A procedure for landslide susceptibility zonation by the conditional analysis method

Numerous methods have been proposed for landslide probability zonation of the landscape by means of a Geographic Information System (GIS). Among the multivariate methods, i.e. those methods which simultaneously take into account all the factors contributing to instability, the Conditional Analysis method applied to a subdivision of the territory into Unique Condition Units is particularly straightforward from a conceptual point of view and particularly suited to the use of a GIS. In fact, working on the principle that future landslides are more likely to occur under those conditions which led to past instability, landslide susceptibility is defined by computing the landslide density in correspondence with different combinations of instability factors. The conceptual simplicity of this method, however, does not necessarily imply that it is simple to implement, especially as it requires rather complex operations and a high number of GIS commands. Moreover, there is the possibility that, in order to achieve satisfactory results, the procedure has to be repeated a few times changing the factors or modifying the class subdivision. To solve this problem, we created a shell program which, by combining the shell commands, the GIS Geographical Research Analysis Support System (GRASS) commands and the gawk language commands, carries out the whole procedure automatically. This makes the construction of a Landslide Susceptibility Map easy and fast for large areas too, and even when a high spatial resolution is adopted, as shown by application of the procedure to the Parma River basin, in the Italian Northern Apennines.     

Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey)

Considering damage to man-made structures by natural hazards in Turkey, landslides are the second most important hazard after earthquakes. For this reason, a large-scale study titled Turkish Landslide Inventory Project, has been carried out since 1998. During this project, some special, susceptibility, hazard and risk assessments have been performed. In this study, a landslide susceptibility map of a part of tectonic Kelkit Valley in the north of central Turkey was produced, employing binary logistic regression analyses. To achieve the most appropriate results some sensitivity analyses were also carried out. For this purpose, four different data sets were constructed considering conditioning factors used and sampling strategies applied for the training data sets in this study. As a consequence of the analyses, the most proper outcomes were obtained by using the data set in which continuous topographical parameters and lithological dummy variables were implemented together and 50% of training data set was taken from seed cells at random. Correct classification percentage and Root Mean Square Error (RMSE) values for the validation data for that case were estimated as 84.16% and 0.36, respectively. This prediction capability shows that the landslide susceptibility map produced in this research paper can be used for the planning of protective and mitigation measures in the region.     

Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

A geomorphological study focussing on slope instability and landslide susceptibility modelling was performed on a 278 km² area in the Nalón River Basin (Central Coalfield, NW Spain). The methodology of the study includes: 1) geomorphological mapping at both 1:5000 and 1:25,000 scales based on air-photo interpretation and field work; 2) Digital Terrain Model (DTM) creation and overlay of geomorphological and DTM layers in a Geographical Information System (GIS); and 3) statistical treatment of variables using SPSS and development of a logistic regression model. A total of 603 mass movements including earth flow and debris flow were inventoried and were classified into two groups according to their size. This study focuses on the first group with small mass movements (10⁰ to 10¹ m in size), which often cause damage to infrastructures and even victims. The detected conditioning factors of these landslides are lithology (soils and colluviums), vegetation (pasture) and topography. DTM analyses show that high instabilities are linked to slopes with NE and SW orientations, curvature values between − 6 and − 0.7, and slope values from 16° to 30°. Bedrock lithology (Carboniferous sandstone and siltstone), presence of Quaternary soils and sediments, vegetation, and the topographical factors were used to develop a landslide susceptibility model using the logistic regression method. Application of “zoom method” allows us to accurately detect small mass movements using a 5-m grid cell data even if geomorphological mapping is done at a 1:25,000 scale.     

Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea

The purpose of this study is to develop and apply the technique for landslide susceptibility analysis using geological structure in a Geographic Information System (GIS). In the study area, the Janghung area of Korea, landslide locations were detected from Indian Remote Sensing (IRS) satellite images by change detection, where the geological structure of foliation was surveyed and analysed. The landslide occurrence factors (location of landslide, geological structure and topography) were constructed into a spatial database. Then, strike and dip of the foliation and the aspect and slope of the topography were compared and the results, which were verified using landslide location data, show that foliation of gneiss has a geometrical relation to the joint or fault that leads to a landslide. Using the geometrical relations, the landslide susceptibility was assessed and verified. The verification results showed satisfactory agreement between the susceptibility map and the landslide location data.     

