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Sample records for landslide susceptibility mapping

  1. A landslide susceptibility map of Africa

    NASA Astrophysics Data System (ADS)

    Broeckx, Jente; Vanmaercke, Matthias; Duchateau, Rica; Poesen, Jean

    2017-04-01

    Studies on landslide risks and fatalities indicate that landslides are a global threat to humans, infrastructure and the environment, certainly in Africa. Nonetheless our understanding of the spatial patterns of landslides and rockfalls on this continent is very limited. Also in global landslide susceptibility maps, Africa is mostly underrepresented in the inventories used to construct these maps. As a result, predicted landslide susceptibilities remain subject to very large uncertainties. This research aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. As a first step, we compiled all available landslide inventories for Africa. This data was supplemented by additional landslide mapping with Google Earth in underrepresented regions. This way, we compiled 60 landslide inventories from the literature (ca. 11000 landslides) and an additional 6500 landslides through mapping in Google Earth (including 1500 rockfalls). Various environmental variables such as slope, lithology, soil characteristics, land use, precipitation and seismic activity, were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in our dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides, tested the significance of the considered environmental variables and evaluated the performance of the fitted multiple logistic regression model against another subset of mapped landslides. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding rockfalls. In both maps, topography, lithology and seismic activity were the most significant variables. The latter factor may be surprising, given the overall limited degree of seismicity in Africa. However, its significance indicates that frequent seismic events may serve as in important

  2. 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

  3. National landslide susceptibility map for Germany

    NASA Astrophysics Data System (ADS)

    Glade, T.; Dikau, R.; Bell, R.

    2003-04-01

    Landslide susceptibility is generally based on historical data and field mapping, Resulting maps usually cover regions ranging between local and regional scales. However, also national scale analysis is important to delineate regions most prone to landsliding. Herein it is crucial to define the parameters, which are most important within this scale, and indeed, which can be derived from national data sets. This study aims to demonstrate a method on how to obtain national scale landslide susceptibility maps. In this study, German landslide literature was extensively reviewed. Due to the varying nature of the different sources and publications, only the information on lithology and slope angle was compiled. To include local knowledge, returned questionnaires send to experts in landslide research were evaluated and respective information summarized. For regions with no information, generalized geotechnical properties for existing lithology were applied. Additionally, a geological map at a scale of 1:1.000.000 and a nationwide digital terrain model with a resolution of 25 m x 25 m were available. The combination of slope angle and lithology was qualitatively classified in negligible, minor, moderate and high landslide susceptibility classes and applied to the data. Due to the resolution of the geology map, the 25 m resolution has been aggregated to 150 m, which seemed appropriate considering the extend of most of the landslides. Coastal landslide susceptibility has been derived from an existing data set. The map delineates areas of different landslide susceptibilities. The regions include cuestas, steep slopes in rolling midland topography and in the Alps, as well as slopes of deeply dissected rivers. Work in progress includes an evaluation of the calculated landslide susceptibility map using regional data sets. Although it is a preliminary result, this study presents the potential of such maps for planning and management purposes.

  4. Multiscale/multiresolution landslides susceptibility mapping

    NASA Astrophysics Data System (ADS)

    Grozavu, Adrian; Cătălin Stanga, Iulian; Valeriu Patriche, Cristian; Toader Juravle, Doru

    2014-05-01

    Within the European strategies, landslides are considered an important threatening that requires detailed studies to identify areas where these processes could occur in the future and to design scientific and technical plans for landslide risk mitigation. In this idea, assessing and mapping the landslide susceptibility is an important preliminary step. Generally, landslide susceptibility at small scale (for large regions) can be assessed through qualitative approach (expert judgements), based on a few variables, while studies at medium and large scale requires quantitative approach (e.g. multivariate statistics), a larger set of variables and, necessarily, the landslide inventory. Obviously, the results vary more or less from a scale to another, depending on the available input data, but also on the applied methodology. Since it is almost impossible to have a complete landslide inventory on large regions (e.g. at continental level), it is very important to verify the compatibility and the validity of results obtained at different scales, identifying the differences and fixing the inherent errors. This paper aims at assessing and mapping the landslide susceptibility at regional level through a multiscale-multiresolution approach from small scale and low resolution to large scale and high resolution of data and results, comparing the compatibility of results. While the first ones could be used for studies at european and national level, the later ones allows results validation, including through fields surveys. The test area, namely the Barlad Plateau (more than 9000 sq.km) is located in Eastern Romania, covering a region where both the natural environment and the human factor create a causal context that favor these processes. The landslide predictors were initially derived from various databases available at pan-european level and progressively completed and/or enhanced together with scale and the resolution: the topography (from SRTM at 90 meters to digital

  5. Mapping landslide susceptibility using data-driven methods.

    PubMed

    Zêzere, J L; Pereira, S; Melo, R; Oliveira, S C; Garcia, R A C

    2017-07-01

    Most epistemic uncertainty within data-driven landslide susceptibility assessment results from errors in landslide inventories, difficulty in identifying and mapping landslide causes and decisions related with the modelling procedure. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from: (i) the selection of the statistical method; (ii) the selection of the terrain mapping unit; and (iii) the selection of the feature type to represent landslides in the model (polygon versus point). The work is performed in a single study area (Silveira Basin - 18.2km(2) - Lisbon Region, Portugal) using a unique database of geo-environmental landslide predisposing factors and an inventory of 82 shallow translational slides. The logistic regression, the discriminant analysis and two versions of the information value were used and we conclude that multivariate statistical methods perform better when computed over heterogeneous terrain units and should be selected to assess landslide susceptibility based on slope terrain units, geo-hydrological terrain units or census terrain units. However, evidence was found that the chosen terrain mapping unit can produce greater differences on final susceptibility results than those resulting from the chosen statistical method for modelling. The landslide susceptibility should be assessed over grid cell terrain units whenever the spatial accuracy of landslide inventory is good. In addition, a single point per landslide proved to be efficient to generate accurate landslide susceptibility maps, providing the landslides are of small size, thus minimizing the possible existence of heterogeneities of predisposing factors within the landslide boundary. Although during last years the ROC curves have been preferred to evaluate the susceptibility model's performance, evidence was found that the model with the highest AUC ROC is not necessarily the best landslide susceptibility model, namely when terrain

  6. Landslide Inventory and Susceptibility Mapping in Tropical Areas - Southern Mexico

    NASA Astrophysics Data System (ADS)

    Gaidzik, K.; Ramirez-Herrera, M. T.; Regmi, N. R.; Leshchinsky, B. A.

    2016-12-01

    Landslides are one of the common catastrophic phenomena in the world. In regions of humid-warm tropical climate they are triggered by extreme storms causing loss of life and economic devastation. In this study we mapped susceptibility to landslides in the tropical mountains of Guerrero (South Mexico) based on the inventory of landslide features triggered by the hurricane Manuel of September 2013. Landslide inventory was produced using interpretation of satellite images and automatic identification of landslides employing the Contour Connection Method (CCM). A map of susceptibility to landslides was developed by computing probability of landslide occurrence from statistical relationships of existing landslides using LiDAR elevation model and derived landslide-causing factors using a logistic regression method. Landslide inventory includes 419 features produced by the hurricane Manuel on the area of 22 km2, and > 1,000 older features, suggesting high landslide activity in this area. Most landslides in the region are small, but some large slides exist, such as the catastrophic landslide in La Pintada that caused 71 fatalities and destroyed a large part of the village. Our results indicate that the distance to streams, human activity, presence or absence of dense vegetation and orientation of slopes (on some areas) strongly influence the spatial distribution of landslides. Results showed high susceptibility zones encompass 30% of the study area and occur mostly along topographic convergence. Applied approach identified most of the landslides within the high susceptibility zone and suggested that it is a valid applicable method to map areas susceptible to landslides in southern Mexico but also on other humid-warm tropical regions.

  7. Comparison of satellite and air photo based landslide susceptibility maps

    NASA Astrophysics Data System (ADS)

    Weirich, Frank; Blesius, Leonhard

    2007-07-01

    Landslide susceptibility maps can be prepared in a variety of ways. Many geoscientists favour the use of an overlay model approach in which several map layers are combined by some arithmetic rules to determine the potential for sliding in an area or region. The resulting susceptibility maps, although based on a subjective weighting of relevant factors, can often be of high accuracy and utility. In order to obtain the relevant input data for this type of analysis, remotely sensed data are often used. To date, susceptibility mapping, just as the mapping of historic and individual landslides, has tended to require higher-resolution imagery. This has somewhat limited the application of landslide susceptibility mapping. While high-resolution air photo or satellite imagery is superior to lower resolution imagery for the purpose of mapping of historic and individual landslides, such higher levels of resolution may not be required for the development of landslide susceptibility maps. In order to determine if medium-resolution satellite imagery, such as SPOT or ASTER, could provide the needed data for landslide susceptibility mapping, a comparison was undertaken of landslide susceptibility model output resulting from the use of stereo NAPP aerial photography versus the use of data obtained from stereo SPOT imagery. The test area selected for this study consisted of two watersheds, Pena Canyon and Big Rock Canyon, situated west of Santa Monica, California, USA, along the Pacific Coast Highway. Both watersheds have a long and well-documented history of landslide activity and sufficient geologic variability and complexity to provide a good test site. The specific overlay model used in this evaluation required input data consistent with the needs of many other models of this type. The model output derived from the two different data sources and presented here in the form of susceptibility maps were virtually identical. Statistical and difference analysis confirmed that both

  8. 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

  9. 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

  10. Landslide susceptibility mapping using geographically-weighted principal component analysis

    NASA Astrophysics Data System (ADS)

    Faraji Sabokbar, Hassanali; Shadman Roodposhti, Majid; Tazik, Esmaeil

    2014-12-01

    Landslide susceptibility mapping (LSM) documents the extent of probable landslide events in a region to investigate the distribution, pattern, recurrence and statistics of slope failure and consequent mass movement. Similar to other analyses of quantitative sources of spatial data, LSM sometimes uses principal component analysis (PCA), a form of multivariate statistical analysis. This approach helps identify susceptibility by grouping locations or by measuring the variation between groups. The present study outlines the principles and examines the capability of the proposed methodology for landslide mapping, considers optimized shapes for spatial units, estimates an efficient kernel size using alternating least squares (ALS) analysis confirmed by cross-validation, and uses geographically-weighted principal component analysis (GWPCA) to calculate landslide susceptibility using a fuzzy gamma operator. RMSE and PBIAS statistical estimators were then used to assess operational efficiency of all LSMs using fuzzy gamma operators (0.1 to 0.9). ROC curves were drawn for the best result for LSM using a landslide inventory containing 82 landslide points, with an area under curve of 0.889. The new tools can improve the quality of landslide-related analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluation.

  11. Uncertainty into statistical landslide susceptibility models resulting from terrain mapping units and landslide input data

    NASA Astrophysics Data System (ADS)

    Zêzere, José Luis; Pereira, Susana; Melo, Raquel; Oliveira, Sérgio; Garcia, Ricardo

    2017-04-01

    There are multiple sources of uncertainty within statistically-based landslide susceptibility assessment that needs to be accounted and monitored. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from the selection of the terrain mapping unit and the selection of the feature type to represent landslides (polygon vs point). The work is performed in the Silveira Basin (18.2 square kilometres) located north of Lisbon, Portugal, using a unique database of geo-environmental landslide predisposing factors and an inventory of 81 shallow translational slides. The Logistic Regression is the statistical method selected to combine the predictive factors with the dependent variable. Four landslide susceptibility models were computed using the complete landslide inventory and considering the total landslide area over four different terrain mapping units: Slope Terrain Units (STU), Geo-Hydrological Terrain Units (GHTU), Census Terrain Units (CTU) and Grid Cell Terrain Units (GCTU). Four additional landslide susceptibility models were made over the same four terrain mapping units using a landslide training group (50% of the inventory randomly selected). These models were independently validated with the other 50% of the landslide inventory (landslide test group). Lastly, two additional landslide susceptibility models were computed over GCTU, one using the landslide training group represented as point features corresponding to the centroid of landslide, and other using the centroid of landslide rupture zone. In total, 10 landslide susceptibility maps were constructed and classified in 10 classes of equal number of terrain units to allow comparison. The evaluation of the prediction skills of susceptibility models was made using ROC metrics and Success and Prediction rate curves. Lastly, the landslide susceptibility maps computed over GCTU were compared using the Kappa statistics. With this work we conclude that large differences

  12. 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

  13. 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.

  14. 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.

  15. Physically-Based Shallow Landslide Susceptibility Mapping, NW of Portugal

    NASA Astrophysics Data System (ADS)

    Teixeira, Manuel; Bateira, Carlos; Soares, Laura

    2013-04-01

    Two physically-based models - Shallow Landslide Stability Analysis (SHALSTAB) and Safety Factor (SF) - are applied in Serra da Peneda (northwest of Portugal) to evaluate shallow landslide susceptibility in Tibo drainage basin. This small basin is located in an area of granitic and metasedimentary substrate, covered by different types of surficial formations (weathering mantles and slope deposits). The application of the selected models requires the determination of a set of mechanical and hydrological parameters, and the use of high resolution topographic information to create an accurate DTM. To fulfill this goal we have applied the Shallow Landslide Stability Analysis (SHALSTAB) and the SF (Safety Factor) models. The shallow landslide area was inventoried on the field. The cohesion was assessed by back analysis and the other mechanical and hydrological soil parameters were assessed on the field survey. Several susceptibility scenarios were tested with SHALSTAB model. The best SHALSTAB scenario used to assess the susceptibility is achieved using the following parameters: cohesion (c) = 2000 N/m2, soil thickness (z) = 1,2 m, internal friction (?)=32o and soil weight (?s)=14,7 KN/m3. Shallow landslide susceptibility mapping using the SF model, was based on the cartography of the factors registered on the field survey and used the following parameters: cohesion (c) = 2000 - 6000 N/m2, soil thickness (z) =1,2 m, internal friction (?)=30 - 40o; soil volumic weight (?m) = 13,7 - 15,7 KN/m3 and Hydraulic conductivity = 0 - 3,9-03 kfs. SHALSTAB scenarios were validated by overlaying the shallow landslide area (scar concentration) and selected the better susceptibility modeling. The parameters used on the SF model applied spatially variable values registered in the field survey (using the superficial formation cartography). To validate the SF model we used the AUC (Area Under the Curve) method. The two models were compared by the scar concentration and landslide potential

  16. Application of Logistic Regression for Landslide Susceptibility Mapping in the Alishan Area, Southern Taiwan

    NASA Astrophysics Data System (ADS)

    Chan, H. C.; Chang, C. C.; Laio, P. Y.

    2012-04-01

    Landslide susceptibility analysis usually combines several factors, including the terrain, geology, and hydrology. The analysis tries to find a suitable combination of these factors in order to establish a landslide susceptibility model and calculate the susceptibility value. A potential landslide map can be established by using the calculated the susceptibility value of landslide. This study took Alishan area as an example and aimed to assess landslide susceptibility analysis by Logistic regression, a multivariate analysis method. In order to select the factors efficiently, the calibration and selection procedure were performed. The results were verified by a previous typhoon event. The classification error matrix was used to evaluate the accuracy of landslide predicted by the present model. Finally, this study applied 10-, 25-, 50-, and 100-year return periods precipitation to estimate the susceptibility values for the study area. The landslide susceptibilities were separated into four levels, including high, medium-high, medium, and low, to delineate the map of potential landslide.

  17. Spatial agreement of predicted results in landslide susceptibility maps

    NASA Astrophysics Data System (ADS)

    Sterlacchini, Simone; Ballabio, Cristiano; Blahut, Jan; Masetti, Marco; Sorichetta, Alessandro

    2010-05-01

    Landslides occur worldwide in response to a broad variety of natural predisposing conditions and triggering factors that include heavy rainfalls, earthquakes, and human activity. Landslides constitute a serious source of danger causing environmental damage and substantial human and financial losses. At a regional scale, landslide susceptibility zonation constitutes the first effective step to achieve a thorough risk assessment and management and contribute to public safety. For this reason, the predicted susceptibility maps must be carefully analysed and critically reviewed before disseminating the results. The tuning of statistical techniques and the independent validation of the results are already recognized as fundamental steps in any natural hazard study to assess model accuracy and predictive power. Validation also may permit to establish the degree of confidence in the model and to compare results from different models. For this reason, the spatial agreement among susceptibility maps, produced by different models, should also be tested, especially if these models have similar prediction power. This is usually a rather common occurrence as it may happen that two or more maps with similar predictive power may not have the same agreement in term of predicted spatial patterns. This study is aimed at assessing the degree of spatial agreement among different patterns of predicted values in susceptibility maps with almost similar success and prediction rate curves and areas under curves (AUC). A data-driven Bayesian method (Weights of Evidence modelling technique) is applied and the output maps reclassified to compare the predicted results. A relative classification, based on the proportion of area classified as susceptible, is performed. Maps are investigated by Kappa Statistic, Principal Component Analysis, and Distance Weighted Entropy procedures. The results show great differences within the output spatial patterns of the predicted maps and also within the

  18. 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.

  19. A proposed cell model for multiple-occurrence regional landslide events: Implications for landslide susceptibility mapping

    NASA Astrophysics Data System (ADS)

    Crozier, M. J.

    2017-10-01

    Multiple-occurrence regional landslide events (MORLEs) consist of hundreds to thousands of shallow landslides occurring more or less simultaneously within defined areas, ranging from tens to thousands of square kilometres. While MORLEs can be triggered by rainstorms and earthquakes, this paper is confined to those landslide events triggered by rainstorms. Globally, MORLEs occur in a range of geological settings in areas of moderate to steep slopes subject to intense rainstorms. Individual landslides in rainstorm-triggered events are dominantly small, shallow debris and earth flows, and debris and earth slides involving regolith or weathered bedrock. The model used to characterise these events assumes that energy distribution within the event area is represented on the land surface by a cell structure; with maximum energy expenditure within an identifiable core and rapid dissipation concentrically away from the centre. The version of the model presented here has been developed for rainfall-triggered landslide events. It proposes that rainfall intensity can be used to determine different critical landslide response zones within the cell (referred to as core, middle, and periphery zones). These zones are most readily distinguished by two conditions: the proportion of the slope that fails and the particular type of the slope stability factor that assumes dominance in determining specific sites of landslide occurrence. The latter condition means that the power of any slope stability factor to distinguish between stable and unstable sites varies throughout the affected area in accordance with the landslide response zones within the cell; certain factors critical for determining the location of landslide sites in one part of the event area have little influence in other parts of the event area. The implication is that landslide susceptibility maps (and subsequently derived mitigation measures) based on conventional slope stability factors may have only limited validity

  20. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran

    NASA Astrophysics Data System (ADS)

    Pourghasemi, Hamid Reza; Jirandeh, Abbas Goli; Pradhan, Biswajeet; Xu, Chong; Gokceoglu, Candan

    2013-04-01

    The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the

  1. 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

  2. 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

  3. 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

  4. An application of adaptive neuro-fuzzy inference system to landslide susceptibility mapping (Klang valley, Malaysia)

    NASA Astrophysics Data System (ADS)

    Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan

    2010-05-01

    Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient

  5. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

    NASA Astrophysics Data System (ADS)

    Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

    2017-10-01

    In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

  6. Spatially explicit shallow landslide susceptibility mapping over large areas

    Treesearch

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

    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...

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

    PubMed

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

    2014-12-01

    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".

  8. Landslide susceptibility mapping based on GIS modle on Shicheng Jiangxi province, china

    NASA Astrophysics Data System (ADS)

    Qi, Wufu; Chen, Yu; Cheng, Xianfeng; Wang, Qinjun; Wei, Yongmin

    2017-02-01

    A GIS model-information index model was presented for landslide susceptibility mapping on Shicheng, Jiangxi province, China.140 landslides were identified from SPOT6 fusion image with 1.5 meters resolution, and they were verified by field investigation. Application of the information index model showed that the landslides more likely occur in areas nearby the road, the river and the lower vegetation covery. The high elevation accuracy of 71% was reached using a receiver operating characteristic (ROC). The result indicates that the northeast and parts of the south of Shicheng County are highly susceptible to damages from landslides, which provides useful information for disaster management and decision making.

  9. 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%.

  10. 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

  11. 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

  12. Comparing landslide inventory maps

    NASA Astrophysics Data System (ADS)

    Galli, Mirco; Ardizzone, Francesca; Cardinali, Mauro; Guzzetti, Fausto; Reichenbach, Paola

    Landslide inventory maps are effective and easily understandable products for both experts, such as geomorphologists, and for non experts, including decision-makers, planners, and civil defense managers. Landslide inventories are essential to understand the evolution of landscapes, and to ascertain landslide susceptibility and hazard. Despite landslide maps being compiled every year in the word at different scales, limited efforts are made to critically compare landslide maps prepared using different techniques or by different investigators. Based on the experience gained in 20 years of landslide mapping in Italy, and on the limited literature on landslide inventory assessment, we propose a general framework for the quantitative comparison of landslide inventory maps. To test the proposed framework we exploit three inventory maps. The first map is a reconnaissance landslide inventory prepared for the Umbria region, in central Italy. The second map is a detailed geomorphological landslide map, also prepared for the Umbria region. The third map is a multi-temporal landslide inventory compiled for the Collazzone area, in central Umbria. Results of the experiment allow for establishing how well the individual inventories describe the location, type and abundance of landslides, to what extent the landslide maps can be used to determine the frequency-area statistics of the slope failures, and the significance of the inventory maps as predictors of landslide susceptibility. We further use the results obtained in the Collazzone area to estimate the quality and completeness of the two regional landslide inventory maps, and to outline general advantages and limitations of the techniques used to complete the inventories.

  13. Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS.

    PubMed

    Lee, Saro

    2004-08-01

    For landslide susceptibility mapping, this study applied and verified a Bayesian probability model, a likelihood ratio and statistical model, and logistic regression to Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite imagery and field surveys; and a spatial database was constructed from topographic maps, soil type, forest cover, geology and land cover. The factors that influence landslide occurrence, such as slope gradient, slope aspect, and curvature of topography, were calculated from the topographic database. Soil texture, material, drainage, and effective depth were extracted from the soil database, while forest type, diameter, and density were extracted from the forest database. Land cover was classified from Landsat TM satellite imagery using unsupervised classification. The likelihood ratio and logistic regression coefficient were overlaid to determine each factor's rating for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared with known landslide locations. The logistic regression model had higher prediction accuracy than the likelihood ratio model. The method can be used to reduce hazards associated with landslides and to land cover planning.

  14. 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

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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

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

    PubMed

    Shahabi, Himan; Hashim, Mazlan

    2015-04-22

    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.

  1. Landslide susceptibility mapping on a global scale using the method of logistic regression

    NASA Astrophysics Data System (ADS)

    Lin, Le; Lin, Qigen; Wang, Ying

    2017-08-01

    This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

  2. 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.

