CHAPTER 3 AN OVERVIEW OF LANDSLIDE HAZARD ASSESSMENT TECHNIQUES

Transcription

1 CHAPTER 3 AN OVERVIEW OF LANDSLIDE HAZARD ASSESSMENT TECHNIQUES

2 3.1 General As a consequence of an urgent demand for slope instability hazard mapping, a large amount of research on landslide hazard zonation has been done during the last 30 years. The concept of hazard zonation (maps that show the spatial distribution of hazard classes) is central to the theme of spatial analysis and hazard prediction of landslide occurrence. Although it is yet difficult to predict a landslide event in space and time, an area may be divided into near-homogeneous domains and ranked according to degrees of potential hazard due to mass movements (Varnes, 1984). As a result, landslide hazard is often represented by landslide susceptibility (Brabb, 1984). Such maps are called landslide hazard zonation (LHZ) or landslide susceptibility zonation (LSZ) maps. These maps identify areas potentially affected and does not imply a time frame when a landslide might occur. In landslide hazard mapping, when, where and what scale of landslides are important aspects in prediction (Chi, et al., 2002). All predictions related to future events are always subject to uncertainties. For the planning of future land use, an essential component is the identification of areas that are affected by future landslides. In the last decades, several qualitative and quantitative studies have been carried out to prepare landslide hazard maps (Carrara, 1983; Gussetti, et al., 1999; Suzen and Doyuran, 2004b; Brenning, 2005; Guinau, et al., 2005; Lee and Sambath, 2006). The availability of remote sensing and GIS technologies has tremendously improved the preparation of susceptibility maps with greater efficiency and accuracy than before. This is primarily due to the fact that through these technologies, it is possible to collect, manipulate and analyze a variety of spatial and non-spatial data about the causative factors (Varnes, 1984; Carrara, et al., 1991; Nagarajan, et al., 1998; van Westen, 2000; Saha, et al., 2002; 2005; Roessner, et al., 2005; Acharya, et al., 2006; Dahal, et al., 2007; Pandey et al., 2007). This chapter describes the state of the art of landslide hazard assessment techniques with focus on the application of remote sensing and GIS modeling for landslide mapping and hazard evaluation in different scales. 3.2 Landslide Hazard Zonation - Scale of Analysis The use of landslide susceptibility and hazard maps for land use planning has considerably increased in the recent past. The aim of these maps is to rank different sections of land surface according to the degree of actual or potential landslide hazard. 24

3 Thus, planners are capable of selecting favorable sites for urban and rural development. The reliability of these maps depends mostly on the methodology applied as well as the available data used for hazard risk estimation. The development of a clear hierarchical methodology in hazard zonation is a necessary condition to obtain an acceptable cost/benefit ratio and to ensure its practical applicability. The working scale for a slope instability analysis is determined by the requirements of the user for whom the survey is executed. Therefore, the appropriate scale on which the data is collected and the final result presented varies considerably. The work scale could be chosen on the basis of three factors, such as the purpose of assessment, the extent of the studied areas and data availability (Aleotti and Chowdhury, 1999; Huabin, et al., 2005). More detailed hazard maps require more detailed input data. The International Association of Engineering Geologists (IAEG, 1976) has differentiated the following scales of analysis for landslide hazard zonation. National scale (< 1:1,000,000) The detail at national scale is very low as it covers an entire country. The aim is to generate awareness among national policy makers and the public and to indicate problematic areas and types of hazards. Regional scale (1:100,000 1:1,000,000) The maps at regional scale are intended for agencies in charge of agricultural, urban or infrastructure planning and may indicate areas where severe landsliding problems can threaten different facilities. The hazard zoning is primarily based on regional geomorphological terrain mapping units (TMU) or regional geologic units. Medium scale (1:25,000 1:100,000) Areas covered at medium scale have an extent of several 10 km 2 to several 100km 2, including a municipality or smaller catchment area. The main characteristic in terms of morphometric features such as slope angle or curvatures are distinguished within bigger units, be it a geologic, land-use or terrain unit. Additionally a precise landslide distribution map is obtained from aerial photo interpretation. A field campaign emphasizes quantitative description and additional information about the mass movements themselves and the relevant sliding factors. 25

