Relation between geomorphological parameters and MM 

Get Complete Project Material File(s) Now! »

MM mapping/zoning, susceptibility and assessment

The term “mapping” or “zoning” of a hazard designs the partition of a given territory into elementary surfaces, which are classified into degrees or hazard levels. Those can be indicated quantitatively or qualitatively (low, medium, high, etc.) and express the intensity of the hazard. Microzoning covers the same definition applied to studies with large scales (superior to 1:25,000). Different approaches to hazard-risk mapping or analysis are found in Baldelli et al. (1996), Chowdhury and Flentje (1996), Leroi (1996), Borga et al. (1997), Chung and Fabbri, (1999), Codebo et al. (2000), Parise (2001), Vaunat and Leroueil (2002), Zerger (2002), Babu and Mukesh (2003), Fernandez et al. (2003), Ferrier and Emdad Haque (2003), Remondo et al. (2003), Chau and Lo (2004), Rocha (2004), Spizzichino et al. (2004), Ayenew and Barbieri (2005). The sensitivity or susceptibility of a MM hazard is a constant of proportionality between causes of MM and their effect. It measures the maximal local intensity possible for a conjunction of causes. The degree of sensitivity is known if the reaction is already produced (traces, proofs). In this case, there is certainty of danger and the magnitude can be derived. If traces are not shown, one should focus interest on causes and factors [state (permanent) factors and triggering (occasional) factors]; once existing, the reaction will be proportional to the combination of factors. The recurrence of triggering factors allows assessing the occurrence probability of hazard. While susceptibility maps provide zonations of areas with similar instability or similar conditions that generate landslides, a true landslide hazard map should offer a zonation of areas with similar probabilities of landslides in a given period of time, based on quantitative analysis of data. Assessment of a hazard is to determine the importance or size, or amount of danger of MM, which may include the mapping, but also the set of procedures needed to understand the hazard.

Regional appraisal modeling

Recently, an increasing trend towards the use of regional appraisal modeling in the Mediterranean environments is noted. This is due to the recognition and importance of accounting for spatial heterogeneity, newly developed tools and technology, especially Geographic Information System (GIS), remotely sensed (RS) imagery, and digital terrain analysis, which make it possible to consider small-scale heterogeneity in these environments, and analyze a variety of spatial and non spatial data about the causative factors responsible for the MM activity in a region. Regional appraisals typically range from simply allocating the mass movements to the more complex zonation of MM hazard based on causative interacting factors. This zonation leads to the mapping of areas with an equal probability of occurrence within a specified period of time. In some cases, these appraisals deal with mass movement potential for specific types of projects. Such maps are called Landslide Hazard Zonation (LHZ) or Landslide Susceptibility Zonation (LSZ) maps.

Methods of MM hazard/susceptibility zonation

A number of methodologies for landslide hazard/susceptibility zonation over large areas are proposed which are belonging to qualitative, semi-quantitative and quantitative GIS-assisted approaches. Each of these approaches has its value for certain applications and disadvantages for other objectives, given the specific conditions of the Mediterranean environments. In this context, the GIS technology has been applied to examine landslide hazard in several Mediterranean countries, such as Jordan (Al-Homoud and Masanat, 1998; Husein et al., 2000); northern Spain (Duarte and Marquinez, 2000); Tuscany, Italy (Luzi et al., 2000), central Italy (Carrara et al., 1991), Southern Italy (Budetta et al., 1994; Refice and Capolongo, 2002), and Fabriano, Italy (Luzi and Pergalani, 1996). Despite the methodological and technical differences, most proposed methods consider that geological and geomorphological conditions of future MM should be similar to those conditions that led to past and present slope instability. Therefore, mapping past and recent slope movements, together with the identification and mapping of the inducing or preparatory factors of slope instability, are the keys in predicting future landslides (Carrara et al., 1999). Very few methods tackle rockfalls on a regional scale. Studies of rockfalls are often based on field surveys, and hazard is estimated either by an empirical assessment of the susceptibility to failure, or by the calculation of a safety factor derived from models of rock mechanics (Carere et al., 2001; Crosta et al., 2001). Several methods for the evaluating landslide hazard can be distinguished.

