SLOPE STABILITY ANALYSIS USING REMOTE SENSING DATA

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1 SLOPE STABILITY ANALYSIS USING REMOTE SENSING DATA Hamdan Omar, Ab. Latif Ibrahim, Mazlan Hashim Department of Remote Sensing, Faculty of Geoinformation Science and Engineering, Universiti Teknologi Malaysia Tel: KEY WORDS: Slope stability, remote sensing, factor of safety, KL-Karak Highway. ABSTRACT: Analysis of slope stability is carried out to minimise the occurrences of slope failure or landslide. Slope along the highway which have been experiencing deep excavations are very susceptible to slope failure and thus landslide. The highway engineers must therefore give serious considerations before any construction or development is executed to ensure the designed slopes remain stable. Slope failure can be determined through appropriate measurement of slope stability. In this research, remote sensing approach is used to determine the safety factors of the highway slopes stability. Multispectral Landsat TM and Radarsat SAR data are utilized to derive several crucial factors involved in the determination of slope stability. Factors such as soil cohesion (c), internal friction angle (ø), and soil unit weight (γ) are determined through correlations with soil moisture (ω) derived from Landsat TM data. Another important parameter, the slope angle (α) is derived from Digital Elevation Model (DEM) generated from stereo Radarsat SAR data. The calculation of factor of safety (FS) is based on infinite slope stability models for shallow landslide, considering common unsaturated residual soil conditions in Malaysia. Slopes stability is presented in terms of susceptibility map along the whole stretch of Kuala Lumpur-Karak Highway, with 1: mapping scale. 1. INTRODUCTION Slope stability analysis is a computational measure to determine the stability of a slope. It plays an important role in civil engineering, especially in the design of highways, railroads, canals, surface mining, refuse disposal earth embankments and dams; as well as many other human activities involving construction and excavations. These analyses are generally carried out at the beginning, and sometimes throughout the life of projects during planning, design, construction, improvement, rehabilitation, and maintenance. Planners, engineers, geologists, contractors, technicians, and maintenance workers may involve in this process. This quantitative analysis determines the safety factors accounting variety of causes that representing the state of strength and possibilities of the slopes to fail, typically turned in an index value called Factor of Safety (FS). The quantitative determination of the stability of slopes, in general, must be based on knowledge regarding the geological structure of the area, the detailed composition and orientation of the strata, and the geomorphological history of the land surface. Hamdan Omar, Ab Latif Ibrahim and Mazlan Hashim (2007). Slope Stability Analysis using Remote Sensing and GIS. 28th Asian Conference on Remote Sensing (ACRS 2007), Putra World Center, Kuala Lumpur November 2007.

2 In this research, the limit equilibrium method of infinite slope stability has been applied to determine the stability of slopes along the Kuala Lumpur-Karak Highway. 3D surface (or commonly known as Digital Elevation Model- DEM) of the slopes were derived from StereoSAR technique using Rdarsat-1 images. Derived DEM from this technique are major input for the slope stability models used in this study. This research also attempts to derive several parameters that significantly control the stability of slopes. The parameters are; (i) cohesion, (ii) internal friction angle, and (iii) soil unit weight. The values of these parameters are quantified through the correlations to soil moisture estimated from remotely sensed data, which is Landsat TM. Infinite slope stability models will be used to determine factor of safety and hence to map the susceptibility of landslides along the study area. Karak Highway (Figure 1), which cuts through the backbone of Peninsular Malaysia, the Main Range that was previously covered by dense tropical rain forests is now very vulnerable to the environmental changes. Like other roads and highways that were constructed on highland areas, the slopes of Karak Highway are very susceptible to failures especially during rainy season. This is because many parts of the slopes along the highway have been cut of more than 60 metres high. Additionally, the cut slope basically composed of a residual soil that had developed over the more commonly outcropping Permo-Triassic mesozonal granite rock of Peninsular Malaysia (Raj, 1985). This kind of soil is particularly vulnerable to soil erosion. Moreover this region receives a relatively higher rainfall compared to other regions in Peninsular Malaysia. Being in the tropics the average temperature throughout the year is about 21 o C and due to relief, an annual rainfall of over 2500 mm are common compared to Kuala Lumpur (27 o C and 2410 mm for annual temperature and rainfall, respectively).