4 Large scale (1:2,000-1:25,000) Large scale hazard analyses will cover several square kilometers, like a town or part of a city. The methodology is based on detailed geotechnical knowledge. Detailed fieldwork is indispensable and may be accomplished by laboratory analysis. Playing with different scenarios of triggering factors like rain or horizontal seismic acceleration, critical mass movement areas can be identified. They are used for disaster prevention and generation of risk maps. Site investigation scale (1:200 1: 2,000) This scale of investigation will cover the area where engineering works will be carried out or a single landslide area. They are used for the detailed design of roads, bridges, tunnels, dams and also for choosing the appropriate slope stabilization techniques. Whatever the analysis scale is, different work scales affect the selection of the approach. There are several techniques for landslide hazard zonation making use of remote sensing (Aleotti and Chowdhury, 1999; van Westen, 2000). Generally, landslide susceptibility analysis methods used consist of landslide distribution analysis, landslide density analysis, landslide activity analysis, geomorphologic analysis, qualitative map combination and safety factor analysis. On a regional scale, landslide distribution analysis, landslide density analysis, geomorphologic analysis and qualitative map combinations are used. On a medium scale, the relationship between the landslide and contributing factors is analysed statistically. On a large scale, safety factors analysis is chosen as one main method to assess the landslide hazard subsequent to the analysis of landslide distribution, landslide activity and geomorphologic features. In the present study, the scale of landslide susceptibility zonation is set to 1:50,000 and categorized as regional scale. This is because, the topographic map on a 1:50,000 scale cannot fully reflect the micro-topographic conditions which may lead to the occurrence of landslides in the region. The landslide occurrence in the study area is characterized by small volumes, and a slight change in micro-scale landform may have a strong influence on the occurrence of landslides. 26

5 3.3 Use of Earth Observation (EO) Data in Landslide Hazard Assessment Landslides represent a serious threat to human life and activities in most high mountain chains. Collecting information on landslide occurrence and activity over wide areas is a crucial task for landslide hazard assessment. However, due to the difficult nature of such terrain, it is often difficult to assess directly the susceptibility of slopes to landsliding (Zhu and Huang, 20006). Field techniques, despite being very precise, are usually not sufficient to achieve this goal, since they mostly provide point based measurements. Hence, remote sensing (aerial and satellite) offers many advantages for the examination of landslide potential, especially in less developed nations where resources are stretched and levels of environmental information are limited. This is mainly because of its synoptic view and its capability for repetitive observations. The optical (visible-infrared) images acquired at different dates and at high spatial resolution can be considered as an effective complementary tool for field techniques (McKean, et al., 1991; Mantovani, et al., 1996; Hervas, et al., 2003). However, there is a need to ensure that the techniques are effective, reliable, and offer value for money in terms of the amount and accuracy of data that can be extracted. The development of modern earth observation techniques, in particular the multitemporal remote sensing with high resolution, improves the mapping and monitoring possibilities. The use of EO data, whether air, satellite or ground based varies according to three main stages of a landslide related study, namely a) detection and identification of landslide related factors b) monitoring and characterization of landslide deposits and c) spatial analysis and hazard prediction. Traditional methods used for mapping slope instabilities can benefit from the use of EO systems which allow rapid and easily updatable acquisition of data over wide areas, reducing the field work and the costs. The two main types of EO data are aerial photograph and satellite images Aerial Photographs in Landslide Hazard Assessment Aerial photographs are a generally accepted resource used in landslide studies. They not only provide a metric model from which quantitative measurements can be 27

6 obtained, but also give a qualitative description of the earth surface. Slope failures directly affect the ground surface and generally distort the ground with distinct traces or marking over a long period of time. The analysis of historical and after-event aerial photographs can be crucial in most slope studies. Aerial photographs are normally taken to provide stereoscopic coverage, producing a three-dimensional image of the terrain. It provides geomorphologic, vegetative and drainage condition of the area. Geomorphology and other relevant information such as the various contributing factors for slope failure are best studied by close examination of the stereoscopic model. Aerial photography has been used extensively to characterize landslides and to produce landslide inventory maps, particularly because of their stereo viewing capability and high spatial resolution. The use of stereoscopic images, mostly from aerial photographs is considered important in slope instability studies because the diagnostic morphology created by some mass movements (disrupted vegetation cover, scarps) can clearly be seen in large scale aerial photographs. Considering the size of most landslides in the order of several tens to a few hundred meters (Lorente, et al., 2002), the most useful photographic scale is around 1: 15,000. This scale enables mapping slope instability features and other individual elements of a landslide. In late 1990s stereoscopic air-photo interpretation was the most frequent remote sensing tool applied in the mapping and monitoring of landslide characteristics (distribution and classification) and factors (slope, lithology, geo-structure, land use/land cover, rock anomalies etc.), though the extent and detail of interpretation vary significantly. However, the conventional photo-interpretation is a time-consuming and costly approach. Currently, air photos are used extensively to produce landslide inventory maps, because they allow features demonstrating slope movement that range from small terracettes (indicating soil creep) to large landslides Satellite Images in Landslide Hazard Assessment Remote Sensing is a fundamental tool for the detection, classification and monitoring of landslide actions. Such technology allows one to obtain historical series, faster collection of data and information at a relatively lower cost (Mantovani, et al., 1996; Schowengerdt, 1997). The application of remote sensing to environmental studies, including the mapping and monitoring of mass movements and gullies, is controlled by the spatial, spectral and temporal resolutions of the data (Zinck, et al., 2001). Spectral 28