  3. Rainfall thresholds and susceptibility mapping for shallow landslides and debris flows in Scotland

    NASA Astrophysics Data System (ADS)

    Postance, Benjamin; Hillier, John; Dijkstra, Tom; Dixon, Neil

    2017-04-01

    Shallow translational slides and debris flows (hereafter 'landslides') pose a significant threat to life and cause significant annual economic impacts (e.g. by damage and disruption of infrastructure). The focus of this research is on the definition of objective rainfall thresholds using a weather radar system and landslide susceptibility mapping. In the study area Scotland, an inventory of 75 known landslides was used for the period 2003 to 2016. First, the effect of using different rain records (i.e. time series length) on two threshold selection techniques in receiver operating characteristic (ROC) analysis was evaluated. The results show that thresholds selected by 'Threat Score' (minimising false alarms) are sensitive to rain record length and which is not routinely considered, whereas thresholds selected using 'Optimal Point' (minimising failed alarms) are not; therefore these may be suited to establishing lower limit thresholds and be of interest to those developing early warning systems. Robust thresholds are found for combinations of normalised rain duration and accumulation at 1 and 12 day's antecedence respectively; these are normalised using the rainy-day normal and an equivalent measure for rain intensity. This research indicates that, in Scotland, rain accumulation provides a better indicator than rain intensity and that landslides may be generated by threshold conditions lower than previously thought. Second, a landslide susceptibility map is constructed using a cross-validated logistic regression model. A novel element of the approach is that landslide susceptibility is calculated for individual hillslope sections. The developed thresholds and susceptibility map are combined to assess potential hazards and impacts posed to the national highway network in Scotland.

  4. 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

  5. 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

  6. 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

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

    PubMed

    Feizizadeh, Bakhtiar; Blaschke, Thomas

    2014-03-04

    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 operation

  8. 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

  9. Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

    NASA Astrophysics Data System (ADS)

    Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

    2017-07-01

    All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

  10. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale

    NASA Astrophysics Data System (ADS)

    Schicker, Renée; Moon, Vicki

    2012-08-01

    Landslide susceptibility assessment was undertaken for the Waikato Region, New Zealand. Landslide inventory data were extracted from a pre-existing database that included few landslides in the region (1.4% of area), and is limited in terms of completeness of record and location uncertainty. This database is in contrast to those normally used for research, which are derived for the research project and are complete and accurate, but is representative of those that may exist within government bodies. This paper applies statistical methods to derive a meaningful predictive map for planning purposes from such a relatively poorly defined database. Susceptibility maps for both logistic regression and weights of evidence were derived and evaluated using success, prediction, and ROC curves. Both statistical methods gave models with fair predictive capacity for validation samples from the original database with areas under ROC curves (AUC) of 0.71 to 0.75. An independent set of landslide data compiled from observations made in Google Earth showed lower overall prediction quality, with the logistic regression method giving the best prediction (AUC = 0.71). For this regional assessment, categorical data proved a major constraint on the application of logistic regression as the area considered has complex geology and geomorphology. As a result, the large number of categories required led to a complex and unwieldy statistical model, whereas division into fewer categories meant that real variability in the area could not be adequately represented. This limited the result to a model with two continuous variables, slope and mean monthly rainfall. The incomplete record in the database proved of little concern for the logistic regression method as the model was able to generalise landslide locations from the known sites well, giving a similar AUC value for the original and independent data; the same was not true for the weights of evidence method which was not successful at

  11. An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software

    NASA Astrophysics Data System (ADS)

    Sezer, E. A.; Nefeslioglu, H. A.; Osna, T.

    2017-01-01

    The main purpose of this study is to introduce an expert-based LSM module developed for Netcad Architect Software. A landslide-prone area located at the eastern Black Sea region of Turkey was selected as the experimental site for this study. The investigations were performed in four stages: (i) introducing technical details of LSM module and theoretical background of the methods implemented in the module, (ii) experiments; landslide susceptibility evaluations by applying the methods M-AHP and Mamdani type FIS by using the expert-based LSM module, (iii) map similarity assessments and evaluations for the generalization capacities of the expert-based models, and (iv) performance assessments of the LSM module. When considering the areal distributions of matching ratios obtained from the map similarity evaluations, it is revealed that M-AHP is more pessimistic and covers a greater area in higher hazard classes, whereas the Mamdani type FIS behaves more optimistically and restricts the area of higher hazard classes in the experimental site. According to the Receiver Operating Characteristics (ROC) curve analyses, the value of Area Under the ROC Curve (AUC) was obtained as 0.66 for the resultant map produced with Mamdani type FIS and 0.82 for the map produced with M-AHP. To compare the time consumptions of the expert methods, experiments were implemented. Mamdani type FIS completes its task in 3 h and 39 min, whereas M-AHP only requires 47 s. As a consequence, (i) the LSM module developed for Netcad Architect Software presents full-featured expert-based landslide susceptibility mapping abilities, and (ii) M-AHP is a useful method for obtaining an expert opinion and modeling landslide susceptibility.

  12. Landslide Susceptibility Mapping of Tegucigalpa, Honduras Using Artificial Neural Network, Bayesian Network and Decision Trees

    NASA Astrophysics Data System (ADS)

    Garcia Urquia, E. L.; Braun, A.; Yamagishi, H.

    2016-12-01

    Tegucigalpa, the capital city of Honduras, experiences rainfall-induced landslides on a yearly basis. The high precipitation regime and the rugged topography the city has been built in couple with the lack of a proper urban expansion plan to contribute to the occurrence of landslides during the rainy season. Thousands of inhabitants live at risk of losing their belongings due to the construction of precarious shelters in landslide-prone areas on mountainous terrains and next to the riverbanks. Therefore, the city is in the need for landslide susceptibility and hazard maps to aid in the regulation of future development. Major challenges in the context of highly dynamic urbanizing areas are the overlap of natural and anthropogenic slope destabilizing factors, as well as the availability and accuracy of data. Data-driven multivariate techniques have proven to be powerful in discovering interrelations between factors, identifying important factors in large datasets, capturing non-linear problems and coping with noisy and incomplete data. This analysis focuses on the creation of a landslide susceptibility map using different methods from the field of data mining, Artificial Neural Networks (ANN), Bayesian Networks (BN) and Decision Trees (DT). The input dataset of the study contains geomorphological and hydrological factors derived from a digital elevation model with a 10 m resolution, lithological factors derived from a geological map, and anthropogenic factors, such as information on the development stage of the neighborhoods in Tegucigalpa and road density. Moreover, a landslide inventory map that was developed in 2014 through aerial photo interpretation was used as target variable in the analysis. The analysis covers an area of roughly 100 km2, while 8.95 km2 are occupied by landslides. In a first step, the dataset was explored by assessing and improving the data quality, identifying unimportant variables and finding interrelations. Then, based on a training

  13. Preliminary Detection Model of Rapid Mapping Technique for Landslide Susceptibility Zone Using Multi Sensor Imagery (Case Study in Banjarnegara Regency)

    NASA Astrophysics Data System (ADS)

    Yanuarsyah, I.; Khairiah, R. N.

    2017-01-01

    This study as a preliminary stage as part of disaster mitigation landslide in Banjarnegara Regency, by utilizing a combination of multi-sensor image to overview the pattern forest cover changes with supported by other parameters such as rainfall, slope, aspect, curvature patterns hill (curvature). The objective is how to develop detection model in rapid mapping technique for detection landslide susceptibility zone. This information is used as basis an early detection for estimating landslide potentially happen in the future. there are four main processes which are optical image processing, SAR image processing, DEM processing and Scoring Geoprocessing. The final zone might be verified by particular landslide event location whether it exist on the result map. It obtain “big five” district with higher prone landslide susceptibility zone such as Batur district, Pejawaran district, Wanayasa district, Kalibening district and Rakit district. Total susceptibility zone in Banjarnegara regency approximately 604.79 Ha with 15,250 prone point location. Thus, it classified as 14.16 Ha of low zone, 286.41 Ha of moderate zone and 304.22 Ha of high zone. This study demonstrates as rapid mapping the enormous potential landslide occurrences investigated by susceptibility zone. In term of landslide prone point, the combination optical image and SAR image quite enough to perform post forest cover changes and it also can overlay with another causative parameter.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    NASA Astrophysics Data System (ADS)

    Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

    2014-06-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

  19. Susceptibility Evaluation and Mapping of CHINA'S Landslide Disaster Based on Multi-Temporal Ground and Remote Sensing Satellite Data

    NASA Astrophysics Data System (ADS)

    Liu, C.; Li, W.; Lu, P.; Sang, K.; Hong, Y.; Li, R.

    2012-07-01

    Under the circumstances of global climate change, nowadays landslide occurs in China more frequently than ever before. The landslide hazard and risk assessment remains an international focus on disaster prevention and mitigation. It is also an important approach for compiling and quantitatively characterizing landslide damages. By integrating empirical models for landslide disasters, and through multi-temporal ground data and remote sensing data, this paper will perform a landslide susceptibility assessment throughout China. A landslide susceptibility (LS) map will then be produced, which can be used for disaster evaluation, and provide basis for analyzing China's major landslide-affected regions. Firstly, based on previous research of landslide susceptibility assessment, this paper collects and analyzes the historical landslide event data (location, quantity and distribution) of past sixty years in China as a reference for late-stage studies. Secondly, this paper will make use of regional GIS data of the whole country provided by the National Geomatics Centre and China Meteorological Administration, including regional precipitation data, and satellite remote sensing data such as from TRMM and MODIS. By referring to historical landslide data of past sixty years, it is possible to develop models for assessing LS, including producing empirical models for prediction, and discovering both static and dynamic key factors, such as topography and landforms (elevation, curvature and slope), geologic conditions (lithology of the strata), soil type, vegetation cover, hydrological conditions (flow distribution). In addition, by analyzing historical data and combining empirical models, it is possible to synthesize a regional statistical model and perform a LS assessment. Finally, based on the 1km×1km grid, the LS map is then produced by ANN learning and multiplying the weighted factor layers. The validation is performed with reference to the frequency and distribution of

  20. 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.

  1. Progress in national-scale landslide susceptibility mapping in Romania using a combined statistical-heuristical approach

    NASA Astrophysics Data System (ADS)

    Bălteanu, Dan; Micu, Mihai; Malet, Jean-Philippe; Jurchescu, Marta; Sima, Mihaela; Kucsicsa, Gheorghe; Dumitrică, Cristina; Petrea, Dănuţ; Mărgărint, Ciprian; Bilaşco, Ştefan; Văcăreanu, Radu; Georgescu, Sever; Senzaconi, Francisc

    2017-04-01

    Landslide processes represent a very widespread geohazard in Romania, affecting mainly the hilly and plateau regions as well as the mountain sectors developed on flysch formations. Two main projects provided the framework for improving the existing national landslide susceptibility map (Bălteanu et al. 2010): the ELSUS (Pan-European and nation-wide landslide susceptibility assessment, EC-CERG) and the RO-RISK (Disaster Risk Evaluation at National Level, ESF-POCA) projects. The latter one, a flagship project aiming at strengthening risk prevention and management in Romania, focused on a national-level evaluation of the main risks in the country including landslides. The strategy for modeling landslide susceptibility was designed based on the experience gained from continental and national level assessments conducted in the frame of the International Programme on Landslides (IPL) project IPL-162, the European Landslides Expert Group - JRC and the ELSUS project. The newly proposed landslide susceptibility model used as input a reduced set of landslide conditioning factor maps available at scales of 1:100,000 - 1:200,000 and consisting of lithology, slope angle and land cover. The input data was further differentiated for specific natural environments, defined here as morpho-structural units in order to incorporate differences induced by elevation (vertical climatic zonation), morpho-structure as well as neotectonic features. In order to best discern the specific landslide conditioning elements, the analysis has been carried out for one single process category, namely slides. The existence of a landslide inventory covering the whole country's territory ( 30,000 records, Micu et al. 2014), although affected by incompleteness and lack of homogeneity, allowed for the application of a semi-quantitative, mixed statistical-heuristical approach having the advantage of combining the objectivity of statistics with expert-knowledge in calibrating class and factor weights. The

  2. Characterization and quantification of path dependency in landslide susceptibility

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

    Landslides cause major environmental damage, economic losses and casualties. Although susceptibility to landsliding is usually considered an exclusively location-specific phenomenon, indications exist that landslide history co-determines susceptibility to future landslides. In this contribution, we quantified the role of landslide path dependency (the effect of landslides on landslides) using a multi-temporal landslide inventory from Italy. The fraction of landslides following earlier landslides in the same location exhibited an exponential decay, with susceptibility increasing 15-fold right after an initial landslide, and returning to pre-landslide values after about 25 years. We investigated the role of the geometry and location of a previous landslide for the occurrence of follow-up landslides. Larger landslides are more likely to cause follow-up landslides. Also landslide shape, topographic wetness index, the vertical distance to the nearest channel network, the absolute profile curvature and relative slope position of an earlier landslide, however, are important in predicting whether a follow-up landslide occurs. Combined in a binary logistic model, these attributes correctly predict 60% of times whether a landslide will be followed-up. These findings open the way for time-variant mapping of susceptibility to landslides, by including the effect of the spatio-temporal history of landsliding on susceptibility.

  3. 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.

  4. Producing landslide susceptibility maps by utilizing machine learning methods. The case of Finikas catchment basin, North Peloponnese, Greece.

    NASA Astrophysics Data System (ADS)

    Tsangaratos, Paraskevas; Ilia, Ioanna; Loupasakis, Constantinos; Papadakis, Michalis; Karimalis, Antonios

    2017-04-01

    The main objective of the present study was to apply two machine learning methods for the production of a landslide susceptibility map in the Finikas catchment basin, located in North Peloponnese, Greece and to compare their results. Specifically, Logistic Regression and Random Forest were utilized, based on a database of 40 sites classified into two categories, non-landslide and landslide areas that were separated into a training dataset (70% of the total data) and a validation dataset (remaining 30%). The identification of the areas was established by analyzing airborne imagery, extensive field investigation and the examination of previous research studies. Six landslide related variables were analyzed, namely: lithology, elevation, slope, aspect, distance to rivers and distance to faults. Within the Finikas catchment basin most of the reported landslides were located along the road network and within the residential complexes, classified as rotational and translational slides, and rockfalls, mainly caused due to the physical conditions and the general geotechnical behavior of the geological formation that cover the area. Each landslide susceptibility map was reclassified by applying the Geometric Interval classification technique into five classes, namely: very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic and the area under the success and predictive rate curves. The computation process was carried out using RStudio an integrated development environment for R language and ArcGIS 10.1 for compiling the data and producing the landslide susceptibility maps. From the outcomes of the Logistic Regression analysis it was induced that the highest b coefficient is allocated to lithology and slope, which was 2.8423 and 1.5841, respectively. From the

  5. A comparative study of Dempster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: An experience from Zagros Mountains, SW Iran

    NASA Astrophysics Data System (ADS)

    Tangestani, Majid H.

    2009-06-01

    A catchment area at the Zagros Mountains, NW Shiraz, Iran is selected as a test site to comparing the output results of the Dempster-Shafer (D-S) and fuzzy models in landslide hazard mapping. Lithology, slope angle, slope aspect, land cover, and soil depth were considered as landslide causal factors. The factor maps were input into a GIS and a modified landslide hazard evaluation factor (MLHEF) rating and fuzzy membership functions as well as belief function values were assessed for each class of the factor maps. The fuzzy sum, product and gamma combination approaches were examined and output maps were assessed based on the known landslides. The outputs of fuzzy sum and product combination rules were not reasonable because these approaches classified the area into 'very-high' or 'very low' susceptibility zones respectively, which were not compatible to the field and factor maps criteria. A γ value of 0.94 yielded the most reliable susceptibility for landslides. Overlay of the known landslides with the output favorability map showed that the identified landslides were located in the high- and very-high susceptible zones. The output results of the Dempster-Shafer model: plausibility, belief, and uncertainty images were also evaluated based on the known landslides. The results of this approach revealed that although it was expected that most of the known landslides correspond the plausibility, or the belief map, only a few of them supported the case, and some landslides were coincided into the disbelief, or uncertainty maps. It is concluded that in comparison to the fuzzy model, the D-S model obtains less reliable results for landslide hazard mapping. Since the belief functions were assigned based on the fuzzy membership functions this might be due to the integration equations used by the model, or the number of evidence maps used as input layers.

  6. 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

  7. The features of the landslide distribution and assessment of landslide susceptibility in Japan

    NASA Astrophysics Data System (ADS)

    Doshida, S.

    2013-12-01

    Many landslides occur in the place which the landslide generated in the past, or its surrounding area. The causes are considered to be formation of slipping surface, the moving mass which becomes vulnerable by deformation or destruction and geological structures in which a slipping surface is easily formed. Therefore, it is very important for prevention and mitigation of the landslide damages to create the landslide inventory map which is shown the place which the landslide generated in the past. National Research Institute for Earth Science and Disaster Prevention (NIED), Japan, have published the landslide inventory map "landslide distribution maps" for preventing and mitigating landslide disasters. The landslide distribution map have mapped the 380,000 or more landslide topographies in whole Japan by interpretation of aerial photographs. The individual landslide not less than 150 m wide is drawn in the landslide distribution map. The objects of this research are to clarify geological and geomorphological features of landslide distributions by analyzing the landslide distribution map and to make the landslide susceptibility map for the assessment of landslide in whole Japan. The landside distribution in whole Japan is not equal and there is a difference in the density. I propose the method of the wide area landslide assessment used by the features and distributions according to of geological setting. I calculate the landslide body ratio in each geological unit. The landslide body ratio is that the rate of the landslide body area in each geological unit and the whole area in each geological unit. The landslide body ratio can be considered that landslide susceptibility (occurrence probability of landslides) in each geological unit. As a result, an average of the landslide body ratio is about 5.2 % in whole Japan. The area consist of the accretionary complex based on volcanic rocks and plutonic rocks have comparatively high-risk landslide susceptibility, and the

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China

    NASA Astrophysics Data System (ADS)

    He, Sanwei; Pan, Peng; Dai, Lan; Wang, Haijun; Liu, Jiping

    2012-10-01

    more robust and less sensitive to different ratios of samples. The susceptibility map produced by KFDA shows that the regions around rivers are highly at risk to the occurrence of landslides in the study area.

  14. 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

  15. 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

    2016-05-11

    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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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

  2. 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

  3. Landslide Susceptibility Assessment Through Fuzzy Logic Inference System (flis)

    NASA Astrophysics Data System (ADS)

    Bibi, T.; Gul, Y.; Rahman, A. Abdul; Riaz, M.

    2016-09-01

    Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chauhan, Shivani; Sharma, Mukta; Arora, M. K.; Gupta, N. K.

    2010-10-01

    In the present study, Artificial Neural Network (ANN) has been implemented to derive ratings of categories of causative factors, which are then integrated to produce a landslide susceptibility zonation map in an objective manner. The results have been evaluated with an ANN based black box approach for Landslide Susceptibility Zonation (LSZ) proposed earlier by the authors. Seven causative factors, namely, slope, slope aspect, relative relief, lithology, structural features (e.g., thrusts and faults), landuse landcover, and drainage density, were placed in 42 categories for which ratings were determined. The results indicate that LSZ map based on ratings derived from ANN performs exceedingly better than that produced from the earlier ANN based approach. The landslide density analysis clearly showed that susceptibility zones were in close agreement with actual landslide areas in the field.

  9. Shallow landslide hazard map of Seattle, Washington

    USGS Publications Warehouse

    Harp, Edwin L.; Michael, John A.; Laprade, William T.

    2008-01-01

    Landslides, particularly debris flows, have long been a significant cause of damage and destruction to people and property in the Puget Sound region. Following the years of 1996 and 1997, the Federal Emergency Management Agency designated Seattle as a “Project Impact” city with the goal of encouraging the city to become more disaster resistant to landslides and other natural hazards. A major recommendation of the Project Impact council was that the city and the U.S. Geological Survey collaborate to produce a landslide hazard map. An exceptional data set archived by the city containing more than 100 yr of landslide data from severe storm events allowed comparison of actual landslide locations with those predicted by slope-stability modeling. We used an infinite-slope analysis, which models slope segments as rigid friction blocks, to estimate the susceptibility of slopes to debris flows, which are water-laden slurries that can form from shallow failures of soil and weathered bedrock and can travel at high velocities down steep slopes. Data used for the analysis consisted of a digital slope map derived from recent light detection and ranging (LiDAR) imagery of Seattle, recent digital geologic mapping of the city, and shear-strength test data for the geologic units found in the surrounding area. The combination of these data layers within a geographic information system (GIS) platform allowed us to create a shallow landslide hazard map for Seattle.

  10. Landslide susceptibility analysis using an artificial neural network model

    NASA Astrophysics Data System (ADS)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  11. Landslide inventory maps: New tools for an old problem

    NASA Astrophysics Data System (ADS)

    Guzzetti, Fausto; Mondini, Alessandro Cesare; Cardinali, Mauro; Fiorucci, Federica; Santangelo, Michele; Chang, Kang-Tsung

    2012-04-01

    Landslides are present in all continents, and play an important role in the evolution of landscapes. They also represent a serious hazard in many areas of the world. Despite their importance, we estimate that landslide maps cover less than 1% of the slopes in the landmasses, and systematic information on the type, abundance, and distribution of landslides is lacking. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the distribution, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass-wasting processes. Conventional methods for the production of landslide maps rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field surveys. These methods are time consuming and resource intensive. New and emerging techniques based on satellite, airborne, and terrestrial remote sensing technologies, promise to facilitate the production of landslide maps, reducing the time and resources required for their compilation and systematic update. In this work, we first outline the principles for landslide mapping, and we review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories. Next, we examine recent and new technologies for landslide mapping, considering (i) the exploitation of very-high resolution digital elevation models to analyze surface morphology, (ii) the visual interpretation and semi-automatic analysis of different types of satellite images, including panchromatic, multispectral, and synthetic aperture radar images, and (iii) tools that facilitate landslide field mapping. Next, we discuss the advantages and the limitations of the new remote sensing data and technology for the production of geomorphological, event, seasonal, and multi-temporal inventory maps

  12. 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.

  13. 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.

  14. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

  16. 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

  17. Rainfall-induced Landslide Susceptibility assessment at the Longnan county

    NASA Astrophysics Data System (ADS)

    Hong, Haoyuan; Zhang, Ying

    2017-04-01

    Landslides are a serious disaster in Longnan county, China. Therefore landslide susceptibility assessment is useful tool for government or decision making. The main objective of this study is to investigate and compare the frequency ratio, support vector machines, and logistic regression. The Longnan county (Jiangxi province, China) was selected as the case study. First, the landslide inventory map with 354 landslide locations was constructed. Then landslide locations were then randomly divided into a ratio of 70/30 for the training and validating the models. Second, fourteen landslide conditioning factors were prepared such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Using the frequency ratio, support vector machines, and logistic regression, a total of three landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using the Receiver operating characteristic (ROC) curve technique. The result showed that the support vector machines model is the best model in the study area. The success rate is 88.39 %; and prediction rate is 84.06 %.

  18. 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.

  19. 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

  20. Neural Network Aided Evaluation of Landslide Susceptibility in Southern Italy

    NASA Astrophysics Data System (ADS)

    Rampone, Salvatore; Valente, Alessio

    Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors. In heuristic approaches, these factors are rated by the attribution of scores based on the assumed role played by each of them in controlling the development of a sliding process. The objective of this research is to forecast landslide susceptibility through the application of Artificial Neural Networks. In particular, given the availability of past events data, we mainly focused on the Calabria region (Italy). Vectors of eight hillslope factors (features) were considered for each considered event in this area (lithology, permeability, slope angle, vegetation cover in terms of type and density, land use, yearly rainfall and yearly temperature range). We collected 106 vectors and each one was labeled with its landslide susceptibility, which is assumed to be the output variable. Subsequently a set of these labeled vectors (examples) was used to train an artificial neural network belonging to the category of Multi-Layer Perceptron (MLP) to evaluate landslide susceptibility. Then the neural network predictions were verified on the vectors not used in the training (validation set), i.e. in previously unseen locations. The comparison between the expected output and the artificial neural network output showed satisfactory results, reporting a prediction discrepancy of less than 4.3%. This is an encouraging preliminary approach towards a systematic introduction of artificial neural network in landslide hazard assessment and mapping in the considered area.

  1. 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

  2. Application of Physically based landslide susceptibility models in Brazil

    NASA Astrophysics Data System (ADS)

    Carvalho Vieira, Bianca; Martins, Tiago D.

    2017-04-01

    Shallow landslides and floods are the processes responsible for most material and environmental damages in Brazil. In the last decades, some landslides events induce a high number of deaths (e.g. Over 1000 deaths in one event) and incalculable social and economic losses. Therefore, the prediction of those processes is considered an important tool for land use planning tools. Among different methods the physically based landslide susceptibility models having been widely used in many countries, but in Brazil it is still incipient when compared to other ones, like statistical tools and frequency analyses. Thus, the main objective of this research was to assess the application of some Physically based landslide susceptibility models in Brazil, identifying their main results, the efficiency of susceptibility mapping, parameters used and limitations of the tropical humid environment. In order to achieve that, it was evaluated SHALSTAB, SINMAP and TRIGRS models in some studies in Brazil along with the Geotechnical values, scales, DEM grid resolution and the results based on the analysis of the agreement between predicted susceptibility and the landslide scar's map. Most of the studies in Brazil applied SHALSTAB, SINMAP and to a lesser extent the TRIGRS model. The majority researches are concentrated in the Serra do Mar mountain range, that is a system of escarpments and rugged mountains that extends more than 1,500 km along the southern and southeastern Brazilian coast, and regularly affected by heavy rainfall that generates widespread mass movements. Most part of these studies used conventional topographic maps with scales ranging from 1:2000 to 1:50000 and DEM-grid resolution between 2 and 20m. Regarding the Geotechnical and hydrological values, a few studies use field collected data which could produce more efficient results, as indicated by international literature. Therefore, even though they have enormous potential in the susceptibility mapping, even for comparison

  3. 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

  4. 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.

  5. Multitemporal landslides inventory map updating using spaceborne SAR analysis

    NASA Astrophysics Data System (ADS)

    Del Ventisette, C.; Righini, G.; Moretti, S.; Casagli, N.

    2014-08-01

    Deep seated gravitational slope deformation and slow moving landslides on large areas were analyzed by spaceborne SAR interferometry: a test site in the Italian Alps of about 300 km2 was selected for updating pre-existing landslide inventory maps based on the advanced interferometric processing technique (A-DInSAR). SAR images from ERS-1/2 satellites (1995-2000) and from Envisat satellite (2002-2009) have been used, allowing the deferred-time analysis of past movements and the record of recent slope movements. In the multi-temporal updated landslide inventory database, the characteristics of the landslides were highlighted: geometry, state of activity, typology, monitoring systems, interventions, source of information and the updating time and actions. Furthermore, for each landslide area, the occurrence of persistent scatterers points and the statistical description of their velocities were reported. This methodology may allow the systematic updating of landslides inventory maps keeping all information on each landslide, becoming the basic tool for the realization and updating of thematic maps such as the landslide susceptibility map.

  6. Landslide susceptibility and risk assessment: specificities for road networks

    NASA Astrophysics Data System (ADS)

    Pellicani, Roberta; Argentiero, Ilenia; Parisi, Alessandro; Spilotro, Giuseppe

    2017-04-01

    A regional-scale assessment of landslide susceptibility and risk along the main road corridors crossing the provincial territory of Matera (Basilicata Region, Southern Italy) was carried out. The entire provincial road network extends for about 1,320 km through a territory, of which represents the main connection infrastructure among thirty-one municipalities due to the lack of an efficient integrated transportation system through the whole regional territory. For this reason, the strategic importance of these roads consists in their uniqueness in connecting every urban center with the socio-economic surrounding context. These roads and their vehicular traffic are continuously exposed to instability processes (about the 40% of the total length is disrupted by landslides), characterized both by high intensity and low frequency and by low intensity and high frequency. This last typology, consisting in small shallow landslides, is particularly hazardous for the roads since it is widespread along the road network, its occurrence is connected to rainfalls and determines high vulnerability conditions for the road in terms of interruption of vehicular traffic. A GIS-based heuristic-bivariate statistical predictive model was performed to assess and map the landslide susceptibility in the study area, by using a polynomial function of eight predisposing factors, weighted according to their influence on the landslide phenomena, recognized and collected in an inventory. Susceptibility associated to small shallow phenomena was assessed by using a polynomial function of specific factors, such as slope angle and aspect, lithological outcrops, rainfalls, etc. In absence of detailed input data, the spatial distribution of landslide risk along the road corridors was assessed and mapped using a qualitative hazard-consequence matrix approach, by which risk is obtained by combining hazard categories with consequence classes pairwise in a two-dimensional table or matrix. Landslide

  7. 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

  8. 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

  9. 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.

  10. Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

    NASA Astrophysics Data System (ADS)

    Ngan Nguyen, Thi To; Liu, Cheng-Chien

    2013-04-01

    How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of

  11. 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

  12. Shallow-landslide hazard map of Seattle, Washington

    USGS Publications Warehouse

    Harp, Edwin L.; Michael, John A.; Laprade, William T.

    2006-01-01

    Landslides, particularly debris flows, have long been a significant cause of damage and destruction to people and property in the Puget Sound region. Following the years of 1996 and 1997, the Federal Emergency Management Agency (FEMA) designated Seattle as a 'Project Impact' city with the goal of encouraging the city to become more disaster resistant to the effects of landslides and other natural hazards. A major recommendation of the Project Impact council was that the city and the U.S. Geological Survey (USGS) collaborate to produce a landslide hazard map of the city. An exceptional data set archived by the city, containing more than 100 years of landslide data from severe storm events, allowed comparison of actual landslide locations with those predicted by slope-stability modeling. We used an infinite-slope analysis, which models slope segments as rigid friction blocks, to estimate the susceptibility of slopes to shallow landslides which often mobilize into debris flows, water-laden slurries that can form from shallow failures of soil and weathered bedrock, and can travel at high velocities down steep slopes. Data used for analysis consisted of a digital slope map derived from recent Light Detection and Ranging (LIDAR) imagery of Seattle, recent digital geologic mapping, and shear-strength test data for the geologic units in the surrounding area. The combination of these data layers within a Geographic Information System (GIS) platform allowed the preparation of a shallow landslide hazard map for the entire city of Seattle.

  13. 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

  14. 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

  15. Landslide susceptibility in the Sierra Nevada National Park (SE Spain) using a multivariate statistics method.

    NASA Astrophysics Data System (ADS)

    Azañón, J. M.; Pérez-Peña, V.; Yesares, J. M.; Roldán, F. J.; Mateos, R. M.; Rodríguez-Fernández, J.; Rodríguez-Peces, M. J.; Ureña, C.

    2012-04-01

    In this work we have evaluated the landslide susceptibility of the Sierra Nevada National Park area. In order to assess the landslide susceptibility, as well as the traditional factors extracted from the Digital Elevation Model and the lithology, we analyzed many important variables that had not been taken into account in previous studies such as; normalized vegetation index (NDVI), distance to active tectonic structures (folds and faults), snow melting cycles, snow duration, and runoff coefficient (P0). We have differentiated three types of slope instabilities; rotational landslides, fluxes, and rocks failures. For each landslide type we carried out a field inventory using aerial photographs and field work. We used a multivariate statistic approach to obtain those factors that better explain the variance of the landslide distribution through a Principal Component Analysis (PCA). In order to produce the different susceptibility maps for each landslide type, we performed a discrimination analysis to weigh the different factors. The three resulting susceptibility maps have been combined to obtain a general susceptibility map for slope movements in the Sierra Nevada National Park area. This study indicates that some of the new analyzed factors as NVDI index, tectonic activity, and runoff coefficient have a strong influence in the landslide susceptibility in the Sierra Nevada National Park.

  16. 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.

  17. 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

  18. The relationship among probability of failure, landslide susceptibility and rainfall

    NASA Astrophysics Data System (ADS)

    Huang, Chuen Ming; Lee, Chyi-Tyi

    2016-04-01

    Landslide hazard included spatial probability, temporal probability and size probability. Many researches evaluate spatial probability in landslide susceptibility, but it is not many in temporal probability and size probability. Because of it must own enough landslide inventories that covered entire study area and large time range. In seismology, using Poisson model to calculate temporal probability is a well-known inference. However, it required a long term and complete records to analyze. In Taiwan, the remote sensing technology made us to establish multi landslide inventories easily, but it is still lack in time series. Thus the landslide susceptibility through changed different return period triggering factor was often assumed landslide hazard. Compare with landslide inventory, collected a long tern rainfall gauge records is easy. However, landslide susceptibility is a relative spatial probability. No matter using different event or analyzing in different area, the landslide susceptibility is not equal. So which model is representative that is difficult to be decided. This study adopted histogram matching to construct basic landslide susceptibility of the region. Then the relationship between landslide susceptibility, probability of failure and rainfall in multi-event can be found out.

  19. Application of Persistent Scatterers deformation inventories to assess regional landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Oliveira, S. C.; Nico, G.; Zêzere, J. L.; Catalão, J.; Garcia, R. A. C.; Benevides, P.; Piedade, A.

    2010-05-01

    The consistency of landslide inventories is an important issue when analyzing a hazard scenario. Landslide Inventory maps depends on the scope, the available resources, and the scale of investigation, and are conditioned by factors such as the chosen data acquisition technique (e.g. field survey or aerial photo-interpretation), the experience of the geomorphologist, and the complexity of the study area (Guzzetti et al. 2000). In addition, the time available to complete the landslide inventory may be a constrain regarding its reliability. It is now generally accepted that landslide inventories must be permanently up to date. However, it is not easy to guarantee the complete update as well as the robustness of landslide inventories for large areas, because of the time consuming process of landslide data acquisition. In this context, Interferometric Synthetic Aperture Radar (InSAR) methods can provide data to turn more reliable the existent landslide inventories and consequently improve landslide susceptibility assessment at the regional/basin scales. The aim of this work is: i) to evaluate the possibility to use Interferometric Synthetic Aperture Radar data to generate landslide inventories; ii) to assess landslide susceptibility at a regional/basin scale with Persistent Scatterers-based landslide inventories; and iii) to validate the reliability of this landslide susceptibility map with an independent filed survey-based landslide inventory. A dataset of 58 ERS-1/2 SAR images, from 1992 to 1998, and a second dataset of 25 ENVISAT/ASAR images, from 2003 to 2009, were processed. The Persistent Scatters (PS) technique was used to estimate the Line Of Sight (LOS) surface deformation. All PSs located on a slope and with a positive LOS velocity (subsidence) are believed to be indicative of landslide activity. The main assumption after images processing and verification (validation) is that the resultant PS data-base corresponds to landslide activity, so, each PS is assumed

  20. Landslide susceptibility assessment in ash-fall pyroclastic deposits surrounding Mount Somma-Vesuvius: Application of geophysical surveys for soil thickness mapping

    NASA Astrophysics Data System (ADS)

    De Vita, P.; Agrello, D.; Ambrosino, F.

    2006-06-01

    Along the steep slopes of the carbonate mountains that surround the Campanian Plain and Mount Somma-Vesuvius, rainfall-triggered debris slides occur in unconsolidated ash-fall pyroclastic deposits. The initial debris slides evolve into debris flows that often cause significant property damage and loss of human life in the towns located at the foot of the slopes. In this particular geological situation, the pyroclastic soil thickness, the slope angle, and the morphological variations of the slope profile are the most important factors that contribute to landslide susceptibility. In this paper, the results of an experimental application of shallow resistivity and refraction seismic soundings in mapping the thickness of pyroclastic soils are presented. These geophysical methods are proposed as low-cost and versatile methods to be used in the difficult morphological conditions of the steep slopes in which debris-slides initiate. The methods have been used experimentally in a sample area located on the upper slope of Mount Pizzo d'Alvano, from which debris flows initiated that dramatically hit the town of Sarno on 5-6 May 1998. The inversion of geoelectrical soundings has been calibrated with resistivity values measured directly on pyroclastic outcrops and with soil thickness estimations derived from refraction seismic soundings and from the application of a mobile dynamic penetrometer. The results of the field experimentation can be summarised as follows: (i) unconsolidated ash-fall pyroclastic deposits, ranging in particle size from fine ash to lapilli, can be differentiated from fractured carbonate bedrock by means of electrical resistivity and velocity values of longitudinal seismic waves; (ii) thickness of ash-fall pyroclastic soils can be empirically related to the slope angle using an inverse relationship; and (iii) the empirical model has been applied to Digital Elevation Model data, allowing pyroclastic soil thickness mapping in the sample area.

  1. 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.

  2. 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

  3. Artificial Neural Networks applied to landslide susceptibility assessment

    NASA Astrophysics Data System (ADS)

    Ermini, Leonardo; Catani, Filippo; Casagli, Nicola

    2005-03-01

    Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors, usually managed as thematic data within geographic information systems (GIS). In heuristic approaches, these factors are rated by the attribution of scores based on the assumed role played by each of them in controlling the development of a sliding process. Other more refined methods, based on the principle that the present and the past are keys to the future, have also been developed, thus allowing less subjective analyses in which landslide susceptibility is assessed by statistical relationships between past landslide events and hillslope instability factors. The objective of this research is to define a method with the ability to forecast landslide susceptibility through the application of Artificial Neural Networks (ANNs). The Riomaggiore catchment, a subwatershed of the Reno River basin located in the Northern Apennines (Italy), was chosen as an ideal test site, as it is representative of many of the geomorphological settings within this region. In the present application, two different ANNs, used in classification problems, were set up and applied: one belonging to the category of Multi-Layered Perceptron (MLP) and the other to the Probabilistic Neural Network (PNN) family. The hillslope factors that have been taken into account in the analysis were the following: (a) lithology, (b) slope angle, (c), profile curvature, (d) land cover and (e) upslope contributing area. These factors have been classified on nominal scales, and their intersection allowed 3342 homogeneous domains (Unique Condition Unit, UCU) to be singled out, which correspond to the terrain units utilized in this analysis. The model vector used to train the ANNs is a subset of that derived from the production of Unique Condition Units and consists of 3342 records organized in input and output variable vectors. In particular, the hillslope factors, once classified on nominal

  4. 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.

  5. Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslides susceptibility models

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

    The aim of this study is to identify the landslide predisposing factors combination, using a bivariate statistical model that best predict landslide susceptibility. The best predictive model should have a good performance in terms of suitability and predictive power, and should be based on landslide predisposing factors that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km2) located in the Northern Portugal. Several destructive landslides occurred in this area in the last decades promoting landscape degradation and other negative human and economic impacts. A landslide inventory was built in 2005-2009 using aerial photo-interpretation (1/5.000 scale) and field work validation. This inventory contains 767 shallow translational slides. The landslide density is 11 events/square kilometre, and each landslide has, on average, 136 m2 and the depth of the slip surface typically ranges from 1 to 1.5 m. The landslide layer was crossed individually with seven landslide predisposing factors (Aspect; Curvature; Slope Angle; Geomorphological Units; Land Use; Inverse Wetness Index; Lithology) and each class within each predisposing theme was weighted using the Information Value Method. In order to identify the best combination of landslide predisposing factors, all possible combinations were tested which resulted in 120 predictive models. The goodness of fit of each landslide susceptibility model was evaluated by constructing the Success Rate Curves and by computing the Area Under the Curve (AUC). The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. Two tests were performed to the entire dataset to assess conditional independence: the Overall Conditional Independence (OCI) and the Agterberg & Cheng Conditional Independence Test (ACCIT) (Agterberg and Cheng, 2002). The best landslide susceptibility model was constructed with only three

  6. Landslide susceptibility and early warning model for shallow landslide in Taiwan

    NASA Astrophysics Data System (ADS)

    Huang, Chun-Ming; Wei, Lun-Wei; Chi, Chun-Chi; Chang, Kan-Tsun; Lee, Chyi-Tyi

    2017-04-01

    This study aims to development a regional susceptibility model and warning threshold as well as the establishment of early warning system in order to prevent and reduce the losses caused by rainfall-induced shallow landslides in Taiwan. For the purpose of practical application, Taiwan is divided into nearly 185,000 slope units. The susceptibility and warning threshold of each slope unit were analyzed as basic information for disaster prevention. The geological characteristics, mechanism and the occurrence time of landslides were recorded for more than 900 cases through field investigation and interview of residents in order to discuss the relationship between landslides and rainfall. Logistic regression analysis was performed to evaluate the landslide susceptibility and an I3-R24 rainfall threshold model was proposed for the early warning of landslides. The validations of recent landslide cases show that the model was suitable for the warning of regional shallow landslide and most of the cases can be warned 3 to 6 hours in advanced. We also propose a slope unit area weighted method to establish local rainfall threshold on landslide for vulnerable villages in order to improve the practical application. Validations of the local rainfall threshold also show a good agreement to the occurrence time reported by newspapers. Finally, a web based "Rainfall-induced Landslide Early Warning System" is built and connected to real-time radar rainfall data so that landslide real-time warning can be achieved. Keywords: landslide, susceptibility analysis, rainfall threshold

  7. Comparison of non-landslide sampling strategies to counteract inventory-based biases within national-scale statistical landslide susceptibility models

    NASA Astrophysics Data System (ADS)

    Lima, Pedro; Steger, Stefan; Glade, Thomas

    2017-04-01

    Landslides can represent a significant threat for people and infrastructure in hilly and mountainous landscapes worldwide. The understanding and prediction of those geomorphic processes is crucial to avoid economic loses or even casualties to people and their properties. Statistical based landslide susceptibility models are well known for being highly reliant on the quality, representativeness and availability of input data. In this context, several studies indicate that the landslide inventory represents the most important input data. However each landslide mapping technique or data collection has its drawbacks. Consequently, biased landslide inventories may be commonly introduced into statistical models, especially at regional or even national scale. It remains to the researcher to be aware of potential limitations and design strategies to avoid or reduce the potential propagation of input data errors and biases influences on the modelling outcomes. Previous studies have proven that such erroneous landslide inventories may lead to unrealistic landslide susceptibility maps. We assume that one possibility to tackle systematic landslide inventory-based biases might be a concentration on sampling strategies that focus on the distribution of non-landslide locations. For this purpose, we test an approach for the Austrian territory that concentrates on a modified non-landslide sampling strategy, instead the traditional applied random sampling. It is expected that the way non-landslide locations are represented (e.g. equally over the area or within those areas where mapping campaigns have been conducted) is important to reduce a potential over- or underestimation of landslide susceptibility within specific areas caused by bias. As presumably each landslide inventory is known to be systematically incomplete, especially in those areas where no mapping campaign was previously conducted. This is also applicable to the one currently available for the Austrian territory

  8. Spatial probabilistic approach on landslide susceptibility assessment from high resolution sensors derived parameters

    NASA Astrophysics Data System (ADS)

    Aman, S. N. A.; Abd Latif, Z.; Pradhan, B.

    2014-02-01

    Landslide occurrence depends on various interrelating factors which consequently initiate to massive mass of soil and rock debris that move downhill due to the gravity action. LiDAR has come with a progressive approach in mitigating landslide by permitting the formation of more accurate DEM compared to other active space borne and airborne remote sensing techniques. The objective of this research is to assess the susceptibility of landslide in Ulu Klang area by investigating the correlation between past landslide events with geo environmental factors. A high resolution LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive second deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI) as well as NDVI generated from IKONOS imagery. Subsequently, a probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factor. Factor ratings were summed up to obtain Landslide Susceptibility Index (LSI) to construct the landslide susceptibility map.

  9. The comparison between a ground based and a space based probabilistic landslide susceptibility assessment

    NASA Astrophysics Data System (ADS)

    Reichenbach, P.; Mondini, A.; Guzzetti, F.; Rossi, M.; Ardizzone, F.; Cardinali, M.

    2009-04-01

    Probabilistic landslide susceptibility assessments attempt to predict the location and threat posed by known landslides. Under the assumption that landslides will occur in the future because of the same conditions that produced them in the past, geomorphologists use susceptibility assessments to predict the location of future landslides. We present an attempt to exploit satellite data to prepare a landslide susceptibility zonation for a the Collazzone area that extends for 79 sq km in the Umbria region, Central Italy. For the study area we have prepared a map of the Normalized Difference Vegetation Index (NDVI) obtained by processing raw NIR and RED channels (b2 and b3 bands) at 15 m x 15 m resolution of an image acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), on board the TERRA satellite, and a map of Land Surface Temperature (LST) obtained by processing raw TIR channels (b11 to b15 bands) at 90 m × 90 m resolution from the same image. Both maps, in general proxy for soil moisture maps, were obtained through standard algorithms. As expected, there is a strong correspondence between NDVI and LST, but, when the NDVI does not change, elevation effects and others are predominant in LST. For the Collazzone area we prepared two different susceptibility models. The first was prepared through multivariate analysis of thematic data (including morphometry, lithology, structure and land use) obtained through traditional methods, primarily the interpretation of aerial photographs and field work. The second susceptibility model was prepared using terrain morphology and information obtained processing satellite data. The two models were compared in term of model fit and model performance and were validated exploiting landslide inventories not used to build the models. The two susceptibility models are very similar from a geographic and a classification point of view. This is good news, as it tells us that for landslide susceptibility

  10. 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

  11. 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

  12. 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.

