Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan

This paper proposes a statistical decision-tree model to analyze landslide susceptibility in a wide area of the Akaishi Mountains, Japan. The objectives of this study were to validate the decision-tree model by comparing landslide susceptibility and actual landslide occurrence, and to reveal the relationships among landslide occurrence, topography, and geology. Landslide susceptibility was examined through ensemble learning with a decision tree. Decision trees are advantageous in that estimation processes and order of important explanatory variables are explicitly represented by the tree structures. Topographic characteristics (elevation, slope angle, profile curvature, plan curvature, and dissection and undissection height) and geological data were used as the explanatory variables. These topographic characteristics were calculated from digital elevation models (DEMs). The objective variables were landslide occurrence and reactivation data between 1992 and 2002 that were depicted by satellite image analysis. Landslide susceptibility was validated by comparing actual data on landslides that occurred and reactivated after the model was constructed (between 2002 and 2004).This study revealed that, from 2002 to 2004, landslides tended to occur and reactivate in catchments with high landslide susceptibility. The landslide susceptibility map thus depicts the actual landslide occurrence and reactivation in the Akaishi Mountains. This result indicates that the decision-tree model has appropriate accuracy for estimating the probabilities of future landslides. The tree structure indicates that landslides occurred and reactivated frequently in the catchments that had an average slope angle exceeding ca. 29° and a mode of slope angle exceeding 33°, which agree well with previous studies. A decision tree also quantitatively expresses important explanatory variables at the higher order of the tree structure.     

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

The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km² was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919–2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region.The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of rainfalls as well as by improving the accuracy of the DEM and the locations of shallow landslide initiation.     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

During the last decade, slope failures were reported in a 500 km² study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis.     

GIS支持下的黄土高原地震滑坡区划研究

分析了影响黄土滑坡的各项影响因子,利用层次分析法(AHP)确定各影响因子的权重。在GIS支持下,建立包括各因子图的空间数据库,对各因子进行分级赋值,然后进行因子加权叠加分析,完成三种超越概率下(50年超越概率2%、10%和63.5%)黄土高原地震滑坡区划图。黄土地震滑坡灾害最严重地区一个是宁夏南部及与其相邻的甘肃白银地区,另一个是甘肃天水地区。     

Magnitude and frequency of landsliding in a large New Zealand catchment

Knowledge of long-term average rates of erosion is necessary if factors affecting sediment yields from catchments are to be understood. Without such information, it is not possible to assess the potential influence of extreme storms, and, therefore, to evaluate the relative importance of various components of a sediment budget. A study of the sediment budget for the Waipaoa catchment, North Island, New Zealand, included evaluation of long-term rates of landsliding for six landslide-prone land systems in the catchment. The number of landslides per unit area generated by each of several storms was counted on sequential aerial photographs and correlated with the magnitude of the corresponding storm. The resulting relationships were combined with magnitude–frequency relationships derived for storms from 70- to 100-year rainfall records in the area to estimate a long-term magnitude–frequency relationship for landsliding for each land system. The long-term average values of the areal landslide frequency (number of slides per unit area per unit time) were then calculated from these relationships. The volumes of a sample of landslide scars were measured in the field, and the proportion of slides that deliver sediment to channels was determined from aerial photographs. These measurements then allowed calculation of the long-term average rate of sediment production to streams from landslides for different land systems and types of vegetation. Results suggest that shallow landslides currently contribute about 15±5% of the suspended sediment load in the Waipaoa River above the Kanakanaia gauging station, and that 75% of the sediment production from the landslides occurs during storms with recurrence intervals of less than 27 years. Reforestation of 6.3% (93 km²) of the slide-prone lands in the catchment between 1990 and 1995 resulted in a calculated decrease in slide-derived sediment of 10%. Calculations suggest that reforestation of an additional 3% (66 km²) of the catchment in areas with the most sensitive combinations of land system and storm regime could decrease the total sediment inputs from landsliding by about 20%.     

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

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 vegetated terrain using conventional techniques, and tend to be subjective. In addition, most landslide inventories simply outline landslide boundaries and do not offer information about landslide mechanics as manifested by internal deformation features. In an alternative approach, we constructed accurate, high-resolution DEMs from airborne laser altimetry (LIDAR) data to characterize a large landslide complex and surrounding terrain near Christchurch, New Zealand. One-dimensional, circular (2-D) and spherical (3-D) statistics are used to map the local topographic roughness in the DEMs over a spatial scale of 1.5 to 10 m. The bedrock landslide is rougher than adjacent unfailed terrain and any of the statistics can be employed to automatically detect and map the overall slide complex. Furthermore, statistics that include a measure of the local variability of aspect successfully delineate four kinematic units within the gently sloping lower half of the slide. Features with a minimum size of surface folds that have a wavelength of about 11 to 12 m and amplitude of about 1 m are readily mapped. Two adjacent earthflows within the landslide complex are distinguished by a contrast in median roughness, and texture and continuity of roughness elements. The less active of the earthflows has a surface morphology that presumably has been smoothed by surface processes. The Laplacian operator also accurately maps the kinematic units and the folds and longitudinal levees within and at the margins of the units. Finally, two-dimensional power spectra analyses are used to quantify how roughness varies with length scale. These results indicate that no dominant length scale of roughness exists for smooth, unfailed terrain. In contrast, zones with different styles of landslide deformation exhibit distinctive spectral peaks that correspond to the scale of deformation features, such as the compression folds. The topographic-based analyses described here may be used to objectively delineate landslide features, generate mechanical inferences about landslide behavior, and evaluate relatively the recent activity of slides.     

Comparing landslide inventory maps

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.     

Analysing the influence of upslope bedrock outcrops on shallow landsliding

A model for the prediction of topographic and climatic control on shallow landsliding in mountainous terrain is enhanced to analyse the impact of upslope rocky outcrops on downslope shallow landsliding. The model uses a ‘generalised quasi-dynamic wetness index’ to describe runoff propagation on bare rock surfaces connected to downslope soil-mantled topographic elements. This approach yields a simple enhanced model capable of describing the influence of upslope bedrock outcrops on the pattern of downslope soil saturation. The model is applied in both diagnostic and predictive modes to a small catchment in the eastern Italian Alps for which a detailed inventory of shallow landslides in areas dominated by rocky outcrops is available. In the diagnostic mode, the model is used with satisfactory results to reproduce the pattern of instability generated by an intense short-duration storm occurred on 14 September 1994, which triggered a large percentage of the surveyed landslides. In the predictive mode, the model is used for hazard assessment, and the return time of the critical rainfall needed to cause instability for each topographic element is determined. Modelling results obtained in the predictive mode are evaluated against all the surveyed landslides. It is revealed that the generalised quasi-dynamic model offers considerable improvement over the non-generalised quasi-dynamic model and the steady-state model in predicting existing landslides as represented in the considered landslide inventory.     

Probability maps of landslide reactivation derived from tree-ring records: Pra Bellon landslide, southern French Alps

Probability maps of landslide reactivation are presented for the Pra Bellon landslide located in the southern French Alps based on results obtained with dendrogeomorphic analysis. Spatiotemporal patterns of past landslide activity was derived from tree-ring series of 403 disturbed mountain pine trees growing in the landslide body. In total, 704 growth disturbances were identified in the samples indicating 22 reactivation phases of the landslide body between 1910 and 2011. The mean return period was 4.5 years. Given the spatiotemporal completeness of the reconstruction, probabilities of landslide reactivation were computed and illustrated using a Poisson distribution model and for 5, 20, 50, and 100 years. Probability of landslide reactivation is highest in the central part of the landslide body and increases from 0.13 for a 5-year period to 0.94 for a 100-year period. Conversely, probabilities of reactivation are lower at its margins. The proposed method differs from conventional approaches based on statistical analyses or physical modeling that have demonstrated to have limitations in the prediction of spatiotemporal reactivation of landslides. Our approach is, in contrast, based on extensive data on past landslides and therefore allowed determination of quantitative probability maps of reactivation derived directly from the frequency of past events. This approach is considered a valuable tool for land managers in charge of protecting and forecasting people and their assets from the negative effects of landslides as well as for those responsible for land use planning and management. It demonstrates the reliability of dendrogeomorphic mapping that should be used systematically in forested shallow landslides.     