7 resolution refer not only to the number of spectral bands offered by the sensor, but also to the ability of specific portions of the electromagnetic spectrum to provide enough spectral separability amongst surface features related to mass movement processes. This requires a good understanding of the interactions between ecosystem characteristics and incoming solar radiation or artificially propagated electromagnetic energy as in the case of radar sensors. The spatial resolution of the sensor determines the scale at which the data may be useful for mass movement analysis and mapping. The concept of a minimum map unit, which makes it possible to consistently delineate the smallest ground features of interest over a selected area, is an important consideration when establishing spatial data requirements. Table 3.1 shows minimum object size needed for identification and interpretation of landslides. The current trend of using spatially explicit approaches based on available remote sensing, GIS and digital terrain analysis techniques allow researchers to consider local heterogeneity of the landscape. Finally, temporal resolution is determined by the revisiting cycle of the sensor. Ideally, the data acquired for mass movement or gully mapping should have a temporal resolution higher than the changes evidenced by the phenomena. According to Mantovani, et al., (1996) remote sensing technique has not been fully exploited in the study of landslides with only a limited number of researchers making full use of multispectral images for evaluating landslide activity. Today there are at least a dozen earth observation satellites collecting data useful for natural disaster mapping including floods and landslides. These provide great capabilities by offering high spatial resolution, <1m, and short periods to revisit the same spot on earth, since their orbits are either polar or sun-synchronous. The mapping of landslide related factors that fall more within the environmental and human categories can be carried out with the help of optical- IR sun-synchronic satellites and aerial photographs. They provide morphological, land use, and geological details required for analysing the relationships between landslides and causative factors. Many of the approaches that incorporate these terrain parameters within GIS based spatial models of deterministic, inventory, statistical or heuristic nature to assess the probability of occurrence of landslides in areas where the similar characteristics are present. 29

8 Table 3.1: Minimum object size needed for identification and interpretation of landslides Size m 2 needed for GRC size (m) High Contrast Low Contrast Identification Interpretation Identification Interpretation Landsat MSS Landsat 5 TM ~ Spot Multispectral Spot Panchromatic IRS 1C/1D PAN* Cartosat-1 PAN* Aerial Photographs 1: :15000 ~ (*modified after Soeters and van Westen, 1996) Furthermore, there are several image processing techniques that permit the improvement of image visual quality, making up the poor spatial resolutions of the multispectral sensors (Carper, et al., 1990; Chavez, et al., 1991; Garguet-Duport, et al., 1996; Yocky, 1996). The spatial distribution of past (relict) and recent landslides is the key for predicting slope movements in advance. Hence, the first step is the identification and mapping of all landslide phenomena occurring in the area under study. 3.4 Geographical Information System in Landslide Hazard Assessment An increasing number of planning agencies throughout the world are attempting to undertake natural hazard mitigation activities through development planning studies. However, while the expertise and baseline data in the form of maps, documents, and 30

9 statistics may exist, a systematic approach is often lacking. The volume of information needed for natural hazards management, particularly in the context of integrated development planning, exceeds the capacity of manual methods and makes the use of computerized techniques compelling. Geographic information systems (GIS) offer a technological framework for supporting efficient and effective data capture, storage, management, retrieval, analysis, integration and display (Burrough, 1986), and have already shown great benefits to the study and mapping of landslide distributions and hazard potential (Carrara, et al., 1995; Guzzetti, et al., 1999). A GIS is a systematic means of combining various bits of information about a unit of geographic space. The concept is analogous to a panel of post-office boxes, each representing a specified area. As each element of information about a particular attribute (soil, rainfall, population) that applies to the area is identified, it can be placed into the corresponding box. Since there is theoretically no limit to the amount of information that can be entered into each box, huge volumes of data can be compiled in an orderly manner, generating a collection of mapped information which reveals spatial relationships between the different attributes, e.g., hazardous events, natural resources, and socioeconomic phenomena. It can thus help planners assess the impact of natural events on existing and proposed development activities. Modelling of the landslides is a factor analysis process. A factor analysis process is a step-by-step approach used to prepare landslide hazard zones of an area (Carrara, 1983; 1988; Aleotti and Chowdhury, 1999; van Westen, 2000; van Westen, et al., 2003; 2006). There are four steps in the factor analysis producing a hazard map: (1) map the existing landslides and combine the factor maps one by one into individual map units; (2) overlay the landslide inventory on the combined factor map; (3) prepare a combined factor analysis for all combinations of the factors and group combinations of these factors in a way that defines the levels of landslide hazard; and (4) produce a map with landslide hazard zones from the grouped combinations. Softwares are used to produce both the evidence maps and the factor maps. 31