  13. Assessment of landslide distribution map reliability in Niigata prefecture - Japan using frequency ratio approach

    NASA Astrophysics Data System (ADS)

    Rahardianto, Trias; Saputra, Aditya; Gomez, Christopher

    2017-07-01

    Research on landslide susceptibility has evolved rapidly over the few last decades thanks to the availability of large databases. Landslide research used to be focused on discreet events but the usage of large inventory dataset has become a central pillar of landslide susceptibility, hazard, and risk assessment. Indeed, extracting meaningful information from the large database is now at the forth of geoscientific research, following the big-data research trend. Indeed, the more comprehensive information of the past landslide available in a particular area is, the better the produced map will be, in order to support the effective decision making, planning, and engineering practice. The landslide inventory data which is freely accessible online gives an opportunity for many researchers and decision makers to prevent casualties and economic loss caused by future landslides. This data is advantageous especially for areas with poor landslide historical data. Since the construction criteria of landslide inventory map and its quality evaluation remain poorly defined, the assessment of open source landslide inventory map reliability is required. The present contribution aims to assess the reliability of open-source landslide inventory data based on the particular topographical setting of the observed area in Niigata prefecture, Japan. Geographic Information System (GIS) platform and statistical approach are applied to analyze the data. Frequency ratio method is utilized to model and assess the landslide map. The outcomes of the generated model showed unsatisfactory results with AUC value of 0.603 indicate the low prediction accuracy and unreliability of the model.

  14. Establish susceptibility and risk assessment models for rainfall-induced landslide: A case in Central Taiwan

    NASA Astrophysics Data System (ADS)

    Wu, Chunhung; Huang, Jyuntai

    2017-04-01

    .823 for LR. The study normalized the susceptibility value range of three landslide susceptibility models to 0 to 1 to deeply compare the model performance. The normalized landslide susceptibility value > 0.5 and ≦0.5 are regarded as predicted-landslide area and predicted-not-landslide area. The ratio of the area in the predicted-landslide area to the total area is 3.0% for FR, 71.4% for WOE, and 26.5% for LR. And the correct ratio is 65.5% for FR, 61.9% for WOE, 74.5% for LR. The study adopted 14 rainfall stations with more than 20 years daily rainfall data in Renai Township to estimate the 24 hrs accumulated rainfall with different RPYs. Landslide susceptibility map under 24 hrs accumulated rainfall distribution with different RPYs is used to estimate the landslide disaster location and scale. The landslide risk under different RPYs in Renai Township is calculated as 2.62 billion for 5 RPYs, 3.06 billion for 10 RPYs, 4.69 billion for 25 RPYs, 5.97 billion for 50 RPYs, 6.98 billion for 100 RPYs, and 8.23 billion for 200 RPYs, respectively.

  15. 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

  16. Structure and characteristics of landslide input data and consequences on landslide susceptibility assessment and prediction capability

    NASA Astrophysics Data System (ADS)

    Oliveira, S. C.; Zezere, J. L.; Garcia, R. A. C.; Piedade, A.

    2009-04-01

    For the territorial planning and management it is of crucial importance the knowledge of the landslide susceptibility, in order to minimize the physical damages and economic losses associated to a certain instability scenario. Resultant mitigation measures can only be effective if we were able to predict where future landslides will occur. In order to improve the quality of data driven landslide susceptibility assessment, recent research developed worldwide as been focused on some fundamental questions: What is the quality of landslide inventories? What is the most appropriate terrain-unit to adopt? What is the most reliable statistical model? What are the best tools to validate results? In contrast, little attention has been given in the literature to the consequences on the landslide susceptibility assessment resulting from the structure and characteristics of the landslide database. Under the assumption that the conditions that led to slope instability in the past are more likely to generate new instability in the future, the statistically-based landslide susceptibility evaluation for a specific area is based on the spatial correlation between a set of independent, predisposing landslide geo-environmental factors, and the distribution of past landslides, which are considered the dependent variable. Landslides are usually included in the susceptibility models as a single point or as a polygon representing the entire unstable area. The selection of the way landslide information enter into prediction models (point vs polygon) is frequently conditioned by software constrains, and surprisingly, the effects of this choice in landslide susceptibility results has not been made. The purpose of this study is to evaluate the quality of susceptibility results obtained for rotational slides in a 12 km2 test site located at north of Lisbon, Portugal considering: (i) the structure and characteristics of landslide input data; (ii) the capacity of different landslide inventories

  17. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

    NASA Astrophysics Data System (ADS)

    Xu, Chong; Dai, Fuchu; Xu, Xiwei; Lee, Yuan Hsi

    2012-04-01

    Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

    There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce. The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available. The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions. The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The

  19. Extreme rainfall-induced landslide changes based on landslide susceptibility in China, 1998-2015

    NASA Astrophysics Data System (ADS)

    Li, Weiyue; Liu, Chun; Hong, Yang

    2017-04-01

    Nowadays, landslide has been one of the most frequent and seriously widespread natural hazards all over the world. Rainfall, especially heavy rainfall is a trigger to cause the landslide occurrence, by increasing soil pore water pressures. In China, rainfall-induced landslides have risen up over to 90% of the total number. Rainfall events sometimes generate a trend of extremelization named rainfall extremes that induce the slope failure suddenly and severely. This study shows a method to simulate the rainfall-induced landslide spatio-temporal distribution on the basis of the landslide susceptibility index. First, the study on landslide susceptibility in China is introduced. We set the values of the index to the range between 0 and 1. Second, we collected TRMM 3B42 precipitation products spanning the years 1998-2015 and extracted the daily rainfall events greater than 50mm/day as extreme rainfall. Most of the rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate extreme rainfall-induced landslide distribution and illustrate the changes in 17 years. This study shows a useful tool to be part of rainfall-induced landslide simulation methodology for landslide early warning.

  20. A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China

    NASA Astrophysics Data System (ADS)

    Hong, Haoyuan; Ilia, Ioanna; Tsangaratos, Paraskevas; Chen, Wei; Xu, Chong

    2017-08-01

    The present study proposed a hybrid fuzzy weight of evidence model for constructing a landslide susceptibility map in the Wuyuan area, China, where disastrous landslide events have occurred. The model combines the knowledge of experts obtained through a fuzzy logic approach and a hybrid weight of evidence method. The estimated knowledge-based fuzzy membership value of each environmental variable is combined with data-based conditional probabilities to derive fuzzy posterior probabilities and landslide susceptibility. The developed model was compared with a landslide susceptibility map produced using the data-driven weight of evidence method, based on 510 landslide and non-landslide sites. The sites were identified by analyzing airborne imagery, field investigation and previous studies. Landside susceptibility for these sites was analyzed using 10 geo-environmental variables: slope, aspect, lithology, soil, rainfall, plan curvature, the normalized difference vegetation index, distance to roads, distance to rivers and distance to faults. The resultant hybrid fuzzy weight of evidence method showed high predictive power, with the area under the success and predictive curves being 0.770 and 0.746, respectively. Additional analyses showed that the developed model could work effectively even with limited data.

  1. An unconventional GIS-based method to assess landslide susceptibility using point data features

    NASA Astrophysics Data System (ADS)

    Adami, S.; Bresolin, M.; Carraretto, M.; Castelletti, P.; Corò, D.; Di Mario, F.; Fiaschi, S.; Frasson, T.; Gandolfo, L.; Mazzalai, L.; Padovan, T.; Sartori, F.; Viganò, A.; Zulian, A.; De Agostini, A.; Pajola, M.; Floris, M.

    2012-04-01

    In this work are reported the results of a project performed by the students attending the course "GIS techniques in Applied Geology", in the master level of the Geological Sciences degree from the Department of Geosciences, University of Padua. The project concerns the evaluation of landslide susceptibility in the Val d'Agno basin, located in the North-Eastern Italian Alps and included in the Vicenza Province (Veneto Region, NE Italy). As well known, most of the models proposed to assess landslide susceptibility are based on the availability of spatial information on landslides and related predisposing environmental factors. Landslides and related factors are spatially combined in GIS systems to weight the influence of each predisposing factor and produce landslide susceptibility maps. The first and most important input factor is the layer landslide, which has to contain as minimum information shape and type of landslides, so it must be a polygon feature. In Italy, as well as in many countries all around the world, location and type of landslides are available in the main spatial databases (AVI project and IFFI project), but in few cases mass movements are delimited, thus they are spatially represented by point features. As an example, in the Vicenza Province, the IFFI database contains 1692 landslides stored in a point feature, but only 383 were delimited and stored in a polygon feature. In order to provide a method that allows to use all the information available and make an effective spatial prediction also in areas where mass movements are mainly stored in point features, punctual data representing landslide in the Val d'Agno basin have been buffered obtaining polygon features, which have been combined with morphometric (elevation, slope, aspect and curvature) and non-morphometric (land use, distance of roads and distance of river) factors. Two buffers have been created: the first has a radius of 10 meters, the minimum required for the analysis, and the second

  2. 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

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. Susceptibility analysis for landslides in the Xiangxi catchment (Three Gorges Reservoir area / China)

    NASA Astrophysics Data System (ADS)

    Rohn, J.; Ehret, D.; Xiang, W.

    2009-04-01

    The Xiangxi River is a tributary of the Yangtze River. In 2009 the Three Gorges Reservoir will reach its final retention water elevation level (175 m asl). Parts of the Xiangxi valley will then flooded. Especially Jurassic sedimentary layers are predestined for intense landslides in this area. As a first step a landslide inventory map is produced. All slopes influenced directly by impoundment are mapped geotechnically in detail to assess the spatial distribution of the landslides and their shape. Furthermore, two sub-catchments in the wide-stretched catchment area of the Xiangxi River were chosen for intense investigation. All in all, about 200 km2will finally be mapped geotechnically in detail to provide data for continuative investigations. The investigation fields are divided into test and training areas for further analysis using the neural networks method. By this means the susceptibility for landslides in dependency of different features, like lithology, slope angle, exposition, distance to the river, etc will be analysed. In a second step the results of the neural network analysis will be the base of a more regional landslide susceptibility analysis for the whole catchment area of the Xiangxi River. The performance of the method will be tested by additional inspections in areas that have been found to have a high susceptibility for landslides. These works are part of the joint research project "Yangtze: land use change - erosion - landslides" financed by the German Federal Ministry of Education and Research (BMBF). Joint aim of this project is to produce a landslide and erosion risk map for the whole region and to analyse the land use change caused by the impoundment of the Three Gorges Dam in this area.

  9. Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

    NASA Astrophysics Data System (ADS)

    Ceppi, C.; Mancini, F.; Ritrovato, G.

    2009-04-01

    This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. Dealing with heterogeneous landslide information for landslide susceptibility assessment: comparing a pixel-based and slope unit-based approach

    NASA Astrophysics Data System (ADS)

    Jacobs, Liesbet; Kervyn, Matthieu; Poesen, Jean; Reichenbach, Paola; Rossi, Mauro; Marchesini, Ivan; Alvioli, Massimiliano; Dewitte, Olivier

    2017-04-01

    In the Rwenzori Mountains, various multi-disciplinary data collection initiatives have resulted in a heterogeneous database counting 247 fully characterized landslides with known size and shape (polygon dataset) and 307 landslides represented as single points taken at an unknown location within the landslide body (point dataset). While the polygon dataset covers only 9% of the inhabited highlands, the point dataset extends the total inventoried area to 18% of the entire inhabited highland region. A regional susceptibility model for the total area should therefore include both information from polygon- as well as point datasets. This involves two distinct methodological challenges with regard to the use of points and polygons respectively. Firstly, the point dataset, where the location of the point within the landslide body is unknown, may not be fully representative for the spatial conditions under which the landslides occurred. Here we aim to identify a robust approach, to limit this uncertainty and maximize the point location representativeness. For this purpose, a pixel-based approach is tested and compared to a slope unit-based approach. To mimic the uncertainty related to the localization of the points, 50 random samplings of single points within each landslide were performed and then fed into a logistic regression model. The model was thus run 50 times using both the slope unit-based and the pixel-based approach. The results show that the slope unit-based alternative has an overall better performance than the pixel-based with comparable stability over the runs. Based on these results, the slope unit seems a more appropriate mapping unit for a susceptibility model based on point-data. A second significant methodological issue, when using polygon-based models, concerns the decision on when a slope unit is considered to be landslide-prone. A threshold representing the fraction of the slope unit affected by landslides above which a slope unit is assigned to be

  18. 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

  19. Predictive susceptibility analysis of typhoon induced landslides in Central Taiwan

    NASA Astrophysics Data System (ADS)

    Shou, Keh-Jian; Lin, Zora

    2017-04-01

    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, such as 2004 Mindulle and 2009 Morakot, hit Taiwan and induced serious flooding and landslides. 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 adopted Wu River watershed in Central Taiwan. To assess the spatial hazard of the landslides, landslide susceptibility analysis was also applied. Different types of rainfall factors were tested in the susceptibility models for a better accuracy. In addition, the routes of typhoons were also considered in the predictive analysis. The results of predictive analysis can be applied for risk prevention and management in the study area.

  20. Susceptibility and triggering scenarios at a regional scale for shallow landslides

    NASA Astrophysics Data System (ADS)

    Gullà, G.; Antronico, L.; Iaquinta, P.; Terranova, O.

    2008-07-01

    The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an ˜ 15,075 km 2 extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing. Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors. The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps. On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility. Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been

  1. Rainfall-induced landslide susceptibility zonation of Puerto Rico

    Treesearch

    Chiara Lepore; Sameer A. Kamal; Peter Shanahan; Rafael L. Bras

    2011-01-01

    Landslides are a major geologic hazard with estimated tens of deaths and $1–2 billion in economic losses per year in the US alone. The island of Puerto Rico experiences one or two large events per year, often triggered in steeply sloped areas by prolonged and heavy rainfall. Identifying areas susceptible to landslides thus has great potential value for Puerto Rico and...

  2. 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

  3. 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.

  4. Landslide overview map of the conterminous United States

    USGS Publications Warehouse

    Radbruch-Hall, Dorothy H.; Colton, Roger B.; Davies, William E.; Lucchitta, Ivo; Skipp, Betty A.; Varnes, David J.

    1982-01-01

    The accompanying landslide overview map of the conterminous United States is one of a series of National Environmental Overview Maps that summarize geologic, hydrogeologic, and topographic data essential to the assessment of national environmental problems. The map delineates areas where large numbers of landslides exist and areas which are susceptible to landsliding. It was prepared by evaluating the geologic map of the United States and classifying the geologic units according to high, medium, or low landslide incidence (number) and high, medium, or low susceptibility to landsliding. Rock types, structures, topography, precipitation, landslide type, and landslide incidence are mentioned for each physical subdivision of the United States. The differences in slope stability between the Colorado Plateau, the Appalachian Highlands, the Coast Ranges of California, and the Southern Rocky Mountains are compared in detail, to illustrate the influence of various natural factors on the types of landsliding that occur in regions having different physical conditions. These four mountainous regions are among the most landslide-prone areas in the United States. The Colorado Plateau is a deformed platform where interbedded sedimentary rocks of varied lithologic properties have been gently warped and deeply eroded. The rocks are extensively fractured. Regional fracture systems, joints associated with individual geologic structures, and joints parallel to topographic surfaces, such as cliff faces, greatly influence slope stability. Detached blocks at the edges of mesas, as well as columns, arched recesses, and many natural arches on the Colorado Plateau, were formed wholly or in part by mass movement. In the Appalachian Highlands, earth flows, debris flows, and debris avalanches predominate in weathered bedrock and colluvium. Damaging debris avalanches result when persistent steady rainfall is followed by a sudden heavy downpour. Landsliding in unweathered bedrock is controlled

  5. 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.

  6. 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

  7. 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

  8. 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

  9. Evaluating performance of simplified physically based models for shallow landslide susceptibility

    NASA Astrophysics Data System (ADS)

    Formetta, Giuseppe; Capparelli, Giovanna; Versace, Pasquale

    2016-11-01

    Rainfall-induced shallow landslides can lead to loss of life and significant damage to private and public properties, transportation systems, etc. Predicting locations that might be susceptible to shallow landslides is a complex task and involves many disciplines: hydrology, geotechnical science, geology, hydrogeology, geomorphology, and statistics. Two main approaches are commonly used: statistical or physically based models. Reliable model applications involve automatic parameter calibration, objective quantification of the quality of susceptibility maps, and model sensitivity analyses. This paper presents a methodology to systemically and objectively calibrate, verify, and compare different models and model performance indicators in order to identify and select the models whose behavior is the most reliable for particular case studies.The procedure was implemented in a package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslide susceptibility analysis (M1, M2, and M3) and a component for model verification. It computes eight goodness-of-fit indices by comparing pixel-by-pixel model results and measurement data. The integration of the package in NewAge-JGrass uses other components, such as geographic information system tools, to manage input-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. The area is extensively subject to rainfall-induced shallow landslides mainly because of its complex geology and climatology. The analysis was carried out considering all the combinations of the eight optimized indices and the three models. Parameter calibration, verification, and model performance assessment were performed by a comparison with a detailed landslide inventory map for the

  10. 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

  11. Strategies for 2nd Grade zonation on susceptibility to seismic-induced landslides in Southern Apennines, Italy

    NASA Astrophysics Data System (ADS)

    Tarallo, D.; Rapolla, A.; Paoletti, V.; di Nocera, S.; Matano, F.

    2010-12-01

    Key Words: Seismic-induced Landslides, Landslide Susceptibility, Evaluation Strategies, Southern Italy Southern Apennines, Italy, are characterized by a very high seismic hazard and the shaking related to this seismicity has been a major cause for landslide triggering. An effective study of seismic hazard of a landslide-prone region should therefore include a detailed assessment of the seismic slope stability, to be carried out at different scales (Fell et al., 2008). Due to the complexity of the different factors controlling slope stability there are currently only a few grade-2 methods (scales 1:50.000-1:10.000) for assessing seismic-induced landslide susceptibility. We here present an application to different seismic areas (Sannio and Irpinia) in Southern Apennines, Italy, of a new strategy for zonation on seismic-induced landslide susceptibility. These areas are characterized by several landslides most of them triggered by the main 1980 Irpinia earthquake. The new GIS-based approach (Rapolla et al., 2010) employs only three factors that we believe are most significant in the susceptibility assessment: i) the properties of outcropping rock/soil expressed as transversal seismic velocity (Vs) ii) the slope angle, iii) the MCS (Mercalli-Cancani-Sieberg) Intensity. The lithological characteristics of the study areas were obtained from geological maps with 1:25000 scale. The attribution of the representative shear wave velocity value to the lithological units required a careful evaluation of their geotechnical and geophysical behavior. The slope angles were obtained from high resolution digital elevation model of the topography of the investigated areas. Finally, the seismic input for computing the MCS Intensity was derived from the macroseismic scenario for the worst event expected in the study area. Each of the three parameters was expressed as a Significance percentage and the resulting Seismic Landslide Susceptibility level was given by the average of the

  12. 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.

  13. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods

    NASA Astrophysics Data System (ADS)

    Pourghasemi, Hamid Reza; Rossi, Mauro

    2016-08-01

    Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide

  14. 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.

  15. 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

  16. Maps showing seismic landslide hazards in Anchorage, Alaska

    USGS Publications Warehouse

    Jibson, Randall W.

    2014-01-01

    The devastating landslides that accompanied the great 1964 Alaska earthquake showed that seismically triggered landslides are one of the greatest geologic hazards in Anchorage. Maps quantifying seismic landslide hazards are therefore important for planning, zoning, and emergency-response preparation. The accompanying maps portray seismic landslide hazards for the following conditions: (1) deep, translational landslides, which occur only during great subduction-zone earthquakes that have return periods of =300-900 yr; (2) shallow landslides for a peak ground acceleration (PGA) of 0.69 g, which has a return period of 2,475 yr, or a 2 percent probability of exceedance in 50 yr; and (3) shallow landslides for a PGA of 0.43 g, which has a return period of 475 yr, or a 10 percent probability of exceedance in 50 yr. Deep, translational landslide hazards were delineated based on previous studies of such landslides, with some modifications based on field observations of locations of deep landslides. Shallow-landslide hazards were delineated using a Newmark-type displacement analysis for the two probabilistic ground motions modeled.

  17. Maps Showing Seismic Landslide Hazards in Anchorage, Alaska

    USGS Publications Warehouse

    Jibson, Randall W.; Michael, John A.

    2009-01-01

    The devastating landslides that accompanied the great 1964 Alaska earthquake showed that seismically triggered landslides are one of the greatest geologic hazards in Anchorage. Maps quantifying seismic landslide hazards are therefore important for planning, zoning, and emergency-response preparation. The accompanying maps portray seismic landslide hazards for the following conditions: (1) deep, translational landslides, which occur only during great subduction-zone earthquakes that have return periods of =~300-900 yr; (2) shallow landslides for a peak ground acceleration (PGA) of 0.69 g, which has a return period of 2,475 yr, or a 2 percent probability of exceedance in 50 yr; and (3) shallow landslides for a PGA of 0.43 g, which has a return period of 475 yr, or a 10 percent probability of exceedance in 50 yr. Deep, translational landslide hazard zones were delineated based on previous studies of such landslides, with some modifications based on field observations of locations of deep landslides. Shallow-landslide hazards were delineated using a Newmark-type displacement analysis for the two probabilistic ground motions modeled.

  18. 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

  19. 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.

  20. 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

  1. Is it beneficial to approximate pre-failure topography to predict landslide susceptibility with empirical models?

    NASA Astrophysics Data System (ADS)

    Steger, Stefan; Schmaltz, Elmar; Glade, Thomas

    2017-04-01

    Empirical landslide susceptibility maps spatially depict the areas where future slope failures are likely due to specific environmental conditions. The underlying statistical models are based on the assumption that future landsliding is likely to occur under similar circumstances (e.g. topographic conditions, lithology, land cover) as past slope failures. This principle is operationalized by applying a supervised classification approach (e.g. a regression model with a binary response: landslide presence/absence) that enables discrimination between conditions that favored past landslide occurrences and the circumstances typical for landslide absences. The derived empirical relation is then transferred to each spatial unit of an area. Literature reveals that the specific topographic conditions representative for landslide presences are frequently extracted from derivatives of digital terrain models at locations were past landslides were mapped. The underlying morphology-based landslide identification becomes possible due to the fact that the topography at a specific locality usually changes after landslide occurrence (e.g. hummocky surface, concave and steep scarp). In a strict sense, this implies that topographic predictors used within conventional statistical landslide susceptibility models relate to post-failure topographic conditions - and not to the required pre-failure situation. This study examines the assumption that models calibrated on the basis of post-failure topographies may not be appropriate to predict future landslide locations, because (i) post-failure and pre-failure topographic conditions may differ and (ii) areas were future landslides will occur do not yet exhibit such a distinct post-failure morphology. The study was conducted for an area located in the Walgau region (Vorarlberg, western Austria), where a detailed inventory consisting of shallow landslides was available. The methodology comprised multiple systematic comparisons of models

  2. 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

  3. Landslide susceptibility assessment and validation in the Abadia Basin, Estremadura - Portugal

    NASA Astrophysics Data System (ADS)

    Garcia, R.; Zezere, J. L.