3 Figure 1 Location of Study Area 2. OBJECTIVES OF RESEARCH Landslides are among the major geohazards occurred in many highways in Peninsular Malaysia. However, the information on them is limited especially when remote sensing and GIS are of concern. The first essential step in this research is to establish spatial parameterization of some factors involve in slope stability model by using remote sensing and GIS technique and to determine the susceptibility of slope failure. To make the research accomplished successfully, specific objectives of this research are as follow; i) To determine Factor of Safety (FS) of slope stability using remote sensing technique, and ii) To map susceptibility of landslide using FS derived from (i). 3. MATERIALS AND METHODS 3.1 Materials Data used in this study include satellite data, ancillary data and also primary data collected during field observation. For satellite data, Landsat TM and a stereo pair of Radarsat-1 satellites images were used. Types and characteristics of satellites date are shown in Table 1. While two sheets of topographic maps series L7030 (3758 and 3858)

4 covering the whole stretch of the study area were used as ancillary data. Data collected during field observation are soil moisture and surface temperature using soil tester and thermal camera, respectively. Table 1 Type and Characteristics of Satellite Images Image Landsat-5 TM Image Radarsat-1 Images Characteristics Scene 1 Scene 2 Scene date June 2, 2005 September 4, 2005 December 12, 2005 Scene number 127/ Orbit* - Descending Descending Pixel spacing 30 m 12.5 m 12.5 m Beam mode* - Standard 7 Standard 2 Data type Unsigned 8-bit Unsigned 16-bit Unsigned 16-bit Note: All satellite images are provided by Malaysian Centre for Remote Sensing (MACRES). 3.2 Methods Field Observation Parameters such as soil moisture content and surface temperature of 19 selected points were measured during field observation that was carried out on Saturday, 19 th May Soil samples for all the 19 points were also taken Samples Measurement/Laboratory Test Samples that were taken from the site were analysed in the laboratory. Soil parameters i.e. soil density, volumetric water content, gravimetric water content, soil bulk density, and soil unit weight are measured. The samples are oven-dried at 110 O Celsius for 8 hours. All parameters are then derived from both wet and dry soil samples Soil Samples and Satellite Images Integration The main parameter, soil moisture (ω) or also known as volumetric water content is calculated from the correlation of soil moisture and ω = [(wet soil weight)-(dry soil weight)/(volume of sample)(density of water)]x100 (1) In metric units, the density of water is 1,000 kg/m3 (1 g/cm3). Measured soil moisture is correlated to the surface temperature that was observed using thermal camera during field data collection. The relationship is found to be: ω = (T s ) (2) For the Landsat-TM image, T s is derived from Radiative Transfer Equation (RTE), and the T s is obtained from the following mono-window algorithm applied to the thermal infrared region (Qin et al. 2001):

5 T s 1 = ) C [ a(1 C D) + b(1 C D) + C + D T DT ] sensor a (3) with C = ετ, D = (1 - τ) [1 + (1 - ε) τ], a = , b = , and where ε is the land surface emissivity, τ is the total atmospheric transmissivity, T sensor is at-sensor brightness temperature and T a represents the mean atmospheric temperature. When the surface temperature was derived from Landsat TM image, eq. 2 is then applied to the image to obtain the soil moisture content. The crucial parameters in slope stability model are i) cohesion, ii) friction angle, and iii) soil unit weight. From the correlations made to the soil moisture that was derived from the image, the following equations for each parameters are derived; i) Cohesion, c = ω ω (4) where c is soil cohesion (kpa) and ω is the soil moisture ii) Friction angle, φ = 11212ω (5) Equations (4) and (5) are adapted from Cocka et al. (2004) while the for soil unit weight (γ) is derived from the soil properties measured from the soil samples. The soil unit weight can be expressed as γ = (ω g ) (6) where γ is unit weight (kn/m3) and ω g is gravimetric water content, can be written as StereoSAR Processing ω g = (ω) (7) StereoSAR or also known as radargrammetry technique is applied to the stereo pair Radarsat-1 images to produce Digital Elevation Model (DEM). This DEM is used to derive slope angle (α) along the road sides of Karak Highway Slope Stability Modeling Infinite model which is adapted from Rahardjo et al. (1995) is used in this research. Factor of safety (FS) for the hydrostatic condition can be written as follow