10 GIS technologies could provide a powerful tool to model the landslide hazards for their spatial analysis and prediction. This is because the collection, manipulation and analysis of the environmental data on landslide hazard can be accomplished much more efficiently and cost effectively (Carrara and Guzzetti, 1999; Guzzetti, et al., 1999). Many GIS-based analysis models and quantitative prediction models of landslide hazard have been proposed since the beginning of GIS application in geohazards research in the late 1980s (Carrara, 1983;; Carrara, et al., 1991, 1995, 1999; Jade and Sarkar, 1993; Chung, et al., 1995,Chung and Fabbri, 1998, 1999; van Westen, 2000; van Westen, et al., 2006). The present chapter outlines the available methodologies for landslide hazard zonation at small scales for regional assessments, medium scales for feasibility studies, and large scales for local and more detailed studies. The literature related to the above methodologies and the application of remote sensing and GIS for the determination of various factors controlling the landslides were closely examined to finalize the methodology for the present study Slope Instability Hazard Zonation Techniques In recent years, the international interest in large-scale landslide hazard, risk and susceptibility maps has increased. The reasons for the increasing international interest in landslides are due to an increasing awareness of the socio-economic significance of landslides and the increased pressure of development and urbanisation on the environment. Therefore, the landslide susceptibility mapping has a great significance in landslide hazard mitigation efforts. The primary objective of modelling landslide hazards is the prediction of landslide prone areas in space and/or time (Brenning, 2005). An assumption is usually made in slope instability hazard assessment. The assumption is that the conditions which led in the past to slope failures would also result in potential unstable conditions in the present. This involves mapping hazards and identifying future hazardous areas through analysis of the dominant variables influencing hazard initiation and occurrence; evaluation of degree of hazard and identification and classification of different types of slope failure (Varnes, 1984; van Westen, 2000; Hutchinson, 1995). Therefore, mapping these phenomena and the factors thought to be of influence is very important in hazard zonation. 32

11 In relation to the analysis of the terrain conditions leading to slope instability, two basic methodologies can be recognized and is further divided into six categories and are given in Table 3.2 (Gizzetti, et al., 1999; van Westen, 2000; Mantovani, et al., 1996; Saha, et al., 2005). 1. The first mapping methodology is the experience-driven appliedgeomorphic approach, by which the earth scientist evaluates direct relationships between landslides and their geomorphic and geologic settings by employing direct observations during a survey of as many existing landslide sites as possible. This is also known as direct mapping technology. 2. The opposite of this experience based, or heuristic approach is the indirect mapping methodology, which consists of mapping a large number of parameters considered to potentially affect landsliding and subsequently analyzing (statistically) all these possible contributing factors with respect to the occurrence of slope instability phenomena. In this way the relationships between the terrain conditions and the occurrence of the landslides may be identified. On the basis of the result of this analysis, statements are made regarding the conditions under which slope failures occur. However, there are essential pre-requisites to identify the preparatory and triggering variables, before the development or application of any method of hazard assessment. These include an understanding of geology, hydrogeology and geomorphology from available maps, reports and field surveys, preliminary geological and geotechnical investigations, historical information on landsliding, rainfall records and historical seismicity (Aleotti and Chowdhury, 1999). At present, when assessing the probability of landsliding on regional scales, it might be feasible to consider landslide susceptibility as the probability of landsliding based on the assumption that long-term historic landslide records tend to smooth-out the spatio-temporal effect of triggering factors on landslide occurrence. This is because, the probability of landsliding depends on both the preparatory and triggering variables and the triggering variables may change over a very short time span, and are thus very difficult to estimate (Dai et al, 2002). 33

12 Overviews and classification of GIS based landslide hazard assessment methods can be found in Soeters and van Westen, 1996; Carrara, et al., 1995; 1999; Cruden and Fell, 1997; Aleotti and Chowdury, 1999; Guzzetti, et al., 1999; van Westen, 2000; Dai, et al., 2002; Huabin, et al., 2005; van Westen, et al., Table 3.2 Landslide hazard/susceptibility zonation techniques LHZ/ LSZ methods Main feature Direct mapping of mass movement features resulting Distribution in a map, which gives information only for those sites analysis where landslides have occurred in the past Direct or semi-direct methods in which the Qualitative / Qualitative geomorphological map is renumbered to a hazard / Direct analysis/ susceptibility map or in which several maps are Geomorphic combined into one using subjective decision rules analysis based on the experience of the earth scientist Statistical Indirect method in which statistical analysis are used analysis to obtain predictions of the mass-movement from a number of parameter maps. Deterministic Indirect methods in which parameters are combined in analysis slope stability calculation. Quantitative / Indirect methods in which earthquakes and/or rainfall Indirect Landslide records or hydrological models are used for frequency correlation with known landslide dates to obtain analysis threshold values with a certain frequency (after Mantovani, et al, 1996; Aleotti and Chowdury, 1999; van Westen, 2000) Qualitative (Direct) Landslide Hazard Zonation Methods Qualitative approach is a direct or semi-direct mapping methodology, which implies that during the landslide inventory a direct relationship is made between the occurrence of slope failures and the causative terrain parameters (van Westen, 2000). Qualitative models rely on expert knowledge of the person or persons carrying out the susceptibility or hazard 34

13 assessment, which dictates the selection, the weighting and the combination function of the variables. In the direct mapping approach the degree of hazard is mapped directly in the field, or is determined after fieldwork using a very detailed geomorphologic map, that in most cases is derived from stereoscopic interpretation of large scale aerial photographs. The indirect approach uses data integration techniques, including qualitative parameter combination, with the analyst assigning weighting values to a series of terrain parameters and to individual classes within each parameter. The parameters are then combined within a GIS to produce hazard values. Dai, et al., (2002) mention the need for long-term information on the landslides and their causative factors; the reproducibility of the results and the subjectivity of weightings and ratings of the variables as the main limitations to the applicability of such models. The distribution analyses and qualitative analyses are generally used for very large areas with very low detail such as national hazard maps. In general qualitative approaches are based entirely on the judgment of the person or persons carrying out the susceptibility or hazard assessment Landslide Distribution Analysis The simplest of all, the distribution analysis only depicts direct mapping of landslide locations from field surveys or aerial photographic interpretation and thus do not provide information on predictive behaviour of future landslide activity. Distribution analysis is a site specific method considered as the basic requirement for landslide hazard and risk assessment and management (Hansen, 1984; Wieczorek, 1984; Guzzetti, et al., 1999; Malamud, et al., 2004). It allows a prompt assessment of the widely dispersed landslide problems which may be triggered by rainstorm events. The input data are usually derived from assessment during field visits possibly supported by aerial photo interpretation and a database of historical occurrences of landslides in an area. Landslide activity maps are indispensable to study the effects of temporal variation of a factor such as land use on landsliding. Landslide distribution can also be shown in the form of a density map. The resulting density values are interpolated and used as landslide isopleths. They can also be used to cite out the current situation of the landslide density per terrain mapping unit or catchment or a predefined geological unit. 35

14 The method is most appropriate at medium or large scales. At the regional scale the construction of a mass movement distribution or activity map is very time consuming and too detailed for procedures of general zoning. In this type of analysis, GIS is used to digitize landslides prepared from field survey maps, aerial photographs and remote sensing images (Saha, et al., 2005). The final product gives the spatial distribution of mass movements, which may be represented on a map either as affected areas to scale or point symbols (Aleotti and Chowdhury, 1999; Ko Ko, et al., 2004). The application of landslide distribution analysis technique for the preparation of LZS map can found in Nagarajan, et al., 1998; Zhou, et al., 2002; van Westen and Getahun, 2003: Cheng, et al., Qualitative/ Heuristic (Geomorphic) Analysis The heuristic approach is based on the a priori knowledge of the causes of landsliding in the area under investigation; hence, instability factors are ranked and weighted according to their assumed or expected importance in causing mass movement (Barredo, et al., 2000; Dai, et al., 2002). This is also called expert-driven approach in which expert opinions make great difference during assessing of the type and degree of landslide hazard and is divided in to two: field geomorphological analysis and the combination or overlaying of index maps (Leroi, 1996; Aleotti and Chowdhury, 1999; Guzzetti, et al., 1999). Geomorphological mapping of landslide hazard is a direct, qualitative method that relies on the ability of the investigator to estimate actual and potential slope failures (Humbert, 1977; Kienholz, et al., 1983; Bosi, et al., 1985; Seeley and West, 1990; Hansen, et al., 1995; Guzzetti, et al., 1999). With the availability of a wide range of GIS softwares, it is now possible to prepare different thematic layers corresponding to the causative factors that are responsible for the occurrence of landslides in a region. In overlay analysis, the instability factors are ranked and weighted (with or without weighting) according to their assumed or expected importance in causing mass movements (Kanungo, et al., 2006). At present, maps obtained by this method cannot be readily evaluated in terms of reliability or certainty. There has been many papers on heuristic/ geomorphic analysis with or with out the use of GIS, some of which can be listed chronologically as: Nilsen and Brabb, 1977; Hollingsworth and Kovacs, 1981; Neeley and Rice, 1990; Montgomery, et al., 1991; 36

15 Anbalagan, 1992; Gee, 1992; Pachauri and Pant, 1992; Gupta, et al., 1993; Sarkar, et al., 1995; Guzzetti, et al., 1999; Barredo, et al., 2000; Moeyersons, et al., 2004; Kanungo, et al., Quantitative (Indirect) Landslide Hazard Zonation Methods The increasing popularity of GIS over the last decades has led to many studies using indirect susceptibility mapping approaches (Aleotti and Chowdury, 1999; van Westen, et al., 2006). Quantitative models involve the use of mathematics, and statistics, to express relationships between variables. Most scientists recognize the superiority of quantitative techniques due to their rigorous scientific framework which promote objectivity. Quantitative or indirect hazard assessment is based on the analysis of relationships between factors preconditioning landslides and the occurrence of landslides using analytical and statistical models (Chung et al., 1995). The development of these factor based methods has been closely related to the evolving GIS and remote sensing technologies opening up new ways in generating and analyzing spatial information (Carrara, et al., 1991, 1999; van Westen, 2000; Aleotti and Chowdury, 1999; Donati and Turrini, 2002) Statistical Analysis for Landslide Hazard zonation To remove subjectivity in qualitative analysis, various statistical methods have been employed for LSZ studies. Statistical landslide hazard assessment has become very popular, especially with the use of geographic information systems (GIS) and the possibility of applying data integration techniques that have been developed in other disciplines. All forms of statistical methods are used to estimate the relative contributions of the factors responsible for slope instability, and to create some predictions based on these factors. However, they still have some differences in the way that they handle the data. They can be categorized into two subgroups according to data analysis methods as: bivariate and multivariate methods. In multivariate methods, all the factors are treated together and their interactions assist to resolve the phenomena statistically (Carrara, 1988; Carrara, et al., 1991); whereas bivariate methods assume that the factors are not correlated with each other (van Westen, 2000; Huabin, et al., 2005). Statistical techniques are generally considered the most appropriate approach for landslide susceptibility mapping at medium scales of 1:10,000 1:50,000, because on this scale it is possible to map out in detail the occurrence of past landslides. These techniques 37

16 are useful in collecting sufficient information on the variables that are considered to be relevant to the occurrence of landslides. (Dai, et al., 2002) Bivariate methods for Landslide Hazard Zonation In bivariate analysis, the core of the analysis is to get the densities of landslide occurrences within each parameter map and within each parameter maps classes, and to get some data driven weights based on the class distribution and the landslide density Probabilistic Likelihood (Frequency ratio) Analysis This method is based on the concept of the favourability function (Chung and Fabbri, 1993; Fabbri, et al., 2002). It assumes that the likelihood of landslide occurrence can be measured by statistical relationships between past landslides of a given type and specified spatial data sets. The relationship between the landslide occurrence area and the landslide related factors could be deduced from the relationship between areas where landslides had not occurred and the landslide related factors. To represent this distinction quantitatively, the probability likelihood (frequency ratio) was used. The probability likelihood (frequency ratio) is the ratio of the probability of an occurrence to the probability of a non-occurrence for given attributes (Bonham-Carter 1994). In this method, the prediction of landslide susceptibility is considered as the joint conditional probability that a given small area will be affected by a future landslide conditioned by the physical characteristics of the area. The calculation of prior and conditional probabilities is a compulsive condition for the development of the remaining procedures, and represents the first step in the cartographic data integration. The procedure for calculating the probabilities, based on the correlation between the landslide map and several independent data layers, and on the relationship between affected areas and total areas, are the following: i) Prior probability of finding a landslide = affected area/total area ii) Prior probability of finding a class of a layer = class area/total area iii) Conditional probability of finding a landslide in each class, for each layer = 1 (1 1/class area) 38 affected area in the class

17 The results obtained from Eq. (iii) can be considered as favourability values (probability likelihood ratio/ frequency ratio) or susceptibility indicators of the different spatial units within a given layer, if this layer is independent from the other factors (Chung and Fabbri, 1993; Remondo, et al., 2003). If this ratio is greater than 1, then the relationship between a landslide and the factor s range or type is strong. If the ratio is less than 1, then the relationship between a landslide and the factor s range or type is weak. Finally, the landslide susceptibility index (LSI), is calculated by summation of each factor s ratio value. LSI = fr (fr: probabilistic likelihood /frequency ratio of each factor s type or range). Some of the studies that have applied probability likelihood (frequency ratio) model to prepare LSZ map are: Jibson, et al., 2000; Luzi, et al., 2000; Lee and Min, 2001; Clerici, et al., 2002; Donati and Turrini, 2002; Lee, et al., 2002a; Lee, et al., 2004; Zezere, et al., 2004; Lee and Talib, 2005; Lee and Sambath, 2006; Akgun, et al., 2007; Lee and Pradhan, Information Value (InfoVal) Method The Information Value Method (InfoVal) has the advantage of assessing landslide susceptibility in an objective way. The method allows for the quantified prediction of susceptibility by means of a score, even on terrain units not yet affected by landslide occurrence. Each instability factor is crossed with the landslide distribution, and weighting values based on landslide densities are calculated for each parameter class, as it happens with all bivariate statistical methods. The method implies the prior definition of terrain units and the selection of a set of instability factors. The information value Ii of each variable Xi is given by (Yin and Yan, 1988): Si/Ni Ii = ln S/N Where: Si = the number of terrain units with landslides of type Y and the presence of variable Xi; Ni = the number of terrain units with variable Xi; S = the total number of terrain units with landslides of type Y; N = the total number of terrain units. 39

18 Negative values of Ii mean that the presence of the variable is not relevant in landslide development. Positive values of Ii indicate a relevant relationship between the presence of the variable and landslide distribution. The total information value Ij for a terrain unit j is given by (Yin and Yan, 1988): m Ij = X ji li i = 1 Where: m = number of variables, Xji is either 0 if the variable is not present in the terrain unit j, or 1 if the variable is present. Therefore, the relative susceptibility of a terrain unit to the occurrence of a particular type of slope movement is given by the total information value Ij. The applications of InfoVal method have been discussed by many researchers, some of which can be listed chronologically as: Yin and Yan, 1988; van Westen, 1997; Wu, et al., 2000; Zezere, 2002; Saha, et al., Weights of Evidence (WofE) Method Among the bivariate statistical analysis, the weights of evidence modeling method is widely used as it offers a flexible way of testing the importance of input factors for landslide susceptibility and can be used as a supporting tool in expert-based mapping ( Lee, et al., 2002; Suzen and Doyuran, 2004b; van Westen, et al., 2003). This method was developed at the Canadian Geological Survey (Agterberg, et al., 1990; Bonham-Carter, et al., 1989) and was applied to the mapping of mineral potential. Sabto, (1991) applied the method for landslide hazard analysis. The method consists of reducing each set of landslide related factors on a map to a pattern of a few discrete states. In its simplest form, the pattern for a feature is binary, representing its presence or absence within a pixel. The spatial association between a set of evidential themes and a set of known landslide locations, which are expressed as the weights of evidence, is combined with the prior probability of occurrence of landslides to derive the posterior probability of occurrence of landslides, provided the evidential themes are conditionally independent with respect to the slides. It uses a log-linear form of the Bayesian probability model to estimate the relative importance of evidences by statistical means. In the Bayesian approach, prior and posterior probabilities are the most important concepts. The concepts and steps involved in the weights of evidence (WofE) modelling is described in detail in chapter 5. 40

19 Landslide Normal Risk Factor The Landslide Nominal Risk Factor (LNRF) method is proposed by Gupta and Joshi, (1990). This method attempts to reduce each map layer thoughts to be important to a single metric value and adds up the scores to produce an overall index. The metric value chosen will usually be some arbitrary score for each category, on an assumed underlying scale of susceptibility (Doratti, et al., 2002; Saha, et al., 2005). In this method the nominal risk factor is determined by taking the relative incidence of known landslides and converted it into a risk index by taking the ratio of their incidence in each category of each factor, and then dividing by the average incidence over all categories of the factor concerned. LNRFi = Npix(Si) ( n i=1npix(si))/n Where, Npix(Si) is the number of pixels containing landslides in i th thematic class, and n is the number of classes present in the particular thematic map. An LNRF value greater than 1 implies high susceptibility to landslides. When the LNRF value is less than 1, it indicates low susceptibility. An LNRF value equal to 1 is a sign of average landslide susceptibility. These individual factor risk indices were then assigned to classes on an ordinal scale (low=0, medium=1, high=2) and these index values were aggregated to prepare the final LHI/LSI map. The resulting assessment of the spatial distribution of landslide hazards was found to be satisfactorily accurate, but there are certain drawbacks of using this method. First, the method gives no guidance as to which data layers are most important in determining the final outcome and which are of minor importance that can be ignored. Secondly, it does not allow for the interactions between factors used and therefore runs the risk of missing significant effects (Shu-Quiang and Unwin, 1992) Multivariate methods for Landslide Hazard Zonation Multivariate statistical analysis models for landslide hazard zonation were developed in Italy, mainly by Carrara, (1983, 1988) and his colleagues (Carrara, et al., 1990, 1991, 1992, 1995). Multivariate statistical analyses of important causal factors controlling landslide occurrence may indicate the relative contribution of each of these 41

20 factors to the degree of hazard within a defined land unit. The strategy of sampling in these applications was based either on a large grid basis or in morphometric units. In either case the presence or absence of landslides were represented in a binary manner. All attributes believed to be relevant were sampled (Gorsevski, et al., 2006). When using multivariate statistical methods all parameters at sites of instability can be analysed by multiple regression techniques or parameter maps are crossed with landslide distribution maps and the correlation is established for stable and unstable areas using discriminant analysis (Yin and Yan, 1988; Carrara, et al., 1991; Dai, et al., 2002; van Westen, 2000). The use of multivariate statistical models has always been hindered by the need for continuous data. Categorical data, such as geology, can be used but it commonly involves the use of dummy variables to indicate the presence or absence of a variable. This can result in an enormous increase in the number of variables, with the increase being directly related to the number of categories in each explanatory variable. Moreover, both techniques showed limited value when the dependent variables take only two values, that is, whether landsliding occurs or not. Under these circumstances, the assumption needed to test the hypothesis in regression analysis is violated. In such cases, another multivariate technique known as logistic regression is used for estimating the probability of an event. (Carrara, et al., 1991; Chung and Fabbri, 1999) Multiple Logistic Regression Regression analysis establishes the relationships between numerous input variables and presents the relationships in a succinct manner (usually as a number or a series of numbers). Multiple regression is used to examine the relationship between a dependent variable whose value is known at a number of locations and a set of independent variables whose values are also known for those locations. Logistic regression, which is a multivariate analysis model, is useful for predicting the presence or absence of a characteristic/ phenomenon or outcome based on values of a set of predictor variables. In logistic regression, the magnitude of occurrence of the phenomenon being modeled by the dependent variable is unknown. The advantage of logistic regression is, through the addition of an appropriate link function to the usual linear regression model, the variables may be either continuous or discrete, or any combination of both types, and they do not necessarily have normal distributions. In the present situation, 42