    2003-04-01

    The detailed geomorphological survey of the Abadia Basin (Torres Vedras, Portuguese Estremadura) allows the mapping of 105 slope movements, which were integrated into a database. Landslide density is 15.8 per km2 and the total unstable area corresponds to 1.4% of the total area of the basin. Rotational and shallow translational slides are the most common types of slope movements in the study area. Most of these landslides (70%) were classified as recent and old movements. The remaining 30% of slope instabilities were triggered during the rainy winter of 2000-2001 (namely in January 2001) and were directly followed during field work. Susceptibility analysis was carried out using a data driven approach over unique-condition terrain units in a matrix basis (GIS environment). The general assumption is that the spatial correlation between landslides occurred in the past and a series of mappable conditioning parameters (e.g. lithology, geological structure, slope, aspect, slope profile, vegetation cover, land use, anthropogenic cuts, fluvial channels) provides the means to predict the future distribution of slope instabilities. Landslide susceptibility was assessed using two types of mathematical procedures: the Information Value Method (Yin &Yan, 1988) and discriminant analysis. These statistical methods were applied both to the total set of rotational and shallow translational slides, and to each type of slope movement. Success rates of the models were evaluated comparing each susceptibility assessment with the landslides used in the analysis. The obtained results, although always showing a high 'goodness of fit' of the data, are higher for the models applied to individual landslide types, testifying that the different types of slope movements are not equally controlled by the considered instability factors. In order to validate the prediction models, the landslide dataset was partitioned in two temporal subgroups: pre-2000 events and cases occurred in the winter 2000

  4. 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.

  5. Landslide susceptibility estimations in the Gerecse hills (Hungary).

    NASA Astrophysics Data System (ADS)

    Dávid, Gerzsenyi; Gáspár, Albert

    2017-04-01

    Surface movement processes are constantly posing threat to property in populated and agricultural areas in the Gerecse hills (Hungary). The affected geological formations are mainly unconsolidated sediments. Pleistocene loess and alluvial terrace sediments are overwhelmingly present, but fluvio-lacustrine sediments of the latest Miocene, and consolidated Eocene and Mesozoic limestones and marls can also be found in the area. Landslides and other surface movement processes are being studied for a long time in the area, but a comprehensive GIS-based geostatistical analysis have not yet been made for the whole area. This was the reason for choosing the Gerecse as the focus area of the study. However, the base data of our study are freely accessible from online servers, so the used method can be applied to other regions in Hungary. Qualitative data was acquired from the landslide-inventory map of the Hungarian Surface Movement Survey and from the Geological Map of Hungary (1 : 100 000). Morphometric parameters derived from the SRMT-1 DEM were used as quantitative variables. Using these parameters the distribution of elevation, slope gradient, aspect and categorized geological features were computed, both for areas affected and not affected by slope movements. Then likelihood values were computed for each parameters by comparing their distribution in the two areas. With combining the likelihood values of the four parameters relative hazard values were computed for each cell. This method is known as the "empirical probability estimation" originally published by Chung (2005). The map created this way shows each cell's place in their ranking based on the relative hazard values as a percentage for the whole study area (787 km2). These values provide information about how similar is a certain area to the areas already affected by landslides based on the four predictor variables. This map can also serve as a base for more complex landslide vulnerability studies involving

  6. 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

  7. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0

    NASA Astrophysics Data System (ADS)

    Rossi, Mauro; Reichenbach, Paola

    2016-10-01

    Landslide susceptibility (LS) assessment provides a relative estimate of landslide spatial occurrence based on local terrain conditions. A literature review revealed that LS evaluation has been performed in many study areas worldwide using different methods, model types, different partition of the territory (mapping units) and a large variety of geo-environmental data. Among the different methods, statistical models have been largely used to evaluate LS, but the minority of articles presents a complete and comprehensive LS assessment that includes model performance analysis, prediction skills evaluation, and estimation of the errors and uncertainty. The aim of this paper is to describe LAND-SE (LANDslide Susceptibility Evaluation) software that performs susceptibility modelling and zonation using statistical models, quantifies the model performances, and the associated uncertainty. The software is implemented in R, a free software environment for statistical computing and graphics. This provides users with the possibility to implement and improve the code with additional models, evaluation tools, or output types. The paper describes the software structure, explains input and output, and illustrates specific applications with maps and graphs. The LAND-SE script is delivered with a basic user guide and three example data sets.

  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. 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

  10. 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

  11. Mapping Surface Features Produced by an Active Landslide

    NASA Astrophysics Data System (ADS)

    Parise, Mario; Gueguen, Erwan; Vennari, Carmela

    2016-10-01

    A large landslide reactivated on December 2013, at Montescaglioso, southern Italy, after 56 hours of rainfall. The landslide disrupted over 500 m of a freeway, involved a few warehouses, a supermarket, and private homes. After the event, it has been performed field surveys, aided by visual analysis of terrestrial and helicopter photographs, to compile a map of the surface deformations. The geomorphological features mapped included single fractures, sets of fractures, tension cracks, trenches, and pressure ridges. In this paper we present the methodology used, the map obtained through the intensive field work, and discuss the main surface features produced by the landslide.

  12. 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.

  13. Landslides Inventory Maps in the Region of Tizi-Ouzou

    NASA Astrophysics Data System (ADS)

    Nacira, Bouaziz; Bachir, Melbouci

    2016-10-01

    Landslides are a complex natural phenomenon that constitutes a worldwide serious natural hazard. Northern Algeria, as all the Mediterranean countries, suffers by this hazard in many towns (JIJEL, Bejaia, Algiers, Constantine, Mila, Media...). Landslides constitute a significant problem for development and urban planning particularly in the city of Tizi-Ouzou, where after each pluvial season; landslides cause many damages for constructions, soils and human lives. The region of Tizi-Ouzou is situated in an area with a variable geology characterised by the presence of different loose formations, where the landslides are widespread. The inventory map of landslides was constructed by field surveys and historical phenomenon, the number of major and significant landslides considered exceeds 25, scattered all about this region. Our paper aims to present the first inventory map of the major landslides induced by different parameters as lithology, geology, slopes, precipitations, urbanization and seismic activities in this region since 1950. Each landslide will be presented and characterized with different geotechnical and geophysical parameters. The results of this study show the importance of landslides inventory in the region of Tizi-Ouzou, to preserve and reduce the hazard to build in risked region, to save human lives and provide useful tools to take decisions.

  14. 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

  15. 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

  16. Large-area landslide susceptibility with optimized slope-units

    NASA Astrophysics Data System (ADS)

    Alvioli, Massimiliano; Marchesini, Ivan; Reichenbach, Paola; Rossi, Mauro; Ardizzone, Francesca; Fiorucci, Federica; Guzzetti, Fausto

    2017-04-01

    A Slope-Unit (SU) is a type of morphological terrain unit bounded by drainage and divide lines that maximize the within-unit homogeneity and the between-unit heterogeneity across distinct physical and geographical boundaries [1]. Compared to other terrain subdivisions, SU are morphological terrain unit well related to the natural (i.e., geological, geomorphological, hydrological) processes that shape and characterize natural slopes. This makes SU easily recognizable in the field or in topographic base maps, and well suited for environmental and geomorphological analysis, in particular for landslide susceptibility (LS) modelling. An optimal subdivision of an area into a set of SU depends on multiple factors: size and complexity of the study area, quality and resolution of the available terrain elevation data, purpose of the terrain subdivision, scale and resolution of the phenomena for which SU are delineated. We use the recently developed r.slopeunits software [2,3] for the automatic, parametric delineation of SU within the open source GRASS GIS based on terrain elevation data and a small number of user-defined parameters. The software provides subdivisions consisting of SU with different shapes and sizes, as a function of the input parameters. In this work, we describe a procedure for the optimal selection of the user parameters through the production of a large number of realizations of the LS model. We tested the software and the optimization procedure in a 2,000 km2 area in Umbria, Central Italy. For LS zonation we adopt a logistic regression model implemented in an well-known software [4,5], using about 50 independent variables. To select the optimal SU partition for LS zonation, we want to define a metric which is able to quantify simultaneously: (i) slope-unit internal homogeneity (ii) slope-unit external heterogeneity (iii) landslide susceptibility model performance. To this end, we define a comprehensive objective function S, as the product of three

  17. 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.

  18. Map showing landslides and areas of potential landsliding in the Salina quadrangle, Utah

    USGS Publications Warehouse

    Williams, Paul L.

    1972-01-01

    The term “landslide” is broadly defined as any “downward and outward movement of slope-forming materials composed of natural rock, soils, artificial fills, or combinations of these materials. The moving mass may proceed by any one of three principal types of movement: falling, sliding, or flossing, or by their combinations” (Varnes, 1958). Landslides and areas of potential landslides are fairly common in the rugged terrain of the Salina quadrangle. In much of the western half of the map area, relatively high rainfall, steep slopes, and flat layers of hard rock on top of very soft incompetent rock all favor landsliding, chiefly as slides and earth flows. In arid parts of the quadrangle, principally in the east half, alternating flat layers of hard and soft rocks are eroded to bare cliffs separated by benches, and rockfalls are the dominant type of landsliding. Landslides were more active in the wetter climate of the Pleistocene Epoch, which ended several thousand years ago (Smith and others, 1963, p. 52). Although landslide deposits are abundant in the Salina quadrangle, few landslide movements have been documented during historic time, partly because landslides are generally less active now than during Pleistocene times, partly because movement is commonly very slow and thus escapes notice, and partly because of the remoteness and sparse population of the area.

  19. 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

  20. 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.

  1. 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.

  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. 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

  4. Coupling a landslide susceptibility model and a ground water model for predicting the timing of shallow landsliding

    NASA Astrophysics Data System (ADS)

    Chiang, S.; Chang, K.

    2012-12-01

    A coupled model has been developed to predict the timing of shallow landslides. The model comprises a landslide susceptibility model to estimate critical wetness responsible for landslide initiation and a ground water model to simulate changes of soil wetness affected by storm rainfall. The model determines the timing of landsliding when the simulated soil wetness exceeds the calculated critical wetness of soil. To better capture the transient dynamics of ground water over hillslopes, we simulate two important processes at the same time: (1) the vertical infiltration within a layered soil, and (2) the lateral subsurface flow driven by hilly topography. The coupled model was first tested and calibrated in two small experiment sites located at Coos Bay in western Oregon, the United States, and Hsiuluan Village in northern Taiwan. At the sites, the landslide data and timing had been investigated and recorded in past landslide events. The model was then applied to the 116.6 km-sq Huagoushan Watershed in southern Taiwan, and the simulation results were validated by comparing them with a landslide inventory prepared after Typhoon Morakot (2009), including landslide locations and their timing. Among the results, we have found that (1) fractures in bedrocks can strongly affect the development of soil wetness, and (2) the dynamics of ground water during a storm and the position of failure plane can critically influence the predictions of the landslide timing and unstable area. These and other results will be presented and discussed in the conference.

  5. 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

  6. 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

  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. 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

  9. Spatial models for landslide susceptibility using logistic regression method with different landslide inventories. Application in Moldavian Plateau, north-east Romania

    NASA Astrophysics Data System (ADS)

    Ciprian Margarint, Mihai; Niculita, Mihai

    2013-04-01

    Quantitative methods for landslide susceptibility at medium scale are considered to have a high level of objectivity. This is because of the acquisition and preparation mode of the geospatial data, but also due to the possibilities of model error and robustness estimation. Beside this, cross-validation procedure, have a good predictive power on the models realized on multi-temporal data sets. In this study we have chosen a representative area of approx. 120 km2 situated in central part of Moldavian Plateau (north-east Romania). This is an area in which landslides have an important frequency, at the moment almost 30% being covered by these processes. Their extension and distribution is governed by the geologic monoclinal structure, clay predominance of the bessarabian strata, landform dissection and climatic conditions. Landslide susceptibility assessment was realized using logistic regression, on a multiple landslide inventory. This inventory was created using ortorectified aerial images from 1978 and 2010, for each periods being considered both old and active landslides. The covariates for modelling were based on a Digital Elevation Model at 10x10 m obtained using 2.5 m contours lines extracted from 1:5,000 topographic maps. As causal factors and predictors of landslide initiation the main geomorphometric variables (elevation, slope angle, slope aspect, plan, profile and mean curvature, modified catchment area, topographic wetness index), statistical indices of them (standard deviation of elevation, slope gradient and slope aspect), distance to drainage network and roads, soil types and land use, were considered. The land use data, also included the land use changes between 1978-2010. The predictive performance of the models was assessed by Receiver Operating Characteristic (ROC) curve. The results show a good correspondence between the actual distribution of landslides and the susceptibility estimation for the two periods. In both cases AUROC (Area Under the ROC

  10. Landslide Susceptibility Across the Pacific Northwest: The Heavy Influence of Transportation Inventories

    NASA Astrophysics Data System (ADS)

    Stanley, Thomas; Kirschbaum, Dalia

    2017-04-01

    Some of the largest and best landslide databases in the United States of America describe the Pacific Northwest region. Nevertheless, these inventories are not a comprehensive listing of historic landslides. In particular, landslide dates tend to be recorded by state transportation agencies, which imposes a spatial bias upon any subsequent analysis. This reporting bias complicates not only the identification of landslide triggering conditions, but also hinders empirical calculations of landslide susceptibility. Although many strategies for bias mitigation could be employed, the simplest approach delivers generally plausible results that are most reliable in the most critical locations: along major highways and rail lines. This work tests logistic regression models that were fitted in zones with landslide reports, then applied regionally. Due to the destabilizing effects of excavation and other anthropogenic disturbances, the models may overestimate susceptibility in undeveloped areas. However, the susceptibility of developed sites should be as accurate as the modeling technique and input data allow.

  11. Map showing 1983 landslides in Utah

    USGS Publications Warehouse

    Brabb, Earl E.; Wieczorek, Gerald F.; Harp, Edwin L.

    1989-01-01

    The State of Utah sustained direct damages from landslides and flooding in excess of $400 million during approximately three months in the spring of 1983.  These disastrous events were declared national disaster areas (Anderson and others, 1985).

  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)

    NASA Astrophysics Data System (ADS)

    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.

  14. 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.

  15. Sentinel-2 for rapid operational landslide inventory mapping

    NASA Astrophysics Data System (ADS)

    Stumpf, André; Marc, Odin; Malet, Jean-Philippe; Michea, David

    2017-04-01

    Landslide inventory mapping after major triggering events such as heavy rainfalls or earthquakes is crucial for disaster response, the assessment of hazards, and the quantification of sediment budgets and empirical scaling laws. Numerous studies have already demonstrated the utility of very-high resolution satellite and aerial images for the elaboration of inventories based on semi-automatic methods or visual image interpretation. Nevertheless, such semi-automatic methods are rarely used in an operational context after major triggering events; this is partly due to access limitations on the required input datasets (i.e. VHR satellite images) and to the absence of dedicated services (i.e. processing chain) available for the landslide community. Several on-going initiatives allow to overcome these limitations. First, from a data perspective, the launch of the Sentinel-2 mission offers opportunities for the design of an operational service that can be deployed for landslide inventory mapping at any time and everywhere on the globe. Second, from an implementation perspective, the Geohazards Exploitation Platform (GEP) of the European Space Agency (ESA) allows the integration and diffusion of on-line processing algorithms in a high computing performance environment. Third, from a community perspective, the recently launched Landslide Pilot of the Committee on Earth Observation Satellites (CEOS), has targeted the take-off of such service as a main objective for the landslide community. Within this context, this study targets the development of a largely automatic, supervised image processing chain for landslide inventory mapping from bi-temporal (before and after a given event) Sentinel-2 optical images. The processing chain combines change detection methods, image segmentation, higher-level image features (e.g. texture, shape) and topographic variables. Based on a few representative examples provided by a human operator, a machine learning model is trained and

  16. Comparative analysis of three data treatment techniques for landslide susceptibility assessment in the Eastern Pyrenees, Spain.

    NASA Astrophysics Data System (ADS)

    Amorim, S.; Corominas, J.; Lantada, N.; Angulo, C.; Baeza, C.

    2009-04-01

    In this communication, the comparison of three data treatment methodologies for assessing landslide susceptibility is presented. The artificial neural networks (ANN) analysis, discriminant analysis (DA) and logistic regression (LR) have been performed in a test area of the Eastern Pyrenees (Spain), at a local scale (1:5.000). The main objective of our research is the comparison of the results obtained with the different techniques and to discuss the advantages and drawbacks of each of them. A database containing an inventory of 280 shallow landslides triggered during the intense rainy event of November of 1982 has been used. Additional information on significant conditioning factors such as vegetation cover and presence of superficial formation has been included as well as other input variables captured automatically from the Digital Elevation Model (DEM). ANN was performed with MATLAB while DA and LR with the statistical package SPSS. The input data and the results have been implemented on a GIS platform (ArcGIS). The test area has a size of about 40km2 and the susceptibility has been determined at each cell (15x15m). The input variables were selected from previous susceptibility assessment studies carried out in the area. The training and validation analyses have worked with two input cell classes (stable and unstable) and final maps with five susceptibility levels have been prepared. DA and LR classify dichotomous variables. The ANN analysis has been carried out with both classification and regression structures. The Receiver Operating Characteristic (ROC) curves obtained are similar in all the models. However, frequency histograms on stable and unstable populations show significant differences in the distance between the mean values of the populations and in the distribution of the overlapping area. The susceptibility maps prepared with ANN and LR minimize the potentially unstable area. The cumulative percentage curves (Duman et al. 2006) show that using

  17. 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.

  18. 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.

  19. 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

  20. 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

  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. 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

  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 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.

  5. 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

  6. Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

    NASA Astrophysics Data System (ADS)

    Albrecht, F.; Hölbling, D.; Friedl, B.

    2017-09-01

    Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

  7. Participatory three dimensional mapping for the preparation of landslide disaster risk reduction program

    NASA Astrophysics Data System (ADS)

    Kusratmoko, Eko; Wibowo, Adi; Cholid, Sofyan; Pin, Tjiong Giok

    2017-07-01

    This paper presents the results of applications of participatory three dimensional mapping (P3DM) method for fqcilitating the people of Cibanteng' village to compile a landslide disaster risk reduction program. Physical factors, as high rainfall, topography, geology and land use, and coupled with the condition of demographic and social-economic factors, make up the Cibanteng region highly susceptible to landslides. During the years 2013-2014 has happened 2 times landslides which caused economic losses, as a result of damage to homes and farmland. Participatory mapping is one part of the activities of community-based disaster risk reduction (CBDRR)), because of the involvement of local communities is a prerequisite for sustainable disaster risk reduction. In this activity, participatory mapping method are done in two ways, namely participatory two-dimensional mapping (P2DM) with a focus on mapping of disaster areas and participatory three-dimensional mapping (P3DM) with a focus on the entire territory of the village. Based on the results P3DM, the ability of the communities in understanding the village environment spatially well-tested and honed, so as to facilitate the preparation of the CBDRR programs. Furthermore, the P3DM method can be applied to another disaster areas, due to it becomes a medium of effective dialogue between all levels of involved communities.

  8. 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

  9. 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

  10. 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

  11. A new approach to reduce the mapping error of landslide inventory maps

    NASA Astrophysics Data System (ADS)

    Santangelo, Michele; Marchesini, Ivan; Bucci, Francesco; Cardinali, Mauro; Rossi, Mauro; Taylor, Faith; Malamud, Bruce; Guzzetti, Fausto

    2013-04-01

    Landslide inventory maps are key in documenting the type and extent of mass movements in local to regional areas, for both geomorphological studies and landslide hazard assessment. Geomorphologists usually prepare landslide inventories by aerial photo interpretation (API) of stereoscopic images aided by field surveys. Criteria adopted for visual image analyses are derived from the heuristic interpretation of photographic and morphological features of the image, such as shape, size, color tone, texture and pattern. The established (traditional) procedure for transferring photo-interpreted information to a GIS environment involves the manual drawing of information from the aerial photograph to the topographic base map. In this stage, mapping (i.e., positioning, shape, size) errors can occur due to (i) the change in scale, from the aerial photographs to the topographic map, (ii) object deformation in the stereoscopic model, due to the vertical exaggeration and the conical projection of the aerial photographs, (iii) differences in topography in the different cartographic media (aerial photographs and base maps). We recently developed a method to reduce mapping errors which exploits the ortho-rectification of the aerial photograph and the photo-interpreted thematic layers, thus avoiding manual transferring of information to the topographic map. The technique was evaluated in a test area of about 50 km2 in the neighboring of Taormina (Sicily, Southern Italy), where the information concerning mass movement was transferred to two inventory maps using the traditional and ortho-rectification technique. More than 500 landslides pairs have been compared in this test region, ranging in landlside area between 102 and 107 m2. The mapping error associated with the mapped features has been evaluated by calculating the mismatch index for each landslide pair as: E = (A U B)-(A ? B)/(A U B), where A is a landslide of the inventory obtained using the manual drawing approach and B is a

  12. 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.

  13. Overview of quantitative susceptibility mapping.

    PubMed

    Deistung, Andreas; Schweser, Ferdinand; Reichenbach, Jürgen R

    2017-04-01

    Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and represents an important parameter in the field of MRI. With the recently introduced method of quantitative susceptibility mapping (QSM) and its conceptual extension to susceptibility tensor imaging (STI), the non-invasive assessment of this important physical quantity has become possible with MRI. Both methods solve the ill-posed inverse problem to determine the magnetic susceptibility from local magnetic fields. Whilst QSM allows the extraction of the spatial distribution of the bulk magnetic susceptibility from a single measurement, STI enables the quantification of magnetic susceptibility anisotropy, but requires multiple measurements with different orientations of the object relative to the main static magnetic field. In this review, we briefly recapitulate the fundamental theoretical foundation of QSM and STI, as well as computational strategies for the characterization of magnetic susceptibility with MRI phase data. In the second part, we provide an overview of current methodological and clinical applications of QSM with a focus on brain imaging. Copyright © 2016 John Wiley & Sons, Ltd.

  14. 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

  15. 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

  16. 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.