Aerial photographs (stereoscopic and stereo-oblique)

Until recently, remote sensing technologies, relying upon the interpretation of aerial photographs, were applied widely for regional MM investigations (McDonald and Grubbs, 1975). These photographs are obtained from aircrafts, with sufficient overlap between successive ones enabling stereoscopic pairs to be produced. The conventional stereopairs, even those of black and white mode, are still convenient for the identification of MM due to their high spatial resolution, but are costly and also time consuming. Using parallax equations, measurements of the heights of MM surface structures can be made from stereo aerial photographs providing information on flow emplacement parameters (such as emplacement rate, and velocity). Stereo Oblique Aerial Photography (SOAP) consists of acquiring photographs from a low flying helicopter with the doors removed to facilitate a clear uninterrupted view of the slopes. When compared to a walk-over field surveys, this technique provides a relatively rapid, cost-effective means of assessing large areas of ground, which may be otherwise with inaccessible, dangerous and high slopes.

Microwave (Synthetic Aperture Radar Interferometry, InSAR)

Radar imageries, at different wavelengths and polarisation, can be obtained from both satellites and aircrafts. Radar data can be acquired either night or day and effectively “sees” through clouds. Currently available SAR (synthetic aperture radar) data including ERS (25 m spatial resolution), RADARSAT with 10-15 m spatial resolution and stereo capability, JERS (18 m) and others have proved efficient for mass movement evaluation in gentle sloping areas (Chorowicz et al., 1998). The analysis of the SAR time series with the permanent scatterer technique (Ferretti et al., 2000, 2001; Kimura and Yamaguchi, 2000; Rizzo and Tesauro, 2000; Refice et al., 2001; Colesanti et al., 2003, Schiavon et al., 2003; Singhroy and Molch, 2004) enables the detection of very small movements. With the advent of repeat-pass interferometry, it has become possible to detect subtle changes (at mm scales) in the landscape such as seismic displacement (Singhroy, 1995; Massonnet et al., 1996) and monitoring of large structures, i.e. hydrological sites at risk (Pieraccini et al., 2001; Leva et al., 2003). Differential synthetic aperture radar (SAR) interferometry from spaceborne platforms has been shown capable of measuring landslide displacement fields of centimetric order over relatively large areas (Fruneau et al., 1996; Massonnet and Feigl, 1998; Rott et al., 1999; Vietmeier et al., 2000). Nevertheless, its application is restricted in the rugged mountainous Mediterranean areas (Buchroithner, 1995; Fruneau et al., 1996) because it can experience ground deformations in excess of the phase gradient limit (Carnec et al., 1996), which eliminate interferometric correlation (Massonnet and Feigl, 1998; Luzi et al., 2005). Attempts are being made to better integrate radar interferograms, field measurements, and ancillary remote sensing of MM to obtain calibrated interferograms which will provide useful geologic and geophysical information to the MM monitoring community (Bulmer and Wilson, 1999; Catani et al., 2005). However, even such improved technologies are rarely utilized to their full potential in hazard assessment.

Multispectral video and hyperspectral scanners

Multispectral video cameras operate at the visible to near infrared portion of the spectrum and can be mounted on low-flying aircraft. They generate pixels of less than 1 m, and therefore suitable for mapping of landslide groundwater systems (Hansen, 1984). Airborne hyperspectral scanners are much more complicated and expensive instruments that the multispectral video and can be conveniently mounted onto a helicopter or fixed wings aircraft. They are able to record complete reflectance spectrum from blue wavelengths (450 nm), through the visible and near-infrared regions, to the short-wave infrared (2500 nm). Therefore, the system is able to resolve individual vegetation or mineral species which may be particularly useful for the identification of stream deposits which emerge from the toe of landslides, and for the mapping of lithological units (Gibson et al., 2000; Ramlin et al., 2000).

READ  Evaluating the Resilience of Browser Fingerprinting to Block Adversarial Crawlers

Heuristic methods

Heuristic or predictive GIS models (parameter models) are based on rules and expert knowledge (Gupta et al., 1999; Meisina et al., 2001; Saha et al., 2002). They are considered as direct or semi-direct methods in which the geomorphological maps are renumbered to hazard/susceptibility maps, or built on the results of exploratory subjective analysis, which identifies cause-effect relationships from the spatial coincidence between observed erosion features and landscape factors. Such models lack deterministic capability, because they neither simulate nor explain the mechanisms involved in mass movement. They are not able to take into consideration, for the purpose of prediction, the role played by the activating factors. This lack must be overcast especially when in instances certain triggering factors like earthquakes are the main causes of MM occurrence in the Mediterranean environments. However, the scarcity of seismic and rainfall intensities records in many Mediterranean countries can be one of the principal problems towards the integration of earthquake and rainfall activity in the GIS predictive models.

Semi-quantitative methods

Also, semi-qualitative susceptibility, hazard or slope instability maps based firstly on analysis of slope angles, lithology and relative amounts of landslip material have been published (Blanc and Cleveland, 1968; Bowman, 1972; Radbruch and Crowther, 1973; Nilsen and Brabb, 1977; Nilsen and Wrigth, 1979). But according to Varnes (1978), these maps are considered closer to MM inventories than to MM hazard maps. In addition, the use of the term ‘‘susceptible’’ to indicate the degree of instability relates these maps to susceptibility maps in the sense developed by Brabb et al. (1972). The latter introduced a semi-quantitative method consisting of a bivariate analysis of MM area percentages in slope angle intervals, expressed by relative susceptibility numbers, from which a susceptibility zonation was obtained. The susceptibility classes were I (0–1%), II (2–8%), III (9–25%); IV (26–42%); V (43–53%) and VI (54–70%) and L (landslide deposits, 100%). The logical analytical method proposed by Bughi et al. (1996) is a variation of the qualitative methods, where the field survey data on slope deformation helps to decide the numerical weights. Remote sensing and GIS techniques may be utilized here for thematic map preparation and overlay analysis. Another approach to mapping landslides involves landslide density or isopleth maps. The landslide isopleth or density contour technique was adopted to
25generalise and quantify the areal distribution of landslide deposits in a form that would combine more easily with other data when preparing derived maps (Schmidt and MacCannel, 1955; Varnes, 1978).

Table of contents :

1. Distribution and consequences of mass movements
2. Concepts
2.1. Definitions
2.2. Classification of mass movements (MM)
2.2.1. Slides
2.2.2. Falls
2.2.3. Flows
2.3. Magnitudes of mass movements (MM)
3. Factors causing mass movements
3.1. Climate
3.2. Neotectonic movements
3.3. Geology and soils
3.4. Topography
3.5. Vegetation
3.6. Human activities
4. Definitions and clarifications of MM-related terms
4.1. MM hazard term
4.2. MM vulnerability term
4.3. MM risk term
4.4. MM mapping/zoning, susceptibility and assessment
5. Evaluation of mass movements hazard
5.1. Individual sites
5.2. Regional appraisal modelling
6. Zonation of mass movements (MM)
6.1. Methods of MM hazard/susceptibility zonation
6.1.1. Qualitative methods
a. Distribution analysis
a.1. Aerial photographs (stereoscopic and stereo-oblique)
a.2. Optical satellite imagery
a.3. Microwave (Synthetic Aperture Radar Interferometry, InSAR)
a.4. Airborne thermal and lidar data
a.5. Multispectral video and hyperspectral scanners
b. Heuristic methods
6.1.2. Semi-quantitative methods
6.1.3. Quantitative methods
a. Factor overlay
b. Statistical techniques
c. Physically-based indexes
d. Distribution-free methods
e. Geotechnical/deterministic methods
6.2. MM risk assessment and mapping
6.3. MM hazard/susceptibility zonation maps
6.3.1. Hazard zonation map types
6.3.2. Spatial scale 30 6.3.3. Time scale
7. Major constraints in evaluating MM hazard zonation and relevant improvements
7.1. Uncertainties in the collected MM hazard factors
7.2. Uncertainty in the models
7.3. Inherent complexity and validation of MM hazard evaluation
8. Conclusion
1. Geographic location
2. Physical and morphodynamic characteristics
2.1. Geomorphology
2.1.1. Physiographic surfaces
2.1.2. Karst
2.2. Geology
2.2.1. Stratigraphy-lithology
2.2.2. Structural geology
2.3. Soils
2.4. Land cover/use
2.5. Hydrology
2.6. Climate
3. Socio-economic characteristics
4. Natural hazards in the study area
4.1. Seismic activity
4.2. Soil erosion
4.3. Flooding
4.4. Forest fires
5. Mass movements (MM) in Lebanon
5.1. Examples of mass movements in Lebanon
5.2. Socio-economic impacts of MM in Lebanon
5.3. Mitigation measures
5.4. Existing studies of MM in Lebanon
6. Conclusion
1. Introduction
2. Methodology
2.1. Approach
2.2. Image characteristics of MM features
2.2.1. Elements of visual interpretation
a. Landslides’ characteristics
c. Earth flows’ characteristics
2.2.2. Ancillary data
2.2.3. Satellite data types
a. Choice
b. Sensors and processing
3. Results and discussion
3.1. Measurement of accuracy
3.3.1. Landslide detection
3.1.2. Rock and debris fall detection
3.1.3. Earth flows detection
3.2. Comparative analysis between image treatments
3.3. Field analysis and IKONOS imagery
4. Conclusion
1. Introduction
2. Choice and preparation of the influencing parameters
2.1. Approach
2.2. Field observations and causes of mass movements
2.3. Preparation of MM parameters
2.3.1. Geomorphological parameters
2.3.2. Geological and soil parameters
2.3.3. Hydrological and climatic parameters
2.3.4. Anthropogenic parameters
3. Parameters’ manipulation in a GIS environment
4. Statistical analysis
5. Results and discussion
5.1. Univariate statistical obtained correlations
5.1.1. Usefulness of univariate correlations
5.1.2. Relation between geomorphological parameters and MM
5.1.3. Relation between geological/soil parameters and MM
5.1.4. Relation between hydrologic/climatic parameters and MM
5.1.5. Relation between anthropogenic influence and MM
5.1.6. Relation between natural hazards and MM
5.2. Bivariate GIS statistical correlations obtained
5.2.1. Levels of correlation significance
5.2.2. Parameters with major correlations (1%)
5.2.3. Parameters with minor correlations (5%)
5.2.4. Parameters without any correlation 105
5.3. Low and high effect classes on MM occurrence
6. Conclusion
1. Introduction
2. Factors of susceptibility/hazard mapping
3. Methods for susceptibility/hazard mapping
3.1. The valuing analytical bi-univariate method (VABU)
3.2. Valuing area accumulation method (VAA)
3.3. Information value method (InfoVal)
4. Classification of MM susceptibility/hazard maps
5. Comparison and validation of the obtained susceptibility/hazard maps
6. Results and discussion
6.1. Attribute and spatial analysis of MM susceptibility maps
6.2. Correspondence analysis of MM susceptibility maps
6.3. Spatial and attribute analysis of MM hazard maps
6.4. Correspondence analysis between MM hazard maps
6.5. Examination of reliability of MM susceptibility maps
6.6. Advantages and problems of the MM used methods
7. Conclusion
1. Introduction
2. Reasons for using decision-tree modeling
3. Modeling approach
3.1. Workflow
3.2. Study site
3.3. Field survey
3.4. Statistical analysis
3.5. Building and pruning decision-tree models
4. Results and discussion
4.1. Character of block falls’ occurrence
4.2. Model performance evaluation
4.3. Production of the volumetric map of block falls
4.3.1. Mapping block falls’ volumes using the unpruned decision-trees
4.3.2. Mapping block falls’ volumes using the pruned decision-trees
4.3.3. Comparative between volumetric block falls’ maps produced using unpruned and pruned decision-trees
4.4. Advantages and problems of the preferred decision-tree model
5. Conclusion
1. Introduction
2. Interferometric synthetic aperture radar (InSAR)
2.1. Background
2.2. Producing interferograms
2.3. Permanent scatter techniques
2.4. Data processing
2.5. Preliminary results and discussion
3. Global Positioning System (GPS)
3.1. Background
3.2. GPS setting
3.3. GPS campaigns
4. Conclusion


Related Posts