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

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

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

Validation of a region-wide model of landslide susceptibility in the Manawatu–Wanganui region of New Zealand

Since European settlement 160 years ago, much of the indigenous forest in New Zealand hill country has been cleared for pastoral agriculture, resulting in increased erosion and sedimentation. To prioritise soil conservation work in the Manawatu–Wanganui region, we developed a model of landslide susceptibility. It assigns high susceptibility to steep land not protected by woody vegetation and low susceptibility everywhere else, following the commonly used approach for identifying inappropriate land use. A major storm on 15–16 February 2004 that produced many landslides was used to validate the model. The model predicted hills at risk to landsliding with moderate accuracy: 58% of erosion scars in the February storm occurred on hillsides considered to be susceptible. The model concept of slope thresholds, above which the probability of landsliding is high and below which the probability is low, is not adequate because below 30° the probability of landsliding is approximately linearly related to slope. Thus, reforestation of steep slopes will need to be combined with improved vegetation management for soil conservation on moderate slopes to significantly reduce future landsliding.     

A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia

Landslides cause damage to property and unfortunately pose a threat even to human lives. Good landslide susceptibility, hazard, and risk models could help mitigate or even avoid the unwanted consequences resulted from such hillslope mass movements. For the purpose of landslide susceptibility assessment the study area in the central Slovenia was divided to 78 365 slope units, for which 24 statistical variables were calculated. For the land-use and vegetation data, multi-spectral high-resolution images were merged using Principal Component Analysis method and classified with an unsupervised classification. Using multivariate statistical analysis (factor analysis), the interactions between factors and landslide distribution were tested, and the importance of individual factors for landslide occurrence was defined. The results show that the slope, the lithology, the terrain roughness, and the cover type play important roles in landslide susceptibility. The importance of other spatial factors varies depending on the landslide type. Based on the statistical results several landslide susceptibility models were developed using the Analytical Hierarchy Process method. These models gave very different results, with a prediction error ranging from 4.3% to 73%. As a final result of the research, the weights of important spatial factors from the best models were derived with the AHP method. Using probability measures, potentially hazardous areas were located in relation to population and road distribution, and hazard classes were assessed.     

Biomass estimation for native perennial grasses in the plain of Mendoza, Argentina

Plant allometric relationships were studied at the end of the 1999–2000 growing season for eight grasses. Logarithmic regressions were developed relating above-ground biomass to dimensional measurements or to tiller density. Basal diameters (the longest and the greatest perpendicular to the first) and plant height (defined as that reached by vegetative tillers) were recorded on individual plants of tussock grasses. The number of tillers per 1 m² plot was counted for a rhizomatous grass. Our study proved that regression models including basal area or a combination of basal area and height as independent variables gave a good fit to the biomass data for tussock grasses. Density of tillers proved to be a good predictor of biomass for a rhizomatous species. A validation test using 20% of the data not used for estimating the regression equations indicated that these models made accurate prediction of grass biomass. Further work is needed to prove if there are year-to-year differences between models.     

Monthly botanical composition of the diet of cattle in the rangelands of Mendoza plain, Argentina

Monthly botanical composition of cattle diet was determined for the period July 1994 to June 1995 in faecal samples by microhistological analysis. Principal components and cluster analyses were used to evaluate diet homogeneity among months. November through March diets were mainly composed of grasses and forbs (50 to 73%). During the April–September period, shrubs represented 36 to 57% of the overall diet. In October more than half the diet came from two species, one shrub (32%) and one grass (25%) making this month very different from the rest. The composition of cattle diets was significantly correlated with temperature and/or rainfall.     

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.     

Changes in the ionic composition of a saline lake,Mar Chiquita,Province of Córdoba,Argentina

Mar Chiquita is the largest water-body in Argentina according to area. This ranges from 2,000 km² in dry periods to 5,000 km² in humid ones. Its very high salinity ranged this century from 200 to 300 g/L, but an increase in the average rainfall in the basin since 1973 has led to a rise in lake level and the dilution of its waters to 78 g/L in 1978 and about 30 g/L in the late 1980s. Ionic strength decreased from 6.61 in 1970 to 0.541 in 1986. The composition of the water also changed, with increases in the proportions of sulphate, calcium and magnesium concentrations. The most important processes taking place in solution are calcite and gypsum equilibrium, formation of clay minerals, and sulphate reduction in the interstitial waters of the bottom sediments. The thermodynamic state of the solutions for different years has been obtained. Results show that the solution shifted from gypsum oversaturation in 1970 to gypsum equilibrium in 1978, and to gypsum undersaturation since 1982. A geochemical simulation of the composition of the solution from its origin from mean tributary composition, following a modified Hardie-Eugster model, has been developed using PHRQPITZ. The results agree with empirical observations.     