一种基于地理环境相似度的滑坡负样本可信度度量方法

滑坡负样本在统计型滑坡危险度制图中具有重要作用,能抑制统计模型对滑坡危险度的高估。当前滑坡负样本采样方法采集的负样本可信度未知,在负样本采样过程中,极有可能将那些潜在滑坡点错选为负样本,这些假的负样本会降低负样本集的质量和训练样本集的质量,进而影响统计模型的精度。本文基于“地理环境越相似、地理特征越相似”的地理学常识,认为与正样本有着相似地理环境的点极有可能是未来发生滑坡的点;与正样本的地理环境越不相似的点,则越有可能是负样本。基于此假设提出一种基于地理环境相似度的负样本可信度度量方法,将该方法应用于滑坡灾害频发的陇南山区油房沟流域,对油房沟进行滑坡负样本可信度评价制图;使用油房沟流域的滑坡发生初始面来验证该方法的有效性。结果发现：滑坡发生初始面上所有栅格点的负样本可信度平均值为0.26,超过95%的栅格点的负样本可信度都小于0.5,说明本文提出的负样本可信度度量方法合理。     

Pore Water Pressure as a Trigger of Shallow Landslides in the Western Ghats of Kerala,India: Some Preliminary Observations from an Experimental Catchment

The Western Ghats mountain chain of Kerala, India, is prone to landslides mainly caused by anthropogenic disturbances and very high rainfall amounts. Here, some initial observations on the apparent relationship among pore water pressure fluctuations, rainfall characteristics, and landslide initiation are presented based on monitoring in an experimental catchment in the upper Tikovil River basin. On June 21 and 22, 2007, continuous rain fell for over 10 hours with a total precipitation of 147 mm, causing three shallow landslides in the catchment. Measurements using piezometers in three hollows of the catchment indicate that the rain spell resulted in the development of high pore water pressure from the beginning of the storm that persisted through the time of occurrence of shallow landslides. The pore water pressure patterns in these monitored hollows are possible representatives of the pore water pressure pattern in the hollows where the landslides initiated. This similarity of response pattern enables such data to be used for the calibration and validation of physically based slope hydrology models coupled with slope stability models.     

Comparison of satellite and air photo based landslide susceptibility maps

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 methods of obtaining input data provide similar results and successfully identified landslide prone areas. These results suggest that satellite imagery, in this instance, SPOT images, could potentially be used in lieu of conventional air photos, to evaluate landslide susceptibility. In many situations, especially in the case of remote locations and/or developing countries, this capability should result in substantial savings in terms of time, financial resources, and overall viability.     

Susceptibility and triggering scenarios at a regional scale for shallow landslides

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 km² 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 utilized to identify the relevant pluviometric triggering scenarios. By using 205 daily rainfall series, different triggering pluviometric scenarios have been identified with reference to CG and FG covers: a value of 365 mm of the total rainfall of the event and/or 170 mm/d of the rainfall maximum intensity and a value of 325 mm of the total rainfall of the event and/or 158 mm/d of the rainfall maximum intensity are able to trigger shallow landsliding events for CG and FG covers, respectively.The results obtained from this study can help administrative authorities to plan future development activities and mitigation measures in shallow landslide-prone areas. In addition, the proposed methodology can be useful in managing emergency situations at a regional scale for shallow landsliding events triggered by intense rainfalls; through this approach, the susceptibility and the pluviometric triggering scenario maps will be improved by means of finer calibration of the involved factors.     

Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina

A qualitative landslide susceptibility zonation map (scale 1:100,000) of a sector of the Río Mendoza Valley was prepared by overlapping thematic maps of conditioning factors. The main parameters affecting the distribution of landslides in the area were ranked according to slope instability. Geomorphological surveys and field observations enabled identification of 300 historical or prehistoric landslides. A landslide inventory was developed classifying the processes involved in slope instability, and analysing the degree of activity. The two most influential factors in decreasing slope stability were lithology and slope angle.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district,Bartın,northern Turkey

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.     

基于信息量与逻辑回归模型的次生滑坡灾害敏感性评价——以汶川县北部为例

次生滑坡灾害的影响是震后较长时间里人们持续关注的焦点,对其开展敏感性评价具有重要意义。选取5.12地震的重灾区汶川县北部作为研究区,利用遥感与地理信息技术提取地震滑坡信息,在全面分析滑坡与高程、坡度、坡向、岩性、断裂带、地震烈度以及水系等7个影响因子相关特性的基础上,采用信息量法与逻辑回归模型进行灾害敏感性评价,将研究区划分为极轻度、轻度、中度、高度和极高危险5个级别,并对不同模型的适用性开展分析和对比。结果表明,逻辑回归模型在描述区域滑坡灾害危险度总体特征方面稍具优势。     

Landslide Representation Strategies in Susceptibility Studies using Weights-of-Evidence Modeling Technique