  17. 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

  18. Proposed method for hazard mapping of landslide propagation zone

    NASA Astrophysics Data System (ADS)

    Serbulea, Manole-Stelian; Gogu, Radu; Manoli, Daniel-Marcel; Gaitanaru, Dragos Stefan; Priceputu, Adrian; Andronic, Adrian; Anghel, Alexandra; Liviu Bugea, Adrian; Ungureanu, Constantin; Niculescu, Alexandru

    2013-04-01

    Sustainable development of communities situated in areas with landslide potential requires a fully understanding of the mechanisms that govern the triggering of the phenomenon as well as the propagation of the sliding mass, with catastrophic consequences on the nearby inhabitants and environment. Modern analysis methods for areas affected by the movement of the soil bodies are presented in this work, as well as a new procedure to assess the landslide hazard. Classical soil mechanics offer sufficient numeric models to assess the landslide triggering zone, such as Limit Equilibrium Methods (Fellenius, Janbu, Morgenstern-Price, Bishop, Spencer etc.), blocks model or progressive mobilization models, Lagrange-based finite element method etc. The computation methods for assessing the propagation zones are quite recent and have high computational requirements, thus not being sufficiently used in practice to confirm their feasibility. The proposed procedure aims to assess not only the landslide hazard factor, but also the affected areas, by means of simple mathematical operations. The method can easily be employed in GIS software, without requiring engineering training. The result is obtained by computing the first and second derivative of the digital terrain model (slope and curvature maps). Using the curvature maps, it is shown that one can assess the areas most likely to be affected by the propagation of the sliding masses. The procedure is first applied on a simple theoretical model and then used on a representative section of a high exposure area in Romania. The method is described by comparison with Romanian legislation for risk and vulnerability assessment, which specifies that the landslide hazard is to be assessed, using an average hazard factor Km, obtained from various other factors. Following the employed example, it is observed that using the Km factor there is an inconsistent distribution of the polygonal surfaces corresponding to different landslide

  19. 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

  20. Spatial patterns of landslide dimension: A tool for magnitude mapping

    NASA Astrophysics Data System (ADS)

    Catani, Filippo; Tofani, Veronica; Lagomarsino, Daniela

    2016-11-01

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

  1. 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

  2. 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

  3. 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

  4. Comparison and validation of Logistic Regression and Analytic Hierarchy Process models of landslide susceptibility in monoclinic regions. A case study in Moldavian Plateau, N-E Romania

    NASA Astrophysics Data System (ADS)

    Ciprian Margarint, Mihai; Niculita, Mihai

    2014-05-01

    The regions with monoclinic geological structure are large portions of earth surface where the repetition of similar landform patterns is very distinguished, the scarps of cuestas being characterized by similar values of morphometrical variables. Landslides are associated with these scarps of cuestas and consequently, a very high value of landslide susceptibility can be reported on its surface. In these regions, landslide susceptibility mapping can be realized for the entire region, or for test areas, with accurate, reliable, and available datasets, concerning multi-temporal inventories and landslide predictors. Because of the similar geomorphologic and landslide distribution we think that if any relevance of using test areas for extrapolating susceptibility models is present, these areas should be targeted first. This study case try to establish the level of usability of landslide predictors influence, obtained for a 90 km2 sample located in the northern part of the Moldavian Plateau (N-E Romania), in other areas of the same physio-geographic region. In a first phase, landslide susceptibility assessment was carried out and validated using logistic regression (LR) approach, using a multiple landslide inventory. This inventory was created using ortorectified aerial images from 1978 and 2005, for each period being considered both old and active landslides. The modeling strategy was based on a distinctly inventory of depletion areas of all landslide, for 1978 phase, and on a number of 30 covariates extracted from topographical and aerial images (both from 1978 and 2005 periods). The geomorphometric variables were computed from a Digital Elevation Model (DEM) obtained by interpolation from 1:5000 contour data (2.5 m equidistance), at 10x10 m resolution. Distance from river network, distance from roads and land use were extracted from topographic maps and aerial images. By applying Akaike Information Criterion (AIC) the covariates with significance under 0.001 level

  5. Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future.

    PubMed

    Pisano, L; Zumpano, V; Malek, Ž; Rosskopf, C M; Parise, M

    2017-12-01

    Land cover is one of the most important conditioning factors in landslide susceptibility analysis. Usually it is considered as a static factor, but it has proven to be dynamic, with changes occurring even in few decades. In this work the influence of land cover changes on landslide susceptibility are analyzed for the past and for future scenarios. For the application, an area representative of the hilly-low mountain sectors of the Italian Southern Apennines was chosen (Rivo basin, in Molise Region). With this purpose landslide inventories and land cover maps were produced for the years 1954, 1981 and 2007. Two alternative future scenarios were created for 2050, one which follows the past trend (2050-trend), and another one more extreme, foreseeing a decrease of forested and cultivated areas (2050-alternative). The landslide susceptibility analysis was performed using the Spatial Multi-Criteria Evaluation method for different time steps, investigating changes to susceptibility over time. The results show that environmental dynamics, such as land cover change, affect slope stability in time. In fact there is a decrease of susceptibility in the past and in the future 2050-trend scenario. This is due to the increase of forest or cultivated areas, that is probably determined by a better land management, water and soil control respect to other land cover types such as shrubland, pasture or bareland. Conversely the results revealed by the alternative scenario (2050-alternative), show how the decrease in forest and cultivated areas leads to an increase in landslide susceptibility. This can be related to the assumed worst climatic condition leading to a minor agricultural activity and lower extension of forested areas, possibly associated also to the effects of forest fires. The results suggest that conscious landscape management might contribute to determine a significant reduction in landslide susceptibility. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. 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

  7. Debris flows susceptibility mapping under tropical rain conditions in Rwanda.

    NASA Astrophysics Data System (ADS)

    Nduwayezu, Emmanuel; Nsengiyumva, Jean-Baptiste; BUgnon, Pierre-Charles; Jaboyedoff, Michel; Derron, Marc-Henri

    2017-04-01

    Rwanda is a densely populated country. It means that all the space is exploited, including sometimes areas with very steep slopes. This has as for consequences that during the rainy season slopes with human activities are affected by gravitational processes, mostly debris and mud flows and shallow landslides. The events of early May 2016 (May 8 and 9), with more than 50 deaths, are an illustration of these frequents landslides and inundations. The goal of this work is to produce a susceptibility map for debris/mud flows at regional/national scale. Main available pieces of data are a national digital terrain model at 10m resolution, bedrock and soil maps, and information collected during field visits on some specific localities. The first step is the characterization of the slope angle distribution for the different types of bedrock or soils (decomposition in Gaussian populations). Then, the combination of this information with other geomorphic and hydrologic parameters is used to define potential source areas of debris flows. Finally, propagation maps of debris flows are produced using FLOW-R (Horton et al. 2013). Horton, P., Jaboyedoff, M., Rudaz, B., and Zimmermann, M.: Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale, Nat. Hazards Earth Syst. Sci., 13, 869-885, doi:10.5194/nhess-13-869-2013, 2013. The paper is in open access.

  8. Time-dependent landslide probability mapping

    USGS Publications Warehouse

    Campbell, Russell H.; Bernknopf, Richard L.; ,

    1993-01-01

    Case studies where time of failure is known for rainfall-triggered debris flows can be used to estimate the parameters of a hazard model in which the probability of failure is a function of time. As an example, a time-dependent function for the conditional probability of a soil slip is estimated from independent variables representing hillside morphology, approximations of material properties, and the duration and rate of rainfall. If probabilities are calculated in a GIS (geomorphic information system ) environment, the spatial distribution of the result for any given hour can be displayed on a map. Although the probability levels in this example are uncalibrated, the method offers a potential for evaluating different physical models and different earth-science variables by comparing the map distribution of predicted probabilities with inventory maps for different areas and different storms. If linked with spatial and temporal socio-economic variables, this method could be used for short-term risk assessment.

  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. 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.

  11. 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.

  12. 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.

    2017-04-01

    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.

  13. 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.

  14. 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

  15. Neural network-based model for landslide susceptibility and soil longitudinal profile analyses: Two case studies

    NASA Astrophysics Data System (ADS)

    Farrokhzad, F.; Barari, A.; Choobbasti, A. J.; Ibsen, L. B.

    2011-12-01

    The purpose of this study was to create an empirical model for assessing the landslide risk potential at Savadkouh Azad University, which is located in the rural surroundings of Savadkouh, about 5 km from the city of Pol-Sefid in northern Iran. The soil longitudinal profile of the city of Babol, located 25 km from the Caspian Sea, also was predicted with an artificial neural network (ANN). A multilayer perceptron neural network model was applied to the landslide area and was used to analyze specific elements in the study area that contributed to previous landsliding events. The ANN models were trained with geotechnical data obtained from an investigation of the study area. The quality of the modeling was improved further by the application of some controlling techniques involved in ANN. The observed >90% overall accuracy produced by the ANN technique in both cases is promising for future studies in landslide susceptibility zonation.

  16. 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.

  17. The susceptibility analysis of landslides induced by earthquake in Aso volcanic area, Japan, scoping the prediction

    NASA Astrophysics Data System (ADS)

    Kubota, Tetsuya; Takeda, Tsuyoshi

    2017-04-01

    Kumamoto earthquake on April 16th 2016 in Kumamoto prefecture, Kyushu Island, Japan with intense seismic scale of M7.3 (maximum acceleration = 1316 gal in Aso volcanic region) yielded countless instances of landslide and debris flow that induced serious damages and causalities in the area, especially in the Aso volcanic mountain range. Hence, field investigation and numerical slope stability analysis were conducted to delve into the characteristics or the prediction factors of the landslides induced by this earthquake. For the numerical analysis, Finite Element Method (FEM) and CSSDP (Critical Slip Surface analysis by Dynamic Programming theory based on limit equilibrium method) were applied to the landslide slopes with seismic acceleration observed. These numerical analysis methods can automatically detect the landslide slip surface which has minimum Fs (factor of safety). The various results and the information obtained through this investigation and analysis were integrated to predict the landslide susceptible slopes in volcanic area induced by earthquakes and rainfalls of their aftermath, considering geologic-geomorphologic features, geo-technical characteristics of the landslides and vegetation effects on the slope stability. Based on the FEM or CSSDP results, the landslides occurred in this earthquake at the mild gradient slope on the ridge have the safety factor of slope Fs=2.20 approximately (without rainfall nor earthquake, and Fs>=1.0 corresponds to stable slope without landslide) and 1.78 2.10 (with the most severe rainfall in the past) while they have approximately Fs=0.40 with the seismic forces in this earthquake (horizontal direction 818 gal, vertical direction -320 gal respectively, observed in the earthquake). It insists that only in case of earthquakes the landslide in volcanic sediment apt to occur at the mild gradient slopes as well as on the ridges with convex cross section. Consequently, the following results are obtained. 1) At volcanic

  18. 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)

  19. 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)

  20. 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

  1. Rainfall-Triggered Landslides Bury Sri Lankan Villages

    NASA Technical Reports Server (NTRS)

    Kirschbaum, Dalia; Stanley, Thomas

    2016-01-01

    On the afternoon of May 17th, 2016, a major landslide event caused at least 92 deaths, with 109 still missing*. The site was rated highly susceptible to landslides in a new global landslide susceptibility map. GPM precipitation data suggest that both antecedent and current rainfall as well as complex topography played a role in the slope failures.

  2. 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

  3. Preliminary map of landslide deposits, Denver 1° by 2° Quadrangle, Colorado

    USGS Publications Warehouse

    Colton, Roger B.; Holligan, Jeffrey A.; Anderson, Larry W.

    1975-01-01

    Areas inferred to be underlain by landslide deposits resulting from landsliding, avalanching, block gliding, debris sliding or flowing, earthflows, mudflows, rocksliding, rockfalls, rotational slides, slab or flake sliding, slumping, talus accumulation, and translational sliding. Rock glacier deposits, colluvium, and solifluction deposits are included in some areas. Some till is mapped with landslide deposits because distinguishing these two deposits from one another is difficult: Furthermore, in some areas till has failed by landsliding and other types of mass movements. Movement within the deposits varies from none to rapid; rates of movement may also be variable in any given landslide within the same year. Ages of deposits' range from early Pleistocene to Holocene.

  4. 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.

  5. Mapping of hazard from rainfall-triggered landslides in developing countries: Examples from Honduras and Micronesia

    USGS Publications Warehouse

    Harp, E.L.; Reid, M.E.; McKenna, J.P.; Michael, J.A.

    2009-01-01

    Loss of life and property caused by landslides triggered by extreme rainfall events demonstrates the need for landslide-hazard assessment in developing countries where recovery from such events often exceeds the country's resources. Mapping landslide hazards in developing countries where the need for landslide-hazard mitigation is great but the resources are few is a challenging, but not intractable problem. The minimum requirements for constructing a physically based landslide-hazard map from a landslide-triggering storm, using the simple methods we discuss, are: (1) an accurate mapped landslide inventory, (2) a slope map derived from a digital elevation model (DEM) or topographic map, and (3) material strength properties of the slopes involved. Provided that the landslide distribution from a triggering event can be documented and mapped, it is often possible to glean enough topographic and geologic information from existing databases to produce a reliable map that depicts landslide hazards from an extreme event. Most areas of the world have enough topographic information to provide digital elevation models from which to construct slope maps. In the likely event that engineering properties of slope materials are not available, reasonable estimates can be made with detailed field examination by engineering geologists or geotechnical engineers. Resulting landslide hazard maps can be used as tools to guide relocation and redevelopment, or, more likely, temporary relocation efforts during severe storm events such as hurricanes/typhoons to minimize loss of life and property. We illustrate these methods in two case studies of lethal landslides in developing countries: Tegucigalpa, Honduras (during Hurricane Mitch in 1998) and the Chuuk Islands, Micronesia (during Typhoon Chata'an in 2002).

  6. Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry

    Treesearch

    J. McKean; J. Roering

    2004-01-01

    A map of extant slope failures is the most basic element of any landslide assessment. Without an accurate inventory of slope instability, it is not possible to analyze the controls on the spatial and temporal patterns of mass movement or the environmental, human, or geomorphic consequences of slides. Landslide inventory maps are tedious to compile, difficult to make in...

  7. Highlighting landslides and other geomorphological features using sediment connectivity maps

    NASA Astrophysics Data System (ADS)

    Bossi, Giulia; Crema, Stefano; Cavalli, Marco; Marcato, Gianluca; Pasuto, Alessandro

    2016-04-01

    Landslide identification is usually made through interpreting geomorphological features in the field or with remote sensing imagery. In recent years, airborne laser scanning (LiDAR) has enhanced the potentiality of geomorphological investigations by providing a detailed and diffuse representation of the land surface. The development of algorithms for geomorphological analysis based on LiDAR derived high-resolution Digital Terrain Models (DTMs) is increasing. Among them, the sediment connectivity index (IC) has been used to quantify sediment dynamics in alpine catchments. In this work, maps of the sediment connectivity index are used for detecting geomorphological features and processes not exclusively related to water-laden processes or debris flows. The test area is located in the upper Passer Valley in South Tyrol (Italy). Here a 4 km2 Deep-seated Gravitational Slope Deformation (DGSD) with several secondary phenomena has been studied for years. The connectivity index was applied to a well-known study area in order to evaluate its effectiveness as an interpretative layer to assist geomorphological analysis. Results were cross checked with evidence previously identified by means of in situ investigations, photointerpretation and monitoring data. IC was applied to a 2.5 m LiDAR derived DTM using two different scenarios in order to test their effectiveness: i) IC derived on the hydrologically correct DTM; ii) IC derived on the original DTM. In the resulting maps a cluster of low-connectivity areas appears as the deformation of the DGSD induce a convexity in the central part of the phenomenon. The double crests, product of the sagging of the landslide, are extremely evident since in those areas the flow directions diverge from the general drainage pattern, which is directed towards the valley river. In the crown area a rock-slab that shows clear evidence of incumbent detachment is clearly highlighted since the maps emphasize the presence of traction trenches and

  8. Preliminary Map of Landslide Deposits in the Mesa Verde National Park Area, Colorado

    USGS Publications Warehouse

    Carrara, Paul E.

    2009-01-01

    This report presents a preliminary map of landslide deposits in the Mesa Verde National Park area (see map sheet) at a compilation scale of 1:50,000. Landslide is a general term for landforms produced by a wide variety of gravity-driven mass movements, including various types of flows, slides, topples and falls, and combinations thereof produced by the slow to rapid downslope transport of surficial materials or bedrock. The map depicts more than 200 landslides ranging in size from small (0.01 square miles) earthflows and rock slumps to large (greater than 0.50 square miles) translational slides and complex landslides (Varnes, 1978). This map has been prepared to provide a regional overview of the distribution of landslide deposits in the Mesa Verde area, and as such constitutes an inventory of landslides in the area. The map is suitable for regional planning to identify broad areas where landslide deposits and processes are concentrated. It should not be used as a substitute for detailed site investigations. Specific areas thought to be subject to landslide hazards should be carefully studied before development. Many of the landslides depicted on this map are probably stable as they date to the Pleistocene (approximately 1.8-0.011 Ma) and hence formed under a different climate regime. However, the recognition of these landslides is important because natural and human-induced factors can alter stability. Reduction of lateral support (by excavations or roadcuts), removal of vegetation (by fire or development), or an increase in pore pressure (by heavy rains) may result in the reactivation of landslides or parts of landslides.

  9. Changes and future trends in landslide risk mapping for mountain communities: application to the Vars catchment and Barcelonnette basin (French Alps)

    NASA Astrophysics Data System (ADS)

    Puissant, Anne; Wernert, Pauline; Débonnaire, Nicolas; Malet, Jean-Philippe; Bernardie, Séverine; Thomas, Loic

    2017-04-01

    Landslide risk assessment has become a major research subject within the last decades. In the context of the French-funded ANR Project SAMCO which aims at enhancing the overall resilience of societies on the impacts of mountain risks, we developed a procedure to quantify changes in landslide risk at catchment scales. First, we investigate landslide susceptibility, the spatial component of the hazard, through a weight of evidence probabilistic model. This latter is based on the knowledge of past and current landslides in order to simulate their spatial locations in relation to environmental controlling factors. Second, we studied potential consequences using a semi-quantitative region-scale indicator-based method, called method of the Potential Damage Index (PDI). It allows estimating the possible damages related to landslides by combining weighted indicators reflecting the exposure of the element at risk for structural, functional and socio-economic stakes. Finally, we provide landslide risk maps by combining both susceptibility and potential consequence maps resulting from the two previous steps. The risk maps are produced for the present time and for the future (e.g. period 2050 and 2100) taking into account four scenarios of future landcover and landuse development (based on the Prelude European Project) that are consistent with the likely evolution of mountain communities. Results allow identifying the geographical areas that are likely to be exposed to landslide risk in the future. The results are integrated on a web-based demonstrator, enabling the comparison between various scenarios, and could thus be used as decision-support tools for local stakeholders. The method and the demonstrator will be presented through the analysis of landslide risk in two catchments of the French Alps: the Vars catchment and the Barcelonnette basin, both characterized by a different exposure to landslide hazards.

  10. 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

  11. Toward accelerating landslide mapping with interactive machine learning techniques

    NASA Astrophysics Data System (ADS)

    Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

    2013-04-01

    Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

  12. Predictive analysis of landslide susceptibility in the Kao-Ping watershed, Taiwan under climate change conditions

    NASA Astrophysics Data System (ADS)

    Shou, K. J.; Wu, C. C.; Lin, J. F.

    2015-01-01

    Among the most critical issues, climatic abnormalities caused by global warming also affect 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 8 August 2009 and induced serious flooding and landslides. In this study, the Kao-Ping River watershed was adopted as the study area, and the typical events 2007 Krosa Typhoon and 2009 Morakot Typhoon were adopted to train the susceptibility model. 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 Kao-Ping River watershed. The rainfall estimates were introduced in the landslide susceptibility model to produce the predictive landslide susceptibility for various rainfall scenarios, including abnormal climate conditions. These results can be used for hazard remediation, mitigation, and prevention plans for the Kao-Ping River watershed.

  13. 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.

  14. 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

  15. 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

  16. Map and description of the active part of the Slumgullion Landslide, Hinsdale County, Colorado

    USGS Publications Warehouse

    Fleming, R.W.; Baum, Rex L.; Giardino, Marco

    1999-01-01

    This text accompanies a map of many of the features on the active part of the Slumgullion landslide, Hinsdale County, Colo. Long-term movement creates and destroys a variety of structural features on the surface of the landslide including faults, fractures, and folds, as well as basins and ridges. The Slumgullion landslide consists of a large volume of inactive landslide deposits and a much smaller volume that is actively moving within the deposits of the older landslide. Previously, collapse of the south side of the scarp on Mesa Seco produced materials that blocked the Lake Fork of the Gunnison River and created Lake San Cristobal. The current landslide activity was triggered by a collapse, which apparently extended the preexisting headscarp toward the north. The loading induced by the deposition of the collapsed materials reactivated some of the older landslide deposits. Displacement rates in the active part of the landslide range from about 0.2 m/yr at the uppermost fractures to a maximum of 7.4 m/yr in the narrowest part of the landslide. From this maximum rate, displacement rate declines to 2 or less m/yr at the toe. The interplay between different displacement rates, varying width, and curving boundaries gives rise to the structures within the landslide. For purposes of description, the landslide has been divided into seven zones: head, zone of stretching, the hopper and neck, zone of pull-apart basins, pond deposits and emergent toe, zone of shortening and spreading, and active toe. Each zone has its characteristic kinematic expression that provides information on the internal deformation of the landslide. In general, the upper part of the landslide is characterized by features such as normal faults and tension cracks associated with stretching. The lowermost part of the landslide is characterized by thrust faults and other features associated with shortening. In between, features are a result of widening, bending, or narrowing of the landslide. Also, in

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. A Bayesian approach for mapping event landslides using optical remote sensing imagery and digital terrain data

    NASA Astrophysics Data System (ADS)

    Guzzetti, F.; Mondini, A. C.; Marchesini, I.; Rossi, M.; Chang, K.; Pasquariello, G.

    2012-12-01

    Event landside inventory maps can be prepared using conventional or new mapping methods. Conventional methods, including field mapping and the visual interpretation of stereoscopic aerial photographs, are time consuming and resource intensive, restricting the ability to prepare event inventory maps rapidly, repeatedly, and for large and very large areas. This is a significant drawback for regional landslide studies and post event remedial efforts. Investigators are currently experimenting new methods for preparing landslide event inventories exploiting remotely sensed data, including qualitative (visual) and quantitative (numerical) analysis of very-high resolution (VHR) digital elevation models obtained chiefly through LiDAR surveys, and the interpretation and analysis of satellite images, including panchromatic, multispectral, and synthetic aperture radar images. We devised a stepwise, semi-automatic approach to detect, map, and classify internally rainfall-induced shallow landslides exploiting multispectral satellite images taken shortly after a landslide-triggering event, and information on the topographic signature of landslides obtained from a pre-event digital elevation model. In a Bayesian framework, the approach combines a standard image classification obtained by a supervised classifier (e.g., the Mahalanobis Distance classifier) applied to a post-event image, with information on the morphometric landslide signature measured by statistics of terrain slope and cross section convexity in landslide and stable areas. The semi-automatic approach is applied in two steps. First, the rainfall-induced landslides are detected and mapped, separating them from the stable areas. Next, the mapped landslides are classified internally, separating the source from the run out areas. We have applied the approach in a 117 km2 study area in Taiwan, where shallow landslides triggered by high intensity rainfall brought by typhoon Morakot in august 2009 were abundant. Comparison

  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. Landslides

    USGS Publications Warehouse

    Nilsen, T.H.