Productivity, management and development problems in the arid rangelands of the central Mendoza plains (Argentina)

The central Mendoza plains (56,000 km²), located in the eastern foothills of the Andes, belong to the vast phytogeographic province of ‘Monte’ (c.500,000 km²) in Argentina. This area is a typical warm arid zone and vegetation is typically a shrubland. Perennial grasses are predominantly C₄species. Land-use is for livestock grazing; the average stocking rate is 28 ha per large stock unit (LSU), year-long. Ecological and range research have been actively pursued in the region by IADIZA over the past 25 years. Two experiment stations were established 25 and 10 years ago, respectively: The Ñacuñán Biosphere Reserve and the El Divisadero Cattle and Range Station. Primary production and its relationship to climatic and edaphic parameters were analysed and compared in the two stations. Rain-use efficiency (RUE, kg DM ha⁻¹year⁻¹mm⁻¹) and useful rains efficiency (URE, P −0·1 PETp) were similar in both cases (RUE = 1·7 and 2·0; URE = 3·0 and 2·9, respectively), when herbs are concerned. However, the variability of annual production was much higher on the silty soils of Ñacuñán than on the coarse sands of El Divisadero; PRVR (Production to Rain Variability Ratio) was 2·5 and 0·53, respectively. Utilization of such experimental figures allows for the prediction of primary production and carrying capacity for a given year, based on rainfall probabilities and, therefore, to determine stocking rates in a rational manner. Basing stocking rates on annual dependable rains (f 0·8) and on useful rains probability (P − 0·1 PETp) proved to be a sound and realistic assessment, validated by long-term experiments and experience. The results of the studies on brush control, cattle diet composition, animal productivity and feed value of forage species were analysed. Current and future research programmes in IADIZA for the Mendoza plains are mentioned. Although research on resource inventory and basic ecological processes in the Mendoza plains should carry on, the emphasis and therefore the resources should henceforth be devoted to the consequences of resource management alternatives on overall arid land productivity, i.e. research for development. A better integration of applied ecology for management and investigations on basic arid land ecological processes should appear in IADIZA programmes.     

Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method

This work provides a landslide susceptibility assessment model for rainfall-induced landslides in Central Taiwan based on the analytical hierarchy process method. The model considers rainfall and six site factors, including slope, geology, vegetation, soil moisture, road development and historical landslides. The rainfall factor consists of 10-day antecedent rainfall and total rainfall during a rainfall event. Landslide susceptibility values are calculated for both before and after the beginning of a rainfall event. The 175 landslide cases with detailed field surveys are used to determine a landslide-susceptibility threshold value of 9.0. When a landslide susceptibility assessment value exceeds the threshold value, slope failure is likely to occur. Three zones with different landslide susceptibility levels (below, slightly above, and far above the threshold) are identified. The 9149 landslides caused by Typhoon Toraji in Central Taiwan are utilized to validate the study's result. Approximately, 0.2%, 0.4% and 15.3% of the typhoon-caused landslides are located in the three landslide susceptibility zones, respectively. Three villages with 6.6%, 0.4% and 4.9% of the landslides respectively are used to validate the accuracy of the landslide susceptibility map and analyze the main causes of landslides. The landslide susceptibility assessment model can be used to evaluate susceptibility relative to accumulated rainfall, and is useful as an early warning and landslide monitoring tool.     

Botanical composition of the seasonal diet of cattle in the rangelands of the Monte Desert of Mendoza, Argentina

Botanical composition of cattle seasonal diets (faecal sampling with microhistological analysis) was determined in a four-pasture, one-herd grazing system during a 3-year period. Diets were classified using cluster analysis. Three diet groups were identified: beginning of rainy season (spring), peak of rainy season (summer) and dry season (fall and winter). Contribution of grasses and woody species to these diet groups was significantly different (p< 0·001). Grasses were the most important component of cattle diets throughout the rainy season, while shrubs and trees were important in the dry season. The major shifts in diets occurred in response to changes in grass phenology.     

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.     

Population mobility and the survival of small farming in the Rio Grande Valley, Jamaica

The relationship between population mobility and farming is complex and has been the focus of numerous studies. Despite differing perspectives on the subject, there is an increasing realization that migration sustains farming in many rural communities, but with contradictory effects. Notwithstanding this conclusion, there is a paucity of village level studies that explain the precise ways in which migration in its various forms affects the survival of small farms. This paper reports on the results of a village level study of migration and small farming in the Rio Grande Valley of Jamaica. Apart from highlighting the various ways in which migration affects small farming at the local level, the study confirms the contradictory impact of migration on small farming in this remote agricultural community. It is noted, however, that the net effect is positive and the capital and labour resources, which are made available as a result of migration, play pivotal roles in the survival of small-scale farming as an economic activity in the area.     

