 1,100-1,300 m, a slope gradient of 15°-35° and a slope aspect between 0°-67.5° and 157°-247°. Loss of life and property may be avoided by the construction of structures to check the debris mass in Eğlence Creek, the cleaning of the canal which passes through Yaka, the broadening of the canal’s base area, elevating the protective edges along the canal and the establishment of a protective zone at least 10-m wide on each side of the canal to deter against damage from probable landslide occurrence and mud flow.

  11. Use of Satellite Remote Sensing Data in the Mapping of Global Landslide Susceptibility

    NASA Technical Reports Server (NTRS)

    Hong, Yang; Adler, Robert F.; Huffman, George J.

    2007-01-01

    Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIs-based weighted linear combination method. First , six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor's relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall. 1

  12. Map showing landslide susceptibility in the municipality of Ponce, Puerto Rico

    USGS Publications Warehouse

    Larsen, Matthew C.; Santiago, Marilyn; Jibson, Randall W.; Questell, Eduardo

    2004-01-01

    The risk of landslides during intense or prolonged rainfall is high in steeply sloping areas such as the municipality of Ponce, where 56 percent of the 301-square-kilometer municipality has slopes 10 degrees or greater. These are areas where the possibility of landsliding increases when triggering conditions such as heavy rainfall or excavation and construction occur. Using a 30-meter digital elevation model to classify hillslope angle, a digital map of bedrock geology, and maps showing the locations of landslides associated with a severe storm in October 1985, the municipality was classified into areas of low, moderate, and high susceptibility to landslides triggered by heavy rainfall. Areas defined by geology as having 0-0.1 landslides per square kilometer were mapped as having low landslide susceptibility, areas having 0.1-0.5 landslides per square kilometer were mapped as having moderate susceptibility, and areas having more than 0.5 landslides per square kilometer were mapped as having high landslide susceptibility. Areas with hillslope angles of 5 degrees or less were not classified as they are considered too flat for significant landslide susceptibility. The result of this classification indicates that 34 percent of the municipality has high susceptibility to rainfall-triggered landsliding, 24 percent has moderate susceptibility, and 9 percent has low susceptibility. Approximately 34 percent of the municipality, mainly areas with slopes of 5 degrees or less and water bodies, was not classified. Because of the uncertainties inherent in the susceptibility classification of extensive landscape areas as well as timing of landslide triggers, landslide susceptibility maps should be used with caution. The results of this study are valid for generalized planning and assessment purposes, but may be less useful at the site-specific scale where local geologic and geographic heterogeneities may occur. Construction in areas of moderate to high landslide susceptibility

  13. A different aspect to use of some soft computing methods for landslide susceptibility mapping

    NASA Astrophysics Data System (ADS)

    Akgün, Aykut

    2014-05-01

    In landslide literature, several applications of soft computing methods such as artifical neural networks (ANN), fuzzy inference systems, and decision trees for landslide susceptibility mapping can be found. In many of these studies, the effectiveness and validation of the models used are also discussed. To carry out analyses, more than one software, for example one statistical package and one geographical information systems software (GIS), are generally used together. In this study, four different soft computing techniques were applied for obtaining landslide susceptibility mapping only by one GIS software. For this purpose, Multi Layer Perceptron (MLP) back propagation neural network, Fuzzy Adaptive Resonance Theory (ARTMAP) neural network, Self-organizing Map (SOM) and Classification Tree Analysis (CTA) approaches were applied to the study area. The study area was selected from a part of Trabzon (North Turkey) city which is one of the most landslide prone areas in Turkey. Initially, five landslide conditioning parameters such as lithology, slope gradient, slope aspect, stream power index (SPI), and topographical wetness index (TWI) for the study area were produced in GIS. Then, these parameters were analysed by MLP, Fuzzy ARTMAP, SOM and CART soft computing classifiers of the IDRISI Taiga GIS and remote sensing software. To accomplish the analyses, two main input groups are needed. These are conditioning parameters and training areas. For training areas, initially, landslide inventory map which was obtained by both field studies and topographical analyses was compared with lithological unit classes. With the help of these comparison, frequency ratio (FR) values of landslide occurrence in the study area were determined. Using the FR values, five landslide susceptibility classes were differentiated from the lowest FR to highest FR values. After this differentiation, the training areas representing the landslide susceptibility classes were determined by using FR

  14. Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters

    NASA Astrophysics Data System (ADS)

    Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

    Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86

  15. Earthquake-induced landslide-susceptibility mapping using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Lee, S.; Evangelista, D. G.

    2006-07-01

    The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS) applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from faults were derived from the geology database. Land cover was identified from the topographic database. Terrain map units were interpreted from aerial photographs. These factors were used with an artificial neural network to analyze landslide susceptibility. Each factor weight was determined by a back-propagation exercise. Landslide-susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from GIS data. The susceptibility map was compared with known landslide locations and verified. The demonstrated prediction accuracy was 93.20%.

  16. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping

    PubMed Central

    Feizizadeh, Bakhtiar; Shadman Roodposhti, Majid; Jankowski, Piotr; Blaschke, Thomas

    2014-01-01

    Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”. PMID:26089577

  17. The landslide susceptibility map of Italy at 1:1 Million scale

    NASA Astrophysics Data System (ADS)

    Trigila, A.; Catani, F.; Casagli, N.; Crosta, G.; Esposito, C.; Frattini, P.; Iadanza, C.; Lagomarsino, D.; Lari, S.; Scarascia Mugnozza, G.; Segoni, S.; Spizzichino, D.; Tofani, V.

    2012-04-01

    Landslides are among the most problematic natural hazards in Italy, in terms of both casualties and economic losses. Landslide susceptibility maps are key tools for land use planning, management and risk mitigation. The aim of the work is to present the methodology adopted by ISPRA (Italian National Institute for Environmental Protection and Research), University of Florence, University of Milano-Bicocca and University of Rome "La Sapienza" for the development of a Landslide susceptibility map of Italy at 1:1,000,000 scale. The Landslide susceptibility map of Italy has been realized by using the Italian Landslide Inventory - Progetto IFFI which contains more than 486,000 landslides, and a set of contributing factors such as surface parameters derived from 20x20 m DEM, lithological map obtained from the Geological map of Italy 1:500,000, and land use map (Corine Land Cover). These databases have been subjected to a quality analysis with the aim of assessing the completeness, homogeneity and reliability of data, and identifying representative areas which may be used as training and test areas for the implementation of landslide susceptibility models. Physiographic domains of homogeneous geology and geomorphology have been identified, and landslides have been divided into three main classes in order to take into account specific sets of conditioning factors: a) rockfalls and rock-avalanches; b) slow mass movements, c) debris flows. Bivariate statistical analyses have been performed to assess the frequency distribution of contributing factors on the landslide area. The tests of different techniques (Discriminant Analysis, Logistic Regression, Bayesian Tree Random Forest) have been performed in selected areas of Italy in order to assess advantages, disadvantages and applicability of the models at the scale of analysis. The modelling tests provided good performance with all techniques, once applied with the appropriate selection of training and validations sets and with

  18. Progress in landslide susceptibility mapping over Europe using Tier-based approaches

    NASA Astrophysics Data System (ADS)

    Günther, Andreas; Hervás, Javier; Reichenbach, Paola; Malet, Jean-Philippe

    2010-05-01

    The European Thematic Strategy for Soil Protection aims, among other objectives, to ensure a sustainable use of soil. The legal instrument of the strategy, the proposed Framework Directive, suggests identifying priority areas of several soil threats including landslides using a coherent and compatible approach based on the use of common thematic data. In a first stage, this can be achieved through landslide susceptibility mapping using geographically nested, multi-step tiered approaches, where areas identified as of high susceptibility by a first, synoptic-scale Tier ("Tier 1") can then be further assessed and mapped at larger scale by successive Tiers. In order to identify areas prone to landslides at European scale ("Tier 1"), a number of thematic terrain and environmental data sets already available for the whole of Europe can be used as input for a continental scale susceptibility model. However, since no coherent landslide inventory data is available at the moment over the whole continent, qualitative heuristic zonation approaches are proposed. For "Tier 1" a preliminary, simplified model has been developed. It consists of an equally weighting combination of a reduced, continent-wide common dataset of landslide conditioning factors including soil parent material, slope angle and land cover, to derive a landslide susceptibility index using raster mapping units consisting of 1 x 1 km pixels. A preliminary European-wide susceptibility map has thus been produced at 1:1 Million scale, since this is compatible with that of the datasets used. The map has been validated by means of a ratio of effectiveness using samples from landslide inventories in Italy, Austria, Hungary and United Kingdom. Although not differentiated for specific geomorphological environments or specific landslide types, the experimental model reveals a relatively good performance in many European regions at a 1:1 Million scale. An additional "Tier 1" susceptibility map at the same scale and using

  19. An innovative tool for landslide susceptibility mapping in Kyrgyzstan, Central Asia

    NASA Astrophysics Data System (ADS)

    Saponaro, Annamaria; Pilz, Marco; Wieland, Marc; Bindi, Dino; Parolai, Stefano

    2013-04-01

    Kyrgyzstan is among the most exposed countries in the world to landslide susceptibility. The high seismicity of the area, the presence of high mountain ridges and topographic relieves, the geology of the local materials and the occurrence of heavy precipitations represent the main factors responsible for slope failures. In particular, the large variability of material properties and slope conditions as well as the difficulties in forecasting heavy precipitations locally and in quantifying the level of ground shaking call for harmonized procedures for reducing the negative impact of these factors. Several studies have recently been carried out aiming at preparing landslide susceptibility and hazard maps; however, some of them - qualitative-based - suffer from the application of subjective decision rules from experts in the classification of parameters that influence the occurrence of a landslide. On the other hand, statistical methods provide objectivity over qualitative ones since they allow a numerical evaluation of landslide spatial distribution with landslide potential factors. For this reason, we will make use of a bivariate technique known as Weight-Of-Evidence method to evaluate the influence of landslide predictive factors. The aim of this study is to identify areas in Kyrgyzstan being more prone to earthquake-triggered landslides. An innovative approach which exploits the new advances of GIS technology together with statistical concepts is presented. A range of conditioning factors and their potential impact on landslide activation is quantitatively assessed on the basis of landslide spatial distribution and seismic zonation. Results show areas which are more susceptible to landslides induced by earthquakes. Our approach can be used to fill the gap of subjectivity that typically affects already performed qualitative analysis. The resulting landslide susceptibility map represents a potentially supportive tool for disaster management and planning activities

  20. Landslide Susceptibility Mapping Using Geospatial Technology in South Eastern Part of Nilgiri District, Tamilnadu, India

    NASA Astrophysics Data System (ADS)

    Thangasamy, N.; Varathan, R.

    2013-05-01

    Landslides are often destructive and periodically affect the Nilgiris district. Two method viz., Frequency ratio (FR) and Weights of evidence (WofE) were used to reclassify the sub-variables and the landslide susceptibility index (LSI) was calculated by weighted sum overlay analysis. The final LS Zonation map was prepared from the LSI and the area was classified into two zones. Validation of the LSM was the next step and was accomplished by excluding some landslide points in the GIS analyses and overlying the unused landslides points over the LSM. The LSMs prepared using the FR and WofE methods are reliable as more than 75% of the excluded slides fall in high and very high landslide susceptibility zones and the error of mismatch in the two maps is negligible.During the course of this study landslides devastated the Kethi, Coonoor, Barliyar and Kothagiri areas due to an extreme event with 374 to 1,171 mm rainfall received in these stations in just three days on 8th to 10th November, 2009. The rainfall event is unprecedented and such extreme rainfall has not occurred in the region since meteorological records are maintained. Over 100 landslides took place in the area of which 75 are major slides and more 43 people died and 200 houses were damaged. The event was documented and a data base containing the location, details of death, slide characteristics and photographs was prepared. Further, the probability of landslide occurrence may change over time due to changes in land use, unscientific massive developmental activities and establishing settlements without adopting proper safety measures. The study also highlights the need for maintenance of landslide database and installation of more rain gauge stations to update and improve the LSM so as to reduce the risk of landslide hazard faced by the Community. NaveenRaj.T INDIA LANDSLIDE SUSCEPTIBILITY MAPPING USING GEOSPATIAL TECHNOLOGY IN SOUTH EASTERN PART OF NILGIRI DISTRICT, TAMILNADU, INDIA.

  1. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps

    NASA Astrophysics Data System (ADS)

    Steger, Stefan; Brenning, Alexander; Bell, Rainer; Petschko, Helene; Glade, Thomas

    2016-06-01

    Empirical models are frequently applied to produce landslide susceptibility maps for large areas. Subsequent quantitative validation results are routinely used as the primary criteria to infer the validity and applicability of the final maps or to select one of several models. This study hypothesizes that such direct deductions can be misleading. The main objective was to explore discrepancies between the predictive performance of a landslide susceptibility model and the geomorphic plausibility of subsequent landslide susceptibility maps while a particular emphasis was placed on the influence of incomplete landslide inventories on modelling and validation results. The study was conducted within the Flysch Zone of Lower Austria (1,354 km2) which is known to be highly susceptible to landslides of the slide-type movement. Sixteen susceptibility models were generated by applying two statistical classifiers (logistic regression and generalized additive model) and two machine learning techniques (random forest and support vector machine) separately for two landslide inventories of differing completeness and two predictor sets. The results were validated quantitatively by estimating the area under the receiver operating characteristic curve (AUROC) with single holdout and spatial cross-validation technique. The heuristic evaluation of the geomorphic plausibility of the final results was supported by findings of an exploratory data analysis, an estimation of odds ratios and an evaluation of the spatial structure of the final maps. The results showed that maps generated by different inventories, classifiers and predictors appeared differently while holdout validation revealed similar high predictive performances. Spatial cross-validation proved useful to expose spatially varying inconsistencies of the modelling results while additionally providing evidence for slightly overfitted machine learning-based models. However, the highest predictive performances were obtained for

  2. Landslide Hazard Assessment and Mapping in the Guil Catchment (Queyras, Southern French Alps): From Landslide Inventory to Susceptibility Modelling

    NASA Astrophysics Data System (ADS)

    Roulleau, Louise; Bétard, François; Carlier, Benoît; Lissak, Candide; Fort, Monique

    2016-04-01

    Landslides are common natural hazards in the Southern French Alps, where they may affect human lives and cause severe damages to infrastructures. As a part of the SAMCO research project dedicated to risk evaluation in mountain areas, this study focuses on the Guil river catchment (317 km2), Queyras, to assess landslide hazard poorly studied until now. In that area, landslides are mainly occasional, low amplitude phenomena, with limited direct impacts when compared to other hazards such as floods or snow avalanches. However, when interacting with floods during extreme rainfall events, landslides may have indirect consequences of greater importance because of strong hillslope-channel connectivity along the Guil River and its tributaries (i.e. positive feedbacks). This specific morphodynamic functioning reinforces the need to have a better understanding of landslide hazards and their spatial distribution at the catchment scale to prevent local population from disasters with multi-hazard origin. The aim of this study is to produce a landslide susceptibility mapping at 1:50 000 scale as a first step towards global estimation of landslide hazard and risk. The three main methodologies used for assessing landslide susceptibility are qualitative (i.e. expert opinion), deterministic (i.e. physics-based models) and statistical methods (i.e. probabilistic models). Due to the rapid development of geographical information systems (GIS) during the last two decades, statistical methods are today widely used because they offer a greater objectivity and reproducibility at large scales. Among them, multivariate analyses are considered as the most robust techniques, especially the logistic regression method commonly used in landslide susceptibility mapping. However, this method like others is strongly dependent on the accuracy of the input data to avoid significant errors in the final results. In particular, a complete and accurate landslide inventory is required before the modelling

  3. Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach

    NASA Astrophysics Data System (ADS)

    Liu, Chia-Nan; Wu, Chia-Chen

    2008-08-01

    To prepare a landslide susceptibility map is essential to identify hazardous regions, construct appropriate mitigation facilities, and plan emergency measures for a region prone to landslides triggered by rainfall. The conventional mapping methods require much information about past landslides records and contributing terrace and rainfall. They also rely heavily on the quantity and quality of accessible information and subjectively of the map builder. This paper contributes to a systematic and quantitative assessment of mapping landslide hazards over a region. Geographical Information System is implemented to retrieve relevant parameters from data layers, including the spatial distribution of transient fluid pressures, which is estimated using the TRIGRS program. The factor of safety of each pixel in the study region is calculated analytically. Monte Carlo simulation of random variables is conducted to process the estimation of fluid pressure and factor of safety for multiple times. The failure probability of each pixel is thus estimated. These procedures of mapping landslide potential are demonstrated in a case history. The analysis results reveal a positive correlation between landslide probability and accumulated rainfall. This approach gives simulation results compared to field records. The location and size of actual landslide are well predicted. An explanation for some of the inconsistencies is also provided to emphasize the importance of site information on the accuracy of mapping results.

  4. GIS-aided Statistical Landslide Susceptibility Modeling And Mapping Of Antipolo Rizal (Philippines)

    NASA Astrophysics Data System (ADS)

    Dumlao, A. J.; Victor, J. A.

    2015-09-01

    Slope instability associated with heavy rainfall or earthquake is a familiar geotechnical problem in the Philippines. The main objective of this study is to perform a detailed landslide susceptibility assessment of Antipolo City. The statistical method of assessment used was logistic regression. Landslide inventory was done through interpretation of aerial photographs and satellite images with corresponding field verification. In this study, morphologic and non-morphologic factors contributing to landslide occurrence and their corresponding spatial relationships were considered. The analysis of landslide susceptibility was implemented in a Geographic Information System (GIS). The 17320 randomly selected datasets were divided into training and test data sets. K- cross fold validation is done with k= 5. The subsamples are then fitted five times with k-1 training data set and the remaining fold as the validation data set. The AUROC of each model is validated using each corresponding data set. The AUROC of the five models are; 0.978, 0.977, 0.977, 0.974, and 0.979 respectively, implying that the models are effective in correctly predicting the occurrence and nonoccurrence of landslide activity. Field verification was also done. The landslide susceptibility map was then generated from the model. It is classified into four categories; low, moderate, high and very high susceptibility. The study also shows that almost 40% of Antipolo City has been assessed to be potentially dangerous areas in terms of landslide occurrence.

  5. Why a high statistical performance cannot be equated with a high plausibility of landslide susceptibility maps

    NASA Astrophysics Data System (ADS)

    Steger, Stefan; Brenning, Alexander; Bell, Rainer; Petschko, Helene; Glade, Thomas

    2015-04-01

    Statistical landslide susceptibility maps express a relative estimate of where landslides are more likely to occur in the future due to a set of geo-environmental conditions. Their predictive capability is regularly deduced by interpreting threshold independent performance measures like the area under the receiver operating characteristic curve (AUROC). These quantitative estimates frequently serve as a decision tool to favour a certain classifier over another and/or to select a suitable combination of predictors. Literature exposes that many authors consider their final maps as a valuable instrument for spatial planners and decision makers. However, most often the susceptibility maps are selected by solely interpreting such quantitative estimates. We assume that a high statistical quality is necessary but not sufficient in order to produce plausible landslide susceptibility maps. This assumption was tested by quantitatively and qualitatively validating 16 susceptibility models for a study area (1354 km²) located in Lower Austria. The models were generated by applying two statistical and two machine learning classifiers separately for two landslide inventories and two sets of predictors. Quantitative validation was conducted by estimating the AUROC with non spatial hold-out validation and a repeated spatial cross validation technique. The spatial differentiation of the final maps was evaluated at different scales by interpreting semivariograms. Maps of the location of major variations illustrate the spatial structure of the final susceptibility maps and allowed to deduce the most influential predictors and predictor classes. According to the hold-out validation, all 16 susceptibility models performed similarly well. However, spatial cross validation revealed considerable differences between models generated by different landslide inventories. Semivariograms exposed that the predicted landslide susceptibility pattern differs substantially between maps generated by

  6. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

    PubMed Central

    Hashim, Mazlan

    2015-01-01

    This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning. PMID:25898919

  7. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

    PubMed

    Shahabi, Himan; Hashim, Mazlan

    2015-01-01

    This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning. PMID:25898919

  8. Landslide susceptibility mapping in Mawat area, Kurdistan Region, NE Iraq: a comparison of different statistical models

    NASA Astrophysics Data System (ADS)

    Othman, A. A.; Gloaguen, R.; Andreani, L.; Rahnama, M.

    2015-03-01

    During the last decades, expansion of settlements into areas prone to landslides in Iraq has increased the importance of accurate hazard assessment. Susceptibility mapping provides information about hazardous locations and thus helps to potentially prevent infrastructure damage due to mass wasting. The aim of this study is to evaluate and compare frequency ratio (FR), weight of evidence (WOE), logistic regression (LR) and probit regression (PR) approaches in combination with new geomorphological indices to determine the landslide susceptibility index (LSI). We tested these four methods in Mawat area, Kurdistan Region, NE Iraq, where landslides occur frequently. For this purpose, we evaluated 16 geomorphological, geological and environmental predicting factors mainly derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite. The available reference inventory includes 351 landslides representing a cumulative surface of 3.127 km2. This reference inventory was mapped from QuickBird data by manual delineation and partly verified by field survey. The areas under curve (AUC) of the receiver operating characteristic (ROC), and relative landslide density (R index) show that all models perform similarly and that focus should be put on the careful selection of proxies. The results indicate that the lithology and the slope aspects play major roles for landslide occurrences. Furthermore, this paper demonstrates that using hypsometric integral as a prediction factor instead of slope curvature gives better results and increases the accuracy of the LSI.

  9. Implementation of landslide susceptibility maps in Lower Austria as part of risk governance

    NASA Astrophysics Data System (ADS)

    Bell, Rainer; Petschko, Helene; Bauer, Christian; Glade, Thomas; Granica, Klaus; Heiss, Gerhard; Leopold, Philip; Pomaroli, Gilbert; Proske, Herwig; Schweigl, Joachim

    2013-04-01

    Landslides frequently cause damage to agricultural land and infrastructure in Lower Austria - a province of Austria. Also settlements and people are threatened by landslides. To reduce landslide risks and to prevent the establishment of new settlements in highly landslide prone areas, the project "MoNOE" (Method development for landslide susceptibility modeling in Lower Austria) was set up by the provincial government. The main aim of the project is the development of methods to model rock fall and slide susceptibility for an area of approx. 15,900 km2 and to implement the resulting susceptibility maps into the spatial planning strategies of the state. Right from the beginning of the project a close cooperation between the involved scientists and the stakeholders from the Geological Survey of Lower Austria and the Department of Spatial Planning and Regional Policy of Lower Austria was established to ensure that method development and final susceptibility maps meet exactly the needs and demands of the stakeholders. This posed huge challenges, together with its realization in the large study area and a (heterogeneous) complex geological situation,. Limitations were given by restricted data availability (e.g. for geology or landslide inventories) in such a large study area. Rock fall susceptibility was modeled by a combined approach of determining rock fall release areas by empirical slope thresholds (dependent on geology) followed by empirical run-out modeling. Slide susceptibility was modeled based on the statistical approaches of weights of evidence (WofE) and generalized additive models (GAM) by two different research groups. Huge efforts were spent on the validation of all susceptibility models. In a later stage of the project we found that the best scientific maps are not necessarily the best maps to be implemented in spatial planning strategies. Thus, in close cooperation with the stakeholders, decisions had to be taken to find the best resolution of the maps

  10. Spatial three-dimensional landslide susceptibility mapping tool and its applications

    NASA Astrophysics Data System (ADS)

    Xie, Mowen; Tetsuro, Esaki; Qiu, Cheng; Jia, Lin

    There are three methods of zoning landslide susceptibility: qualitative, statistical methodologies, and geotechnical model. Qualitative approaches are based on the judgment of those conducting the susceptibility or hazard assessment; the statistical approach uses a predictive function or index derived from a combination of weighted factors; and the deterministic, or physically based, models are based on the physical laws of conservation of mass, energy, and momentum. Two-dimensional deterministic models are widely used in the design of civil engineering, and the infinite slope model (one-dimensional) is always employed in the deterministic-model-based landslide hazard mapping. This article presents a new GIS (Geographic Information Systems)-based landslide susceptibility mapping system which can be used to identify the three-dimensional (3-D) landslide bodies from complex topography. All slope-related spatial information (vector or raster dataset) was integrated in the system, by dividing the study area into slope units and assuming the initial slip to be the lower part of an ellipsoid. The 3-D critical slip surface in the 3-D slope stability analysis was located by minimizing the 3-D safety factor using the Monte Carlo random simulation. The failure probability of the landslide was calculated using an approximate method in which effective cohesion, effective friction angle, and 3-D safety factor were assumed to be in normal distribution. A computational program called 3-DSlopeGIS, in which a GIS Developer kit (ArcObjects of ESRI) had been used to fulfill the GIS spatial analysis function and effective data management, has been developed to implement all the calculations of the 3-D slope problem. By using the spatial analysis functions, the data management, and the visualization of GIS for processing the complicated slope-related data, the 3-D slope stability problem is easier to be studied through a friendly visual graphical user interface. The system has been

  11. Shallow Landslide Susceptibility Mapping for Selected Areas in the Philippines Severely affected by Super Typhoon Haiyan

    NASA Astrophysics Data System (ADS)

    Felix, Raquel; Rabonza, Maricar; Ortiz, Iris Jill; Alejandrino, Ian Kaye; Aquino, Dakila; Narod Eco, Rodrigo; Mahar Francisco Lagmay, Alfredo

    2014-05-01

    Super Typhoon Haiyan, considered as one of the most powerful storms recorded in 2013, devastated the central Philippines region on 8 November 2013. In its wake, Haiyan left 6,190 fatalities, 28,626 injured and 1,785 missing, as well as damage amounting to more than USD 823 million. To mitigate damage from similar events in the future, it is imperative to characterize hazards associated with tropical cyclones such as those brought by Haiyan, with detailed studies of storm surges, landslides and floods. Although strong winds and powerful storm surges up 15-17 feet were the primary causes of damage, landslides studies are also vital in the rehabilitation of typhoon damaged areas. Cities and municipalities of Leyte (7,246.7 sq. km) and Samar (13,121 sq. km) provinces, the heaviest cities area during the onslaught of Haiyan, require detailed and up-to-date hazard maps for their rebuilding and disaster mitigation programs. In order to delineate areas susceptible to rainfall induced shallow landslides, Stability INdex MAPping (SINMAP) software was used over a 6-meter Synthetic Aperture Radar (SAR)-derived DEM grid. Soil calibration parameters from previous studies were used as parameter input to generate a worst-case scenario hazard map of the two provinces. Topographic, hydrologic and soil parameters (cohesion, angle of friction, bulk density and hydraulic conductivity) were used for each pixel of a given digital elevation model (DEM) grid to compute for the corresponding factor of safety. The landslide maps generated using SINMAP are found to be consistent with the landslide inventory derived from high-resolution satellite imagery 2003-2013. The landslide susceptibility classification found in the landslide hazard maps are useful to identify no-build zones, areas that can be built upon but with slope intervention and monitoring as well as places that are safe from shallow landslides. These maps complement the debris flow and structurally-controlled landslide hazard maps

  12. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

    PubMed Central

    Feizizadeh, Bakhtiar; Blaschke, Thomas

    2014-01-01

    GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC

  13. Validating national landslide susceptibility and hazard maps for Caribbean island countries: the case of Dominica and tropical storm Erika.

    NASA Astrophysics Data System (ADS)

    van Westen, Cees; Jetten, Victor; Alkema, Dinand

    2016-04-01

    The aim of this study was to generate national-scale landslide susceptibility and hazard maps for four Caribbean islands, as part of the World Bank project CHARIM (Caribbean Handbook on Disaster Geoinformation Management, www.charim.net). This paper focuses on the results for the island country of Dominica, located in the Eastern part of the Caribbean, in-between Guadalupe and Martinique. The available data turned out to be insufficient to generate reliable results. We therefore generated a new database of disaster events for Dominica using all available data, making use of many different sources. We compiled landslide inventories for five recent rainfall events from the maintenance records of the Ministry of Public Works, and generated a completely new landslide inventory using multi-temporal visual image interpretation, and generated an extensive landslide database for Dominica. We analyzed the triggering conditions for landslides as far as was possible given the available data, and generated rainfall magnitude-frequency relations. We applied a method for landslide susceptibility assessment which combined bi-variate statistical analysis, that provided indications on the importance of the possible contributing factors, with an expert-based iterative weighing approach using Spatial Multi-Criteria Evaluation. The method is transparent, as the stakeholders (e.g. the engineers and planners from the four countries) and other consultants can consult the criteria trees and evaluate the standardization and weights, and make adjustments. The landslide susceptibility map was converted into a landslide hazard map using landslide density and frequencies for so called major, moderate and minor triggering events. The landslide hazard map was produced in May 2015. A major rainfall event occurred on Dominica following the passage of tropical storm Erika on 26 to 28 August 2015. An event-based landslide inventory for this event was produced by UNOSAT using very high resolution

  14. Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin

    NASA Astrophysics Data System (ADS)

    Tazik, E.; Jahantab, Z.; Bakhtiari, M.; Rezaei, A.; Kazem Alavipanah, S.

    2014-10-01

    Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.

  15. A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models

    NASA Astrophysics Data System (ADS)

    Wang, Qiqing; Li, Wenping; Wu, Yanli; Pei, Yabing; Xing, Maolin; Yang, Dongdong

    2016-04-01

    The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning.

  16. A comparison of bivariate statistical model and deterministic model-based landslide susceptibility mapping methods: An example from North Turkey

    NASA Astrophysics Data System (ADS)

    Akgun, Aykut; Erkan, Oguzhan

    2015-04-01

    In Turkey, landslide is one of the most important natural hazards. Due to landslide occurrence, several landforms and man made structures are adversely affected, and may cause many injuries and loss of life. In this context, landslide susceptibility assessment is important task to determine susceptible areas to landslide occurrence. Especially, several dam reservoir areas in Turkey are threated by landslide phenomena. For this reason, in this study, a dam reservoir area located in North Turkey was selected, and investigated in point of landslide susceptibility assessment. A landslide susceptibility assessment for the Kurtun dam reservoir area (Gumushane, North Turkey) was carried out by geographical information systems (GIS)-based statistical and deterministic models. For this purpose, frequency ratio (FR) and stability index mapping (SINMAP) methodologies were applied. In this context, eight conditioning parameters such as altitude, lithology, slope gradient, slope aspect, distance to drainage, distance to lineament, stream power index (SPI) and topographical wetness index (TWI) were considered. After assessment of these parameters by FR and SINMAP methods in a GIS environment, two landslide susceptibility maps were obtained. Then, the maps obtained were analyzed for verification purpose. For this purpose, area under curvature (AUC) approach was used. At the end of this process, the AUC values of 0.73 and 0.70 were found for FR and SINMAP methods, respectively. Additionally, the SINMAP statistical results showed that the 93.8% of the observed landslides in the area falls into the lower and upper threshold showing the stability index classes. These values indicate that the accuracies of landslide susceptibility maps are acceptable, and the maps are feasible for further natural hazard management affairs in the area.

  17. Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models

    NASA Astrophysics Data System (ADS)

    Das, Iswar; Stein, Alfred; Kerle, Norman; Dadhwal, Vinay K.

    2012-12-01

    Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular.

  18. Landslide susceptibility mapping along PLUS expressways in Malaysia using probabilistic based model in GIS

    NASA Astrophysics Data System (ADS)

    Yusof, Norbazlan M.; Pradhan, Biswajeet

    2014-06-01

    PLUS Berhad holds the concession for a total of 987 km of toll expressways in Malaysia, the longest of which is the North-South Expressway or NSE. Acting as the backbone' of the west coast of the peninsula, the NSE stretches from the Malaysian-Thai border in the north to the border with neighbouring Singapore in the south, linking several major cities and towns along the way. North-South Expressway in Malaysia contributes to the country economic development through trade, social and tourism sector. Presently, the highway is good in terms of its condition and connection to every state but some locations need urgent attention. Stability of slopes at these locations is of most concern as any instability can cause danger to the motorist. In this paper, two study locations have been analysed; they are Gua Tempurung (soil slope) and Jelapang (rock slope) which are obviously having two different characteristics. These locations passed through undulating terrain with steep slopes where landslides are common and the probability of slope instability due to human activities in surrounding areas is high. A combination of twelve (12) landslide conditioning factors database on slope stability such as slope degree and slope aspect were extracted from IFSAR (interoferometric synthetic aperture radar) while landuse, lithology and structural geology were constructed from interpretation of high resolution satellite data from World View II, Quickbird and Ikonos. All this information was analysed in geographic information system (GIS) environment for landslide susceptibility mapping using probabilistic based frequency ratio model. Consequently, information on the slopes such as inventories, condition assessments and maintenance records were assessed through total expressway maintenance management system or better known as TEMAN. The above mentioned system is used by PLUS as an asset management and decision support tools for maintenance activities along the highways as well as for data

  19. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression

    NASA Astrophysics Data System (ADS)

    Colkesen, Ismail; Sahin, Emrehan Kutlug; Kavzoglu, Taskin

    2016-06-01

    Identification of landslide prone areas and production of accurate landslide susceptibility zonation maps have been crucial topics for hazard management studies. Since the prediction of susceptibility is one of the main processing steps in landslide susceptibility analysis, selection of a suitable prediction method plays an important role in the success of the susceptibility zonation process. Although simple statistical algorithms (e.g. logistic regression) have been widely used in the literature, the use of advanced non-parametric algorithms in landslide susceptibility zonation has recently become an active research topic. The main purpose of this study is to investigate the possible application of kernel-based Gaussian process regression (GPR) and support vector regression (SVR) for producing landslide susceptibility map of Tonya district of Trabzon, Turkey. Results of these two regression methods were compared with logistic regression (LR) method that is regarded as a benchmark method. Results showed that while kernel-based GPR and SVR methods generally produced similar results (90.46% and 90.37%, respectively), they outperformed the conventional LR method by about 18%. While confirming the superiority of the GPR method, statistical tests based on ROC statistics, success rate and prediction rate curves revealed the significant improvement in susceptibility map accuracy by applying kernel-based GPR and SVR methods.

  20. Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China

    NASA Astrophysics Data System (ADS)

    Peng, Ling; Niu, Ruiqing; Huang, Bo; Wu, Xueling; Zhao, Yannan; Ye, Runqing

    2014-01-01

    This paper aims to develop a novel hybrid model for assessing landslide susceptibility at the regional scale using multisource data to produce a landslide susceptibility map of the Zigui-Badong area near the Three Gorges Reservoir, China. This area is subject to anthropogenic influences because the reservoir's water level fluctuates cyclically between 145 and 175 m; in addition, the area suffers from extreme rainfall events due to the local climate. The area has experienced significant and widespread landslide events in recent years. In our study, a novel hybrid model is proposed to produce landslide susceptibility maps using geographical information systems (GIS) and remote sensing. The hybrid model is based on rough set (RS) theory and a support vector machine (SVM). RS theory is employed as an attribute reduction tool to identify the significant environmental parameters of a landslide, and an SVM is used to predict landslide susceptibility. Four data domains were considered in this research: geological, geomorphological, hydrology, and land cover. The original group of 20 environmental parameters and 202 landslides were used as the inputs to produce a landslide susceptibility map. According to the map, 19.7% of the study area was identified as medium- and high-susceptibility zones encompassing 89.5% of the historical landslides. The results indicate high levels of landslide hazard in and around the main inhabited areas, such as Badong County and other towns, as well as in rural residential areas and transportation areas along the Yangtze River and its tributaries. The predicted map indicates a good correlation between the classified high hazard areas and slope failures confirmed in the field. Furthermore, the quality of the proposed model was comprehensively evaluated, including the degree of model fit, the robustness of the model, the uncertainty associated with the probabilistic estimate, and the model prediction skill. The proposed model was also compared

  1. Mapping of risk and susceptibility of shallow-landslide in the city of São Paulo, Brazil

    NASA Astrophysics Data System (ADS)

    Listo, Fabrizio de Luiz Rosito; Carvalho Vieira, Bianca

    2012-10-01

    In the city of São Paulo, where about 11 million people live, landslides and flooding occur frequently, especially during the summer. These landslides cause the destruction of houses and urban equipment, economic damage, and the loss of lives. The number of areas threatened by landslides has been increasing each year. The objective of this article is to analyze the probability of risk and susceptibility to shallow landslides in the Limoeiro River basin, which is located at the head of the Aricanduva River basin, one of the main hydrographic basins in the city of São Paulo. To map areas of risk, we created a cadastral survey form to evaluate landslide risk in the field. Risk was categorized into four levels based on natural and anthropogenic factors: R1 (low risk), R2 (average risk), R3 (high risk), and R4 (very high risk). To analyze susceptibility to shallow landslides, we used the SHALSTAB (Shallow Landsliding Stability) mathematical model and calculated the Distribution Frequency (DF) of the susceptibility classes for the entire basin. Finally, we performed a joint analysis of the average Risk Concentration (RC) and Risk Potential (RP). We mapped 14 risk sectors containing approximately 685 at-risk homes, more than half of which presented a high (R3) or very high (R4) probability of risk to the population. In the susceptibility map, 41% of the area was classified as stable and 20% as unconditionally unstable. Although the latter category accounted a smaller proportion of the total area, it contained a concentration (RC) of 41% of the mapped risk areas with a risk potential (RP) of 12%. We found that the locations of areas predicted to be unstable by the model coincided with the risk areas mapped in the field. This combination of methods can be applied to evaluate the risk of shallow landslides in densely populated areas and can assist public managers in defining areas that are unstable and inappropriate for occupation.

  2. Tien Shan Geohazards Database: Landslide susceptibility analysis

    NASA Astrophysics Data System (ADS)

    Havenith, H. B.; Torgoev, A.; Schlögel, R.; Braun, A.; Torgoev, I.; Ischuk, A.

    2015-11-01

    This paper is the second part of a new geohazards analysis applied to a large part of the Tien Shan, Central Asia, focused on landslide susceptibility computations that are based on recently compiled geographic, geological and geomorphological data. The core data are a digital elevation model, an updated earthquake catalogue, an active fault map as well as a new landslide inventory. The most recently added digital data are a new simplified geological map, an annual precipitation map, as well as river and road network maps that were produced for the Kyrgyz and Tajik parts of the Tien Shan. On the basis of these records we determine landslide densities with respect to morphological (M), geological (G), river distance (R), precipitation (P), earthquake (E) and fault (F) distance factors. Correlations were also established between scarp locations and the slope angle, distance to rivers, curvature. These correlations show that scarps tend to be located on steeper slopes, farther from rivers and on more convex terrain than the entire landslides. On the basis of the landslide density values computed for each class of the aforementioned factors, two landslide susceptibility maps are created according to the Landslide Factor analysis: the first one considers correlations between the landslide occurrences and the first four factors (MGRP); the second one is based on the first map (MGRP) combined with the seismo-tectonic influence (+ E + F) on landslide distributions. From the comparison of these two maps with actual landslide distributions we infer that the distances to rivers as well as to faults and past earthquakes most strongly constrain the susceptibility of slopes to landslides. We highlight several zones where the landslide susceptibilities computed for the MGRP + E + F factors fit better the observed concentration of landslides than those computed for the MGRP factors alone. For a few zones, both maps produce high landslide susceptibilities that do not well reflect

  3. Landslide susceptibility mapping in the coastal region in the State of São Paulo, Brazil

    NASA Astrophysics Data System (ADS)

    Alvala, R. C.; Camarinha, P. I.; Canavesi, V.

    2013-05-01

    , land use and pedology. As a result, we obtain 5 susceptibility classes: very low, low, medium, high and very high. To validate the methodology, there was overlapped the Landslides Susceptibility Map with real risk areas previously mapped, provided by the National Centre for Monitoring and Alert of Natural Disasters. This step is important especially to assess the methodology adherence to evaluate the classes that was mapped with high and very high susceptibility. The preliminary results indicate that over 70% of the the mapped risks areas are located into the classes more susceptible. We observed small inconsistencies that are related with spatial displacement of the various databases considered, which has different resolutions and scales. Therefore, the results indicated that the methodology is robust and showed the high vulnerability of the counties analyzed, which further highlights that the landslides susceptibility should be monitored carefully by the decision makers in order to prevent and minimize the natural disasters impact, so that provide better territorial planning.

  4. Regional geomorphic analysis and gis susceptibility mapping of landslides in the blue nile and the tekeze river basins of ethiopia

    NASA Astrophysics Data System (ADS)

    Ismail, Elamin Hassan Dai

    The Plateau region of Ethiopia lies within a seismically active continental extensional regime, which is being rapidly incised by the Blue Nile and the Tekeze Rivers. Extremely large landslides pose serious hazards in this highly populated region (>27 million), which is in the process of developing its hydrologic resources. This research sought to develop cost-effective methods to compile regional landslide inventory and landslide susceptibility maps, using geomorphic tools and GIS technologies. This work also sought to evaluate the relationships between landslide dams and knickpoints, caused by channel bed incision from those caused by slope failures, by utilizing identified knickpoints along 56 tributary channels across the study area. The study employed the weighted overlay technique to produce regional landslide susceptibility hazard maps, and for the first time, employing wind-driven and integrated rainfall/aspect rasters at various inclination to more realistically model the actual precipitation that is felt by hillsides of varying azimuth, shape, and height. Landslides greater than 500m long were tentatively identified on 1:200,000 topographic maps draped over 30m hill-shade generated ASTER GDEMv2. The mapping revealed different types of landslides, and also revealed a considerable number of old, dormant landslide features. The use of wind-driven rainfall with integrated rainfall and aspect rasters provided a much more detailed and asymmetric distribution of precipitation. Spatial distribution of the very high and high hazard areas, during the Kermit and Belg rainy seasons by a range of 0.38% for an inclination of 40o and 1.7% for inclinations on 60o, as compared to the traditional assumption of 90o vertical rainfall, without integration of a slope aspect raster.

  5. Relocation of a Problematic Segment of a Natural Gas Pipeline Using GIS-Based Landslide Susceptibility Mapping, Hendek (Turkey)

    NASA Astrophysics Data System (ADS)

    Cevik, Engin; Topal, Tamer

    A segment of natural gas pipeline constructed in 1997 to supply natural gas to a steel factory was broken due to a landslide with fire near Hendek, Turkey. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map of the corresponding segment. In this study, statistical methods namely statistical index (Wi) and weighting factor (WF) have been used with geographic information systems (GIS) by analyzing several intrinsic factors controlling the landslides to prepare landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides mainly occur in the unconsolidated to semi-consolidated clayey unit and regolith. Lithology, land use/land cover, elevation, slope, and distance to stream are found to be the important parameters for the study area whereas aspect is not. Nevertheless, the drainage density has a very low contribution. Based on the findings obtained in this study, an alternative route to be studied for detailed engineering geological investigations is proposed.

  6. Map Showing Susceptibility to Earthquake-Induced Landsliding, San Juan Metropolitan Area, Puerto Rico

    USGS Publications Warehouse

    Santiago, Marilyn; Larsen, Matthew C.

    2001-01-01

    Analysis of slope angle and rock type using a geographic information system indicates that about 68 percent of the San Juan metropolitan area has low to no susceptibility to earthquake-induced landslides. This is at least partly due to the fact that 45 percent of the San Juan metropolitan area is constructed on slopes of 3 degrees or less, which are too gentle for landslides to occur. The areas with the highest susceptibility to earthquake-induced landslides account for 6 percent of the surface area. Almost one-quarter (24 percent) of the San Juan metropolitan area is moderately susceptible to earthquake-induced landslides. These areas are mainly in the southern portions of the San Juan metropolitan area, where housing development pressures are currently high because of land availability and the esthetics of greenery and hillside views. The combination of new development and moderate earthquake-induced landslide susceptibility indicate that the southern portions of the San Juan metropolitan area are be at greatest risk.

  7. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

    NASA Astrophysics Data System (ADS)

    Yao, X.; Tham, L. G.; Dai, F. C.

    2008-11-01

    The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping.

  8. Comparison of two methods for landslide susceptibility mapping in the Champagne region (France) where viticultural activity is threatened by slope failure

    NASA Astrophysics Data System (ADS)

    van den Eeckhaut, M.; Marre, A.; Poesen, J.

    2009-04-01

    The vineyards of the Champagne region are planted on steep south-oriented cuesta fronts receiving a maximum of sun radiation. However, due to the location of the vineyards on steep hillslopes, the viticultural activity is threatened by slope failures. In this study we attempt to better understand the spatial variability of landslides by comparing two techniques for landslide susceptibility assessment in a 1120 km² study area in the Champagne Ardenne. The first landslide susceptibility map was derived from an heuristic model adopted from the Bureau de Recherches Géologiques et Minières, geomorphologists of Reims University and the Comité Interprofessionnel du Vin de Champagne. In this qualitative model expert knowledge of the Champagne region was used to assign weights to all slope classes and lithologies located in the area. The second landslide susceptibility map was developed in this study by the application of a statistical model, logistic regression, to a calibration dataset of ‘old' (Holocene) landslides. This map was successfully evaluated using ROC curves and κ values. Both models indicate that present and future landslides are mainly controlled by slope gradient and lithology. However, the comparison of the two landslide susceptibility maps through (1) evaluation with the location of mapped ‘old' (Holocene) landslides and through (2) temporal validation with spatial data of ‘recent' (between forty and ten years old) and ‘very recent' (less than ten years old) landslides showed that the statistical model produced in this study allowed better prediction of sites already affected by landslides. In total the statistically-derived susceptibility map succeeded in correctly classifying 83.2% of the ‘old' and 84.0% of the ‘recent' and ‘very recent' landslides. The heuristic model on the other hand classified only 50.6% of the ‘old' and 58.5% of the ‘recent' and ‘very recent' landslides correctly as unstable. Taking into account the

  9. A GIS-based susceptibility map for landslides at the Franconian Alb, Germany

    NASA Astrophysics Data System (ADS)

    Jaeger, Daniel; Wilde, Martina; Lorenz, Michael; Terhorst, Birgit; Neuhäuser, Bettina; Damm, Bodo; Bemm, Stefan

    2014-05-01

    In general, slopes of cuesta scarps like the Franconian Alb are highly prone to slide activity due to susceptible geological and geomorphological conditions. The geological setting with alternating permeable and non-permeable bedrock results in the characteristic cuesta landforms of almost flat backslopes and steeper front slopes. Furthermore, this bipartite structure leads to a strong disposition for mass movements. The slopes of the study area near the town of Ebermannstadt are affected by different types of mass movements, such as topples, slides, lateral spreads and flows, either in single or in combined occurrence. In the years 1625, 1957, 1961 and 1979, four large landslides took place in the area of Ebermannstadt, reaching close to the town limits and causing major destructions to traffic facilities. In the study area, slopes are covered by debris and slide masses, thus they are prone to remobilization and further mass movements. In order to assess hazardous areas, a GIS-based susceptibility modelling was generated for the study area. The susceptibtibility modeling was processed with the slope stability model SINMAP (Stability Index Mapping), developed by TARBOTON (1997) and PACK et al. (1999). As SINMAP was particularly designed to model shallow translational slides, it should be well designed for describing the conditions of the study area in a sufficient way. SINMAP is based on the "infinite slope stability model" by HAMMONT et al. (1992) and MONTGOMERY & DIETRICH (1994), which focuses on the relation of stabilizing (cohesiveness, friction angle) and destabilizing (gravitation) factors on a plain surface. By adding a slope gradient, as well as soil mechanical and climatical data, indices of slope stabilities are calculated. For a more detailed modeling of the slope conditions, SINMAP computes different "calibration regions", which merge similar parameters of soil, land-use, vegetation, and geology. Due to the fact that vegetation, land-use, and soils only

  10. A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines)

    NASA Astrophysics Data System (ADS)

    Clerici, Aldo; Perego, Susanna; Tellini, Claudio; Vescovi, Paolo

    2006-08-01

    Among the many GIS based multivariate statistical methods for landslide susceptibility zonation, the so called “Conditional Analysis method” holds a special place for its conceptual simplicity. In fact, in this method landslide susceptibility is simply expressed as landslide density in correspondence with different combinations of instability-factor classes. To overcome the operational complexity connected to the long, tedious and error prone sequence of commands required by the procedure, a shell script mainly based on the GRASS GIS was created. The script, starting from a landslide inventory map and a number of factor maps, automatically carries out the whole procedure resulting in the construction of a map with five landslide susceptibility classes. A validation procedure allows to assess the reliability of the resulting model, while the simple mean deviation of the density values in the factor class combinations, helps to evaluate the goodness of landslide density distribution. The procedure was applied to a relatively small basin (167 km2) in the Italian Northern Apennines considering three landslide types, namely rotational slides, flows and complex landslides, for a total of 1,137 landslides, and five factors, namely lithology, slope angle and aspect, elevation and slope/bedding relations. The analysis of the resulting 31 different models obtained combining the five factors, confirms the role of lithology, slope angle and slope/bedding relations in influencing slope stability.

  11. Effect of suction-dependent soil deformability on landslide susceptibility maps

    NASA Astrophysics Data System (ADS)

    Lizarraga, Jose J.; Buscarnera, Giuseppe; Frattini, Paolo; Crosta, Giovanni B.

    2016-04-01

    This contribution presents a physically-based, spatially-distributed model for shallow landslides promoted by rainfall infiltration. The model features a set of Factor of Safety values aimed to capture different failure mechanisms, namely frictional slips with limited mobility and flowslide events associated with the liquefaction of the considered soils. Indices of failure associated with these two modes of instability have been derived from unsaturated soil stability principles. In particular, the propensity to wetting-induced collapse of unsaturated soils is quantified through the introduction of a rigid-plastic model with suction-dependent yielding and strength properties. The model is combined with an analytical approach (TRIGRS) to track the spatio-temporal evolution of soil suction in slopes subjected to transient infiltration. The model has been tested to reply the triggering of shallow landslides in pyroclastic deposits in Sarno (1998, Campania Region, Southern Italy). It is shown that suction-dependent mechanical properties, such as soil deformability, have important effects on the predicted landslide susceptibility scenarios, resulting on computed unstable zones that may encompass a wide range of slope inclinations, saturation levels, and depths. Such preliminary results suggest that the proposed methodology offers an alternative mechanistic interpretation to the variability in behavior of rainfall-induced landslides. Differently to standard methods the explanation to this variability is based on suction-dependent soil behavior characteristics.

  12. Logistic regression and artificial neural network models for mapping of regional-scale landslide susceptibility in volcanic mountains of West Java (Indonesia)

    NASA Astrophysics Data System (ADS)

    Ngadisih, Bhandary, Netra P.; Yatabe, Ryuichi; Dahal, Ranjan K.

    2016-05-01

    West Java Province is the most landslide risky area in Indonesia owing to extreme geo-morphological conditions, climatic conditions and densely populated settlements with immense completed and ongoing development activities. So, a landslide susceptibility map at regional scale in this province is a fundamental tool for risk management and land-use planning. Logistic regression and Artificial Neural Network (ANN) models are the most frequently used tools for landslide susceptibility assessment, mainly because they are capable of handling the nature of landslide data. The main objective of this study is to apply logistic regression and ANN models and compare their performance for landslide susceptibility mapping in volcanic mountains of West Java Province. In addition, the model application is proposed to identify the most contributing factors to landslide events in the study area. The spatial database built in GIS platform consists of landslide inventory, four topographical parameters (slope, aspect, relief, distance to river), three geological parameters (distance to volcano crater, distance to thrust and fault, geological formation), and two anthropogenic parameters (distance to road, land use). The logistic regression model in this study revealed that slope, geological formations, distance to road and distance to volcano are the most influential factors of landslide events while, the ANN model revealed that distance to volcano crater, geological formation, distance to road, and land-use are the most important causal factors of landslides in the study area. Moreover, an evaluation of the model showed that the ANN model has a higher accuracy than the logistic regression model.

  13. Integration of landslide susceptibility products in the environmental plans

    NASA Astrophysics Data System (ADS)

    Fiorucci, Federica; Reichenbach, Paola; Rossi, Mauro; Cardinali, Mauro; Guzzetti, Fausto

    2015-04-01

    Landslides are one of the most destructive natural hazard that causes damages to urban area worldwide. The knowledge of where a landslide could occur is essential for the strategic management of the territory and for a good urban planning . In this contest landslide susceptibility zoning (LSZ) is crucial to provide information on the degree to which an area can be affected by future slope movements. Despite landslide susceptibility maps have been prepared extensively during the last decades, there are few examples of application is in the environmental plans (EP). In this work we present a proposal for the integration of the landslide inventory map with the following landslide susceptibility products: (i) landslide susceptibility zonation , (ii) the associated error map and (iii) the susceptibility uncertainty map. Moreover we proposed to incorporate detailed morphological studies for the evaluation of landslide risk associated to local parceling plan. The integration of all this information is crucial for the management of landslide risk in urban expansions forecasts. Municipality, province and regional administration are often not able to support the costs of landslide risk evaluation for extensive areas but should concentrate their financial resources to specific hazardous and unsafe situations defined by the result of the integration of landslide susceptibility products. Zonation and detail morphological analysis should be performed taking into account the existing laws and regulations, and could become a starting point to discuss new regulations for the landslide risk management.

  14. Landslide susceptibility assessment based on different rainfall-triggered landslide events

    NASA Astrophysics Data System (ADS)

    Oliveira, Sergio C.; Zêzere, José L.; Garcia, Ricardo A. C.

    2015-04-01

    The availability of several complete landslide event inventory maps associated to different rainfall conditions is uncommon for a single region. Nevertheless, it could contribute to a better recognition of the total extent and magnitude of landslides under specific triggering conditions. The motivation of the present work is related with three problems that should be solved: (i) How representative of the landslide activity and distribution in a study area can be a landslide event? (ii) How reliable can be a landslide event-based susceptibility map? (iii) How adequate can be a landslide event-based map to independent validate a landslide susceptibility map? To answer the previous questions two independent rainfall-triggered landslide event inventories, available for the Grande da Pipa river basin, north of Lisbon, Portugal, are used to assess landslide susceptibility at the regional scale. The 1983 landslide event was triggered by a single day of intense precipitation and originates 220 landslides that affected 0.15% (161413 m2) of the study area. The 2010 landslide event was associated with a long lasting rainfall period up to 90 days and generated 254 landslides that affected 0.46 % (511820 m2) of the study area. The two landslide-event inventories are compared according the following topics: (i) the landslide typology; (ii) the landslide morphometric characteristics; (iii) the analysis of the landslide predisposing factors; (iv) the assessment of magnitude-frequency relationships; (v) the predictive capability of landslide event-based susceptibility models. For the last topic, the Information Value method is used to establish the statistical relationships between the dependent landslide inventory map and the data-set of independent predisposing factors. Two landslide event-based susceptibility maps are produced using independently the landslide inventories of 1983 and 2010. The independent validation is obtained by crossing each landside susceptibility map with

  15. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

    NASA Astrophysics Data System (ADS)

    Pradhan, Biswajeet

    2013-02-01

    The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

  16. An integrated remote sensing approach for landslide susceptibly mapping at the volcanic islands of Vulcano and Lipari (Eolian Island, Italy)

    NASA Astrophysics Data System (ADS)

    Scifoni, Silvia; Palenzuela Baena, José A.; Marsella, Maria; Pepe, Susi; Sansosti, Eugenio; Solaro, Giuseppe; Tizzani, Piero

    2015-10-01

    Volcanic Island can be affected by instability phenomena such as landslide and partial collapse events, even in quiescent period. Starting from data collected by an aerial laser scanning survey at cm-level accuracy), a GIS based approach was implemented in order to perform a landslide-susceptibility analysis. The results of this analysis were compared and integrated with data derived from Differential Synthetic Aperture Radar Interferometry (DinSAR) analysis able to identify the most active areas and quantify the on-going deformation processes. The analysis is focused on the on the active volcanic edifice of Vulcano Island and in some areas of Lipari island, both include in the Eaolian Islands in Sicily (Italy). The developed approach represent a step-forward for the compilation of hazard maps furnishing in an overall contest, updated and georeferenced quantitative data, describing the morphology and the present behaviour of the slopes in the area of investigation.

  17. On the influence of temporal change on the validity of landslide susceptibility maps in an alpine catchment, Switzerland

    NASA Astrophysics Data System (ADS)

    Meusburger, K.; Alewell, C.

    2009-04-01

    Global change (as a combination of climate and land use change) poses a risk to stability of alpine soils, and may enhance landslide hazard. The occurrence of landslides depends on static catchment characteristics (e.g. geology, topography etc.), as well as triggering factors that are variable in time (dynamic factors), such as event characteristics and land use. However, in literature the effects of temporal change are still discussed controversially and most statistical landslide prediction models rely on static catchment characteristics alone. In this study, we aim to assess the additional influence of dynamic factors on landslide susceptibility and on the validity of commonly used statistical landslide models. The Urseren Valley (Central Swiss Alps) was chosen as study area due to the evidence of climate and land use change. To assess the influence of catchment characteristics on landslide susceptibility, we set up a logistic regression model using 20 static predictor variables. The additional impact of dynamic risk factors was evaluated with historic data (aerial photographs and meteorological time series). We found that geology, slope and stream density were the most significant static predictors and could explain 70% of the landslide variation. However, the area affected by landslides increased by 92% from 1959 to 2004, which highlights the crucial role of dynamic landslide triggering factors. Furthermore, more recent landslides (since 2000) could only in part be predicted, which confirmed our proposed hypothesis that the validity of statistical hazard models may worsen over time. Discrepancies between predicted susceptibility and observed landslides mainly occurred in areas that have undergone land use changes. Consequently, slopes, that have formerly been classified as only "medium" landslide susceptibility may nonetheless have a high probability to fail under changed management. Spatial information of the impact of land use on landslide susceptibility

  18. Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models

    NASA Astrophysics Data System (ADS)

    Mohammady, Majid; Pourghasemi, Hamid Reza; Pradhan, Biswajeet

    2012-11-01

    The purpose of the present study is to investigate the landslide susceptibility mapping using three statistical models such as frequency ratio, Dempster-Shafer, and weights-of-evidence at southern part of Golestan province. At first, landslide locations were identified from the interpretation of aerial photographs, and field surveys. A total of 392 landslides were mapped in GIS out of which 275 (70%) locations were chosen for the modeling purpose and the remaining 118 (30%) cases were used for the model validation. Then layers of the landslide conditioning factors were prepared. The relationship between the conditioning factors and the landslides were calculated using three models. For verification, the results were compared with landslides which were not used during the training of the models. Subsequently, the ROC (Receiver operating characteristic) curves and area under the curves (AUC) for three landslide susceptibility maps were constructed and the areas under curves were assessed for validation purpose. The validation results showed that the area under the curve for frequency ratio, Dempster-Shafer, and weights-of-evidence models are 0.8013 (80.13%), 0.7832 (78.32%), and 0.7460 (74.60%) with prediction accuracy 0.7516 (75%), 0.7396 (73%), and 0.6998 (69%) respectively. The results revealed that frequency ratio model has higher AUC than the other models. In general, all the three models produced reasonable accuracy. The resultant maps would be useful for general land use planning.

  19. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

    PubMed Central

    Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

    2016-01-01

    In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

  20. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

    PubMed

    Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

    2016-01-01

    In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

  1. Susceptibility mapping and estimation of rainfall threshold using space based input for assessment of landslide hazard in Guwahati city in North East India

    NASA Astrophysics Data System (ADS)

    Bhusan, K.; Kundu, S. S.; Goswami, K.; Sudhakar, S.

    2014-11-01

    Slopes are the most common landforms in North Eastern Region (NER) of India and because of its relatively immature topography, active tectonics, and intense rainfall activities; the region is susceptible to landslide incidences. The scenario is further aggravated due to unscientific human activities leading to destabilization of slopes. Guwahati, the capital city of Assam also experiences similar hazardous situation especially during monsoon season thus demanding a systematic study towards landslide risk reduction. A systematic assessment of landslide hazard requires understanding of two components, "where" and "when" that landslides may occur. Presently no such system exists for Guwahati city due to lack of landslide inventory data, high resolution thematic maps, DEM, sparse rain gauge network, etc. The present study elucidates the potential of space-based inputs in addressing the problem in absence of field-based observing networks. First, Landslide susceptibility map in 1 : 10,000 scale was derived by integrating geospatial datasets interpreted from high resolution satellite data. Secondly, the rainfall threshold for dynamic triggering of landslide was estimated using rainfall estimates from Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis. The 3B41RT data for 1 hourly rainfall estimates were used to make Intensity-Duration plot. Critical rainfall was estimated for every incidence by analysing cumulative rainfall leading to a landslide for total of 19 incidences and an empirical rainfall intensity-duration threshold for triggering shallow debris slides was developed (Intensity = 5.9 Duration-0.479).

  2. Landslide risk mapping and modeling in China

    NASA Astrophysics Data System (ADS)

    Li, W.; Hong, Y.