    1977-01-01

    Each of the major earthquakes described above had magnitudes greater than 6.5. Although smaller earthquakes may cause less damage to manmade structures by ground shaking, they are capable of triggering slope failures, especially renewed movements of old, marginally stable landslide deposits (fig. 5), in hillside areas. 

  5. Landslide!

    NASA Image and Video Library

    2017-05-12

    This image from NASA Mars Reconnaissance Orbiter shows a fresh well-preserved landslide scarp and rocky deposit off the edge of a streamlined mesa in Simud Valles, a giant outflow channel carved by ancient floods. A stereo anaglyph is available at https://photojournal.jpl.nasa.gov/catalog/PIA21633

  6. Preliminary soil-slip susceptibility maps, southwestern California

    USGS Publications Warehouse

    Morton, Douglas M.; Alvarez, Rachel M.; Campbell, Russell H.; Digital preparation by Bovard, Kelly R.; Brown, D.T.; Corriea, K.M.; Lesser, J.N.

    2003-01-01

    This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of “where”, “when”, and “how big”. These maps empirically show part of the “where” of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to “when” the process might begin is developed. “When” is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. “When” information is not provided on the maps but is described later in this narrative. The prediction of “how big” is addressed indirectly by restricting the maps to a single type of landslide process—soil slip-debris flows. The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as

  7. Regional sinkhole susceptibility maps: The Latium Region case (central Italy)

    NASA Astrophysics Data System (ADS)

    La Vigna, F.; Teoli, P.; Mazza, R.; Leoni, G.; Capelli, G.

    2012-04-01

    Several and frequent studies were internationally presented about landslide susceptibility, meanwhile in literature is missing a broad diffusion of studies regarding sinkhole susceptibility. That's why sinkhole recurrence depends on several geological conditions related to specific geological and hydrogeological context (sinkhole prone area) that vary case by case. Notwithstanding this regionalization problem of sinkhole recurrence, in the central Appenine sedimentary basins (Italy) a certain number of geological, geomorphologic and hydrogeological conditions (sinkhole predisposing issues) can be considered in common between the surveyed sinkholes. Eventually this could be compared with similar geological conditions and sinkhole occurrence in the rest of Italy or in other countries. In this case study is presented a probabilistic approach regarding the Latium Region deriving from the comparison between the regional sinkhole inventory realized during a precedent project and the dataset of the new Hydrogeological Map of Latium Region (scale 1:100.000). Indexed elements, chosen because associated to the majority of sinkhole phenomena, are: outcropping lithologies, water table depth, main faults (even if buried), hydrothermal springs, land use and the epicentres of recent earthquakes. These indexed elements were weighted and combined in a matrix which preliminary result is the sinkhole susceptibility map of Latium Region. When definitively validated, this approach could be suitable for local authorities to planning more targeted studies in major hazard areas.

  8. 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

  9. 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.

  10. Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan

    NASA Astrophysics Data System (ADS)

    Wu, C. Y.; Chen, S. C.

    2013-09-01

    Landslide spatial, temporal, and size probabilities were used to perform a landslide hazard assessment in this study. Eleven intrinsic geomorphological, and two extrinsic rainfall factors were evaluated as landslide susceptibility related factors as they related to the success rate curves, landslide ratio plots, frequency distributions of landslide and non-landslide groups, as well as probability-probability plots. Data on landslides caused by Typhoon Aere in the Shihmen watershed were selected to train the susceptibility model. The landslide area probability, based on the power law relationship between the landslide area and a noncumulative number, was analyzed using the Pearson type 5 probability density function. The exceedance probabilities of rainfall with various recurrence intervals, including 2, 5, 10, 20, 50, 100 and 200 yr, were used to determine the temporal probabilities of the events. The study was conducted in the Shihmen watershed, which has an area of 760 km2 and is one of the main water sources for northern Taiwan. The validation result of Typhoon Krosa demonstrated that this landslide hazard model could be used to predict the landslide probabilities. The results suggested that integration of spatial, area, and exceedance probabilities to estimate the annual probability of each slope unit is feasible. The advantage of this annual landslide probability model lies in its ability to estimate the annual landslide risk, instead of a scenario-based risk.

  11. Towards an EO-based Landslide Web Mapping and Monitoring Service

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

    National and regional authorities and infrastructure maintainers in mountainous regions require accurate knowledge of the location and spatial extent of landslides for hazard and risk management. Information on landslides is often collected by a combination of ground surveying and manual image interpretation following landslide triggering events. However, the high workload and limited time for data acquisition result in a trade-off between completeness, accuracy and detail. Remote sensing data offers great potential for mapping and monitoring landslides in a fast and efficient manner. While facing an increased availability of high-quality Earth Observation (EO) data and new computational methods, there is still a lack in science-policy interaction and in providing innovative tools and methods that can easily be used by stakeholders and users to support their daily work. Taking up this issue, we introduce an innovative and user-oriented EO-based web service for landslide mapping and monitoring. Three central design components of the service are presented: (1) the user requirements definition, (2) the semi-automated image analysis methods implemented in the service, and (3) the web mapping application with its responsive user interface. User requirements were gathered during semi-structured interviews with regional authorities. The potential users were asked if and how they employ remote sensing data for landslide investigation and what their expectations to a landslide web mapping service regarding reliability and usability are. The interviews revealed the capability of our service for landslide documentation and mapping as well as monitoring of selected landslide sites, for example to complete and update landslide inventory maps. In addition, the users see a considerable potential for landslide rapid mapping. The user requirements analysis served as basis for the service concept definition. Optical satellite imagery from different high resolution (HR) and very high

  12. Rapid Landslide Mapping by Means of Post-Event Polarimetric SAR Imagery

    NASA Astrophysics Data System (ADS)

    Plank, Simon; Martinis, Sandro; Twele, Andre

    2016-08-01

    Rapid mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response. Reviewing the literature shows that most synthetic aperture radar (SAR) data-based landslide mapping procedures use change detection techniques. However, the required very high resolution (VHR) pre-event SAR imagery, acquired shortly before the landslide event, is commonly not available. Due to limitations in onboard disk space and downlink transmission rates modern VHR SAR missions do not systematically cover the entire world. We present a fast and robust procedure for mapping of landslides, based on change detection between freely available and systematically acquired pre-event optical and post-event polarimetric SAR data.

  13. 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.

  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. Towards the Optimal Pixel Size of dem for Automatic Mapping of Landslide Areas

    NASA Astrophysics Data System (ADS)

    Pawłuszek, K.; Borkowski, A.; Tarolli, P.

    2017-05-01

    Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Monitoring of Landslide Areas with the Use of Contemporary Methods of Measuring and Mapping

    NASA Astrophysics Data System (ADS)

    Skrzypczak, Izabela; Kogut, Janusz; Kokoszka, Wanda; Zientek, Dawid

    2017-03-01

    In recent years, there is an increase of landslide risk observed, which is associated with intensive anthropogenic activities and extreme weather conditions. Appropriate monitoring and proper development of measurements resulting as maps of areas at risk of landslides enables us to estimate the risk in the social and economic aspect. Landslide monitoring in the framework of SOPO project is performed by several methods of measurements: monitoring of surface (GNSS measurement and laser scanning), monitoring in-deepth (inclinometer measurements) and monitoring of the hydrological changes and precipitation (measuring changes in water-table and rainfall).

  2. 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.

  3. 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

  4. 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

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

    USGS Publications Warehouse

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

    2016-04-12

    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.

  6. Application of Landsat-8 and ALOS-2 data for structural and landslide hazard mapping in Kelantan, Malaysia

    NASA Astrophysics Data System (ADS)

    Beiranvand Pour, Amin; Hashim, Mazlan

    2017-07-01

    Identification of high potential risk and susceptible zones for natural hazards of geological origin is one of the most important applications of advanced remote sensing technology. Yearly, several landslides occur during heavy monsoon rainfall in Kelantan River basin, Peninsular Malaysia. Flooding and subsequent landslide occurrences generated significant damage to livestock, agricultural produce, homes and businesses in the Kelantan River basin. In this study, remote sensing data from the recently launched Landsat-8 and Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) on board the Advanced Land Observing Satellite-2 (ALOS-2) were used to map geologic structural and topographical features in the Kelantan River basin for identification of high potential risk and susceptible zones for landslides and flooding areas. The data were processed for a comprehensive analysis of major geological structures and detailed characterizations of lineaments, drainage patterns and lithology at both regional and district scales. The analytical hierarchy process (AHP) approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index (NDVI), land cover, distance to drainage, precipitation, distance to fault and distance to the road were extracted from remote sensing satellite data and fieldwork to apply the AHP approach. Directional convolution filters were applied to ALOS-2 data for identifying linear features in particular directions and edge enhancement in the spatial domain. Results indicate that lineament occurrence at regional scale was mainly linked to the N-S trending of the Bentong-Raub Suture Zone (BRSZ) in the west and Lebir Fault Zone in the east of the Kelantan state. The combination of different polarization channels produced image maps that contain important information related to water bodies, wetlands and lithological units. The N-S, NE-SW and NNE-SSW lineament trends and

  7. Map showing recent and historic landslide activity on coastal bluffs of Puget Sound between Shilshole Bay and Everett, Washington

    USGS Publications Warehouse

    Baum, R.L.; Harp, E.L.; Hultman, W.A.

    2000-01-01

    Many landslides occurred on the coastal bluffs between Seattle and Everett, Washington during the winters of 1996 and 1997. Shallow earth slides and debris flows were the most common, but a few deep-seated rotational earth slides also occurred. The landslides caused significant property damage and interfered with rail traffic; future landslides in the area pose significant hazards to property and public safety. Field observations indicate that ground-water seepage, runoff concentration, and dumping at the tops of the bluffs all contributed to instability of the bluffs. Most landslides in the study area occurred in colluvium, residuum, and landslide deposits derived from the Vashon Drift, particularly the advance outwash. In the northern part of the area, colluvium derived from the Pleistocene Whidbey Formation was also involved in shallow landslides. Comparison of recent activity with historic records in the southern part of the map area indicates that landslides tend to occur in many of the same areas as previous landslides.

  8. Regional landslide-hazard evaluation using landslide slopes, Western Wasatch County, Utah

    USGS Publications Warehouse

    Hylland, M.D.; Lowe, Mark

    1997-01-01

    Landsliding has historically been one of the most damaging geologic hazards in western Wasatch County, Utah. Accordingly, we mapped and analyzed landslides (slumps and debris slides) in the area to provide an empirical basis for regional landslide-hazard evaluation. The 336 landslides in the 250-sq-mi (650-km2) area involve 20 geologic units, including Mississippian- to Quaternary-aged rock and unconsolidated deposits. Landsliding in western Wasatch County is characterized by a strong correlation between geologic material and landslide-slope inclination. From a simple statistical analysis of overall slope inclinations of late Holocene landslides, we determined "critical" slope inclinations above which late Holocene landsliding has typically occurred and used these as the primary basis for defining relative landslide hazard. The critical slopes vary for individual geologic units and range from 15 to 50 percent (9??-27??). The critical slope values and landslide locations were used in conjunction with geologic and slope maps to construct qualitative landslide-susceptibility maps for use by county planners. The maps delineate areas of low, moderate, and high relative hazard and indicate where studies should be completed prior to development to evaluate site-specific slope-stability conditions. Critical slopes as determined in this study provide a consistent empirical reference that is useful for evaluating relative landslide hazard and guiding land-use-planning decisions in large, geologically complex areas.

  9. History of landslide susceptibility and a chorology of landslide-prone areas in the Western Ghats of Kerala, India

    NASA Astrophysics Data System (ADS)

    Kuriakose, Sekhar L.; Sankar, G.; Muraleedharan, C.

    2009-06-01

    Kerala is the third most densely populated state in India. It is a narrow strip of land, of which 47% is occupied by the most prominent orographic feature of peninsular India, The Western Ghats mountain chain. The highlands of Kerala experience several types of landslides, of which debris flows are the most common. They are called “Urul Pottal” in the local vernacular. The west-facing Western Ghats scarps that runs the entire extent of the mountain system is the most prone physiographic unit for landslides. The highlands of the region experience an annual average rainfall as high as 500 cm through the South-West, North-East and Pre-Monsoon showers. A survey of ancient documents and early news papers indicates a reduced rate of slope instability in the past. The processes leading to landslides were accelerated by anthropogenic disturbances such as deforestation since the early 18th century, terracing and obstruction of ephemeral streams and cultivation of crops lacking capability to add root cohesion in steep slopes. The events have become more destructive given the increasing vulnerability of population and property. Majority of mass movements have occurred in hill slopes >20° along the Western Ghats scarps, the only exception being the coastal cliffs. Studies conducted in the state indicates that prolonged and intense rainfall or more particularly a combination of the two and the resultant pore pressure variations are the most important trigger of landslides. The initiation zone of most of the landslides was typical hollows generally having degraded natural vegetation. A survey of post-landslide investigation and news paper reports enabled the identification of 29 major landslide events in the state. All except one of the 14 districts in the state are prone to landslides. Wayanad and Kozhikode districts are prone to deep seated landslides, while Idukki and Kottayam are prone to shallow landslides.

  10. 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. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. 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.

  12. Quantitative susceptibility mapping differentiates between parkinsonian disorders.

    PubMed

    Sjöström, Henrik; Granberg, Tobias; Westman, Eric; Svenningsson, Per

    2017-09-01

    It is often challenging to clinically distinguish between Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Quantitative susceptibility mapping (QSM) is an accurate indirect method for estimating brain iron levels in vivo. This method has yet to be applied in atypical parkinsonism. We aimed to investigate differences in brain iron accumulation parkinsonian disorders and healthy controls using QSM. 15 patients with PSP, 11 patients with MSA, 62 patients with PD and 14 healthy controls were included in the study and their phase and magnitude data from susceptibility-weighted magnetic resonance imaging were retrospectively analyzed with an in-house pipeline to create susceptibility maps. Two-way ANCOVA were used to assess group differences. Pairwise comparisons within the ANCOVA were corrected for multiple comparisons. Red nucleus susceptibility was higher in PSP compared with PD (p < 0.001), MSA (p < 0.001) and controls (p < 0.001), which separated PSP from these groups with areas under receiver operating characteristic curve of 0.97, 0.75 and 0.98 respectively. PSP showed higher globus pallidus susceptibility compared with PD (p < 0.001), MSA (p = 0.006) and controls (p < 0.001). Putamen susceptibility was higher in MSA than in PD (p = 0.022) and controls (p = 0.026). Substantia nigra susceptibility was increased in PD compared to controls (p = 0.030). We show that all studied parkinsonian disorders have increased susceptibility subcortically, reflecting distinct topographical patterns of abnormal brain iron accumulation. QSM, particularly of the red nucleus, is a promising biomarker in differentiating parkinsonian disorders, and would be interesting to study longitudinally for monitoring disease progression and treatment effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. 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.

  14. 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.

  15. Whole brain susceptibility mapping using compressed sensing.

    PubMed

    Wu, Bing; Li, Wei; Guidon, Arnaud; Liu, Chunlei

    2012-01-01

    The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this article, compressed sensing is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the compressed sensing based method outperforms regularization based methods. The key difference between the regularized inversions and compressed sensing compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the compressed sensing based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings.

  16. Whole Brain Susceptibility Mapping Using Compressed Sensing

    PubMed Central

    Wu, Bing; Li, Wei; Guidon, Arnaud; Liu, Chunlei

    2011-01-01

    The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this paper, compressed sensing (CS) is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the CS based method outperforms regularization based methods. The key difference between the regularized inversions and CS compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the CS based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings. PMID:21671269

  17. Quantitative Susceptibility Mapping in Parkinson's Disease

    PubMed Central

    Seiler, Stephan; Deistung, Andreas; Schweser, Ferdinand; Franthal, Sebastian; Homayoon, Nina; Katschnig-Winter, Petra; Koegl-Wallner, Mariella; Pendl, Tamara; Stoegerer, Eva Maria; Wenzel, Karoline; Fazekas, Franz; Ropele, Stefan; Reichenbach, Jürgen Rainer; Schmidt, Reinhold; Schwingenschuh, Petra

    2016-01-01

    Background Quantitative susceptibility mapping (QSM) and R2* relaxation rate mapping have demonstrated increased iron deposition in the substantia nigra of patients with idiopathic Parkinson’s disease (PD). However, the findings in other subcortical deep gray matter nuclei are converse and the sensitivity of QSM and R2* for morphological changes and their relation to clinical measures of disease severity has so far been investigated only sparsely. Methods The local ethics committee approved this study and all subjects gave written informed consent. 66 patients with idiopathic Parkinson’s disease and 58 control subjects underwent quantitative MRI at 3T. Susceptibility and R2* maps were reconstructed from a spoiled multi-echo 3D gradient echo sequence. Mean susceptibilities and R2* rates were measured in subcortical deep gray matter nuclei and compared between patients with PD and controls as well as related to clinical variables. Results Compared to control subjects, patients with PD had increased R2* values in the substantia nigra. QSM also showed higher susceptibilities in patients with PD in substantia nigra, in the nucleus ruber, thalamus, and globus pallidus. Magnetic susceptibility of several of these structures was correlated with the levodopa-equivalent daily dose (LEDD) and clinical markers of motor and non-motor disease severity (total MDS-UPDRS, MDS-UPDRS-I and II). Disease severity as assessed by the Hoehn & Yahr scale was correlated with magnetic susceptibility in the substantia nigra. Conclusion The established finding of higher R2* rates in the substantia nigra was extended by QSM showing superior sensitivity for PD-related tissue changes in nigrostriatal dopaminergic pathways. QSM additionally reflected the levodopa-dosage and disease severity. These results suggest a more widespread pathologic involvement and QSM as a novel means for its investigation, more sensitive than current MRI techniques. PMID:27598250

  18. 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.

  19. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy)

    NASA Astrophysics Data System (ADS)

    Conoscenti, Christian; Ciaccio, Marilena; Caraballo-Arias, Nathalie Almaru; Gómez-Gutiérrez, Álvaro; Rotigliano, Edoardo; Agnesi, Valerio

    2015-08-01

    In this paper, terrain susceptibility to earth-flow occurrence was evaluated by using geographic information systems (GIS) and two statistical methods: Logistic regression (LR) and multivariate adaptive regression splines (MARS). LR has been already demonstrated to provide reliable predictions of earth-flow occurrence, whereas MARS, as far as we know, has never been used to generate earth-flow susceptibility models. The experiment was carried out in a basin of western Sicily (Italy), which extends for 51 km2 and is severely affected by earth-flows. In total, we mapped 1376 earth-flows, covering an area of 4.59 km2. To explore the effect of pre-failure topography on earth-flow spatial distribution, we performed a reconstruction of topography before the landslide occurrence. This was achieved by preparing a digital terrain model (DTM) where altitude of areas hosting landslides was interpolated from the adjacent undisturbed land surface by using the algorithm topo-to-raster. This DTM was exploited to extract 15 morphological and hydrological variables that, in addition to outcropping lithology, were employed as explanatory variables of earth-flow spatial distribution. The predictive skill of the earth-flow susceptibility models and the robustness of the procedure were tested by preparing five datasets, each including a different subset of landslides and stable areas. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The results demonstrate that the overall accuracy of LR and MARS earth-flow susceptibility models is from excellent to outstanding. However, AUC values of the validation datasets attest to a higher predictive power of MARS-models (AUC between 0.881 and 0.912) with respect to LR-models (AUC between 0.823 and 0.870). The adopted procedure proved to be resistant to overfitting and stable when changes of the learning and validation samples are

  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. 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.

  2. 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

  3. 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

  4. Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

    NASA Astrophysics Data System (ADS)

    Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

    2014-12-01

    This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. Landslide databases review in the Geological Surveys of Europe

    NASA Astrophysics Data System (ADS)

    Herrera, Gerardo

    2017-04-01

    Landslides are one of the most widespread geohazards in Europe, producing significant social and economic damages. Rapid population growth in urban areas throughout many countries in Europe and extreme climatic scenarios can considerably increase landslide risk in the near future. However, many European countries do not include landslide risk into their legislation. Countries lack official methodological assessment guidelines and knowledge about landslide impacts. Although regional and national landslide databases exist in most countries, they are often not integrated because they are owed by different institutions. Hence, a European Landslides Directive, that provides a common legal framework for dealing with landslides, is necessary. With this long-term goal in mind, we present a review of the landslide databases from the Geological Surveys of Europe focusing on their interoperability. The same landslide classification was used for the 849,543 landslide records from the Geological Surveys, from which 36% are slides, 10 % falls, 20% flows, 11% complex slides and 24% remain either unclassified or correspond to another typology. A landslide density map was produced from the available records of the Geological Surveys of 17 countries showing the variable distribution of landslides. There are 0.2 million km2 of landslide prone areas. The comparison of this map with the European landslide susceptibility map ELSUS v1 was successful for 73% of the predictions, and permitted identification of 25% of susceptible areas where landslide records are not available from the Geological Surveys. Taking these results into account the completeness of these landslide databases was evaluated, revealing different landslide hazard management approaches between surveys and countries.

  11. 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).

  12. A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal

    NASA Astrophysics Data System (ADS)

    Yu, Bo; Chen, Fang

    2017-03-01

    This paper presents a new technique for landslide mapping from large-scale Landsat8 images. The method introduces saliency enhancement to enhance the landslide regions, making the landslides salient objects in the image. Morphological operations are applied to the enhanced image to remove most background objects. Afterwards, digital elevation model is applied to further remove the ground objects of plain areas according to the height of landscape, since most landslides occur in mountainous areas. Final landslides are extracted by the proposal regions from selective search. The study area covers 2°x2°, making it more similar with practical cases, such as emergency response and landslide inventory mappings. The proposed method performs satisfactorily by detecting 99.1% of the landslides in the image, and obtains an overall accuracy of 99.8% in the landslides/background classification problem, which gets further validated in another Landsat8 image of a different site. The experiment shows that the proposed method is feasible for landslide detection from large-scale area, which may contribute to the further landslide-related research.

  13. 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

  14. A new concept in seismic landslide hazard analysis for practical application

    NASA Astrophysics Data System (ADS)

    Lee, Chyi-Tyi

    2017-04-01

    A seismic landslide hazard model could be constructed using deterministic approach (Jibson et al., 2000) or statistical approach (Lee, 2014). Both approaches got landslide spatial probability under a certain return-period earthquake. In the statistical approach, our recent study found that there are common patterns among different landslide susceptibility models of the same region. The common susceptibility could reflect relative stability of slopes at a region; higher susceptibility indicates lower stability. Using the common susceptibility together with an earthquake event landslide inventory and a map of topographically corrected Arias intensity, we can build the relationship among probability of failure, Arias intensity and the susceptibility. This relationship can immediately be used to construct a seismic landslide hazard map for the region that the empirical relationship built. If the common susceptibility model is further normalized and the empirical relationship built with normalized susceptibility, then the empirical relationship may be practically applied to different region with similar tectonic environments and climate conditions. This could be feasible, when a region has no existing earthquake-induce landslide data to train the susceptibility model and to build the relationship. It is worth mentioning that a rain-induced landslide susceptibility model has common pattern similar to earthquake-induced landslide susceptibility in the same region, and is usable to build the relationship with an earthquake event landslide inventory and a map of Arias intensity. These will be introduced with examples in the meeting.

  15. Probabilistic modelling of rainfall induced landslide hazard assessment

    NASA Astrophysics Data System (ADS)

    Kawagoe, S.; Kazama, S.; Sarukkalige, P. R.

    2010-01-01

    To evaluate the frequency and distribution of landslides hazards over Japan, this study uses a probabilistic model based on multiple logistic regression analysis. Study particular concerns several important physical parameters such as hydraulic parameters, geographical parameters and the geological parameters which are considered to be influential in the occurrence of landslides. Sensitivity analysis confirmed that hydrological parameter (hydraulic gradient) is the most influential factor in the occurrence of landslides. Therefore, the hydraulic gradient is used as the main hydraulic parameter; dynamic factor which includes the effect of heavy rainfall and their return period. Using the constructed spatial data-sets, a multiple logistic regression model is applied and landslide susceptibility maps are produced showing the spatial-temporal distribution of landslide hazard susceptibility over Japan. To represent the susceptibility in different temporal scales, extreme precipitation in 5 years, 30 years, and 100 years return periods are used for the evaluation. The results show that the highest landslide hazard susceptibility exists in the mountain ranges on the western side of Japan (Japan Sea side), including the Hida and Kiso, Iide and the Asahi mountainous range, the south side of Chugoku mountainous range, the south side of Kyusu mountainous and the Dewa mountainous range and the Hokuriku region. The developed landslide hazard susceptibility maps in this study will assist authorities, policy makers and decision makers, who are responsible for infrastructural planning and development, as they can identify landslide-susceptible areas and thus decrease landslide damage through proper preparation.

  16. Deep seated landslide mapping and sliding mass assessment with DInSAR and UAV model

    NASA Astrophysics Data System (ADS)

    Wang, Kuo-Lung; Lin, Jun-Tin; Lin, Meei-Ling; Liao, Ray-Tang; Chen, Chao-Wei; Chi, Chung-Chi

    2017-04-01

    Landslide mapping is simple work in case landslide is sliding and scar appears with optical images. However, it is difficult to reveal the sliding depth and sliding mass from ground surface monitoring. Several deep-seated landslides were investigated in central Taiwan and monitoring systems were installed several years. The sliding depth and sliding mass are defined in these area. A proposed method to define sliding scars and sliding depth from surface deformation observation in this research. Unmanned vehicle produced very high resolution and accuracy digital surface model to help geomorphology identification. SAR images from ALOS, ALOS2, Sentinel-1 and Terra-X are adopted to compare results. DInSAR and SBAS methods are used in this research to discover different landslide details and deformation magnitude. The results shows that ALOS and ALOS2 images are more suitable than other SAR images in this area. This might be owing to the wave length of SAR image, which longer wave length is more suitable for faster landslide and vegetation area.

  17. 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

  18. New potentials of laser scanning in landslide hazard assessments

    NASA Astrophysics Data System (ADS)

    Bell, Rainer; Petschko, Helene

    2010-05-01

    of the landslides can be roughly estimated based on freshness of the structures and the land use. If possible, general ideas of landslide activity in respective regions can be checked and revised if necessary. Multi-temporal DTM's might be very helpful in this respect, but are very rarely available at present. Furthermore, such return periods might be calculated for a larger region if complete landslide inventories are mapped for a sub-region and a maximum age of the landslides is assumed. Regarding landslide susceptibility modelling quite often important information is not spatially available, e.g. the location of important natural or artificial structures (terraces, road cuts, etc.). The challenge is in which form such information can be extracted from ALS DTM's to improve subsequently the landslide susceptibility models. Potentials and limitations of these aspects are discussed and examples are given.

  19. 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

  20. Mapping, Assessment and Analysis of Large-Scale Landslides Based on Airborne LIDAR Data

    NASA Astrophysics Data System (ADS)

    Elsner, Bernhard

    2015-04-01

    In the context of the integrated risk management of the Austrian Service for Torrent and Avalanche Control (WLV) large-scale landslides - per definition only areas larger than 10 ha - were mapped in a study area in the South of Innsbruck/Tyrol. The large-scale landslides to be identified are mostly very slow and deep-seated including complex processes like mountain slope deformations ("Talzuschübe"). The mapping method for the first time developed in this study is based on hillshades of high-resolution airborne LIDAR data which recently has been made nearly nationwide available for Austria. These data have the advantage faced with other remote-sensing data like orthophotos that dense and high vegetation and other distracting objects on the ground are eliminated. This guarantees everywhere a high visibility of the terrain surface which is very helpful for the detection of landslides. These aspects allowed developing a new systematic approach for the identification and rough classification of large-scale landslides according to their activity. Using an iterative comparison of first results with other existing methods and field observations the methodology was developed as objective as possible. For this reason these landslides are divided in four distinct segments showing typical characteristics like double ridges or a surface of rupture. According to the characteristics and to the importance of these four segments all detected landslides are then assigned to one of the four classes of activity: "active", "likely active", "inactive" or "possibly inactive". Using this method, large-scale landslides were identified in 26 % of the entire study area. The major part of these landslides (65 %) is supposed to be "inactive" and only 0.5 % are classified as "active". In addition some analyses in the context of natural hazard research were carried out to interpret the quantitative occurrence and spatial distribution of the mapped landslides. Glacial overdeepening in valleys

  1. Mapping basin-wide subaquatic slope failure susceptibility as a tool to assess regional seismic and tsunami hazards

    NASA Astrophysics Data System (ADS)

    Strasser, Michael; Hilbe, Michael; Anselmetti, Flavio S.

    2010-05-01

    occurred. Comparison of reconstructed critical stability conditions with the known distribution of landslide deposits reveals minimum and maximum threshold conditions for slopes that failed or remained stable, respectively. The resulting correlations reveal good agreements and suggest that the slope stability model generally succeeds in reproducing past events. The basin-wide mapping of subaquatic slope failure susceptibility through time thus can also be considered as a promising paleoseismologic tool that allows quantification of past earthquake ground shaking intensities. Furthermore, it can be used to assess the present-day slope failure susceptibility allowing for identification of location and estimation of size of future, potentially tsunamigenic subaquatic landslides. The new approach presented in our comprehensive lake study and resulting conceptual ideas can be vital to improve our understanding of larger marine slope instabilities and related seismic and oceanic geohazards along formerly glaciated ocean margins and closed basins worldwide.

  2. 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.

  3. 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.

  4. 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

  5. Determination of important topographic factors for landslide mapping analysis using MLP network.

    PubMed

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

  6. Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling

    NASA Astrophysics Data System (ADS)

    Alvioli, Massimiliano; Marchesini, Ivan; Reichenbach, Paola; Rossi, Mauro; Ardizzone, Francesca; Fiorucci, Federica; Guzzetti, Fausto

    2016-11-01

    Automatic subdivision of landscapes into terrain units remains a challenge. Slope units are terrain units bounded by drainage and divide lines, but their use in hydrological and geomorphological studies is limited because of the lack of reliable software for their automatic delineation. We present the r.slopeunits software for the automatic delineation of slope units, given a digital elevation model and a few input parameters. We further propose an approach for the selection of optimal parameters controlling the terrain subdivision for landslide susceptibility modeling. We tested the software and the optimization approach in central Italy, where terrain, landslide, and geo-environmental information was available. The software was capable of capturing the variability of the landscape and partitioning the study area into slope units suited for landslide susceptibility modeling and zonation. We expect r.slopeunits to be used in different physiographical settings for the production of reliable and reproducible landslide susceptibility zonations.

  7. The variability of root cohesion as an influence on shallow landslide susceptibility in the Oregon Coast Range

    USGS Publications Warehouse

    Schmidt, K.M.; Roering, J.J.; Stock, J.D.; Dietrich, W.E.; Montgomery, D.R.; Schaub, T.

    2001-01-01

    Decades of quantitative measurement indicate that roots can mechanically reinforce shallow soils in forested landscapes. Forests, however, have variations in vegetation species and age which can dominate the local stability of landslide-initiation sites. To assess the influence of this variability on root cohesion we examined scarps of landslides triggered during large storms in February and November of 1996 in the Oregon Coast Range and hand-dug soil pits on stable ground. At 41 sites we estimated the cohesive reinforcement to soil due to roots by determining the tensile strength, species, depth, orientation, relative health, and the density of roots ???1 mm in diameter within a measured soil area. We found that median lateral root cohesion ranges from 6.8-23.2 kPa in industrial forests with significant understory and deciduous vegetation to 25.6-94.3 kPa in natural forests dominated by coniferous vegetation. Lateral root cohesion in clearcuts is uniformly ???10 kPa. Some 100-year-old industrial forests have species compositions, lateral root cohesion, and root diameters that more closely resemble 10-year-old clearcuts than natural forests. As such, the influence of root cohesion variability on landslide susceptibility cannot be determined solely from broad age classifications or extrapolated from the presence of one species of vegetation. Furthermore, the anthropogenic disturbance legacy modifies root cohesion for at least a century and should be considered when comparing contemporary landslide rates from industrial forests with geologic background rates.

  8. Empirical Relationship for Probability of Earthquake induced Landslide Failure

    NASA Astrophysics Data System (ADS)

    Lee, Chyi-Tyi

    2016-04-01

    The estimation of probability of landslide failure at each grid point under an expected earthquake ground shaking is fundamental in seismic landslide study. We attempt to build an empirical relationship among probability of failure, basic susceptibility, and Arias intensity using Taiwan data set. At the first step, we use the Chi-Chi earthquake-induced landslide inventory as a training data set to build a susceptibility model for the region. Because the model included Arias intensity of the earthquake, it is event-dependent; the landslide distribution is highly dependent to the earthquake intensity. However, if we extract the Arias intensity factor from the susceptibility model, then it becomes event-independent, and it is similar in pattern to the event-independent model trained by storm-induced landslides. Also, the event-independent models are similar in pattern to the susceptibility model trained by a multi-temporal landslide inventory. We found there is a basic susceptibility model for a region, no matter an event-based model or a model trained by a multi-temporal landslide inventory. After the basic susceptibility of a region is determined, then we can analyse the probability of failure of a certain event at each basic susceptibility and Arias intensity bins and build their relationship. Again, the Chi-Chi earthquake-induced landslide inventory and the Chi-Chi Arias intensity map are used in the analyses together with a basic susceptibility model in central Taiwan. A new empirical relationship is developed to estimate probability of landslide failure as a function of basic susceptibility and Arias intensity based on the Chi-Chi data set. The results show that the relation is good; the probability of failure increases with an increase in Arias intensity and also increases with an increase in the basic susceptibility. This relationship could be a prediction model for earthquake-induced landslide, providing Arial intensity and basic susceptibility are given.

  9. Sentinel-1 Data for the Detection and Mapping of Landslides: A Case Study from Western Peloponnese, Greece

    NASA Astrophysics Data System (ADS)

    Kyriou, Aggeliki S.; Nikolakopoulos, Konstantinos G.

    2016-08-01

    Interferometry is one of the most modern techniques of acquisition earth surface height information and it has a wide range of applications such as surface monitoring, volcanic hazards, seismic events etc. This work focus on exploitation of Sentinel-1 data for the monitoring of an active landslide in a village of Ilia Prefecture, Greece. Sentinel-1 mission provides timely, with global coverage, operational and easily accessible data with satisfactory spatial resolution. These advantages make Sentinel-1 data the best solution for the observation of landslides. In particular, at an initial level of landslide's observation, interferometry contributed to Digital Surface Model (DSM) generation, utilizing the phase difference between the representations of the interferometric pairs. Thus two DSMs were created, one before the landslide and one after it, which were compared to each other in order to identify height differences and ground subsidence and map the landslide zone. The results are presented below.

  10. An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping

    NASA Astrophysics Data System (ADS)

    Abedi Gheshlaghi, Hassan; Feizizadeh, Bakhtiar

    2017-09-01

    Landslides in mountainous areas render major damages to residential areas, roads, and farmlands. Hence, one of the basic measures to reduce the possible damage is by identifying landslide-prone areas through landslide mapping by different models and methods. The purpose of conducting this study is to evaluate the efficacy of a combination of two models of the analytical network process (ANP) and fuzzy logic in landslide risk mapping in the Azarshahr Chay basin in northwest Iran. After field investigations and a review of research literature, factors affecting the occurrence of landslides including slope, slope aspect, altitude, lithology, land use, vegetation density, rainfall, distance to fault, distance to roads, distance to rivers, along with a map of the distribution of occurred landslides were prepared in GIS environment. Then, fuzzy logic was used for weighting sub-criteria, and the ANP was applied to weight the criteria. Next, they were integrated based on GIS spatial analysis methods and the landslide risk map was produced. Evaluating the results of this study by using receiver operating characteristic curves shows that the hybrid model designed by areas under the curve 0.815 has good accuracy. Also, according to the prepared map, a total of 23.22% of the area, amounting to 105.38 km2, is in the high and very high-risk class. Results of this research are great of importance for regional planning tasks and the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area.

  11. 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

  12. A Tool for Modelling the Probability of Landslides Impacting Road Networks

    NASA Astrophysics Data System (ADS)

    Taylor, Faith E.; Santangelo, Michele; Marchesini, Ivan; Malamud, Bruce D.; Guzzetti, Fausto

    2014-05-01

    Triggers such as earthquakes or heavy rainfall can result in hundreds to thousands of landslides occurring across a region within a short space of time. These landslides can in turn result in blockages across the road network, impacting how people move about a region. Here, we show the development and application of a semi-stochastic model to simulate how landslides intersect with road networks during a triggered landslide event. This was performed by creating 'synthetic' triggered landslide inventory maps and overlaying these with a road network map to identify where road blockages occur. Our landslide-road model has been applied to two regions: (i) the Collazzone basin (79 km2) in Central Italy where 422 landslides were triggered by rapid snowmelt in January 1997, (ii) the Oat Mountain quadrangle (155 km2) in California, USA, where 1,350 landslides were triggered by the Northridge Earthquake (M = 6.7) in January 1994. For both regions, detailed landslide inventory maps for the triggered events were available, in addition to maps of landslide susceptibility and road networks of primary, secondary and tertiary roads. To create 'synthetic' landslide inventory maps, landslide areas (AL) were randomly selected from a three-parameter inverse gamma probability density function, consisting of a power law decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL. The number of landslide areas selected was based on the observed density of landslides (number of landslides km-2) in the triggered event inventories. Landslide shapes were approximated as ellipses, where the ratio of the major and minor axes varies with AL. Landslides were then dropped over the region semi-stochastically, conditioned by a landslide susceptibility map, resulting in a synthetic landslide inventory map. The originally available landslide susceptibility maps did not take into account susceptibility changes in the immediate vicinity of roads, therefore

  13. 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.

  14. Inundation Mapping and Hazard Assessment of Tectonic and Landslide Tsunamis in Southeast Alaska

    NASA Astrophysics Data System (ADS)

    Suleimani, E.; Nicolsky, D.; Koehler, R. D., III

    2014-12-01

    The Alaska Earthquake Center conducts tsunami inundation mapping for coastal communities in Alaska, and is currently focused on the southeastern region and communities of Yakutat, Elfin Cove, Gustavus and Hoonah. This activity provides local emergency officials with tsunami hazard assessment, planning, and mitigation tools. At-risk communities are distributed along several segments of the Alaska coastline, each having a unique seismic history and potential tsunami hazard. Thus, a critical component of our project is accurate identification and characterization of potential tectonic and landslide tsunami sources. The primary tectonic element of Southeast Alaska is the Fairweather - Queen Charlotte fault system, which has ruptured in 5 large strike-slip earthquakes in the past 100 years. The 1958 "Lituya Bay" earthquake triggered a large landslide into Lituya Bay that generated a 540-m-high wave. The M7.7 Haida Gwaii earthquake of October 28, 2012 occurred along the same fault, but was associated with dominantly vertical motion, generating a local tsunami. Communities in Southeast Alaska are also vulnerable to hazards related to locally generated waves, due to proximity of communities to landslide-prone fjords and frequent earthquakes. The primary mechanisms for local tsunami generation are failure of steep rock slopes due to relaxation of internal stresses after deglaciation, and failure of thick unconsolidated sediments accumulated on underwater delta fronts at river mouths. We numerically model potential tsunami waves and inundation extent that may result from future hypothetical far- and near-field earthquakes and landslides. We perform simulations for each source scenario using the Alaska Tsunami Model, which is validated through a set of analytical benchmarks and tested against laboratory and field data. Results of numerical modeling combined with historical observations are compiled on inundation maps and used for site-specific tsunami hazard assessment by

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. 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

  20. Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain.

    PubMed

    Schweser, Ferdinand; Sommer, Karsten; Deistung, Andreas; Reichenbach, Jürgen Rainer

    2012-09-01

    Quantitative susceptibility mapping (QSM) is a novel magnetic resonance-based technique that determines tissue magnetic susceptibility from measurements of the magnetic field perturbation. Due to the ill-posed nature of this problem, regularization strategies are generally required to reduce streaking artifacts on the computed maps. The present study introduces a new algorithm for calculating the susceptibility distribution utilizing a priori information on its regional homogeneity derived from gradient echo phase images and analyzes the impact of erroneous a priori information on susceptibility map fidelity. The algorithm, Homogeneity Enabled Incremental Dipole Inversion (HEIDI), was investigated with a special focus on the reconstruction of subtle susceptibility variations in a numerical model and in volunteer data and was compared with two recently published approaches, Thresholded K-space Division (TKD) and Morphology Enabled Dipole Inversion (MEDI). HEIDI resulted in susceptibility maps without streaking artifacts and excellent depiction of subtle susceptibility variations in most regions. By investigating HEIDI susceptibility maps acquired with the volunteers' heads in different orientations, it was demonstrated that the apparent magnetic susceptibility distribution of human brain tissue considerably depends on the direction of the main magnetic field.

  1. An Assessment of Conditioning Parameter Selection Efficiency on Medium Scale Erosion Susceptibility Mapping by GIS and Remote Sensing methodologies : An Example from Northwest Turkey

    NASA Astrophysics Data System (ADS)

    Akgün, Aykut; Turk, Necdet

    2013-04-01

    To make a medium scale erosion susceptibility map, several conditioning parameters can be considered to be input parameter in the model constructed. However, to select appropriate conditioning parameters is an important task in order to provide a comprehensive erosion susceptibility map. In this context, this study examines the efficiency of conditioning parameter selection in a case study. For this purpose, Ayvalık district (Northwest Turkey) was selected where a serious surface erosion problem is available. To make an erosion susceptibility map of the area, two methodologies were considered, namely logistic regression (LR) and analytical hierarchy process (AHP). Weathering of rock units, slope gradient, stream power index (SPI), structural lineament density, drainage density and land cover were considered to be conditioning parameters. Initally, an erosion susceptibility map considering by all the conditioning parameters were produced by LR and AHP methodologies. Then, six different parameter combinations were created, and six different erosion susceptibility maps were also produced for two modelling methods. After obtaining twelve different erosion susceptibility maps, performance analyses were carried out for all produced maps by area under curvature (AUC) procedure. The maps produced were also compared with each other. For this purpose, cross correlation were done, and both similarities and dissimilarities were determined between the maps by Kappa Index (KIA) assessment. After all these process, the obtained erosion susceptibility maps were also compared with the landslide occurrence locations which are another natural hazard problem in the area to investigate the relationship between erosion susceptibility and landslide occurrence. At the end of the performance analysis, the most successful estimations by LR and AHP were obtained, and the results were also discussed in frame of cause-result relationship. Keywords: Erosion, AHP, Logistic regression, Turkey

  2. 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.

  3. 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.

  4. 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

  5. Quantitative Susceptibility Mapping in Cerebral Cavernous Malformations: Clinical Correlations.

    PubMed

    Tan, H; Zhang, L; Mikati, A G; Girard, R; Khanna, O; Fam, M D; Liu, T; Wang, Y; Edelman, R R; Christoforidis, G; Awad, I A

    2016-07-01

    Quantitative susceptibility mapping has been shown to assess iron content in cerebral cavernous malformations. In this study, our aim was to correlate lesional iron deposition assessed by quantitative susceptibility mapping with clinical and disease features in patients with cerebral cavernous malformations. Patients underwent routine clinical scans in addition to quantitative susceptibility mapping on 3T systems. Data from 105 patients met the inclusion criteria. Cerebral cavernous malformation lesions identified on susceptibility maps were cross-verified by T2-weighted images and differentiated on the basis of prior overt hemorrhage. Mean susceptibility per cerebral cavernous malformation lesion (χ̄lesion) was measured to correlate with lesion volume, age at scanning, and hemorrhagic history. Temporal rates of change in χ̄lesion were evaluated in 33 patients. Average χ̄lesion per patient was positively correlated with patient age at scanning (P < .05, 4.1% change with each decade of life). Cerebral cavernous malformation lesions with prior overt hemorrhages exhibited higher χ̄lesion than those without (P < .05). Changes in χ̄lesion during 3- to 15-month follow-up were small in patients without new hemorrhage between the 2 scans (bias = -0.0003; 95% CI, -0.06-0.06). The study revealed a positive correlation between mean quantitative susceptibility mapping signal and patient age in cerebral cavernous malformation lesions, higher mean quantitative susceptibility mapping signal in hemorrhagic lesions, and minimum longitudinal quantitative susceptibility mapping signal change in clinically stable lesions. Quantitative susceptibility mapping has the potential to be a novel imaging biomarker supplementing conventional imaging in cerebral cavernous malformations. The clinical significance of such measures merits further study. © 2016 by American Journal of Neuroradiology.

  6. 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

  7. 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