This paper is focused primarily on how to represent landslide scarp areas, how to analyze results achieved by the application of specific strategies of representation and how to compare the outcomes derived by different tests, within a general framework related to landslide susceptibility assessment. These topics are analyzed taking into account the scale of data survey (1:10,000) and the role of a landslide susceptibility map into projects targeted toward the definition of prediction, prevention, and mitigation measures, in a wider context of civil protection planning. These aims are achieved by using ArcSDM (Arc Spatial Data Modeler), a software extension to ArcView GIS useful for developing spatial prediction models using regional datasets. This extension requires a representation by points of the investigated problems (landslide susceptibility, aquifer vulnerability, detection of mineral deposits, identification of natural habitats of animals, and plants, etc.). Maps of spatial evidence from regional geological and geomorphological datasets were used to generate maps showing susceptibility to slope failures in two different study areas, located in the northern Apennines and in the central Alps (Italy), respectively. The final susceptibility maps for both study areas were derived by the application of the weights-of-evidence (WofE) modeling technique. By this method a series of subjective decisions were required, strongly dependent on an understanding of the natural processes under study, supported by statistical analysis of the spatial associations between known landslides and evidential themes. Except for maps of attitude, permeability, and structure, that were not available for both study areas, the other data were the same and comprised geological, land use, slope, and internal relief maps. The paper illustrates how different representations of scarp areas by points (in terms of different number of points) did not greatly influence the final response map, considering the scale of this work. On the contrary, some differences were observed in the capability of the model to describe the relations between predictor variables and landslides. In effect, a representation of the scarp areas using one point every 50 m led to a more efficient model able to better define relationships of this type. It avoided both problems of redundancy of information, deriving by the use of too many points, and problems related to a random positioning of the centroid. Moreover, it permitted to minimize the uncertainty related with identification and mapping of landslides.     

GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region

The M_w 7.6 October 8, 2005 Kashmir earthquake triggered several thousand landslides throughout the Himalaya of northern Pakistan and India. These were concentrated in six different geomorphic–geologic–anthropogenic settings. A spatial database, which included 2252 landslides, was developed and analyzed using ASTER satellite imagery and geographical information system (GIS) technology. A multi-criterion evaluation was applied to determine the significance of event-controlling parameters in triggering the landslides. The parameters included lithology, faults, slope gradient, slope aspect, elevation, land cover, rivers and roads. The results showed four classes of landslide susceptibility. Furthermore, they indicated that lithology had the strongest influence on landsliding, particularly when the rock is highly fractured, such as in shale, slate, clastic sediments, and limestone and dolomite. Moreover, the proximity of the landslides to faults, rivers, and roads was also an important factor in helping to initiate failures. In addition, landslides occurred particularly in moderate elevations on south facing slopes. Shrub land, grassland, and also agricultural land were highly susceptible to failures, while forested slopes had few landslides. One-third of the study area was highly or very highly susceptible to future landsliding and requires immediate mitigation action. The rest of the region had a low or moderate susceptibility to landsliding and remains relatively stable. This study supports the view that (1) earthquake-triggered landslides are concentrated in specific zones associated with event-controlling parameters; and (2) in the western Himalaya deforestation and road construction contributed significantly to landsliding during and shortly after earthquakes.     

Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Landslide inventory maps are necessary for assessing landslide hazards and addressing the role slope stability plays in landscape evolution over geologic timescales. However, landslide inventory maps produced with traditional methods — aerial photograph interpretation, topographic map analysis, and field inspection — are often subjective and incomplete. The increasing availability of high-resolution topographic data acquired via airborne Light Detection and Ranging (LiDAR) over broad swaths of terrain invites new, automated landslide mapping procedures. We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides. Power spectra produced using the two-dimensional discrete Fourier transform and the two-dimensional continuous wavelet transform identify the characteristic spatial frequencies of deep-seated landslide morphologic features such as hummocky topography, scarps, and displaced blocks of material. Spatial patterns in the amount of spectral power concentrated in these characteristic frequency bands highlight past slope instabilities and allow the delineation of landslide terrain. When calibrated by comparison with detailed, independently compiled landslide inventory maps, our algorithms correctly classify an average of 82% of the terrain in our five study areas. Spectral analysis also allows the creation of dominant wavelength maps, which prove useful in analyzing meter-scale topographic expressions of landslide mechanics, past landslide activity, and landslide-modifying geomorphic processes. These results suggest that our automated landslide mapping methods can create accurate landslide maps and serve as effective, objective, and efficient tools for digital terrain analysis.