Diet of the desert lizard Liolaemus pseudoanomalus (Iguania: Liolaemini) in northern La Rioja Province, Argentina

The diet of a species can help us to understand its natural history, ecological requirements, and strategies involved in searching for food. Liolaemus pseudoanomalus is a rare desert lizard where previous studies showed no seasonal, ontogenetic or sexual differences observed in microhabitat use; however the sexual dimorphism is evident in the number of precloacal pores and in the snout-vent length. In this work, we investigate if there are differences in the diet composition between sexes and age groups. Herein we describe the diet of L. pseudoanomalus, of the Central West Monte desert of Argentina, based on the analysis of 63 stomach contents. We found that ants are the most important food item and some characteristics of foraging behavior suggest similarities with other ant-eating lizards.     

Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a susceptibility evaluation model based on the back-propagation neural network including slope, aspect, plan curvature, strata and lithology, distance to faults, land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, distance from roads, and effect of rivers. During model development, a three-layered interconnected neural network structure of 10 (input layer) × 20 (hidden layer) × 1 (output layer) was used for evaluating the landslide susceptibility in Guojiaba. At the same time, a back-propagation algorithm was applied to calculate the weights between the input layer and the hidden layer and between the hidden layer and the output layer. The results showed that the effect of slope has the highest weight value (0.2051), which is more than two times that of the other factors, followed by strata and lithology (0.1213) and then the effect of rivers (0.1201). At the end of the susceptibility evaluation, the area was divided into four zones such as very high, high, moderate and low susceptibility. For verification, the receiver operating characteristic curve for the back-propagation neural network-derived landslide susceptibility evaluation model was drawn, and the results showed that the area under the receiver operating characteristic curve was 0.8790 and the prediction accuracy was 88%. Furthermore, the results obtained from this article were then verified by comparing with the existing landslide historical data and multiple field-verified results. Lastly, the landslide susceptibility map will help decision makers in risk management, site selection, site planning, and the design of control engineering.     

Modern Diatom Assemblages in Surface Sediments from Estuarine Systemsin the Southeastern Buenos Aires Province, Argentina

Paleoecological reconstructions of Holocene sea-level changes in Argentinean coastal regions were based mainly on ecological data gathered from other regions, as there was a lack of information on modern estuarine diatom distributions. The aim of the present work was to assess the spatial variation of diatom assemblages in two representative estuaries of Argentina in order to gather ecological information for paleoecological reconstructions in the region. The two selected estuaries have different geomorphologic features and salinity regimes: Mar Chiquita Lagoon is shallow, which prevents the development of a stable salinity gradient as it occurs in the Quequén Grande River. Surface sediment samples were taken from selected stations representative of the environmental gradient from the inlet to the inner reaches of both estuaries. Cluster analysis defined three diatom zones at Mar Chiquita: marine/brackish assemblages dominate the inlet (zone I), where salinity, tidal range and current speed are higher. The brackish/freshwater tychoplankton Staurosira construens var. venter and Staurosirella pinnata dominate the inner lagoon (zone II), where environmental conditions are very variable and concentrations of suspended sediments are higher. Brackish/freshwater euryhaline diatoms dominate the headwaters (zone III). On the other hand, the Quequén Grande River was divided into three diatom zones: coastal taxa are distributed at the inlet (zone I), while the middle estuary (zone II) is dominated by brackish/freshwater euryhaline taxa. At the upper estuary region (zone III), freshwater diatoms dominate, and the halophobous Nitzschia denticula increased in abundance values. Diatom distributions were most closely related to the salinity gradient at Quequén Grande River than at Mar Chiquita Lagoon. Fossil data of a sequence from Mar Chiquita Lagoon (Las Gallinas Creek) were compared to the modern data set in order to search for analogies between fossil and modern diatom assemblages. DCA results showed that fossil diatom assemblages have modern counterparts. Most diatom assemblages of Las Gallinas Creek fall within Mar Chiquita zone III, representing a shallow brackish/freshwater environment, with low salinity fluctuations (~1–9‰) and no tidal influence. Therefore, our modern diatom data provide useful analogs to interpret paleoenvironments in the region.