21 the dependent variable is a binary variable representing the presence or absence of landslides (Afifi and Clark 1998; Dai et al., 2001; Lee and Min 2001; Santacana, et al., 2003; Ercanoglu, et al., 2004; Gorsevski et al., 2006). The logistic regression therefore models the probability of presence and absence given observed values of predictor variables. Logistic regression fits a special s-shaped curve by taking a linear regression that may produce any y value between and +, and transforming it with the function that produces a probability (p -probability) between 0 (as y approaches minus infinity) and 1 (as y approaches plus infinity). p(y = 1 x) = exp (βo + β 1 x) 1 + exp (βo + β 1 x) In the logistic regression model, x is the data vector for a randomly selected experimental unit, β is the coefficient(s) of the independent variable(s), and y is the value of the binary outcome variable. The maximum likelihood method may be used to estimate β prior to prediction. The output of a statistically based model is an equation that can be used for prediction or estimation. However, the accuracy and the reliability of the prediction of these explicit empirical models are a function of the sampling strategy and choice of explanatory variables may relate to the basic processes operating at specific measurement scales. Further, the logistic model can be written in its simplest form as: P = e -z Where, P is the probability of an event occurring and also is the estimated probability of landslide occurrence. The probability varies from 0 to 1 on an s-shaped curve and z is the linear combination. Logistic regression returns a constant value and a coefficient for each of the independent input grids or variables. To create a grid of predicted values for the dependent variable at each location using these coefficients (which produces a friction or preference surface), the following formula with the GRID map algebra language is employed: 43

22 Prediction (Z) = (B 0 + B 1 X 1 + B 2 X 2 + B 3 X B n X n ) Where, B 0 is the intercept of the model, n is the number of independent variables, B i (i = 1,2,3,...,n) is the slope coefficient of the model and X i (i = 1,2,3,...,n) is the independent variable. There have been many studies on landslide susceptibility assessment using multivariate logistic regression analysis, some of which are listed chronologically as: Carrara et al, 1991 ; Atkinson and Massari, 1998; Carrara, et al., 1999; Guzzetti, et al., 1999; Dai, et al., 2001; Lee and Min, 2001; Dai and Lee 2002; Ohlmacher and Davis, 2003; Santacana, et al., 2003; Ercanoglu, et al., 2004; Suzen and Doyuran, 2004b; Ayalew and Yamagishi, 2005; Gorsevski, et al, 2006; Yesilnacar and Topal, 2005; Akgu and Bulut, 2007; Greco, et al., 2007; Lee and Pradhan, 2007; Lee, et al., Discriminant Analysis Discriminant function analysis is used to determine which continuous variables discriminate between two or more naturally occurring groups. The task of this analysis is the same as that of regression analysis. The objective of the analysis is to find the best discrimination between two groups: units or pixels with and without mass movements. The analysis results in a discriminant function: D s = B 0 + B 1 X 1 + B 2 X B n X n Where, X 1 are the values of the variables and B 1 the calculated coefficients. Before any further analysis can be performed, the success of the formula in separating the two groups must be tested. For this purpose three tests can be used. 1. The variability between the two groups and within the groups, and the total variability of the data, are calculated. The ratio of the variability between the two groups and the variability within the groups is called the eigen value. It should be maximized for a good discriminant function. 2. The ratio of the variability between the two groups and the total variability is called Wilk s λ. A small value indicates strong variation between groups and less variation within groups. A Wilk s λ of 1 indicates that there is equally great 44

23 variation within groups as between groups (i.e. that the function does not discriminate). 3. The χ 2 test to determine if the two groups are significantly different. Furthermore, as the slope stability depends on several factors acting at the same time, some efforts have been directed towards the acquisition of simply and quickly determined parameters. Stevenson (1977) using scored factors proposed a method to evaluate relative landslide risk in clayey slopes. Discriminant analysis provides a more accurate stability assessment and the studies carried out using the discriminant analysis are: Ardizzone, et al., 2002; Baeza and Corominas, Artificial Neural Network (ANN) Method Due to slope geological complexity and self-organized system, many variables are involved in slope stability evaluation, and these variables have a highly nonlinear relation with the evaluation results. Artificial neural networks have been introduced to produce the landslide hazard maps considering the nonlinear characteristics of sliding process (Huabin, et al. 2005; van Westen, et al., 2006). Artificial neural network methods have previously been applied to land use and cover classification using satellite imagery (Schaale and Furrer 1995; Serpico and Roli 1995). In particular, the multi-layer perception method using a back-propagation algorithm was used widely in a supervised classification with training area data (Atkinson and Tatnall, 1997). Artificial neural network (ANN) approach belongs to the category of statistical methods but, has many advantages. An ANN offers a computational mechanism that is able to acquire, represent and compute a map taking data from one multivariate space of information to another, given a set of data representing the relationships (Garrett, 1994; Lee, et al., 2003; Lu and Rosenbaum, 2003). They have certain similarities to time series extrapolations, but, has distinct advantages for the prediction of slow displacement movements of slopes. An ANN is a computerized system composed of a large number of very simple processors: the artificial neurons. These neurons have multiple input parameters and an output parameter and perform elementary calculations (often weighted sums) (Mayoraz and 45