    2015-12-01

    Under circumstances of global climate change, tectonic stress and human effect, landslides are among the most frequent and severely widespread natural hazards on Earth, as demonstrated in the World Atlas of Natural Hazards (McGuire et al., 2004). Every year, landslide activities cause serious economic loss as well as casualties (Róbert et al., 2005). How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China (Hong, 2007). Since the 1950s, landslide events have been recorded in the statistical yearbooks, newspapers, and monographs in China. As disasters have been increasingly concerned by the government and the public, information about landslide events is becoming available from online news reports (Liu et al., 2012).This study presents multi-scale landslide risk mapping and modeling in China. At the national scale, based on historical data and practical experiences, we carry out landslide susceptibility and risk mapping by adopting a statistical approach and pattern recognition methods to construct empirical models. Over the identified landslide hot-spot areas, we further evaluate the slope-stability for each individual site (Sidle and Hirotaka, 2006), with the ultimate goal to set up a space-time multi-scale coupling system of Landslide risk mapping and modeling for landslide hazard monitoring and early warning.

  3. ALISSA: Abridged Landslide Inventory of Spain for synoptic Susceptibility Assessment

    NASA Astrophysics Data System (ADS)

    Hervás, Javier

    2014-05-01

    ALISSA is a concise although fairly spatially distributed, small-scale landslide inventory covering peninsular Spain and the Balearic Islands. The inventory was primarily aimed to provide point locations of undifferentiated landslides to calibrate and validate the susceptibility model used to produce the first version of the 1 km cell size (approximately 1:1 million scale), generic European Landslide Susceptibility Map (ELSUS 1000 v1) in 2013. The map is the result of collaborative work between BGR (Hanover, Germany), JRC (Ispra, Italy), CNRS-IPGS (Strasbourg, France) and CNR-IRPI (Perugia, Italy), with help from many mapping organisations throughout Europe which provided landslide locations, in support to the EU Thematic Strategy for Soil Protection regarding the identification of landslide priority areas in Europe. This limited landslide inventory was needed to complete pan-European landslide susceptibility assessment since no nationwide inventory fairly representing landslide occurrence in Spain was published. ALISSA is compiled from published documents, including mainly scientific literature, technical reports, and geological, geotechnical and geomorphological maps, complemented with media news for very recent landslides not yet published in the literature and unpublished work by the author in some areas. The spatial dataset (inventory map) consists of point features corresponding to landslide centroids, which have been crosschecked, validated and geo-referenced on Google Earth to a location accuracy generally within 100 m, which for the smaller landslides is mainly dependent on Google Earth spatial accuracy. In areas where Google Earth imagery does not provide suitable spatial resolution landslide location validation is performed using web-based 2-D satellite/aerial imagery viewers available in the country such as Iberpix or SigPac, or even through interpretation of Panoramio photos on Google Earth. Landslide type, when documented, and locations are thus

  4. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

    NASA Astrophysics Data System (ADS)

    Ozdemir, Adnan; Altural, Tolga

    2013-03-01

    This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model's relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are

  5. Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

    NASA Astrophysics Data System (ADS)

    Fressard, M.; Thiery, Y.; Maquaire, O.

    2014-03-01

    This paper aims at assessing the impact of the data set quality for landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted on the Pays d'Auge plateau (Normandy, France) with a scale objective of 1 / 10 000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, and geomorphological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlight that only high-quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formation maps) can predict a satisfying proportion of landslides in the study area.

  6. Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

    NASA Astrophysics Data System (ADS)

    Fressard, M.; Thiery, Y.; Maquaire, O.

    2013-04-01

    The objective of this paper is to assess the impact of the datasets quality for the landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted in the Pays d'Auge plateau (Normandy, France) with a scale objective of 1/10000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, geomophological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlights that only high quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formations maps) can predict a satisfying proportion of landslides on the study area.

  7. Application of LiDAR Date to Assess the Landslide Susceptibility Map Using Weights of Evidence Method - AN Example from Podhale Region (southern Poland)

    NASA Astrophysics Data System (ADS)

    Kamiński, Mirosław

    2016-06-01

    Podhale is a region in southern Poland, which is the northernmost part of the Central Carpathian Mountains. It is characterized by the presence of a large number of landslides that threaten the local infrastructure. In an article presents application of LiDAR data and geostatistical methods to assess landslides susceptibility map. Landslide inventory map were performed using LiDAR data and field work. The Weights of Evidence method was applied to assess landslides susceptibility map. Used factors for modeling: slope gradient, slope aspect, elevation, drainage density, faults density, lithology and curvature. All maps were subdivided into different classes. Then were converted to grid format in the ArcGIS 10.0. The conditional independence test was carried out to determine factors that are conditionally independent of each other with landslides. As a result, chi-square test for further GIS analysis used only five factors: slope gradient, slope aspect, elevation, drainage density and lithology. The final prediction results, it is concluded that the susceptibility map gives useful information both on present instability of the area and its possible future evolution in agreement with the morphological evolution of the area.

  8. Susceptibility map of triggering landslides due to rainfall forecast as a part of innovative inspire compliant cloud based infrastructure - InGeoCloudS

    NASA Astrophysics Data System (ADS)

    Šinigoj, Jasna; Podboj, Martin; Komac, Marko, ,, dr.; Požar, Mitja; Krivic, Matija; Jemec-Auflič, Mateja, ,, dr.

    2014-05-01

    Slovenian area is relatively highly exposed to slope mass movement processes due to its geological and morphological settings. Intense short and less intense, but long duration rainfall events are primary causes of shallow landslides' occurrence that are predominant type of slope mass movements in Slovenia. Past studies show that the total proportion of exposed area to slope mass movement processes is roughly one quarter of Slovenian territory. Although landslides are very locally related problem, the 15-years average landslide damage represents 7.6% of total damages due to disasters in Slovenia (and 0.03% of GDP). In the past 15 years more than 10 people have been killed in landslide events. Yet, consequences (and the loss of lives) could be mitigated, in some cases even prevented with a reliable near real-time landslide hazard forecast system that would continuously draw information from three data/model pillars: the precipitation forecast model, the landslide susceptibility model and the rainfall triggering values for landslide occurrence. Consequentially a project has been set up by the Administration of the Republic of Slovenia for civil protection and disaster relief and the Ministry of Defense of the Republic of Slovenia to tackle the minimization of the landslide hazard potential with a goal to develop a near real-time online publicly available regional landslide forecasting system. The system is fully operational from September 2013, yet due to the testing phase of hazard model prediction the results need to be treated with care and within their reliability. The system is designed and built in a cloud infrastructure (InGeoCloudS) and provides an efficient, flexible scalable and in all ways innovative infrastructure for Geodata services. It is fully automated systems which automatically pushes data in to the cloud and execute GIS modelling for calculating the landslide susceptibility map and creating WMS or WFS map services using open-source tools. The

  9. Landslide Susceptibility Statistical Methods: A Critical and Systematic Literature Review

    NASA Astrophysics Data System (ADS)

    Mihir, Monika; Malamud, Bruce; Rossi, Mauro; Reichenbach, Paola; Ardizzone, Francesca

    2014-05-01

    Landslide susceptibility assessment, the subject of this systematic review, is aimed at understanding the spatial probability of slope failures under a set of geomorphological and environmental conditions. It is estimated that about 375 landslides that occur globally each year are fatal, with around 4600 people killed per year. Past studies have brought out the increasing cost of landslide damages which primarily can be attributed to human occupation and increased human activities in the vulnerable environments. Many scientists, to evaluate and reduce landslide risk, have made an effort to efficiently map landslide susceptibility using different statistical methods. In this paper, we do a critical and systematic landslide susceptibility literature review, in terms of the different statistical methods used. For each of a broad set of studies reviewed we note: (i) study geography region and areal extent, (ii) landslide types, (iii) inventory type and temporal period covered, (iv) mapping technique (v) thematic variables used (vi) statistical models, (vii) assessment of model skill, (viii) uncertainty assessment methods, (ix) validation methods. We then pulled out broad trends within our review of landslide susceptibility, particularly regarding the statistical methods. We found that the most common statistical methods used in the study of landslide susceptibility include logistic regression, artificial neural network, discriminant analysis and weight of evidence. Although most of the studies we reviewed assessed the model skill, very few assessed model uncertainty. In terms of geographic extent, the largest number of landslide susceptibility zonations were in Turkey, Korea, Spain, Italy and Malaysia. However, there are also many landslides and fatalities in other localities, particularly India, China, Philippines, Nepal and Indonesia, Guatemala, and Pakistan, where there are much fewer landslide susceptibility studies available in the peer-review literature. This

  10. Linking landslide susceptibility to sediment yield in the Romanian Carpathians

    NASA Astrophysics Data System (ADS)

    Broeckx, Jente; Vanmaercke, Matthias; Bǎlteanu, Dan; Chendeş, Viorel; Sima, Mihaela; Enciu, Petru; Poesen, Jean

    2016-04-01

    Recent studies revealed the importance of seismic activity in explaining regional patterns of catchment sediment yield (SY). This relation is often explained by the fact that seismic activity induces landslides that contribute to SY. Nevertheless, only a few studies focused on the effects of landslides on SY and even fewer studies have explored the potential of landslide susceptibility as a predictor for SY. The objective of this study is therefore to explore the potential of landslide susceptibility maps to explain the spatial variation of SY in the Romanian Carpathians, a region with moderate to high seismicity. 133 catchments, covering 63% of Romania, for which SY was measured during a period of at least 10 years and for which SY was not significantly affected by upstream reservoirs, were compiled and selected. 78 of these catchments were 'less disturbed', being covered for at least 50% by forest and semi-natural areas and confined to the Carpathian mountains. Landslide susceptibility in each catchment was assessed, using an earlier published state of the art landslide susceptibility map of Romania. Mean landslide susceptibility for each catchment shows a highly significant correlation with SY (r² = 0.44). This indicates that landslides are an important contributor to SY in Romania and suggests that regional and national landslide susceptibility maps can indeed be a useful tool to predict SY. Nevertheless, the susceptibility map did not explain much more of the observed variance in SY than some other individual catchment characteristics such as seismicity (r² = 0.40) and lithology (r² = 0.33). Also taking into account the spatial patterns of landslide susceptibility within the catchment did not significantly improve the observed correlations. Surprisingly, topography showed a nonsignificant correlation with SY, which can be attributed to the overwhelming effect of seismicity and lithology. Overall, our results suggest that seismicity is indeed a highly

  11. An overview of a GIS method for mapping landslides and assessing landslide susceptibility in the Río La Carbonera watershed, on the SE flank of Pico de Orizaba Volcano, Mexico.

    NASA Astrophysics Data System (ADS)

    Legorreta Paulin, G.; Bursik, M. I.; Contreras, T.

    2015-12-01

    This poster provides an overview of the on-going research project (Grant PAPIIT # IN102115) from the Institute of Geography at the National Autonomous University of Mexico (UNAM) that seeks to conduct a multi-temporal landslide inventory, produce a landslide susceptibility map, and estimate sediment production by using Geographic Information Systems (GIS). The Río La Carbonera watershed on the southeastern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The catchment covers 71.9 km2 with elevations ranging from 1224 to 3643 m a.s.l. and hillslopes between <5° and 68°. The stream system of Río La Carbonera catchment erodes Tertiary and Quaternary lavas, pyroclastic flows, and fall deposits. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The methodology encompasses three main stages of analysis to assess landslide hazards: Stage 1 builds a historic landslide inventory. In the study area, an inventory of more than 200 landslides is created from multi-temporal aerial-photo-interpretation and local field surveys to assess landslide distribution. All landslides were digitized into a geographic information system (GIS), and a spatial geo-database of landslides was constructed from standardized GIS datasets. Stage 2 calculates the susceptibility for the watershed. During this stage, (SINMAP using default values) is evaluated. Stage 3 Estimate the potential total material delivered to the main stream drainage channel by all landslides in the catchment. Detailed geometric measurements of individual landslides visited during the field work will be carried out to obtain the landslide area and volume. These measurements revealed an empirical relationship between area and volume that took the form of a power law. This relationship will be used to estimate the potential volume of material delivered to the

  12. A multi-annual landslide inventory for the assessment of shallow landslide susceptibility - Two test cases in Vorarlberg, Austria

    NASA Astrophysics Data System (ADS)

    Zieher, Thomas; Perzl, Frank; Rössel, Monika; Rutzinger, Martin; Meißl, Gertraud; Markart, Gerhard; Geitner, Clemens

    2016-04-01

    Geomorphological landslide inventories provide crucial input data for any study on the assessment of landslide susceptibility, hazard or risk. Several approaches for assessing landslide susceptibility have been proposed to identify areas particularly vulnerable to this natural hazard. What they have in common is the need for data of observed landslides. Therefore the first step of any study on landslide susceptibility is usually the compilation of a geomorphological landslide inventory using a geographical information system. Recent research has proved the feasibility of orthophoto interpretation for the preparation of an inventory aimed at the delineation of landslides with the use of distinctive signs in the imagery data. In this study a multi-annual landslide inventory focusing on shallow landslides (i.e. translational soil slides of 0-2 m in depth) was compiled for two study areas in Vorarlberg (Austria) from the interpretation of nine orthophoto series. In addition, derivatives of two generations of airborne laser scanning data aided the mapping procedure. Landslide scar areas were delineated on the basis of a high-resolution differential digital terrain model. The derivation of landslide volumes, depths and depth-to-length ratios are discussed. Results show that most mapped landslides meet the definition of a shallow landslide. The inventory therefore provides the data basis for the assessment of shallow landslide susceptibility and allows for the application of various modelling techniques.

  13. Landslide susceptibility-certainty mapping by a multi-method approach: A case study in the Tertiary basin of Puy-en-Velay (Massif central, France)

    NASA Astrophysics Data System (ADS)

    Poiraud, Alexandre

    2014-07-01

    The present study discusses the use of integrated variables along with a combination of multi-method forecasts for landslide susceptibility mapping. The study area is located in the south-eastern French Massif central, a volcanic region containing Tertiary sedimentary materials that are prone to landslides. The flowage-type landslides within the study area are very slow-moving phenomena which affect the infrastructures and human settlements. The modelling process is based on a training set of landslides (70% of total landslides) and a set of controlling factor (slope, lithology, surficial formation, the topographic wetness index, the topographic position index, distance to thalweg, and aspect). We create a composite variable (or integrated variable), corresponding to the union of geology and surficial formation, in order to avoid the conditional dependence between these two variables and to build a geotechnical variable. We use five classical modelling methods (index, weight-of-evidence, logistic regression, decision tree, and unique condition unit) with the same training set but with different architectures of input data made up of controlling factors. All the models are tested with a validation group (30% of total landslides), using the Area Under the Receiver Operating Characteristic curve (AUC) to quantify their predictive performance. We finally select a single “best” model for each method. However, these five models are all equivalent in quality, despite their differences in detail, so no single model stands out against another. Finally, we combine the five models into a unique susceptibility map with a calculation of median susceptibility class. The final AUC value of this combined map is better than that for a single model (except for Unique Condition Unit), and we can evaluate the certainty of the susceptibility class pixel by pixel. In agreement with the sparse literature on this topic, we conclude that i) integrated variables increase the performance

  14. The influence of different type of landslide for the preparation of statistical multivariate landslide susceptibility models

    NASA Astrophysics Data System (ADS)

    Ghosh, S.; Reichenbach, P.; Rossi, M.; Guzzetti, F.; van Westen, C.; Carranza, E. J. M.

    2009-04-01

    The results of multivariate landslide statistical susceptibility models are highly sensitive to the type of statistical and spatial distribution of the mass movement used as grouping variable, and to the type of geofactors used as explanatory variables. Different classification of landslide data set could result in different model performance and validation fit. Exploiting a discriminant analysis (DA) and a logistic regression (LR) models, we prepared different landslide susceptibility zonation for a study area around Kurseong town in the Darjeeling Himalaya region, Eastern India. To prepare the models, we used as training data set, 342 shallow translational rock slides and 168 shallow translational debris slides, which occurred between 1968 and 2003. To validate the models we used a different set of landslide that occurred between 2004 and 2007. 62 relevant factors including morphometric and geo-environmental parameters were used as explanatory variables. We present and discuss the performance and the validation results of the landslide susceptibility zonation prepared with the two different statistical multivariate models using as grouping variables - the rock slides data set, the debris slides data set and the two type of landslides data set together. The discriminate analysis performs better than the logistic regression and this is probably due to the: a) lack of coherence in the selected training data set and the corresponding explanatory variables; b) landslide type classification problems; c) frequency distribution of landslide/no-landslide mapping units.

  15. Landsat applied to landslide mapping

    NASA Technical Reports Server (NTRS)

    Sauchyn, D. J.; Trench, N. R.

    1978-01-01

    A variety of features characteristic of rotational landslides may be identified on Landsat imagery. These include tonal mottling, tonal banding, major and secondary scarps, and ponds. Pseudostereoscopic viewing of 9 by 9 in. transparencies was useful for the detailed identification of landslides, whereas 1:250,000 prints enlarged from 70 mm negatives were most suitable for regional analysis. Band 7 is the most useful band for landslide recognition, due to accentuation of ponds and shadows. Examination of both bands 7 and 5, including vegetation information, was found to be most suitable. Although, given optimum terrain conditions, some landslides in Colorado may be recognized, many smaller landslides are not identifiable. Consequently, Landsat is not recommended for detailed regional mapping, or for use in areas similar to Colorado, where alternative (aircraft) imagery is available. However, Landsat may prove useful for preliminary landslide mapping in relatively unknown areas.

  16. Landslide susceptibility revealed by LIDAR imagery and historical records, Seattle, Washington

    USGS Publications Warehouse

    Schulz, W.H.

    2007-01-01

    Light detection and ranging (LIDAR) data were used to visually map landslides, headscarps, and denuded slopes in Seattle, Washington. Four times more landslides were mapped than by previous efforts that used aerial photographs. The mapped landforms (landslides, headscarps, and denuded slopes) were created by many individual landslides. The spatial distribution of mapped landforms and 1308 historical landslides show that historical landslide activity has been concentrated on the mapped landforms, and that most of the landslide activity that created the landforms was prehistoric. Thus, the spatial densities of historical landslides on the landforms provide approximations of the landforms' relative susceptibilities to future landsliding. Historical landslide characteristics appear to be closely related to landform type so relative susceptibilities were determined for landslides with various characteristics. No strong relations were identified between stratigraphy and landslide occurrence; however, landslide characteristics and slope morphology appear to be related to stratigraphic conditions. Human activity is responsible for causing about 80% of historical Seattle landslides. The distribution of mapped landforms and human-caused landslides suggests the probable characteristics of future human-caused landslides on each of the landforms. The distribution of mapped landforms and historical landslides suggests that erosion of slope-toes by surface water has been a necessary condition for causing Seattle landslides. Human activity has largely arrested this erosion, which implies that landslide activity will decrease with time as hillsides naturally stabilize. However, evaluation of glacial-age analogs of areas of recent slope-toe erosion suggests that landslide activity in Seattle will continue for the foreseeable future. ?? 2006 Elsevier B.V. All rights reserved.

  17. Landslide susceptibility in the Tully Valley area, Finger Lakes region, New York

    USGS Publications Warehouse

    Jager, Stefan; Wieczorek, Gerald E.

    1994-01-01

    As a consequence of a large landslide in the Tully Valley, Onondaga County, New York, an investigation was undertaken to determine the factors responsible for the landslide in order to develop a model for regional landslide susceptibility. The April 27, 1993 Tully Valley landslide occurred within glacial lake clays overlain by till and colluvium on gentle slopes of 9-12 degrees. The landslide was triggered by extreme climatic events of prolonged heavy rainfall combined with rapid melting of a winter snowpack. A photoinventory and field checking of landslides within a 415 km2 study area, including the Tully Valley, revealed small recently-active landslides and other large dormant prehistoric landslides, probably Pleistocene in age. Similar to the larger Tully Valley landslide, the smaller recently-active landslides occurred in red, glacial lake clays very likely triggered by seasonal rainfall. The large dormant landslides have been stable for long periods as evidenced by slope denudational processes that have modified the landslides. These old and ancient landslides correspond with proglacial lake levels during the Pleistocene, suggesting that either inundation or rapid drainage was responsible for triggering these landslides. A logistic regression analysis was performed within a Geographic Information System (GIS) environment to develop a model of landslide susceptibility for the Tully Valley study area. Presence of glacial clays, slope angle, and glacial lake levels were used as explanatory variables for landslide incidence. The spatial probability of landsliding, categorized as low, moderate and high, is portrayed within 90-m square cells on the susceptibility map.

  18. Spatially and temporally distributed modeling of landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Gorsevski, Pece V.; Gessler, Paul E.; Boll, Jan; Elliot, William J.; Foltz, Randy B.

    2006-10-01

    Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km 2 Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly

  19. Susceptibility assessment of earthquake-triggered landslide in Wenchuan

    NASA Astrophysics Data System (ADS)

    Tao, Shu; Hu, Deyong; Zhao, Wenji

    2010-11-01

    The Ms 8.0 Wenchuan earthquake, occurred on 12 May 2008 in Sichuan Province, collapsed a great many houses and injured hundreds of thousands of people. Undoubtedly, it can be predicted that secondary earthquake landslides will draw much attention during a long time after the earthquake due to the severe geological hazard. In order to remove threat from the secondary disasters effectively, this study used techniques of remote sensing and GIS to generate susceptibility maps, taking the case of Wenchuan County. Seven factors controlling landslide occurrence have been taken account into the susceptibility assessment, including elevation, slop, aspect, lithology, seismic intensity, distance to faults and rivers. According to the probability that predicts the possibility of landslide occurrence calculated applying information value method and logistic regression separately, the study zone was ultimately categorized into five classes, specifically, "extremely low", "low", "moderate", "high" and "very high". These results have been proved to reflect closely the spatial distributions of landslides in the study area.

  20. Susceptibility assessment of earthquake-triggered landslide in Wenchuan

    NASA Astrophysics Data System (ADS)

    Tao, Shu; Hu, Deyong; Zhao, Wenji

    2009-09-01

    The Ms 8.0 Wenchuan earthquake, occurred on 12 May 2008 in Sichuan Province, collapsed a great many houses and injured hundreds of thousands of people. Undoubtedly, it can be predicted that secondary earthquake landslides will draw much attention during a long time after the earthquake due to the severe geological hazard. In order to remove threat from the secondary disasters effectively, this study used techniques of remote sensing and GIS to generate susceptibility maps, taking the case of Wenchuan County. Seven factors controlling landslide occurrence have been taken account into the susceptibility assessment, including elevation, slop, aspect, lithology, seismic intensity, distance to faults and rivers. According to the probability that predicts the possibility of landslide occurrence calculated applying information value method and logistic regression separately, the study zone was ultimately categorized into five classes, specifically, "extremely low", "low", "moderate", "high" and "very high". These results have been proved to reflect closely the spatial distributions of landslides in the study area.

  1. Assessment of the regional landslide susceptibility based on GIS

    NASA Astrophysics Data System (ADS)

    Sun, Ze; Xie, Shijie; Zhang, Kexin; Zheng, Xinshen; Zhu, Yunhai

    2007-06-01

    Landslide is one of the major geological disasters in Minhe area bounded on Gansu and Qinghai. Based on field detailed investigation on landslide susceptibility of Minhe area, the said paper selected four principal controlling factors to establish digital assessment standard of regional landslide susceptibility via construction of mathematical model as well as making scoring diagram of regional landslide susceptibility. Meanwhile, the method and flow of geological mapping multisource-data integration was initially set up. Two premises for conducting multisource-data integration during regional geological survey on digital basis were determined, namely, geological problem and mathematical model applicable for various geoscience research data. Two mathematical methods cited during the whole flow were Analytic Hierarchy Process (AHP) and decision tree. Digital quantification of different data types such as qualitative data and qualitative data was realized with AHP, so that those data can be imported into mathematical formula and participated in calculation as variables. Decision tree achieved artificial intelligent classification of space data such as remote sensing. Finally, landslide susceptibility assessment diagram of Minhe area was obtained, which was basically in accordance with the actual landslide distribution principle of the region via comparison with actual conditions.

  2. Non-Susceptible Landslide Areas in Italy and in the Mediterranean Region

    NASA Astrophysics Data System (ADS)

    Alvioli, Massimiliano; Ardizzone, Francesca; Guzzetti, Fausto; Marchesini, Ivan; Rossi, Mauro

    2014-05-01

    Landslide susceptibility is the likelihood of a landslide occurring in a given area. Over the past three decades, researchers, and planning and environmental organisations have worked to assess landslide susceptibility at different geographical scales, and to produce maps portraying landslide susceptibility zonation. Little effort was made to determine where landslides are not expected, where susceptibility is null, or negligible. This is surprising because planners and decision makers are also interesting in knowing where landslides are not foreseen, or cannot occur in an area. We propose a method for the definition of non-susceptible landslide areas, at the synoptic scale. We applied the method in Italy and to the territory surrounding the Mediterranean Sea and we produced two synoptic-scale maps showing areas where landslides are not expected in Italy and in the Mediterranean area. To construct the method we used digital terrain elevation and landslide information. The digital terrain consisted in the 3-arc-second SRTM DEM, the landslide information was obtained for 13 areas in Italy where landslide inventory maps were available to us. We tested three different models to determine the non-susceptible landslide areas, including a linear model (LR), a quantile linear model (QLR), and a quantile non-linear model (QNL). Model performances have been evaluated using independent landslide information represented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non- susceptible landslide areas was intersected in a GIS with geographical census data for Italy. The results show that the 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility was expected to be null or negligible, while the remaining 42.5% in areas where some landslide susceptibility was significant or not negligible. We applied the QNL model to the

  3. Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

    Technology Transfer Automated Retrieval System (TEKTRAN)

    As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote sensing methods have the spatial and temporal resolution required to...

  4. A logical framework for ranking landslide inventory maps

    NASA Astrophysics Data System (ADS)

    Santangelo, Michele; Fiorucci, Federica; Bucci, Francesco; Cardinali, Mauro; Ardizzone, Francesca; Marchesini, Ivan; Cesare Mondini, Alessandro; Reichenbach, Paola; Rossi, Mauro; Guzzetti, Fausto

    2014-05-01

    Landslides inventory maps are essential for quantitative landslide hazard and risk assessments, and for geomorphological and ecological studies. Landslide maps, including geomorphological, event based, multi-temporal, and seasonal inventory maps, are most commonly prepared through the visual interpretation of (i) monoscopic and stereoscopic aerial photographs, (ii) satellite images, (iii) LiDAR derived images, aided by more or less extensive field surveys. Landslide inventory maps are the basic information for a number of different scientific, technical and civil protection purposes, such as: (i) quantitative geomorphic analyses, (ii) erosion studies, (iii) deriving landslide statistics, (iv) urban development planning (v) landslide susceptibility, hazard and risk evaluation, and (vi) landslide monitoring systems. Despite several decades of activity in landslide inventory making, still no worldwide-accepted standards, best practices and protocols exist for the ranking and the production of landslide inventory maps. Standards for the preparation (and/or ranking) of landslide inventories should indicate the minimum amount of information for a landslide inventory map, given the scale, the type of images, the instrumentation available, and the available ancillary data. We recently attempted at a systematic description and evaluation of a total of 22 geomorphological inventories, 6 multi-temporal inventories, 10 event inventories, and 3 seasonal inventories, in the scale range between 1:10,000 and 1:500,000, prepared for areas in different geological and geomorphological settings. All of the analysed inventories were carried out by using image interpretation techniques, or field surveys. Firstly, a detailed characterisation was performed for each landslide inventory, mainly collecting metadata related (i) to the amount of information used for preparing the landslide inventory (i.e. images used, instrumentation, ancillary data, digitalisation method, legend, validation

  5. Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

    2013-03-01

    In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence

  6. Assessing Landslide Characteristics and Developing a Landslide Potential Hazard Map in Rwanda and Uganda Using NASA Earth Observations

    NASA Astrophysics Data System (ADS)

    Sinclair, L.; Conner, P.; le Roux, J.; Finley, T.

    2015-12-01

    The International Emergency Disasters Database indicates that a total of 482 people have been killed and another 27,530 have been affected by landslides in Rwanda and Uganda, although the actual numbers are thought to be much higher. Data for individual countries are poorly tracked, but hotspots for devastating landslides occur throughout Rwanda and Uganda due to the local topography and soil type, intense rainfall events, and deforestation. In spite of this, there has been little research in this region that utilizes satellite imagery to estimate areas susceptible to landslides. This project utilized Landsat 8 Operational Land Imager (OLI) data and Google Earth to identify landslides that occurred within the study area. These landslides were then added to SERVIR's Global Landslide Catalog (GLC). Next, Landsat 8 OLI, the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM), and Shuttle Radar Topography Mission Version 2 (SRTM V2) data were used to create a Landslide Susceptibility Map. This was combined with population data from the Socioeconomic Data and Applications Center (SEDAC) to create a Landslide Hazard map. A preliminary assessment of the relative performance of GPM and TRMM in identifying landslide conditions was also performed. The additions to the GLC, the Landslide Susceptibility Map, the Landslide Hazard Map, and the preliminary assessment of satellite rainfall performance will be used by SERVIR and the Regional Centre for Mapping of Resources for Development (RCMRD) for disaster risk management, land use planning, and determining landslide conditions and moisture thresholds.

  7. Developing a Seismic Landslide Hazard Map for Taiwan

    NASA Astrophysics Data System (ADS)

    Lee, Chyi-Tyi

    2013-04-01

    Following Lee et al. (2008), the statistical approach is applied to the seismic landslide hazard analysis for whole Taiwan and all the works are done by using new data sets. These new data include a new and carefully mapped Chi-Chi earhquake-incuced landslide inventory, a 5mx5m DEM, and a new version of 1 to 50,000 scale geologic map for whole Taiwan. Landslide causative factors used in the susceptibility analysis include slope gradient, slope aspect, terrain roughness, slope roughness, total curvature, total slope height, and lithology. A corrected Arias intensity which considered topographic amplification is used as a triggering factor. Firstly, a susceptibility model is built by using the 1999 Chi-Chi shallow landslides as a training data set and mulitivariate logistic regression as an analytical tool. This model is validated by using the 1998 Jueili earhtquake-induced landslide data. Then, a probability of failure curve is established by comparing the Chi-Chi landslide data and the susceptibility values, and the spatial probability of landslide occurrence may be drawn. The temporal probability may be accounted by the triggering factor - hazard level of Arias intensity, which may be got through a regular probabilistic seismic hazard analysis. Finally, the susceptibility model and the probability of failure curve are applied to whole Taiwan by using a topographic corrected 475-year Arias intensity as triggering factor, so that a seismic shallow landslide probability map for 475-year earthquake is completed.

  8. Assessments on landslide susceptibility in the Tseng-wen reservoir watershed, Taiwan

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Chin; Chen, Yung-Chau; Chen, Wen-Fu

    2014-05-01

    Typhoon Morakot under the strong influence of southwestern monsoon wind struck Taiwan on 8 August 2009, and dumped record-breaking rains in southern Taiwan. It triggered enormous landslides in mountains and severe flooding in low-lying areas. In addition, it destroyed or damaged houses, agricultural fields, roads, bridges, and other infrastructure facilities, causing massive economic loss and, more tragically, human casualties. In order to evaluate landslide hazard and risk assessment, it is important to understand the potential sites of landslide and their spatial distribution. Multi-temporal satellite images and geo-spatial data are used to build landslide susceptibility map for the post-disaster in the Tseng-wen reservoir watershed in this research. Elevation, slope, aspect, NDVI (normalized differential vegetation index), relief, roughness, distance to river, and distance to road are the considered factors for estimating landslide susceptibility. Maximum hourly rainfall and total rainfall, accompanied with typhoon event, are selected as the trigger factors of landslide events. Logistic regression analysis is adopted as the statistical method to model landslide susceptibility. The assessed susceptibility is represented in 4 levels which are high, high-intermediate, intermediate, and low level, respectively. Landslide spatial distribution can be depicted as a landslide susceptibility map with respect to each considered influence factors for a specified susceptible level. The landslide areas are about 358 ha and 1,485 ha before and after typhoon Morakot. The new landslide area, induced by typhoon Morakot, is as almost 4 times as the landslide area before typhoon Morakot. In addition, there is about 44.56% landslide area elevation ranging from 500m to 1000m and about 57.22% average slope ranging from 30° to 45° of landslide area. Furthermore, the devastating landslides were happened at those sites close to rivers, exposed area, and area with big land cover change

  9. Typologically-differentiated landslide susceptibility assessment for Romania.

    NASA Astrophysics Data System (ADS)

    Micu, Mihai; Malet, Jean-Philippe; Balteanu, Dan; Margarint, Ciprian; Niculita, Mihai; Jurchescu, Marta; Chitu, Zenaida; Sandric, Ionut; Simota, Catalin; Mathieu, Alexandre

    2014-05-01

    Alongside floods and earthquakes, landslides are representing one of the main geomorphic hazards in Romania, a country with more then 2/3 of its territory consisting of mountains, hills and tablelands prone to such slope processes. Diversity of morphostructural and lithological features are imposing a large variety in landslide typology, dominated by mud and debris-slides and mud-debris-flows and rockfalls. The purpose of this paper is to propose the first national inventory-based landslide susceptibility assessment. By compiling literature data, personal or institutional landslide inventories, a database of more that 27,900 cases (split in three main categories, i.e. slide, flow, fall) was set up as the basis for a multi-criteria modelling approach. For this assessment, a restricted number of spatial predictors is used (lithology, land-cover, slope). The assessment is based on a DEM of 90×90 m derived from ASTERGDEM v2; a classification of different topographic regions is proposed. Following classification and weighting procedures, a pairwise comparison was performed in order to rank the importance of each conditioning factor. The results (consisting in three nation-wide maps; slides, flows and falls) outline very well the correlation between the major morphostructural units and different susceptibility classes. The medium and high Carpathians, built mainly on metamorphic and igneous rock formations (sometimes on limestone and dolostones), present the highest susceptibility to (rock/debris) falls and (debris) flows. The low Carpathians, consisting of more or less cohesive flysch formations are very prone to (mud/debris) slides. The Subcarpathian hills and the extended homocline or hilly tablelands shows high susceptibility to (mud/debris) slides and (mud) flows. Further steps will include the integration of dynamic factors (climate maps, peak ground acceleration map) in the analysis. The work is performed in the framework of the "Pan-European and nation

  10. An approach to reduce mapping errors in the production of landslide inventory maps

    NASA Astrophysics Data System (ADS)

    Santangelo, M.; Marchesini, I.; Bucci, F.; Cardinali, M.; Fiorucci, F.; Guzzetti, F.

    2015-09-01

    Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that makes use of GIS technology for the digitization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure (a) increases the interpreter's overall efficiency by a factor of 2, (b) reduces significantly the subjectivity introduced by the visual (manual) transfer of the landslide information to the digital database, resulting in more accurate LIMs. With the new procedure, the landslide positional error decreases with increasing landslide size, following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.

  11. Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS

    NASA Astrophysics Data System (ADS)

    Kumar, Rohan; Anbalagan, R.

    2015-03-01

    A comprehensive study for the identification of landslide susceptible zones using landslide frequency ratio and fuzzy logic in GIS environment is presented for Tehri reservoir rim region (Uttarakhand, India). Temporal remote sensing data was used to prepare important landslide causative factor layers and landslide inventory. Primary and secondary topographic attributes namely slope, aspect, relative relief, profile curvature, topographic wetness index, and stream power index, were derived from digital elevation model. Landslide frequency ratio technique was adopted to correlate factors with landslides. Further, fuzzy logic method was applied for the integration of factors (causative factor) to map landslide susceptible zones. Normalized landslide frequency ratio value was used for the fuzzy membership function and different fuzzy operators were considered for the preparation of landslide susceptibility/hazard index map. The factors considered in this study were found to be carrying a wide range of information. Accordingly, a methodology was evolved to integrate the factors using combined fuzzy gamma and fuzzy OR operation. Fuzzy gamma integration was performed for six different gamma values (range: 0-1). Gamma value of 0.95 was selected for the preparation of final susceptibility map. Landslide susceptibility index map was divided into the following five hazard zones - very low, low, moderate, high, and very high - on the basis of natural break classification. Validation of the model was performed by using cumulative percentage curve technique. Area under curve value of cumulative percentage curve of proposed landslide susceptibility map (gamma = 0.95) was found to be 0.834 and it can be said that 83.4% accuracy was achieved by applying combined fuzzy logic and landslide frequency ratio method.

  12. Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France)

    NASA Astrophysics Data System (ADS)

    Den Eeckhaut, M. Van; Marre, A.; Poesen, J.

    2010-02-01

    The vineyards of the Montagne de Reims are mostly planted on steep south-oriented cuesta fronts receiving a maximum of sun radiation. Due to the location of the vineyards on steep hillslopes, the viticultural activity is threatened by slope failures. This study attempts to better understand the spatial patterns of landslide susceptibility in the Champagne-Ardenne region by comparing a heuristic (qualitative) and a statistical (quantitative) model in a 1120 km² study area. The heuristic landslide susceptibility model was adopted from the Bureau de Recherches Géologiques et Minières, the GEGEAA - Reims University and the Comité Interprofessionnel du Vin de Champagne. In this model, expert knowledge of the region was used to assign weights to all slope classes and lithologies present in the area, but the final susceptibility map was never evaluated with the location of mapped landslides. For the statistical landslide susceptibility assessment, logistic regression was applied to a dataset of 291 'old' (Holocene) landslides. The robustness of the logistic regression model was evaluated and ROC curves were used for model calibration and validation. With regard to the variables assumed to be important environmental factors controlling landslides, the two models are in agreement. They both indicate that present and future landslides are mainly controlled by slope gradient and lithology. However, the comparison of the two landslide susceptibility maps through (1) an evaluation with the location of mapped 'old' landslides and through (2) a temporal validation with spatial data of 'recent' (1960-1999; n = 48) and 'very recent' (2000-2008; n = 46) landslides showed a better prediction capacity for the statistical model produced in this study compared to the heuristic model. In total, the statistically-derived landslide susceptibility map succeeded in correctly classifying 81.0% of the 'old' and 91.6% of the 'recent' and 'very recent' landslides. On the susceptibility map

  13. Linking landslide susceptibility to sediment yield at regional scale: application to Romania

    NASA Astrophysics Data System (ADS)

    Broeckx, Jente; Vanmaercke, Matthias; Bălteanu, Dan; Chendeş, Viorel; Sima, Mihaela; Enciu, Petru; Poesen, Jean

    2016-09-01

    It is generally accepted that catchment sediment yield (SY, t km- 2 y- 1) can be strongly influenced by landsliding. Nevertheless, due to data requirements, only few studies investigated this effect at a regional scale. The objective of this study is therefore to explore the potential of a landslide susceptibility map for explaining the spatial variation of SY in Romania. We selected 133 catchments in Romania for which SY was measured during a period of at least 10 years. For each catchment, we derived a variety of proxies that potentially explain SY, including several indicators of landslide occurrence. The latter were derived from a published landslide susceptibility map. Results show that SY is significantly correlated with mean landslide susceptibility (r2 = 0.30). Estimates of average sheet and rill erosion rates showed a much weaker correlation with SY (r2 = 0.06). Further analyses showed that the strong correlation between SY and landslide susceptibility is mainly attributed to regional variations in lithology and seismicity. Especially the latter may play a crucial role in understanding denudation rates at regional scales, e.g. by facilitating the occurrence of landslides. Using landslide proxies that also account for sediment connectivity did not result in stronger correlations. Overall, our results show that landslide susceptibility maps can be a highly useful tool to predict SY at regional scales, provided that they incorporate all relevant factors.

  14. The impact of systematically incomplete and positionally inaccurate landslide inventories on statistical landslide susceptibility models

    NASA Astrophysics Data System (ADS)

    Steger, Stefan; Brenning, Alexander; Bell, Rainer; Glade, Thomas

    2016-04-01

    Several publications emphasize that the quality of statistical landslide susceptibility maps is highly dependent on the completeness and positional accuracy of the landslide inventory used as a response variable to produce the underlying models. We assume that erroneous landslide inventories distort relationships between a landslide inventory and its predictors while we hypothesize that the predictive performance of the underlying models is not necessarily worse in comparison to models generated with an accurate and unbiased landslide inventory. The objective of this study was to investigate the effect of incomplete and positionally inaccurate landslide inventories on the results of statistical landslide susceptibility models. An additional aim was to explore the potential of applying multilevel models to tackle the problem of confounded model coefficients as a results of inventory-based biases. The study was conducted for a landslide-prone study area (100 km²) located in the western part of Lower Austria. An accurate earth-slide point inventory (n = 591) was available for that region. The methodological approach consisted of an artificial introduction of biases and positional inaccuracies into the present landslide inventory and a subsequent quantitative (odds ratios, variable importance, non-spatial and spatial cross validation) and qualitative (geomorphic plausibility) evaluation of the modelling results. Two mapping biases were introduced separately by gradually thinning landslide data (0%, 20%, 80%) within (i) forested areas and (ii) selected municipalities. Positional inaccuracies were simulated by gradually changing the original landslide position (0, 5, 10, 20, 50 and 120 m). The resulting inventories were introduced into a logistic regression model while we considered the effects of including or excluding predictors directly related to the respective incompleteness. All incomplete inventories were additionally introduced into a two-level generalized

  15. An approach to reduce mapping errors in the production of landslide inventory maps

    NASA Astrophysics Data System (ADS)

    Santangelo, M.; Marchesini, I.; Bucci, F.; Cardinali, M.; Fiorucci, F.; Guzzetti, F.

    2015-07-01

    Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that exploits GIS technology for the digitalization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitalized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure is more efficient and results in a more accurate LIM. With the new procedure, the landslide positional error decreases with increasing landslide size following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.

  16. Recent development of shallow landslide susceptibility for two alpine catchments with different land use history, Switzerland

    NASA Astrophysics Data System (ADS)

    Meusburger, Katrin; Alewell, Christine

    2010-05-01

    Worldwide inventories conducted between 1964 and 1999 show a steady increase in the number of landslide disasters. The alpine valleys of Switzerland have always been subject to landslides, however, the recent development of landslide susceptibility is unknown. In this study we aim to evaluate and compare the development of shallow landslide susceptibly and its possible causes for two alpine valleys. Assessment of the shallow landslide incidence over time is done by aerial photograph interpretation using photos of several years since 1959. Based on these inventory maps shallow landslide susceptibility maps are constructed using multivariate logistic regression. An increasing trend of shallow landslide incidence could be observed for the alpine valley Urseren (30 km2; Central Swiss Alps) during the last 45 years. The valley is characterized by susceptible geology and rapid land use change that increased landslide susceptibility. The second site is the neighboring valley of Obergoms (65 km2). The Obergoms was chosen because it is characterized by a similar geological situation as the Urseren valley but a differing land use and land cover development. The Obergoms also showed an increase in shallow landslide incidence of 62% since 1967. Compared with the Urseren Valley the Obergoms is generally less affected by shallow landslides. This might be due to the fact that the highly susceptible geological formations in the Obergoms are forested and not pastured (as in the Urseren valley). The spatial pattern- and development of shallow landslide susceptibility will be discussed in connection with documented land use- and climate changes. Further the comparison between the valleys points to potential drivers for the increased shallow landslide incidence.

  17. A multidisciplinary approach for analysing landslide susceptibility in Abruzzo piedmont (Italy)

    NASA Astrophysics Data System (ADS)

    Sciarra, Marco; Urbano, Tullio; Coco, Laura

    2016-04-01

    Landslide susceptibility is the probability or likelihood that a landslide phenomenon happens in a specific area and in a not determined date, based on the correlation of controlling factors with distribution of past events. The present work presents a landslide susceptibility analysis assessment in the Feltrino Stream basin and minor surrounding coastal basins in south-eastern Abruzzo Region (Central Italy). The work was based on a multidisciplinary approach involving GIS (Geographic Information System) processing and geomorphological field survey. The study area, as well as the whole Italian Adriatic hills, is characterized by moderate to high landslide susceptibility, because of the complex geological, geomorphological and climatic features. Geologically, the bedrock is mainly characterised by marine deposits composed by clay-sandstone-conglomerate lithology belonging to Upper Pliocene - Lower Pleistocene, and locally by marine to continental transitional deposits belonging to Middle Pleistocene. The bedrock is largely covered by near-surface continental deposits composed by clay-silt-sand-gravel lithology ranging in age from Upper Pleistocene to Holocene. From the geomorphological viewpoint, the area is involved in different landslides phenomena (rock falls, rotational, translational and complex landslides, earth flows) which affect ~15% of the overall surface area. The landslide susceptibility study was carried out through a geostatistical analysis of landslides driver factors. Air-photos analysis was conducted for larger landslides and hillslope areas. The identified landslides were corroborated through a detailed geomorphological field survey. The methodology involved three main steps. Firstly, the main driver factors, directly or indirectly linked to slope instability, were defined and mapped by DTM processing, air-photos analysis and detailed geomorphological field survey. Morphological, geological and geomorphological factors were considered: slope

  18. Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility.

    PubMed

    Eskandari, Mahnaz; Homaee, Mehdi; Falamaki, Amin

    2016-06-01

    Several cities across the world are located in mountainous and landslide prone areas. Any landfill siting without considering landslide susceptibility in such regions may impose additional environmental adversity. This study was aimed to propose a practical method for selecting waste disposal site that accounts for landslide exposure. The proposed method was applied to a city which is highly proneness to landslide due to its geology, morphology, and climatic conditions. First, information on the previously occurred landslides of the region was collected. Based on this information, proper landslide causative factors were selected and their thematic maps were prepared. Factors' classes were then standardized in 0-1 domain, and thematic layers were weighted by using analytical hierarchy process (AHP). The landslide susceptibility map was prepared afterwards. Unsuitable areas for landfill location were masked in GIS environment by Boolean method, retaining sufficient areas for further evaluation. Nine remaining alternatives were selected through comprehensive field visits and were ranked by using AHP. Consequently, 17 factors in three environmental, economical, and social perspectives were employed. Sensitivity analyses were performed to assess the stability of the alternatives ranking with respect to variations in criterion weights. Based on the obtained landslide susceptible map, nearly 36 % of the entire region is proneness to landslide. The prepared Boolean map indicates that potential areas for landfill construction cover 11 % of the whole region. The results further indicated that if landslide susceptible areas are not considered in landfill site selection, the potential landfill sites would become more than twice. It can be concluded that if any of these landslide prone sites are selected for landfilling, further environmental disaster would be terminated in the future. It can be further concluded that the proposed method could reasonably well be adjusted to

  19. Landslides Mapped from LIDAR Imagery, Kitsap County, Washington

    USGS Publications Warehouse

    McKenna, Jonathan P.; Lidke, David J.; Coe, Jeffrey A.

    2008-01-01

    Landslides are a recurring problem on hillslopes throughout the Puget Lowland, Washington, but can be difficult to identify in the densely forested terrain. However, digital terrain models of the bare-earth surface derived from LIght Detection And Ranging (LIDAR) data express topographic details sufficiently well to identify landslides. Landslides and escarpments were mapped using LIDAR imagery and field checked (when permissible and accessible) throughout Kitsap County. We relied almost entirely on derivatives of LIDAR data for our mapping, including topographic-contour, slope, and hill-shaded relief maps. Each mapped landslide was assigned a level of 'high' or 'moderate' confidence based on the LIDAR characteristics and on field observations. A total of 231 landslides were identified representing 0.8 percent of the land area of Kitsap County. Shallow debris topples along the coastal bluffs and large (>10,000 m2) landslide complexes are the most common types of landslides. The smallest deposit mapped covers an area of 252 m2, while the largest covers 0.5 km2. Previous mapping efforts that relied solely on field and photogrammetric methods identified only 57 percent of the landslides mapped by LIDAR (61 percent high confidence and 39 percent moderate confidence), although nine landslides previously identified were not mapped during this study. The remaining 43 percent identified using LIDAR have 13 percent high confidence and 87 percent moderate confidence. Coastal areas are especially susceptible to landsliding; 67 percent of the landslide area that we mapped lies within 500 meters of the present coastline. The remaining 33 percent are located along drainages farther inland. The LIDAR data we used for mapping have some limitations including (1) rounding of the interface area between low slope surfaces and vertical faces (that is, along the edges of steep escarpments) which results in scarps being mapped too far headward (one or two meters), (2) incorrect laser

  20. Improving Landslide Susceptibility Modeling Using an Empirical Threshold Scheme for Excluding Landslide Deposition

    NASA Astrophysics Data System (ADS)

    Tsai, F.; Lai, J. S.; Chiang, S. H.

    2015-12-01

    Landslides are frequently triggered by typhoons and earthquakes in Taiwan, causing serious economic losses and human casualties. Remotely sensed images and geo-spatial data consisting of land-cover and environmental information have been widely used for producing landslide inventories and causative factors for slope stability analysis. Landslide susceptibility, on the other hand, can represent the spatial likelihood of landslide occurrence and is an important basis for landslide risk assessment. As multi-temporal satellite images become popular and affordable, they are commonly used to generate landslide inventories for subsequent analysis. However, it is usually difficult to distinguish different landslide sub-regions (scarp, debris flow, deposition etc.) directly from remote sensing imagery. Consequently, the extracted landslide extents using image-based visual interpretation and automatic detections may contain many depositions that may reduce the fidelity of the landslide susceptibility model. This study developed an empirical thresholding scheme based on terrain characteristics for eliminating depositions from detected landslide areas to improve landslide susceptibility modeling. In this study, Bayesian network classifier is utilized to build a landslide susceptibility model and to predict sequent rainfall-induced shallow landslides in the Shimen reservoir watershed located in northern Taiwan. Eleven causative factors are considered, including terrain slope, aspect, curvature, elevation, geology, land-use, NDVI, soil, distance to fault, river and road. Landslide areas detected using satellite images acquired before and after eight typhoons between 2004 to 2008 are collected as the main inventory for training and verification. In the analysis, previous landslide events are used as training data to predict the samples of the next event. The results are then compared with recorded landslide areas in the inventory to evaluate the accuracy. Experimental results

  1. A comparison landslide susceptibility assessment methods in the Luncavat watershed, Romania

    NASA Astrophysics Data System (ADS)

    Irimia, Radu; Cosmin Sandric, Ionut

    2015-04-01

    This paper uses quantitative approaches to generate a landslide susceptibility maps (LSM) for the Luncavăț watershed, located in the Getic Subcarpathians, Romania. The purpose of this study is to evaluate and to compare the results of multivariate and bivariate landslide susceptibility methods. For this study it was decided to use only three predictor factors: slope gradient, lithology and a land-use/land-cover. The study area spreads over approximate 300 km2. This area is highly affected by mass movements, over 65% of the area is moderately or highly susceptible to landslides. Bivariate and multivariate statistical analyses were applied to calculate a landslide susceptibility index. It was found that the results from all the methods converge for aproximate 75% of the Luncavat watershed. However, the bivariate method had some severe deficiencies and parts of the high and medium landslides susceptibility classess did not overlapped with the actual landslide inventory map. The result of the multivariate technique was more sensitive to the different local features of the test zone and it resulted in more accurate and homogeneous susceptibility maps. It is anticipated that the result of this work can be used by local authorithies to help identify high risk landslide areas, and those that are safe for building and other human activities

  2. Comparison and validation of shallow landslides susceptibility maps generated by bi-variate and multi-variate linear probabilistic GIS-based techniques. A case study from Ribeira Quente Valley (S. Miguel Island, Azores)

    NASA Astrophysics Data System (ADS)

    Marques, R.; Amaral, P.; Zêzere, J. L.; Queiroz, G.; Goulart, C.

    2009-04-01

    Slope instability research and susceptibility mapping is a fundamental component of hazard assessment and is of extreme importance for risk mitigation, land-use management and emergency planning. Landslide susceptibility zonation has been actively pursued during the last two decades and several methodologies are still being improved. Among all the methods presented in the literature, indirect quantitative probabilistic methods have been extensively used. In this work different linear probabilistic methods, both bi-variate and multi-variate (Informative Value, Fuzzy Logic, Weights of Evidence and Logistic Regression), were used for the computation of the spatial probability of landslide occurrence, using the pixel as mapping unit. The methods used are based on linear relationships between landslides and 9 considered conditioning factors (altimetry, slope angle, exposition, curvature, distance to streams, wetness index, contribution area, lithology and land-use). It was assumed that future landslides will be conditioned by the same factors as past landslides in the study area. The presented work was developed for Ribeira Quente Valley (S. Miguel Island, Azores), a study area of 9,5 km2, mainly composed of volcanic deposits (ash and pumice lapilli) produced by explosive eruptions in Furnas Volcano. This materials associated to the steepness of the slopes (38,9% of the area has slope angles higher than 35°, reaching a maximum of 87,5°), make the area very prone to landslide activity. A total of 1.495 shallow landslides were mapped (at 1:5.000 scale) and included in a GIS database. The total affected area is 401.744 m2 (4,5% of the study area). Most slope movements are translational slides frequently evolving into debris-flows. The landslides are elongated, with maximum length generally equivalent to the slope extent, and their width normally does not exceed 25 m. The failure depth rarely exceeds 1,5 m and the volume is usually smaller than 700 m3. For modelling

  3. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model

    NASA Astrophysics Data System (ADS)

    Hussin, Haydar Y.; Zumpano, Veronica; Reichenbach, Paola; Sterlacchini, Simone; Micu, Mihai; van Westen, Cees; Bălteanu, Dan

    2016-01-01

    This study had three aims. The first was to assess the performance of the weights-of-evidence (WofE) landslide susceptibility model in areas that are very different in terms of size, geoenvironmental settings, and landslide types. The second was to test the appropriate strategies to sample the mapped landslide polygon. The final aim was to evaluate the performance of the method to changes in the landslide sample size used to train the model. The method was applied to two areas: the Fella River basin (eastern Italian Alps) containing debris flows, and Buzau County (Romanian Carpathians) with shallow landslides. The three landslide sampling strategies used were: (1) the landslide scarp centroid, (2) points populating the scarp on a 50-m grid, and (3) the entire scarp polygon. The highest success rates were obtained when sampling shallow landslides as 50-m grid-points and debris flow scarps as polygons. Prediction rates were highest when using the entire scarp polygon method for both landslide types. The sample size test using the landslide centroids showed that a sample of 104 debris flow scarps was sufficient to predict the remaining 941 debris flows in the Fella River basin, while 161 shallow landslides was the minimum required number to predict the remaining 1451 scarps in Buzau County. Below these landslide sample thresholds, model performance was too low. However, using more landslides than the threshold produced a plateau effect with little to no increase in the model performance rates.

  4. 3D Landslides Susceptibility Analysis in Romanian Subcarpathians

    NASA Astrophysics Data System (ADS)

    Sandric, Ionuc; Ilinca, Viorel; Chitu, Zenaida; Jurchescu, Marta

    2015-04-01

    Most of the present day studies make use the 2.5D raster data formats for the landslide susceptibility analysis at regional scales. This data format has some disadvantages when geological and lithological settings are spatial discretized, hence these disadvantages propagate in the landslides susceptibility analysis and especially where only surface lithology is used. The main disadvantage when using 3D data models for the assessment of landslide susceptibility at regional scales is represented by the quality of the geological and lithological information that is available for a depth of no more than 100m. In order to mitigate this, a sufficient number of boreholes is required and sometimes is not available. In order to overcome the lack of borehole data, our approach was to make use of the present-day geological maps at scales ranging from 1:25,000 to 1:50,000 and to generate a geological 3D model up to a depth of 100m. The geological model was generated based on expert knowledge interpretations and geological cross sections provided on these geological maps. Using the 3D geological model a more complex 3D model was generated for the landslide susceptibility analysis that also contains information from other predictor factors like slope gradient, land-cover and land-use. For the landslide susceptibility analysis instead of using map algebra equations on classic pixel based data sets, the equations were adapted for 3D data models and map algebra equations on voxels. The test sites are located in the areas of Romanian Subcarpathians. The Romanian Subcarpathians are located to the exterior of the Carpathians. They consist of a large variety of rocks, flysch-type deposits in the inner part and molasse deposits in the outer part, ranging from a Cretacic-Paleogene to a Quaternary age. While some parts of the Subcarpathians have a basic geology, with a monoclinal geological structure, other parts like the Curvature Subcarpathians, present acomplex folded and faulted

  5. Assessment of Uncertainty Propagation from DEM's on Small Scale Typologically-Differentiated Landslide Susceptibility in Romania

    NASA Astrophysics Data System (ADS)

    Cosmin Sandric, Ionut; Chitu, Zenaida; Jurchescu, Marta; Malet, Jean-Philippe; Ciprian Margarint, Mihai; Micu, Mihai

    2015-04-01

    An increasing number of free and open access global digital elevation models has become available in the past 15 years and these DEMs have been widely used for the assessment of landslide susceptibility at medium and small scales. Even though the global vertical and horizontal accuracies of each DEM are known, what it is still unknown is the uncertainty that propagates from the first and second derivatives of DEMs, like slope gradient, into the final landslide susceptibility map For the present study we focused on the assessment of the uncertainty propagation from the following digital elevation models: SRTM 90m spatial resolution, ASTERDEM 30m spatial resolution, EUDEM 30m spatial resolution and the latest release SRTM 30m spatial resolution. From each DEM dataset the slope gradient was generated and used in the landslide susceptibility analysis. A restricted number of spatial predictors are used for landslide susceptibility assessment, represented by lithology, land-cover and slope, were the slope is the only predictor that changes with each DEM. The study makes use of the first national landslide inventory (Micu et al, 2014) obtained from compiling literature data, personal or institutional landslide inventories. The landslide inventory contains more than 27,900 cases classified in three main categories: slides flows and falls The results present landslide susceptibility maps obtained from each DEM and from the combinations of DEM datasets. Maps with uncertainty propagation at country level and differentiated by topographic regions from Romania and by landslide typology (slides, flows and falls) are obtained for each DEM dataset and for the combinations of these. An objective evaluation of each DEM dataset and a final map of landslide susceptibility and the associated uncertainty are provided

  6. Characteristics of a Recent and Prehistoric Landslides in the Pine River Valley, BC: a Mapping Effort

    NASA Astrophysics Data System (ADS)

    Heijenk, R.; Geertsema, M.; Miller, B.; de Jong, S. M.

    2015-12-01

    Spreads and other low gradient landslides are common in glacial lake sediments in north eastern British Columbia. Both pre and post glacial lake sediments, largely derived from shale bedrock are susceptible to low-gradient landslides. Bank erosion by rivers and streams and high pore pressures, have contributed to the landslides. We used LiDAR for mapping the extent of the glaciolacustrine sediments and map and characterise landslides in the Pine River valley, near Chetwynd, British Columbia. We included metrics such as travel angle, length, area, and elevation to distinguish rotational and translational landslides. We mapped 45 landslides in the Pine River valley distinguishing between rotational and translational landslides. The rotational landslides commonly have a smaller area and smaller travel length than translational landslides. Most rotational slides involved overlying alluvial fans, while most translational slides involved terraces.

  7. Landslide susceptibility assessment and validation in the framework of municipal planning in Portugal: the case of Loures Municipality.

    PubMed

    Guillard, Clemence; Zezere, Jose

    2012-10-01

    The legislation that demands the evaluation of landslide susceptibility in Portugal at the municipal level is the National Ecological Reserve (NER). A methodology for the evaluation of landslide susceptibility to be used in municipal planning is applied in Loures Municipality (169.3 km²) located north of Lisbon (Portugal). A landslide inventory was made for the whole area interpreting orthophoto maps and aerial photographs and using standard geomorphologic techniques in field work. It consists of 686 polygons, each polygon representing a rotational, a deep translational or a shallow translational slide, and is integrated into a GIS database. Landslide susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Information Value Method) over unique-condition terrain units in a raster basis. Three susceptibility models are elaborated independently according to the type of slide (rotational, deep translational, shallow translational). The landslide susceptibility maps are prepared by sorting all pixels according to the pixel susceptibility value in descending order. The robustness and accuracy of the landslide susceptibility models are evaluated by prediction-rate curves, which are used for the quantitative interpretation of the landslide susceptibility maps. Unstable slopes that have to be included into the National Ecological Reserve are extracted from the three susceptibility maps following the general rules to draw the NER that state that the area to be included in the NER should guarantee the inclusion of at least 70 % of the landslides identified in the landslide inventory. The obtained results allow us to conclude that 70 % of the future landslides should occur in these areas, classified as most susceptible to landslides corresponding to 20.3 % of the total area of Municipality. Thus, the consideration of these 20.3 % as regards prevention and protection of landslide risk could potentially reduce damage resulting from 70

  8. Landslide Susceptibility Assessment and Validation in the Framework of Municipal Planning in Portugal: The Case of Loures Municipality

    NASA Astrophysics Data System (ADS)

    Guillard, Clemence; Zezere, Jose

    2012-10-01

    The legislation that demands the evaluation of landslide susceptibility in Portugal at the municipal level is the National Ecological Reserve (NER). A methodology for the evaluation of landslide susceptibility to be used in municipal planning is applied in Loures Municipality (169.3 km²) located north of Lisbon (Portugal). A landslide inventory was made for the whole area interpreting orthophoto maps and aerial photographs and using standard geomorphologic techniques in field work. It consists of 686 polygons, each polygon representing a rotational, a deep translational or a shallow translational slide, and is integrated into a GIS database. Landslide susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Information Value Method) over unique-condition terrain units in a raster basis. Three susceptibility models are elaborated independently according to the type of slide (rotational, deep translational, shallow translational). The landslide susceptibility maps are prepared by sorting all pixels according to the pixel susceptibility value in descending order. The robustness and accuracy of the landslide susceptibility models are evaluated by prediction-rate curves, which are used for the quantitative interpretation of the landslide susceptibility maps. Unstable slopes that have to be included into the National Ecological Reserve are extracted from the three susceptibility maps following the general rules to draw the NER that state that the area to be included in the NER should guarantee the inclusion of at least 70 % of the landslides identified in the landslide inventory. The obtained results allow us to conclude that 70 % of the future landslides should occur in these areas, classified as most susceptible to landslides corresponding to 20.3 % of the total area of Municipality. Thus, the consideration of these 20.3 % as regards prevention and protection of landslide risk could potentially reduce damage resulting from 70 % of

  9. Exploring model sensitivity issues across different scales in landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Catani, F.; Lagomarsino, D.; Segoni, S.; Tofani, V.

    2013-03-01

    Despite the large number of recent advances and developments in landslide susceptibility mapping there is still a lack of studies focusing on specific aspects of LSM model sensitivity. For example, the influence of factors of paramount importance such as the survey scale of the landslide conditioning variables (LCVs), the resolution of the mapping unit (MUR) and the optimal number and ranking of LCVs have never been investigated analytically, especially on large datasets. In this paper we attempt this experimentation concentrating on the impact of model tuning choice on the final result, rather than on the comparison of methodologies. To this end, we adopt a simple implementation of the random forest (RF) classification family to produce an ensemble of landslide susceptibility maps for a set of different model settings, input data types and scales. Random forest is a combination of tree (usually binary) bayesian predictors that permits to relate a set of contributing factors with the actual landslides occurrence. Being it a nonparametric model, it is possible to incorporate a range of numeric or categorical data layers and there is no need to select unimodal training data. Many classical and widely acknowledged landslide predisposing factors have been taken into account as mainly related to: the lithology, the land use, the land surface geometry (derived from of DTM), the structural and anthropogenic constrains. In addition, for each factor we also included in the parameter set the standard deviation (for numerical variables) or the variety (for categorical ones). The use of random forest enables to estimate the relative importance of the single input parameters and to select the optimal configuration of the regression model. The model was initially applied using the complete set of input parameters then, with progressively smaller subsamples of the parameter space. Considering the best set of parameters we also studies the impact of scale and accuracy of input

  10. Map Showing Inventory and Regional Susceptibility for Holocene Debris Flows, and Related Fast-Moving Landslides in the Conterminous United States

    USGS Publications Warehouse

    Brabb, Earl E.; Colgan, Joseph P.; Best, Timothy C.

    2000-01-01

    Introduction Debris flows, debris avalanches, mud flows and lahars are fast-moving landslides that occur in a wide variety of environments throughout the world. They are particularly dangerous to life and property because they move quickly, destroy objects in their paths, and often strike without warning. This map represents a significant effort to compile the locations of known debris flows in United Stated and predict where future flows might occur. The files 'dfipoint.e00' and 'dfipoly.e00' contain the locations of over 6600 debris flows from published and unpublished sources. The locations are referenced by numbers that correspond to entries in a bibliography, which is part of the pamphlet 'mf2329pamphlet.pdf'. The areas of possible future debris flows are shown in the file 'susceptibility.tif', which is a georeferenced TIFF file that can be opened in an image editing program or imported into a GIS system like ARC/INFO. All other databases are in ARC/INFO export (.e00) format.

  11. Pitfalls in statistical landslide susceptibility modelling

    NASA Astrophysics Data System (ADS)

    Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

    2010-05-01

    The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible

  12. Evaluating performances of simplified physically based models for landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Formetta, G.; Capparelli, G.; Versace, P.

    2015-12-01

    Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

  13. Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information

    NASA Astrophysics Data System (ADS)

    Günther, Andreas; Van Den Eeckhaut, Miet; Malet, Jean-Philippe; Reichenbach, Paola; Hervás, Javier

    2014-11-01

    With the adoption of the EU Thematic Strategy for Soil Protection in 2006, small-scale (1:1 M) assessments of threats affecting soils over Europe received increasing attention. As landslides have been recognized as one of eight threats requiring a Pan-European evaluation, we present an approach for landslide susceptibility evaluation at the continental scale over Europe. Unlike previous continental and global scale landslide susceptibility studies not utilizing spatial information on the events, we collected more than 102,000 landslide locations in 22 European countries. These landslides are heterogeneously distributed over Europe, but are indispensable for the evaluation and classification of Pan-European datasets used as spatial predictors, and the validation of the resulting assessments. For the analysis we subdivided the European territory into seven different climate-physiographical zones by combining morphometric and climatic data for terrain differentiation, and adding a coastal zone defined as a 1 km strip inland from the coastline. Landslide susceptibility modeling was performed for each zone using heuristic spatial multicriteria evaluations supported by analytical hierarchy processes, and validated with the inventory data using the receiver operating characteristics. In contrast to purely data-driven statistical modeling techniques, our semi-quantitative approach is capable to introduce expert knowledge into the analysis, which is indispensable considering quality and resolution of the input data, and incompleteness and bias in the inventory information. The reliability of the resulting susceptibility map ELSUS 1000 Version 1 (1 km resolution) was examined on an administrative terrain unit level in areas with landslide information and through the comparison with available national susceptibility zonations. These evaluations suggest that although the ELSUS 1000 is capable for a correct synoptic prediction of landslide susceptibility in the majority of the

  14. Assessment of neural network, frequency ratio and regression models for landslide susceptibility analysis

    NASA Astrophysics Data System (ADS)

    Pradhan, B.; Buchroithner, M. F.; Mansor, S.

    2009-04-01

    This paper presents the assessment results of spatially based probabilistic three models using Geoinformation Techniques (GIT) for landslide susceptibility analysis at Penang Island in Malaysia. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and supported with field surveys. Maps of the topography, soil type, lineaments and land cover were constructed from the spatial data sets. There are nine landslide related factors were extracted from the spatial database and the neural network, frequency ratio and logistic regression coefficients of each factor was computed. Landslide susceptibility maps were drawn for study area using neural network, frequency ratios and logistic regression models. For verification, the results of the analyses were compared with actual landslide locations in study area. The verification results show that frequency ratio model provides higher prediction accuracy than the ANN and regression models.

  15. A zonation technique for landslide susceptibility in southern Taiwan

    NASA Astrophysics Data System (ADS)

    Chiang, Jie-Lun; Tian, Yu-Qing; Chen, Yie-Ruey; Tsai, Kuang-Jung

    2016-04-01

    In recent years, global climate changes violently, extreme rainfall events occur frequently and also cause massive sediment related disasters in Taiwan. The disaster seriously hit the regional economic development and national infrastructures. For example, in August, 2009, the typhoon Morakot brought massive rainfall especially in the mountains in Chiayi County and Kaohsiung County in which the cumulative maximum rainfall was up to 2900 mm; meanwhile, the cumulative maximum rainfall was over 1500m.m. in Nantou County, Tainan County and Pingtung County. The typhoon caused severe damage in southern Taiwan. The study will search for the influence on the sediment hazards caused by the extreme rainfall and hydrological environmental changes focusing on southern Taiwan (including Chiayi, Tainan, Kaohsiung and Pingtung). The instability index and kriging theories are applied to analyze the factors of landslide to determine the susceptibility in southern Taiwan. We collected the landslide records during the period year, 2007~2013 and analyzed the instability factors including elevation, slope, aspect, soil, and geology. Among these factors, slope got the highest weight. The steeper the slope is, the more the landslides occur. As for the factor of aspect, the highest probability falls on the Southwest. However, this factor has the lowest weight among all the factors. Likewise, Darkish colluvial soil holds the highest probability of collapses among all the soils. Miocene middle Ruifang group and its equivalents have the highest probability of collapses among all the geologies. In this study, Kriging was used to establish the susceptibility map in southern Taiwan. The instability index above 4.21 can correspond to those landslide records. The potential landslide area in southern Taiwan, where collapses more likely occur, belongs to high level and medium-high level; the area is 5.12% and 17.81% respectively.

  16. A Geomorphological Approach To Landslide Susceptibility Assessment and Its Application To The Virginio River Basin

    NASA Astrophysics Data System (ADS)

    Casagli, N.; Catani, F.; Delmonaco, G.; Ermini, L.; Margottini, C.; Puglisi, C.

    The present note presents the results of a research project aimed at developing a ge- omorphology based method for the assessment of landslide susceptibility. The work has been carried out in the Virginio River basin, a tributary of the Arno river with confluence located 20 km downstream from Florence (Italy). Starting from a detailed landslide inventory, essentially carried out by aerial-photograph survey, the first phase of the research was directed to geomorphic field investigations, aimed at determining those hillslope factors influencing landslide triggering. This analysis was carried out for a significant sub-sample of the inventoried landslides, allowing the distinction of the most relevant hillslope factors affecting each type of mass movement. Selected factors are a) the past slope gradient in which landslide originated, b) litology and 3) land use. Once defined as thematic vector data, these factors have been handled by GIS overlay mapping, allowing to single out, for the entire Virginio River basin, homogeneous domains (Unique Condition Units) that contain, for each landslide ty- pology, unique combinations of the selected hillslope factors. Those domains are the basic Terrain Units for the following phase of landslide susceptibility assessment and mapping. The hillslope factors not selected in the first phase, however playing an im- portant role in contributing to the activation of the mass movements, have been taken into account for the definition of a landslide susceptibility function. This function is es- sentially a logic function based on the presence/absence of preparatory factors within the previous selected Unique Condition Terrain Units. The final mapping of the ar- eas characterized by different landslide susceptibility levels was performed by vector and raster based GIS techniques on the basis of the number and rank of predisposing factors. These landslide susceptibility classes, defined by a logic and easily replicable function, can be

  17. Comparing physically-based and statistical landslide susceptibility model outputs - a case study from Lower Austria

    NASA Astrophysics Data System (ADS)

    Canli, Ekrem; Thiebes, Benni; Petschko, Helene; Glade, Thomas

    2015-04-01

    By now there is a broad consensus that due to human-induced global change the frequency and magnitude of heavy precipitation events is expected to increase in certain parts of the world. Given the fact, that rainfall serves as the most common triggering agent for landslide initiation, also an increased landside activity can be expected there. Landslide occurrence is a globally spread phenomenon that clearly needs to be handled. The present and well known problems in modelling landslide susceptibility and hazard give uncertain results in the prediction. This includes the lack of a universal applicable modelling solution for adequately assessing landslide susceptibility (which can be seen as the relative indication of the spatial probability of landslide initiation). Generally speaking, there are three major approaches for performing landslide susceptibility analysis: heuristic, statistical and deterministic models, all with different assumptions, its distinctive data requirements and differently interpretable outcomes. Still, detailed comparison of resulting landslide susceptibility maps are rare. In this presentation, the susceptibility modelling outputs of a deterministic model (Stability INdex MAPping - SINMAP) and a statistical modelling approach (generalized additive model - GAM) are compared. SINMAP is an infinite slope stability model which requires parameterization of soil mechanical parameters. Modelling with the generalized additive model, which represents a non-linear extension of a generalized linear model, requires a high quality landslide inventory that serves as the dependent variable in the statistical approach. Both methods rely on topographical data derived from the DTM. The comparison has been carried out in a study area located in the district of Waidhofen/Ybbs in Lower Austria. For the whole district (ca. 132 km²), 1063 landslides have been mapped and partially used within the analysis and the validation of the model outputs. The respective

  18. Landslide detection and susceptibility analysis using aerial photographs and weight of evidence

    NASA Astrophysics Data System (ADS)

    Saro, Lee; Hyun-Joo, Oh

    2010-05-01

    The aim of this study was to detect landslide using aerial photographs and apply the landslide to probabilistic landslide susceptibility mapping at Jinbu-myeon area, Korea using a Geographic Information System (GIS). For probabilistic landslide susceptibility analysis, accurate detection of the location of landslides is very important. Interpretation of aerial photographs has the advantage of enabling the rapid and accurate detection of landslides. During the Korea rainy season in June 14 to July 19, 2006, a series of typhoons such as EWINIAR, BILIS and KAEMI has hit Gangwon-do area by storm and heavy rainfall. The 2 days-rainfall was 675mm and 3 hours-rainfall was 209mm. As the result, the damage to property was about a value of 449 billion USD. So, among the Gangwon-do area, the Jinbu-myeon area was selected as study area because one of the most landslides occurred area. In this study, the location of landslide detected using web-based digital aerial photographs with 50cm resolution provided from Internet portal site "Daum (www.daum.net) and field work. The photographs were taken before and after this rainy season (4, Arial 2005 and 27, May 2008, respectively). For aerial photograph interpretation, an aerial photograph database was constructed by ortho-rectification and by merging many aerial photographs. About 90% of the landslide locations detected by photographic interpretation (comparison of the two photographs) were verified by fieldwork. Landslides were observed in aerial photographs as a break in the forest canopy, bare soil, or other geomorphic characteristics typical of landslide scars; for example, head and side scarps, flow tracks, and soil and debris deposits below the scar. In total, 1,801 landslides were mapped within a total study area of 59.78km2. In this study area, the majority of the landslide is soil slide and debris flow. The weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide

  19. Coseismic and Post-seismic landsliding: insights from seismological modeling and landslide map time series.

    NASA Astrophysics Data System (ADS)

    Marc, Odin; Hovius, Niels; Meunier, Patrick; Uchida, Taro; Gorum, Tolga

    2016-04-01

    Earthquakes impart a catastrophic forcing on hillslopes, that often lead to widespread landsliding and can contribute significantly to sedimentary and organic matter fluxes. We present a new expression for the total area and volume of populations of earthquake-induced landslides.This model builds on a set of scaling relationships between key parameters, such as landslide density, ground acceleration, fault size, earthquake source depth and seismic moment, derived from geomorphological and seismological observations. To assess the model we have assembled and normalized a catalogue of landslide inventories for 40 earthquakes. We have found that low landscape steepness systematically leads to over-prediction of the total area and volume of landslides.When this effect is accounted for, the model is able to predict within a factor of 2 the landslide areas and associated volumes for about two thirds of the cases in our databases. This is a significant improvement on a previously published empirical expression based only on earthquake moment. This model is suitable for integration into landscape evolution models, and application to the assessment of secondary hazards and risks associated with earthquakes. However, it only models landslides associated to the strong ground shaking and neglects the intrinsic permanent damage that also occurred on hillslopes and persist for longer period. With time series of landslide maps we have constrained the magnitude of the change in landslide susceptibility in the epicentral areas of 4 intermediate to large earthquakes. We propose likely causes for this transient ground strength perturbations and compare our observations to other observations of transient perturbations in epicentral areas, such as suspended sediment transport increases, seismic velocity reductions and hydrological perturbations. We conclude with some preliminary observations on the coseismic mass wasting and post-seismic landslide enhancement caused by the 2015 Mw.7

  20. Mapping and analysis of Martian landslides

    NASA Astrophysics Data System (ADS)

    Crosta, Giovanni B.; Frattini, Paolo; Valbuzzi, Elena; Russo, Valeria

    2013-04-01

    This work is part of a larger effort aimed to a more quantitative description of landslide phenomena on Mars and the understanding of rock mass properties and landslide mobility with respect to their Earth equivalents. Recently, large satellite imagery datasets have become available and they have been mosaicked in different suitable tools making mapping an easier job than before. Furthermore, the availability of other georeferenced database makes possible and easily feasible some spatially distributed analyses. We prepared a new landslide inventory to acquire information about: landslide size distribution and areal density, controls of geometrical condition along Martian slopes, landslide typology and mechanism, relationship with impact craters distribution, runout, volume estimates, characteristic features. We adopted Google Earth, Google, Inc. as a mapping tool using both visible and CTX images. Landslides have been mapped according to standard geomorphological criteria, by two landslide experts delineating both the landslide scar and accumulation limits, associating each scarp to a deposit. Multiple accumulations have been differentiated where possible to obtain a more sound dataset. We prevalently mapped landslides located along the Martian valleys and Chasma flanks with only minor attention to classical block and slump instabilities typical of crater rim failures. This because we were mainly interested in long runout landslides or complex failures which could allow to define some rock mass characteristics along these slopes, and to study landslide mobility with respect to Earth equivalent phenomena. So long runout landslides have been mapped also when recognized within crater rims. Topographic characteristics have been extracted by means of the available MOLA dataset. The inventory presently consists of 1232 landslides covering a total area of about 180,000 km2. Landslide size ranges from 0.15 km2 to a maximum of 12,000 km2. We examined area

  1. Assessing Degree of Susceptibility to Landslide Hazard

    NASA Astrophysics Data System (ADS)

    Sheridan, M. F.; Cordoba, G. A.; Delgado, H.; Stefanescu, R.

    2013-05-01

    The modeling of hazardous mass flows, both dry and water saturated, is currently an area of active research and several stable models have now emerged that have differing degrees of physical and mathematical fidelity. Models based on the early work of Savage and Hutter (1989) assume that very large dense granular flows could be modeled as incompressible continua governed by a Coulomb failure criterion. Based on this concept, Patra et al. (2005) developed a code for dry avalanches, which proposes a thin layer mathematical model similar to shallow-water equations. This concept was implemented in the widely-used TITAN2D program, which integrates the shock-capturing Godunov solution methodology for the equation system. We propose a method to assess the susceptibility of specific locations susceptible to landslides following heavy tephra fall using the TIATN2D code. Successful application requires that the range of several uncertainties must be framed in the selection of model input data: 1) initial conditions, like volume and location of origin of the landslide, 2) bed and internal friction parameters and 3) digital elevation model (DEM) uncertainties. Among the possible ways of coping with these uncertainties, we chose to use Latin Hypercube Sampling (LHS). This statistical technique reduces a computationally intractable problem to such an extent that is it possible to apply it, even with current personal computers. LHS requires that there is only one sample in each row and each column of the sampling matrix, where each row (multi-dimensional) corresponds to each uncertainty. LHS requires less than 10% of the sample runs needed by Monte Carlo approaches to achieve a stable solution. In our application LHS output provides model sampling for 4 input parameters: initial random volumes, UTM location (x and y), and bed friction. We developed a simple Octave script to link the output of LHS with TITAN2D. In this way, TITAN2D can run several times with successively different

  2. Multidisciplinary approach to evaluate landslide susceptibility along highway in northern Calabria, Italy

    NASA Astrophysics Data System (ADS)

    Muto, Francesco; Conforti, Massimo; Critelli, Salvatore; Fabbricatore, Davide; Filomena, Luciana; Rago, Valeria; Robustelli, Gaetano; Scarciglia, Fabio; Versace, Pasquale

    2014-05-01

    The interaction of landslides with linear infrastructures is often the cause of disasters. In Italy landslide impact on roads, railways and buildings cause millions of Euro per year in damage and restoration as well. The proposed study is aimed to the landslide susceptibility evaluation using a multidisciplinary approach: geological and geomorphological survey, statistical analysis and GIS technique, along a section of highway "A3 (Salerno-Reggio Calabria)" between Cosenza Sud and Altilia, northern Calabria. This study is included in a wider research project, named: PON01-01503, Landslides Early Warning-Sistemi integrati per il monitoraggio e la mitigazione del rischio idrogeologico lungo le grandi vie di comunicazione - aimed at the hydrogeological risk mitigation and at the early warning along the highways. The work was first based on air-photo interpretations and field investigations, in order to realize the geological map, geomorphological map and landslide inventory map. In the study area the geomorphology is strongly controlled by its bedrock geology and tectonics. The bedrock geology consists of Neogene sedimentary rocks that cover a thick stack of allochthonous nappes. These nappes consist of crystalline rocks mainly gneiss, phyllite and schist. A total of 835 landslides were mapped and the type of movement are represented mainly by slides and complex and subordinately flow. In order to estimate and validate landslide susceptibility the landslides were divided in two group. One group (training set) was used to prepare susceptibility map and the second group (validation set) to validate the map. Then, the selection of predisposing factors was performed, according with the geological and geomorphological settings of the study area: lithology, distance from tectonic elements, land use, slope, aspect, stream power index (SPI) and plan curvature. In order to evaluate landslide susceptibility Conditional Analysis was applied to Unique Conditions Units (UCUs

  3. UAV for landslide mapping and deformation analysis

    NASA Astrophysics Data System (ADS)

    Shi, Beiqi; Liu, Chun

    2015-12-01

    Unmanned aerial vehicle (UAV) can be a flexible, cost-effective, and accurate method to monitor landslides with high resolution aerial images. Images acquired on 05 May 2013 and 13 December 2014 of the Xishan landslide, China, have been used to produce a high-resolution ortho-mosaic of the entire landslide and digital elevation model (DEM). The UAV capability for imaging detection and displacements on the landslide surface has been evaluated, and the subsequent image processing approaches for suitably georectifying the data have been assessed. Objects derived from the segmentation of a multispectral image were used as classifying units for landslide object-oriented analysis. Spectral information together with various morphometric characteristics was applied for recognizing landslides from false positives. Digital image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two temporal UAV images corresponded to a visual interpretation of landslide change. Therefore, the UAV can demonstrate its capability for producing valuable landslide mapping data and deformation information.

  4. Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event

    NASA Astrophysics Data System (ADS)

    Park, D. W.; Nikhil, N. V.; Lee, S. R.

    2013-06-01

    This paper presents the results from application of a regional, physically-based stability model: Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) for a catchment on Woomyeon Mountain, Seoul, Korea. This model couples an infinite-slope stability analysis with a one-dimensional analytical solution to predict the transient pore pressure response to the infiltration of rainfall. TRIGRS also adopts the Geographic Information Systems (GIS) framework for determining the whole behaviour of a slope. In this paper, we suggest an index for evaluating the results produced by the model. Particular attention is devoted to the prediction of routes of debris flow, using a runoff module. In this context, the paper compares observed landslide and debris flow events with those predicted by the TRIGRS model. The TRIGRS model, originally developed to predict shallow landslides, has been extended in this study for application to debris flows. The results predicted by the TRIGRS model are presented as safety factor (FS) maps corresponding to transient rainfall events, and in terms of debris flow paths using methods proposed by several researchers in hydrology. In order to quantify the accuracy of the model, we proposed an index called LRclass (landslide ratio for each predicted FS class). The LRclass index is mainly applied in regions where the landslide scar area is not well defined (or is unknown), in order to avoid over-estimation of the model results. The use of the TRIGRS routing module was proposed to predict the paths of debris flow, especially in areas where the rheological properties and erosion rates of the materials are difficult to obtain. Although an improvement in accuracy is needed, this module is very useful for preliminary spatiotemporal assessment over wide areas. In summary, the TRIGRS model is a powerful tool of use to decision makers for susceptibility mapping, particularly when linked with various advanced applications using

  5. Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event

    NASA Astrophysics Data System (ADS)

    Park, D. W.; Nikhil, N. V.; Lee, S. R.

    2013-11-01

    This paper presents the results from the application of a regional, physically based stability model: Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) for a region on Woomyeon Mountain, Seoul, South Korea. This model couples an infinite-slope stability analysis with a one-dimensional analytical solution to predict the transient pore pressure response to the infiltration of rainfall. TRIGRS also adopts the geographic information system (GIS) framework for determining the whole behaviour of a slope. In this paper, we suggest an index for evaluating the results produced by the model. Particular attention is devoted to the prediction of routes of debris flow, using a runoff module. In this context, the paper compares observed landslide and debris flow events with those predicted by the TRIGRS model. The TRIGRS model, originally developed to predict shallow landslides, has been extended in this study for application to debris flows. The results predicted by the TRIGRS model are presented as safety factor (FS) maps corresponding to transient rainfall events, and in terms of debris flow paths using methods proposed by several researchers in hydrology. In order to quantify the effectiveness of the model, we proposed an index called LRclass (landslide ratio for each predicted FS class). The LRclass index is mainly applied in regions where the landslide scar area is not well defined (or is unknown), in order to avoid overestimation of the model results. The use of the TRIGRS routing module was proposed to predict the paths of debris flow, especially in areas where the rheological properties and erosion rates of the materials are difficult to obtain. Although an improvement in accuracy is needed, this module is very useful for preliminary spatio-temporal assessment over wide areas. In summary, the TRIGRS model is a powerful tool of use to decision makers for susceptibility mapping, particularly when linked with various advanced

  6. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm

    NASA Astrophysics Data System (ADS)

    Akgun, A.; Sezer, E. A.; Nefeslioglu, H. A.; Gokceoglu, C.; Pradhan, B.

    2012-01-01

    In this study, landslide susceptibility mapping using a completely expert opinion-based approach was applied for the Sinop (northern Turkey) region and its close vicinity. For this purpose, an easy-to-use program, "MamLand," was developed for the construction of a Mamdani fuzzy inference system and employed in MATLAB. Using this newly developed program, it is possible to construct a landslide susceptibility map based on expert opinion. In this study, seven conditioning parameters characterising topographical, geological, and environmental conditions were included in the FIS. A landslide inventory dataset including 351 landslide locations was obtained for the study area. After completing the data production stage of the study, the data were processed using a soft computing approach, i.e., a Mamdani-type fuzzy inference system. In this system, only landslide conditioning data were assessed, and landslide inventory data were not included in the assessment approach. Thus, a file depicting the landslide susceptibility degrees for the study area was produced using the Mamdani FIS. These degrees were then exported into a GIS environment, and a landslide susceptibility map was produced and assessed in point of statistical interpretation. For this purpose, the obtained landslide susceptibility map and the landslide inventory data were compared, and an area under curve (AUC) obtained from receiver operating characteristics (ROC) assessment was carried out. From this assessment, the AUC value was found to be 0.855, indicating that this landslide susceptibility map, which was produced in a data-independent manner, was successful.

  7. Landslide susceptibility assessment in the Pays d'Auge plateau (Normandy, France): application at different scales

    NASA Astrophysics Data System (ADS)

    Fressard, M.; Thiery, Y.; Maquaire, O.

    2012-04-01

    This research takes place in the hilly valleys of the Pays d'Auge where few scientific works have been conducted on landslide risk in spite of the activity of the processes. Moreover, the local authorities are still lacking operational mapping resources in order to improve the landuse planning and risk reduction. The susceptibility or hazard maps performed by statistical approaches can sometimes be difficult to understand by end-users. Therefore, they usually prefer to work with direct methods (i.e. expert mapping), even if they are often considered as subjective by scientists. Independently of the mapping method, it is difficult to obtain rapidly susceptibility maps on large areas that fit to the operational scale. These small scale maps are often not accepted by end-users, particularly because of the lack of accuracy of the available datasets. Then, this presentation focus on the production of landslide susceptibility maps at different scales, using GIS as a first stage towards operational landslide hazard assessment. The main objective is to show the research process coupling the geomorphological approach and the statistical modelling. This study is splitted in three major steps: (1) a geomorphological approach at the landslide scale; (2) a landslide susceptibility mapping at regional scale; and finally (3) a landslide susceptibility mapping at detailed scale. (1) Due to the lack of bibliographical and expert references on the existing landslides in this area, a first geomorphological study was conducted in order to build a landslide inventory with a detailed typology. Then, for each landslide type, the predisposing and triggering factors were defined. This first step is essential in order to supply the geomorphologist's expert opinion on this specific site. (2) These observations on predisposing factors were formalized into a heuristic model (SMCE) in order to assess the regional landslide susceptibility at small scale i.e. 1/100.000. In this case, only simple

  8. Integration of data-driven and physically-based methods to assess shallow landslides susceptibility

    NASA Astrophysics Data System (ADS)

    Lajas, Sara; Oliveira, Sérgio C.; Zêzere, José Luis

    2016-04-01

    Approaches used to assess shallow landslides susceptibility at the basin scale are conceptually different depending on the use of statistic or deterministic methods. The data-driven methods are sustained in the assumption that the same causes are likely to produce the same effects and for that reason a present/past landslide inventory and a dataset of factors assumed as predisposing factors are crucial for the landslide susceptibility assessment. The physically-based methods are based on a system controlled by physical laws and soil mechanics, where the forces which tend to promote movement are compared with forces that tend to promote resistance to movement. In this case, the evaluation of susceptibility is supported by the calculation of the Factor of safety (FoS), and dependent of the availability of detailed data related with the slope geometry and hydrological and geotechnical properties of the soils and rocks. Within this framework, this work aims to test two hypothesis: (i) although conceptually distinct and based on contrasting procedures, statistic and deterministic methods generate similar shallow landslides susceptibility results regarding the predictive capacity and spatial agreement; and (ii) the integration of the shallow landslides susceptibility maps obtained with data-driven and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow landslides occurrence. To evaluate these two hypotheses, we select the Information Value data-driven method and the physically-based Infinite Slope model to evaluate shallow landslides in the study area of Monfalim and Louriceira basins (13.9 km2), which is located in the north of Lisbon region (Portugal). The landslide inventory is composed by 111 shallow landslides and was divide in two independent groups based on temporal criteria (age ≤ 1983 and age > 1983): (i) the modelling group (51 cases) was used to define the weights for each predisposing factor

  9. Farmers' awareness on landslide susceptibility on their plots: a first step towards household resilience in the Rwenzori region, Western Uganda

    NASA Astrophysics Data System (ADS)

    Mertens, Kewan; Jacobs, Lies; Maes, Jan; Kervyn, Matthieu; Vranken, Liesbet

    2016-04-01

    In the mountainous area of the Rwenzori region, western Uganda, landslides frequently destroy houses and plots of farmers living and cultivating on unstable slopes. The impact of these landslides on the local livelihoods depends on the exposure and the resilience of the households. Both the exposure and the resilience can be modified to a certain extent with specific measures, e.g. planting slope stabilizing trees of paying for (informal) insurance. The adoption of such measures and the willingness to accept measures imposed by local governments crucially depends on the local awareness of landslide risk. The aim of this research is to estimate awareness on landslide susceptibility, as a proxy for landslide risk, among household heads in a landslide prone area in the Rwenzori region, Western Uganda. The objective is to compare household and plot characteristics between aware and unaware households. This will allow us to identify those households which are less aware of landslide susceptibility and therefore most likely to be less resilient when exposed to landslide risk. We use data from a susceptibility map constructed in 2016 and a structured household survey conducted in the Rwenzori region in 2015. The susceptibility map is based on a SRTM 30m DEM and validated with field observations, while the household survey includes the answers of more than 450 households that have been asked to evaluate the landslide susceptibility on their plots. Simple probit models at plot level are used to compare the estimated landslide susceptibility with the modelled susceptibility. We use this comparison to identify the household characteristics of those households that do not correctly estimate the landslide susceptibility on their plots. We will exploit the fact that landslide susceptibility is very space specific and that households can therefore have plots in both susceptible and unsusceptible areas. The research is currently ongoing, but we hypothesize that younger farmers

  10. Spatial Resolution Effects of Digital Terrain Models on Landslide Susceptibility Analysis

    NASA Astrophysics Data System (ADS)

    Chang, K. T.; Dou, J.; Chang, Y.; Kuo, C. P.; Xu, K. M.; Liu, J. K.

    2016-06-01

    The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.

  11. The landslide inventory as the basis of susceptibility and hazard assessment

    NASA Astrophysics Data System (ADS)

    Copons, Ramon; Linares, Rogelio; Cirés, Jordi; Tallada, Anna

    2010-05-01

    Landslide inventory involves the location, classification, volume, activity and others characteristics of the landslides in an area (Fell et al, 2008). Landslide inventory can includes also the location of lithologies prone to instability, structural conditions and silent witnesses (affected vegetation, damaged buildings, etc). This high quality information about landslides requires the use of images acquired from remote sensing and the field observation. Landslide inventory is the basis for susceptibility, hazard and risk assessment (Fell et al., 2008) because supplies information contrasted on the field. Unfortunately, landslide inventory has limitations so it usually is not totally complete or landslides boundaries mapped are influenced by the techniques used, resources and the ability of the field geologist. These usual errors included in the landslide inventory are difficult to estimate but are crucial to know since can create greater errors on results of susceptibility, hazard and risk assessed by further approaches including heuristic, empirical and deterministic ones. In many cases it is not possible to make an inventory including all the landslides occurred in the past because morphology of older landslides could be extremely eroded, or they are partially or totally covered by younger vents. Moreover, several external factors (like extreme forestation, urbanization or erosion) do not allow their identification or difficult their delimitation. Our work focuses on: (i) the establishment of a procedure for gathering data to complete a landslide inventory, and (ii) the determination of the error included in the landslide inventory whichever the field geologist. These issues are useful for administrations for: (i) undertaking landslide inventories across the country by several geologists, and (ii) managing hazard knowing limitations of the hazard zoning obtained from the landslide inventory. For accomplishing our purposes we have selected an area located about

  12. Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events

    NASA Astrophysics Data System (ADS)

    Deb, Sanjit K.; El-Kadi, Aly I.

    2009-07-01

    The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km 2 was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919-2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region. The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of

  13. Landslide susceptibility modelling using Fuzzy Logic in the oued Larbaa basin (Oriental Rif, Morocco)

    NASA Astrophysics Data System (ADS)

    Zezere, J. L.; Sadiki, A.; Faleh, A.; Elkoulali, E.; Garcia, R. A. C.; Oliveira, S. C.

    2009-04-01

    Together with flash floods and soil erosion, landslides are relevant natural hazards that affect marly slopes in the oued Larbaa basin, located in the Oriental Rif, Morocco. Landslides have been generated important economic, social and ecological effects, by the destruction of farming lands, and by the collapse and interruption of roads and other human infrastructures (e.g., houses). The reduction of socio-economic losses due to landslide activity needs to be accomplished through the implementation of a comprehensive mitigation landslide risk program. The first task of this program is the definition of landslide susceptible areas based on the study of relationships between spatial distribution of past landslides and the cartographic set of landslide predisposing factors. Therefore, the major aim of this work is to create a landslide susceptibility map for the study area. The oued Larbaa basin, located northwards the Taza city, has an area of 245 km2 and the elevation ranges between 450 m and 1300 m. Morphology is characterized by rounded hills cutting marly formations essentially of Cretaceous age. Land use is dominated by cereal cultures and a few sparse tree plantations. Natural vegetation shows a very high level of degradation and usually appears as shrub tufts. The inventory of instability events has been made for the study area and it includes both rainfall-triggered rotational and shallow translational slides. These landslides were included into a GIS database that comprises also the following landslide predisposing factors: slope angle, aspect and curvature, inverse wetness index, lithology and land use. The susceptibility assessment was carried out for each type of landslide (rotational slides and shallow translational slides) under the assumption that future landslides will occur under the same environmental patterns that generated landslides in the past. The modelling of landslide susceptibility was made using the Fuzzy Logic method (Fuzzy Algebraic

  14. Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy

    NASA Astrophysics Data System (ADS)

    Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

    2013-08-01

    Several mathematical models are used to predict the spatial distribution characteristics of landslides to mitigate damage caused by landslide disasters. Although some studies have achieved excellent results around the world, few studies take the inter-relationship of the selected points (training points) into account. In this paper, we present the Fuzzy c-means (FCM) algorithm as an optimal method for choosing the appropriate input landslide points as training data. Based on different combinations of the Fuzzy exponent (m) and the number of clusters (c), five groups of sampling points were derived from formal seed cells points and applied to analyze the landslide susceptibility in Mizunami City, Gifu Prefecture, Japan. A logistic regression model is applied to create the models of the relationships between landslide-conditioning factors and landslide occurrence. The pre-existing landslide bodies and the area under the relative operative characteristic (ROC) curve were used to evaluate the performance of all the models with different m and c. The results revealed that Model no. 4 (m=1.9, c=4) and Model no. 5 (m=1.9, c=5) have significantly high classification accuracies, i.e., 90.0%. Moreover, over 30% of the landslide bodies were grouped under the very high susceptibility zone. Otherwise, Model no. 4 and Model no. 5 had higher area under the ROC curve (AUC) values, which were 0.78 and 0.79, respectively. Therefore, Model no. 4 and Model no. 5 offer better model results for landslide susceptibility mapping. Maps derived from Model no. 4 and Model no. 5 would offer the local authorities crucial information for city planning and development.

  15. A landslide susceptibility assessment in urban areas based on existing data: an example from the Iguaná Valley, Medellín City, Colombia

    NASA Astrophysics Data System (ADS)

    Klimeš, J.; Rios Escobar, V.

    2010-10-01

    Fast urbanization and the morphological conditions of the Iguaná River Basin, Medellín, Colombia have forced many people to settle on landslide prone slopes as evidenced by extensive landslide induced damage. In this study we used existing disaster databases (inventories) in order to examine the spatial and temporal variability of landsliding within this watershed. The spatial variability of landsliding was examined using "expert-based" and "weighted" landslide susceptibility models. The constructed landslide susceptibility maps demonstrate consistent results irrespective of the underlying method. These show that at least 55.9% of the watershed is highly or very highly susceptible to landsliding. In addition, the temporal distribution of landsliding was analyzed and compared with climatic data. Results show that the area has a distinct bimodal rainfall distribution, and it is clear that landsliding is particularly frequent during the later rainy season between October and November. Moreover, landslides are more common during La Niña years. It is recommended that the existing landslide inventories are improved so as to be of greater use in the future land use planning of the watershed. The construction of landslide susceptibility maps based on existing data represents a significant step towards landslide mitigation in the area. Using susceptibility and hazard assessment during the developmental process should lessen the need for disaster response at a later stage.

  16. Typologically-differentiated landslide susceptibility assessment for the territory of Georgia

    NASA Astrophysics Data System (ADS)

    Tsamalashvili, Tamar; Chelidze, Tamaz; Malet, Jean-Philippe; Mathieu, Alexandre

    2014-05-01

    Mass movement is one of the major natural hazards affecting mountainous regions, which lead to the damage to infrastructure, economical harm and life loss. Georgia is highly affected by landslides because of the complex geological and geomorphological structure, the high geodynamic activity of the region and the possibility of important rainfall events. Up to now, most of the research has been carried out on landslide hazard assessment in Georgia consisted in landslide qualitative description, data collection and inventory mapping. The objective of this work is to propose a national scale and typologically-differentiated landslide susceptibility map based on a spatial database constructed in the framework of the "Pan-European and nation-wide landslide susceptibility assessment" project of Council of Europe. The development of such a map has a significant importance from the scientific view as well as from the practical vision for Georgian stakeholders. A database with more than 3300 mass movement events have been created during the project. The database contains information on the location, date, event type and intensity of the event. The database distinguishes slide, falls and flows processes. A first susceptibility map is created using three types predictors (lithology, slope, landuse) for different climatic and topographic regions of the country. Further, the dependence of the mass movements location to triggering factors such as GPA (ground peak acceleration) and precipitation is investigated. The results of the analysis are presented and discussed.

  17. Do landslides follow landslides?

    NASA Astrophysics Data System (ADS)

    Samia, Jalal; Temme, Arnaud; Bregt, Arnold; Wallinga, Jakob; Guzzetti, Fausto; Ardizzone, Francesca; Rossi, Mauro

    2016-04-01

    Landslide susceptibility maps are typically obtained by quantifying relations between landslides and conditioning attributes. Here, we take a fundamentally different starting point: path dependency and self-organization, i.e. the effect of landslides on landslides. We test two hypotheses: first, that landslides do preferentially follow landslides, and second, that follow-up landslides are different from those that do not follow other slides. Results indicate that there is indeed a considerable amount of overlap among landslides that affect the overall affected area by landsliding. This is more than expected: the number of overlaps among landslides is more than would occur of slides were randomly placed in the study area. Overlaps of slides with previous slides occur frequently within a period of about ten years after a previous slide, yet decrease considerably over time. Also the second hypothesis is confirmed: follow-up landslides indeed have different properties in terms of power law and shape than those that are not associated. Particularly, follow-up landslides are larger and more elongated than non-follow up landslides. Moreover, after fitting an inverse gamma function to the magnitude-frequency distributions of follow-up and non-follow-up slides, it was found that the alpha parameter that controls the prevalence of very extreme events, is much larger for follow-up slides than for non-follow-up slides. Also the rollover value is substantially larger for follow-up landslides than non-follow up landslides . The prevalence of follow-up slides in the first approximately ten years after a previous slides, and the fact that follow-up slides are different from other slides, should have implications for susceptibility studies. Apparently, susceptibility (conventionally a purely spatial concept) changes with the time since previous landslides happened. We explore possible mechanisms for this that may allow us to include these temporal changes in landslide

  18. Optimal landslide susceptibility zonation based on multiple forecasts

    NASA Astrophysics Data System (ADS)

    Rossi, Mauro; Guzzetti, Fausto; Reichenbach, Paola; Mondini, Alessandro Cesare; Peruccacci, Silvia

    2010-01-01

    Environmental and multi-temporal landslide information for an area in Umbria, Italy, was exploited to produce four single and two combined landslide susceptibility zonations. The 78.9 km 2 study area was partitioned in 894 slope units, and the single susceptibility zonations were obtained through linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR), and by training a neural network (NN). The presence or absence of landslides in the slope units in the period from pre-1941 to 1996 (training set) was used as the dependent variable for the terrain classification. Next, adopting a regression approach, two "optimal" combinations of the four single zonations were prepared. The single and the combined zonations were tested against landslides in the 9-year period from 1997 to 2005 (validation set). Different metrics were used to evaluate the quality of the susceptibility zonations, including degree of model fit, uncertainty in the probability estimates, and model prediction skills. These metrics showed that the degree of model fit was not a good indicator of the model forecasting skills. Zonations obtained through classical multivariate classification techniques (LDA, QDA and LR) produced superior predictions when compared to the NN model, that over fitted the landslide information in the training set. LDA and LR produced less uncertain zonations than QDA and NN. The combined models resulted in a reduced number of errors and in less uncertain predictions; an important result that suggests that the combination of landslide susceptibility zonations can provide "optimal" susceptibility assessments.

  19. Landslide susceptibility assessment in the Southern part of Vrancea-Buzau Seismic Region

    NASA Astrophysics Data System (ADS)

    Micu, M.; Balteanu, D.; Jurchescu, M.; Chendes, V.; Sima, M.; Zumpano, V.

    2012-04-01

    Vrancea-Buzau Region (extending across some 8,000 km2 of the Romania's Curvature Carpathians) represents one of Europe's most seismically-active areas. The sub-crustal earthquakes occurring there are responsible for the damages inflicted to half of Romania's territory, and its effects are extending towards NW in Ukraine and to the SW, to Bulgaria. The region's seismicity represents an important landslide preparing (and in certain conditions even triggering) factor, integrating itself into a landslide-prone framework which includes heavy summer rainfalls alternating with long-lasting droughts and spring showers overlapping snowmelt. The purpose of this paper is to outline the landslides mechanisms, forms and processes, and also to present a landslide susceptibility assessment for the southern half of the region, a case-study within FP7 MC-ITN Project CHANGES, as a basis for the entire region's landslide risk assessment. The GIS database includes thematic maps, aerial images, different-scale DEM's (and derived parameters), climatic data and landslide inventories derived from geomorphological field mapping and local authorities (Buzau County Inspectorate for Emergency Situations) databases. The evaluation of susceptibility classes was performed through statistical analysis (bivariate and multivariate), the results being also validated through statistical methods and also on field. In the mean time, the susceptibility classes resulted helped in assessing the quality of a previously-done, expert-judgment-based landslide susceptibility assessment which covers the entire Romanian territory (Balteanu at al., 2010). The importance of this paper is that it provides a strong background for the assessment of landslide hazard, vital in establishing proper risk management strategies, necessary for such an affected yet poor region of Europe.

  20. Using the statistical analysis method to assess the landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Chan, Hsun-Chuan; Chen, Bo-An; Wen, Yo-Ting

    2015-04-01

    This study assessed the landslide susceptibility in Jing-Shan River upstream watershed, central Taiwan. The landslide inventories during typhoons Toraji in 2001, Mindulle in 2004, Kalmaegi and Sinlaku in 2008, Morakot in 2009, and the 0719 rainfall event in 2011, which were established by Taiwan Central Geological Survey, were used as landslide data. This study aims to assess the landslide susceptibility by using different statistical methods including logistic regression, instability index method and support vector machine (SVM). After the evaluations, the elevation, slope, slope aspect, lithology, terrain roughness, slope roughness, plan curvature, profile curvature, total curvature, average of rainfall were chosen as the landslide factors. The validity of the three established models was further examined by the receiver operating characteristic curve. The result of logistic regression showed that the factor of terrain roughness and slope roughness had a stronger impact on the susceptibility value. Instability index method showed that the factor of terrain roughness and lithology had a stronger impact on the susceptibility value. Due to the fact that the use of instability index method may lead to possible underestimation around the river side. In addition, landslide susceptibility indicated that the use of instability index method laid a potential issue about the number of factor classification. An increase of the number of factor classification may cause excessive variation coefficient of the factor. An decrease of the number of factor classification may make a large range of nearby cells classified into the same susceptibility level. Finally, using the receiver operating characteristic curve discriminate the three models. SVM is a preferred method than the others in assessment of landslide susceptibility. Moreover, SVM is further suggested to be nearly logistic regression in terms of recognizing the medium-high and high susceptibility.

  1. Geomorphological mapping of shallow landslides using UAVs

    NASA Astrophysics Data System (ADS)

    Fiorucci, Federica; Giordan, Daniele; Dutto, Furio; Rossi, Mauro; Guzzetti, Fausto

    2015-04-01

    The mapping of event shallow landslides is a critical activity, due to the large number of phenomena, mostly with small dimension, affecting extensive areas. This is commonly done through aerial photo-interpretation or through field surveys. Nowadays, landslide maps can be realized exploiting other methods/technologies: (i) airborne LiDARs, (ii) stereoscopic satellite images, and (iii) unmanned aerial vehicles (UAVs). In addition to the landslide maps, these methods/technologies allow the generation of updated Digital Terrain Models (DTM). In December 2013, in the Collazzone area (Umbria, Central Italy), an intense rainfall event triggered a large number of shallow landslides. To map the landslides occurred in the area, we exploited data and images obtained through (A) an airborne LiDAR survey, (B) a remote controlled optocopter (equipped with a Canon EOS M) survey, and (C) a stereoscopic satellite WorldView II MS. To evaluate the mapping accuracy of these methods, we select two landslides and we mapped them using a GPS RTK instrumentation. We consider the GPS survey as the benchmark being the most accurate system. The results of the comparison allow to highlight pros and cons of the methods/technologies used. LiDAR can be considered the most accurate system and in addition it allows the extraction and the classification of the digital surface models from the surveyed point cloud. Conversely, LiDAR requires additional time for the flight planning, and specific data analysis user capabilities. The analysis of the satellite WorldView II MS images facilitates the landslide mapping over large areas, but at the expenses of a minor resolution to detect the smaller landslides and their boundaries. UAVs can be considered the cheapest and fastest solution for the acquisition of high resolution ortho-photographs on limited areas, and the best solution for a multi-temporal analysis of specific landslide phenomena. Limitations are due to (i) the needs of optimal climatic

  2. A method for producing digital probabilistic seismic landslide hazard maps

    USGS Publications Warehouse

    Jibson, R.W.; Harp, E.L.; Michael, J.A.

    2000-01-01

    The 1994 Northridge, California, earthquake is the first earthquake for which we have all of the data sets needed to conduct a rigorous regional analysis of seismic slope instability. These data sets include: (1) a comprehensive inventory of triggered landslides, (2) about 200 strong-motion records of the mainshock, (3) 1:24 000-scale geologic mapping of the region, (4) extensive data on engineering properties of geologic units, and (5) high-resolution digital elevation models of the topography. All of these data sets have been digitized and rasterized at 10 m grid spacing using ARC/INFO GIS software on a UNIX computer. Combining these data sets in a dynamic model based on Newmark's permanent-deformation (sliding-block) analysis yields estimates of coseismic landslide displacement in each grid cell from the Northridge earthquake. The modeled displacements are then compared with the digital inventory of landslides triggered by the Northridge earthquake to construct a probability curve relating predicted displacement to probability of failure. This probability function can be applied to predict and map the spatial variability in failure probability in any ground-shaking conditions of interest. We anticipate that this mapping procedure will be used to construct seismic landslide hazard maps that will assist in emergency preparedness planning and in making rational decisions regarding development and construction in areas susceptible to seismic slope failure. ?? 2000 Elsevier Science B.V. All rights reserved.

  3. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    NASA Astrophysics Data System (ADS)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  4. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks

    NASA Astrophysics Data System (ADS)

    Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

    2012-10-01

    This study investigates the potential application of artificial neural networks in landslide susceptibility mapping in the Hoa Binh province of Vietnam. A landslide inventory map of the study area was prepared by combining landslide locations investigated through three projects during the last 10 years. Some recent landslide locations were identified based on SPOT satellite images, field surveys, and existing literature. The images have a spatial resolution of 2.5 m. Ten landslide conditioning factors were utilized in the multilayer feed-forward neural network analysis: slope, aspect, relief amplitude, lithology, land use, soil type, rainfall, distance to roads, distance to rivers and distance to faults. Two back-propagation training algorithms, Levenberg-Marquardt and Bayesian regularization, were utilized to determine synoptic weights using a training dataset. Relative importance of each landslide conditioning factor was assessed using the above mentioned synoptic weights. The final connection weights obtained in the training phase were applied to the entire study area to produce landslide susceptibility indexes. The results were then imported to a GIS and landslide susceptibility maps were constructed. Landslide locations not used in the training phase were used to verify and compare the results of the landslide susceptibility maps. Finally, the two landslide susceptibility maps were validated using the prediction-rate method. Subsequently, areas under the prediction curves were assessed. The prediction accuracy of landslide susceptibility maps produced by the Bayesian regularization neural network and the Levenberg-Marquardt neural network were 90.3% and 86.1% respectively. These results indicate that the two models seem to have good predictive capability. The Bayesian regularization network model appears more robust and efficient than the Levenberg-Marquardt network model for landslide susceptibility mapping.

  5. Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

    PubMed

    Dou, Jie; Tien Bui, Dieu; Yunus, Ali P; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

    2015-01-01

    This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. PMID:26214691

  6. Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

    PubMed Central

    Dou, Jie; Tien Bui, Dieu; P. Yunus, Ali; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

    2015-01-01

    This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. PMID:26214691

  7. Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region.

    PubMed

    Pineda, M C; Viloria, J; Martínez-Casasnovas, J A

    2016-04-01

    Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions. PMID:27030353

  8. Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region.

    PubMed

    Pineda, M C; Viloria, J; Martínez-Casasnovas, J A

    2016-04-01

    Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions. PMID:27358998

  9. GIS Applied to Landslide Hazard Mapping and Evaluation in North-East Wales

    NASA Astrophysics Data System (ADS)

    Miller, S. A.; Degg, M.

    2009-04-01

    Slope instability is a significant environmental hazard in North-East Wales, responsible for important damage to roads and built-up areas. During the late 1980s and the 1990s, systematic landslide mapping and hazard modelling was completed for a number of landslide prone areas within Great Britain, but no such study has to date been carried out for North Wales. This research reports on the creation of a digital landslide inventory for North-East Wales and the use of a Geographical Information System (GIS) to create the first landslide susceptibility models for the area. The research has resulted in the most comprehensive landslide inventory of North-East Wales completed to date. This was accomplished through a combination of aerial photograph interpretation, field mapping and data collection from secondary sources (e.g. consultancy reports, newspapers), yielding a database that records 430 landslides for the area. This represents a 76% (186 landslides) increase on the number of landslides recorded for the area in the UK national landslides database. The landslides in North-East Wales are almost entirely situated inland, with less than 1% on the coast. Approximately 84% of the landslides occur within drift geology and 16% in solid geology. For the slides of known type, 46% are translational slides, 47% rotational slides, 3% flows, 3% falls and 1% complex failures. The type and distribution of landsliding in the area shows notable differences to that found in areas of similar bedrock geology elsewhere in the UK (e.g. Derbyshire and South Wales). Analysis shows that the main landslide controlling parameters in North-East Wales are: lithology, drift material, slope angle, proximity to known faults (structural weaknesses) and proximity to fluvial channels (undercutting). These factors were weighted statistically based on their estimated contribution to slope instability, and combined to create the landslide susceptibility models using a statistical (multiple logistic

  10. GeoFIS: An integrated tool for the assessment of landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Osna, Turgay; Sezer, Ebru Akcapinar; Akgun, Aykut

    2014-05-01

    In this study, requirements of landslide susceptibility mapping by a Mamdani fuzzy inference system (FIS) are identified, and a single standalone application (GeoFIS) is developed. GeoFIS includes two main open source libraries, one for GIS operations and the other for creating Mamdani FIS. As a result, it is possible to construct a landslide susceptibility map based on expert opinion, to visualize maps instantly and to measure model performance. GeoFIS supports all steps of the landslide susceptibility mapping process, starting from data deployment and ending with performance measurement. In GeoFIS, visual controls allow use of the inferred results and actual landslide occurrence information, and ROC-AUC values are calculated automatically. Moreover, a confusion matrix is produced, and alternative measurement schemes such as recall are suggested, to reveal those performance details not observable with ROC-AUC and to create trust in the inferred results. GeoFIS is applied to the Trabzon region of northern Turkey, and the recall and ROC-AUC values were .902 and .602, respectively.

  11. The contribution of PSInSAR interferometry to landslide susceptibility assessment in weak rock-dominated areas

    NASA Astrophysics Data System (ADS)

    Oliveira, Sérgio C.; Zêzere, José L.; Catalão, João; Nico, Giovanni

    2015-04-01

    In the Grande da Pipa river basin (north of Lisbon, Portugal), 64% of the landslides inventoried occur on a particular weak rock lithological unit composed by clay and with sandstone intercalations, that is present in 58% of the study (Oliveira et al., 2014). Deep-seated slow moving rotational slides occur essentially on this lithological unit and are responsible for the major damages verified along roads and buildings in the study area. Within this context, landslide hazard assessment, is limited by two major constrains: (i) the slope instability signs may not be sufficiently clear and observable and consequently may not be correctly identifiable through traditional geomorphologic survey techniques and (ii) the non-timely recognition of precursor signs of instability both in landslides activated for the first time and in previously landslide-affected areas (landslide reactivation). To encompass these limitations, the Persistent Scatterer synthetic aperture radar interferometry technique is applied to a data set of 16 TerraSAR-X SAR images, from April of 2010 to March of 2011, available for a small test site of 12.5 square kilometers (Laje-Salema) located on south-central part of the study area. This work's specific objectives are the following: (i) to evaluate the capacity of the Persistent Scatterer displacement maps in assessing landslide susceptibility at the regional scale, and (ii) to assess the capacity of landslide susceptibility maps based on historical landslide inventories to predict the location of actual terrain displacement measured by the Persistent Scatterers technique. Landslide susceptibility was assessed for the test site using the Information Value bivariate statistical method and the susceptibility scores were exported to the Grande da Pipa river basin. The independent validation of the landslide susceptibility maps was made using the historical landslide inventory and the Persistent Scatterer displacement map. Results are compared by computing

  12. Evaluation of Landslide Mapping Techniques and LiDAR-based Conditioning Factors

    NASA Astrophysics Data System (ADS)

    Mahalingam, R.; Olsen, M. J.

    2014-12-01

    Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities to plan and prepare for these damaging events. Mapping landslide susceptible locations using GIS and remote sensing techniques is gaining popularity in the past three decades. These efforts use a wide variety of procedures and consider a wide range of factors. Unfortunately, each study is often completed differently and independently of others. Further, the quality of the datasets used varies in terms of source, data collection, and generation, which can propagate errors or inconsistencies into the resulting output maps. Light detection and ranging (LiDAR) has proved to have higher accuracy in representing the continuous topographic surface, which can help minimize this uncertainty. The primary objectives of this paper are to investigate the applicability and performance of terrain factors in landslide hazard mapping, determine if LiDAR-derived datasets (slope, slope roughness, terrain roughness, stream power index and compound topographic index) can be used for predictive mapping without data representing other common landslide conditioning factors, and evaluate the differences in landslide susceptibility mapping using widely-used statistical approaches. The aforementioned factors were used to produce landslide susceptibility maps for a 140 km2 study area in northwest Oregon using six representative techniques: frequency ratio, weights of evidence, logistic regression, discriminant analysis, artificial neural network, and support vector machine. Most notably, the research showed an advantage in selecting fewer critical conditioning factors. The most reliable factors all could be derived from a single LiDAR DEM, reducing the need for laborious and costly data gathering. Most of the six techniques showed similar statistical results; however, ANN showed less accuracy for predictive mapping. Keywords : Li

  13. Comparing performances of heuristic and logistic regression models for a spatial landslide susceptibility assessment in Maramures, County, Northwestern Romania

    NASA Astrophysics Data System (ADS)

    Mǎgut, F. L.; Zaharia, S.; Glade, T.; Irimuş, I. A.

    2012-04-01

    Various methods exist in analyzing spatial landslide susceptibility and classing the results in susceptibility classes. The prediction of spatial landslide distribution can be performed by using a variety of methods based on GIS techniques. The two very common methods of a heuristic assessment and a logistic regression model are employed in this study in order to compare their performance in predicting the spatial distribution of previously mapped landslides for a study area located in Maramureš County, in Northwestern Romania. The first model determines a susceptibility index by combining the heuristic approach with GIS techniques of spatial data analysis. The criteria used for quantifying each susceptibility factor and the expression used to determine the susceptibility index are taken from the Romanian legislation (Governmental Decision 447/2003). This procedure is followed in any Romanian state-ordered study which relies on financial support. The logistic regression model predicts the spatial distribution of landslides by statistically calculating regressive coefficients which describe the dependency of previously mapped landslides on different factors. The identified shallow landslides correspond generally to Pannonian marl and Quaternary contractile clay deposits. The study region is located in the Northwestern part of Romania, including the Baia Mare municipality, the capital of Maramureš County. The study focuses on the former piedmontal region situated to the south of the volcanic mountains Gutâi, in the Baia Mare Depression, where most of the landslide activity has been recorded. In addition, a narrow sector of the volcanic mountains which borders the city of Baia Mare to the north has also been included to test the accuracy of the models in different lithologic units. The results of both models indicate a general medium landslide susceptibility of the study area. The more detailed differences will be discussed with respect to the advantages and

  14. Comparing landslide inventories: The map depends on the method

    USGS Publications Warehouse

    Wills, C.J.; McCrink, T.P.

    2002-01-01

    Landslide inventory maps are generally prepared by interpreting the geomorphic expression of landsliding on aerial photos, topographic maps, or on the ground. Distinctive landslide geomorphology allows the recognition and mapping of landslides, although there are always landslides that have very subtle expression and are not identified. The difficulties of mapping landslides based on their geomorphic expression are amplified in heavily forested terrain. The ground surface is obscured by tree cover on aerial photographs, and landslide-related features are often hidden. This limitation affects not only aerial photo interpretation, but also interpretation of topographic maps, which are based on aerial photographs. We compared five maps showing landslides in the Laurel Quadrangle in the Santa Cruz Mountains, California. These include a geologic map, a map prepared for the county based on interpretation of aerial photographs, a map prepared by us based on aerial photographs and compilation of previous work, a map of features interpreted from the U.S. Geological Survey 7.5-minute topographic map, and a detailed field-based landslide map. Comparison of these maps shows that the geologic map identifies few landslides, but most landslides on the geologic map are also shown on the other maps. The two maps based mainly on aerial photo interpretation tend to show the larger slides, but there is only about 60 percent correspondence of landslide areas between the two. Comparing the reconnaissance techniques with the much more detailed field mapping shows that the reconnaisance maps emphasize the large slides of bedrock and identify a lower percentage of shallow debris slides and debris flows.

  15. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)

    NASA Astrophysics Data System (ADS)

    Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele

    2015-11-01

    The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire

  16. Seismic controls on contemporary sediment yields in Italy: the link with landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Vanmaercke, Matthias; Ardizzone, Francesca; Rossi, Mauro; Guzzetti, Fausto

    2015-04-01

    Recent studies showed that contemporary catchment sediment yields (SY, [t/km²/y]) at regional and continental scales are strongly correlated to spatial patterns of seismic activity. Nonetheless, we currently have little insights into the mechanisms that explain these correlations. We therefore investigated how spatial patterns of SY in Italy are linked to patterns of seismic activity. Based on inventories of registered and historical earthquakes in Italy we generated maps indicating the cumulative peak ground acceleration (PGA) associated with different ranges of earthquake magnitudes and explored to which extent these maps explained observed spatial patterns in SY for 106 catchments across Italy. Results showed that SY was significantly correlated to the cumulative PGA associated with small (Mw < 3) but frequent (i.e. thousands per year) earthquakes, but not to the cumulative PGA associated with large earthquakes (Mw > 6) that occurred over the past 1000 years. Analyses of a dataset of ca. 500 000 landslides across Italy showed very similar trends: spatial patterns of landsliding are correlated to seismicity. However, landslides in similar lithological units were generally stronger correlated to patterns of weak but frequent seismicity than to the occurrence of large earthquake events. Differences in catchment sediment yield were also correlated to spatial patterns of mapped landslides and landslide-prone lithological units. This clearly indicates that seismicity may lead to higher sediment yields by increasing the occurrence of landslides. However, these increases are more likely attributable to an increased landslide susceptibility than to the direct triggering of landslides. This also suggests that, on average and at a regional scale, the geomorphic impact of weak but frequent earthquakes can be much larger than the geomorphic impact of large but rare earthquake events.

  17. Continental level landslide susceptibility assessment in the context of the European Union's Soil Thematic Strategy

    NASA Astrophysics Data System (ADS)

    Günther, A.; Van Den Eeckhaut, M.; Reichenbach, P.; Hervás, J.; Malet, J.; Guzzetti, F.

    2011-12-01

    classifies 13% of the EU territory as generally prone to landslides, thus requiring more detailed, quantitative inventory-based susceptibility evaluations ("Tier 2"). Compared to globally parameterized susceptibility models, the terrain-differentiated assessment is able to spatially predict landslide occurrences more accurately at the continental scale. Future work will focus on the preparation of typologically differentiated continental-level landslide susceptibility models and maps over Europe.

  18. Challenges and limitations of a statistical Pan-European landslide susceptibility evaluation

    NASA Astrophysics Data System (ADS)

    Jurchescu, Marta-Cristina; Günther, Andreas; Malet, Jean-Philippe; Reichenbach, Paola; Micu, Mihai

    2016-04-01

    In the framework of the European Thematic Strategy for Soil Protection, landslides are regarded as one of the several soil threats which need to be considered in view of a sustainable soil use. With the aim of identifying landslide priority areas in Europe, research related to the Pan-European landslide susceptibility assessment is progressing on a second version of the map (ELSUS v2) which bases upon a heuristic spatial multi-criteria evaluation (SMCE). In the context of an enriching continental landslide inventory from various European countries, this study aims at exploring the degree to which statistical and typologically-differentiated European-wide landslide susceptibility modeling can be conducted, while looking into the challenges and limitations raised by spatial analyses at small scales (1: 1Mill.). Despite the efforts put into collecting a continent-wide dataset of landslides, the present data is still characterized by large incoherencies. In order to comply with the current assessment requirements for objectivity and typological differentiation, the European landslide database is analyzed and classified according to the main quality indicators (completeness and spatial accuracy) as well as landslide categories (topple/falls versus slide/flows). The selected thematic environmental input data (lithology, slope angle and land cover) are classified separately according to their relevance for the occurrence of the landslide types. Statistical assessments, using modern multivariate data mining techniques like Classification and Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS), are attempted separately for each of the seven climate-physiographic zones used for the preparation of ELSUS, distinguished on the basis of morphometric and climatic data. To ensure that information is collected in an objective and unbiased manner, a sampling strategy is proposed for each zone. Accordingly, areas for sampling landslide absences are restricted

  19. Analysis of Rainfall induced landslides susceptibility along 50-110k section of the Southern Cross Island Highway in Taiwan

    NASA Astrophysics Data System (ADS)

    Wu, Tzu-Chia; Chan, Hsun-Chuan; Hong, Yu-Jou

    2014-05-01

    -duration and high-intensity, such as Morakot event, the model with the rainfall factor will increase the landslide predictive capability. The other two events remain similar landslide predictive capability with and without the landslide causative factor in the model. Moreover, the maps of potential landslide were delineated to discuss the influence of rainfall on the landslide susceptibility analysis. The landslide susceptibilities were separated into four levels, including high, medium, low, and steady. The results can support planning of the surrounding area along the highway and be a reference for disaster warning

  20. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

    NASA Astrophysics Data System (ADS)

    Vasu, Nikhil N.; Lee, Seung-Rae

    2016-06-01

    An ever-increasing trend of extreme rainfall events in South Korea owing to climate change is causing shallow landslides and debris flows in mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion KRW (South Korean Won) in losses in addition to fatalities every year. The most common type of landslide observed is the shallow landslide, which occurs at 1-3 m depth, and may mobilize into more catastrophic flow-type landslides. Hence, to predict potential landslide areas, susceptibility maps are developed in a geographical information system (GIS) environment utilizing available morphological, hydrological, geotechnical, and geological data. Landslide susceptibility models were developed using 163 landslide points and an equal number of nonlandslide points in Mt. Woomyeon, Seoul, and 23 landslide conditioning factors. However, because not all of the factors contribute to the determination of the spatial probability for landslide initiation, and a simple filter or wrapper-based approach is not efficient in identifying all of the relevant features, a feedback-loop-based hybrid algorithm was implemented in conjunction with a learning scheme called an extreme learning machine, which is based on a single-layer, feed-forward network. Validation of the constructed susceptibility model was conducted using a testing set of landslide inventory data through a prediction rate curve. The model selected 13 relevant conditioning factors out of the initial 23; and the resulting susceptibility map shows a success rate of 85% and a prediction rate of 89.45%, indicating a good performance, in contrast to the low success and prediction rate of 69.19% and 56.19%, respectively, as obtained using a wrapper technique.

  1. Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images

    NASA Astrophysics Data System (ADS)

    Fiorucci, F.; Cardinali, M.; Carlà, R.; Rossi, M.; Mondini, A. C.; Santurri, L.; Ardizzone, F.; Guzzetti, F.

    2011-06-01

    We tested the possibility of using digital, color aerial ortho-photographs and monoscopic, panchromatic satellite images of comparable spatial and radiometric resolution, to map recent landslides in Italy and to update existing measures of landslide mobilization. In a 90-km 2 area in Umbria, central Apennines, rainfall resulted in abundant landslides in the period from September 2004 to June 2005. Analysis of the rainfall record determined the approximate dates of landslide occurrence and revealed that the slope failures occurred in response to moderately wet rainfall periods. The slope failures occurred primarily in cultivated terrain and left subtle morphological and land cover signatures, making the recognition and mapping of the individual landslides problematic. Despite the difficulty with the identification of the landslides without the use of stereoscopic visualization, visual analysis of the aerial and satellite images allowed mapping 457 new landslides, ranging in area 3.0 × 10 1 < AL < 2.5 × 10 4 m 2, for a total landslide area ALT = 6.92 × 10 5 m 2. To identify the landslides, the investigators adopted the interpretation criteria commonly used to identify and map landslides on aerial photography. The result confirms that monoscopic, very high resolution images taken by airborne and satellite sensors can be used to prepare landslide maps even where slope failures are difficult to detect, provided the imagery has sufficient geometric and radiometric resolutions. The different dates of the aerial (March 2005) and the satellite (June-July 2005) images allowed the temporal segmentation of the landslide information, and studying the statistics of landslide area and volume for different periods. Compared to pre-existing information on the abundance and size of the landslides in the area, the inventory obtained by studying the aerial and satellite images proved more complete. The new mapping showed 145% more landslides and 85% more landslide area than a pre

  2. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models

    NASA Astrophysics Data System (ADS)

    Hong, Haoyuan; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat

    2016-04-01

    Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua

  3. Root reinforcement and its implications in shallow landsliding susceptibility on a small alpine catchment

    NASA Astrophysics Data System (ADS)

    Morandi, M. C.; Farabegoli, E.; Onorevoli, G.

    2012-04-01

    Roots shear resistance offers a considerable contribution to hill-slope stability on vegetated terrains. Through the pseudo-cohesion of shrubs, trees and turf's roots, the geomechanical properties of soils can be drastically increased, exerting a positive influence on the hillslope stability. We analysed the shallow landsliding susceptibility of a small alpine catchment (Duron valley, Central Dolomites, Italy) that we consider representative of a wide altitude belt of the Dolomites (1800 - 2400 m a.s.l). The catchment is mostly mantled by grass (Nardetum strictae s.l.), with clustered shrubs (Rhododendron hirsutum and Juniperus nana), and trees (Pinus cembra, Larix decidua and Picea abies). The soil depth, investigated with direct and indirect methods, ranges from 0 to 180 cm, with its peak at the hollow axes. Locally, the bedrock, made of Triassic volcanic rocks, is deeply incised by the Holocene drainage network. Intensive grazing of cows and horses pervades the catchment area and cattle-trails occupy ca 20% of the grass cover. We used laboratory and field tests to characterize the geotechnical properties of these alpine soils; moreover we designed and tested an experimental device that measures, in situ, the shear strengths of the grass mantle. In the study area we mapped 18 shallow landslides, mostly related to road cuts and periodically reactivated as retrogressive landslides. The triggering mechanisms of these shallow landslides were qualitatively analysed at large scale and modelled at smaller scale. We used SHALSTAB to model the shallow landsliding susceptibility of the catchment at the basin scale and SLIDE (RocScience) to compute the Safety Factor at the versant scale. Qualitative management solutions are provided, in order to reduce the shallow landsliding susceptibility risk in this alpine context.

  4. Slope stability susceptibility evaluation parameter (SSEP) rating scheme - An approach for landslide hazard zonation

    NASA Astrophysics Data System (ADS)

    Raghuvanshi, Tarun Kumar; Ibrahim, Jemal; Ayalew, Dereje

    2014-11-01

    In this paper a new slope susceptibility evaluation parameter (SSEP) rating scheme is presented which is developed as an expert evaluation approach for landslide hazard zonation. The SSEP rating scheme is developed by considering intrinsic and external triggering parameters that are responsible for slope instability. The intrinsic parameters which are considered are; slope geometry, slope material (rock or soil type), structural discontinuities, landuse and landcover and groundwater. Besides, external triggering parameters such as, seismicity, rainfall and manmade activities are also considered. For SSEP empirical technique numerical ratings are assigned to each of the intrinsic and triggering parameters on the basis of logical judgments acquired from experience of studies of intrinsic and external triggering factors and their relative impact in inducing instability to the slope. Further, the distribution of maximum SSEP ratings is based on their relative order of importance in contributing instability to the slope. Finally, summation of all ratings for intrinsic and triggering parameter based on actual observation will provide the expected degree of landslide in a given land unit. This information may be utilized to develop a landslide hazard zonation map. The SSEP technique was applied in the area around Wurgessa Kebelle of North Wollo Zonal Administration, Amhara National Regional State in northern Ethiopia, some 490 km from Addis Ababa. The results obtained indicates that 8.33% of the area fall under Moderately hazard and 83.33% fall within High hazard whereas 8.34% of the area fall under Very high hazard. Further, in order to validate the LHZ map prepared during the study, active landslide activities and potential instability areas, delineated through inventory mapping was overlain on it. All active landslide activities and potential instability areas fall within very high and high hazard zone. Thus, the satisfactory agreement confirms the rationality of

  5. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy

    NASA Astrophysics Data System (ADS)

    Conoscenti, Christian; Rotigliano, Edoardo; Cama, Mariaelena; Caraballo-Arias, Nathalie Almaru; Lombardo, Luigi; Agnesi, Valerio

    2016-05-01

    A statistical approach was employed to model the spatial distribution of rainfall-triggered landslides in two areas in Sicily (Italy) that occurred during the winter of 2004-2005. The investigated areas are located within the Belice River basin and extend for 38.5 and 10.3 km2, respectively. A landslide inventory was established for both areas using two Google Earth images taken on October 25th 2004 and on March 18th 2005, to map slope failures activated or reactivated during this interval. Geographic Information Systems (GIS) were used to prepare 5 m grids of the dependent variables (absence/presence of landslide) and independent variables (lithology and 13 DEM-derivatives). Multivariate Adaptive Regression Splines (MARS) were applied to model landslide susceptibility whereas receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate model performance. To evaluate the robustness of the whole procedure, we prepared 10 different samples of positive (landslide presence) and negative (landslide absence) cases for each area. Absences were selected through two different methods: (i) extraction from randomly distributed circles with a diameter corresponding to the mean width of the landslide source areas; and (ii) selection as randomly distributed individual grid cells. A comparison was also made between the predictive performances of models including and not including the lithology parameter. The models trained and tested on the same area demonstrated excellent to outstanding fit (AUC > 0.8). On the other hand, predictive skill decreases when measured outside the calibration area, although most of the landslides occur where susceptibility is high and the overall model performance is acceptable (AUC > 0.7). The results also showed that the accuracy of the landslide susceptibility models is higher when lithology is included in the statistical analysis. Models whose absences were selected using random circles showed a

  6. Smartphone-based Data Acquisition for Landslide Susceptibility Assessment

    NASA Astrophysics Data System (ADS)

    Son, J.; Lee, S.; Oh, M.; Yun, D. E.; Park, H. D.; Kim, S.

    2015-12-01

    The objective of this paper is to suggest a smartphone application for collecting geological data in the assessment process of landslide susceptibility. Spatial and geological information collection is the most crucial part in risk assessment while the evaluation of large area is often conducted using geographic information system (GIS). GIS-based landslide susceptibility assessment requires large database to ensure the reliability of analyses. The digitalized collection tool can help its users obtaining precise measurements and reducing time for the data collection process. In this study, the mobile application which measures structural information from inbuilt sensors and accepts various data types had been developed. It can collect the triggering factors that promote or inhibit the hazard occurrence in the training domain areas. The database includes various spatial information such as slope, aspect, joint set and their coordinates, as well as hazard inventory. Collected geospatial datasets with smartphone application can be applied to a series of geospatial processes regarding the assessment of the landslide susceptibility.

  7. MORFEO project: use of remote sensing technology for mapping, monitoring and forecasting landslides

    NASA Astrophysics Data System (ADS)

    Guzzetti, F.; Candela, L.; Carlà, R.; Fornaro, G.; Lanari, R.; Mondini, A.; Ober, G.; Fiorucci, F.; Zeni, G.

    2009-04-01

    MORFEO, an Italian acronym for Monitoring Landslide Risk exploiting Earth Observation Technology, is a 3-year research and development project of the Italian Space Agency, carried out in the framework of the Italian national earth observation programme. The project primary contract is Carlo Gavazzi Space, a leading enterprise in space technology and remote sensing applications in Italy. The project research team is composed by seven research institutes of the Italian National Research Council, and six university departments. The team has consolidated experience in landslide detection and mapping, landslide hazard assessment and risk evaluation, remote sensing technology (e.g., laser, optical, radar, GPS) for landslide detection, mapping and monitoring. MORFEO aims at the design, development and demonstration of a prototype system that exploits multiple satellite technologies to support the Italian national civil protection offices to manage landslide risk in Italy. Research activities conducted within the MORFEO project consist chiefly in testing, evaluating and improving EO technologies to increase the current capabilities to detect, map, monitor and forecast landslides in Italy. More precisely, the activities include: (i) detection and mapping landslides exploiting medium-resolution to very-high resolution satellite optical images, (ii) landslide monitoring, through the integration of ground based and satellite technologies, including GPS and DInSAR, (iii) landslide susceptibility, hazard and risk modelling using information obtained processing optical and radar data, (iv) vulnerability and damage assessment, exploiting optical and radar sensors, and (v) landslides forecasting, using thresholds, models and remote sensing data. We provide examples of some of the preliminary results obtained in the MOFEO project.

  8. Evaluating performances of simplified physically based landslide susceptibility models.

    NASA Astrophysics Data System (ADS)

    Capparelli, Giovanna; Formetta, Giuseppe; Versace, Pasquale

    2015-04-01

    Rainfall induced shallow landslides cause significant damages involving loss of life and properties. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. This paper presents a package of GIS based models for landslide susceptibility analysis. It was integrated in the NewAge-JGrass hydrological model using the Object Modeling System (OMS) modeling framework. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices (GOF) by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system offers the possibility to investigate and fairly compare the quality and the robustness of models and models parameters, according a procedure that includes: i) model parameters estimation by optimizing each of the GOF index separately, ii) models evaluation in the ROC plane by using each of the optimal parameter set, and iii) GOF robustness evaluation by assessing their sensitivity to the input parameter variation. This procedure was repeated for all three models. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, Average Index (AI) optimization coupled with model M3 is the best modeling solution for our test case. This research was funded by PON Project No. 01_01503 "Integrated Systems for Hydrogeological Risk

  9. Object-based Landslide Mapping: Examples, Challenges and Opportunities

    NASA Astrophysics Data System (ADS)

    Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris

    2016-04-01

    Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple

  10. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues

    NASA Astrophysics Data System (ADS)

    Catani, F.; Lagomarsino, D.; Segoni, S.; Tofani, V.

    2013-11-01

    Despite the large number of recent advances and developments in landslide susceptibility mapping (LSM) there is still a lack of studies focusing on specific aspects of LSM model sensitivity. For example, the influence of factors such as the survey scale of the landslide conditioning variables (LCVs), the resolution of the mapping unit (MUR) and the optimal number and ranking of LCVs have never been investigated analytically, especially on large data sets. In this paper we attempt this experimentation concentrating on the impact of model tuning choice on the final result, rather than on the comparison of methodologies. To this end, we adopt a simple implementation of the random forest (RF), a machine learning technique, to produce an ensemble of landslide susceptibility maps for a set of different model settings, input data types and scales. Random forest is a combination of Bayesian trees that relates a set of predictors to the actual landslide occurrence. Being it a nonparametric model, it is possible to incorporate a range of numerical or categorical data layers and there is no need to select unimodal training data as for example in linear discriminant analysis. Many widely acknowledged landslide predisposing factors are taken into account as mainly related to the lithology, the land use, the geomorphology, the structural and anthropogenic constraints. In addition, for each factor we also include in the predictors set a measure of the standard deviation (for numerical variables) or the variety (for categorical ones) over the map unit. As in other systems, the use of RF enables one to estimate the relative importance of the single input parameters and to select the optimal configuration of the classification model. The model is initially applied using the complete set of input variables, then an iterative process is implemented and progressively smaller subsets of the parameter space are considered. The impact of scale and accuracy of input variables, as well as

  11. Assessing the impact of input data quality on the modelling of shallow landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Zieher, Thomas; Rutzinger, Martin; Geitner, Clemens

    2015-04-01

    Shallow landslides are a widespread phenomenon in mountain regions of the world often posing a serious threat to human living. Hence many recent studies aim at assessing landslide susceptibility in space and time involving various kinds of models (i.e. heuristical, statistical or physically-based). Among others these models commonly require for digital terrain models (DTM) and their derivatives as well as detailed landslide inventories as input data. On the basis of a detailed multitemporal landslide inventory covering three selected communities in Vorarlberg (Austria) focussing on shallow landslides (i.e. debris slides with a maximum scar depth of 1-2 m) and two series of airborne laser scanning data the impact of (i) the DTM used, (ii) varying spatial resolutions and (iii) the influence of different algorithms for the calculation of derivatives are discussed. The distributions of slope, measures of curvature and topographic indices as well as more complex neighbourhood indices (e.g. landform elements derived by the GRASS-tool r.geomorphon) are evaluated within landslide scar areas. In addition the sensitivity of an expert-based approach to the various input data is assessed in ROC-space. Results show that the time of acquisition and spatial resolution of the DTM are essential factors for the quality of the resulting susceptibility map while the algorithm used for the calculation of derivatives plays a minor role. This work has been conducted within C3S-ISLS, which is funded by the Austrian Climate and Energy Fund, 5th ACRP Program.

  12. Experiences of a WEB based test site platform for landslide susceptibility and the use of remote sensing interferometric techniques for monitoring landslide movements in Sweden

    NASA Astrophysics Data System (ADS)

    Löfroth, H.; Hultén, C.; Ledwith, M.; Nisser-Larsson, M.; Righini, G.

    2009-04-01

    GIS platform was developed, which comprises two test sites in Sweden, Vagnhärad and Sundsvall, in which the Swedish methodology has been applied. One purpose of the platform was to illustrate the stability conditions for end-users as the municipalities for easier decision making. In addition, within the PREVIEW project, the applicability of resistivity measurements to obtain an overview of the soil profile as a basis for geotechnical field investigations and the use of satellite radar data (differential SAR interferometry (DIFSAR)) for detection of small movements of the ground, as an early warning of a large landslide have been tested for Swedish conditions. In Vagnhärad, a 200 m wide and 50 m long landslide occurred in 1997 which destroyed or undermined seven single-family houses (Andersson et. al. 2000). In the Sundsvall area two minor slides in vegetated areas occurred 2006 and 2007. The Italian private enterprise Telespazio (a Finmeccanica/Thales company) has been responsible for the WebGIS platform and conducted the DIFSAR analyses, while the Italian research Institute for Environmental Methodological Analysis, (IMAA-CNR) carried out the resistivity measurements. The WebGIS platform for the Swedish test sites consists of one landslides inventory map, three landslides susceptibility maps (1a, 1b and 2) based on the Swedish methodology, DIFSAR displacement maps, 2D Electrical Resistivity Tomographies (only Vagnhärad test site) and a satellite image as a background. The landslides inventory map contains areas with old landslide scars, gullies, ongoing erosion and fill. The intention is that the information on this map could be combined with the susceptibility maps. The susceptibility maps 1a and 1b contains the results from Sub-stage 1a and 1b of the overview stability mapping and these results are presented on the WebGIS the same way as it is normally done in Sweden. Susceptibility map 1a is a map divided into stability zones including areas with or without

  13. A preliminary regional assessment of earthquake-induced landslide susceptibility for Vrancea Seismic Region

    NASA Astrophysics Data System (ADS)

    Micu, Mihai; Balteanu, Dan; Ionescu, Constantin; Havenith, Hans; Radulian, Mircea; van Westen, Cees; Damen, Michiel; Jurchescu, Marta

    2015-04-01

    ) with head scarps near mountain tops and close to faults is similar to the one of large mass movements for which a seismic origin is proved (such as in the Tien Shan, Pamir, Longmenshan, etc.). Thus, correlations between landslide occurrence and combined seismotectonic and climatic factors are needed to support a regional multi-hazard risk assessment. The purpose of this paper is to harmonize for the first time at a regional scale the landslide predisposing factors and seismotectonic triggers and to present a first qualitative insight into the earthquake-induced landslide susceptibility for the Vrancea Seismic Region in terms of a GIS-based analysis of Newmark displacement (ND). In this way, it aims at better defining spatial and temporal distribution patterns of earthquake-triggered landslides. Arias Intensity calculation involved in the assessment considers both regional seismic hazard aspects and singular earthquake scenarios (adjusted by topography amplification factors). The known distribution of landslides mapped through digital stereographic interpretation of high-resolution aerial photos is compared with digital active fault maps and the computed ND maps to statistically outline the seismotectonic influence on slope stability in the study area. The importance of this approach resides in two main outputs. The fist one, of a fundamental nature, by providing the first regional insight into the seismic landslides triggering framework, is allowing us to understand if deep-focus earthquakes may trigger massive slope failures in an area with a relatively smooth relief (compared to the high mountain regions in Central Asia, the Himalayas), considering possible geologic and topographic site effects. The second one, more applied, will allow a better accelerometer instrumentation and monitoring of slopes and also will provide a first correlation of different levels of seismic shaking with precipitation recurrences, an important relationship within a multi-hazard risk

  14. Landslide hazard mapping with selected dominant factors: A study case of Penang Island, Malaysia

    NASA Astrophysics Data System (ADS)

    Tay, Lea Tien; Alkhasawneh, Mutasem Sh.; Ngah, Umi Kalthum; Lateh, Habibah

    2015-05-01

    Landslide is one of the destructive natural geohazards in Malaysia. In addition to rainfall as triggering factos for landslide in Malaysia, topographical and geological factors play important role in the landslide susceptibility analysis. Conventional topographic factors such as elevation, slope angle, slope aspect, plan curvature and profile curvature have been considered as landslide causative factors in many research works. However, other topographic factors such as diagonal length, surface area, surface roughness and rugosity have not been considered, especially for the research work in landslide hazard analysis in Malaysia. This paper presents landslide hazard mapping using Frequency Ratio (FR) and the study area is Penang Island of Malaysia. Frequency ratio approach is a variant of probabilistic method that is based on the observed relationships between the distribution of landslides and each landslide-causative factor. Landslide hazard map of Penang Island is produced by considering twenty-two (22) landslide causative factors. Among these twenty-two (22) factors, fourteen (14) factors are topographic factors. They are elevation, slope gradient, slope aspect, plan curvature, profile curvature, general curvature, tangential curvature, longitudinal curvature, cross section curvature, total curvature, diagonal length, surface area, surface roughness and rugosity. These topographic factors are extracted from the digital elevation model of Penang Island. The other eight (8) non-topographic factors considered are land cover, vegetation cover, distance from road, distance from stream, distance from fault line, geology, soil texture and rainfall precipitation. After considering all twenty-two factors for landslide hazard mapping, the analysis is repeated with fourteen dominant factors which are selected from the twenty-two factors. Landslide hazard map was segregated into four categories of risks, i.e. Highly hazardous area, Hazardous area, Moderately hazardous area

  15. Landslide hazard mapping with selected dominant factors: A study case of Penang Island, Malaysia

    SciTech Connect

    Tay, Lea Tien; Alkhasawneh, Mutasem Sh.; Ngah, Umi Kalthum; Lateh, Habibah

    2015-05-15

    Landslide is one of the destructive natural geohazards in Malaysia. In addition to rainfall as triggering factos for landslide in Malaysia, topographical and geological factors play important role in the landslide susceptibility analysis. Conventional topographic factors such as elevation, slope angle, slope aspect, plan curvature and profile curvature have been considered as landslide causative factors in many research works. However, other topographic factors such as diagonal length, surface area, surface roughness and rugosity have not been considered, especially for the research work in landslide hazard analysis in Malaysia. This paper presents landslide hazard mapping using Frequency Ratio (FR) and the study area is Penang Island of Malaysia. Frequency ratio approach is a variant of probabilistic method that is based on the observed relationships between the distribution of landslides and each landslide-causative factor. Landslide hazard map of Penang Island is produced by considering twenty-two (22) landslide causative factors. Among these twenty-two (22) factors, fourteen (14) factors are topographic factors. They are elevation, slope gradient, slope aspect, plan curvature, profile curvature, general curvature, tangential curvature, longitudinal curvature, cross section curvature, total curvature, diagonal length, surface area, surface roughness and rugosity. These topographic factors are extracted from the digital elevation model of Penang Island. The other eight (8) non-topographic factors considered are land cover, vegetation cover, distance from road, distance from stream, distance from fault line, geology, soil texture and rainfall precipitation. After considering all twenty-two factors for landslide hazard mapping, the analysis is repeated with fourteen dominant factors which are selected from the twenty-two factors. Landslide hazard map was segregated into four categories of risks, i.e. Highly hazardous area, Hazardous area, Moderately hazardous area

  16. Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

    NASA Astrophysics Data System (ADS)

    Epifânio, Bruno; Zêzere, José Luís; Neves, Mário

    2014-10-01

    The coastal zone of Lourinhã (located in Central Portugal) is characterized by beach-cliff systems, where beaches are narrow and cliffs have notorious slope instability. These cliffs evolve by different types of landslides, which are one of the main sources of natural hazard and risk in this coastal region. In this work, aerial photo interpretation and a systematic field survey were performed in order to obtain an inventory of landslides of the following types: rotational slides, translational slides and debris flows. The entire coast was then split into 261 terrain mapping units. For each unit, landslide predisposing factors were derived and classified: cliff elevation, slope angle (maximum, mean and standard deviation), potential solar radiation, slope curvature (profile and plan), lithological units and geologic structure. The predictive susceptibility models were computed for each type of landslide using a bi-variate statistical method - the Information Value Method. The degree of fit and the predictive capacity of the models were assessed using the Effectiveness Ratio, the standard Receiver Operator Characteristic curves and the respective Area Under Curve. Results show that each landslide typology occurs in particular terrain conditions. Individual susceptibility models evidence better predictive capacity than susceptibility model for total landslides.

  17. Relationship among Probability of Failure, Landslide Susceptibility Index, and Rainfall Parameters

    NASA Astrophysics Data System (ADS)

    Lee, C. T.

    2015-12-01

    Common or basic susceptibility may exist in a region. This common feature has been tested to be true in several drainage basins in Taiwan in recent years. A multi-temporal landslide inventory was commonly used in building a landslide susceptibility model for a region. An event landslide inventory and triggering factors were also used to build an event landslide susceptibility model in recent years. An event-based landslide susceptibility model is dependent on that event, and different event models exhibit different susceptibility pattern. However, if we extract the triggering factors from the event model, then the model would be event-independent and can represent the basic susceptibility of the region. We found that different event-independent susceptibility model for the same region are similar in pattern, and is similar also to a susceptibility model built by a multi-temporal landslide inventory at that region. On this basis, we can use an event-independent susceptibility model to represent the basic susceptibility of the region with confidence. We tested the relationship between probability of failure and rainfall intensity, as well as total rainfall at each basic susceptibility bin. It was found that the relation is good; the probability of failure increases with an increase in the rainfalls and also an increase in the susceptibility. A fitting surface of probability of landslide failure using a rainfall parameter and the basic susceptibility as two independent variables was done and the example will be presented.

  18. Hazard mapping related to structurally controlled landslides in Southern Leyte, Philippines

    NASA Astrophysics Data System (ADS)

    Luzon, Paul Kenneth; Montalbo, Kristina; Galang, Jam; Sabado, Jasmine May; Escape, Carmille Marie; Felix, Raquel; Mahar Francisco Lagmay, Alfredo

    2016-04-01

    The 2006 Guinsaugon landslide in Saint Bernard, Southern Leyte, is one of the largest known landslides in the Philippines in recent history. It consists of a 15-20 million m3 rockslide-debris avalanche from an approximately 675 m high mountain weakened by continuous movement of the Philippine Fault. The catastrophic Guinsaugon landslide killed 1221 people and displaced 19 000 residents over its 4.5 km path. To investigate the present-day morphology of the scar and potential failure that may occur, analysis of a 5 m resolution InSAR-derived digital elevation model was conducted using Coltop3D and Matterocking software, leading to the generation of a landslide hazard map for the province of Southern Leyte in central Philippines. The dip and dip direction of discontinuity sets that contribute to gravitational failure in mountainous areas of the province were identified and measured using a lower Schmidt-Lambert color scheme. After measurement of the morpho-structural orientations, potential sites of failure were analyzed. Conefall was then utilized to compute the extent of rock mass runout. Results of the analysis show instability in the scarp area of the 2006 Guinsaugon landslide and in adjacent slopes because of the presence of steep discontinuities that range from 45 to 60°. Apart from the 2006 Guinsaugon landslide site, runout models simulated farther rock mass extent in its adjacent slopes, revealing a high potential for fatal landslides to happen in the municipality of Saint Bernard. Concerned agencies may use maps produced in the same manner as this study to identify possible sites where structurally controlled landslides can occur. In a country like the Philippines, where fractures and faults are common, this type of simulated hazard maps would be useful for disaster prevention and facilitate disaster risk reduction efforts for landslide-susceptible areas.

  19. A study of large scaled landslide susceptibility by using Weight-of-Evidence method: A case study from the Laonung River Watershed, Southern Taiwan

    NASA Astrophysics Data System (ADS)

    Chen, Chih-Hao; Lin, Ching-Weei; Tseng, Chih-Ming

    2013-04-01

    The Laonung River watershed which covered an area 1367 km2 is selected as the study area to construct large scaled landslides susceptibility model by using Weight-of-Evidence method. Within the study area, 950 landslides with an area more than 10 ha are identified from FORMOSAT 2 images, aerial photos, and LiDAR derived 1 m high resolution Digital-Elevation-Model (DEM) taken after typhoon Moratko in 2009. Among these, 271 landslides occurred recently and they show bare ground in aerial photos and satellite images. 318 landslides are vegetation recovery, and they are inferred from their topographic characteristics by using aerial photos with topographic map. Additionally, 361 landslides with topographic features of deep seated landslide such as crown main escarpment, down slop scarp, up slop scarp, and transverse cracks are identified from 1m resolution LiDAR derived DEM. Weight-of-Evidence method is a bivariate statistical approach which uses the concept of Bayes' theorem and odds ratio to calculate the weighting of each evaluation parameter. In this study, ten parameters including slope gradient, slope aspect, landform, elevation, lithology, dip-slope, undercut slope, normalized difference vegetation index (NDVI), the distance from geological structure and the distance from stream are selected as evaluation factors. For each parameter, the weighting for landslide susceptibility is calculated, and the weighting of all parameters are then summed to generate the landslide susceptibility map. The study results show the area under the success rate curves reaching 80%, and 70% of large scaled landslides falls within top 30% susceptibility index. It implies that the susceptibility model constructed by this study can effectively predict the location of large scaled landslides in the study area. The results can benefit to the management of mitigation plan of the large scaled landslides in southern Taiwan.

  20. GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China

    NASA Astrophysics Data System (ADS)

    Wang, Qiqing; Li, Wenping; Chen, Wei; Bai, Hanying

    2015-10-01

    The main goal of this study is to produce landslide susceptibility maps for the Qianyang County of Baoji city, China, using both certainty factor (CF) and index of entropy (IOE) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 81 landslide locations were detected. Out of these, 56 (70%) landslides were randomly selected as training data for building landslide susceptibility models and the remaining 25 (30%) were used for the validation purposes. Then, a total number of 15 landslide causative factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, the sediment transport index (STI), the stream power index (SPI), the topographic wetness index (TWI), geomorphology, lithology, and rainfall, were used in the analysis. The susceptibility maps produced using CF and IOE models had five different susceptibility classes such as very low, low, moderate, high, and very high. Finally, the output maps were validated using the validation data (i.e., 30% landslide location data that was not used during the model construction), using the area under the curve (AUC) method. The `success rate' curve showed that the area under the curve for CF and IOE models were 0.8433 (84.33%) and 0.8227 (82.27%) accuracy, respectively. Similarly, the validation result showed that the susceptibility map using CF model has the higher prediction accuracy of 82.32%, while for IOE model it was 80.88%. The results of this study showed that the two landslide susceptibility maps obtained were successful and can be used for preliminary land use planning and hazard mitigation purpose.

  1. Improving accuracy in shallow-landslide susceptibility analyses at regional scale

    NASA Astrophysics Data System (ADS)

    Iovine, Giulio G. R.; Rago, Valeria; Frustaci, Francesco; Bruno, Claudia; Giordano, Stefania; Muto, Francesco; Gariano, Stefano L.; Pellegrino, Annamaria D.; Conforti, Massimo; Pascale, Stefania; Distilo, Daniela; Basile, Vincenzo; Soleri, Sergio; Terranova, Oreste G.

    2015-04-01

    Calabria (southern Italy) is particularly exposed to geo-hydrological risk. In the last decades, slope instabilities, mainly related to rainfall-induced landslides, repeatedly affected its territory. Among these, shallow landslides, characterized by abrupt onset and extremely rapid movements, are among the most destructive and dangerous phenomena for people and infrastructures. In this study, a susceptibility analysis to shallow landslides has been performed by refining a method recently applied in Costa Viola - central Calabria (Iovine et al., 2014), and only focusing on landslide source activations (regardless of their possible evolution as debris flows). A multivariate approach has been applied to estimating the presence/absence of sources, based on linear statistical relationships with a set of causal variables. The different classes of numeric causal variables have been determined by means of a data clustering method, designed to determine the best arrangement. A multi-temporal inventory map of sources, mainly obtained from interpretation of air photographs taken in 1954-1955, and in 2000, has been adopted to selecting the training and the validation sets. Due to the wide extend of the territory, the analysis has been iteratively performed by a step-by-step decreasing cell-size approach, by adopting greater spatial resolutions and thematic details (e.g. lithology, land-use, soil, morphometry, rainfall) for high-susceptible sectors. Through a sensitivity analysis, the weight of the considered factors in predisposing shallow landslides has been evaluated. The best set of variables has been identified by iteratively including one variable at a time, and comparing the results in terms of performance. Furthermore, susceptibility evaluations obtained through logistic regression have been compared to those obtained by applying neural networks. Obtained results may be useful to improve land utilization planning, and to select proper mitigation measures in shallow-landslide

  2. GIS based probabilistic analysis for shallow landslide susceptibility using Point Estimate Method

    NASA Astrophysics Data System (ADS)

    Park, Hyuck-Jin; Lee, Jung-Hyun

    2016-04-01

    The mechanical properties of soil materials (such as cohesion and friction angle) used in physically based model for landslide susceptibility analyses have been identified as the major source of uncertainty caused by complex geological conditions and spatial variability. In addition, limited sampling is another source of the uncertainty since the input parameters were obtained from broad areas. Therefore, in order to properly account for the uncertainty in mechanical parameters, the parameters were considered as random variables and the probabilistic analysis method has been used. In many previous researches, the Monte Carlo simulation has been widely used as the probabilistic analysis. However, since the Monte Carlo method requires a large number of repeated calculations and a great deal of calculation time to evaluate the probability of failure, it is not easy to adopt this approach to extensive study area due to a huge amount of computation time for regional study area. Therefore, this study proposes the alternative probabilistic analysis approach using the Point Estimate method (PEM), which has the advantage overcoming the shortcomings of the Monte Carlo simulation. This is because PEM requires only the mean and standard deviation of random variables and can obtain the probability of failure with a simple calculation. This proposed approach was performed in GIS based environments and applied to the study are which was experienced a large amount of landslides. The spatial database for input parameters and landslide inventory map were constructed in a grid-based GIS environment. To evaluate the performance of the model, the results of the landslide susceptibility assessment were compared with the landslide inventories using ROC graph.

  3. Landslide Mapping Using SqueeSAR Data

    NASA Astrophysics Data System (ADS)

    Ferretti, A.; Bellotti, F.; Alberti, S.; Allievi, J.; Del Conte, S.; Tamburini, A.; Broccolato, M.; Ratto, S.; Alberto, W.

    2011-12-01

    SqueeSAR represents the most recent advancement of PSInSAR algorithm. By exploiting signal radar returns both from Permanent and Distributed Scatterers (PS and DS), it is able to detect millimetre displacements over long periods and large areas and to obtain a significant increase in the spatial density of ground measurement points. SqueeSAR analysis is complementary to conventional geological and geomorphological studies in landslide mapping over wide areas, traditionally based on aerial-photo interpretation and field surveys. However, whenever surface displacement rates are low (mm to cm per year), assessing landslide activity is difficult or even impossible without a long-term monitoring tool, as in the case of Deep-seated Gravitational Slope Deformations (DGSD), typically characterized by large areal extent and subtle surface displacement. The availability of surface displacement time series per each measurement point allows one to have both a synoptic overview, at regional scale, as well as an in depth characterization of the instability phenomena analyzed, a meaningful support to the design of traditional monitoring networks and the efficiency testing of remedial works. When data archives are available, SqueeSAR can also provide valuable information before the installation of any terrestrial measurement system. The Italian authorities increasing interest in the application of SqueeSAR as a standard monitoring tool to help hydrogeological risk assessment, resulted in a national project, Piano Straordinario di Telerilevamento (PST), founded by the Ministry of the Environment. The aim of the project was to create the first interferometric database on a national scale for mapping unstable areas. More than 12,000 ERS and ENVISAT radar scenes acquired over Italy were processed spanning the period 1992-2010, proving that, in less than ten years, radar interferometry has become a standard monitoring tool. Recently, many regional governments in Italy have applied

  4. Landslides in Nicaragua - Mapping, Inventory, Hazard Assessment, Vulnerability Reduction, and Forecasting Attempts

    NASA Astrophysics Data System (ADS)

    Dévoli, G.; Strauch, W.; Álvarez, A.; Muñoz, A.; Kjekstad, O.

    2009-04-01

    access, manage, update and distribute in a short time to all sectors and users; and finally, the need of a comprehensive understanding of landslide processes. Many efforts have been made in the last 10 years to gain a more comprehensive and predictive understanding of landslide processes in Nicaragua. Since 1998, landslide inventory GIS based maps have been produced in different areas of the country, as part of international and multidisciplinary development projects. Landslide susceptibility and hazard maps are available now at INETEŔs Website for all municipalities of the country. The insights on landslide hazard have been transmitted to governmental agencies, local authorities, NGÓs, international agencies to be used in measures for risk reduction. A massive application example was the integration of hazard assessment studies in a large house building program in Nicaragua. Hazards of landslides, and other dangerous phenomena, were evaluated in more than 90 house building projects, each with 50 - 200 houses to be build, sited mainly in rural areas of the country. For more than 7000 families, this program could finally assure that their new houses were build in safe areas. Attempts have been made to develop a strategy for early warning of landslides in Nicaragua. First approaches relied on precipitation gauges with satellite based telemetry which were installed in some Nicaraguan volcanoes where lahars occur frequently. The occurrence of lahars in certain gullies could be detected by seismic stations. A software system gave acoustic alarm at INETEŔs Monitoring Centre when certain trigger levels of the accumulated precipitation were reached. The monitoring and early warning for all areas under risk would have required many rain gauges. A new concept is tested which uses near real time precipitation estimates from NOAA meteorological satellite data. A software system sends out alarm messages if strong or long lasting rains are observed over certain landslide "hot spots

  5. Landslide Mapping and Modeling Using Remote Sensing, GIS and Statistical Analysis of District Muzaffarabad, Pakistan

    NASA Astrophysics Data System (ADS)

    Khalid, Nimrah; Mushtaq, Saman

    2016-07-01

    Occurrence factors of Landslide hazard can be natural such as high slopes, geological conditions and lineaments, faults, rain, and river cutting. Man-made factors such as road cuttings, deforestation or development can also contribute to the landsliding. The focus of this study was to model those landslides susceptible prone to hazard areas which in turn can help for the development, urbanization and for setting up rules or regulations to save nature and environment of the area. The focal of the current research work was the Earthquake of October, 2005 also known as Kashmir Earthquake, the epicenter location of the earthquake 34°29'35″N 73°37'44″E at height of ~2000 from mean sea level and ~20 Km North-East from Muzaffarabad city, Azad Jammu & Kashmir, at the scale of 1:50000 Geological map of 43-F/11, tehsil Nauseri area. The techniques used in this research is based on theorem of Bayes's bivariat statistic (weight of evidence) which predicts the events geographically and on input layers and the relationship of event. A relationship between event of landslide and factors was studied and analyzed using this method. Subsequently a prediction of the occurrence of the spatial location of the landslide event was established successfully. The relationship of distribution of landslide and factors layers was calculated using the statistical methods which enabled to predict the landslides zones in different areas. The methodology applied proved that the success rate was 80% landslide occurred in 18% area and prediction rate was 70% of landslides occurred in 70% of area. The use satellite remote sensing data, and GIS with the integration of statistical method are definitely an effective tool for predicting the future landslide prone areas.

  6. Potential of SENTINEL-1A for Nation-Wide Routine Updates of Active Landslide Maps

    NASA Astrophysics Data System (ADS)

    Lazecky, M.; Canaslan Comut, F.; Nikolaeva, E.; Bakon, M.; Papco, J.; Ruiz-Armenteros, A. M.; Qin, Y.; de Sousa, J. J. M.; Ondrejka, P.

    2016-06-01

    Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.

  7. Non-susceptible landslide areas in Italy and in the Mediterranean region

    NASA Astrophysics Data System (ADS)

    Marchesini, I.; Ardizzone, F.; Alvioli, M.; Rossi, M.; Guzzetti, F.

    2014-04-01

    We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3 arc-second SRTM DEM to determine areas where landslide susceptibility is expected to be null or negligible in Italy, and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted in the local terrain slope computed in a square 3 × 3 cell moving window, and in the regional relative relief computed in a circular 15 × 15 cell moving window. We tested three different models to determine the non-susceptible landslide areas, including a linear model (LR), a quantile linear model (QLR), and a quantile non-linear model (QNL). We tested the performance of the three models using independent landslide information represented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a GIS with geographical census data for Italy. The result allowed determining that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be null or negligible, and that the remaining 42.5% was located in areas where some landslide susceptibility is expected. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results proved that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the Mediterranean area. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible and susceptible landslide areas, at the synoptic scale.

  8. Sensitivity analysis of conditioning factors for landslide susceptibility evaluation in Santa Marta de Penaguiño (Douro valley - Portugal

    NASA Astrophysics Data System (ADS)

    Pereira, S.; Zêzere, J. L.; Bateira, C.

    2009-04-01

    The MapRisk project intends to develop a landslide hazard and risk analysis to support planning decisions at the municipal level. The municipality of Santa Marta de Penaguião (70 square kilometres) is one of the test sites of the project. The study area has been affected in recent years by destructive landslides that were responsible for deaths and house and roads destruction. Despite these losses, mitigation and zonation landslide programs are missing, and the land use planning at the municipal level did not solve yet the problem. The study area is located in the Douro Valley region, mainly composed by metamorphic rocks (e.g., schist and quartzites). These rocks are strongly fractured, and weathered materials are abundant in clayed schist, mainly in those areas with agricultural terraces. From the geomorphological point of view, the study area is characterized by a transition landscape between the Marão mountain and the transmontano plateau, with deep incised valleys, tectonic depressions and slopes controlled by the geological structure. This area is characterised by the vineyard monoculture cultivated in agricultural terraces over centuries to produce Oporto wine. The main landslide triggering factor is rainfall and the mean annual precipitation ranges from 2500 mm near Marão mountain to 700 mm in the Corgós Valley. In this area there are historical records of destructive landslides, although they were aggregated in a landslide geodatabase only recently. The most complete landslide inventory was performed in 2005-2008 using aerial photographs interpretation at 1/5.000 scale and field work verification. The geodatabase includes 725 landslides, most of shallow translational slide type (80% of total slope movements). The landslide density is 10.5 events/square kilometre, and the average landslide area is 535 square meters. In this work we present the results of GIS based landslide susceptibility assessment for the shallow translational slides using two

  9. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

    NASA Astrophysics Data System (ADS)

    Goetz, J. N.; Brenning, A.; Petschko, H.; Leopold, P.

    2015-08-01

    Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings. Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan

  10. Probabilistic, Seismically-Induced Landslide Hazard Mapping of Western Oregon

    NASA Astrophysics Data System (ADS)

    Olsen, M. J.; Sharifi Mood, M.; Gillins, D. T.; Mahalingam, R.

    2015-12-01

    Earthquake-induced landslides can generate significant damage within urban communities by damaging structures, obstructing lifeline connection routes and utilities, generating various environmental impacts, and possibly resulting in loss of life. Reliable hazard and risk maps are important to assist agencies in efficiently allocating and managing limited resources to prepare for such events. This research presents a new methodology in order to communicate site-specific landslide hazard assessments in a large-scale, regional map. Implementation of the proposed methodology results in seismic-induced landslide hazard maps that depict the probabilities of exceeding landslide displacement thresholds (e.g. 0.1, 0.3, 1.0 and 10 meters). These maps integrate a variety of data sources including: recent landslide inventories, LIDAR and photogrammetric topographic data, geology map, mapped NEHRP site classifications based on available shear wave velocity data in each geologic unit, and USGS probabilistic seismic hazard curves. Soil strength estimates were obtained by evaluating slopes present along landslide scarps and deposits for major geologic units. Code was then developed to integrate these layers to perform a rigid, sliding block analysis to determine the amount and associated probabilities of displacement based on each bin of peak ground acceleration in the seismic hazard curve at each pixel. The methodology was applied to western Oregon, which contains weak, weathered, and often wet soils at steep slopes. Such conditions have a high landslide hazard even without seismic events. A series of landslide hazard maps highlighting the probabilities of exceeding the aforementioned thresholds were generated for the study area. These output maps were then utilized in a performance based design framework enabling them to be analyzed in conjunction with other hazards for fully probabilistic-based hazard evaluation and risk assessment. a) School of Civil and Construction

  11. Landslide Hazard Mapping in Rwanda Using Logistic Regression

    NASA Astrophysics Data System (ADS)

    Piller, A.; Anderson, E.; Ballard, H.

    2015-12-01

    Landslides in the United States cause more than $1 billion in damages and 50 deaths per year (USGS 2014). Globally, figures are much more grave, yet monitoring, mapping and forecasting of these hazards are less than adequate. Seventy-five percent of the population of Rwanda earns a living from farming, mostly subsistence. Loss of farmland, housing, or life, to landslides is a very real hazard. Landslides in Rwanda have an impact at the economic, social, and environmental level. In a developing nation that faces challenges in tracking, cataloging, and predicting the numerous landslides that occur each year, satellite imagery and spatial analysis allow for remote study. We have focused on the development of a landslide inventory and a statistical methodology for assessing landslide hazards. Using logistic regression on approximately 30 test variables (i.e. slope, soil type, land cover, etc.) and a sample of over 200 landslides, we determine which variables are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda has been produced, using the variables selected from the logistic regression analysis.

  12. Shallow landslide susceptibility model for the Oria river basin, Gipuzkoa province (North of Spain). Application of the logistic regression and comparison with previous studies.

    NASA Astrophysics Data System (ADS)

    Bornaetxea, Txomin; Antigüedad, Iñaki; Ormaetxea, Orbange

    2016-04-01

    In the Oria river basin (885 km2) shallow landslides are very frequent and they produce several roadblocks and damage in the infrastructure and properties, causing big economic loss every year. Considering that the zonification of the territory in different landslide susceptibility levels provides a useful tool for the territorial planning and natural risk management, this study has the objective of identifying the most prone landslide places applying an objective and reproducible methodology. To do so, a quantitative multivariate methodology, the logistic regression, has been used. Fieldwork landslide points and randomly selected stable points have been used along with Lithology, Land Use, Distance to the transport infrastructure, Altitude, Senoidal Slope and Normalized Difference Vegetation Index (NDVI) independent variables to carry out a landslide susceptibility map. The model has been validated by the prediction and success rate curves and their corresponding area under the curve (AUC). In addition, the result has been compared to those from two landslide susceptibility models, covering the study area previously applied in different scales, such as ELSUS1000 version 1 (2013) and Landslide Susceptibility Map of Gipuzkoa (2007). Validation results show an excellent prediction capacity of the proposed model (AUC 0,962), and comparisons highlight big differences with previous studies.

  13. The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy).

    PubMed

    Reichenbach, P; Busca, C; Mondini, A C; Rossi, M

    2014-12-01

    The spatial distribution of landslides is influenced by different climatic conditions and environmental settings including topography, morphology, hydrology, lithology, and land use. In this work, we have attempted to evaluate the influence of land use change on landslide susceptibility (LS) for a small study area located in the southern part of the Briga catchment, along the Ionian coast of Sicily (Italy). On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion. After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area. Moreover, two different land use maps were developed: the first was obtained through a semi-automatic classification of digitized aerial photographs acquired in 1954, the second through the combination of supervised classifications of two recent QuickBird images. Exploiting the two land use maps and different land use scenarios, LS zonations were prepared through multivariate statistical analyses. Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation. Susceptibility maps show an increase in the areal percentage and number of slope units classified as unstable related to the increase in bare soils to the detriment of forested areas. PMID:25164982

  14. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics

    NASA Astrophysics Data System (ADS)

    Meinhardt, Markus; Fink, Manfred; Tünschel, Hannes

    2015-04-01

    Vietnam is regarded as a country strongly impacted by climate change. Population and economic growth result in additional pressures on the ecosystems in the region. In particular, changes in landuse and precipitation extremes lead to a higher landslide susceptibility in the study area (approx. 12,400 km2), located in central Vietnam and impacted by a tropical monsoon climate. Hence, this natural hazard is a serious problem in the study area. A probability assessment of landslides is therefore undertaken through the use of bivariate statistics. However, the landslide inventory based only on field campaigns does not cover the whole area. To avoid a systematic bias due to the limited mapping area, the investigated regions are depicted as the viewshed in the calculations. On this basis, the distribution of the landslides is evaluated in relation to the maps of 13 parameters, showing the strongest correlation to distance to roads and precipitation increase. An additional weighting of the input parameters leads to better results, since some parameters contribute more to landslides than others. The method developed in this work is based on the validation of different parameter sets used within the statistical index method. It is called "omit error" because always omitting another parameter leads to the weightings, which describe how strong every single parameter improves or reduces the objective function. Furthermore, this approach is used to find a better input parameter set by excluding some parameters. After this optimization, nine input parameters are left, and they are weighted by the omit error method, providing the best susceptibility map with a success rate of 92.9% and a prediction rate of 92.3%. This is an improvement of 4.4% and 4.2%, respectively, compared to the basic statistical index method with the 13 input parameters.

  15. Directions of the US Geological Survey Landslide Hazards Reduction Program

    USGS Publications Warehouse

    Wieczorek, G.F.

    1993-01-01

    The US Geological Survey (USGS) Landslide Hazards Reduction Program includes studies of landslide process and prediction, landslide susceptibility and risk mapping, landslide recurrence and slope evolution, and research application and technology transfer. Studies of landslide processes have been recently conducted in Virginia, Utah, California, Alaska, and Hawaii, Landslide susceptibility maps provide a very important tool for landslide hazard reduction. The effects of engineering-geologic characteristics of rocks, seismic activity, short and long-term climatic change on landslide recurrence are under study. Detailed measurement of movement and deformation has begun on some active landslides. -from Author

  16. Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: a case study in Mizunami City, Japan

    NASA Astrophysics Data System (ADS)

    Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

    2013-01-01

    To mitigate the damage caused by landslide disasters, different mathematical models have been applied to predict landslide spatial distribution characteristics. Although some researchers have achieved excellent results around the world, few studies take the spatial resolution of the database into account. Four types of digital elevation model (DEM) ranging from 2 to 20 m derived from light detection and ranging technology to analyze landslide susceptibility in Mizunami City, Gifu Prefecture, Japan, are presented. Fifteen landslide-causative factors are considered using a logistic-regression approach to create models for landslide potential analysis. Pre-existing landslide bodies are used to evaluate the performance of the four models. The results revealed that the 20-m model had the highest classification accuracy (71.9%), whereas the 2-m model had the lowest value (68.7%). In the 2-m model, 89.4% of the landslide bodies fit in the medium to very high categories. For the 20-m model, only 83.3% of the landslide bodies were concentrated in the medium to very high classes. When the cell size decreases from 20 to 2 m, the area under the relative operative characteristic increases from 0.68 to 0.77. Therefore, higher-resolution DEMs would provide better results for landslide-susceptibility mapping.

  17. State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk

    NASA Astrophysics Data System (ADS)

    Van Den Eeckhaut, Miet; Hervás, Javier

    2012-02-01

    A landslide inventory is the most important information source for quantitative zoning of landslide susceptibility, hazard and risk. It should give insight into the location, date, type, size, activity and causal factors of landslides as well as resultant damage. In Europe, many countries have created or are creating national and/or regional landslide databases (LDBs). Yet little is known on their contents, completeness, format, structure, language use and accessibility, and hence on their ability to perform national or transnational landslide zoning. Therefore, this study presents a detailed analysis of existing national LDBs in the EU member states, EU official candidate and potential candidate countries, and EFTA countries, and their possible use for landslide zoning. These national LDBs were compared with a subset of 22 regional databases. Twenty-two out of 37 contacted European countries currently have national LDBs, and six other countries have only regional LDBs. In total, the national LDBs contain 633,696 landslides, of which 485,004 are located in Italy, while Austria, the Czech Republic, France, Norway, Poland, Slovakia, and the UK also have > 10,000 landslides in their LDBs. National LDBs are generally created in the official language of each country and 58% of them contain other natural hazards (e.g. floods and sinkholes). About 68% of the LDBs contain less than 50% of all landslides in each country, but a positive observation is that 60% of the LDBs are updated at least once a year or after a major event. Most landslide locations are collected with traditional methods such as field surveys, aerial photo interpretation and analysis of historical records. Currently, integration of landslide information from different national LDBs is hampered because of differences in language and classification systems for landslide type and activity. Other problems are that currently only half of the national LDBs have a direct link between spatial and alphanumeric

  18. The role of method of production and resolution of the DEM on slope-units delineation for landslide susceptibility assessment - Ubaye Valley, French Alps case study

    NASA Astrophysics Data System (ADS)

    Schlögel, Romy; Marchesini, Ivan; Alvioli, Massimiliano; Reichenbach, Paola; Rossi, Mauro; Malet, Jean-Philippe

    2016-04-01

    Landslide susceptibility assessment forms the basis of any hazard mapping, which is one of the essential parts of quantitative risk mapping. For the same study area, different susceptibility maps can be achieved depending on the type of susceptibility mapping methods, mapping unit, and scale. In the Ubaye Valley (South French Alps), we investigate the effect of resolution and method of production of the DEM to delineate slope units for landslide susceptibility mapping method. Slope units delineation has been processed using multiple combinations of circular variance and minimum area size values, which are the input parameters for a new software for terrain partitioning. We rely on this method taking into account homogeneity of aspect direction inside each unit and inhomogeneity between different units. We computed slope units delineation for 5, 10 and 25 meters resolution DEM, and investigate statistical distributions of morphometric variables within the different polygons. Then, for each different slope units partitioning, we calibrated a landslide susceptibility model, considering landslide bodies and scarps as a dependent variable (binary response). This work aims to analyse the role of DEM resolution on slope-units delineation for landslide susceptibility assessment. Area Under the Curve of the Receiver Operating Characteristic is investigated for the susceptibility model calculations. In addition, we analysed further the performance of the Logistic Regression Model by looking at the percentage of significant variable in the statistical analyses. Results show that smaller slope units have a better chance of containing a smaller number of thematic and morphometric variables, allowing for an easier classification. Reliability of the models according to the DEM resolution considered as well as scarp area and landslides bodies presence/absence as dependent variable are discussed.

  19. Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India)

    NASA Astrophysics Data System (ADS)

    Ghosh, Saibal; Carranza, Emmanuel John M.; van Westen, Cees J.; Jetten, Victor G.; Bhattacharya, Dipendra N.

    2011-08-01

    In this paper, we created predictive models for assessing the susceptibility to shallow translational rocksliding and debris sliding in the Darjeeling Himalayas (India) by empirically selecting and weighting spatial predictors of landslides. We demonstrate a two-stage methodology: (1) quantifying associations of individual spatial factors with landslides of different types using bivariate analysis to select predictors; and (2) pairwise comparisons of the quantified associations using an analytical hierarchy process to assign predictor weights. We integrate the weighted spatial predictors through multi-class index overlay to derive predictive models of landslide susceptibility. The resultant model for shallow translational landsliding based on selected and weighted predictors outperforms those based on all weighted predictors or selected and unweighted predictors. Therefore, spatial factors with negative associations with landslides and unweighted predictors are ineffective in predictive modeling of landslide susceptibility. We also applied logistic regression to model landslide susceptibility, but some of the selected predictors are less realistic than those from our methodology, and our methodology gives better prediction rates. Although previous predictive models of landslide susceptibility indicate that multivariate analyses are superior to bivariate analyses, we demonstrate the benefit of the proposed methodology including bivariate analyses.

  20. Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi'an Region, Shaanxi Province, China

    NASA Astrophysics Data System (ADS)

    Zhuang, Jianqi; Peng, Jianbing; Iqbal, Javed; Liu, Tieming; Liu, Na; Li, Yazhe; Ma, Penghui

    2015-09-01

    Landslides are among the most serious of geohazards in the Xi'an Region, Shaanxi, China, and are responsible for extensive human and property loss. In order to understand the distribution of landslides and assess their associated hazards in this region, we used a combination of frequency analysis, logistic analysis, and Geographic Information System (GIS) analysis, with consideration of the spatial distribution of landslides. Using the GIS approach, the five key factors of surface topography, including slope gradient, topographic wetness index (TWI), height difference, profile curvature and slope aspect, were considered. First, the distribution and frequency of landslides were considered in relation to all of the five factors in each of three sub-regions susceptible to landslides (Qin Mountain, Li Mountain, and Loess Tableland). Secondly, each factor's influence was determined by a logistic regression method, and the relative importance of each of these independent variables was evaluated. Finally, a landslide susceptibility map was generated using GIS tools. Locations that had recorded landslides were used to validate the results of the landslide susceptibility map and the accuracy obtained was above 84%. The validation proved that there is sufficient agreement between the susceptibility map and existing records of landslide occurrences. The logistic regression model produced acceptable results (the areas under the Receiver Operating Characteristics (ROC) curve were 0.865, 0.841, and 0.924 in the Qin Mountain, Li Mountain and Loess Tableland). We are confident that the results of this study can be useful in preliminary planning for land use, particularly for construction work in high-risk areas.

  1. Non-susceptible landslide areas in Italy and in the Mediterranean region

    NASA Astrophysics Data System (ADS)

    Marchesini, I.; Ardizzone, F.; Alvioli, M.; Rossi, M.; Guzzetti, F.

    2014-08-01

    We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.

  2. Map and map database of susceptibility to slope failure by sliding and earthflow in the Oakland area, California

    USGS Publications Warehouse

    Pike, R.J.; Graymer, R.W.; Roberts, Sebastian; Kalman, N.B.; Sobieszczyk, Steven

    2001-01-01

    Map data that predict the varying likelihood of landsliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km2) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits. Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area

  3. Selecting statistical or machine learning techniques for regional landslide susceptibility modelling by evaluating spatial prediction

    NASA Astrophysics Data System (ADS)

    Goetz, Jason; Brenning, Alexander; Petschko, Helene; Leopold, Philip

    2015-04-01

    With so many techniques now available for landslide susceptibility modelling, it can be challenging to decide on which technique to apply. Generally speaking, the criteria for model selection should be tied closely to end users' purpose, which could be spatial prediction, spatial analysis or both. In our research, we focus on comparing the spatial predictive abilities of landslide susceptibility models. We illustrate how spatial cross-validation, a statistical approach for assessing spatial prediction performance, can be applied with the area under the receiver operating characteristic curve (AUROC) as a prediction measure for model comparison. Several machine learning and statistical techniques are evaluated for prediction in Lower Austria: support vector machine, random forest, bundling with penalized linear discriminant analysis, logistic regression, weights of evidence, and the generalized additive model. In addition to predictive performance, the importance of predictor variables in each model was estimated using spatial cross-validation by calculating the change in AUROC performance when variables are randomly permuted. The susceptibility modelling techniques were tested in three areas of interest in Lower Austria, which have unique geologic conditions associated with landslide occurrence. Overall, we found for the majority of comparisons that there were little practical or even statistically significant differences in AUROCs. That is the models' prediction performances were very similar. Therefore, in addition to prediction, the ability to interpret models for spatial analysis and the qualitative qualities of the prediction surface (map) are considered and discussed. The measure of variable importance provided some insight into the model behaviour for prediction, in particular for "black-box" models. However, there were no clear patterns in all areas of interest to why certain variables were given more importance over others.

  4. Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

    NASA Astrophysics Data System (ADS)

    Sdao, F.; Lioi, D. S.; Pascale, S.; Caniani, D.; Mancini, I. M.

    2013-02-01

    The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the

  5. Landslide Susceptibility Evaluation on agricultural terraces of DOURO VALLEY (PORTUGAL), using physically based mathematical models.

    NASA Astrophysics Data System (ADS)

    Faria, Ana; Bateira, Carlos; Laura, Soares; Fernandes, Joana; Gonçalves, José; Marques, Fernando

    2016-04-01

    predictive capacity of the models is related with the construction methods of contributory areas. The SHALSTAB susceptibility map shows better discrimination of the unstable areas, which is important to the estates decision makers in order to organize the priority of the hazard mitigation process. References Dietrich, W. E.; Reiss, R.; Hsu, M-L.; Montgomery, D.(1995) - A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrological Processes. ISSN 1099-1085. Vol. 9, n.° 3-4, pp.383-400. Fawcett, T.(2006) - An introduction to ROC analysis. Pattern Recognition Letters. ISSN 0167-8655. Vol. 27, n.° 8, pp.861-874. Montgomery, David R.; Dietrich, William E.- A physically based model for the topographic control on shallow landsliding. Water Resources Research. ISSN 1944-7973. Vol. 30, n.° 4 (1994), p.1153-1171. Raia, S., [et al.]- Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach. Geoscientific Model Development. ISSN 1991-959X. Vol. 7, n.° 2 (2014), p.495-514.

  6. Generating an optimal DTM from airborne laser scanning data for landslide mapping in a tropical forest environment

    NASA Astrophysics Data System (ADS)

    Razak, Khamarrul Azahari; Santangelo, Michele; Van Westen, Cees J.; Straatsma, Menno W.; de Jong, Steven M.

    2013-05-01

    Landslide inventory maps are fundamental for assessing landslide susceptibility, hazard, and risk. In tropical mountainous environments, mapping landslides is difficult as rapid and dense vegetation growth obscures landslides soon after their occurrence. Airborne laser scanning (ALS) data have been used to construct the digital terrain model (DTM) under dense vegetation, but its reliability for landslide recognition in the tropics remains surprisingly unknown. This study evaluates the suitability of ALS for generating an optimal DTM for mapping landslides in the Cameron Highlands, Malaysia. For the bare-earth extraction, we used hierarchical robust filtering algorithm and a parameterization with three sequential filtering steps. After each filtering step, four interpolations techniques were applied, namely: (i) the linear prediction derived from the SCOP++ (SCP), (ii) the inverse distance weighting (IDW), (iii) the natural neighbor (NEN) and (iv) the topo-to-raster (T2R). We assessed the quality of 12 DTMs in two ways: (1) with respect to 448 field-measured terrain heights and (2) based on the interpretability of landslides. The lowest root-mean-square error (RMSE) was 0.89 m across the landscape using three filtering steps and linear prediction as interpolation method. However, we found that a less stringent DTM filtering unveiled more diagnostic micro-morphological features, but also retained some of vegetation. Hence, a combination of filtering steps is required for optimal landslide interpretation, especially in forested mountainous areas. IDW was favored as the interpolation technique because it combined computational times more reasonably without adding artifacts to the DTM than T2R and NEN, which performed relatively well in the first and second filtering steps, respectively. The laser point density and the resulting ground point density after filtering are key parameters for producing a DTM applicable to landslide identification. The results showed that the

  7. Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling.

    PubMed

    Guri, Pardeep Kumar; Ray, P K Champati; Patel, Ramesh Chandra

    2015-06-01

    Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment

  8. Temporal and Spatial Variability in Landslide Susceptibility Analyses

    NASA Astrophysics Data System (ADS)

    Trizzino, Rosamaria; Pagliarulo, Rossella

    2014-05-01

    The geomorphic processes in landscape evolution are commonly assumed deterministic, although their high variability in rates and time. As the stability analyses of slopes are concerned, the classical methods consider threshold values of the different elements (slope angle, friction angle, climatic conditions, hydrogeological conditions, seismicity) that condition the safety factors, but often widespread landscape instabilities occur when the threshold values are not exceeded. To analyze these phenomena we studied a model for defining an "average" pattern of landscape evolution starting from the single deterministic process. Many previous studies demonstrated the driving role of weathering and erosion processes in landslide evolution. Among these, the "instability principle of geomorphic equilibrium" (Scheidegger, 1983) stated the relevancy of exogenic processes (weathering, erosion, etc.) particularly in those places where preexisting micro topographic irregularities or lithological variations are recognizable. The present paper gives an example of the unstable growth of small perturbations from the initial conditions up to the landslide initiation, even if there were no measurable variations in external controls. In this analysis the geo- materials are considered as a weathering system mathematically depicted as an n-components nonlinear dynamical system. A hierarchical multiscale model of instability is applied. The model treats four spatial scales: 1) local regolith scale (weathering processes, in situ breakdown of geo-materials), 2) hill slope scale (allocation of weathered products: soil removal in solid form, via erosion and mass wasting, or in dissolved form via surface water flow), 3) landscape units (relationships between weathering and denudation), 4) broadest landscape scale (topographic and isostatic response to weathering-limited denudation, unloading or depositional loading). The landslide susceptibility analysis for the present study is located in

  9. Susceptibility mapping in the Río El Estado watershed, Pico de Orizaba volcano, Mexico

    NASA Astrophysics Data System (ADS)

    Legorreta Paulin, G.; Bursik, M. I.; Lugo Hubp, J.; Paredes Mejía, L.; Aceves Quesada, F.

    2013-12-01

    In volcanic terrains, dormant stratovolcanoes are very common and can trigger landslides and debris flows continually along stream systems, thereby affecting human settlements and economic activities. It is important to assess their potential impact and damage through the use of landslide inventory maps and landslide models. This poster provides an overview of the on-going research project (Grant SEP-CONACYT no 167495) from the Institute of Geography at the National Autonomous University of Mexico (UNAM) that seeks to conduct a multi-temporal landslide inventory and produce a landslide susceptibility map by using Geographic Information Systems (GIS). The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The catchment covers 5.2 km2 with elevations ranging from 2676.79 to 4248.2 m a.s.l. and hillslopes between 5° and 56°. The stream system of Río El Estado catchment erodes Tertiary and Quaternary lavas, pyroclastic flows, and fall deposits. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The method encompasses two main levels of analysis to assess landslide susceptibility. The first level builds a historic landslide inventory. In the study area, an inventory of more than 100 landslides was mapped from interpretation of multi-temporal aerial orthophotographs and local field surveys to assess and describe landslide distribution. All landslides were digitized into a GIS, and the spatial geo-database of landslides was constructed from standardized GIS datasets. The second level calculates the susceptibility for the watershed. Multiple Logistic Regression (MLR) was used to examine the relationship between landsliding and several independent variables (elevation, slope, terrain curvature, flow direction, saturation, contributing area, land use, and geology

  10. Physically-based landslide susceptibility modelling: geotechnical testing and model evaluation issues

    NASA Astrophysics Data System (ADS)

    Marchesini, Ivan; Mergili, Martin; Schneider-Muntau, Barbara; Alvioli, Massimiliano; Rossi, Mauro; Guzzetti, Fausto

    2015-04-01

    tests may help to further improve the geotechnical parameterization of the model and, consequently, how much effort and resources should be put into geotechnical sampling and testing for physically-based landslide susceptibility modelling. (ii) What is the spatial unit most suitable to discretize landslide susceptibility maps? Whilst the GIS pixel is the most commonly used level of discretization, slope units represent a valid alternative. Tests have shown that the area under the ROC curve increases significantly when evaluating the slope failure probabilities yielded with r.slope.stability at the level of slope units instead of pixels. At the level of slope units, the physically-based model r.slope.stability outperforms statistical models applied to the Collazzone Area. However, there is good reason to discuss the validity and the usefulness of different levels of discretization.

  11. Susceptibility Assessment and Rainfall Thresholding: Application to Landslide Hazard Management in Jamaica.

    NASA Astrophysics Data System (ADS)

    Miller, S.; Harris, N.

    2009-04-01

    The parish of St Thomas in Jamaica has one of the highest densities of landslides on the island, landslides that continue to have negative impact on lives, the local economy, and the built and natural environment. The occurrence of these landslides is a result of a combination of steep slopes, faulting, heavy rainfall and highly weathered geology (volcanics, sandstones, limestones and sandstone/shale series) that occur within this area. The problem of slope instability in the parish is a recurring one particularly during the hurricane season (June- November) when they are triggered by heavy rainfall associated with hurricanes. Two methods, rainfall thresholding and landslide susceptibility assessment, that may be used in the management of slope instability in landslide prone areas of the parish, were explored in this research. Both methods have yielded good results which in combination may be used as management tool to better determine when and where landslides are likely to occur and in the process mitigate the effects of landslides in the area. Keywords: Landslide; susceptibility; GIS; logistic regression; Jamaica; rainfall thresholding.

  12. Predictive landslide susceptibility analysis along the mountain highway in central Taiwan

    NASA Astrophysics Data System (ADS)

    Shou, Keh-Jian; Lin, Zora

    2016-04-01

    Climate change caused by global warming affects Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary Typhoon Morakot hit Southern Taiwan, on August 8, 2009, and induced serious flooding and landslides. Considering the existence of various types of large scale landslides (shallow and deep-seated) and the importance of protection targets (the landslide might affect a residential area, cut a road, isolate a village, etc.), this study aims to analyze the landslide susceptibility along the Nantou County Road # 89 of Taiwan, in the upstream of Wu River. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the Wu River watershed. Based on the data of Li-DAR and the information from boreholes, the temporal behavior and the complex mechanism of large scale landslides were analyzed. To assess the spatial hazard of the landslides, landslide susceptibility analysis was also implemented. The results of this study can be applied for risk prevention and management in the study area.

  13. A seismic landslide susceptibility rating of geologic units based on analysis of characterstics of landslides triggered by the 17 January, 1994 Northridge, California earthquake

    USGS Publications Warehouse

    Parise, M.; Jibson, R.W.

    2000-01-01

    One of the most significant effects of the 17 January, 1994 Northridge, California earthquake (M=6.7) was the triggering of thousands of landslides over a broad area. Some of these landslides damaged and destroyed homes and other tructures, blocked roads, disrupted pipelines, and caused other serious damage. Analysis of the distribution and characteristics of these landslides is important in understanding what areas may be susceptible to landsliding in future earthquakes. We analyzed the frequency, distribution, and geometries of triggered landslides in the Santa Susana 7.5??? quadrangle, an area of intense seismic landslide activity near the earthquake epicenter. Landslides occured primarily in young (Late Miocene through Pleistocene) uncemented or very weakly cemented sediment that has been repeatedly folded, faulted, and uplifted in the past 1.5 million years. The most common types of landslide triggered by the earthquake were highly disrupted, shallow falls and slides of rock and debris. Far less numerous were deeper, more coherent slumps and block slides, primarily occuring in more cohesive or competent materials. The landslides in the Santa Susana quadrangle were divided into two samples: single landslides (1502) and landslide complexes (60), which involved multiple coalescing failures of surficial material. We described landslide, morphologies by computing simple morphometric parameters (area, length, width, aspect ratio, slope angle). To quantify and rank the relative susceptibility of each geologic unit to seismic landsliding, we calculated two indices: (1) the susceptibility index, which is the ratio (given as a percentage) of the area covered by landslide sources within a geologic unit to the total outcrop area of that unit: and (2) the frequency index [given in landslides per square kilometer (ls/km2)], which is the total number of landslides within each geologic unit divided by the outcrop area of that unit. Susceptibility categories include very high

  14. Integration between ground based and satellite SAR data in landslide mapping: The San Fratello case study

    NASA Astrophysics Data System (ADS)

    Bardi, Federica; Frodella, William; Ciampalini, Andrea; Bianchini, Silvia; Del Ventisette, Chiara; Gigli, Giovanni; Fanti, Riccardo; Moretti, Sandro; Basile, Giuseppe; Casagli, Nicola

    2014-10-01

    The potential use of the integration of PSI (Persistent Scatterer Interferometry) and GB-InSAR (Ground-based Synthetic Aperture Radar Interferometry) for landslide hazard mitigation was evaluated for mapping and monitoring activities of the San Fratello landslide (Sicily, Italy). Intense and exceptional rainfall events are the main factors that triggered several slope movements in the study area, which is susceptible to landslides, because of its steep slopes and silty-clayey sedimentary cover. In the last three centuries, the town of San Fratello was affected by three large landslides, developed in different periods: the oldest one occurred in 1754, damaging the northeastern sector of the town; in 1922 a large landslide completely destroyed a wide area in the western hillside of the town. In this paper, the attention is focussed on the most recent landslide that occurred on 14 February 2010: in this case, the phenomenon produced the failure of a large sector of the eastern hillside, causing severe damages to buildings and infrastructures. In particular, several slow-moving rotational and translational slides occurred in the area, making it suitable to monitor ground instability through different InSAR techniques. PS-InSAR™ (permanent scatterers SAR interferometry) techniques, using ERS-1/ERS-2, ENVISAT, RADARSAT-1, and COSMO-SkyMed SAR images, were applied to analyze ground displacements during pre- and post-event phases. Moreover, during the post-event phase in March 2010, a GB-InSAR system, able to acquire data continuously every 14 min, was installed collecting ground displacement maps for a period of about three years, until March 2013. Through the integration of space-borne and ground-based data sets, ground deformation velocity maps were obtained, providing a more accurate delimitation of the February 2010 landslide boundary, with respect to the carried out traditional geomorphological field survey. The integration of GB-InSAR and PSI techniques proved to

  15. Using geotypes for landslide hazard assessment and mapping: a coupled field and GIS-based method

    NASA Astrophysics Data System (ADS)

    Bilgot, S.; Parriaux, A.

    2009-04-01

    Switzerland is exceptionally subjected to landslides; indeed, about 10% of its area is considered as unstable. Making this observation, its Department of the Environment (BAFU) introduces in 1997 a method to realize landslide hazard maps. It is routinely used but, like most of the methods applied in Europe to map unstable areas, it is mainly based on the signs of previous or current phenomena (geomorphologic mapping, archive consultation, etc.) even though instabilities can appear where there is nothing to show that they existed earlier. Furthermore, the transcription from the geomorphologic map to the hazard map can vary according to the geologist or the geographer who realizes it: this method is affected by a certain lack of transparency. The aim of this project is to introduce the bedrock of a new method for landslide hazard mapping; based on instability predisposition assessment, it involves the designation of main factors for landslide susceptibility, their integration in a GIS to calculate a landslide predisposition index and the implementation of new methods to evaluate these factors; to be competitive, these processes have to be both cheap and quick. To identify the most important parameters to consider for assessing slope stability, we chose a large panel of topographic, geomechanic and hydraulic parameters and tested their importance by calculating safety factors on theoretical landslides using Geostudio 2007®; thus, we could determine that slope, cohesion, hydraulic conductivity and saturation play an important role in soil stability. After showing that cohesion and hydraulic conductivity of loose materials are strongly linked to their granulometry and plasticity index, we implemented two new field tests, one based on teledetection and one coupled sedimentometric and blue methylen test to evaluate these parameters. From these data, we could deduce approximated values of maximum cohesion and saturated hydraulic conductivity. The hydraulic conductivity of

  16. Comparison and applicability of landslide susceptibility models based on landslide ratio-based logistic regression, frequency ratio, weight of evidence, and instability index methods in an extreme rainfall event

    NASA Astrophysics Data System (ADS)

    Wu, Chunhung

    2016-04-01

    Few researches have discussed about the applicability of applying the statistical landslide susceptibility (LS) model for extreme rainfall-induced landslide events. The researches focuses on the comparison and applicability of LS models based on four methods, including landslide ratio-based logistic regression (LRBLR), frequency ratio (FR), weight of evidence (WOE), and instability index (II) methods, in an extreme rainfall-induced landslide cases. The landslide inventory in the Chishan river watershed, Southwestern Taiwan, after 2009 Typhoon Morakot is the main materials in this research. The Chishan river watershed is a tributary watershed of Kaoping river watershed, which is a landslide- and erosion-prone watershed with the annual average suspended load of 3.6×107 MT/yr (ranks 11th in the world). Typhoon Morakot struck Southern Taiwan from Aug. 6-10 in 2009 and dumped nearly 2,000 mm of rainfall in the Chishan river watershed. The 24-hour, 48-hour, and 72-hours accumulated rainfall in the Chishan river watershed exceeded the 200-year return period accumulated rainfall. 2,389 landslide polygons in the Chishan river watershed were extracted from SPOT 5 images after 2009 Typhoon Morakot. The total landslide area is around 33.5 km2, equals to the landslide ratio of 4.1%. The main landslide types based on Varnes' (1978) classification are rotational and translational slides. The two characteristics of extreme rainfall-induced landslide event are dense landslide distribution and large occupation of downslope landslide areas owing to headward erosion and bank erosion in the flooding processes. The area of downslope landslide in the Chishan river watershed after 2009 Typhoon Morakot is 3.2 times higher than that of upslope landslide areas. The prediction accuracy of LS models based on LRBLR, FR, WOE, and II methods have been proven over 70%. The model performance and applicability of four models in a landslide-prone watershed with dense distribution of rainfall

  17. Landslide triggering-thickness susceptibility, a simple proxy for landslide hazard? A test in the Mili catchment (North-Eastern Sicily, Italy)

    NASA Astrophysics Data System (ADS)

    Lombardo, Luigi; Fubelli, Giandomenico; Amato, Gabriele; Bonasera, Mauro; Mai, Martin

    2016-04-01

    This study implements a landslide triggering-thickness susceptibility approach in order to investigate the landslide scenario in the catchment of Mili, this being located in the north-easternmost sector of Sicily (Italy). From a detailed geomorphological campaign, thicknesses of mobilised materials at the triggering zone of each mass movement were collected and subsequently used as a dependent variable to be analysed in the framework of spatial predictive models. The adopted modelling methodology consisted of a presence-only learning algorithm which differently from classic presence-absence methods does not rely on stable conditions in order to derive functional relationships between dependent and independent variables. The dependent was pre-processed by reclassifying the crown thickness spectrum into a binary condition expressing thick (values equal or greater than 1m) and thin (values less than 1m) landslide crown classes. The explanatory variables were selected to express triggering-thickness dependency at different scales, these being in close proximity to the triggering point through primary and secondary attributes from a 2m-cell side Lidar HRDEM, at a medium scale through vegetation indexes from multispectral satellite images (ASTER) and a coarser scale through a geological, land use and tectonic maps. The choice of a presence-only approach allowed to effectively discriminate between the two types of landslide thicknesses at the triggering zone, producing excellent prediction skills associated with relatively low variances across a set of 50 randomly generated replicates. In addition, the role of each predictor was assessed for the two considered classes as relevant differences arose in terms of their contribution to the final models. In this regard, predictor importance, Jack-knife tests and response curves were used to assess the reliability of the models together with their geomorphological reasonability. This work attempts to capitalize on fieldwork data

  18. Characterization of past landslides and slope susceptibility analysis for Lima and Callao provinces, Peru

    NASA Astrophysics Data System (ADS)

    Tatard, Lucile; Villacorta, Sandra; Metzger, Pascale

    2013-04-01

    85% of people exposed to earthquakes, hurricanes, floods and drought live in developing countries (IPU, 2010). This population is also exposed to the landslide risk as this phenomenon is mainly triggered by earthquakes and rainfall. There is an urgent need to propose methods to evaluate and mitigate the landslide risk for developing countries, where few studies were undergone and data, and information on data, are scarce. In this study, we characterize a landslide inventory set up for the megalopolis of Lima, Peru, by the local geological bureau (INGEMMET). This inventory was set up using satellite images and includes landslides of all ages. It is composed of two landslide types: rockfalls and debris flows (huaycos) that we investigate together and separately. First, we describe qualitatively the landslide occurrences in terms of geology, slope steepness, altitude, etc. We notably find that debris flows occur at altitudes larger than the ones of the rockfalls, probably due to the climatic conditions. Then we find that the rockfalls and debris flows area distributions follow a power law when investigated separately whereas it does not follow a power law when investigated together. This highlights a logical difference of mechanics between the two landslide types. Then, using the dimension of correlation D (Grassberger and Procaccia, 1983) we show that the event spatial occurrences are not uniformly distributed but clustered. It supports the existence of controlling parameters on the spatial occurrence of landslides and the research to identify them. Last, we investigate the relationships between different landslide parameters (geology, altitude, slope steepness, ...) using the linear correlation coefficient r, and we find that all these parameters are independent to each other. This allows us to investigate each parameter separately in terms of landslide susceptibility and to define values for which the landslide susceptibility is low, medium or high for each

  19. Modelling increased landslide susceptibility near highways in the Andes of southern Ecuador

    NASA Astrophysics Data System (ADS)

    Brenning, Alexander; Muenchow, Jannes

    2016-04-01

    Modelling increased landslide susceptibility near highways in the Andes of southern Ecuador A. Brenning (1), J. Muenchow (1) (1) Department of Geography, Friedrich Schiller University Jena, Loebdergraben 32, 07743 Jena, Germany Mountain roads are affected by and also affect themselves landslide suceptibility. Especially in developing countries, inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading are known processes through which road construction and maintenance can enhance landslide activity within the immediate surroundings of road infrastructure. In the Andes of southern Ecuador, strong precipitation gradients as well as lithological differences provide an excellent study site in which the relationship between highways and landslide susceptibility and its regional differentiation can be studied. This study uses Generalized Additive Models (GAM) to investigate patterns of landslide susceptibility along two paved interurban highways in the tropical Andes of southern Ecuador. The relationship of landslides to distance from road is modeled while accounting for topographic, climatic and lithological predictors as possible confounders and modifiers, focusing on the odds ratio of landslide occurrence at 25 m versus 200 m distance from the highway. Spatial attention is given to uncertainties in estimated odds ratios of landslide occurrence using spatial block bootstrap techniques. The GAM is able to represent nonlinear additive terms as well as bivariate smooth interaction terms, providing a good tradeoff between model complexity and interpretability. The estimated odds of landslide occurrence were 18-21 times higher near the highway than at 200 m distance, based on different analyses, with lower 95% confidence limits always >13. (Semi-) parametric estimates confirmed this general range of values but suggests slightly higher odds ratios (95% confidence interval: 15.5-25.3). Highway-related effects were observed to

  20. Assessing the effects of land-use changes on landslide susceptibility: a case study in the upper Rivo basin (Molise, Italy)

    NASA Astrophysics Data System (ADS)

    Pisano, Luca; Parise, Mario; Rosskopf, Carmen

    2015-04-01

    Landslides are the results of the complex spatial-temporal interaction of various predisposing and triggering factors, among which land-use is one of the most important. Especially in the short term perspective, whilst geological and geomorphological factors change in relatively long periods, land-use can evolve in few decades, which explains why variations in the land use may determine significant changes in the landslide frequency and distribution, even in a short time span. In this study, have been analyzed land use changes occurred during the second part of the last century in order to get some hints about their likely influence on activity, dimensions and distribution of landslide-prone areas in small sized rural catchments. The selected study area is the upper sector of the Rivo basin, located in Molise region (Italy). The main goal of this study is to understand the effects of land use changes resulting from land management activities on landslide susceptibility. In the study area, major socio-economical transformations have been identified during the period 1954-2003, regarding both land management and land-use pattern. To this aim, multi-temporal land-use and landslide inventory maps have been compiled for the considered period, by means of air photo interpretation. Then, using the obtained data sets, different landslide susceptibility maps have been developed in order to quantify the changes in land use and evaluate their effects on landslide proneness. The analysis of the most recent aerial photos reveals a decrease in the landslides occurrence, but, at the same time, an increase in the landslide extinction rate. The preliminary results show that the increase in forested areas, due to the corresponding decrease in pasture and bushes, determines changes in the stability of the slopes, and the development of smaller-size areas with high susceptibility to landslides. These outcomes represent an important step towards the better understanding of the past

  1. PREPARING A DETAILED LANDSLIDE-INVENTORY MAP FOR HAZARD EVALUATION AND REDUCTION.

    USGS Publications Warehouse

    Wieczorek, Gerald F.

    1983-01-01

    A method of preparing a detailed landslide-inventory map has been developed which provides the engineering geologist with the basic information for evaluating and reducing landslide hazards or risk on a regional or community level. For each landslide, the map depicts state of activity, certainty of identification, dominant type of slope movement, primary direction of movement, estimated thickness of material involved in landsliding, and date(s) of known activity. This information is developed from interpreting aerial photographs and examining landslide features in the field. Although preparing detailed landslide-inventory maps involves considerably more time and effort than landslide reconnaissance mapping, these maps are directly useable by planners and decisionmakers as a basis for requiring site-specific investigations prior to development or adopting land-use regulations. Refs.

  2. The use of IFSAR data in GIS-based landslide susceptibility evaluation

    NASA Astrophysics Data System (ADS)

    Floris, M.; Squarzoni, C.; Hundseder, C.; Mason, M.; Genevois, R.

    2010-05-01

    GIS-based landslide susceptibility evaluation is based on the spatial relationships between landslides and their related factors. The analyses are highly conditioned by precision and accuracy of input factors, in particular landslides identification and characterization. Factors influencing landslide spatial hazard consist of geological, geomorphological, hydrogeological and tectonic features, geomechanical and geotechnical properties, land use and management, and DEM-derived factors (elevation, slope, aspect, curvature, superficial flow). The choice of influencing factors depends on: method of analysis, scale of inputs, aim of the outputs, availability and quality of the input data. Then, the choice can be made a priori, on the bases of an in-deep territorial knowledge and experts' judgements, or by performing statistical analyses, finalized to identify the significance of each of the influencing factor. Due to the large availability of terrain data, spatial models often include DEM-derived factors, but the resolution and accuracy of DEMs influence the final outputs. In this work the relationships between landslides occurred in the volcanic area of the Euganean Hills Regional Park (SE of Padua, Veneto region, Italy) and morphometric factors (slope, aspect and curvature) will be examined through a simple probability method. The use of complex and time consuming mathematical or statistical models is not always recommended, because often simple models can lead to more accurate results. Morphometric input factors are derived from DEMs created from vector elevation data of the regional cartography at 1:5.000 scale and with NEXTMap® data (http://www.intermap.com). NEXTMap® Digital Surface Model (DSM) and Digital Terrain Model (DTM) are generated using Intermap's IFSAR (Interferometric Synthetic Aperture Radar) technology mounted on an aircraft at a flight height of 8500 m above Mean Sea Level and under a side viewing angle of about 45°. The DSM represents the first

  3. Completing the Publication of 1:50,000 Landslide Distribution Maps in Japan

    NASA Astrophysics Data System (ADS)

    Uchiyama, S.; Oyagi, N.; Doshida, S.; Shimizu, F.; Sano, A.; Ogura, M.

    2013-12-01

    We have published the 1:50,000 Landslide Distribution Maps since 1981. The purpose is to reduce the damage caused by a landslide disaster. In 2013, we will complete a publication that covers all of Japan. There are two final products that will come out of this long project. One is the 1:50,000 Landslide Distribution Maps Series 1 to 60 which is a printed publication. The other is the Seamless Landslide GIS Data which digitized all landslide topography from the Landslide Distribution Maps. The map was created based on interpretation by stereoscopic vision of aerial photographs. We used the 1:40,000 monochrome aerial photographs taken in the 1970s for manual interpretation. Norio Oyagi and Fumitake Shimizu carried out all interpretation of landslide landforms mostly from the start of the project to the end. In creation of Seamless Landslide GIS Data, Shoichiro Uchiyama and Ayako Sano did all the work. After we complete the publication of the Landslide Distribution Maps in 2013, we must continue to provide these products. Our current goal is building a sustainable mechanism to provide products after dissolution of this project. Published area of 1:50,000 Landslide Maps and publishing schedule in 2013 The list of all series of the 1:50,000 Landslide Maps in Japan

  4. Landslide susceptibility from mathematical model in Sarno area

    NASA Astrophysics Data System (ADS)

    Capparelli, G.; Versace, P. P.

    2013-10-01

    Rainfall is accepted as a major precursor for many types of slope movements (rapid, shallow soil slips and deeper landslides) and the technical literature is rich in examples of study cases and analysis models, related to landslides induced by rainfall. In general, the developed model can be regrouped in two categories: hydrological and complete. The first ones involve simple empirical relationships linking antecedent precipitation to the time that the landslide occurs; the latter consist of more complex expressions that take several components into account, including specific site conditions, mechanical, hydraulic and physical soil properties, local seepage conditions, and the contribution of these to soil strength. In this study, the analysis was carried out by using a model belonging to the second category for a landslide-prone area in Campania region (Southern Italy), were disastrous mud-flows occurred on 5 May 1998. In details, the model named SUSHI (Saturated Unsaturated Simulation for Hillslope Instability) was used and the obtained results made possible to better define the triggering conditions and differentiate the scenarios leading to instability of those slopes.

  5. Landslide Susceptibility Analysis by the comparison and integration of Random Forest and Logistic Regression methods; application to the disaster of Nova Friburgo - Rio de Janeiro, Brasil (January 2011)

    NASA Astrophysics Data System (ADS)

    Esposito, Carlo; Barra, Anna; Evans, Stephen G.; Scarascia Mugnozza, Gabriele; Delaney, Keith

    2014-05-01

    The study of landslide susceptibility by multivariate statistical methods is based on finding a quantitative relationship between controlling factors and landslide occurrence. Such studies have become popular in the last few decades thanks to the development of geographic information systems (GIS) software and the related improved data management. In this work we applied a statistical approach to an area of high landslide susceptibility mainly due to its tropical climate and geological-geomorphological setting. The study area is located in the south-east region of Brazil that has frequently been affected by flood and landslide hazard, especially because of heavy rainfall events during the summer season. In this work we studied a disastrous event that occurred on January 11th and 12th of 2011, which involved Região Serrana (the mountainous region of Rio de Janeiro State) and caused more than 5000 landslides and at least 904 deaths. In order to produce susceptibility maps, we focused our attention on an area of 93,6 km2 that includes Nova Friburgo city. We utilized two different multivariate statistic methods: Logistic Regression (LR), already widely used in applied geosciences, and Random Forest (RF), which has only recently been applied to landslide susceptibility analysis. With reference to each mapping unit, the first method (LR) results in a probability of landslide occurrence, while the second one (RF) gives a prediction in terms of % of area susceptible to slope failure. With this aim in mind, a landslide inventory map (related to the studied event) has been drawn up through analyses of high-resolution GeoEye satellite images, in a GIS environment. Data layers of 11 causative factors have been created and processed in order to be used as continuous numerical or discrete categorical variables in statistical analysis. In particular, the logistic regression method has frequent difficulties in managing numerical continuous and discrete categorical variables

  6. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

    NASA Astrophysics Data System (ADS)

    Pham, Binh Thai; Tien Bui, Dieu; Pourghasemi, Hamid Reza; Indra, Prakash; Dholakia, M. B.

    2015-12-01

    The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.

  7. Landslides hazard mapping integrating remote sensing and geo-morphological data in the Sorrentina Peninsula coastal areas

    NASA Astrophysics Data System (ADS)

    spinetti, claudia; bisson, marina; tolomei, cristiano; colini, laura; galvani, alessandro; moro, marco; saroli, michele; sepe, vincenzo

    2016-04-01

    The densely inhabited Campania region (Southern Italy) is affected by numerous and dangerous landslides. In particular, the coastal area of Sorrentina Peninsula is one of the zones most subjected to two types of landslides: volcanoclastic debris flows and rock fall. The first type occurs during intensive or persistent precipitations and on significant hillslopes where carbonatic bedrock is covered by pyroclastic deposits related to the Somma-Vesuvius and Phlegrean Fields explosive activity. The second type could be triggered by seismic events and occurs in areas where outcropping bedrock with steep slopes (e.g. the cliffs) is subjected to coastal erosion generating cliff instability. In order to improve the landslides hazard zonation in the Sorrentina Peninsula coastal area, we show a multidisciplinary approach to identify the areas more prone to generate such types of landslide. Our approach involves the analyses of ERS (temporal span between 1992-2000), Envisat (2003-2010), and COSMO-SkyMed (2013-2015) SAR data elaborated applying multi-temporal InSAR techniques to obtain the ground displacement maps and the relative displacement time series, integrated by means of GPS data. These maps were used to identify the instability areas and subsequently investigated by field survey, airborne photogeological interpretation and morphometric elaborations derived from airborne Lidar information. In addition, the land cover mapping was obtained using satellite high-medium resolution data. The analysis was performed in a GIS environment allowing to identify the main parameters that influence the slope instability and to obtain the landslide hazard map. finally, the comparison with the landslides historical database provides the different landslides susceptibility degrees classes.

  8. Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)

    NASA Astrophysics Data System (ADS)

    Das, Iswar; Sahoo, Sashikant; van Westen, Cees; Stein, Alfred; Hack, Robert

    2010-02-01

    Landslide studies are commonly guided by ground knowledge and field measurements of rock strength and slope failure criteria. With increasing sophistication of GIS-based statistical methods, however, landslide susceptibility studies benefit from the integration of data collected from various sources and methods at different scales. This study presents a logistic regression method for landslide susceptibility mapping and verifies the result by comparing it with the geotechnical-based slope stability probability classification (SSPC) methodology. The study was carried out in a landslide-prone national highway road section in the northern Himalayas, India. Logistic regression model performance was assessed by the receiver operator characteristics (ROC) curve, showing an area under the curve equal to 0.83. Field validation of the SSPC results showed a correspondence of 72% between the high and very high susceptibility classes with present landslide occurrences. A spatial comparison of the two susceptibility maps revealed the significance of the geotechnical-based SSPC method as 90% of the area classified as high and very high susceptible zones by the logistic regression method corresponds to the high and very high class in the SSPC method. On the other hand, only 34% of the area classified as high and very high by the SSPC method falls in the high and very high classes of the logistic regression method. The underestimation by the logistic regression method can be attributed to the generalisation made by the statistical methods, so that a number of slopes existing in critical equilibrium condition might not be classified as high or very high susceptible zones.

  9. Landslide movement mapping by sub-pixel amplitude offset tracking - case study from Corvara landslide

    NASA Astrophysics Data System (ADS)

    Darvishi, Mehdi; Schlögel, Romy; Cuozzo, Giovanni; Callegari, Mattia; Thiebes, Benni; Bruzzone, Lorenzo; Mulas, Marco; Corsini, Alessandro; Mair, Volkmar

    2016-04-01

    Despite the advantages of Differential Synthetic Aperture Radar Interferometry (DInSAR) methods for quantifying landslide deformation over large areas, some limitations remain. These include for example geometric distortions, atmospheric artefacts, geometric and temporal decorrelations, data and scale constraints, and the restriction that only 1-dimentional line-of-sight (LOS) deformations can be measured. At local scale, the major limitations are dense vegetation, as well as large displacement rates which can lead to decorrelation between SAR acquisitions also for high resolution images and temporal baselines. Sub-pixel offset tracking was proposed to overcome some of these limitations. Two of the most important advantages of this technique are the mapping of 2-D displacements (azimuth and range directions), and the fact that there is no need for complex phase unwrapping algorithms which could give wrong results or fail in case of decorrelation or fast ground deformations. As sub-pixel offset tracking is highly sensitive to the spatial resolution of the data, latest generations of SAR sensors such as TerraSAR-X and COSMO-SkyMed providing high resolution data (up to 1m) have great potential to become established methods in the field of ground deformation monitoring. In this study, sub-pixel offset tracking was applied to COSMO SkyMed X-band imagery in order to quantify ground displacements and to evaluate the feasibility of offset tracking for landslide movement mapping and monitoring. The study area is the active Corvara landslide located in the Italian Alps, described as a slow-moving and deep-seated landslide with annual displacement rates of up to 20 m. Corner reflectors specifically designed for X-band were installed on the landslide and used as reference points for sub-pixel offset tracking. Satellite images covering the period from 2013 to 2015 were analyzed with an amplitude tracking tool for calculating the offsets and extracting 2-D displacements. Sub

  10. Presence-only approach to assess landslide triggering-thickness susceptibility. A test for the Mili catchment (North-Eastern Sicily, Italy)

    NASA Astrophysics Data System (ADS)

    Lombardo, Luigi; Fubelli, Giandomenico; Amato, Gabriele; Bonasera, Mauro; Hochschild, Volker; Rotigliano, Edoardo

    2015-04-01

    This study aims at comparing the performances of a presence only approach, namely Maximum Entropy, in assessing landslide triggering-thickness susceptibility within the Mili catchment, located in the north-eastern Sicily, Italy. This catchment has been recently exposed to three main meteorological extreme events, resulting in the activation of multiple fast landslides, which occurred on the 1st October 2009, 10th March 2010 and 1st March 2011. Differently from the 2009 event, which only marginally hit the catchment, the 2010 and 2011 storms fully involved the area of the Mili catchment. Detailed field data was collected to associate the thickness of mobilised materials at the triggering zone to each mass movement within the catchment. This information has been used to model the landslide susceptibility for two classes of processes clustered into shallow failures for maximum depths of 0.5m and deep ones in case of values equal or greater than 0.5m. As the authors believed that the peculiar geomorphometry of this narrow and steep catchment played a fundamental role in generating two distinct patterns of landslide thicknesses during the initiation phase, a HRDEM was used to extract topographic attributes to express near-triggering geomorphological conditions. On the other hand, medium resolution vegetation indexes derived from ASTER scenes were used as explanatory variables pertaining to a wider spatial neighbourhood, whilst a revised geological map, the land use from CORINE and a tectonic map were used to convey an even wider area connected to the slope instability. The choice of a presence-only approach allowed to effectively discriminate between the two types of landslide thicknesses at the triggering zone, producing outstanding prediction skills associated with relatively low variances across a set of 20 randomly generated replicates. The validation phase produced indeed average AUC values of 0.91 with a standard deviation of 0.03 for both the modelled landslide

  11. Application of multi-temporal landform analysis in landslide susceptibility assessment for mountainous highway - a case study in southeastern Taiwan

    NASA Astrophysics Data System (ADS)

    Liu-Xuan, Jian; Wei-Kai, Huang; Po-Shen, Lin

    2016-04-01

    This study divided a coastal mountainous highway into small sections with slope unit, plot the multi-temporal landslide inventories, and analyze the relationships between the revegetation areas of the existing landslide and newly activated landslide to calculate landslide status Index (LSI). The RI represents the multi-temporal status of landslide status in each slope unit; three statuses and their representing colors were defined in this study. Red representing slope unit with continuously landslides, yellow for those with previous landslide but stable and revegetating, green are those without landslides. The regression lines became one of the parameters in establishing landslide status map. The study area, 407K to 439K of Provincial Highway No. 9, located in southeastern Taiwan and is the most important transport corridor connecting southern Taiwan and the east coast. In 2009 this mountainous highway was hit by Typhoon Morakot and several landslides, debris slides were triggered in the study area. The debris blocked the traffic and residential communities alone the highway became isolated. To this date some section of the highway still suffer from landslide hazard and transportation had to be temporarily interrupted during some occasions. The landslide status map of this transport corridor was established combining the result of field investigation, remote sensing interpretation, and the regression lines of LSI. The preliminary result shows that out of the 258 slope units, 13 (5%) showing continuous landslides, 44 (17%) became stable and revegetating. The result of this study could provide better information for mountainous highway safety management.

  12. Application of PALSAR-2 Remote Sensing Data for Landslide Hazard Mapping in Kelantan River Basin, Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Beiranvand Pour, Amin; Hashim, Mazlan

    2016-06-01

    Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.

  13. Contribution of physical modelling to climate-driven landslide hazard mapping: an alpine test site

    NASA Astrophysics Data System (ADS)

    Vandromme, R.; Desramaut, N.; Baills, A.; Hohmann, A.; Grandjean, G.; Sedan, O.; Mallet, J. P.

    2012-04-01

    The aim of this work is to develop a methodology for integrating climate change scenarios into quantitative hazard assessment and especially their precipitation component. The effects of climate change will be different depending on both the location of the site and the type of landslide considered. Indeed, mass movements can be triggered by different factors. This paper describes a methodology to address this issue and shows an application on an alpine test site. Mechanical approaches represent a solution for quantitative landslide susceptibility and hazard modeling. However, as the quantity and the quality of data are generally very heterogeneous at a regional scale, it is necessary to take into account the uncertainty in the analysis. In this perspective, a new hazard modeling method is developed and integrated in a program named ALICE. This program integrates mechanical stability analysis through a GIS software taking into account data uncertainty. This method proposes a quantitative classification of landslide hazard and offers a useful tool to gain time and efficiency in hazard mapping. However, an expertise approach is still necessary to finalize the maps. Indeed it is the only way to take into account some influent factors in slope stability such as heterogeneity of the geological formations or effects of anthropic interventions. To go further, the alpine test site (Barcelonnette area, France) is being used to integrate climate change scenarios into ALICE program, and especially their precipitation component with the help of a hydrological model (GARDENIA) and the regional climate model REMO (Jacob, 2001). From a DEM, land-cover map, geology, geotechnical data and so forth the program classifies hazard zones depending on geotechnics and different hydrological contexts varying in time. This communication, realized within the framework of Safeland project, is supported by the European Commission under the 7th Framework Programme for Research and Technological

  14. Using online database for landslide susceptibility assessment with an example from the Veneto Region (north-eastern Italy).

    NASA Astrophysics Data System (ADS)

    Floris, Mario; Squarzoni, Cristina; Zorzi, Luca; D'Alpaos, Andrea; Iafelice, Maria

    2010-05-01

    Landslide susceptibility maps describe landslide-prone areas by the spatial correlation between landslides and related factors, derived from different kinds of datasets: geological, geotechnical and geomechanical maps, hydrogeological maps, landslides maps, vector and raster terrain data, real-time inclinometer and pore pressure data. In the last decade, thanks to the increasing use of web-based tools for management, sharing and communication of territorial information, many Web-based Geographical Information Systems (WebGIS) were created by local governments or nations, University and Research Centres. Nowadays there is a strong proliferation of geological WebGIS or GeoBrowser, allowing free download of spatial information. There are global Cartographical Portals that provide a free download of DTM and other vector data related to the whole planet (http://www.webgis.com). At major scale, there are WebGIS regarding entire nation (http://www.agiweb.org), or specific region of a country (http://www.mrt.tas.gov.au), or single municipality (http://sitn.ne.ch/). Moreover, portals managed by local government and academic government (http://turtle.ags.gov.ab.ca/Peace_River/Site/) or by a private agency (http://www.bbt-se.com) are noteworthy. In Italy, the first national projects for the creation of WebGIS and web-based databases begun during the 1980s, and evolved, through years, to the present number of different WebGIS, which have different territorial extensions: national (Italian National Cartographical Portal, http://www.pcn.minambiente.it; E-GEO Project, http://www.egeo.unisi.it), interregional (River Tiber Basin Authority, www.abtevere.it ), and regional (Veneto Region, www.regione.veneto.it). In this way we investigated most of the Italian WebGIS in order to verify their geographic range and the availability and quality of data useful for landslide hazard analyses. We noticed a large variability of the accessing information among the different browsers. In

  15. Landslide!

    ERIC Educational Resources Information Center

    Hall-Wallace, Michelle; Mitchell, Carl

    1996-01-01

    Presents a unit that focuses on landslides and integrates earth science, physics, chemistry, and math. Includes activities to investigate porosity, permeability, cohesion, saturation, and gravity. (JRH)

  16. The Rock Engineering System (RES) applied to landslide susceptibility zonation of the northeastern flank of Etna: methodological approach and results

    NASA Astrophysics Data System (ADS)

    Apuani, Tiziana; Corazzato, Claudia

    2015-04-01

    Ground deformations in the northeastern flank of Etna are well known. Despite only a few landslide events have been documented, these have significantly involved and damaged lifelines and buildings. These events are mainly related to the activity of the volcano-tectonic structures and associated seismicity, as in the case of the 2002 reactivation of the Presa landslide during an increased activity of the Pernicana fault system. In order to highlight the areal distribution of potentially unstable slopes based on a detailed, site-specific study of the factors responsible for landslide, and to ultimately contribute to risk management, a landslide susceptibility analysis of the northeastern flank of Etna in the Pernicana area was carried out, and a susceptibility map at 1:10.000 scale was produced, extending over an area of 168 km2. Different methods are proposed in the literature to obtain the regional distribution of potentially unstable slopes, depending on the problem scale, the slope dynamic evolution in the geological context, and the availability of data. Among semi-quantitative approaches, the present research combines the Rock Engineering System (RES) methodology with parameter zonation mapping in a GIS environment. The RES method represents a structured approach to manage a high number of interacting factors involved in the instability problem. A numerically coded, site-specific interaction matrix (IM) analyzes the cause-effect relationship in these factors, and calculates the degree of interactivity of each parameter, normalized by the overall interactivity of the system (weight factor). In the specific Etna case, the considered parameters are: slope attitude, lithotechnical properties (lithology, structural complexity, soil and rock mass quality), land use, tectonic structures, seismic activity (horizontal acceleration) and hydrogeological conditions (groundwater and drainage). Thematic maps are prepared at 1:10.000 scale for each of these parameters, and

  17. A method for producing digital probabilistic seismic landslide hazard maps; an example from the Los Angeles, California, area

    USGS Publications Warehouse

    Jibson, Randall W.; Harp, Edwin L.; Michael, John A.

    1998-01-01

    The 1994 Northridge, California, earthquake is the first earthquake for which we have all of the data sets needed to conduct a rigorous regional analysis of seismic slope instability. These data sets include (1) a comprehensive inventory of triggered landslides, (2) about 200 strong-motion records of the mainshock, (3) 1:24,000-scale geologic mapping of the region, (4) extensive data on engineering properties of geologic units, and (5) high-resolution digital elevation models of the topography. All of these data sets have been digitized and rasterized at 10-m grid spacing in the ARC/INFO GIS platform. Combining these data sets in a dynamic model based on Newmark's permanent-deformation (sliding-block) analysis yields estimates of coseismic landslide displacement in each grid cell from the Northridge earthquake. The modeled displacements are then compared with the digital inventory of landslides triggered by the Northridge earthquake to construct a probability curve relating predicted displacement to probability of failure. This probability function can be applied to predict and map the spatial variability in failure probability in any ground-shaking conditions of interest. We anticipate that this mapping procedure will be used to construct seismic landslide hazard maps that will assist in emergency preparedness planning and in making rational decisions regarding development and construction in areas susceptible to seismic slope failure.

  18. GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy)

    NASA Astrophysics Data System (ADS)

    Conoscenti, Christian; Di Maggio, Cipriano; Rotigliano, Edoardo

    Landslide hazard assessment, effected by means of geostatistical methods, is based on the analysis of the relationships between landslides and the spatial distributions of some instability factors. Frequently such analyses are based on landslide inventories in which each record represents the entire unstable area and is managed as a single instability landform. In this research, landslide susceptibility is evaluated through the study of a variety of instability landforms: landslides, scarps and areas uphill from crown . The instability factors selected were: bedrock lithology, steepness, topographic wetness index and stream power index. The instability landform densities computed for all the factors, which were arranged in Unique Condition Unit, allowed us to derive a total of three prediction images for each landslide typology. The role of the instability factors and the effects generated by the use of different landforms were analyzed by means of: a) bivariate analysis of the relationships between factors and landslide density; b) predictive power validations of the prediction images, based on a random partition strategy. The test area was the Iato River Basin (North-Western Sicily), whose slopes are moderately involved in flow and rotational slide landslides (219 and 28, respectively). The area is mainly made up of the following complexes: Numidian Flysch clays (19%, 1%), Terravecchia sandy clays (5%, 1%), Terravecchia clayey sands (3%, 0.3%) and San Cipirello marly clays (9%, 0%). The steepness parameter shows the highest landslide density in the [11-19°] class for both the typologies (8%, 1%), even if the density distributions for rotational slides are right-asymmetric and right-shifted. We obtained significant differences in shape when we used different instability landforms. Unlike scarps and areas uphill from crowns, landslide areas produce left-asymmetric and left-shifted density distributions for both the typologies. As far as the topographic wetness

  19. Evaluating landslide susceptibility in hillslopes of the Daunia Apennines (Apulia, Italy)

    NASA Astrophysics Data System (ADS)

    Andriani, G. F.; Parise, M.; Spagnoletta, A.; Walsh, N.

    2009-04-01

    Landslide susceptibility, defined as the probability of occurrence of slope movements in a given territory, is evaluated in this contribution by means of a computerized methodology in GIS environment, based upon geomorphological surveys, geotechnical characterization of involved materials, and hydrological analysis of time series of hourly rainfall. The Daunia Apennines are located at the north-western border of Apulia region (southern Italy), representing the outer front of the southern Italian Apenninic Range, and the transition to the Apulian foreland. They are characterized by hilly landscapes, rarely above 1,000 m a.s.l., and present outcropping rocks consisting of pre-Pliocene terrigenous sediments, and recent colluvial and alluvial deposits. The area is intensely affected by several types of slope movements, the most common being complex landslides (roto-translational slides evolving to debris- and/or earth-flows). Locally, rock failures in the more competent lithotypes, and mud flows in the prevailing clay deposits are also present. In most of the cases, slope movements are related to partial or total re-activation of dormant phenomena, triggered by prolonged, intense rainstorms. The sector between San Marco la Catola, Volturara Appula, Celenza Valfortore, Alberona and San Bartolomeo in Galdo, in the catchment of La Catola Torrent, a right tributary of the Fortore River, has been selected as sample area. With slope gradients around 20°, the area is highly affected by shallow instabilities, involving mostly clay terranes. The index parameters were determined on both fresh and remoulded samples of involved lithotypes, as well as the consolidated-drained (CD) and consolidated-undrained (CU) shear strength. Permeability was evaluated through determination of the hydraulic conductivity by means of aedometric tests and falling head permeability tests. The digital elevation model (DEM), from which using a class rating method the main environmental factors (slope

  20. The comparison of landslide ratio-based and general logistic regression landslide susceptibility models in the Chishan watershed after 2009 Typhoon Morakot

    NASA Astrophysics Data System (ADS)

    WU, Chunhung

    2015-04-01

    The research built the original logistic regression landslide susceptibility model (abbreviated as or-LRLSM) and landslide ratio-based ogistic regression landslide susceptibility model (abbreviated as lr-LRLSM), compared the performance and explained the error source of two models. The research assumes that the performance of the logistic regression model can be better if the distribution of landslide ratio and weighted value of each variable is similar. Landslide ratio is the ratio of landslide area to total area in the specific area and an useful index to evaluate the seriousness of landslide disaster in Taiwan. The research adopted the landside inventory induced by 2009 Typhoon Morakot in the Chishan watershed, which was the most serious disaster event in the last decade, in Taiwan. The research adopted the 20 m grid as the basic unit in building the LRLSM, and six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The six variables were divided as continuous variables, including elevation, slope, and accumulated rainfall, and categorical variables, including aspect, geological formation and bank erosion in building the or-LRLSM, while all variables, which were classified based on landslide ratio, were categorical variables in building the lr-LRLSM. Because the count of whole basic unit in the Chishan watershed was too much to calculate by using commercial software, the research took random sampling instead of the whole basic units. The research adopted equal proportions of landslide unit and not landslide unit in logistic regression analysis. The research took 10 times random sampling and selected the group with the best Cox & Snell R2 value and Nagelkerker R2 value as the database for the following analysis. Based on the best result from 10 random sampling groups, the or-LRLSM (lr-LRLSM) is significant at the 1% level with Cox & Snell R2 = 0.190 (0.196) and Nagelkerke R2

  1. Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework

    USGS Publications Warehouse

    Godt, J.W.; Baum, R.L.; Savage, W.Z.; Salciarini, D.; Schulz, W.H.; Harp, E.L.

    2008-01-01

    Application of transient deterministic shallow landslide models over broad regions for hazard and susceptibility assessments requires information on rainfall, topography and the distribution and properties of hillside materials. We survey techniques for generating the spatial and temporal input data for such models and present an example using a transient deterministic model that combines an analytic solution to assess the pore-pressure response to rainfall infiltration with an infinite-slope stability calculation. Pore-pressures and factors of safety are computed on a cell-by-cell basis and can be displayed or manipulated in a grid-based GIS. Input data are high-resolution (1.8??m) topographic information derived from LiDAR data and simple descriptions of initial pore-pressure distribution and boundary conditions for a study area north of Seattle, Washington. Rainfall information is taken from a previously defined empirical rainfall intensity-duration threshold and material strength and hydraulic properties were measured both in the field and laboratory. Results are tested by comparison with a shallow landslide inventory. Comparison of results with those from static infinite-slope stability analyses assuming fixed water-table heights shows that the spatial prediction of shallow landslide susceptibility is improved using the transient analyses; moreover, results can be depicted in terms of the rainfall intensity and duration known to trigger shallow landslides in the study area.

  2. Shallow Landslide Susceptibilty Mapping using SINMAP for Selected Areas in the Philippines

    NASA Astrophysics Data System (ADS)

    Rabonza, M. L.; Alejandrino, I. A.; Suarez, J.; Aquino, D. T.; Eco, R. C.; Lagmay, A.

    2013-12-01

    Among the deadliest calamities that plagued the Philippines were the landslide and flooding caused by consecutive tropical storms Mufia, Merbok, Winnie, and Nanmadol in Infanta, Quezon (2004), Typhoon Reming in Guinsaugon, Leyte (2006) and Typhoon Pepeng in Pangasinan (2009). In Quezon alone, the number of death and missing exceeded 1600, and the cost of damage was estimated at US$78.2M. Situated in the humid tropics, the Philippines will inevitably be a locus of climate-related disasters similar to those experienced recently. To aid the local government units in delineating areas located on hazardous zones in the country, the spatial distribution of rainfall-induced shallow landslide susceptibilities was modeled for the provinces of Quezon (906,960 ha), Leyte (651,505 ha), and Pangasinan (545,101 ha). The Stability INdex Mapping (SINMAP) model was applied based on the infinite slope stability model and a steady-state hydrology module. Using the hydrologic, soil and topographic parameters for each pixel on a 6-meter synthetic aperture radar-derived DEM (digital elevation model grid) or on a 1-meter LiDAR-derived DEM, Stability Index Maps were generated for the study areas. Soil properties were based on data available from Bureau of Soils and Water Management as of July 2013. These data were augmented by field data and constrained by values from direct shear strength testing, soil gradation, and hydraulic conductivity tests of soil. The model validation performed using the previously identified landslide inventory was found out to be about 90% accurate. The SINMAP model accuracy can be further improved by additional locations for field tests to better map out the spatial variation of soil properties.

  3. Using online databases for landslide susceptibility assessment: an example from the Veneto Region (northeastern Italy)

    NASA Astrophysics Data System (ADS)

    Floris, M.; Iafelice, M.; Squarzoni, C.; Zorzi, L.; de Agostini, A.; Genevois, R.

    2011-07-01

    In this paper, spatial data available in the Italian portals was used to evaluate the landslide susceptibility of the Euganean Hills Regional Park, located SW of Padua (northeastern Italy). Quality, applicability and possible analysis scales of the online data were investigated. After a brief overview on the WebGIS portals around the world, their contents and tools for natural risk analyses, a susceptibility analysis of the study area was carried out using a simple probabilistic approach that compared landslide distribution and influencing factors. The input factors used in the analysis depended on available data and included landslides, morphometric data (elevation, slope, curvature, profile and plan Curvature) and non-morphometric data (land use, distance to roads and distance to rivers). Great attention was paid to the pre-processing step, in particular the re-classification of continuous data that was performed following objective, geologic and geomorphologic criteria. The results of the study show that the simple probabilistic approach used for the susceptibility evaluation showed quite good accuracy and precision (repeatability). However, heuristic, statistical or deterministic methods could be applied to the online data to improve the prediction. The data available online for the Italian territory allows susceptibility assessment at medium and large scales. Morphometric factors, such as elevation and slope angle, are important because they implicitly include information that is not available, such as lithologic and structural data. The main drawback of the Italian online databases is the lack of information on the frequency of landslides; thus, a complete hazard analysis is not possible. Despite the good results achieved to date, collection and sharing of data on natural risks must be improved in Italy and around the world. The creation of spatial data infrastructure and more WebGIS portals is desirable.

  4. Comparison of event-based landslide inventory maps obtained interpreting satellite images and aerial photographs

    NASA Astrophysics Data System (ADS)

    Fiorucci, Federica; Cardinali, Mauro; Carlà Roberto; Mondini, Alessandro; Santurri, Leonardo; Guzzetti, Fausto

    2010-05-01

    Landslide inventory maps are a common type of map used for geomorphological investigations, land planning, and hazard and risk assessment. Landslide inventory maps covering medium to large areas are obtained primarily exploiting traditional geomorphological techniques. These techniques combine the visual and heuristic interpretation of stereoscopic aerial photographs with more or less extensive field investigations. Aerial photographs most commonly used to prepare landslide inventory maps range in scale from about 1:10,000 to about 1:40,000. Interpretation of satellite images is a relatively recent, powerful tool to obtain information of the Earth surface potentially useful for the production of landslide inventory maps. The usefulness of satellite information - and the associated technology - for the identification of landslides and the production of landslide inventory maps, remains largely unexplored. In this context, it is of interest to investigate the type, quantity, and quality of the information that can be retrieved analyzing images taken by the last generation of high and very-high resolution satellite sensors, and to compare this information with the information obtained from the analysis of traditional stereoscopic aerial photographs, or in the field. In the framework of the MORFEO project for the exploitation of Earth Observation data and technology for landslide identification and risk assessment, of the Italian Space Agency, we have compared two event-based landslide inventory maps prepared exploiting two different techniques. The two maps portray the geographical distribution and types of landslides triggered by rainfall in the period from November 2004 to May 2005 in the Collazzone area, Umbria, central Italy. The first map was prepared through reconnaissance field surveys carried out mostly along roads. The second map was obtained through the combined visual interpretation of 1:10,000 scale, colour ortho-photo maps, and images taken by the IKONOS

  5. An overview of a GIS method for mapping landslides and assessing landslide hazards at Río El Estado watershed, on the SW flank of Pico de Orizaba Volcano, Mexico

    NASA Astrophysics Data System (ADS)

    Legorreta Paulin, G.; Bursik, M. I.; Contreras, T.; Polenz, M.; Ramírez Herrera, M.; Paredes Mejía, L.; Arana Salinas, L.

    2012-12-01

    This poster provides an overview of the on-going research project (Grant SEP-CONACYT no 167495) from the Institute of Geography at the National Autonomous University of Mexico (UNAM) that seeks to conduct a multi-temporal landslide inventory, produce a landslide susceptibility map, and estimate sediment production by using Geographic Information Systems (GIS). The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The catchment covers 5.2 km2 with elevations ranging from 2676.79 to 4248.2 m a.s.l. and hillslopes between 0° and 56°. The stream system of Río El Estado catchment erodes Tertiary and Quaternary lavas, pyroclastic flows, and fall deposits. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The methodology encompasses three main stages of analysis to assess landslide hazards: Stage 1 builds a historic landslide inventory. In the study area, an inventory of more than 170 landslides is created from multi-temporal aerial-photo-interpretation and local field surveys to assess landslide distribution. All landslides were digitized into a geographic information system (GIS), and a spatial geo-database of landslides was constructed from standardized GIS datasets. Stage 2 Calculates the susceptibility for the watershed. During this stage, Multiple Logistic Regression and SINMAP) will be evaluated to select the one that provides scientific accuracy, technical accessibility, and applicability. Stage 3 Estimate the potential total material delivered to the main stream drainage channel by all landslides in the catchment. Detailed geometric measurements of individual landslides visited during the field work will be carried out to obtain the landslide area and volume. These measurements revealed an empirical relationship between area and volume that took the

  6. Landslide!

    NASA Technical Reports Server (NTRS)

    2003-01-01

    MGS MOC Release No. MOC2-486, 17 September 2003

    This August 2003 Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image shows part of a deposit created by a landslide off the wall of a crater near 12.3oN, 21.3oW. The crater wall is not shown; it is several kilometers to the left of this picture. The debris that slid from the crater wall came from the left/upper left (northwest) and moved toward the lower right (southeast). The crater floor onto which the debris was deposited has more small meteor craters on it than does the landslide material; this indicates that there was a considerable interval between the time when the crater floor formed, and when the landslide occurred. This picture covers an area 3 km (1.9 mi) wide. Sunlight illuminates the scene from the lower left.

  7. Geomorphological mapping and geotechnical testing of the March 22, 2014, SR530 landslide near Oso, Washington

    NASA Astrophysics Data System (ADS)

    Collins, B. D.; Reid, M. E.; Vallance, J. W.; Iverson, R. M.; Schmidt, K. M.

    2014-12-01

    The March 22, 2014 landslide near Oso, Washington devastated a community, killing 43 people, destroying dozens of homes, and temporarily closing a section of State Route (SR) 530. The landslide, characterized as a debris avalanche - debris flow - rotational slide, was triggered by heavy precipitation in the region and initiated from a 200 m tall section of Pleistocene glacial deposits. The entire landslide encompassed an area of 1.2 km2. To understand the mobility of this landslide, we performed geological and geomorphological mapping throughout the initiation, transport, and deposition zones. In addition, we mapped a 450-m-long cross-section through the western distal lobe created by the excavation to reopen the SR530 roadbed to temporary traffic. Samples collected during mapping were used for geotechnical testing to evaluate the mobility of the landslide materials. Our detailed (1:300) geological mapping of the excavation revealed the juxtaposition of sand (glacial outwash) and clay (glaciolacustrine) debris avalanche hummocks towards the distal end of the landslide. Further, we found that two sections of the roadbed, having a combined length of at least 150 m, were entrained in the landslide. Throughout the debris avalanche deposit, 1:1200-scale geomorphological mapping identified a preponderance of sand boils located within thinner deposits between hummocks, suggesting that liquefaction played a role in the landslides mobility. In the central distal end of the landslide, we mapped on-lap deposits, wherein distal debris flow material overrode smaller hummocks of the larger debris avalanche deposit. Discovery of these deposits indicates that the run out of the landslide might have been even longer in places had topographic barriers (i.e., the other side of the valley) not reflected the flow back towards itself.

  8. Geoethical Issues in Landslides Hazard Zonation

    NASA Astrophysics Data System (ADS)

    Parkash Gupta, Surya

    2016-04-01

    Landslide hazard zonation is a common geoscientific practice for assessing potential from slope instability problems. Several different approaches and techniques have been applied by various researchers to classify hilly terrains into different degrees or probabilities of landslide hazards. But the study of landslide hazard zonation practices in India reveals that most of these approaches use same factors and approaches for landslide processes. However, the causative and controlling factors for different types of landslides have been found to be different depending on the material (rock, debris or soil) involved in the movement as well as the failure process (fall, topple, slide (rotational, wedge, planar), flow and spread. Each of these landslide process is governed by different factors but during the landslide hazard or susceptibility zonation by many of the geoscientists, same set of factors have been used. Such approaches not only enhance the errors in landslide hazard assessment but also increase the uncertainties in terms of landslide processes. These kind of landslide hazard or susceptibility zonation maps can not be used reliably by the planners, administrators, development agencies, communities and other stakeholders. The approach is likely to affect the credibility of geoscientists among the society. Hence, it is proposed that landslide process specific zonation maps should be generated to classify the hilly terrains into different degrees of hazards. It will also help in establishing responsible factor for each landslide process more accurately and estimating potential landslide hazards with greater reliability.

  9. Geomorphological mapping and mass-wasting analysis in complex landslides using Terrestrial and Airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Corsini, Alessandro; Bertacchini, Eleonora; Borgatti, Lisa; Capra, Alessandro; Cervi, Federico; Dahne, Alexander; Ronchetti, Francesco

    2010-05-01

    High-Resolution Digital Elevation Models (HR DEMs) obtained with Light Detection and Ranging technology, (LiDAR) have proved to be useful in the analysis of landslide processes in mountainous terrains, for different purposes and at different spatial scales, including improvement of landslide inventories, susceptibility assessment and appraisal of landslide geomorphic features. The application of laser scanning techniques results in data sets with enormous data size, extremely high accuracy (up to cm-scale) and very high spatial resolution. The exploitation of HR DEMs in landslide analysis can be manifold, comprising automated spatial data processing as well as expert knowledge - supervised procedures. First goal of this paper is to stress the advantages given by the usage of Shaded Relief datasets obtained from HR DEMs in geomorphological mapping of complex landslides. Through the use of such datasets, surveyors gain an enhanced capacity to identify slope-scale geomorpho-dynamic units, the possibility to increase the precision of scarps and detachment areas zonation, the capacity to identify and map compressive and extensional features all across the landslide body, and the possibility to retrieve other relevant geomorphological indicators of movement. These advantages are exemplified by presenting maps of large-scale earth slide - earth flows and deep seated rock slides located in the Alps and in the Apennines, that have been obtained by making use of shaded relief maps calculated from regional and local datasets using different scene illumination parameters. Second goal of this paper is to exemplify the usage of Differential HR DEMs is mass-wasting analysis applied to active earth slides - earth flows located in the northern Apennines. This simple DEM subtraction procedure can be carried out using multi-temporal airborne or terrestrial surveys, or by fusing airborne and terrestrial data. Examples are presented of landslides for which mass wasting at the slope

  10. Mapping spatial patterns of people's risk perception of landslides

    NASA Astrophysics Data System (ADS)

    Kofler, Christian; Pedoth, Lydia; Elzbieta Stawinoga, Agnieszka; Schneiderbauer, Stefan

    2016-04-01

    The resilience of communities against natural hazards is largely influenced by how the individuals perceive risk. A good understanding of people's risk perception, awareness and hazard knowledge is crucial for developing and improving risk management and communication strategies between authorities and the affected population. A lot of research has been done in investigating the social aspects of risks to natural hazards by means of interviews or questionnaires. However, there is still a lack of research in the investigation of the influence of the spatial distance to a hazard event on peoples risk perception. While the spatial dimension of a natural hazard event is always assessed in works with a natural science approach, it is often neglected in works on social aspects of natural hazards. In the present study, we aimed to overcome these gaps by combining methods from different disciplines and assessing and mapping the spatial pattern of risk perception through multivariate statistical approaches based on empirical data from questionnaires. We will present results from a case study carried out in Badia, located in the Province of South Tyrol- Italy, where in December 2012 a landslide destroyed four residential buildings and led to the evacuation of 36 people. By means of questionnaires distributed to all adults living in the case study area we assessed people's risk perception and asked respondents to allocate their place of residence on a map of the case study area subdivided in 7 zones. Based on the data of the questionnaire results we developed a risk perception factor in order to express various assessed aspects linked to risk perception with one metric. We analyzed and mapped this factor according to the different zones reflecting the spatial distance to the event. Furthermore, a cluster analysis identified various risk behavior profiles within the population. We also investigated the spatial patterns of these risk profiles. We revealed that the residential

  11. Utilization of web-based stationary rainfall data for near-real-time derivation of spatial landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Canli, Ekrem; Glade, Thomas; Loigge, Bernd

    2016-04-01

    Scarcity of high-quality meteorological data is often referred to as one of the main constraints for performing real-time landslide forecasting. Meteorological data may be expensive or not up-to-date any more soon after it is acquired. However, the internet is a great source of freely available, high quality real-time weather data from different sources. Web scraping has emerged into a highly valuable technique for utilizing information from public websites. Hereby, web scraping is the process of automatically gathering data from the internet, extracting these data according to required needs, storing the selected data and using those self-generated databases for further analysis. This technique is of great value, in particular for weather data that is released regularly in short intervals to the public, but may be applicable to any other type of continuously released data. By applying these techniques, research institutions in developing countries may be able to generate their own free data without the need of purchasing expensive, ready-made weather data. However, some weather data providers already offer application programming interfaces (API) that facilitate access to real-time weather data, but those usually have to be purchased. Here we present an approach for integrating web-based rainfall data from different sources into an automated workflow. This workflow ranges from the query of near-real-time data to spatially interpolating those rain gauge measurements into a continuous rainfall raster. Subsequently, this raster is handed over into a dynamic, physical-based landslide model for generating hourly distributed landslide susceptibility maps on a regional scale. Future work involves the establishment or regional intensity-duration rainfall thresholds that are continuously evaluated against the distributed rainfall patterns based on real-time rainfall data.

  12. Using geo-informatics for landslide risk map in northern Thailand

    NASA Astrophysics Data System (ADS)

    Thammapala, Prasong; Weng, Jingnong

    2015-12-01

    The Kingdom of Thailand has been facing with natural disasters every year: landslide, drought, wind storm, landslide etc. especially, the last decade the natural disaster was most frequency and devastated vast areas. Furthermore, landslide occurrences have become more and more recurrence and human impacts have been increasing on seriously natural disasters problem during the past couple of decades. The study has been designed to analyze the risk landslide areas for landslide management in Phetchabun province, Thailand. This study aim to apply the geo-informatics technology, create landslide risk map, and develop landslide monitoring and warning systems used for formulating preparedness and recovery plans. This analyzed the concerned physical and environmental factors though statistical techniques and spatial analysis. The analyzed factors included with river, elevation, street, land use, sub-basin area, slope, drainage and rainfall. Potential Surface Analysis (PSA) technique has been used for analysis included with overlaying and Weighting-Rating Model for landslide risk area. The validation model compared with historical data. The result could show risk areas of landslide in Phetchabun province that high risk areas are covering north-eastern and central of province. In addition, we divided risk area as three levels; high risky, moderate and less. Furthermore, the consequences can be protect or relieved by using appropriate measures; including both publicizing risk information and be prepared for the happening of such disasters. However, some of spatial data have to up to date and improve to high accuracy.

  13. Rapid multi-orientation quantitative susceptibility mapping.

    PubMed

    Bilgic, Berkin; Xie, Luke; Dibb, Russell; Langkammer, Christian; Mutluay, Aysegul; Ye, Huihui; Polimeni, Jonathan R; Augustinack, Jean; Liu, Chunlei; Wald, Lawrence L; Setsompop, Kawin

    2016-01-15

    Three-dimensional gradient echo (GRE) is the main workhorse sequence used for susceptibility weighted imaging (SWI), quantitative susceptibility mapping (QSM), and susceptibility tensor imaging (STI). Achieving optimal phase signal-to-noise ratio requires late echo times, thus necessitating a long repetition time (TR). Combined with the large encoding burden of whole-brain coverage with high resolution, this leads to increased scan time. Further, the dipole kernel relating the tissue phase to the underlying susceptibility distribution undersamples the frequency content of the susceptibility map. Scans at multiple head orientations along with calculation of susceptibility through multi-orientation sampling (COSMOS) are one way to effectively mitigate this issue. Additionally, STI requires a minimum of 6 head orientations to solve for the independent tensor elements in each voxel. The requirements of high-resolution imaging with long TR at multiple orientations substantially lengthen the acquisition of COSMOS and STI. The goal of this work is to dramatically speed up susceptibility mapping at multiple head orientations. We demonstrate highly efficient acquisition using 3D-GRE with Wave-CAIPI and dramatically reduce the acquisition time of these protocols. Using R=15-fold acceleration with Wave-CAIPI permits acquisition per head orientation in 90s at 1.1mm isotropic resolution, and 5:35min at 0.5mm isotropic resolution. Since Wave-CAIPI fully harnesses the 3D spatial encoding capability of receive arrays, the maximum g-factor noise amplification remains below 1.30 at 3T and 1.12 at 7T. This allows a 30-min exam for STI with 12 orientations, thus paving the way to its clinical application. PMID:26277773

  14. GIS-based landslide hazard evaluation at the regional scale: some critical points in the permanent displacement approach for seismically-induced landslide maps

    NASA Astrophysics Data System (ADS)

    Vessia, Giovanna; Parise, Mario

    2013-04-01

    Landslide susceptibility and hazard are commonly developed by means of GIS (Geographic Information Systems) tools. Many products such as DTM (Digital Terrain Models), and geological, morphological and lithological layers (often, to be downloaded for free and integrated within GIS) are nowadays available on the web and ready to be used for urban planning purposes. The multiple sources of public information enable the local authorities to use these products for predicting hazards within urban territories by limited investments on technological infrastructures. On the contrary, the necessary expertise required for conducting pertinent hazard analyses is high, and rarely available at the level of the local authorities. In this respect, taking into account the production of seismically-induced landslide hazard maps at regional scale drawn by GIS tool, these can be performed according to the permanent displacement approach derived by Newmark's sliding block method (Newmark, 1965). Some simplified assumptions are considered for occurrence of a seismic mass movement, listed as follows: (1) the Mohr-Coulomb criterion is used for the plastic displacement of the rigid block; (2) only downward movements are accounted for; (3) a translative sliding mechanism is assumed. Under such conditions, several expressions have been proposed for predicting permanent displacements of slopes during seismic events (Ambresys and Menu, 1988; Luzi and Pergalani 2000; Romeo 2000; Jibson 2007, among the others). These formulations have been provided by researchers for different ranges of seismic magnitudes, and for indexes describing the seismic action, such as peak ground acceleration, peak ground velocity, Arias Intensity, and damage potential. With respect to the resistant properties of the rock units, the critical acceleration is the relevant strength variable in every expressions; it is a function of local slope, groundwater level, unit weight shear resistance of the surficial sediments, and

  15. Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba

    NASA Astrophysics Data System (ADS)

    Castellanos Abella, Enrique A.; Van Westen, Cees J.

    Geomorphological information can be combined with decision-support tools to assess landslide hazard and risk. A heuristic model was applied to a rural municipality in eastern Cuba. The study is based on a terrain mapping units (TMU) map, generated at 1:50,000 scale by interpretation of aerial photos, satellite images and field data. Information describing 603 terrain units was collected in a database. Landslide areas were mapped in detail to classify the different failure types and parts. Three major landslide regions are recognized in the study area: coastal hills with rockfalls, shallow debris flows and old rotational rockslides denudational slopes in limestone, with very large deep-seated rockslides related to tectonic activity and the Sierra de Caujerí scarp, with large rockslides. The Caujerí scarp presents the highest hazard, with recent landslides and various signs of active processes. The different landforms and the causative factors for landslides were analyzed and used to develop the heuristic model. The model is based on weights assigned by expert judgment and organized in a number of components such as slope angle, internal relief, slope shape, geological formation, active faults, distance to drainage, distance to springs, geomorphological subunits and existing landslide zones. From these variables a hierarchical heuristic model was applied in which three levels of weights were designed for classes, variables, and criteria. The model combines all weights into a single hazard value for each pixel of the landslide hazard map. The hazard map was then divided by two scales, one with three classes for disaster managers and one with 10 detailed hazard classes for technical staff. The range of weight values and the number of existing landslides is registered for each class. The resulting increasing landslide density with higher hazard classes indicates that the output map is reliable. The landslide hazard map was used in combination with existing information

  16. Airborne geophysical mapping as an innovative methodology for landslide investigation: evaluation of results from the Gschliefgraben landslide, Austria

    NASA Astrophysics Data System (ADS)

    Supper, R.; Baroň, I.; Ottowitz, D.; Motschka, K.; Gruber, S.; Winkler, E.; Jochum, B.; Römer, A.

    2013-05-01

    In September 2009, a complex airborne geophysical survey was performed in the large landslide affected area of the Gschliefgraben valley, Upper Austria, in order to evaluate the usability of this method for landslide detection and mapping. An evaluation of the results, including different remote sensing and ground based methods, proved that airborne geophysics, especially the airborne electromagnetic method, has a high potential for landslide investigation. This is due to its sensitivity to fluid and clay content and porosity, which are parameters showing characteristic values in landslide prone structures. Resistivity distributions in different depth levels as well as depth-slices along selected profiles are presented and compared with ground geoelectrical profiles for the test area of Gschliefgraben. Further interesting results can be derived from the radiometric survey, whereas the naturally occurring radioisotopes 40K and 232Th, as well as the man-made nuclide 137Cs have been considered. While the content of potassium and thorium in the shallow subsurface layer is expressively related to the lithological composition, the distribution of caesium is mainly determined by mass wasting processes.

  17. Airborne geophysical mapping as an innovative methodology for landslide investigation: evaluation of results from the Gschliefgraben landslide, Austria

    NASA Astrophysics Data System (ADS)

    Supper, R.; Baroň, I.; Ottowitz, D.; Motschka, K.; Gruber, S.; Winkler, E.; Jochum, B.; Römer, A.

    2013-12-01

    In September 2009, a complex airborne geophysical survey was performed in the large landslide affected area of the Gschliefgraben valley, Upper Austria, in order to evaluate the applicability of this method for landslide detection and mapping. An evaluation of the results, including different remote-sensing and ground-based methods, proved that airborne geophysics, especially the airborne electromagnetic method, has a high potential for landslide investigation. This is due to its sensitivity to fluid and clay content and porosity, which are parameters showing characteristic values in landslide prone structures. Resistivity distributions in different depth levels as well as depth slices along selected profiles are presented and compared with ground geoelectrical profiles for the test area of Gschliefgraben. Further interesting results can be derived from the radiometric survey, whereas the naturally occurring radioisotopes 40K and 232Th, as well as the man-made nuclide 137Cs have been considered. While the content of potassium and thorium in the shallow subsurface layer is expressively related to the lithological composition, the distribution of caesium is mainly determined by mass wasting processes.

  18. An Atlas of ShakeMaps for Landslide and Liquefaction Modeling

    NASA Astrophysics Data System (ADS)

    Johnson, K. L.; Nowicki, M. A.; Mah, R. T.; Garcia, D.; Harp, E. L.; Godt, J. W.; Lin, K.; Wald, D. J.

    2012-12-01

    The human consequences of a seismic event are often a result of subsequent hazards induced by the earthquake, such as landslides. While the United States Geological Survey (USGS) ShakeMap and Prompt Assessment of Global Earthquakes for Response (PAGER) systems are, in conjunction, capable of estimating the damage potential of earthquake shaking in near-real time, they do not currently provide estimates for the potential of further damage by secondary processes. We are developing a sound basis for providing estimates of the likelihood and spatial distribution of landslides for any global earthquake under the PAGER system. Here we discuss several important ingredients in this effort. First, we report on the development of a standardized hazard layer from which to calibrate observed landslide distributions; in contrast, prior studies have used a wide variety of means for estimating the hazard input. This layer now takes the form of a ShakeMap, a standardized approach for computing geospatial estimates for a variety of shaking metrics (both peak ground motions and shaking intensity) from any well-recorded earthquake. We have created ShakeMaps for about 20 historical landslide "case history" events, significant in terms of their landslide occurrence, as part of an updated release of the USGS ShakeMap Atlas. We have also collected digitized landslide data from open-source databases for many of the earthquake events of interest. When these are combined with up-to-date topographic and geologic maps, we have the basic ingredients for calibrating landslide probabilities for a significant collection of earthquakes. In terms of modeling, rather than focusing on mechanistic models of landsliding, we adopt a strictly statistical approach to quantify landslide likelihood. We incorporate geology, slope, peak ground acceleration, and landslide data as variables in a logistic regression, selecting the best explanatory variables given the standardized new hazard layers (see Nowicki

  19. Potential Deep Seated Landslide Mapping from Various Temporal Data - Benchmark, Aerial Photo, and SAR

    NASA Astrophysics Data System (ADS)

    Wang, Kuo-Lung; Lin, Jun-Tin; Lee, Yi-Hsuan; Lin, Meei-Ling; Chen, Chao-Wei; Liao, Ray-Tang; Chi, Chung-Chi; Lin, Hsi-Hung

    2016-04-01

    Landslide is always not hazard until mankind development in highly potential area. The study tries to map deep seated landslide before the initiation of landslide. Study area in central Taiwan is selected and the geological condition is quite unique, which is slate. Major direction of bedding in this area is northeast and the dip ranges from 30-75 degree to southeast. Several deep seated landslides were discovered in the same side of bedding from rainfall events. The benchmarks from 2002 ~ 2009 are in this study. However, the benchmarks were measured along Highway No. 14B and the road was constructed along the peak of mountains. Taiwan located between sea plates and continental plate. The elevation of mountains is rising according to most GPS and benchmarks in the island. The same trend is discovered from benchmarks in this area. But some benchmarks are located in landslide area thus the elevation is below average and event negative. The aerial photos from 1979 to 2007 are used for orthophoto generation. The changes of land use are obvious during 30 years and enlargement of river channel is also observed in this area. Both benchmarks and aerial photos have discovered landslide potential did exist this area but how big of landslide in not easy to define currently. Thus SAR data utilization is adopted in this case. DInSAR and SBAS sar analysis are used in this research and ALOS/PALSAR from 2006 to 2010 is adopted. DInSAR analysis shows that landslide is possible mapped but the error is not easy to reduce. The error is possibly form several conditions such as vegetation, clouds, vapor, etc. To conquer the problem, time series analysis, SBAS, is adopted in this research. The result of SBAS in this area shows that large deep seated landslides are easy mapped and the accuracy of vertical displacement is reasonable.

  20. Landslide

    NASA Technical Reports Server (NTRS)

    2005-01-01

    [figure removed for brevity, see original site] Context image for PIA03582 Landslide

    This landslide occurred in Coprates Chasma.

    Image information: VIS instrument. Latitude 12.6S, Longitude 296.9E. 17 meter/pixel resolution.

    Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

    NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

  1. Landslide

    NASA Technical Reports Server (NTRS)

    2006-01-01

    [figure removed for brevity, see original site] Context image for PIA02160 Landslide

    This large landslide is located within Ganges Chasma.

    Image information: VIS instrument. Latitude -7.6N, Longitude 315.8E. 17 meter/pixel resolution.

    Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

    NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

  2. Mapping of post-event earthquake induced landslides in Sg. Mesilou using LiDAR

    NASA Astrophysics Data System (ADS)

    Hanan Mat Yusoff, Habibah; Azahari Razak, Khamarrul; Yuen, Florence; Harun, Afifi; Talib, Jasmi; Mohamad, Zakaria; Ramli, Zamri; Abd Razab, Razain

    2016-06-01

    Earthquake is a common natural disaster in active tectonic regions. The disaster can induce cascading disasters such as debris flow, mudflow and reactivated old landslides. M 6.0 Ranau earthquake dated on June 05, 2015 coupling with intense and prolonged rainfall caused several mass movements such as debris flow, deep-seated and shallow landslides in Mesilou, Sabah. This study aims at providing a better insight into the use of advanced LiDAR mapping technology for recognizing landslide induced by earthquakes particularly in a vegetated terrain, assessing post event hazard and analyzing its distribution for hazard zonation. We developed the landslide inventory using LiDAR-derived visual analysis method and validated in the field. A landslide inventory map improved with the support of LiDAR derivative data. Finally, landslide inventory was analysed by emphasizing its distribution and density in such a way that it provides clues of risky zone as a result of debris flow. We recommend that mitigation action and risk reduction should be taken place at a transport zone of the channel compared to other zones. This study indicates that modern airborne LiDAR can be a good complementary tool for improving landslide inventory in a complex environment, and an effective tool for rapid regional hazard and risk assessment in the tropics.

  3. Landslides in Flanders (Belgium): Where science meets public policy

    NASA Astrophysics Data System (ADS)

    van den Eeckhaut, M.; Poesen, J.; Vandekerckhove, L.

    2009-04-01

    Although scientific research on landslides in the Flemish Ardennes (710 km²; Belgium), has been conducted over the last decades, the Flemish Government only took account of slope failure as a soil degradation process after the occurrence of several damaging landslides in the beginning of the 21st century. Here we aim to present the successful collaboration between the Physical and Regional Geography Research Group (FRG; Dept. Earth and Environmental Sciences K.U.Leuven) and the Environment, Nature and Energy Department (LNE; Flemish Government) in landslide management. We will demonstrate how geomorphologists produced practical tools for landslide management which can be directly applied by LNE as well as other local and regional authorities and planners. Since 2004 three projects on landslide inventory mapping and susceptibility assessment in the Flemish Ardennes have been funded by LNE, and a fourth one on landslide susceptibility assessment in remaining hilly regions in Flanders west of Brussels recently started. Together with a steering committee composed of stakeholders, persons from LNE supervise the research carried out by geomorphologists experienced in landslide studies. For the establishment of the landslide inventory map of the Flemish Ardennes we combined the analysis of LIDAR-derived hillshade and contour line maps with detailed field controls. Additional information was collected through interviews with local authorities and inhabitants and from analysis of newspaper articles and technical reports. Then, a statistical model, logistic regression, was applied to produce a high quality classified landslide susceptibility map. The unique part of this collaboration is that all end products are online available at user-friendly websites designed by LNE. The scientific report containing (1) general information on landslides, (2) a description of the study area, (3) an explanation of the materials and methods used, (4) a presentation of the resulting

  4. Mapping landslide processes in the North Tanganyika - Lake Kivu rift zones: towards a regional hazard assessment

    NASA Astrophysics Data System (ADS)

    Dewitte, Olivier; Monsieurs, Elise; Jacobs, Liesbet; Basimike, Joseph; Delvaux, Damien; Draida, Salah; Hamenyimana, Jean-Baptiste; Havenith, Hans-Balder; Kubwimana, Désiré; Maki Mateso, Jean-Claude; Michellier, Caroline; Nahimana, Louis; Ndayisenga, Aloys; Ngenzebuhoro, Pierre-Claver; Nkurunziza, Pascal; Nshokano, Jean-Robert; Sindayihebura, Bernard; Philippe, Trefois; Turimumahoro, Denis; Kervyn, François

    2015-04-01

    The mountainous environments of the North Tanganyika - Lake Kivu rift zones are part of the West branch of the East African Rift. In this area, natural triggering and environmental factors such as heavy rainfalls, earthquake occurrences and steep topographies favour the concentration of mass movement processes. In addition anthropogenic factors such as rapid land use changes and urban expansion increase the sensibility to slope instability. Until very recently few landslide data was available for the area. Now, through the initiation of several research projects and the setting-up of a methodology for data collection adapted to this data-poor environment, it becomes possible to draw a first regional picture of the landslide hazard. Landslides include a wide range of ground movements such as rock falls, deep failure of slopes and shallow debris flows. Landslides are possibly the most important geohazard in the region in terms of recurring impact on the populations, causing fatalities every year. Many landslides are observed each year in the whole region, and their occurrence is clearly linked to complex topographic, lithological and vegetation signatures coupled with heavy rainfall events, which is the main triggering factor. Here we present the current knowledge of the various slope processes present in these equatorial environments. A particular attention is given to urban areas such as Bukavu and Bujumbura where landslide threat is particularly acute. Results and research perspectives on landslide inventorying, monitoring, and susceptibility and hazard assessment are presented.

  5. Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy

    NASA Astrophysics Data System (ADS)

    Zizioli, D.; Meisina, C.; Valentino, R.; Montrasio, L.

    2013-03-01

    On the 27 and 28 April 2009, the area of Oltrepo Pavese in northern Italy was affected by a very intense rainfall event that caused a great number of shallow landslides. These instabilities occurred on slopes covered by vineyards or recently formed woodlands and caused damage to many roads and one human loss. Based on aerial photographs taken immediately after the event and field surveys, more than 1600 landslides were detected. After acquiring topographical data, geotechnical properties of the soils and land use, susceptibility analysis on a territorial scale was carried out. In particular, different physically based models were applied to two contiguous sites with the same geological context but different typologies and sizes of shallow landslides. This paper presents the comparison between the ex-post results obtained from the different approaches. On the basis of the observed landslide localizations, the accuracy of the different models was evaluated, and the significant results are highlighted.

  6. Sparse methods for Quantitative Susceptibility Mapping

    NASA Astrophysics Data System (ADS)

    Bilgic, Berkin; Chatnuntawech, Itthi; Langkammer, Christian; Setsompop, Kawin

    2015-09-01

    Quantitative Susceptibility Mapping (QSM) aims to estimate the tissue susceptibility distribution that gives rise to subtle changes in the main magnetic field, which are captured by the image phase in a gradient echo (GRE) experiment. The underlying susceptibility distribution is related to the acquired tissue phase through an ill-posed linear system. To facilitate its inversion, spatial regularization that imposes sparsity or smoothness assumptions can be employed. This paper focuses on efficient algorithms for regularized QSM reconstruction. Fast solvers that enforce sparsity under Total Variation (TV) and Total Generalized Variation (TGV) constraints are developed using Alternating Direction Method of Multipliers (ADMM). Through variable splitting that permits closed-form iterations, the computation efficiency of these solvers are dramatically improved. An alternative approach to improve the conditioning of the ill-posed inversion is to acquire multiple GRE volumes at different head orientations relative to the main magnetic field. The phase information from such multi-orientation acquisition can be combined to yield exquisite susceptibility maps and obviate the need for regularized reconstruction, albeit at the cost of increased data acquisition time.

  7. Advanced Susceptibility Mapping for Natural Hazards at a Local Scale - The Case of the Swiss Alpine Valley of Bagnes.

    NASA Astrophysics Data System (ADS)

    Michoud, Clément; Mazotti, Benoît.; Choffet, Marc; Dubois, Jérôme; Breguet, Alain; Métraux, Valentin; Jaboyedoff, Michel

    2010-05-01

    Alpine municipalities are exposed to numerous natural hazards, such as snow avalanches, rockfalls, landslides and debris flows. The Bagnes and Vollèges municipalities in Valais (Switzerland) lie between 600 m and 4200 m m.s.l. with an area of 300 km2. The anthropization is rapid because of the fast growing ski resort of Verbier. In such situation the municipalities needs to have global overview of the natural hazards for landplaning purpose and decision making. The susceptibility mapping at regional scale allows the detection of the areas that are exposed to natural hazards, without considering the intensity and the frequency of the phenomena. The aim of this study is to provide susceptibility maps at 1:25'000 for the following natural hazards: landslides, shallow landslides, rockfalls, debris flows, snow avalanches, flooding and river overflowing. The present method was first developed for the Canton of Vaud (2'800 km2). Because it is applied to a smaller area, more numerical models and field investigations were performed. In addition historical event were included in the study. 1. The landslide mapping identifies deep-seated slope gravitational deformations, landslides and shallow landslides. It is based on the observations of geomorphological criteria on High Resolution DEM, orthophotos and field work. Finally, the activity of each landslide is described by the knowledge of local guides. 2. The shallow landslide susceptibility mapping is realized thanks to the software SInMap, calculating Security Factor (FS) and Stability Index (SI) according to the land use, the topography and the climatic conditions. The model is calibrated on the basis of the 67 shallow landslides already identified for the first map. 3. The rockfall susceptibility mapping is a two steps process. First, the potential source areas of blocks are detected using a statistical analysis of the slope angle distribution, including external knowledge on the geology and land cover. Then the run

  8. Map of landslides triggered by the January 12, 2010, Haiti earthquake

    USGS Publications Warehouse

    Harp, Edwin L.; Jibson, Randall W.; Schmitt, Robert G.

    2016-01-01

    The magnitude (M) 7.0 Haiti earthquake of January 12, 2010, triggered landslides throughout much of Haiti on the island of Hispaniola in the Caribbean Sea. The epicenter of the quake was located at 18.44°N., 72.57°W. at a depth of 13 kilometers (km) approximately 25 km southwest of the capital, Port-au-Prince. Although estimates vary widely, the most reliable surveys of casualties indicate that the earthquake caused 158,679 fatalities and more than 300,000 injuries. The U.S. Geological Survey compared publicly available satellite imagery acquired both before and after the earthquake and mapped 23,567 landslides that were triggered by the strong shaking. Our mapping from aerial photography and satellite imagery was augmented by field observations.Most of the landslides triggered by the earthquake were south of the Léogâne fault on the footwall and were fairly shallow falls and slides in weathered limestone (2–5 meters [m] thick) and volcanic rock and soil (generally <1 m thick). Landslides extended from the north to the south coasts of the southwestern peninsula (southwest of Port-au-Prince) and almost 60 km to the east and west of the epicenter. The highest concentration of landslides was on the steep limestone slopes of incised river valleys, but large numbers of landslides also occurred on gentler slopes in weathered volcanic rocks. Although some high landslide concentrations did occur near areas of maximum fault slip, the overall distribution of landslides appears to involve complex interactions between geology, topography, and strong shaking with limited spatial correlation between fault slip and landslides.

  9. Mass Movement Susceptibility in the Western San Juan Mountains, Colorado: A Preliminary 3-D Mapping Approach

    NASA Astrophysics Data System (ADS)

    Kelkar, K. A.; Giardino, J. R.

    2015-12-01

    Mass movement is a major activity that impacts lives of humans and their infrastructure. Human activity in steep, mountainous regions is especially at risk to this potential hazard. Thus, the identification and quantification of risk by mapping and determining mass movement susceptibility are fundamental in protecting lives, resources and ensuring proper land use regulation and planning. Specific mass-movement processes including debris flows, rock falls, snow avalanches and landslides continuously modify the landscape of the San Juan Mountains. Historically, large-magnitude slope failures have repeatedly occurred in the region. Common triggers include intense, long-duration precipitation, freeze-thaw processes, human activity and various volcanic lithologies overlying weaker sedimentary formations. Predicting mass movement is challenging because of its episodic and spatially, discontinuous occurrence. Landslides in mountain terrain are characterized as widespread, highly mobile and have a long duration of activity. We developed a 3-D model for landslide susceptibility using Geographic Information Systems Technology (GIST). The study area encompasses eight USGS quadrangles: Ridgway, Dallas, Mount Sneffels, Ouray, Telluride, Ironton, Ophir and Silverton. Fieldwork consisted of field reconnaissance mapping at 1:5,000 focusing on surficial geomorphology. Field mapping was used to identify potential locations, which then received additional onsite investigation and photographic documentation of features indicative of slope failure. A GIS module was created using seven terrain spatial databases: geology, surficial geomorphology (digitized), slope aspect, slope angle, vegetation, soils and distance to infrastructure to map risk. The GIS database will help determine risk zonation for the study area. Correlations between terrain parameters leading to slope failure were determined through the GIS module. This 3-D model will provide a spatial perspective of the landscape to

  10. Expert opinion on landslide susceptibility elicted by probabilistic inversion from scenario rankings

    NASA Astrophysics Data System (ADS)

    Lee, Katy; Dashwood, Claire; Lark, Murray

    2016-04-01

    For many natural hazards the opinion of experts, with experience in assessing susceptibility under different circumstances, is a valuable source of information on which to base risk assessments. This is particularly important where incomplete process understanding, and limited data, limit the scope to predict susceptibility by mechanistic or statistical modelling. The expert has a tacit model of a system, based on their understanding of processes and their field experience. This model may vary in quality, depending on the experience of the expert. There is considerable interest in how one may elicit expert understanding by a process which is transparent and robust, to provide a basis for decision support. One approach is to provide experts with a set of scenarios, and then to ask them to rank small overlapping subsets of these with respect to susceptibility. Methods of probabilistic inversion have been used to compute susceptibility scores for each scenario, implicit in the expert ranking. It is also possible to model these scores as functions of measurable properties of the scenarios. This approach has been used to assess susceptibility of animal populations to invasive diseases, to assess risk to vulnerable marine environments and to assess the risk in hypothetical novel technologies for food production. We will present the results of a study in which a group of geologists with varying degrees of expertise in assessing landslide hazards were asked to rank sets of hypothetical simplified scenarios with respect to land slide susceptibility. We examine the consistency of their rankings and the importance of different properties of the scenarios in the tacit susceptibility model that their rankings implied. Our results suggest that this is a promising approach to the problem of how experts can communicate their tacit model of uncertain systems to those who want to make use of their expertise.

  11. Assessing landslide susceptibility, hazards and sediment yield in the Río El Estado watershed, Pico de Orizaba volcano, Mexico

    NASA Astrophysics Data System (ADS)

    Legorreta Paulin, G.; Bursik, M. I.; Lugo Hubp, J.; Aceves Quesada, J. F.

    2014-12-01

    Orizaba, and may prove useful in the assessment of landslide susceptibility and hazard in volcanic terrains.

  12. Prospects and limitations for determining the parameters in physical-based regional landslide susceptibility model using back analysis technique

    NASA Astrophysics Data System (ADS)

    Dong, Jia-Jyun; Liu, Chia-Nan; Lin, Yan-Cheng; Chen, Ci-Ren

    2010-05-01

    Landslide susceptibility analysis is crucial from viewpoint of hazard mitigation. Statistical and deterministic approaches are frequently adopted for landslide susceptibility analysis. Based on physical models, deterministic approaches are superior to the statistical approaches for they fully take the mechanical mechanisms into account. However, it is difficult to input the appropriate mechanical parameters (including strength and hydraulic) in a deterministic model. Back analysis is a promising way to calibrate the required parameters though few researches have paid attention to evaluate the performance of back analysis approach. This research use hypothetical cases (100 cells) to investigate the prospects and limitations for estimating the parameters of a deterministic model by using back-analysis approach. Based on the assigned hydraulic and strength parameters, the corresponding safety factor and landslide inventory (cell with safety factor less than 1), as well as the depth of ground water table for each cell, were calculated using a deterministic model, TRIGRS. The landslide inventory derived from the forward calculation is then used to back-calculate the pre-assigned parameters. Two scenarios of back analysis approaches were examined in this research. The results reveal that the non-uniqueness of back-analyzed hydraulic and strength parameters is detrimental to the performance if only the landslide inventory is utilized to back-calculate the parameters. However, the performance of back-calculation will be improved if the spatial and temporal variation of ground water table is used to calibrate the hydraulic parameters first. Thereafter, the multiple landslide inventories are hopefully helpful to soothe the non-uniqueness on back-calculating the hydraulic and strength parameters for a deterministic landslide susceptibility analysis in regional scale.

  13. An integrated approach coupling physically based models and probabilistic method to assess quantitatively landslide susceptibility at different scale: application to different geomorphological environments

    NASA Astrophysics Data System (ADS)

    Vandromme, Rosalie; Thiéry, Yannick; Sedan, Olivier; Bernardie, Séverine

    2016-04-01

    probability to obtain a safety factor below 1 represents the probability of occurrence of a landslide for a given triggering event. The dispersion of the distribution gives the uncertainty of the result. Finally, a map is created, displaying a probability of occurrence for each computing cell of the studied area. In order to take into account the land-uses change, a complementary module integrating the vegetation effects on soil properties has been recently developed. Last years, the model has been applied at different scales for different geomorphological environments: (i) at regional scale (1:50,000-1:25,000) in French West Indies and French Polynesian islands (ii) at local scale (i.e.1:10,000) for two complex mountainous areas; (iii) at the site-specific scale (1:2,000) for one landslide. For each study the 3D geotechnical model has been adapted. The different studies have allowed : (i) to discuss the different factors included in the model especially the initial 3D geotechnical models; (ii) to precise the location of probable failure following different hydrological scenarii; (iii) to test the effects of climatic change and land-use on slopes for two cases. In that way, future changes in temperature, precipitation and vegetation cover can be analyzed, permitting to address the impacts of global change on landslides. Finally, results show that it is possible to obtain reliable information about future slope failures at different scale of work for different scenarii with an integrated approach. The final information about landslide susceptibility (i.e. probability of failure) can be integrated in landslide hazard assessment and could be an essential information source for future land planning. As it has been performed in the ANR Project SAMCO (Society Adaptation for coping with Mountain risks in a global change COntext), this analysis constitutes a first step in the chain for risk assessment for different climate and economical development scenarios, to evaluate the

  14. Large-scale mapping of landslides in the epicentral area Loma Prieta earthquake of October 17, 1989, Santa Cruz County

    SciTech Connect

    Spittler, T.E.; Sydnor, R.H.; Manson, M.W.; Levine, P.; McKittrick, M.M.

    1990-01-01

    The Loma Prieta earthquake of October 17, 1989 triggered landslides throughout the Santa Cruz Mountains in central California. The California Department of Conservation, Division of Mines and Geology (DMG) responded to a request for assistance from the County of Santa Cruz, Office of Emergency Services to evaluate the geologic hazard from major reactivated large landslides. DMG prepared a set of geologic maps showing the landslide features that resulted from the October 17 earthquake. The principal purpose of large-scale mapping of these landslides is: (1) to provide county officials with regional landslide information that can be used for timely recovery of damaged areas; (2) to identify disturbed ground which is potentially vulnerable to landslide movement during winter rains; (3) to provide county planning officials with timely geologic information that will be used for effective land-use decisions; (4) to document regional landslide features that may not otherwise be available for individual site reconstruction permits and for future development.

  15. A landslide susceptibility prediction on a sample slope in Kathmandu Nepal associated with the 2015's Gorkha Earthquake

    NASA Astrophysics Data System (ADS)

    Kubota, Tetsuya; Prasad Paudel, Prem

    2016-04-01

    In 2013, some landslides induced by heavy rainfalls occurred in southern part of Kathmandu, Nepal which is located southern suburb of Kathmandu, the capital. These landslide slopes hit by the strong Gorkha Earthquake in April 2015 and seemed to destabilize again. Hereby, to clarify their susceptibility of landslide in the earthquake, one of these landslide slopes was analyzed its slope stability by CSSDP (Critical Slip Surface analysis by Dynamic Programming based on limit equilibrium method, especially Janbu method) against slope failure with various seismic acceleration observed around Kathmandu in the Gorkha Earthquake. The CSSDP can detect the landslide slip surface which has minimum Fs (factor of safety) automatically using dynamic programming theory. The geology in this area mainly consists of fragile schist and it is prone to landslide occurrence. Field survey was conducted to obtain topological data such as ground surface and slip surface cross section. Soil parameters obtained by geotechnical tests with field sampling were applied. Consequently, the slope has distinctive characteristics followings in terms of slope stability: (1) With heavy rainfall, it collapsed and had a factor of safety Fs <1.0 (0.654 or more). (2) With seismic acceleration of 0.15G (147gal) observed around Kathmandu, it has Fs=1.34. (3) With possible local seismic acceleration of 0.35G (343gal) estimated at Kathmandu, it has Fs=0.989. If it were very shallow landslide and covered with cedars, it could have Fs =1.055 due to root reinforcement effect to the soil strength. (4) Without seismic acceleration and with no rainfall condition, it has Fs=1.75. These results can explain the real landslide occurrence in this area with the maximum seismic acceleration estimated as 0.15G in the vicinity of Kathmandu by the Gorkha Earthquake. Therefore, these results indicate landslide susceptibility of the slopes in this area with strong earthquake. In this situation, it is possible to predict

  16. Slovenian national landslide database as a basis for statistical assessment of landslide phenomena in Slovenia

    NASA Astrophysics Data System (ADS)

    Komac, Marko; Hribernik, Katarina

    2015-11-01

    Landslide databases on a national scale are an important tool for good spatial planning and for planning prevention measures or remediation activities. We have developed a modern national landslide database that enabled better landslide occurrence understanding, and will in the future help to assess landslide hazard, risk, potential damage, and enable more efficient landslide mitigation. In the paper landslide database construction steps and their properties are described. Following the collection of the landslide data from various sources and their input into the database the consistency of the database was assessed. Based on the data collected we have assessed basic statistical landslide properties, such as their overall spatial distribution, size and volume and the relation between them, landslide distribution in relation to engineering-geological units and different land-use, and past landslide mitigation activities. Analysis of landslide distribution also indicated areas in Slovenia where no landslide mapping was performed in the past, yet it should be, due to the high landslide susceptibility of these areas. Consequentially future national activities in relation to landslide problems should be governed primarily based on the findings of the database analyses to achieve the highest efficiency.

  17. Landslides

    NASA Technical Reports Server (NTRS)

    2003-01-01

    [figure removed for brevity, see original site]

    The slumping of materials in the walls of this impact crater illustrate the continued erosion of the martian surface. Small fans of debris as well as larger landslides are observed throughout the THEMIS image.

    Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

    NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

    Image information: VIS instrument. Latitude 40.9, Longitude 120.5 East (239.5 West). 19 meter/pixel resolution.

  18. Integration of landslide hazard maps into probabilistic risk assessment in context of global changes: an alpine test site

    NASA Astrophysics Data System (ADS)

    Vandromme, Rosalie; Desramaut, Nicolas; Baills, Audrey; Fontaine, Mélanie; Hohmann, Audrey; Grandjean, Gilles; Sedan, Olivier; Puissant, Anne; Malet, Jean-Philippe

    2013-04-01

    The aim of this work is to develop a methodology to integrate global changes scenarios into quantitative risk assessment. This paper describes a methodology to take into account effects of changing climate on landslides activity and impacts of social changes on exposure to provide a complete evaluation of risk for given scenarios. This approach is applied for demonstration purpose on a southern alpine test site. Mechanical approaches represent a solution to quantify landslide susceptibility and to model hazard on unprecedented conditions, as it is likely to occur. However, as the quantity and the quality of data are generally very heterogeneous at a regional scale, it is necessary to take into account their uncertainty in the analysis. In this perspective, a new hazard modeling method has been developed and integrated in a GIS-based software called ALICE®. To go further, climate change scenarios have been computed for the alpine test site (Barcelonnette area, France) using the REMO-COSMO-LM. From the precipitation time series, a daily index of the soil water content has been computed thanks to a reservoir-based model (GARDENIA®). Hence, the program classifies hazard zones depending on the several spatial data (lithological, DEM, etc…) and different hydrological contexts varying in time. The probabilistically initiated landslides are then propagated thank to a semi-empirical model (BORA) to provide real hazard maps. Different scenarios of land-use have been developed using an automate cellular model to cover the probable range of development of potential elements at risks in the future. These exposure maps are then combined with the aforementioned hazard maps to obtain risk maps for the different periods and the different land-use development scenarios. Potential evolutions of landslide risks are then evaluated, with a general increase in the 7 communes. This methodology also allows the analysis of the contributions of both considered global changes (climate and

  19. Mapping landslide source and transport areas in VHR images with Object-Based Analysis and Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Heleno, Sandra; Matias, Magda; Pina, Pedro

    2015-04-01

    Visual interpretation of satellite imagery remains extremely demanding in terms of resources and time, especially when dealing with numerous multi-scale landslides affecting wide areas, such as is the case of rainfall-induced shallow landslides. Applying automated methods can contribute to more efficient landslide mapping and updating of existing inventories, and in recent years the number and variety of approaches is rapidly increasing. Very High Resolution (VHR) images, acquired by space-borne sensors with sub-metric precision, such as Ikonos, Quickbird, Geoeye and Worldview, are increasingly being considered as the best option for landslide mapping, but these new levels of spatial detail also present new challenges to state of the art image analysis tools, asking for automated methods specifically suited to map landslide events on VHR optical images. In this work we develop and test a methodology for semi-automatic landslide recognition and mapping of landslide source and transport areas. The method combines object-based image analysis and a Support Vector Machine supervised learning algorithm, and was tested using a GeoEye-1 multispectral image, sensed 3 days after a damaging landslide event in Madeira Island, together with a pre-event LiDAR DEM. Our approach has proved successful in the recognition of landslides on a 15 Km2-wide study area, with 81 out of 85 landslides detected in its validation regions. The classifier also showed reasonable performance (false positive rate 60% and false positive rate below 36% in both validation regions) in the internal mapping of landslide source and transport areas, in particular in the sunnier east-facing slopes. In the less illuminated areas the classifier is still able to accurately map the source areas, but performs poorly in the mapping of landslide transport areas.

  20. Hazard Mapping of Structurally Controlled Landslide in Southern Leyte, Philippines Using High Resolution Digital Elevation Model

    NASA Astrophysics Data System (ADS)

    Luzon, Paul Kenneth; Rochelle Montalbo, Kristina; Mahar Francisco Lagmay, Alfredo

    2014-05-01

    The 2006 Guinsaugon landslide in St. Bernard, Southern Leyte is the largest known mass movement of soil in the Philippines. It consisted of a 15 million m3 rockslide-debris avalanche from an approximately 700 m high escarpment produced by continuous movement of the Philippine fault at approximately 2.5 cm/year. The landslide was preceded by continuous heavy rainfall totaling 571.2 mm from February 8 to 12, 2006. The catastrophic landslide killed more than 1,000 people and displaced 19,000 residents over its 6,400 km path. To investigate the present-day morphology of the scar and potential failure that may occur, an analysis of a high-resolution digital elevation model (10 m resolution Synthetic Aperture Radar images in 2013) was conducted, leading to the generation of a structurally controlled landslide hazard map of the area. Discontinuity sets that could contribute to any failure mechanism were identified using Coltop 3D software which uses a unique lower Schmidt-Lambert color scheme for any given dip and dip direction. Thus, finding main morpho-structural orientations became easier. Matterocking, a software designed for structural analysis, was used to generate possible planes that could slide due to the identified discontinuity sets. Conefall was then utilized to compute the extent to which the rock mass will run out. The results showed potential instabilities in the scarp area of the 2006 Guinsaguon landslide and in adjacent slopes because of the presence of steep discontinuities that range from 45-60°. Apart from the 2006 Guinsaugon potential landslides, conefall simulation generated farther rock mass extent in adjacent slopes. In conclusion, there is a high probability of landslides in the municipality of St. Bernard Leyte, where the 2006 Guinsaugon Landslide occurred. Concerned agencies may use maps produced from this study for disaster preparedness and to facilitate long-term recovery planning for hazardous areas.

  1. Rapid Offline-Online Post-Disaster Landslide Mapping Tool: A case study from Nepal

    NASA Astrophysics Data System (ADS)

    Olyazadeh, Roya; Jaboyedoff, Michel; Sudmeier-Rieux, Karen; Derron, Marc-Henri; Devkota, Sanjaya

    2016-04-01

    One of the crucial components of post disaster management is the efficient mapping of impacted areas. Here we present a tool designed to map landslides and affected objects after the earthquakes of 2015 in Nepal as well as for intense rainfall impact. Because internet is not available in many rural areas of Nepal, we developed an offline-online prototype based on Open-Source WebGIS technologies to make data on hazard impacts, including damaged infrastructure, landslides or flooding events available to authorities and the general public. This mobile application was designed as a low-cost, rapid and participatory method for recording impacts from hazard events. It is possible to record such events offline and upload them through a server, where internet connection is available. This application allows user authentication, image capturing, and information collation such as geolocation, event description, interactive mapping and finally storing all the data in the server for further analysis and visualisation. This application can be accessed by a mobile phone (Android) or a tablet as a hybrid version for both offline and online versions. The offline version has an interactive-offline map function which allows users to upload satellites image in order to improve ground truthing interpretation. After geolocation, the user can start mapping and then save recorded data into Geojson-TXT files that can be easily uploaded to the server whenever internet is available. This prototype was tested specifically for a rapid assessment of landslides and relevant land use characteristics such as roads, forest area, rivers in the Phewa Lake watershed near Pokhara, Nepal where a large number landslides were activated or reactivated after the 2015 monsoon season. More than 60 landslides were recorded during two days of field trip. Besides, it is possible to use this application for any other kind of hazard event like flood, avalanche, etc. Keywords: Offline, Online, Open source, Web

  2. Root strength evaluation on vineyards in an area susceptible to shallow landslides: preliminary results

    NASA Astrophysics Data System (ADS)

    Meisina, Claudia; Bordoni, Massimiliano; Vercesi, Alberto; Vergani, Chiara; Bassanelli, Chiara; Zizioli, Davide; Valentino, Roberto; Bittelli, Marco; Chersich, Silvia

    2014-05-01

    The role played by plant roots in reinforcing mechanically the soil is widely recognized and numerous studies were carried out in the last years for the quantification of the effect of vegetation on slope stability, especially in terms of root reinforcement. Vegetation can represent an effective instrument to decrease landslides susceptibility, in particular towards shallow landslides, which usually develop in the first 2 meters from the ground level where the majority of plant roots develop. In this work preliminary results of root reinforcement on vineyards located in an area susceptible to shallow landslides are presented. Vineyards have been chosen because there are no studies about the role played by vineyards roots on soil cohesion and because they represent the most common species in the studied area. The objectives of the study were i) to estimate the root strength through laboratory tests on sampled roots of living vine plants, ii) to analyze the distribution pattern of roots of living plants in the soil profile, iii) to assess the root contribution to soil cohesion on the basis of the measured root strength and the distribution pattern of the roots evaluated trough in-situ surveys. The sample study area is in the north-eastern part of Oltrepo Pavese, in northern Italy. In this area, hilly slopes are extensively cultivated with vineyards for the production of wine. In April 2009, this sector of Oltrepo Pavese experienced a great rainfall event, which triggered more than 1600 shallow landslides in an area of about 250 km2. In particular, a great number of these phenomena affected slopes cultivated with vineyards that were completely or partially destroyed with consequent serious economic losses. Roots for mechanical properties evaluation were collected from pits in different test-site slopes characterized by vineyards of the same installation age (about 10-20 years). In correspondence of these pits the distribution pattern of the living roots with depth was

  3. Landslide Mapping Using Imagery Acquired by a Fixed-Wing Uav

    NASA Astrophysics Data System (ADS)

    Rau, J. Y.; Jhan, J. P.; Lo, C. F.; Lin, Y. S.

    2011-09-01

    In Taiwan, the average annual rainfall is about 2,500 mm, about three times the world average. Hill slopes where are mostly under meta-stable conditions due to fragmented surface materials can easily be disturbed by heavy typhoon rainfall and/or earthquakes, resulting in landslides and debris flows. Thus, an efficient data acquisition and disaster surveying method is critical for decision making. Comparing with satellite and airplane, the unmanned aerial vehicle (UAV) is a portable and dynamic platform for data acquisition. In particularly when a small target area is required. In this study, a fixed-wing UAV that equipped with a consumer grade digital camera, i.e. Canon EOS 450D, a flight control computer, a Garmin GPS receiver and an attitude heading reference system (AHRS) are proposed. The adopted UAV has about two hours flight duration time with a flight control range of 20 km and has a payload of 3 kg, which is suitable for a medium scale mapping and surveying mission. In the paper, a test area with 21.3 km2 in size containing hundreds of landslides induced by Typhoon Morakot is used for landslides mapping. The flight height is around 1,400 meters and the ground sampling distance of the acquired imagery is about 17 cm. The aerial triangulation, ortho-image generation and mosaicking are applied to the acquired images in advance. An automatic landslides detection algorithm is proposed based on the object-based image analysis (OBIA) technique. The color ortho-image and a digital elevation model (DEM) are used. The ortho-images before and after typhoon are utilized to estimate new landslide regions. Experimental results show that the developed algorithm can achieve a producer's accuracy up to 91%, user's accuracy 84%, and a Kappa index of 0.87. It demonstrates the feasibility of the landslide detection algorithm and the applicability of a fixed-wing UAV for landslide mapping.

  4. Integrating Geo-Spatial Data for Regional Landslide Susceptibility Modeling in Consideration of Run-Out Signature

    NASA Astrophysics Data System (ADS)

    Lai, J.-S.; Tsai, F.; Chiang, S.-H.

    2016-06-01

    This study implements a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional and rainfall-induced landslide susceptibility model. The developed model also takes account of landslide regions (source, non-occurrence and run-out signatures) from the original landslide inventory in order to increase the reliability of the susceptibility modelling. A total of ten causative factors were collected and used in this study, including aspect, curvature, elevation, slope, faults, geology, NDVI (Normalized Difference Vegetation Index), rivers, roads and soil data. Consequently, this study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses original and edited topographic data in the analysis to understand their impacts to the susceptibility modeling. Experimental results demonstrate that after identifying the run-out signatures, the overall accuracy and Kappa coefficient have been reached to be become more than 85 % and 0.8, respectively. In addition, correcting unreasonable topographic feature of the digital terrain model also produces more reliable modelling results.

  5. Principles and case studies of earthquake-triggered landslide inventory mapping using remote sensing and GIS technologies

    NASA Astrophysics Data System (ADS)

    Xu, Chong

    2014-05-01

    Inventory maps of earthquake-triggered landslides can be constructed using several methods, which are often subject to obvious differences due to lack of commonly accepted criteria or principles. To solve this problem, the author describes the principles for preparing inventory maps of earthquake-triggered landslides, focusing on varied methods and their criteria. The principles include the following key points: all landslides should be mapped as long as they can be recognized from images; both the boundary and source area position of landslides should be mapped; spatial distribution pattern of earthquake-triggered landslides should be continuous; complex landslides should be divided into distinct groups; three types of errors such as precision of the location and boundary of landslides, false positive errors, and false negative errors of earthquake-triggered landslide inventories should be controlled and reduced; and inventories of co-seismic landslides should be constructed by the visual interpretation method rather than automatic extraction of satellite images or/and aerial photographs. In addition, selection of remote sensing images and creation of landslides attribute database are also discussed in this paper. Then the author applies these principles to produce inventory maps of four events: the 12 May 2008 Wenchuan, China Mw 7.9, 14 April 2010 Yushu, China Mw 6.9, 12 January 2010 Haiti Mw 7.0, and 2007 Aysén Fjord, Chile Mw 6.2. The results show obvious differences in comparison with previous studies by other researchers, which again attests to the necessity of establishment of unified principles for preparation of inventory maps of earthquake-triggered landslides. This research was supported by the National Science Foundation of China (41202235).

  6. Communicating Earth Observation (EO)-based landslide mapping capabilities to practitioners

    NASA Astrophysics Data System (ADS)

    Albrecht, Florian; Hölbling, Daniel; Eisank, Clemens; Weinke, Elisabeth; Vecchiotti, Filippo; Kociu, Arben

    2016-04-01

    Current remote sensing methods and the available Earth Observation (EO) data for landslide mapping already can support practitioners in their processes for gathering and for using landslide information. Information derived from EO data can support emergency services and authorities in rapid mapping after landslide-triggering events, in landslide monitoring and can serve as a relevant basis for hazard and risk mapping. These applications also concern owners, maintainers and insurers of infrastructure. Most often practitioners have a rough overview of the potential and limits of EO-based methods for landslide mapping. However, semi-automated image analysis techniques are still rarely used in practice. This limits the opportunity for user feedback, which would contribute to improve the methods for delivering fully adequate results in terms of accuracy, applicability and reliability. Moreover, practitioners miss information on the best way of integrating the methods in their daily processes. Practitioners require easy-to-grasp interfaces for testing new methods, which in turn would provide researchers with valuable user feedback. We introduce ongoing work towards an innovative web service which will allow for fast and efficient provision of EO-based landslide information products and that supports online processing. We investigate the applicability of various very high resolution (VHR), e.g. WorldView-2/3, Pleiades, and high resolution (HR), e.g. Landsat, Sentinel-2, optical EO data for semi-automated mapping based on object-based image analysis (OBIA). The methods, i.e. knowledge-based and statistical OBIA routines, are evaluated regarding their suitability for inclusion in a web service that is easy to use with the least amount of necessary training. The pre-operational web service will be implemented for selected study areas in the Alps (Austria, Italy), where weather-induced landslides have happened in the past. We will test the service on its usability together

  7. Development of community hazard map for landslide risk reduction at the village level in Java, Indonesia

    NASA Astrophysics Data System (ADS)

    Karnawati, D.

    2010-12-01

    Socio-economical loss due to landslides in Indonesia continuously increases, despite the availability of the existing landslide hazard map with the various scales. It is apparent that the existing landslide hazard map is too technical and difficult to be understood by local community living in the landslide prone are. Therefore, a simple hazard map for community-based landslide risk reduction at the village level is proposed by introducing a social engineering approach with respect to the social and geological conditions at the local sites. The mapping is conducted by observing and recording the indicators of slope movements and/ or the key parameters causing such movements such as the slope inclination, fragility of rock/ soil forming the slope, slope saturation and slope landuse conditions, in order to visually illustrate the levels of hazard and risk. Slope deformations which indicate the symptoms of slope movements can also be recorded through the appearance of cracks with typical ”horse-shoe-shape” at the slope surface, or at the structures and infrastructures; the land subsidence or the displacement of any feature at the slope surface; the bulging or deformation at the slope surface or retaining wall; the appearance of seepage or spring (the water discharge from slope surface which is mixed with the sediments or slurry) at the toe or foot slope; and the inclination of any vertical features (piles, trees, etc.) at the slope surface. Due to the simplicity of such method, the hazard mapping can be conducted by the community (which may also facilitated by an adviser), and accordingly they can distinguish the levels of landslide hazard into three different levels with several criteria such as the active slope (where the symptoms of movement can be recorded clearly and these symptoms are quite persistence), non active (no symptom of movement), and moderately active (illustrating the transition conditions from active to non active). Indeed, this method of

  8. A GRASS GIS-based deterministic model for shallow and deep-seated landslide susceptibility analysis over large areas

    NASA Astrophysics Data System (ADS)

    Mergili, Martin; Marchesini, Ivan; Rossi, Mauro; Guzzetti, Fausto; Fellin, Wolfgang

    2013-04-01

    Various deterministic slope stability models, based on the assumption of an infinite slope with a plane, slope-parallel failure plane, have been proposed in the literature. These models are commonly implemented in a GIS environment and are mostly used to model shallow landslides. Other models consider the three-dimensional geometry of possible slope failures and assume an ellipsoidal sliding surface. Such models are best suited to investigate deep-seated landslides. The latter models rely on complex neighbourhood relationships and are difficult to implement in a GIS environment. Here, we present a GIS-based landslide modelling tool that considers the three-dimensional geometry of the sliding surfaces and is capable of dealing with shallow and deep-seated failures. The model is developed in the GRASS GIS software as the C-based raster module r.rotstab, and adopts a modification of the three-dimensional sliding surface model proposed by Hovland and revised and extended by Xie and co-workers. Given a Digital Elevation Model and a set of thematic layers, the model evaluates slope stability for a large number of randomly selected potential slip surfaces, ellipsoidal in shape. Truncated ellipsoids can be used to model the presence of shallow weak layers in the soil or the bedrock. Any single raster cell may be intersected by multiple sliding surfaces, each associated with a computed safety factor. For each grid cell, the lowest value of the safety factor and the depth of the associated slip surface are stored. This information can be used to obtain a spatial overview of the potentially unstable regions in the study area. In addition, a landslide susceptibility index in the range 0 - 1 is calculated. The index relates the number of unstable slip surfaces to the total number of slip surfaces simulated for each pixel. We tested the model in the Collazzone area, Umbria, Central Italy, which is susceptible to landslides of different types. The presence of both shallow

  9. Object-based landslide mapping on satellite images from different sensors

    NASA Astrophysics Data System (ADS)

    Hölbling, Daniel; Friedl, Barbara; Eisank, Clemens; Blaschke, Thomas

    2015-04-01

    Several studies have proven that object-based image analysis (OBIA) is a suitable approach for landslide mapping using remote sensing data. Mostly, optical satellite images are utilized in combination with digital elevation models (DEMs) for semi-automated mapping. The ability of considering spectral, spatial, morphometric and contextual features in OBIA constitutes a significant advantage over pixel-based methods, especially when analysing non-uniform natural phenomena such as landslides. However, many of the existing knowledge-based OBIA approaches for landslide mapping are rather complex and are tailored to specific data sets. These restraints lead to a lack of transferability of OBIA mapping routines. The objective of this study is to develop an object-based approach for landslide mapping that is robust against changing input data with different resolutions, i.e. optical satellite imagery from various sensors. Two study sites in Taiwan were selected for developing and testing the landslide mapping approach. One site is located around the Baolai village in the Huaguoshan catchment in the southern-central part of the island, the other one is a sub-area of the Taimali watershed in Taitung County near the south-eastern Pacific coast. Both areas are regularly affected by severe landslides and debris flows. A range of very high resolution (VHR) optical satellite images was used for the object-based mapping of landslides and for testing the transferability across different sensors and resolutions: (I) SPOT-5, (II) Formosat-2, (III) QuickBird, and (IV) WorldView-2. Additionally, a digital elevation model (DEM) with 5 m spatial resolution and its derived products (e.g. slope, plan curvature) were used for supporting the semi-automated mapping, particularly for differentiating source areas and accumulation areas according to their morphometric characteristics. A focus was put on the identification of comparatively stable parameters (e.g. relative indices), which could be

  10. Map showing landslides in California that have caused fatalities or at least $1,000,000 in damages from 1906 to 1984

    USGS Publications Warehouse

    Taylor, Fred; Brabb, E.E.

    1986-01-01

    Understanding where landslide processes in California have been most severe is helpful in determining priorities for landslide mapping, mitigation measures, and preparedness planning. Although a few studies of landslide damage and fatalities have been published (see sources of data 12, 17, 34, 36, 40), and many more reports mention landslide damage and fatalities incidentally, our map is the first to show where the problem is most severe for the entire state.

  11. Landslide inventory map as a tool for landscape planning and management in Buzau Land Geopark

    NASA Astrophysics Data System (ADS)

    Tatu, Mihai; Niculae, Lucica; Popa, Răzvan-Gabriel

    2015-04-01

    Buzău Land is an aspiring Geopark in Romania, located in the mountainous region of the southern part of the Carpathian Bend Area. From a geologic point of view, the East Carpathians represent a segment of the Alpine - Carpathian orogene, and they are composed of numerous tectonic units put up throughout the Mesozoic and Cenozoic orogenesis. They represent a result of two compressional phases, (1) during Late Cretaceous and (2) during Early and Middle Miocene that were responsible for thrusting of internal units onto external units. The latter cover tectonically the Foredeep folded deposits. Landslides are one of the most widespread and dangerous natural hazards in this region, disrupting access routes and damaging property and habitats at least twice per year, in the rainy seasons. This hazard induces deep changes in the landscape and has serious economic consequences related to the damaging of infrastructure and isolation of localities. The proximity to the Vrancea seismogenic zone increases the risk of landslide triggering. A first step in observing the space and time tendency and amplitude of landslides, in order to distinguish the main vulnerabilities and estimate the risk, is to produce an inventory map. We shall present a landslide inventory map for the Buzău Land territory (~1036 km2), which is the primary base of information for further discussions regarding this phenomenon and an essential tool in observing the development of mass-wasting processes and in landscape planning. The inventory map is in accordance with the recommendations of the IAEG Commission on Landslides and other Mass-Movement, applied across the EU. Based on this work, we can already draw some remarks: - The Geopark territory mostly covers two major tectonic units of the East Carpathians: the external nappes and the folded foredeep; areas with landslide potential are common, but by far the highest landslide frequency is observed in the foredeep. This is related to the soft, argillaceous

  12. Suitability aero-geophysical methods for generating conceptual soil maps and their use in the modeling of process-related susceptibility maps

    NASA Astrophysics Data System (ADS)

    Tilch, Nils; Römer, Alexander; Jochum, Birgit; Schattauer, Ingrid

    2014-05-01

    In the past years, several times large-scale disasters occurred in Austria, which were characterized not only by flooding, but also by numerous shallow landslides and debris flows. Therefore, for the purpose of risk prevention, national and regional authorities also require more objective and realistic maps with information about spatially variable susceptibility of the geosphere for hazard-relevant gravitational mass movements. There are many and various proven methods and models (e.g. neural networks, logistic regression, heuristic methods) available to create such process-related (e.g. flat gravitational mass movements in soil) suszeptibility maps. But numerous national and international studies show a dependence of the suitability of a method on the quality of process data and parameter maps (f.e. Tilch & Schwarz 2011, Schwarz & Tilch 2011). In this case, it is important that also maps with detailed and process-oriented information on the process-relevant geosphere will be considered. One major disadvantage is that only occasionally area-wide process-relevant information exists. Similarly, in Austria often only soil maps for treeless areas are available. However, in almost all previous studies, randomly existing geological and geotechnical maps were used, which often have been specially adapted to the issues and objectives. This is one reason why very often conceptual soil maps must be derived from geological maps with only hard rock information, which often have a rather low quality. Based on these maps, for example, adjacent areas of different geological composition and process-relevant physical properties are razor sharp delineated, which in nature appears quite rarly. In order to obtain more realistic information about the spatial variability of the process-relevant geosphere (soil cover) and its physical properties, aerogeophysical measurements (electromagnetic, radiometric), carried out by helicopter, from different regions of Austria were interpreted

  13. Mapping susceptibility gene in systemic lupus erythematosus.

    PubMed

    Scofield, R Hal; Kaufman, Kenneth M

    2012-01-01

    Genome-wide association studies have identified many dozen genetic intervals that harbor single-nucleotide polymorphisms (SNPs) showing statistical association with systemic lupus erythematosus. Despite the wealth of data produced, there are limitations of these studies. The causal alleles at a given locus are not identified; only SNP is strong linkage disequilibrium with the putative causative alleles. In order to address identification of the causative SNPs for lupus susceptibility genes, we have initiated a candidate gene study for which more than 40 investigators have contributed patient and control samples. In addition, these investigators have designated SNPs to be placed on a custom array. In this way fine mapping of genetic association findings can occur in order to identify causal alleles. These efforts have thus far benefitted greatly from comparisons of different ethnicities. Work on about ten previously identified associations has been published using this resource. Genome-wide association studies cannot identify rare SNPs or mutations, which may impart greater relative risks than common variants. Much of the genetics of lupus may be from rare variants or mutations. In order to approach this aspect of lupus genetics, next-generation sequencing has begun in which all exons will be sequenced in controls and patients. This effort can also be used to identify causal alleles from association intervals not yet otherwise identified. PMID:22933063

  14. A new-old approach for shallow landslide analysis and susceptibility zoning in fine-grained weathered soils of southern Italy

    NASA Astrophysics Data System (ADS)

    Cascini, Leonardo; Ciurleo, Mariantonietta; Di Nocera, Silvio; Gullà, Giovanni

    2015-07-01

    Rainfall-induced shallow landslides involve several geo-environmental contexts and different types of soils. In clayey soils, they affect the most superficial layer, which is generally constituted by physically weathered soils characterised by a diffuse pattern of cracks. This type of landslide most commonly occurs in the form of multiple-occurrence landslide phenomena simultaneously involving large areas and thus has several consequences in terms of environmental and economic damage. Indeed, landslide susceptibility zoning is a relevant issue for land use planning and/or design purposes. This study proposes a multi-scale approach to reach this goal. The proposed approach is tested and validated over an area in southern Italy affected by widespread shallow landslides that can be classified as earth slides and earth slide-flows. Specifically, by moving from a small (1:100,000) to a medium scale (1:25,000), with the aid of heuristic and statistical methods, the approach identifies the main factors leading to landslide occurrence and effectively detects the areas potentially affected by these phenomena. Finally, at a larger scale (1:5000), deterministic methods, i.e., physically based models (TRIGRS and TRIGRS-unsaturated), allow quantitative landslide susceptibility assessment, starting from sample areas representative of those that can be affected by shallow landslides. Considering the reliability of the obtained results, the proposed approach seems useful for analysing other case studies in similar geological contexts.

  15. IHG: an Integrated Hydrological-Geotechnical model for large landslides' susceptibility assessments

    NASA Astrophysics Data System (ADS)

    Passalacqua, R.; Bovolenta, R.

    2012-04-01

    A large area (~ 5 km2) in the north-east sector of the Genoa-Province (Liguria - Italy) is subjected to a diffused, continuous kinematic phenomenon. It is shaped into a top-valley gentle slope (circa 11° ), which downgrades directly from the south-side faces of the Northern Apennines summits (1800 meters a.s.l.). On this endangered site are situated a small town and six of its surrounding hamlets. In consequence of the widespread and differential movements at ground level, many buildings and structures are continuously damaged. Institutions, Land-Authorities, as well as the Citizens, are applying their economical efforts in the rehabilitations and the assessment/control of the active phenomena. From the geological and morphological points of view, the topmost sediment is formed by a pliocenic glacial till and its body of widely assorted sediments had been reckoned as a large relict landslide. The loose-soils' thickness spans from few meters up to 90, before of reaching the local bedrock formations (argillites, sandstones, mudstones, ophiolites and diabases in pillows). Former studies have underlined that the main trigger actions are represented by the seasonal rain/snow falls on the watershed and that the kinematic phenomenon is heavily influenced by the subsoil features. The Authors have recently dealt with the characterization and study of this complex landslide [ref. @: the International Association for Mathematical Geosciences (IAMG) Conference, Salzburg (A), September 5-9, 2011 and the 2nd World Landslide Forum, Rome (I), October 3-9, 2011], giving particular attention to both the geotechnical and the hydrological aspects of the site. Since the buried bedrock spatial morphology, depth and steepness have a key role, geophysical and seismic array techniques were used toinvestigate the micro-tremor characteristics and to correlate the emerging data to the geotechnical and geophysical properties of the shallowsediments. Noise measurements were made at more than

  16. Mapping Shallow Landslide Slope Inestability at Large Scales Using Remote Sensing and GIS

    NASA Astrophysics Data System (ADS)

    Avalon Cullen, C.; Kashuk, S.; Temimi, M.; Suhili, R.; Khanbilvardi, R.

    2015-12-01

    Rainfall induced landslides are one of the most frequent hazards on slanted terrains. They lead to great economic losses and fatalities worldwide. Most factors inducing shallow landslides are local and can only be mapped with high levels of uncertainty at larger scales. This work presents an attempt to determine slope instability at large scales. Buffer and threshold techniques are used to downscale areas and minimize uncertainties. Four static parameters (slope angle, soil type, land cover and elevation) for 261 shallow rainfall-induced landslides in the continental United States are examined. ASTER GDEM is used as bases for topographical characterization of slope and buffer analysis. Slope angle threshold assessment at the 50, 75, 95, 98, and 99 percentiles is tested locally. Further analysis of each threshold in relation to other parameters is investigated in a logistic regression environment for the continental U.S. It is determined that lower than 95-percentile thresholds under-estimate slope angles. Best regression fit can be achieved when utilizing the 99-threshold slope angle. This model predicts the highest number of cases correctly at 87.0% accuracy. A one-unit rise in the 99-threshold range increases landslide likelihood by 11.8%. The logistic regression model is carried over to ArcGIS where all variables are processed based on their corresponding coefficients. A regional slope instability map for the continental United States is created and analyzed against the available landslide records and their spatial distributions. It is expected that future inclusion of dynamic parameters like precipitation and other proxies like soil moisture into the model will further improve accuracy.

  17. Fine mapping of eight psoriasis susceptibility loci.

    PubMed

    Das, Sayantan; Stuart, Philip E; Ding, Jun; Tejasvi, Trilokraj; Li, Yanming; Tsoi, Lam C; Chandran, Vinod; Fischer, Judith; Helms, Cynthia; Duffin, Kristina Callis; Voorhees, John J; Bowcock, Anne M; Krueger, Gerald G; Lathrop, G Mark; Nair, Rajan P; Rahman, Proton; Abecasis, Goncalo R; Gladman, Dafna; Elder, James T

    2015-06-01

    Previous studies have identified 41 independent genome-wide significant psoriasis susceptibility loci. After our first psoriasis genome-wide association study, we designed a custom genotyping array to fine-map eight genome-wide significant susceptibility loci known at that time (IL23R, IL13, IL12B, TNIP1, MHC, TNFAIP3, IL23A and RNF114) enabling genotyping of 2269 single-nucleotide polymorphisms (SNPs) in the eight loci for 2699 psoriasis cases and 2107 unaffected controls of European ancestry. We imputed these data using the latest 1000 Genome reference haplotypes, which included both indels and SNPs, to increase the marker density of the eight loci to 49 239 genetic variants. Using stepwise conditional association analysis, we identified nine independent signals distributed across six of the eight loci. In the major histocompatibility complex (MHC) region, we detected three independent signals at rs114255771 (P = 2.94 × 10(-74)), rs6924962 (P = 3.21 × 10(-19)) and rs892666 (P = 1.11 × 10(-10)). Near IL12B we detected two independent signals at rs62377586 (P = 7.42 × 10(-16)) and rs918518 (P = 3.22 × 10(-11)). Only one signal was observed in each of the TNIP1 (rs17728338; P = 4.15 × 10(-13)), IL13 (rs1295685; P = 1.65 × 10(-7)), IL23A (rs61937678; P = 1.82 × 10(-7)) and TNFAIP3 (rs642627; P = 5.90 × 10(-7)) regions. We also imputed variants for eight HLA genes and found that SNP rs114255771 yielded a more significant association than any HLA allele or amino-acid residue. Further analysis revealed that the HLA-C*06-B*57 haplotype tagged by this SNP had a significantly higher odds ratio than other HLA-C*06-bearing haplotypes. The results demonstrate allelic heterogeneity at IL12B and identify a high-risk MHC class I haplotype, consistent with the existence of multiple psoriasis effectors in the MHC. PMID:25182136

  18. Fine mapping of eight psoriasis susceptibility loci

    PubMed Central

    Das, Sayantan; Stuart, Philip E; Ding, Jun; Tejasvi, Trilokraj; Li, Yanming; Tsoi, Lam C; Chandran, Vinod; Fischer, Judith; Helms, Cynthia; Duffin, Kristina Callis; Voorhees, John J; Bowcock, Anne M; Krueger, Gerald G; Lathrop, G Mark; Nair, Rajan P; Rahman, Proton; Abecasis, Goncalo R; Gladman, Dafna; Elder, James T

    2015-01-01

    Previous studies have identified 41 independent genome-wide significant psoriasis susceptibility loci. After our first psoriasis genome-wide association study, we designed a custom genotyping array to fine-map eight genome-wide significant susceptibility loci known at that time (IL23R, IL13, IL12B, TNIP1, MHC, TNFAIP3, IL23A and RNF114) enabling genotyping of 2269 single-nucleotide polymorphisms (SNPs) in the eight loci for 2699 psoriasis cases and 2107 unaffected controls of European ancestry. We imputed these data using the latest 1000 Genome reference haplotypes, which included both indels and SNPs, to increase the marker density of the eight loci to 49 239 genetic variants. Using stepwise conditional association analysis, we identified nine independent signals distributed across six of the eight loci. In the major histocompatibility complex (MHC) region, we detected three independent signals at rs114255771 (P=2.94 × 10−74), rs6924962 (P=3.21 × 10−19) and rs892666 (P=1.11 × 10−10). Near IL12B we detected two independent signals at rs62377586 (P=7.42 × 10−16) and rs918518 (P=3.22 × 10−11). Only one signal was observed in each of the TNIP1 (rs17728338; P=4.15 × 10−13), IL13 (rs1295685; P=1.65 × 10−7), IL23A (rs61937678; P=1.82 × 10−7) and TNFAIP3 (rs642627; P=5.90 × 10−7) regions. We also imputed variants for eight HLA genes and found that SNP rs114255771 yielded a more significant association than any HLA allele or amino-acid residue. Further analysis revealed that the HLA-C*06-B*57 haplotype tagged by this SNP had a significantly higher odds ratio than other HLA-C*06-bearing haplotypes. The results demonstrate allelic heterogeneity at IL12B and identify a high-risk MHC class I haplotype, consistent with the existence of multiple psoriasis effectors in the MHC. PMID:25182136

  19. Uncertainty on shallow landslide hazard assessment: from field data to hazard mapping

    NASA Astrophysics Data System (ADS)

    Trefolini, Emanuele; Tolo, Silvia; Patelli, Eduardo; Broggi, Matteo; Disperati, Leonardo; Le Tuan, Hai

    2015-04-01

    empirical relations with geotechnical index properties. Site specific information was regionalized at map scale by (hard and fuzzy) clustering analysis taking into account spatial variables such as: geology, geomorphology and hillslope morphometric variables (longitudinal and transverse curvature, flow accumulation and slope), the latter derived by a DEM with 10 m cell size. In order to map shallow landslide hazard, Monte Carlo simulation was performed for some common physically based models available in literature (eg. SINMAP, SHALSTAB, TRIGRS). Furthermore, a new approach based on the use of Bayesian Network was proposed and validated. Different models, such as Intervals, Convex Models and Fuzzy Sets, were adopted for the modelling of input parameters. Finally, an accuracy assessment was carried out on the resulting maps and the propagation of uncertainty of input parameters into the final shallow landslide hazard estimation was estimated. The outcomes of the analysis are compared and discussed in term of discrepancy among map pixel values and related estimated error. The novelty of the proposed method is on estimation of the confidence of the shallow landslides hazard mapping at regional level. This allows i) to discriminate regions where hazard assessment is robust from areas where more data are necessary to increase the confidence level and ii) to assess the reliability of the procedure used for hazard assessment.

  20. Using High-Resolution Airborne LiDAR-Data for Landslide Mapping in the Eastern Alps

    NASA Astrophysics Data System (ADS)

    Kamp, N.

    2012-04-01

    Due to the increasing frequency of natural disasters like floods and landslides, the active remote sensing technique LiDAR (Light Detection and Ranging), has become a topic of great interest to the Federal State Government of Styria, Federal Republic of Austria. In a perennial project from 2008 to 2012 high-resolution 3D Airborne LiDAR Data of the Province of Styria, an area about 16.000km2 in south-eastern Austria were collected. These data were processed to create Digital Terrain Models (DTM) and Digital Surface Models (DSM) at 1m resolution with a vertical accuracy of 15 [cm] and a positional accuracy of 40 [cm]. High resolution DTMs can be used in different geo-related applications like geomorphological mapping or natural hazard mapping. DTMs show because of its high accuracy various natural and anthropogenic terrain features such as erosion scarps, alluvial fans, landslides, old creeks, topographic edges and karstforms, as well as walking paths and roads and in addition to that LiDAR data allows the detection and outlining of these different geomorphological and anthropogenic features with the help of ArcGIS 10 geoprocessing and analysing techniques, mathematical, statistical and image processing methods and the open source scripting language Python. As a result complex workflows and new geoprocessing tools can be implemented in an ArcGIS 10 workspace and are provided as easy to use toolbox contents. The landslide phenomena take in centre stage of the research work of the author. Thereby the main focus is targeted on sliding movements out of soils and bedrock. Factors like gravity take effect on slope stability directly and cause complex mass movements with a downslope directed, gliding movement of bed- and/or loose-rock as well as soil material. In this paper the author presents the result of her master thesis, an automatic ArcGIS 10 landslide mapping tool using high-resolution LiDAR data in the rock masses of the Eastern Alps (Province of Styria, Austria

  1. Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

    PubMed Central

    Alkhasawneh, Mutasem Sh.; Ngah, Umi Kalthum; Mat Isa, Nor Ashidi; Al-batah, Mohammad Subhi

    2013-01-01

    Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors. PMID:24453846

  2. Impacts of Landuse Management and Climate Change on Landslides Susceptibility over the Olympic Peninsula of Washington State

    NASA Astrophysics Data System (ADS)

    Barik, M. G.; Adam, J. C.

    2009-12-01

    The commercial forests on the western side of the Olympic Mountains in Washington State are a region of steep slopes and high annual rainfall (2500-6000 mm/year) and are therefore highly susceptible to landslides. Potential climatic change (more intense and frequent winter storms) may exacerbate landslide susceptibility unless forest management practices are changed. As this area is a critical habitat for numerous organisms, including salmon, this may result in potentially severe consequences to riparian habitat due to increased sediment loads. Therefore, there is a need to investigate potential forest management plans to promote the economic viability of timber extraction while protecting the natural habitat, particularly in riparian areas. The objective of this study is to predict the long term effects of forest management decisions under projected climate change on slope stability. We applied the physically-based Distributed Hydrology Soil Vegetation Model (DHSVM) with its sediment module to simulate mass wasting and sediment delivery under different vegetation and climate scenarios. Sub-basins were selected and classified according to elevation, slope, land cover and soil type. Various land management practices (such as clear-cutting in riparian areas, logging under short rotations, varying amount of timbers left intact in riparian areas) were applied to each of the selected sub-basins. DHSVM was used to simulate landslide volume, frequency, and sediment loads for each of the land cover applications under various future climate scenarios. We comment on the suitability of various harvesting techniques for different parts of the forest to minimize landslide-induced sediment loading to streams in an altered climate. This approach can be developed as a decision making tool that can be used by forest managers to make long-term planning decisions.

  3. A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective

    NASA Astrophysics Data System (ADS)

    Shafique, Muhammad; van der Meijde, Mark; Khan, M. Asif

    2016-03-01

    The 8th October 2005 Kashmir earthquake, in northern Pakistan has triggered thousands of landslides, which was the second major factor in the destruction of the build-up environment, after earthquake-induced ground shaking. Subsequent to the earthquake, several researchers from home and abroad applied a variety of remote sensing techniques, supported with field observations, to develop inventories of the earthquake-triggered landslides, analyzed their spatial distribution and subsequently developed landslide-susceptibility maps. Earthquake causative fault rupture, geology, anthropogenic activities and remote sensing derived topographic attributes were observed to have major influence on the spatial distribution of landslides. These were subsequently used to develop a landslide susceptibility map, thereby demarcating the areas prone to landsliding. Temporal studies monitoring the earthquake-induced landslides shows that the earthquake-induced landslides are stabilized, contrary to earlier belief, directly after the earthquake. The biggest landslide induced dam, as a result of the massive Hattian Bala landslide, is still posing a threat to the surrounding communities. It is observed that remote sensing data is effectively and efficiently used to assess the landslides triggered by the Kashmir earthquake, however, there is still a need of more research to understand the mechanism of intensity and distribution of landslides; and their continuous monitoring using remote sensing data at a regional scale. This paper, provides an overview of remote sensing and GIS applications, for the Kashmir-earthquake triggered landslides, derived outputs and discusses the lessons learnt, advantages, limitations and recommendations for future research.

  4. Landslide mapping with multi-scale object-based image analysis - a case study in the Baichi watershed, Taiwan

    NASA Astrophysics Data System (ADS)

    Lahousse, T.; Chang, K. T.; Lin, Y. H.

    2011-10-01

    We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m2. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.

  5. Landslide hazard mapping using a GIS and a fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Muthu, Kavitha; Petrou, Maria

    2004-10-01

    The aim of this work is to use information from various sources, including remote sensing images from which land use change may be identified, in order to produce landslide hazard maps. We designed a fuzzy neural network which allows us to incorporate all the levels of uncertainty in the informations used in order to draw conclusions about the severity of the landslide hazard. The scale of operation of such a system is at the regional level rather than the local microlevel where ground local measurements may be performed and detailed geotechnical mathematical models may be applied to calculate soil stresses. It is not possible to apply such accurate detailed models for large scale hazard assessment. The proposed system is expected to be less accurate but more widely applicable than what is currently used in geotechnics.

  6. The Framework on Multi-Scale Landslide Hazard Evaluation in China

    NASA Astrophysics Data System (ADS)

    Li, W. Y.; Liu, C.; Gao, J.

    2016-06-01

    Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.

  7. Landslide Susceptibility Assessment in the Central Part of Republic of Moldova

    NASA Astrophysics Data System (ADS)

    Ercanoglu, Murat; Boboc, Nicolae; Sirodoev, Igor; Ahmet Temiz, F.; Sirodoev, Ghenadi

    2010-05-01

    There has been an increasing interest in natural hazard assessments within the scientific community, particularly in the last two decades. In other respect, there is also a dramatically rising trend in the number of natural hazards. Growing population and expansion of settlements and lifelines over hazardous areas have largely increased the impact of natural disasters both in industrialized and developing countries. Furthermore, natural disasters such as earthquakes, landslides, floods have dramatic effects on human life, infrastructures, environment, and so on. Landslides, one of the most destructive natural hazards, constitute a major geological hazard throughout the world, like in Turkey and Moldova. There are a lot of regions affected by landslides in Turkey (particularly the West, Middle and East Black Sea Region) and Moldova (e.g.: area between Nisporeni, Calarasi, Balti, Western Rezina District, Codri Hills in Central Moldova etc.), and consequences of landslides are of great importance in the two countries. In the last 50 years' period, only the economic loss due to landslides in Turkey is estimated about 5 billion , and 12.5 % of the whole settlement areas, including big and populated cities, are facing landslide threat. Similar to Turkey, there are about 16000 areas affected by landslides in Moldova. In February-March, 1998 the intensity of landslides in the central part of Moldova, including Chisinau, considerably increased. In total, 357 private households involving 1400 people were affected, 214 houses were destroyed, and 137 were damaged. The total national damage accounted for 44.3 million Lei. At present on Moldavian territory, there are more than 17000 landslides of various types. These landslides are mostly located within Central Moldavian heights, one of the most complicated geomorphologic structure and territory's fragmentation. Among major landslide triggering factors, in addition to natural ones, one should also consider the anthropogenic

  8. Comparison between different approaches of modeling shallow landslide susceptibility: a case history in the area of Oltrepo Pavese, Northern Italy

    NASA Astrophysics Data System (ADS)

    Zizioli, D.; Meisina, C.; Valentino, R.; Montrasio, L.

    2012-04-01

    Shallow landslides are triggered by intense rainfalls of short duration. Even though they involve only small portions of hilly and mountainous terrains, they are the cause of heavy damages to people and infrastructures. The identification of shallow landslide prone-areas is, therefore, a necessity to plan mitigation measures. On the 27th and 28th of April 2009, the area of Oltrepo Pavese, northern Italy, was affected by a very intense rainfall event, which caused a great number of shallow landslides. These instability phenomena meanly occurred on slopes taken up by vineyards and caused damages to many roads and one human loss. On the basis of aerial photographs taken immediately after the event and field surveys, it was possible to detect more than 1,600 landslides. After acquiring all the information dealing with topography, geotechnical properties of the involved soils and land use, a susceptibility analysis on territorial scale has been carried out. The paper deals with the application and the comparison, on the study area, of different methods for the susceptibility assessment: a) the physically-based stability models TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model, Baum et al., 2008), which is designed for modelling the potential occurrences of shallow landslides by incorporating the transient pressure response to rainfall and downward infiltration processes and SLIP (Shallow Landslides Instability Prediction; Montrasio, 2000; Montrasio and Valentino, 2008), which allows to dynamically take into account the connection between the stability condition of a slope, the characteristics of the soil, and the rainfall amounts, including also previous rainfalls; b) the logistic regression and the Neural Artificial Network (ANN) that take into account some important predisposing factors in the study area (slope angle, landform classification, the potential solar radiation, soil thickness, permeability, topographic ruggedness index

  9. Statistical Seismic Landslide Analysis: an Update

    NASA Astrophysics Data System (ADS)

    Lee, Chyi-Tyi

    2015-04-01

    Landslides are secondary or induced features, whose recurrence is controlled by the repetition of triggering events, such as earthquakes or heavy rainfall. This makes seismic landslide hazard analysis more complicated than ordinary seismic hazard analysis, and it requires multi-stage analysis. First, susceptibility analysis is utilized to divide a region into successive classes. Then, it is necessary to construct a relationship between the probability of landslide failure and earthquake intensity for each susceptibility class for a region, or to find the probability of failure surface using the susceptibility value and earthquake intensity as independent variables at the study region. Then, hazard analysis for the exceedance probability of earthquake intensity is performed. Finally, an analysis of the spatial probability of landslide failure under a certain return-period earthquake is drawn. This study uses data for Chi-Chi earthquake induced landslides as the training data set to perform the susceptibility analysis and probability of failure surface analysis. A regular probabilistic seismic hazard analysis is also conducted to map different return-period Arias intensities. Finally a seismic landslide hazard map for the whole of Taiwan is provided.

  10. Comparison between monitored and modeled pore water pressure and safety factor in a slope susceptible to shallow landslides

    NASA Astrophysics Data System (ADS)

    Bordoni, Massimiliano; Meisina, Claudia; Zizioli, Davide; Valentino, Roberto; Bittelli, Marco; Chersich, Silvia

    2014-05-01

    Shallow landslides can be defined as slope movements affecting superficial deposits of small thicknesses which are usually triggered due to extreme rainfall events, also very concentrated in time. Shallow landslides are hazardous phenomena: in particular, if they happen close to urbanized areas they could cause significant damages to cultivations, structures, infrastructures and, sometimes, human losses. The triggering mechanism of rainfall-induced shallow landslides is strictly linked with the hydrological and mechanical responses of usually unsaturated soils to rainfall events. For this reason, it is fundamental knowing the intrinsic hydro-mechanical properties of the soils in order to assess both susceptibility and hazard of shallow landslide and to develop early-warning systems at large scale. The hydrological data collected by a 20 months monitoring on a slope susceptible to shallow landslides in an area of the North -Eastern Oltrepo Pavese (Northern Apennines, Italy) were used to identify the hydrological behaviors of the investigated soils towards rainfall events. Field conditions under different rainfall trends have also been modeled by using both hydrological and physically-based stability models for the evaluation of the slope safety factor . The main objectives of this research are: (a) to compare the field measured pore water pressures at different depths with results of hydrological models, in order to evaluate the efficiency of the tested models and to determine how precipitations affect pore pressure development; (b) to compare the time trends of the safety factor that have been obtained by applying different stability models; (c) to evaluate, through a sensitivity analysis, the effects of soil hydrological properties on modeling pore water pressure and safety factor. The test site slope where field measurements were acquired is representative of other sites in Northern Apennines affected by shallow landslides and is characterized by medium

  11. Regional Landslide Mapping Aided by Automated Classification of SqueeSAR™ Time Series (Northern Apennines, Italy)

    NASA Astrophysics Data System (ADS)

    Iannacone, J.; Berti, M.; Allievi, J.; Del Conte, S.; Corsini, A.

    2013-12-01

    Space borne InSAR has proven to be very valuable for landslides detection. In particular, extremely slow landslides (Cruden and Varnes, 1996) can be now clearly identified, thanks to the millimetric precision reached by recent multi-interferometric algorithms. The typical approach in radar interpretation for landslides mapping is based on average annual velocity of the deformation which is calculated over the entire times series. The Hotspot and Cluster Analysis (Lu et al., 2012) and the PSI-based matrix approach (Cigna et al., 2013) are examples of landslides mapping techniques based on average annual velocities. However, slope movements can be affected by non-linear deformation trends, (i.e. reactivation of dormant landslides, deceleration due to natural or man-made slope stabilization, seasonal activity, etc). Therefore, analyzing deformation time series is crucial in order to fully characterize slope dynamics. While this is relatively simple to be carried out manually when dealing with small dataset, the time series analysis over regional scale dataset requires automated classification procedures. Berti et al. (2013) developed an automatic procedure for the analysis of InSAR time series based on a sequence of statistical tests. The analysis allows to classify the time series into six distinctive target trends (0=uncorrelated; 1=linear; 2=quadratic; 3=bilinear; 4=discontinuous without constant velocity; 5=discontinuous with change in velocity) which are likely to represent different slope processes. The analysis also provides a series of descriptive parameters which can be used to characterize the temporal changes of ground motion. All the classification algorithms were integrated into a Graphical User Interface called PSTime. We investigated an area of about 2000 km2 in the Northern Apennines of Italy by using SqueeSAR™ algorithm (Ferretti et al., 2011). Two Radarsat-1 data stack, comprising of 112 scenes in descending orbit and 124 scenes in ascending orbit

  12. Exploitation of amplitude and phase of satellite SAR images for landslide mapping: the case of Montescaglioso (South Italy)

    NASA Astrophysics Data System (ADS)

    Raspini, Federico; Ciampalini, Andrea; Lombardi, Luca; Nocentini, Massimiliano; Gigli, Giovanni; Casagli, Nicola; Del Conte, Sara; Ferretti, Alessandro

    2016-04-01

    Pre- event and event landslide deformations have been detected and measured for the landslide that occurred on 3 December 2013 on the south-western slope of the Montescaglioso village (Basilicata Region, southern Italy). The event, triggered by prolonged rainfalls, created significant damage to buildings and local infrastructures. Ground displacements have been mapped through an integrated analysis based on a series of high resolution SAR (Synthetic Aperture Radar) images acquired by the Italian constellation of satellites COSMO-SkyMed. Analysis has been performed by exploiting both phase (through multi-image SAR interferometry) and amplitude information (through speckle tracking techniques) of the satellite images. SAR Interferometry, applied to images taken before the event, revealed a general pre-event movement, in the order of a few mm/yr, in the south-western slope of the Montescaglioso village. Highest pre-event velocities, ranging between 8 and 12 mm/yr, have been recorded in the sector of the slope where the first movement of the landslide took place. Speckle tracking, applied to images acquired before and after the event, allowed the retrieval of the 3D deformation field produced by the landslide. It also showed that ground displacements produced by the landslide have a dominant SSW component, with values exceeding 10 m for large sectors of the landslide area, with local peaks of 20 m in its central and deposit areas. Two minor landslides with a dominant SSE direction, which were detected in the upper parts of the slope, likely also occurred as secondary phenomena as consequence of the SSW movement of the main Montescaglioso landslide. This work demonstrates that this complementary approach, based on the synergistic exploitation of phase and amplitude SAR data, can become a powerful tool for landslide investigation, allowing the detection of slow, precursory deformation patterns as well the retrieval of full 3D surface displacement fields caused by large

  13. Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications

    PubMed Central

    Liu, Chunlei; Wei, Hongjiang; Gong, Nan-Jie; Cronin, Matthew; Dibb, Russel; Decker, Kyle

    2016-01-01

    Quantitative susceptibility mapping (QSM) is a recently developed MRI technique for quantifying the spatial distribution of magnetic susceptibility within biological tissues. It first uses the frequency shift in the MRI signal to map the magnetic field profile within the tissue. The resulting field map is then used to determine the spatial distribution of the underlying magnetic susceptibility by solving an inverse problem. The solution is achieved by deconvolving the field map with a dipole field, under the assumption that the magnetic field is a result of the superposition of the dipole fields generated by all voxels and that each voxel has its unique magnetic susceptibility. QSM provides improved contrast to noise ratio for certain tissues and structures compared to its magnitude counterpart. More importantly, magnetic susceptibility is a direct reflection of the molecular composition and cellular architecture of the tissue. Consequently, by quantifying magnetic susceptibility, QSM is becoming a quantitative imaging approach for characterizing normal and pathological tissue properties. This article reviews the mechanism generating susceptibility contrast within tissues and some associated applications. PMID:26844301

  14. Integrating the effects of forest cover on slope stability in a deterministic landslide susceptibility model (TRIGRS 2.0)

    NASA Astrophysics Data System (ADS)

    Zieher, T.; Rutzinger, M.; Bremer, M.; Meissl, G.; Geitner, C.

    2014-12-01

    The potentially stabilizing effects of forest cover in respect of slope stability have been the subject of many studies in the recent past. Hence, the effects of trees are also considered in many deterministic landslide susceptibility models. TRIGRS 2.0 (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability; USGS) is a dynamic, physically-based model designed to estimate shallow landslide susceptibility in space and time. In the original version the effects of forest cover are not considered. As for further studies in Vorarlberg (Austria) TRIGRS 2.0 is intended to be applied in selected catchments that are densely forested, the effects of trees on slope stability were implemented in the model. Besides hydrological impacts such as interception or transpiration by tree canopies and stems, root cohesion directly influences the stability of slopes especially in case of shallow landslides while the additional weight superimposed by trees is of minor relevance. Detailed data on tree positions and further attributes such as tree height and diameter at breast height were derived throughout the study area (52 km²) from high-resolution airborne laser scanning data. Different scenarios were computed for spruce (Picea abies) in the study area. Root cohesion was estimated area-wide based on published correlations between root reinforcement and distance to tree stems depending on the stem diameter at breast height. In order to account for decreasing root cohesion with depth an exponential distribution was assumed and implemented in the model. Preliminary modelling results show that forest cover can have positive effects on slope stability yet strongly depending on tree age and stand structure. This work has been conducted within C3S-ISLS, which is funded by the Austrian Climate and Energy Fund, 5th ACRP Program.

  15. GIS application on spatial landslide analysis using statistical based models

    NASA Astrophysics Data System (ADS)

    Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred F.

    2009-09-01

    This paper presents the assessment results of spatially based probabilistic three models using Geoinformation Techniques (GIT) for landslide susceptibility analysis at Penang Island in Malaysia. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and supported with field surveys. Maps of the topography, soil type, lineaments and land cover were constructed from the spatial data sets. There are ten landslide related factors were extracted from the spatial database and the frequency ratio, fuzzy logic, and bivariate logistic regression coefficients of each factor was computed. Finally, landslide susceptibility maps were drawn for study area using frequency ratios, fuzzy logic and bivariate logistic regression models. For verification, the results of the analyses were compared with actual landslide locations in study area. The verification results show that bivariate logistic regression model provides slightly higher prediction accuracy than the frequency ratio and fuzzy logic models.

  16. Mapping Landslides in Lunar Impact Craters Using Chebyshev Polynomials and Dem's

    NASA Astrophysics Data System (ADS)

    Yordanov, V.; Scaioni, M.; Brunetti, M. T.; Melis, M. T.; Zinzi, A.; Giommi, P.

    2016-06-01

    Geological slope failure processes have been observed on the Moon surface for decades, nevertheless a detailed and exhaustive lunar landslide inventory has not been produced yet. For a preliminary survey, WAC images and DEM maps from LROC at 100 m/pixels have been exploited in combination with the criteria applied by Brunetti et al. (2015) to detect the landslides. These criteria are based on the visual analysis of optical images to recognize mass wasting features. In the literature, Chebyshev polynomials have been applied to interpolate crater cross-sections in order to obtain a parametric characterization useful for classification into different morphological shapes. Here a new implementation of Chebyshev polynomial approximation is proposed, taking into account some statistical testing of the results obtained during Least-squares estimation. The presence of landslides in lunar craters is then investigated by analyzing the absolute values off odd coefficients of estimated Chebyshev polynomials. A case study on the Cassini A crater has demonstrated the key-points of the proposed methodology and outlined the required future development to carry out.

  17. Towards a National Hazard Map of Landslides: Juan de Grijalva, Chiapas, and Mitlatongo, Oaxaca, two catastrophic landslides on southeastern of Mexico

    NASA Astrophysics Data System (ADS)

    Dominguez-M, L.; Castañeda, A.; Ramirez, A.; González, A. E.

    2013-05-01

    One of the most catastrophic events, with economical losses and deaths, in Mexico and Latin America, is the landslide event. The Juan de Grijalva landslide, which blocked one of the largest rivers in the Chiapas state of Mexico, on November 4, 2007, is considered one of the greatest that have occurred in the world in the last 100 years (Dominguez, 2008) and it could be the one with the largest economic impact in the history of Mexico. This landslide occurred four days after a period of very heavy rains that caused, in the peak of the emergency, flooding in almost 62% of the area of the state of Tabasco (CENAPRED, 2009) and is also one of the most serious disasters that were faced by the Mexican government in the past 10 years. The Juan de Grijalva landslide mobilized the entire government apparatus and required an investment of just over 0.1 billions of US Dollars (CENAPRED, 2009) for the rehabilitation of the river runway and additional works in order to prevent further damages if another landslide occurs in the vicinity. A similar case of interest for Mexican researchers and specialists in earth sciences is the big landslide occurred in the communities of Santa Cruz Mitlatongo, municipality of Magdalena Jaltepec, and Santiago Mitlatongo, municipality of Nochixtlan, both in the state of Oaxaca (Dominguez, 2011). This landslide has dimensions of just over 2,500 m long and 900 m wide, and it remains active from September 2011. Since then, the landslide has moved just over 230 m in length and has destroyed about 850 houses. Given the geological and geotechnical characteristics of these landslides and the economic and social impact caused, the National Center for Disaster Prevention (CENAPRED) has initiated a research project in order to learn the main factors (constraints and triggers) that influenced both landslides. In relation with the National Hazard Landslide Map, developed by CENAPRED, these events are an important task of the National Inventory of Landslides

  18. Landslide hazard mapping in the Göta river valley to limit

    NASA Astrophysics Data System (ADS)

    Tremblay, M.; Svahn, V.; Lind, B.; Lundström, K.; Cederbom, C. E.

    2012-04-01

    Landslide scars are frequent along the river bank of the Göta river in southwest Sweden, and several landslides in quick-clay have resulted in casualties and severe damages on buildings and infrastructure during the last century. Moreover, higher average precipitation and increased occurrence of extreme rainfall events are some expected climate changes in Sweden during the coming 70-100 years. The Swedish Geotechnical Institute (SGI) was therefore commissioned by the Swedish Government to perform an inventory of the landslide potential in the Göta river valley, taking predicted climate changes into consideration. The project was running over three years (2009-2011) and the final report is presented in March 2012. To prevent extensive floodings and damages of cities and infrastructure around Lake Vänern, it is necessary to allow controlled overflow from Lake Vänern through the Göta river. An overflow in the river, in turn, leads to increased risk for erosion and landslides along the river valley. The inventory has included detailed field and laboratory investigations of the geological and hydrological conditions, methodology development, erosion modeling, effects of climate changes on porewater and groundwater conditions as well as an estimation of consequences and probabilities for failure in the present-day and future climate. In the final report risk estimates for the complete study area are presented along with rough cost estimates for first-order preventing measures. This presentation aims to give an overview of the outcome of the inventory, the experience and new knowledge acquired during the project as well as the need of research and development work in different technical areas in order to improve risk mapping of natural slopes.

  19. Prediction of landslides using ASTER imagery and data mining models

    NASA Astrophysics Data System (ADS)

    Song, Kyo-Young; Oh, Hyun-Joo; Choi, Jaewon; Park, Inhye; Lee, Changwook; Lee, Saro

    2012-03-01

    The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS. The generalized bell-shaped built-in membership function of the ANFIS was applied to landslide susceptibility mapping. Analytical results were validated using landslide test location data. In the validation results, the ANN model showed 80.42% prediction accuracy and the ANFIS model showed 86.55% prediction accuracy. These results suggest that the ANFIS model has a better performance than does the ANN in predicting landslide susceptibility.

  20. Digital Compilation of "Preliminary Map of Landslide Deposits in Santa Cruz County, California, By Cooper-Clark and Associates, 1975": A Digital Map Database

    USGS Publications Warehouse

    Report by Roberts, Sebastian; Barron, Andrew D.; Preface by Brabb, Earl E.; Pike, Richard J.

    1998-01-01

    A 1:62,500-scale black-and-white map identifying some 2,000 landslides of various types in Santa Cruz County, California, has been converted to a digital-map database that can be acquired from the U.S. Geological Survey over the Internet or on magnetic tape.

  1. Databases and GIS for landslide research in Europe

    NASA Astrophysics Data System (ADS)

    Dikau, Richard; Cavallin, Angelo; Jäger, Stefan

    1996-04-01

    Within the project "The Temporal occurrence and forecasting of landslides in the European Community" a review of the use of databases and GIS for landslide research has been accomplished. It shows a high potential of these techniques in storing spatial and temporal landslide data (landslide inventories) and in applying different modelling approaches to landslide hazard assessments at various scales. There are three major strategies in European landslide research using GIS and database technologies. At medium and broad scales different combinations of landslide data with factor maps (e.g. slope angle, lithology and geomorphological units) lead to static susceptibility and hazard assessments, which allow probability evaluations for future landslide occurrences. At local scales process models to simulate trajectories of paths for slope processes and deterministic slope stability models are in use. In landslide frequency analysis, temporal database information are correlated with recent and historical triggering factors (e.g. precipitation and precipitation indices) to calculate temporal probabilities for landslide forecasting. However, despite encouraging progress in applying computer technologies in European landslide research, the potential of these tools is still largely untested. Furthermore, it is clear that sophisticated technology cannot replace field work, interdisciplinary research strategies, and critical testing of the reliability of the model results.

  2. Assessing deep-seated landslide susceptibility using 3-D groundwater and slope-stability analyses, southwestern Seattle, Washington

    USGS Publications Warehouse

    Brien, Dianne L.; Reid, Mark E.

    2008-01-01

    In Seattle, Washington, deep-seated landslides on bluffs along Puget Sound have historically caused extensive damage to land and structures. These large failures are controlled by three-dimensional (3-D) variations in strength and pore-water pressures. We assess the slope stability of part of southwestern Seattle using a 3-D limit-equilibrium analysis coupled with a 3-D groundwater flow model. Our analyses use a high-resolution digital elevation model (DEM) combined with assignment of strength and hydraulic properties based on geologic units. The hydrogeology of the Seattle area consists of a layer of permeable glacial outwash sand that overlies less permeable glacial lacustrine silty clay. Using a 3-D groundwater model, MODFLOW-2000, we simulate a water table above the less permeable units and calibrate the model to observed conditions. The simulated pore-pressure distribution is then used in a 3-D slope-stability analysis, SCOOPS, to quantify the stability of the coastal bluffs. For wet winter conditions, our analyses predict that the least stable areas are steep hillslopes above Puget Sound, where pore pressures are elevated in the outwash sand. Groundwater flow converges in coastal reentrants, resulting in elevated pore pressures and destabilization of slopes. Regions predicted to be least stable include the areas in or adjacent to three mapped historically active deep-seated landslides. The results of our 3-D analyses differ significantly from a slope map or results from one-dimensional (1-D) analyses.

  3. Mapping the kinematics of the Blaubach landslide (Austria) using digital photogrammetry

    NASA Astrophysics Data System (ADS)

    Kaufmann, V.; Lieb, G. K.

    2003-04-01

    The Blaubach landslide (12°08'E, 47°12'N, northern margin of the Hohe Tauern range, Austria) is located in the upper part of the catchment area of the Blaubach torrent. The latter follows an important Eastern Alpine fault. The area of interest is built of tectonically fractured rock favoring fluvial erosion, debris flows, and other types of mass movements triggered by widespread deep reaching gravitational slope deformations. The Blaubach landslide is characterized by high surface movement and a front with several secondary slides, which are free of vegetation and provide high quantities of material to the torrent below. This natural hazard has induced the construction of protective measures such as retaining walls in the torrent bed since 1950. However, as of yet no numerical data have been available concerning the surface kinematics of the landslide, such as flow/creep velocity, surface height change, or volumetric change. The Austrian Forest Engineering Service of Torrent and Avalanche Control therefore launched a project related to these questions. One task was to reconstruct the morphodynamics of the landslide area using historical multi-temporal aerial photographs. Aerial photographs at various image scales between 1:9,300 and 1:45,800 of 11 different data acquisition periods between 1953 and 1999 were acquired from the Austrian Federal Office of Surveying and Mapping. The photographs were scanned using the UltraScan 5000 of Vexcel Imaging Austria in order to facilitate digital photogrammetry. A special software package ADVM (Automatic Displacement Vector Measurement), originally developed at the Institute of Geodesy for monitoring debris-covered glaciers and rock glaciers, was used to automatically derive three-dimensional displacement vectors, both area-wide and dense, based on advanced image matching techniques. The digital photogrammetric method applied is based on quasi-orthophotos. This approach supports the fusion of multi-temporal aerial photographs

  4. Disseminating Landslide Hazard Information for California Local Government

    NASA Astrophysics Data System (ADS)

    Wills, C. J.

    2010-12-01

    landslide susceptibility map to give a single value of susceptibility for each census tract. We then calculated the loss ratio, the cost of landslide damage from the 1978 storms divided by the value of light wood frame structures in the census tract. The comparison suggests three general categories of damage: tracts with low landslide susceptibility have no landslide damage: tracts with moderate susceptibility have loss ratios of about 0.016%: and tracts with high susceptibility have loss ratios of 0.096%. Using these values, the susceptibility map becomes a landslide loss ratio map for the average storm intensity and landslide vulnerability of Los Angeles in 1978. Generalization to other storm intensities uses differences in storm intensity and landslide damage data from the 1982 storm in the Bay Area. In Santa Cruz County, that storm had a recurrence interval of over 100 years, and over 3 times the damage as our projection from the 1978 data. In Sonoma County, that storm had a recurrence interval of only 10 years and damage that was only 2% of our projection. If a relationship between storm intensity and the projections from the 1978 Los Angeles data can be developed, we may be able to estimate landslide losses for any projected storm intensity.

  5. Active machine learning for rapid landslide inventory mapping with VHR satellite images (Invited)

    NASA Astrophysics Data System (ADS)

    Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

    2013-12-01

    VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern

  6. Debris Flow Vulnerability Assessment in Urban Area Associated with Landslide Hazard Map : Application to Busan, Korea

    NASA Astrophysics Data System (ADS)

    Okjeong, Lee; Yoonkyung, Park; Mookwang, Sung; Sangdan, Kim

    2016-04-01

    In this presentation, an urban debris flow disaster vulnerability assessment methodology is suggested with major focus on urban social and economic aspect. The proposed methodology is developed based on the landslide hazard maps that Korean Forest Service has utilized to identify landslide source areas. Frist, debris flows are propagated to urban areas from such source areas by Flow-R model, and then urban vulnerability is evaluated by two categories; physical and socio-economic aspect. The physical vulnerability is associated to buildings that can be broken down by a landslide event directly. This study considers two popular building structure types, reinforced concrete frame and non-reinforced concretes frame, to evaluate the physically-based vulnerability. The socio-economic vulnerability is measured as a function of the resistant levels of the exposed people, the intensity and magnitude of indirect or intangible losses, and preparedness level of the local government. An indicator-based model is established to evaluate the life and indirect loss under urban debris flow disasters as well as the resilience ability against disasters. To illuminate the validity of the suggested methodology, physical and socio-economic vulnerability levels are investigated for Daejeon, Korea using the proposed approach. The results reveal that the higher population density areas under a weaker fiscal condition that are located at the downstream of mountainous areas are more vulnerable than the areas in opposite conditions. Key words: Debris flow disasters, Physical vulnerability, Socio-economic Vulnerability, Urban Acknowledgement This research was supported by a grant(13SCIPS04) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport(MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement(KAIA).

  7. Analysis of national and regional landslide inventories in Europe

    NASA Astrophysics Data System (ADS)

    Hervás, J.; Van Den Eeckhaut, M.

    2012-04-01

    A landslide inventory can be defined as a detailed register of the distribution and characteristics of past landslides in an area. Today most landslide inventories have the form of digital databases including landslide distribution maps and associated alphanumeric information for each landslide. While landslide inventories are of the utmost importance for land use planning and risk management through the generation of landslide zonation (susceptibility, hazard and risk) maps, landslide databases are thought to greatly differ from one country to another and often also within the same country. This hampers the generation of comparable, harmonised landslide zonation maps at national and continental scales, which is needed for policy and decision making at EU level as regarded for instance in the INSPIRE Directive and the Thematic Strategy for Soil Protection. In order to have a clear understanding of the landslide inventories available in Europe and their potential to produce landslide zonation maps as well as to draw recommendations to improve harmonisation and interoperability between landslide databases, we have surveyed 37 countries. In total, information has been collected and analysed for 24 national databases in 22 countries (Albania, Andorra, Austria, Bosnia and Herzegovina, Bulgaria, Czech Republic, Former Yugoslav Republic of Macedonia, France, Greece, Hungary, Iceland, Ireland, Italy, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland and UK) and 22 regional databases in 10 countries. At the moment, over 633,000 landslides are recorded in national databases, representing on average less than 50% of the estimated landslides occurred in these countries. The sample of regional databases included over 103,000 landslides, with an estimated completeness substantially higher than that of national databases, as more attention can be paid for data collection over smaller regions. Yet, both for national and regional coverage, the data collection

  8. Landslide inventory development in a data sparse region: spatial and temporal characteristics of landslides in Papua New Guinea

    NASA Astrophysics Data System (ADS)

    Robbins, J. C.; Petterson, M. G.

    2015-08-01

    In Papua New Guinea (PNG) earthquakes and rainfall events form the dominant trigger mechanisms capable of generating many landslides. Large volume and high density landsliding can result in significant socio-economic impacts, which are felt particularly strongly in the largely subsistence-orientated communities which reside in the most susceptible areas of the country. As PNG has undergone rapid development and increased external investment from mining and other companies, population and settled areas have increased, hence the potential for damage from landslides has also increased. Information on the spatial and temporal distribution of landslides, at a regional-scale, is critical for developing landslide hazard maps and for planning, sustainable development and decision making. This study describes the methods used to produce the first, country-wide landslide inventory for PNG and analyses of landslide events which occurred between 1970 and 2013. The findings illustrate that there is a strong climatic control on landslide-triggering events and that the majority (~ 61 %) of landslides in the PNG landslide inventory are initiated by rainfall related triggers. There is also large year to year variability in the annual occurrence of landslide events and this is related to the phase of El Niño Southern Oscillation (ENSO) and mesoscale rainfall variability. Landslide-triggering events occur during the north-westerly monsoon season during all phases of ENSO, but less landslide-triggering events are observed during drier season months (May to October) during El Niño phases, than either La Niña or ENSO neutral periods. This analysis has identified landslide hazard hotspots and relationships between landslide occurrence and rainfall climatology and this information can prove to be very valuable in the assessment of trends and future behaviour, which can be useful for policy makers and planners.

  9. Subsoil compaction in Flanders: from soil map to susceptibility map and risk map for subsoil compaction

    NASA Astrophysics Data System (ADS)

    van de Vreken, Philippe; van Holm, Lieven; Diels, Jan; van Orshoven, Jos

    2010-05-01

    In contrast to topsoil compaction, which can be remediated by normal soil tillage and natural loosening processes, subsoil compaction must be considered as a long term threat to soil productivity as this form of compaction is much more persistent and not easy to alleviate. Therefore we focused on subsoil compaction with a view to demarcate areas prone to soil compaction in Flanders, Belgium. The susceptibility of soil material to compaction is inversely related to its structural strength which can be expressed in terms of precompression stress (PCS). In order to construct maps of subsoil susceptibility we upgraded the soil map of Flanders, originally printed at a scale of 1:20.000, by attributing a ‘typical' PCS-value to the legend units. These PCS-values were estimated by means of pedotransfer functions (PTFs), valid either at pF 1.8 or pF 2.5, elaborated from PCS-measurements on soils in Germany by Lebert and Horn (1991). Predictor values for the PTFs were supplied by or derived by means of other PTFs from a historical database of georeferenced soil profiles, which were analysed between 1947 and 1971. After regional stratification, soil profiles with associated horizons were linked to soil map units based on corresponding classification units. Next, for each map unit the horizon at 40 cm of depth was selected and its characteristics retrieved for use in the PTFs. The two resulting PCS-maps (pF 1.8 or 2.5) show the susceptibility to compaction of almost uncompacted or little compacted arable soils as they were present in the period 1950-1970, when the wheel loads of the agricultural equipment of that time were much lower compared to the wheel loads that are common today. Both maps of inherent susceptibility at fixed pF were combined into a ‘hybrid map' of the inherent susceptibility to subsoil compaction in spring, when the groundwater table is at its highest level and correspondingly also the susceptibility to compaction is highest. Each soil map unit was

  10. Wildfire susceptibility mapping: comparing deterministic and stochastic approaches

    NASA Astrophysics Data System (ADS)

    Pereira, Mário; Leuenberger, Michael; Parente, Joana; Tonini, Marj

    2016-04-01

    Estimating the probability of wildfire-occurrence in a certain area under particular environmental conditions represents a modern tool to support forest protection plans and to reduce fires consequences. This can be performed by the implementation of wildfire susceptibility mapping, normally achieved employing more or less sophisticated models which combine the predisposing variables (as raster datasets) into a geographic information systems (GIS). The selection of the appropriate variables includes the evaluation of success and the implementation of prediction curves, as well as independent probabilistic validations for different scenarios. These methods allow to define the spatial pattern of wildfire-occurrences, characterize the susceptibility of the territory, namely for specific fire causes/types, and can also account for other factors such as human behavior and social aspects. We selected Portugal as the study region which, due to its favorable climatic, topographic and vegetation conditions, is by far the European country most affected by wildfires. In addition, Verde and Zêzere (2010) performed a first assessment and validation of wildfire susceptibility and hazard in Portugal which can be used as benchmarking. The objectives of the present study comprise: (1) assessing the structural forest fire risk in Portugal using updated datasets, namely, with higher spatial resolution (80 m to 25 m), most recent vegetation cover (Corine Land Cover), longer fire history (1975-2013); and, (2) comparing linear vs non-linear approaches for wildfire susceptibility mapping. The data we used includes: (i) a DEM derived from the Shuttle Radar Topographic Mission in a resolution of 1 arc-seconds (DEM-SRTM 25 m) to assess elevation and slope; (ii) the Corine Land Cover inventory provided by the European Environment Agency (http://www.eea.europa.eu/pt) to produce the land use land cover map; (iii) the National Mapping Burnt Areas (NMBA) provided by the Institute for the

  11. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain.

    PubMed

    Liu, Chunlei; Li, Wei; Tong, Karen A; Yeom, Kristen W; Kuzminski, Samuel

    2015-07-01

    Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique that enhances image contrast by using the susceptibility differences between tissues. It is created by combining both magnitude and phase in the gradient echo data. SWI is sensitive to both paramagnetic and diamagnetic substances which generate different phase shift in MRI data. SWI images can be displayed as a minimum intensity projection that provides high resolution delineation of the cerebral venous architecture, a feature that is not available in other MRI techniques. As such, SWI has been widely applied to diagnose various venous abnormalities. SWI is especially sensitive to deoxygenated blood and intracranial mineral deposition and, for that reason, has been applied to image various pathologies including intracranial hemorrhage, traumatic brain injury, stroke, neoplasm, and multiple sclerosis. SWI, however, does not provide quantitative measures of magnetic susceptibility. This limitation is currently being addressed with the development of quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI). While QSM treats susceptibility as isotropic, STI treats susceptibility as generally anisotropic characterized by a tensor quantity. This article reviews the basic principles of SWI, its clinical and research applications, the mechanisms governing brain susceptibility properties, and its practical implementation, with a focus on brain imaging. PMID:25270052

  12. Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping in the Brain

    PubMed Central

    Liu, Chunlei; Li, Wei; Tong, Karen A.; Yeom, Kristen W.; Kuzminski, Samuel

    2015-01-01

    Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique that enhances image contrast by using the susceptibility differences between tissues. It is created by combining both magnitude and phase in the gradient echo data. SWI is sensitive to both paramagnetic and diamagnetic substances which generate different phase shift in MRI data. SWI images can be displayed as a minimum intensity projection that provides high resolution delineation of the cerebral venous architecture, a feature that is not available in other MRI techniques. As such, SWI has been widely applied to diagnose various venous abnormalities. SWI is especially sensitive to deoxygenated blood and intracranial mineral deposition and, for that reason, has been applied to image various pathologies including intracranial hemorrhage, traumatic brain injury, stroke, neoplasm, and multiple sclerosis. SWI, however, does not provide quantitative measures of magnetic susceptibility. This limitation is currently being addressed with the development of quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI). While QSM treats susceptibility as isotropic, STI treats susceptibility as generally anisotropic characterized by a tensor quantity. This article reviews the basic principles of SWI, its clinical and research applications, the mechanisms governing brain susceptibility properties, and its practical implementation, with a focus on brain imaging. PMID:25270052

  13. A first landslide inventory in the Rwenzori Mountains, Uganda

    NASA Astrophysics Data System (ADS)

    Jacobs, Liesbet; Dewitte, Olivier; Poesen, Jean; Sekajugo, John; Maes, Jan; Mertens, Kewan; Kervyn, Matthieu

    2015-04-01

    Landslides have significant impacts in many equatorial regions, particularly in the East-African highlands characterized by mountainous topography, intense rainfalls, deep weathering profiles, high population density and high vulnerability to geohazards. With its exceptionally steep topography, wet climate and active faulting, landslides can be expected to occur in the Rwenzori region as well. Whether or not this region is prone to landsliding is however unclear due to a lack of scientific studies and representation of this region in global landslide databases. In order to address this question, a first landslide inventory based on archive information is built. In total, 48 landslide and flashflood events, or combinations of these, are found. They caused 56 fatalities, considerable damage to road infrastructure, buildings and cropland, and rendered over 14,000 persons homeless. These numbers indicate that the Rwenzori Mountains are landslide-prone and that the impact of these events is significant. This archive inventory provided the basis for a thorough field inventory executed in three sub-regions of each 40-50 km² situated in the three districts of the Rwenzori Mountains and covering the main lithological units. Over 300 landslides were mapped in the field. Various contrasting mass wasting processes occur among which translational debris and soil slides, debris avalanches, debris flows and rotational soil slides. Landslides occur on almost all lithological groups present in the Rwenzori (Gneiss, Schists and Miocene to recent sediments), with the exception of Amphibolite, which does not appear to be susceptible to landslides. The majority of events are triggered by intense rainfall, although also earthquake-triggered landslides are identified, mostly related to the Mw 6.2 earthquake of 1994. The field inventory will be complemented and validated using very high resolution remotely sensed data and aerial photographs. This multi-temporal landslide inventory will

  14. Landsliding, topographic variables and location of cultural terraces in Slovenia

    NASA Astrophysics Data System (ADS)

    Komac, Blaž; Zorn, Matija

    2015-04-01

    For a large number of people living in hilly regions of Slovenia cultural terraces are important landscape elements. We know from previous studies that as many as half of vineyard terraces are built in areas which are highly susceptible to landslides, and a quarter in low landslide susceptibility areas. The contribution will present links between landslide susceptibility in terraced areas in Slovenia. Landslides are frequent element of cultural terraces-landscape. In Slovenia they are frequent in hilly and mountainous regions. The position of landslide areas is strongly influenced by the topography and thus indirectly by the construction of cultural terraces. They trigger during and after terraces construction when the drainage system is altered. Thus, agricultural activity leads to instability of slopes, and increases the production costs. Links between landsliding (Zorn and Komac 2004; 2008; 2009) and cultural terraces were determined using the geographic information systems. For the territory of Slovenia, we have already created landslide susceptibility map (Zorn and Komac 2004; 2008), while here we determined the correlation between landslides, topographic variables and location of cultural terraces. To achieve this aim, all areas of cultural terraces in Slovenia were digitized at the scale of 1:10,000. References Zorn, M., Komac B. 2004: Deterministic modeling of landslide and rockfall risk. Acta geographica Slovenica 44 (2), pp. 53-10. DOI: 10.3986/AGS44203 Zorn, M., Komac, B. 2008: Zemeljski plazovi v Sloveniji (Landslides in Slovenia). Georitem 8. Ljubljana: ZRC Publishing. Zorn, M., Komac, B. 2009: The importance of landsliding in a flysch geomorphic system: The example of the Gori\\vska brda Hills (W Slovenia). Zeitschrift für Geomorphologie N. F., Suppl. 56 (3), pp. 53-79. DOI: 10.1127/0372-8854/2012/S-00104

  15. Transmission tower classification based on landslide risk Map generated by Geographical Information System (GIS) at Cameron Highlands

    NASA Astrophysics Data System (ADS)

    K, Hazwani N.; O, Rohayu C.; U, Fathoni; Baharuddin, I. N. Z.; A, Azwin Z.

    2013-06-01

    Transmission tower is usually locates at remote area which is covered by hilly topography. Landslide is mainly occurring at hilly area and causing failure to the tower structure. This phenomenon subsequently will affect the national electricity supply. A landslide risk hazard map is generated using Geographical Information System (GIS). Risk classification is introduced to initiate the monitoring process along Jor-Bintang transmission line, Cameron Highland, Pahang. The classification has been divided into three categories, which are low, medium and high. This method can be applied in slope monitoring activities since all towers have been classified based on their risk level. Therefore, maintenance schedule can be planned smoothly and efficiently.

  16. Transmission tower classification based on landslide risk map generated by Geographical Information System (GIS) at Cameron Highlands

    NASA Astrophysics Data System (ADS)

    K, Hazwani N.; O, Rohayu C.; U, Fathoni; Baharuddin, Inz

    2013-06-01

    Transmission tower is usually locates at remote area which is covered by hilly topography. Landslide is mainly occurring at hilly area and causing failure to the tower structure. This phenomenon subsequently will affect the national electricity supply. A landslide risk hazard map is generated using Geographical Information System (GIS). Risk classification is introduced to initiate the monitoring process along Jor-Bintang transmission line, Cameron Highland, Pahang. The classification has been divided into three categories, which are low, medium and high. This method can be applied in slope monitoring activities since all towers have been classified based on their risk level. Therefore, maintenance schedule can be planned smoothly and efficiently.

  17. Analysis of Submarine Landslides and Canyons along the U.S. Atlantic Margin Using Extended Continental Shelf Mapping Data

    NASA Astrophysics Data System (ADS)

    Chaytor, J. D.; Brothers, D. S.; Ten Brink, U. S.; Hoy, S. K.; Baxter, C.; Andrews, B.

    2013-12-01

    U.S. Geological Survey (USGS) studies of the U.S. Atlantic continental slope and rise aim to understand the: 1) the role of submarine landslides in tsunami generation, and 2) the linkages between margin morphology and sedimentary processes, particularly in and around submarine canyon systems. Data from U.S. Extended Continental Shelf (ECS) and numerous subsequent mapping surveys have facilitated the identification and characterization of submarine landslides and related features in fine detail over an unprecedented spatial extent. Ongoing analysis of USGS collected piston cores, sub-bottom and multichannel seismic (MCS) reflection profiles, and an extensive suite of legacy MCS data from two landslides, the Southern New England landslide zone and the Currituck Landslide, suggest that the most recent major landslide events are pre-Holocene, but that failures were complex and most likely multi-phase, at times resulting in extensive overlapping debris deposits. Piston core records plus visual observations of the seafloor from recent TowCam deployments and NOAA Ship Okeanos Explorer ROV dives reveal ongoing development of colluvial wedge-style debris aprons at the base of scarps within these landslides, showing that these regions continue to evolve long after the initial failure events. Multibeam bathymetry data and MCS profiles along the upper slope reveal evidence for vertical fluid migration and possible seabed gas expulsion. These observations underscore the need to reevaluate the sources of pore fluid overpressure in slope sediments and their role in landslide generation. ECS and more recent multibeam mapping have provided the opportunity to investigate the full extent of submarine canyon morphology and evolution from Cape Hatteras up to the US-Canadian EEZ, which has led to better understanding of the important role of antecedent margin physiography on their development. Six submarine canyon systems along the margin (Veatch, Hydrographer, Hudson, Wilmington

  18. Landslide risk assessment for individual buildings. A case study from the Prahova Subcarpathians, ROMANIA

    NASA Astrophysics Data System (ADS)

    Armaş, I.

    2012-04-01

    The aim of this study is to quantify the landslide risk for individual buildings using spatial data in a GIS environment. To document the efficiency of the method, a landslide prone area along Prahova Subcarpathian Valley was chosen, where landslide hazard interacts with human settlement and activities. The bivariate landslide susceptibility index (LSI) was used to calculate the spatial probability of landslides occurrence. LSI is a bivariate statistical approach that compares the spatial distribution of landslides with each individual factor that is being considered. The Landslide Susceptibility Index map was produced by numerically adding the weighted thematic maps for slope gradient and aspect, watertable, soil texture, lithology, built environment and land use. The values obtained were in good agreement with the field observations. Validation curves were obtained using the random-split strategy for two combinations of variables: (a) all seven variables and (b) three variables which showed highest individual success rates with respect to landslides occurrences (slope gradient, watertable and land use). The principal pre-disposing factors were found to be slope steepness and groundwater table. Vulnerability was established as the degree of loss to individual buildings resulting from a potential damaging landslide with a given return period in an area. Risk was calculated by multiplying the spatial probability of landslides by the vulnerability for each building and summing up the losses for a 10 years return period.

  19. Using geoelectrical for predicting susceptibility to landslides. Land instability phenomena in Gornet village, Prahova County

    NASA Astrophysics Data System (ADS)

    Maftei, Raluca-Mihaela; Rusu, Emil; Avram, Ovidiu; Filipciuc, Constantina; Ulmeanu, Antonio; Tudor, Elena; Scutelnicu, Ioan

    2015-04-01

    The village is situated at the crossroads Gornet plain area with hills, in a small valley formed by the surrounding hills. There subsequent royal charters mention Gornet village in different periods, such as documents certifying salt mine during the reign of Constantin Brancoveanu mine by 1960, due to landslides was rediscovered by all inhabitants . No one looked at the amount of salt in the underground village, but there is salt. Geoelectrical investigations carried out in the month of September 2014 aimed at obtaining indirect information on the geological structure of the subsoil and identify the causes of instability phenomena produced in recent years, with serious consequences for a significant number of construction and road and villages Gornet and Cuib. Since the area of land affected by movements in an area is occupied by houses, enclosures, courtyards and gardens of the locals, location and length of the profiles was determined by measuring doorways available for stretching cables. The upper surface of the salt occurs about the horizontal lifting and lowering a few meters favoring accumulation and movement of groundwater on the backs of salt in permeable formations that cover (breccia salt). Geoelectrical sections clearly shows that affected homes are positioned on a basement conductor, unstable, which continued under the river, but sooner or later, can have the same trend. This work was supported by a grant of the Romanian National Authority for Scientific Research, CCCDI - UEFISCDI, project number 83/2014

  20. The role of multicollinearity in landslide susceptibility assessment by means of Binary Logistic Regression: comparison between VIF and AIC stepwise selection

    NASA Astrophysics Data System (ADS)

    Cama, Mariaelena; Cristi Nicu, Ionut; Conoscenti, Christian; Quénéhervé, Geraldine; Maerker, Michael

    2016-04-01

    Landslide susceptibility can be defined as the likelihood of a landslide occurring in a given area on the basis of local terrain conditions. In the last decades many research focused on its evaluation by means of stochastic approaches under the assumption that 'the past is the key to the future' which means that if a model is able to reproduce a known landslide spatial distribution, it will be able to predict the future locations of new (i.e. unknown) slope failures. Among the various stochastic approaches, Binary Logistic Regression (BLR) is one of the most used because it calculates the susceptibility in probabilistic terms and its results are easily interpretable from a geomorphological point of view. However, very often not much importance is given to multicollinearity assessment whose effect is that the coefficient estimates are unstable, with opposite sign and therefore difficult to interpret. Therefore, it should be evaluated every time in order to make a model whose results are geomorphologically correct. In this study the effects of multicollinearity in the predictive performance and robustness of landslide susceptibility models are analyzed. In particular, the multicollinearity is estimated by means of Variation Inflation Index (VIF) which is also used as selection criterion for the independent variables (VIF Stepwise Selection) and compared to the more commonly used AIC Stepwise Selection. The robustness of the results is evaluated through 100 replicates of the dataset. The study area selected to perform this analysis is the Moldavian Plateau where landslides are among the most frequent geomorphological processes. This area has an increasing trend of urbanization and a very high potential regarding the cultural heritage, being the place of discovery of the largest settlement belonging to the Cucuteni Culture from Eastern Europe (that led to the development of the great complex Cucuteni-Tripyllia). Therefore, identifying the areas susceptible to

  1. The National Landslide Database and GIS for Great Britain: construction, development, data acquisition, application and communication

    NASA Astrophysics Data System (ADS)

    Pennington, Catherine; Dashwood, Claire; Freeborough, Katy

    2014-05-01

    The National Landslide Database has been developed by the British Geological Survey (BGS) and is the focus for national geohazard research for landslides in Great Britain. The history and structure of the geospatial database and associated Geographical Information System (GIS) are explained, along with the future developments of the database and its applications. The database is the most extensive source of information on landslides in Great Britain with over 16,500 records of landslide events, each documented as fully as possible. Data are gathered through a range of procedures, including: incorporation of other databases; automated trawling of current and historical scientific literature and media reports; new field- and desk-based mapping technologies with digital data capture, and crowd-sourcing information through social media and other online resources. This information is invaluable for the investigation, prevention and mitigation of areas of unstable ground in accordance with Government planning policy guidelines. The national landslide susceptibility map (GeoSure) and a national landslide domain map currently under development rely heavily on the information contained within the landslide database. Assessing susceptibility to landsliding requires knowledge of the distribution of failures and an understanding of causative factors and their spatial distribution, whilst understanding the frequency and types of landsliding present is integral to modelling how rainfall will influence the stability of a region. Communication of landslide data through the Natural Hazard Partnership (NHP) contributes to national hazard mitigation and disaster risk reduction with respect to weather and climate. Daily reports of landslide potential are published by BGS through the NHP and data collected for the National Landslide Database is used widely for the creation of these assessments. The National Landslide Database is freely available via an online GIS and is used by a

  2. Shallow Landslide Assessment using SINMAP in Laguna, Philippines

    NASA Astrophysics Data System (ADS)

    Bonus, A. A. B.; Rabonza, M. L.; Alemania, M. K. B.; Alejandrino, I. K.; Ybanez, R. L.; Lagmay, A. M. A.

    2014-12-01

    Due to the tectonic environment and tropical climate in the Philippines, both rain-induced and seismic-induced landslides are common in the country. Numerous hazard mapping activities are regularly conducted by both academic and government institutions using various tools and software. One such software is Stability Index Mapping (SINMAP), a terrain stability mapping tool applied to shallow translational landslide phenomena controlled by shallow groundwater flow convergence. SINMAP modelling combines a slope stability model with a steady-state hydrology model to delineate areas prone to shallow landslides. DOST- Project NOAH, one of the hazard-mapping initiatives of the government, aims to map all landslide hazard in the Philippines using both computer models as well as validating ground data. Laguna, located in the island of Luzon, is one such area where mapping and modelling is conducted. SINMAP modelling of the Laguna area was run with a 5-meter Interferomteric Synthetic Aperture Radar (IFSAR) derived digital terrain model (DTM). Topographic, soil-strength and physical hydrologic parameters, which include cohesion, angle of friction, bulk density and hydraulic conductivity, were assigned to each pixel of a given DTM grid to compute for the corresponding factor of safety. The landslide hazard map generated using SINMAP shows 2% of the total land area is highly susceptible in Santa Mara, Famy, Siniloan, Pangil, Pakil and Los Baἦos Laguna and 10% is moderately susceptible in the eastern parts of Laguna. The data derived from the model is consistent with both ground validation surveys as well as landslide inventories derived from high resolution satellite imagery from 2003 to 2013. With these combined computer and on-the-ground data, it is useful in identifying no-build zone areas and in monitoring activities of the local government units and other agencies concerned. This provides a reasonable delineation of hazard zones for shallow landslide susceptible areas of

  3. Landslide Hazards - A National Threat

    USGS Publications Warehouse

    U.S. Geological Survey

    2005-01-01

    Landslides occur and can cause damage in all 50 States. Severe storms, earthquakes, volcanic activity, coastal wave attack, and wildfires can cause widespread slope instability. Landslide danger may be high even as emergency personnel are providing rescue and recovery services. To address landslide hazards, several questions must be considered: Where and when will landslides occur? How big will the landslides be? How fast and how far will they move? What areas will the landslides affect or damage? How frequently do landslides occur in a given area? Answers to these questions are needed to make accurate landslide hazard maps and forecasts of landslide occurrence, and to provide information on how to avoid or mitigate landslide impacts. The U.S. Geological Survey develops methods to answer these questions to help protect U.S. communities from the dangers of landslides.

  4. The inner structure of landslides and landslide-prone slopes in south German cuesta landscapes assessed by geophysical, geomorphological and sedimentological approaches

    NASA Astrophysics Data System (ADS)

    Schwindt, Daniel; Sandmeier, Christine; Büdel, Christian; Jäger, Daniel; Wilde, Martina; Terhorst, Birgit

    2016-04-01

    Investigations on landslide activity in the cuesta landscape of Germany, usually characterized by an interbedding of morphologically hard (e.g. sand-/limestones) and soft (clay) sedimentary rocks are relatively sparse. However, spring 2013 has once again revealed a high susceptibility of the slopes in the Franconian and Swabian Alb to mass movements, when enduring rainfalls initiated numerous landslides causing considerable damage to settlements and infrastructure. Many aspects like the spatial distribution of landslides, triggering factors, and process dynamics - especially with view on the reactivation of landslides - require intensive investigations to allow for assessment of the landslide vulnerability and the development of reliable early-warning systems. Aim of the study is to achieve a deeper insight into the triggering factors and the process dynamics of landslides in the cuesta landscape with special regard on landslide proneness of slopes and the potential reactivation of old landslides. A multi-methodological approach was conducted based on geophysical investigations (seismic refraction tomography - SRT, electrical resistivity tomography - ERT), geomorphological mapping, morphometric GIS-based analysis, core soundings and substrate mapping. Study sites are located in the Swabian Alb (southwestern Germany) in the Jurassic escarpment where where Oxfordian marls and limestones superimpose Callovian clays, as well as in the northeastern Franconian Alb, within the escarpment of the so called Rhätolias with with red claystones of the late Norian (Feuerletten formation) below interbedding layers of sand- and claystones of the Rhaetian (Upper Triassic) and Hettangian ( Lower Jurassic). The investigated landslides strongly differ with respect to their age, from young landslides originated in spring 2013 to ancient landslides. Investigations reveal a distinct diversity of landslide types composed of a complex combination of processes. The applied methods allow

  5. Guidelines for the selection of appropriate remote sensing technologies for landslide detection, monitoring and rapid mapping: the experience of the SafeLand European Project.

    NASA Astrophysics Data System (ADS)

    Stumpf, A.; Malet, J.-P.; Kerle, N.; Tofani, V.; Segoni, S.; Casagli, N.; Michoud, C.; Jaboyedoff, M.; Fornaro, G.; Peduto, D.; Cascini, L.; Baron, I.; Supper, R.; Oppikofer, T.; L'Heureux, J.-S.; Van Den Eeckhaut, M.; Hervás, J.; Moya, J.; Raucoules, D.; Carman, M.

    2012-04-01

    New earth observation satellites, innovative airborne platforms and sensors, high precision laser scanners, and enhanced ground-based geophysical investigation tools are a few examples of the increasing diversity of remote sensing technologies used in landslide analysis. The use of advanced sensors and analysis methods can help to significantly increase our understanding of potentially hazardous areas and helps to reduce associated risk. However, the choice of the optimal technology, analysis method and observation strategy requires careful considerations of the landslide process in the local and regional context, and the advantages and limitations of each technique. Guidelines for the selection of the most suitable remote sensing technologies according to different landslide types, displacement velocities, observational scales and risk management strategies have been proposed. The guidelines are meant to aid operational decision making, and include information such as spatial resolution and coverage, data and processing costs, and maturity of the method. The guidelines target scientists and end-users in charge of risk management, from the detection to the monitoring and the rapid mapping of landslides. They are illustrated by recent innovative methodologies developed for the creation and updating of landslide inventory maps, for the construction of landslide deformation maps and for the quantification of hazard. The guidelines were compiled with contributions from experts on landslide remote sensing from 13 European institutions coming from 8 different countries. This work is presented within the framework of the SafeLand project funded by the European Commission's FP7 Programme.

  6. Landslide hazard scenario assessment at a large spatio-temporal scale: the case of a municipality in the Getic Subcarpathians, Romania

    NASA Astrophysics Data System (ADS)

    Jurchescu, Marta; Dragota, Carmen; Borcan, Mihaela

    2014-05-01

    Performing a landslide hazard assessment requires both a spatial and a temporal-probabilistic modelling of landslide occurrence. Nevertheless, most landslide 'hazard' maps only present the zoning of susceptibility (i.e. spatial probability) without including information on the temporal component of the hazard. One of the difficulties in estimating temporal probabilities of landslides lies in identifying frequency-magnitude relationships for landslide occurrences since historical landslide records are usually incomplete. However, even in scarce data conditions, the possibility remains to address recent occurences of landslides that can be related to particular triggering events (e.g. earthquake, rainfall). The present paper proposes to produce a landslide hazard scenario, claiming that by analysing more particular frequency-magnitude relationships and developing hazard scenarios, the assessment of a general landslide hazard map could be more easily envisaged in future. The study is part of a top-down approach, which, in a previous phase, has completed a regional scale (1:100,000) semi-quantitative susceptibility assessment of a wider administrative unit in southern Romania (the Vâlcea county), constantly under the threat of landslides. Based on the landslide hotspots provided by this synoptic map, the case of a municipality in the Subcarpathian hills was selected for perfoming a much more detailed hazard scenario assessment, i.e. at a large scale (1:10,000 spatial scale and daily temporal scale). A first stage of the present study aims at zoning the terrain in terms of spatial probabilities of landslide occurrence by statistically analysing the relation between inventoried landslides and several predisposing factors. Considering the large scale of analysis, a special focus was given to predicting areas probable to generate landslides and thus only the mapped depletion zones were entered into the analysis. Moreover, in order to correctly reflect causal relations, a

  7. Semi-Automated Landslide Mapping by Using an Expert Based Module Running on GIS Environment: Netcad Architect M-AHP Operator

    NASA Astrophysics Data System (ADS)

    Kukul, Elvan; Yilmaz, Ezgi; Nefeslioglu, Hakan A.; Sezer, Ebru A.; Toptas, Tunc E.; Celik, Deniz; Orhun, Koray; Osna, Turgay; Ak, Serdar; Gokceoglu, Candan

    2014-05-01

    In the present study semi-automated landslide mapping of an area locating between the cities Afyon and Usak (west of Turkey) was evaluated by using an expert based modelling operator developed in Netcad Architect environment. The area considerably suffers from landslides. The main public concern of this region within this respect is due to the high speed train railway route which will connect Ankara and Izmir. The study was carried out in three main stages; (i) data production, (ii) modelling for semi-automated landslide mapping, and (iii) validation of the constructed models by using the actual landslides observed in the region. The altitude, slope gradient, slope aspect and the second derivative of topography in terms of topographical wetness index parameters, geology in terms of lithology type, and normalized difference vegetation index in terms of environmental factor were evaluated to be the main conditioning factor of active and old landslides observed in the area. Two expert based models for mapping active and old landslides were constructed by using the M-AHP operator of the Netcad Architect environment. The resultant maps represent both possible active and old landslide areas which could be encountered throughout the region. According to the results of the modelling stages, almost 7 % of the area is found to be active landslide area, and almost 13 % of the area constitutes possible old failures in the region. The validations of the constructed models were performed by using the ROC (Receiver Operating Characteristics) curve operator which was also developed in Netcad Architect environment. The area under ROC curves for the models were calculated to be 0.674 and 0.728 for active and old landslides respectively. Considering expert nature of the constructed models these results are promising, and could be evaluated in route selection assessments and suitable site selection for settlement.

  8. MCD for detection of event-based landslides

    NASA Astrophysics Data System (ADS)

    Mondini, A. C.; Chang, K.; Guzzetti, F.

    2011-12-01

    Landslides play an important role in the landscape evolution of mountainous terrain. They also present a socioeconomic problem in terms of risk for people and properties. Landslide inventory maps are not available for many areas affected by slope instabilities, resulting in a lack of primary information for the comprehension of the phenomenon, evaluation of relative landslide statistics, and civil protection operations on large scales. Traditional methods for the preparation of landslide inventory maps are based on the geomorphological interpretation of stereoscopic aerial photography and field surveys. These methods are expensive and time consuming. The exploitation of new remote sensing data, in particular very high resolution (VHR) satellite images, and new dedicated methods present an alternative to the traditional methods and are at the forefront of modern landslide research. Recent studies have showed the possibility to produce accurate landslide maps, reducing the time and resources required for their compilation and systematic update. This paper presents the Multiple Change Detection (MCD) technique, a new method that has shown promising results in landslide mapping. Through supervised or unsupervised classifiers, MCD combines different algorithms of change detection metrics, such as change in Normalized Differential Vegetation Index, spectral angle, principal component analysis, and independent component analysis, and applies them to a multi-temporal set of VHR satellite images to distinguish new landslides from stable areas. MCD has been applied with success in different geographical areas and with different satellite images, suggesting it is a reliable and robust technique. The technique can distinguish old from new landslides and capture runout features. Results of these case studies will be presented in the conference. Also to be presented are new developments of MCD involving the introduction of a priori information on landslide susceptibility within

  9. Landslide Hazard Zonation and Risk Assessment of Ramganga Basin in Garhwal Himalaya

    NASA Astrophysics Data System (ADS)

    Wasini Pandey, Bindhy; Roy, Nikhil

    2016-04-01

    The Himalaya being unique in its physiographic, tectonic and climatic characteristics coupled with many natural and man-made factors is inherently prone to landslides. These landslides lead to mass loss of property and lives every year in Himalayas. Hence, Landslide Hazard Zonation is important to take quick and safe mitigation measures and make strategic planning for future development. The present study tries to explore the causes of landslides in Ramganga Basin in Garhwal Himalaya, which has an established history and inherent susceptibility to massive landslides has been chosen for landslide hazard zonation and risk assessment. The satellite imageries of LANDSAT, IRS P6, ASTER along with Survey of India (SOI) topographical sheets formed the basis for deriving baseline information on various parameters like slope, aspect, relative relief, drainage density, geology/lithology and land use/land cover. The weighted parametric method will be used to determine the degree of susceptibility to landslides. Finally, a risk map will be prepared from the landslide probability values, which will be classified into no risk, very low to moderate, high, and very high to severe landslide hazard risk zones. Keywords: Landslides, Hazard Zonation, Risk Assessment

  10. New Method for Estimating Landslide Losses for Major Winter Storms in California.

    NASA Astrophysics Data System (ADS)

    Wills, C. J.; Perez, F. G.; Branum, D.

    2014-12-01

    We have developed a prototype system for estimating the economic costs of landslides due to winter storms in California. This system uses some of the basic concepts and estimates of the value of structures from the HAZUS program developed for FEMA. Using the only relatively complete landslide loss data set that we could obtain, data gathered by the City of Los Angeles in 1978, we have developed relations between landslide susceptibility and loss ratio for private property (represented as the value of wood frame structures from HAZUS). The landslide loss ratios estimated from the Los Angeles data are calibrated using more generalized data from the 1982 storms in the San Francisco Bay area to develop relationships that can be used to estimate loss for any value of 2-day or 30-day rainfall averaged over a county. The current estimates for major storms are long projections from very small data sets, subject to very large uncertainties, so provide a very rough estimate of the landslide damage to structures and infrastructure on hill slopes. More importantly, the system can be extended and improved with additional data and used to project landslide losses in future major winter storms. The key features of this system—the landslide susceptibility map, the relationship between susceptibility and loss ratio, and the calibration of estimates against losses in past storms—can all be improved with additional data. Most importantly, this study highlights the importance of comprehensive studies of landslide damage. Detailed surveys of landslide damage following future storms that include locations and amounts of damage for all landslides within an area are critical for building a well-calibrated system to project future landslide losses. Without an investment in post-storm landslide damage surveys, it will not be possible to improve estimates of the magnitude or distribution of landslide damage, which can range up to billions of dollars.

  11. Operational early warning of shallow landslides in Norway: Evaluation of landslide forecasts and associated challenges

    NASA Astrophysics Data System (ADS)

    Dahl, Mads-Peter; Colleuille, Hervé; Boje, Søren; Sund, Monica; Krøgli, Ingeborg; Devoli, Graziella

    2015-04-01

    The Norwegian Water Resources and Energy Directorate (NVE) runs a national early warning system (EWS) for shallow landslides in Norway. Slope failures included in the EWS are debris slides, debris flows, debris avalanches and slush flows. The EWS has been operational on national scale since 2013 and consists of (a) quantitative landslide thresholds and daily hydro-meteorological prognosis; (b) daily qualitative expert evaluation of prognosis / additional data in decision to determine warning levels; (c) publication of warning levels through various custom build internet platforms. The effectiveness of an EWS depends on both the quality of forecasts being issued, and the communication of forecasts to the public. In this analysis a preliminary evaluation of landslide forecasts from the Norwegian EWS within the period 2012-2014 is presented. Criteria for categorizing forecasts as correct, missed events or false alarms are discussed and concrete examples of forecasts falling into the latter two categories are presented. The evaluation show a rate of correct forecasts exceeding 90%. However correct forecast categorization is sometimes difficult, particularly due to poorly documented landslide events. Several challenges has to be met in the process of further lowering rates of missed events of false alarms in the EWS. Among others these include better implementation of susceptibility maps in landslide forecasting, more detailed regionalization of hydro-meteorological landslide thresholds, improved prognosis on precipitation, snowmelt and soil water content as well as the build-up of more experience among the people performing landslide forecasting.

  12. An integrated mass wasting susceptibility assesment by geographical information systems and remote sensing applications: Example from North Turkey

    NASA Astrophysics Data System (ADS)

    Akgün, Aykut

    2016-04-01

    The Northern part of Turkey have been suffering from both landslides and snow avalanches due to the steep topography and climatological characteristics triggering the processes. In order to manage these natural hazard phenomenons, regional hazards assessments are both crucial and essential for the region. In this context, an integrated hazard assesment including landslide and snow avalanche was carried out for a selected area at North Turkey. Caykara (Trabzon) district was one of the most suitable areas for such a purpose, because several landslide and snow avalanche cases occured in the area during the last two decades. To inspect the landslide and snow avalanche susceptibility of the area, geographical information systems and remote sensing based assessments were applied to the area. To produce a landslide susceptibility map, logistic regression model was used by using lithological, topographical and environmental data set. To obtain a snow avalanche susceptibility map, topograhical data such as slope gradient, slope aspect and slope curvature, environmental data such as normaliazed vegetation index (NDVI), snow accumlation areas and landcover were taken into account, and these data set were analyzed by a 2D modelling tool, called as CONEFALL. By obtaining the landslide and snow avalanche susceptibility maps, five susceptibility classes from very low to very high were differentiated in the area. The both susceptibility maps were also verified by the actual field data as well, and it was determined that the obtained maps were successful. Then, the both susceptibility maps were overlaid, and finally an integrated mass wasting susceptibility map was created. In this final map, total susceptible areas to both landslide and snow avalanche occurrence were determined. The final susceptibility map is believed and expected to be used by the govermental and local authorities as a decision makers to mitigate the landslide and snow avalanche based hazards in the area.

  13. Denudation rates across a steep rainfall gradient on Kauai, constrained by cosmogenic nuclides and landslide mapping (Invited)

    NASA Astrophysics Data System (ADS)

    Ferrier, K.; Perron, T.; Mukhopadhyay, S.; Huppert, K. L.

    2010-12-01

    tributary basin and as low as 133 t km-2 yr^{-1} in a drier, gentler tributary basin 10 km downstream of the headwaters. To supplement these ^3He measurements, we mapped >1000 landslides in repeat images of the Hanalei basin from aerial photography and satellite imagery from 2003-2010. Our initial mapping implies an average landslide scar generation rate of 336 m^2 km^{-2} yr^{-1} over the lower two-thirds of the Hanalei basin, where repeat satellite images overlap and permit comparisons of landslide coverage over time. When combined with empirical relationships relating landslide volume to area and our field measurements of soil density (1.1 g cm^{-3}), this scar generation rate corresponds to a short-term landslide erosion rate of 317 t km^{-2} yr^{-1}. To date, our combined measurements of ^3He and landslide occurrence are consistent with faster total erosion rates in wetter, steeper basins.

  14. The landslide hazard in the San Francisco Bay region

    USGS Publications Warehouse

    Brabb, E.E.

    1977-01-01

    Development in hilly or mountainous terrain has resulted in much landslide damage. Areas susceptible to landsliding can be recognized. Practices for minimizing landslides are presented. ?? 1977 D. Reidel Publishing Company.

  15. Landslide mapping and analysis of Korbous area, Cap Bon (Northern Tunisia)

    NASA Astrophysics Data System (ADS)

    Ben Hammouda, Mariam; Jaboyedoff, Michel; Derron, Marc Henri; Bouaziz, Samir

    2015-04-01

    Djbel Korbous is an important relief dominating the south-eastern edge of the Gulf of Tunis. It is an anticline truncated by a NS fault that passes through the axis of the fold, reason of the collapse of western slopes under the sea. This geometry gives the appearance of a large cased fold and the individualization of series of crests forming the massive of Korbous where altitudes exceed sometimes 400.0 m. Different types of landslides, with various origins and evolution, affect this area. Reactivated pre-existing structures, heterogeneity of lithology and water flow infiltration are the main agents of this phenomenon. The degradation of steep cliffs along the road is strongly accentuated by physico-chemical alteration due to the dissolution of rocks by the runoff flowing through a dense network of fractures and cracks and the spalling of the sandstone bar. The situation has become increasingly critical since 2009 when a large rock slide affected the slope over the sea, threatening, especially the only access to the village with heavy consequences for the population of the region (infrastructure, regional medical center, trade and tourism). The present study aims at defining (i) the main structurally controlled failure types;(ii) the detection of potential instabilities from steep slopes and cliff areas; (iii) the preliminary estimation of potential run-out areas. Geographical Information System GIS (generation of slope map and azimuth map), digital elevation modeling (DEM) are among the most useful tools used for a reliable analysis in this area. Additionally, field work in this paper includes a program of in situ recognition of inventoried instabilities (field measurements, discontinuities characterization, stereoplots and kinematic tests ) and digital photogrammetry using a Canon EOS 7D camera (construction of 3D models and discontinuity measurements were all achieved using VisualSFM and CloudCompare software). The application of those techniques on the area of

  16. The Effects of Rainfall Characteristics on Landslides of Alisan Forestry Railway

    NASA Astrophysics Data System (ADS)

    Chang, C.; Chan, H.; Laio, P.

    2012-12-01

    The Alishan Forest Railway is not only an important cultural heritage, but also a transportation between Alishan and the outside world. However, the natural slope disaster lead to the Alishan Forest Railway stopped running were continually happening in recent years. This is a major issue to how to create the warning system of slope landslide. The object of study is the buffer range of 1200 meters of the Alishan Forest Railway. In order to select the factors efficiently, we use the distribution of the landslide, non-landslide, and p-p plot. The landslide group and non-landslide group data were random sampled and the data numbers of two groups were equal. The trigger factor is rainfall of the Morakot event, with the causative factors to execute logistic regression analysis. As a result, a suitable combination of these factors for establishing landslide susceptibility model and evaluate the susceptibility value was proposed. Eventually, this study applied 10-, 25-, 50-, and 100-year return periods precipitation to estimate the susceptibility values for the study area. The landslide susceptibility map with susceptibility index was proposed for engineering and disaster prevention consideration.

  17. Proposal for a probabilistic local level landslide hazard assessment model: The case of Suluktu, Kyrgyzstan

    NASA Astrophysics Data System (ADS)

    Vidar Vangelsten, Bjørn; Fornes, Petter; Cepeda, Jose Mauricio; Ekseth, Kristine Helene; Eidsvig, Unni; Ormukov, Cholponbek

    2015-04-01

    Landslides are a significant threat to human life and the built environment in many parts of Central Asia. To improve understanding of the magnitude of the threat and propose appropriate risk mitigation measures, landslide hazard mapping is needed both at regional and local level. Many different approaches for landslide hazard mapping exist depending on the scale and purpose of the analysis and what input data are available. This paper presents a probabilistic local scale landslide hazard mapping methodology for rainfall triggered landslides, adapted to the relatively dry climate found in South-Western Kyrgyzstan. The GIS based approach makes use of data on topography, geology, land use and soil characteristics to assess landslide susceptibility. Together with a selected rainfall scenario, these data are inserted into a triggering model based on an infinite slope formulation considering pore pressure and suction effects for unsaturated soils. A statistical model based on local landslide data has been developed to estimate landslide run-out. The model links the spatial extension of the landslide to land use and geological features. The model is tested and validated for the town of Suluktu in the Ferghana Valley in South-West Kyrgyzstan. Landslide hazard is estimated for the urban area and the surrounding hillsides. The case makes use of a range of data from different sources, both remote sensing data and in-situ data. Public global data sources are mixed with case specific data obtained from field work. The different data and models have various degrees of uncertainty. To account for this, the hazard model has been inserted into a Monte Carlo simulation framework to produce a probabilistic landslide hazard map identifying areas with high landslide exposure. The research leading to these results has received funding from the European Commission's Seventh Framework Programme [FP7/2007-2013], under grant agreement n° 312972 "Framework to integrate Space-based and in

  18. Inventory of landslides triggered by the 1994 Northridge, California earthquake

    USGS Publications Warehouse

    Harp, Edwin L.; Jibson, Randall W.

    1995-01-01

    The 17 January 1994 Northridge, California, earthquake (M=6.7) triggered more than 11,000 landslides over an area of about 10,000 km?. Most of the landslides were concentrated in a 1,000-km? area that includes the Santa Susana Mountains and the mountains north of the Santa Clara River valley. We mapped landslides triggered by the earthquake in the field and from 1:60,000-scale aerial photography provided by the U.S. Air Force and taken the morning of the earthquake; these were subsequently digitized and plotted in a GIS-based format, as shown on the accompanying maps (which also are accessible via Internet). Most of the triggered landslides were shallow (1-5 m), highly disrupted falls and slides in weakly cemented Tertiary to Pleistocene clastic sediment. Average volumes of these types of landslides were less than 1,000 m?, but many had volumes exceeding 100,000 m?. Many of the larger disrupted slides traveled more than 50 m, and a few moved as far as 200 m from the bases of steep parent slopes. Deeper ( >5 m) rotational slumps and block slides numbered in the hundreds, a few of which exceeded 100,000 m? in volume. The largest triggered landslide was a block slide having a volume of 8X10E06 m?. Triggered landslides damaged or destroyed dozens of homes, blocked roads, and damaged oil-field infrastructure. Analysis of landslide distribution with respect to variations in (1) landslide susceptibility and (2) strong shaking recorded by hundreds of instruments will form the basis of a seismic landslide hazard analysis of the Los Angeles area.

  19. Advancements in near real time mapping of earthquake and rainfall induced landslides in the Avcilar Peninsula, Marmara Region

    NASA Astrophysics Data System (ADS)

    Coccia, Stella

    2014-05-01

    Stella COCCIA (1), Fiona THEOLEYRE (1), Pascal BIGARRE(1) , Semih ERGINTAV(2), Oguz OZEL(3) and Serdar ÖZALAYBEY(4) (1) National Institute of Industrial Environment and Risks (INERIS) Nancy, France, (2) Kandilli Observatory and Earthquake Research Institute (KOERI), Istanbul, Turkey, (3) Istanbul University (IU), Istanbul, Turkey, (4) TUBITAK MAM, Istanbul, Turkey The European Project MARsite (http://marsite.eu/), started in 2012 and leaded by the KOERI, aims to improve seismic risk evaluation and preparedness to face the next dreadful large event expected for the next three decades. MARsite is thus expected to move a "step forward" the most advanced monitoring technologies, and offering promising open databases to the worldwide scientific community in the frame of other European environmental large-scale infrastructures, such as EPOS (http://www.epos-eu.org/ ). Among the 11 work packages (WP), the main aim of the WP6 is to study seismically-induced landslide hazard, by using and improving observing and monitoring systems in geological, hydrogeotechnical and seismic onshore and offshore areas. One of the WP6 specific study area is the Avcilar Peninsula, situated between Kucukcekmece and Buyukcekmece Lakes in the north-west of the region of Marmara. There, more than 400 landslides are located. According to geological and geotechnical investigations and studies, soil movements of this area are related to underground water and pore pressure changes, seismic forces arising after earthquakes and decreasing sliding strength in fissured and heavily consolidated clays. The WP6 includes various tasks and one of these works on a methodology to develop a dynamic system to create combined earthquake and rainfall induced landslides hazard maps at near real time and automatically. This innovative system could be used to improve the prevention strategy as well as in disaster management and relief operations. Base on literature review a dynamic GIS platform is used to combine

  20. Assessing Landslide Mobility Using GIS: Application to Kosrae, Micronesia

    NASA Astrophysics Data System (ADS)

    Reid, M. E.; Brien, D. L.; Godt, J.; Schmitt, R. G.; Harp, E. L.

    2015-12-01

    Deadly landslides are often mobile landslides, as exemplified by the disastrous landslide that occurred near Oso, Washington in 2014 killing 43. Despite this association, many landslide susceptibility maps do not identify runout areas. We developed a simple, GIS-based method for identifying areas potentially overrun by mobile slides and debris flows. Our method links three processes within a DEM landscape: landslide initiation, transport, and debris-flow inundation (from very mobile slides). Given spatially distributed shear strengths, we first identify initiation areas using an infinite-slope stability analysis. We then delineate transport zones, or regions of potential entrainment and/or deposition, using a height/length runout envelope. Finally, where these transport zones intersect the channel network, we start debris-flow inundation zones. The extent of inundation is computed using the USGS model Laharz, modified to include many debris-flow locations throughout a DEM. Potential debris-flow volumes are computed from upslope initiation areas and typical slide thicknesses. We applied this approach to the main island of Kosrae State, Federated States of Micronesia (FSM). In 2002, typhoon Chata'an triggered numerous landslides on the neighboring islands of Chuuk State, FSM, resulting in 43 fatalities. Using an infinite-slope stability model calibrated to the Chuuk event, we identified potential landslide initiation areas on Kosrae. We then delineated potential transport zones using a 20º runout envelope, based on runout observations from Chuuk. Potential debris-flow inundation zones were then determined using Laharz. Field inspections on Kosrae revealed that our resulting susceptibility map correctly classified areas covered by previous debris-flow deposits and did not include areas covered by fluvial deposits. Our map has the advantage of providing a visual tool to portray initiation, transport, and runout zones from mobile landslides.

  1. Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania

    NASA Astrophysics Data System (ADS)

    Malek, Žiga; Boerboom, Luc; Glade, Thomas

    2015-11-01

    This study focuses on future forest cover change in Buzau Subcarpathians, a landslide prone region in Romania. Past and current trends suggest that the area might expect a future increase in deforestation. We developed spatially explicit scenarios until 2040 to analyze the spatial pattern of future forest cover change and potential changes to landslide risk. First, we generated transition probability maps using the weights of evidence method, followed by a cellular automata allocation model. We performed expert interviews, to develop two future forest management scenarios. The Alternative scenario (ALT) was defined by 67 % more deforestation than the Business as Usual scenario (BAU). We integrated the simulated scenarios with a landslide susceptibility map. In both scenarios, most of deforestation was projected in areas where landslides are less likely to occur. Still, 483 (ALT) and 276 (BAU) ha of deforestation were projected on areas with a high-landslide occurrence likelihood. Thus, deforestation could lead to a local-scale increase in landslide risk, in particular near or adjacent to forestry roads. The parallel process of near 10 % forest expansion until 2040 was projected to occur mostly on areas with high-landslide susceptibility. On a regional scale, forest expansion could so result in improved slope stability. We modeled two additional scenarios with an implemented landslide risk policy, excluding high-risk zones. The reduction of deforestation on high-risk areas was achieved without a drastic decrease in the accessibility of the areas. Together with forest expansion, it could therefore be used as a risk reduction strategy.

  2. Plan curvature and landslide probability in regions dominated by earth flows and earth slides

    USGS Publications Warehouse

    Ohlmacher, G.C.

    2007-01-01

    Damaging landslides in the Appalachian Plateau and scattered regions within the Midcontinent of North America highlight the need for landslide-hazard mapping and a better understanding of the geomorphic development of landslide terrains. The Plateau and Midcontinent have the necessary ingredients for landslides including sufficient relief, steep slope gradients, Pennsylvanian and Permian cyclothems that weather into fine-grained soils containing considerable clay, and adequate precipitation. One commonly used parameter in landslide-hazard analysis that is in need of further investigation is plan curvature. Plan curvature is the curvature of the hillside in a horizontal plane or the curvature of the contours on a topographic map. Hillsides can be subdivided into regions of concave outward plan curvature called hollows, convex outward plan curvature called noses, and straight contours called planar regions. Statistical analysis of plan-curvature and landslide datasets indicate that hillsides with planar plan curvature have the highest probability for landslides in regions dominated by earth flows and earth slides in clayey soils (CH and CL). The probability of landslides decreases as the hillsides become more concave or convex. Hollows have a slightly higher probability for landslides than noses. In hollows landslide material converges into the narrow region at the base of the slope. The convergence combined with the cohesive nature of fine-grained soils creates a buttressing effect that slows soil movement and increases the stability of the hillside within the hollow. Statistical approaches that attempt to determine landslide hazard need to account for the complex relationship between plan curvature, type of landslide, and landslide susceptibility. ?? 2007 Elsevier B.V. All rights reserved.

  3. Application of indicators derived by remote sensing for mapping of landslide hazard and vulnerability

    NASA Astrophysics Data System (ADS)

    Eidsvig, Unni; Vidar Vangelsten, Bjørn; Geiss, Christian; Klotz, Martin; Ekseth, Kristine; Taubenböck, Hannes

    2014-05-01

    The choice and the development of methods for risk assessment of landslides depends on several factors. Important factors are the type of landslide and the elements at risk, the choice of spatial and temporal scale, the purpose of the analysis and the needs of the end-users. In addition, data availability is a major constraint, which greatly affects the type of methods and models that can be developed. Remote sensing is a promising tool for an economical and up-to-date data collection, which also could be applied to monitor the dynamic development of risk. The spatial and temporal distribution of the risk for landslides can be assessed by monitoring hazard indicators (e.g. slope height and slope angle), exposure indicators (e.g. number of houses and the total population) and vulnerability indicators (e.g. population density, settlement structures or indicators related to structural vulnerability). Several of the indicators applicable for landslide risk and vulnerability can be obtained by remote sensing techniques. However, for better results, indicators from remote sensing should be combined with other type of data. In this work, a review on the application of indicators for landslide risk assessment in explicit models as well as an assessment of end user needs was conducted in order to determine the most relevant indicators for landslide hazard and vulnerability. Lists of recommended indicators, mainly derivable from remote sensing, have been developed. These indicators are supposed to be used in risk assessment, e.g. by combining hazard, vulnerability and exposure indicators to produce risk indices. Moreover schemes for ranking, weighting and aggregation of the indicators into hazard- and vulnerability indices are provided. The research leading to these results has received funding from the European Community's Seventh Framework Programme [FP7-SPACE-2012-1] under Grant agreement No 312972 Framework to integrate Space-based and in-situ sENSing for dynamic v

  4. Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada

    NASA Astrophysics Data System (ADS)

    Blais-Stevens, A.; Behnia, P.

    2016-02-01

    This research activity aimed at reducing risk to infrastructure, such as a proposed pipeline route roughly parallel to the Yukon Alaska Highway Corridor (YAHC), by filling geoscience knowledge gaps in geohazards. Hence, the Geological Survey of Canada compiled an inventory of landslides including debris flow deposits, which were subsequently used to validate two different debris flow susceptibility models. A qualitative heuristic debris flow susceptibility model was produced for the northern region of the YAHC, from Kluane Lake to the Alaska border, by integrating data layers with assigned weights and class ratings. These were slope angle, slope aspect, surficial geology, plan curvature, and proximity to drainage system. Validation of the model was carried out by calculating a success rate curve which revealed a good correlation with the susceptibility model and the debris flow deposit inventory compiled from air photos, high-resolution satellite imagery, and field verification. In addition, the quantitative Flow-R method was tested in order to define the potential source and debris flow susceptibility for the southern region of Kluane Lake, an area where documented debris flow events have blocked the highway in the past (e.g. 1988). Trial and error calculations were required for this method because there was not detailed information on the debris flows for the YAHC to allow us to define threshold values for some parameters when calculating source areas, spreading, and runout distance. Nevertheless, correlation with known documented events helped define these parameters and produce a map that captures most of the known events and displays debris flow susceptibility in other, usually smaller, steep channels that had not been previously documented.

  5. Geospatial Approach on Landslide Hazard Zonation Mapping Using Multicriteria Decision Analysis: A Study on Coonoor and Ooty, Part of Kallar Watershed, The Nilgiris, Tamil Nadu

    NASA Astrophysics Data System (ADS)

    Rahamana, S. Abdul; Aruchamy, S.; Jegankumar, R.

    2014-12-01

    Landslides are one of the critical natural phenomena that frequently lead to serious problems in hilly area, resulting to loss of human life and property, as well as causing severe damage to natural resources. The local geology with high degree of slope coupled with high intensity of rainfall along with unplanned human activities of the study area causes many landslides in this region. The present study area is more attracted by tourist throughout the year, so this area must be considered for preventive measures. Geospatial based Multicriteria decision analysis (MCDA) technique is increasingly used for landslide vulnerability and hazard zonation mapping. It enables the integration of different data layers with different levels of uncertainty. In this present study, it is used analytic hierarchy process (AHP) method to prepare landslide hazard zones of the Coonoor and Ooty, part of Kallar watershed, The Nilgiris, Tamil Nadu. The study was carried out using remote sensing data, field surveys and geographic information system (GIS) tools. The ten factors that influence landslide occurrence, such as elevation, slope aspect, slope angle, drainage density, lineament density, soil, precipitation, land use/land cover (LULC), distance from road and NDVI were considered. These factors layers were extracted from the various related spatial data's. These factors were evaluated, and then, the individual factor weight and class weight were assigned to each of the related factors. The Landslide Hazard Zone Index (LHZI) was calculated using Multicriteria decision analysis (MCDA) the technique based on the assigned weight and the rating is given by the Analytical Hierarchy Process (AHP) method. The final cumulative map of the study area was categorized into four hazard zones and classified as zone I to IV. There are 3.56% of the area comes under the hazard zone IV fallowed by 48.19% of the area comes under zone III, 43.63 % of the area in zone II and 4.61% of the area comes hazard

  6. Numerical modeling of submarine landslide-generated tsunamis as a component of the Alaska Tsunami Inundation Mapping Project

    USGS Publications Warehouse

    Suleimani, E.; Lee, H.; Haeussler, Peter J.; Hansen, R.

    2006-01-01

    Tsunami waves are a threat for manyAlaska coastal locations, and community preparedness plays an important role in saving lives and property. The GeophysicalInstitute of the University of Alaska Fairbanks participates in the National Tsunami Hazard Mitigation Program by evaluating andmapping potential tsunami inundation of selected coastal communities in Alaska. We develop hypothetical tsunamiscenarios based on the parameters of potential underwater earthquakes and landslides for a specified coastal community. The modeling results are delivered to the community for localtsunami hazard planning and construction of evacuation maps. For the community of Seward, located at the head of Resurrection Bay, tsunami potential from tectonic and submarinelandslide sources must be evaluated for comprehensiveinundation mapping. Recent multi-beam and high-resolution sub-bottom profile surveys of Resurrection Bay show medium- and large-sized blocks, which we interpret as landslide debris that slid in the 1964 earthquake. Numerical modeling of the 1964 underwater slides and tsunamis will help to validate and improve the models. In order to construct tsunami inundation maps for Seward, we combine two different approaches for estimating tsunami risk. First, we observe inundation and runup due to tsunami waves generated by the 1964 earthquake. Next we model tsunami wave dynamics in Resurrection Bay caused by superposition of the local landslide- generated waves and the major tectonic tsunami. We compare modeled and observed values from 1964 to calibrate the numerical tsunami model. In our second approach, we perform a landslide tsunami hazard assessment using underwater slope stability analysis and available characteristics of potentially unstable sediment bodies. The approach produces hypothetical underwater slides and resulting tsunami waves. We use a three-dimensional numerical model of an incompressible viscous slide with full interaction between the slide

  7. Improved Visualization of Cartilage Canals Using Quantitative Susceptibility Mapping

    PubMed Central

    Nissi, Mikko J.; Tóth, Ferenc; Wang, Luning; Carlson, Cathy S.; Ellermann, Jutta M.

    2015-01-01

    Purpose Cartilage canal vessels are critical to the normal function of epiphyseal (growth) cartilage and damage to these vessels is demonstrated or suspected in several important developmental orthopaedic diseases. High-resolution, three-dimensional (3-D) visualization of cartilage canals has recently been demonstrated using susceptibility weighted imaging (SWI). In the present study, a quantitative susceptibility mapping (QSM) approach is evaluated for 3-D visualization of the cartilage canals. It is hypothesized that QSM post-processing improves visualization of the cartilage canals by resolving artifacts present in the standard SWI post-processing while retaining sensitivity to the cartilage canals. Methods Ex vivo distal femoral specimens from 3- and 8-week-old piglets and a 1-month-old human cadaver were scanned at 9.4 T with a 3-D gradient recalled echo sequence suitable for SWI and QSM post-processing. The human specimen and the stifle joint of a live, 3-week-old piglet also were scanned at 7.0 T. Datasets were processed using the standard SWI method and truncated k-space division QSM approach. To compare the post-processing methods, minimum/maximum intensity projections and 3-D reconstructions of the processed datasets were generated and evaluated. Results Cartilage canals were successfully visualized using both SWI and QSM approaches. The artifactual splitting of the cartilage canals that occurs due to the dipolar phase, which was present in the SWI post-processed data, was eliminated by the QSM approach. Thus, orientation-independent visualization and better localization of the cartilage canals was achieved with the QSM approach. Combination of GRE with a mask based on QSM data further improved visualization. Conclusions Improved and artifact-free 3-D visualization of the cartilage canals was demonstrated by QSM processing of the data, especially by utilizing susceptibility data as an enhancing mask. Utilizing tissue-inherent contrast, this method allows

  8. Approaches for delineating landslide hazard areas using receiver operating characteristic in an advanced calibrating precision soil erosion model

    NASA Astrophysics Data System (ADS)

    Ghazvinei, P. T.; Zandi, J.; Ariffin, J.; Hashim, R. B.; Motamedi, S.; Aghamohammadi, N.; Moghaddam, D. A.

    2015-10-01

    Soil erosion is undesirable natural event that causes land degradation and desertification. Identify the erosion-prone areas is a major component of preventive measures. Recent landslide damages at different regions lead us to develop a model of the erosion susceptibility map using empirical method (RUSLE). A landslide-location map was established by interpreting satellite image. Field observation data was used to validate the intensity of soil erosion. Further, a correlation analysis was conducted to investigate the "Receiver Operating Characteristic" and frequency ratio. Results showed a satisfactory correlation between the prepared RUSLE-based soil erosion map and actual landslide distribution. The proposed model can effectively predict the landslide events in soil-erosion area. Such a reliable predictive model is an effective management facility for the regional landslide forecasting system.

  9. Architecture planning and geo-disasters assessment mapping of landslide by using airborne lidar data and UAV images

    NASA Astrophysics Data System (ADS)

    Liu, Chun; Li, Weiyue; Lei, Weigang; Liu, Lin; Wu, Hangbin

    2011-10-01

    After the operation of GPS/IMU direct geo-referencing, segmentation, filtering, classification of scattered point data and aerial triangulation on airborne LiDAR(Light Detection and Ranging) data, the accurate and high-resolution DEM of the study area in the west part of Zengcheng city, Guangdong, China was constructed. In addition, unmanned aerial vehicle (UAV) images were used for ground objects identification. Landslides occur frequently in summer in the city because of heavy rainfall. The LiDAR data (point cloud) and the mosaic images were then combined to produce the suitability distribution maps by considering Several factors, such as slope gradient, slope aspect, on-the-spot investigation data etc The maps can then be used to analyze the potential risk of landslides and assess the risk level around some buildings. The experiment results show that the method based on LiDAR data and UAV images can rapidly and accurately survey the terrain of the study area and also provides useful information for architectural design.

  10. Statistical Patterns of Triggered Landslide Events and their Application to Road Networks

    NASA Astrophysics Data System (ADS)

    Taylor, Faith E.; Malamud, Bruce D.; Santangelo, Michele; Marchesini, Ivan; Guzzetti, Fausto

    2015-04-01

    In the minutes to weeks after a landslide trigger such as an earthquake or heavy rainfall, as part of a triggered landslide event, one individual to tens of thousands of landslides may occur across a region. If in the region, one or more roads become blocked by landslides, this can cause extensive detours and delay rescue and recovery operations. In this paper, we show the development, application and confrontation with real data of a model to simulate triggered landslide events and their impacts upon road networks. This is done by creating a 'synthetic' triggered landslide event inventory by randomly sampling landslide areas and shapes from already established statistical distributions. These landslides are then semi-randomly dropped across a given study region, conditioned by that region's landslide susceptibility. The resulting synthetic triggered landslide event inventory is overlaid with the region's road network map and the number, size, location and network impact of road blockages and landslides near roads calculated. This process is repeated hundreds of times in a Monte Carlo type simulation. The statistical distributions and approaches used in the model are thought to be generally applicable for low-mobility triggered landslides in many medium to high-topography regions throughout the world. The only local data required to run the model are a road network map, a landslide susceptibility map, a map of the study area boundary and a digital elevation model. Coupled with an Open Source modelling approach (in GRASS-GIS), this model may be applied to many regions where triggered landslide events are an issue. We present model results and confrontation with observed data for two study regions where the model has been applied: Collazzone (Central Italy) where rapid snowmelt triggered 413 landslides in January 1997 and Oat Mountain (Northridge, USA), where the Northridge Earthquake triggered 1,356 landslides in January 1994. We find that when the landslide

  11. Toward a Global Model for Predicting Earthquake-Induced Landslides in Near-Real Time

    NASA Astrophysics Data System (ADS)

    Nowicki, M. A.; Wald, D. J.; Hamburger, M. W.; Hearne, M.; Thompson, E.

    2013-12-01

    We present a newly developed statistical model for estimating the distribution of earthquake-triggered landslides in near-real time, which is designed for use in the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) and ShakeCast systems. We use standardized estimates of ground shaking from the USGS ShakeMap Atlas 2.0 to develop an empirical landslide probability model by combining shaking estimates with broadly available landslide susceptibility proxies, including topographic slope, surface geology, and climatic parameters. While the initial model was based on four earthquakes for which digitally mapped landslide inventories and well constrained ShakeMaps are available--the Guatemala (1976), Northridge, California (1994), Chi-Chi, Taiwan (1999), and Wenchuan, China (2008) earthquakes, our improved model includes observations from approximately ten other events from a variety of tectonic and geomorphic settings for which we have obtained landslide inventories. Using logistic regression, this database is used to build a predictive model of the probability of landslide occurrence. We assess the performance of the regression model using statistical goodness-of-fit metrics to determine which combination of the tested landslide proxies provides the optimum prediction of observed landslides while minimizing ';false alarms' in non-landslide zones. Our initial results indicate strong correlations with peak ground acceleration and maximum slope, and weaker correlations with surface geological and soil wetness proxies. In terms of the original four events included, the global model predicts landslides most accurately when applied to the Wenchuan and Chi-Chi events, and less accurately when applied to the Northridge and Guatemala datasets. Combined with near-real time ShakeMaps, the model can be used to make generalized predictions of whether or not landslides are likely to occur (and if so, where) for future earthquakes around the globe, and these estimates

  12. An Experimental Global Monitoring System for Rainfall-triggered Landslides using Satellite Remote Sensing Information

    NASA Technical Reports Server (NTRS)

    Hong, Yang; Adler, Robert F.; Huffman, George J.

    2006-01-01

    Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.

  13. Map showing recent (1997-98 El Nino) and historical landslides, Crow Creek and vicinity, Alameda and Contra Costa Counties, California

    USGS Publications Warehouse

    Coe, Jeffrey A.; Godt, Jonathan; Tachker, Pierre

    2004-01-01

    This report documents the spatial distribution of 3,800 landslides caused by 1997-98 El Ni?o winter rainfall in the vicinity of Crow Creek in Alameda and Contra Costa Counties, California. The report also documents 558 historical (pre-1997-98) landslides. Landslides were mapped from 1:12,000-scale aerial photographs and classified as either debris flows or slides. Slides include rotational and translational slides, earth flows, and com