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Model kerentanan tanah runtuh biasanya tidak termasuk faktor multitemporal seperti kerpasan, terutamanya untuk skala pertengahan. Peta tanah runtuh digunakan untuk menerbitkan hubungan ruang antara kejadian tanah runtuh dan faktor tanah runtuh menggunakan kaedah bivariat statistik.

INTRODUCTION

Background of Study
Problem Statements
Research Objectives and Expected Outcomes
Scope of Study
Contributions of Research
Thesis Organization

NDVI, Soil Moisture and LST, on the modeling of landslide sensitivity, have not yet been explored. To evaluate the developed landslide sensitivity maps, including the accuracy of the maps, the appropriate weighting system and the role of multitemporal.

Fig. 1.1 Rainfall cycle and landslide occurrences in PM

LITERATURE REVIEW

Overview

Landslide Hazard Studies: Terminologies and Associated Concepts

Landslide hazard refers to the potential for the occurrence of a damaging landslide within a given area and within a given time period. Landslide hazard (HL) is formulated by the International Association for Engineering Geology and the Environment (IAEG) and Varnes [38] as the multiplication of probability of landslide size (AL), probability of temporal occurrence (NL) and landslide susceptibility (LS).

Fig. 2.1 Type of mass movement Souce: Sassa [37]

Landslide Triggering and Causative Factors

Geology
Geomorphology

Slope gradient
Slope Aspect
Curvature
Elevation

Hydrology conditions and Climate

Soil Moisture
Factor related to river

Vegetation and Land Use Land Cover

Land use land cover (LULC)
Vegetation Index

Human activities

Factors related to deforestation
Factors related to road network development

Number of Causative Factors Used for LHA
Multi Temporal Environmental Factors

Change in vegetation index
Change in soil moisture
Change in LST

In addition to intrinsic factors, there are factors that produce unfavorable changes, such as those that change stress under slope conditions (e.g. stress varies due to erosion, fluctuations in groundwater, change in land use, removal of lateral support of slopes during cuts for roads, house sites, excavation etc.) and those that change the strength of materials in slopes (eg weathering and other physical and chemical influences). This is why the number of causal factors that investigators use for landslide hazard assessment (LHA) varies.

Table 2.1 Category of terrain attributes associated with landslide Category/Causative Factors Example

Scales of LHZ Maps

Hu and Feng [25] also found that surface air temperature and precipitation influenced soil temperature in the Eurasian continent. These studies show that climate, and more specifically rainfall, contributes to soil temperature control.

Methods for Landslide Hazard Assessment

Qualitative Methods

Field Geomorphology Analysis
Overlay or Combination of Index Maps or Parameter Maps

Quantitative Methods

Bivariate statistical method
Multivariate statistical methods

Production of Final Landslide Susceptibility Map

The assessment of stability in a given area can be carried out quickly by involving a large number of causative factors. This subjectivity exists during the classification of a causative factor map into a number of relevant ones and the division of the final hazard map into hazards.

Fig. 2.2 Landslide hazard assessment methods

Landslide Events in Malaysia and Cameron Highlands

About 55% of the total number of landslide events in Malaysia occurred in hilly areas such as Fraser's Hill, Cameron Highlands, Genting Highlands (all are in Pahang), Gunung Raya (Langkawi), Paya Terubung (Penang), Hulu Kelang (Selangor) mountain ranges and numerous limestone hills in Ipoh. Since 1961 to 2007, the total economic cost due to the threat of landslides in Cameron Highlands is about RM454 million according to JKR [13] records. A large landslide occurred at Km22, Km23.8 and Km24 of the highway connecting Pos Selim, Perak and Kampung Raja, Cameron Highlands, Pahang (Fig. 2.5b).

Due to a massive landslide in 1999, the opening of the third east-west highway was postponed. 153] reported that over 19,000 hectares of forest reserve in the Cameron Highlands had been proposed for agricultural development and new roads.

Fig. 2.4 Landslide events distribution in PM from JKR [13] database (1961-2007)

The Efforts on Reducing Landslides Hazards

Efforts by the Government and Individuals
Previous Works of LHA in Cameron Highlands

LHA in Cameron Highlands using Qualitative Methods
LHA in Cameron Highlands using Quantitative Methods

The accuracy of the developed LHZ map has not been known so far because this work did not include the process of map validation. Meanwhile, about 15 percent and 0.2 percent of the study area were categorized as moderate hazard and high hazard, respectively. Some remarks regarding this work are that expert opinions in the form of the LHEF rating system were used, showing a typical qualitative method; Secondly,.

However, the temporal environmental factors were not included in the LHZ modeling and the LHZ map verification test was not mentioned in this work. This work focused on the introduction of another quantitative method, which proved to be more satisfactory than the previous method in terms of the accuracy of the prediction of sensitive areas.

Remote Sensing and Its Roles in Landslide Hazard Assessment

Remote Sensing: The Concepts
Spectral Signatures and Multi Spectral Sensors
Landsat 7 ETM+ Satellite Mission
SPOT 5 Satellite Mission
Image Pre-processing

Geometric Corrections
Atmospheric Correction

Image Enhancement

Band Combination
Linear stretch

Image Classification for derivation of Land Use and Land Cover

Unsupervised Classification
Supervised Classification
LULC Classification Scheme

Land Surface Temperature
Vegetation Indices
Tasseled Cap Transformation for Landsat ETM 7 data
The Roles of Remote Sensing in Landslide Hazard Assessment

The characteristics of SPOT 5 bands are given in Table 2.6 and the satellite schematic is shown in Figure. The detailed procedure for removing random geometric distortion can be found in most remote sensing image processing literature, such as in Lillesand, et al. The detailed explanation of such methods can be found in most remote sensing image processing literature, such as Jensen [168] , Gibson and Power [94], Gao [165] and Lillesand, et al.

The mathematical expression of the stretched image (DNst) produced from the original image (DN) is given in equation (2.15). The detailed explanation of the classification method can be found in most image processing of remote sensing image such as Gao [165], Gibson and Power [94] and Lillesand, et al.

Fig. 2.6 Spectral signatures of different objects and Landsat ETM+ bands Source: CSIRO [158]

GIS and Its Roles in Landslide Hazard Assessment

GIS roles in Landslide Hazard Assessment
Concept of GIS
GIS Software

The GIS stores the required spatial data such as geology and LULC for the LHA, including remote sensing imagery, in a database during the data collection phase. In the real world, geographic information is represented by location, such as the location of a gauge point, gas stations, avalanche locations. etc.; attributes such as street name, land cover type, etc.; and spatial relationships such as dividing the center line of the river and the national border, etc. In the next phase, the software can be used to extract spatial data from the DEM, such as slope, slope aspect, curvature, etc.

This 3D surface is a master data used by Spatial Analyst for derived data such as slope, slope aspect, curvature, and elevation. Spatial Analyst provided spatial analysis tools required for landslide hazard modeling, such as extraction menu to evaluate the relationship between past landslide occurrences and causative factors and overlay tools to construct the final landslide hazard maps.

Fig. 2.17 GIS roles in phases of LHA Source: Van Westen [108]

Chapter Summary

Basic techniques of image processing of remote sensing data were described to produce corrected satellite images. Procedures for converting corrected satellite data to other spatial data such as LULC, NDVI, soil moisture and LST were explained. The presence of GIS was useful to cover all steps in LHZ from data collection to presentation of final landslide hazard map.

DATA AND METHODOLOGY

Overview

Characteristics of Study Area

Limits of Study Area
Topography, LULC and Soil of Cameron Highlands
Climate of Cameron Highlands
Geology of Cameron Highlands
Landslide Occurrences in Cameron Highlands

The average elevation is 1108 meters, indicating that the study area deserves the name highlands area. 9], the slopes in the study area are mainly dominated by the critical slopes that range from 200 to 350 and cover 47% of the total area. The annual precipitation in the study area is quite high, ranging from 2412 mm to 3172 mm, based on Malaysia Meteorological Department (MMD) data obtained between 2000 and 2005, as shown in Figure 1.

In the western part of PM, the precipitation intensity is smaller than that in the eastern part. A detailed explanation of the geology of the study area can be found in Bakar and Madun [218] and Jamaluddin [219].

Hardwares and Softwares

Pos Selim, located on the Simpang Pulai-Kampung Raja road near the Perak-Pahang border, was considered a large massive landslide in 1999 with wide coverage. Total costs include deaths, injuries, distance-related transportation costs, time-related transportation costs, time-related productivity costs, and restoration costs.

Source of Spatial Data

Topographic Map-Derived Spatial Data
SPOT 5 Satellites Data
Landsat 7 ETM+ Satellite Data and Strategy of Data Selection
Soil Map
Geology Map-Derived Spatial Data
Rainfall Data
Landslide Inventory map

According to the information on the map legend, the map was constructed from aerial photographs taken in 1981. The first was the function of digital topographic map and the second was the function of analog topographic human after being geometrically corrected. Once a scanned topographic map has been geometrically corrected, it can be used as a reference map to correct any remote sensing images.

There were four Landsat images to select according to the peak time of two rainy and dry seasons. Precipitation data were used for a monthly comparison between landslides and rainfall intensity, as shown in Figure 1.

Table 3.3 Data source for deriving landslide causative factors

Data Processing and Derivation

RSO Reference System
Digital Elevation Model (DEM)
Elevation
Slope
Slope Aspect
Curvature
Road Network and River-Lake
River and Lake
Geometric Correction of Topographic Map
Image Pre-Processing for SPOT and Landsat Images

Image Subsetting
Dealing with Atmospheric Effects
Geometric Correction of SPOT and Landsat Images
Image Enhancement

Land use land cover map

Image classification scheme
Ground Truthing, Training Sites and Signatures Generation . 130

Normalized Difference Vegetation Index (NDVI)
Soil Wetness from Tasseled Cap Transformation
Land Surface Temperatures
Dealing with Cloud Cover Problem
Lithology and Lineament Maps
Soil Map
Landslide Inventory

All slope directions, except flat areas, occupy almost the same areas ranging from 12-13% of the study area. The road network map of the study was extracted from the old database of topographic map by separating this layer from the main database. All satellite images were subset to fit the size of the study area to match the size of the corrected topographic map.

The Landsat image signature file was used as input to the dendrogram function. The expected result is a map showing the division of the study area into known LULC classes. The area value of all classes is corrected based on the effect of clouds and their shadow.

Soil wetness can be an indicator of the effect of seasonal rainfall on soil wetness.

Landslide Susceptibility Modeling

Extraction of Terrain Attributes of Landslide Sites
Landslide Attributes Reclassification
Weighting System
Final Landslide Susceptibility Maps and Scenarios
Validation of Final LSMs
Analysis of Relative Role of Causative Factor
Test of Applicability of the Developed Model

The final landslide susceptibility maps (LSMs) were constructed by summing a weighted thematic map of landslide causative factors. Calculation of the weight value of classes of residual landslide causation follows the stated procedures. Landslide model validation uses the well-known and widely applied principle that “the past and the present are the keys to the future,” as stated by Varnes [38] and Carrara, et al.

The landslide map, which expresses the distribution of landslide and slope failure locations in the study area, is an important key to assess the accuracy of the final LSMs. The number of landslide points with both status categories (VHS and HS) is used to measure the accuracy of the final LSMs.

Fig. 3.49 Work flow of landslide susceptibility modeling

Chapter Summary

The data were further processed to produce thematic maps composed of 10 static factors and 3 multitemporal factors. Areas affected by both features were left blank for NDVI, soil moisture, and LST maps and filled with information obtained from the adjacent acquisition image date. Landslide susceptibility modeling begins with determining the spatial relationship between previous landslide occurrences and causative factors.

The first scenario includes only static factors; The second to fourth scenarios include all static factors with additional multitemporal factors, namely NDVI, soil moisture, and LST. The scenarios were designed to answer research objectives on the roles of multitemporal factors in modeling landslide susceptibility.

Fig. 3.53 Flow chart of chapter 3 Selection of study area

RESULTS AND DISCUSSION

Overview
Weighting System and Construction of Thematic Maps

Weight Values and Thematic Map of Land Use Land Cover
Weight Values and Thematic Map of Lithology
Weight Values and Thematic Map of Elevation
Weight Values and Thematic Map of Slope Gradient
Weight Values and Thematic Map of Slope Aspect
Weight Valued and Thematic Map of Curvature
Weight Values and Thematic Map of Proximity to Road
Rating Weight and Thematic Map of Lineament
Weight Values and Thematic Map of River and Lake
Weight Values and Thematic Map of Soil
Weight Values and Thematic Maps of NDVI
Weight Values and Thematic Maps of Soil Wetness
Weight Values and Thematic Maps of LST
Anbalagan‘s LHEF Weighting System

Final Landslide Susceptibility Maps (LSMs) and Maps Validation

Final LSMs of Scenario 1 and Validation of the Maps
Static versus Multi Temporal Causative Factors-Based LSMs

Evaluation on Significance of Landslide Causative Factors

One by One Inclusion of Causative Factors
Accumulative Inclusion of Causative Factors

GIS-Based LSM Constructed Using Selected Causative Factors
Test of Applicability of The developed Landslide Model
Discussion
Chapter Summary

Thematic map of land use land use was compiled based on weight values derived from LSI. The assessment weight values for lithology were calculated in the same way as the causative factor for land use and land cover. For use in GIS analysis, a thematic elevation map was created with the weight values of each elevation range (Fig. 4.7).

The weight values were used as a map attribute to construct a thematic elevation map (Fig. 4.9). The thematic map of the slope aspect, showing the weight values of each aspect, was constructed based on LSI values.

Fig. 4.1 Landslide occurrences number and Landslide Susceptibility Index of land use land cover

CONCLUSIONS AND RECOMMENDATIONS

Overview

Conclusions

Recommendations for Future Works