6 tanφ c 1 y γ w tanφ FS = + + (8) tanα γ ( H y) sinα cosα H y γ tanα Where H is the height of soil from the water table (assumed as 10 m), y is the slab thickness, estimated to be a constant 2.5 m, reflecting the average debris-flow thickness in the region of study area (Rahardjo et al. 1995; Jamaludin et al. 2006) and also suggested by Terlien, (1998) and VanWestern, (2000) for a shallow landslide. 4. RESULTS Factor of safety for the whole study area is mapped at 1: mapping scale. Every pixel in the image represents the value of FS. Factor of safety provide a quantitative indication of slope stability. A value of FS = 1.0 indicate that a slope is on the boundary between stable and unstable; the factor tending to make the slope stable are in precise balance with those tending to make the slope unstable. A calculated value of FS less than 1.0 indicates that a slope would be unstable under the conditions contemplated, and a value of FS greater than 1.0 indicates that a slope would be stable (Duncan and Wright, 2005). The final result of this research is shown in Figure 2. Figure 2 Slope Stability Map Depicting Location of Unstable Slopes (FS < 1.0) This research is still on going and has been discuss are the preliminary finding from the works that have been carried out so far. This preliminary results however show some indication that information from remote sensing data together with other supporting data

7 can be utilized to model slope stability in the study area. This however required further analysis, and trough various accuracy assessment that will be carried out further, it is hope that the final outcome this research will provide a significant contribute in this specific field of study. 5. CONCLUSION Referring to the preliminary findings of this study, it has been proven that soil mechanical parameters can be connected to the remote sensing data. However, accuracy and validity of the parameters can vary depending on the techniques employed as well as the way the data are interpreted. This variation can thus effect the slope stability information that is being analysed. This is a new finding that being unexploited in current time. It will be a great opportunity for remote sensing to be involved into the geotechnical practices, especially in highway slope stability specializes. 6. REFERENCES Duncan, J.M. and Wright S.G. (2005). Soil Strength and Slope Stability. John Wiley & Sons, Inc. Hoboken, New Jersey. Cocka, E., Erol., O. and Armangil, F. (2004), Effects of Comapaction Moisture Content on the Shear Strength of an Unsaturated Clay. Geotech. & Geol. Eng. 22: Jamaludin, S., Ley, L.N. and Pandi, A.R. (2006), Hydrological Triggering Systems in Landslides; An Overview. Proc. National Conference Water for Sustainable Development towards a Developed Nation by 2020, July 2006, Guoman Resort Port Dickson. Qin, Z., Karnieli, A. and Berliner, P. (2001), A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and its Application to the Israel Egypt Border Region. International Journal of Remote Sensing, 22 (18), Rahardjo, H., Lim, T.T., Chang, M.F. and Fredlund D.G. (1995), Shear-Strength Characteristics of a Residual Soil. Can. Geotech. J. 32: Raj, J.K. (1985). Characterization of the weathering profile developed over porphyritic biotite granite in Peninsular Malaysia. Bulletin of the International Association of Engineering Geology. Paris. 32: Terlien, M. (1998), The Determination of Statistical and Deterministic Hydrological Landslide-Triggering Thresholds. Environmental Geology 35: