LANDSLIDE HAZARD AND RISK ASSESSMENT FOR ROAD
NETWORK USING RS AND GIS: A CASE STUDY OF XIN MAN
DISTRICT, VIET NAM
by
Lai Tuan Anh
A thesis submitted in partial fulfillment of the requirements for the
degree of Master of Engineering
Examinat ion Committee: Dr. Kiyoshi Honda (Chairperson)
Dr. Marc Souris
Dr. Ulrich Glawe
Nationality: Vietnamese
Previous Degree: Bachelor of Engineering in Geodesy
Hanoi University of Mining and Geology,
Vietnam
Scholarship Donor:
AIT Fellowship
Asian Institute of Technology
School of Engineering Technology
Thailand
May 2006
i
ACKNOWLEDGEMENT
It is with delight that the author first of all extends his hearties gratitude to the Thesis research
committee Chairperson, DR Kiyoshi Honda for his professional guidance, advice,
encouragement throughout the study.
The technical and conceptual support of Dr Marc Souris, thesis committee member, helped me
to conduct the research for which I express my thanks to him.
Valuable suggestions support of Dr Ulrich Glawe and thesis committee member help me to
work enthusiastically so I am grateful to him.
I would like to express my sincere thanks to DANIDA for the scholarship and Star program
for the research grand, thereby making this study possible.
Special thanks go to RSL staff, Mr. Do Minh Phuong for providing all the necessary on time.
I am grateful to the local in Xin Man province who provided and guide me go to all the
landslide points to measurement GPS.
My vote of thanks goes to all my friends, Mr. Tran Trung Kien, Miss Dao Thi Chau Ha, for
their helps, supports, sharing the difficulties to my life in AIT.
Most of all, I want to express my deep appreciation to my family: Parents, my sister for their
endless love, constant support and encouragement for the graduate study.
ii
ABSTRACT
Xin Man district in the South west Ha Giang has high landslide hazard. However, the
available information on landslide in Xin Man district is still limited. We constructed the
essential spatial database of landslides using GIS techniques. The quantitative relationships
between landslides and factor s affecting landslides are established by the Certainty Factor
(CF). The affecting factors such as slope, elevation, landcover, geology, road distance,
lineament distance, drainage density are recognized. By applying CF value integration and
landslide zonation, the most significant affecting factors are selected.
By using RS&GIS technology landslide occurrences on all these factors have been analyzed.
The vector based GIS has been used for digitizing to produce thematic maps, as analysis for
study was based on the pixel based information therefore Raster based GIS has been used for
the analysis.
Pixel based calculation was made by using the CF value Model. By using the CF model we
obtain the CF value for all classes al all factor maps. On the basis of these CF value all factor
maps are recoded and matrix analysis was perform to produce a Landslide Hazard Zonation
map.
The Landslide Hazard Zonation map has been applied to develop a methodology to produce
hazard maps considering the behavior of landslide and to evaluate potential damage to
infrastructure specific road system. Different factors have been cons idered for this study.
iii
TABLE OF CONTENTS
CHAPTER
TITLE
PAGE
Title page
ACKNOWLEDGEMENT
ABSTRACT
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF MAPS
LIST OF ABBREVIATIONS
i
ii
iii
iv
vi
vii
ix
x
1
INTRODUCTION
1.1 Background
1.2 Statement of problem
1.3 Objectives
1.4 Scope and limitation
1
1
1
2
2
2
LITERARUTE REVIEW
2.1 Hazard, risk & vulnerability
2.2 Landslide Hazard mapping
2.3 Fundamental of Remote sensing
2.4 GIS overview
2.5 Global Positioning System (GPS)
2.6 Web Map Server
2.7 Landslide Studies
3
3
4
5
12
12
12
15
3
DESCRIPTION OF THE STUDY AREA
3.1 Area and situation
3.2 Climate
3.3 Rainfall
3.4 Population
3.5 Geology
3.6 Elevation
3.7 Slope
3.8 Lineament
3.9 Road system
3.10 Drainage density
3.11 Landcover
18
18
18
18
18
20
22
24
25
27
29
30
4
METHODOLOGY AND ANALYSIS
4.1 General
4.2 Compilation of Required data
4.3 Field Survey by using GPS
4.4 Extraction of maps from the source data
33
33
33
34
35
iv
TABLE OF CONTENTS (CONT.)
CHAPTER
TITLE
PAGE
4.5 Methodology and Analysis Data
4.6 Data Entry
4.7 Analysis data
4.8 Landslide Hazard Zonation Map
38
40
41
43
5
RESULTS AND DISCUSSIONS
44
5.1 Characterize several types of landslide in Xin Man district
44
5.2 Landslide Hazard Zonation map
46
5.3 Landslide Hazard Zonation map
54
5.4 Accuracy Check for Landslide Hazard Zonation Map
60
5.5 Develop a methodology to produce hazard maps considering the behavior of
landslides
62
5.6 Publish Landslide Hazard Zonation to Internet using Web Map Server
69
6
CONCLUSIONS AND RECOMMENDATIONS
6.1 Conclusions
6.2 Recommendation
76
76
77
APPENDICES
80
v
LIST OF TABLES
TABLE
2.1
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
4.1
4.2
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
TITLE
PAGE
Landsat 7 ETM image characteristic
Geology, major litho-stratigraphic units with their corresponding classes
Area under Geology
Area under Elevation
Area under slope
Area under distance to lineament
Area under distance to road
Area under drainage density
Area under Landcover
Analysis data from different sources
Hazard zones
CF value of Geological
CF value of distance to lineament
CF value of slope angle
CF value of elevation classes
CF value of drainage density
CF value of landcover layer
CF value of distance to road
The hazard value ranges used for road buffer
The hazard value ranges used for whole area
% area for landslide hazard zone for buffer area
% area for landslide hazard zone for whole area
Defining the risk
Classification risk level
Result of the risk class based on buffer analysis
vi
7
20
21
22
24
26
27
29
30
34
43
47
48
49
51
52
53
54
55
55
56
56
64
66
69
LIST OF FIGURES
FIGURE
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.1
4.2
4.3
4.4
4.5
4.6
4.7
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
TITLE
PAGE
Criteria for risk assessment (Disaster Preparedness and Mitigation-2002)
4
Spectral reflectance of vegetation, soil and water
6
Spectral reflectance of a green left
7
The image interpretation processing
8
Data Flow in Remote sensing
9
The flow of geometric correction
10
Procedures of Classification
11
WFS Processing Request
14
Map of large Landslide areas of Vietnam
17
The yearly rainfall from 1961 to 2003
18
Location of Study area Xin Man district, Viet Nam
19
Geology chart
21
Elevation chart
23
Slope chart in Xin Man district
24
Distance to the lineament chart
26
Road area under the buffer
28
Drainage density chart
29
Landcover chart
31
Flo w Diagram For Landcover Map
35
Flow Diagram for Digitized Map
35
Flow Diagram for Landslide Map using GPS
36
Flow Diagram For TIN and maps extraction from TIN
37
Flow Diagram For Landcover Map extracted From Satellite Data
37
Flow Diagram for Buffered Road and lineament Maps
38
Methodology of thematic data layer preparation
39
Show the landslide attacked road.
44
Wedge slip occur along the road.
45
CF value of geological
48
CF value of distance to lineament
49
Statistical map of slope angle distribution in Xin Man District
50
CF value of slope angle layer
50
CF value of elevation layer
51
CF value of drainage density layer
52
CF value of landcover layer
53
CF value for distance road.
54
Bar chart showing the distribution of various hazard zones
57
for buffer area in Xin Man district
57
Bar chart showing the distribution of various hazard zones for whole area in Xin
Man district.
57
Relative distributions of various hazard zones and landslide probability within
each zone in road buffer in Xin Man district.
60
vii
LIST OF FIGURES
FIGURE
5.14
5.15
5.16
5.17
5.18
5.19
5.20
5.21
5.22
5.23
TITLE
PAGE
Relative distributions of various hazard zones and landslide probability within
each zone whole area in Xin Man district
The description of the road buffer
Show the process landslide
Schematization the Landslide area
Flow chart fo r procedure risk map
Minnesota Mapserver Framework Using CGI
Input data from the Mapfile
Study area on WMS
LHZ map in Xin Man on MapBrowser.
LHZ map on GMapFactory
viii
61
62
63
63
64
70
73
74
74
75
LIST OF MAPS
MAP
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
5.1
5.2
5.3
5.3
5.4
5.5
5.6
TITLE
PAGE
Tin in Xin Man district
Geological in Xin Man district
Elevation in Xin Man district
Slope in Xin Man district
Distance to lineament in Xin Man
Buffer road system in Xin Man
Drainage density in Xin Man
Landcover in Xin Man
Road system in Xin Man
Landslide distribution in Xin Man district
Landslide hazard zonation for buffer area in Xin Man
Landslide hazard zonation for buffer area in Xin Man
Risk of slope in Xin Man’s Road Network
Risk of distance to Xin Man’s road network
Risk in Xin Man
Risk assessment for road network in Xin Man
ix
19
22
23
25
27
28
30
31
32
46
58
59
65
65
67
68
LIST OF ABBREVIATIONS
TIN
Triangulated Irregular Network
DEM
Digital Elevation Model
DTM
Digital terrain Model
RS
Remote Sensing
GIS
Geographical Information System
GPS
Global Position System
GML
Geographical Mark Up Language
JPEG
Join Photographic Experts Group
URL
Uniform Resource Locator
WWW
World Wide Web
WMS
Web Map Service
WFS
Web Feature Service
x
CHAPTER 1
INTRODUCTION
1.1 Background
Landslide has become one of the world’s major natural disasters for the few years in many
countries. Landslides are the most common natural hazard in mountainous terrain. Landslide
can be a major threat to population in the mountainous area. Even when they occur away from
the inhabited areas, landslide can be a significant hazard and have a serious economic impact
by blocking roads and river (Aniya, 1985; J. Achache, B. Fruneau and C. Delacourt 1995).
Landslides are widespread in many countries and cause great economic losses, especially
when engineering constructions are designed and erected without heeding the stability
conditions of the slopes (Q.Zaruba, V.Mencl 1967).
Landslides become a problem when they interfere with human activity. The frequency and
the magnitude of the slope failures can be increased due to human activities such as
deforestation or urban expansion.
Landslide hazard analysis is a difficult task. It requires large number of parameters and
techniques for analysis. Remote sensing and GIS are the powerful analysis tools to handle this
type of problems. A in the analysis of landslide spatial information e.g. topography, geology,
landcover, etc. are involved, therefore application of Remote sensing and GIS will be
effective.
1.2 Statement of problem
- Although landslide usually occur in Xin Man district, but people who live near or in the
landslide’s local do not illustrate the different between them. But actually, there are many
types of landslide which can occur and each of them have separate characterize. We need to
give some information to describe characterize some types of landslide in Xin Man district.
- Landslide is a serious disaster in Viet Nam. In recent 10 years, there are more than 10
areas occurred violent landslide, causing above 300 human deaths and thousands of hectares
of solids was buried by stone, sand, pebble and hundreds of inhabitant settlements having to
change their living places and locations. These are responsible for considerably greater socioeconomic loss than is generally recognized. There are some projects a nd research applying for
landslide but only for mid_center of Viet nam. Up to now, there is not hazard map, risk map
about landslide in Xin Man district, the leader of province only have measure to prevent
landslide every year and they have not had any project to study about landslide in the Xin Man
district. Hence, there is an urgent need to prepare landslide hazard zonation maps in the highly
landslide susceptible mountainous terrain special is Xin Man district.
- No other landslide investigation or risk assessment has been performed in Xin Man
district to date.
- Understanding and prevent landslide hazard is very important for every people. What can
people do when lack of information about natural hazard? Nowadays, internet is popular and
useful for every people. People can update, download all information and all thing which they
need to know. In this regard, we need to publish and share information about landslide on
Internet by using Web Map Sever.
1
1.3 Objectives
The general objective of this study is using Remote sensing and GIS technique to making
landslide hazard zonation mapping in Xin Man district.
The specific objectives of the study are:
1. Characterize several types of landslide in Xin Man district.
2. Create zoning maps for landslide hazard that usually occurs in Xin Man district.
3. Develop a methodology to produce hazard maps considering the behavior of
landslides
4. Publish and share landslide hazard zonation map’s information on internet using
Web Map Server.
1.4 Scope and limitation
- Landslide hazard map zonation will be focuses on critical physical factors by using GIS
overlaying thematic maps.
- To determine and localize area have high risk of landslide base on investigation, study
topology, geology, hydrology, and geomorphology.
- The limitation is associated with the availability of reliable and adequate data sets from
secondary sources to support making landslide hazard zonation map.
- Risk assessment only for road networks, not consider about the others as population,
economics, socia l…
- Data collection is not enough to be analysis.
- Landsat TM images will be used for analysis of landcover of the study area.
- Apply existing program to publish landslide hazard zonation map on internet using Web
Map Server.
2
CHAPTER 2
LITERARUTE REVIEW
Natural Hazard is extreme events in the earth’s ecosystem. The concepts of hazard, risk, and
vulnerability are often confused with one another and with the extreme event itself. Although
the extreme event is inherent in hazard, risk and vulnerability terminology, it is not
synonymous with the terminology. Therefore it is necessary to distinguish between the terms
hazard, risk and vulnerability.
Hazard assessment determines the type of hazardous phenomenon, frequency, magnitude and
the extent of the area that may be affected. Vulnerability indicates the degree of loss caused to
people, infrastructure, buildings, economies etc…distinguishing physical (buildings,
infrastructure), functional (lifelines, communication) and social aspects (health, population
density). Risk combines the knowledge about hazard and vulnerability to make a quantitative
prediction of the elements at risk, like numbers of lives to be possibly lost, people to be
injured, cost of property being damaged and destroyed or economic activities a affected.
2.1 Hazard, risk & vulnerability
In order to provide a systematic approach to study the landslide, Varnes (1984) defined
various types of hazard, risk & vulnerability.
Natural hazard the probability of occurrence of a potentially damaging phenomenon
within a specific period of time and within a given area.
Vulnerability the degree of loss to a given element or set of elements resulting from the
occurrence of a natural phenomenon of a given magnitude.
Element at risk the population, properties, economic activities etc… at risk in a given area.
Risk the expected degree of loss due to a particular natural phenomenon. Hence it is a
product of hazard and vulnerability.
Criteria for risk assessment is represented schematically as below (Figure2-1)
3
Hazard
Zonation
Hazard
Persons
Structures
Elements at
risk
Landuse
Activities&
Funtions
Vulnerability
Human
Vulnerability
Structural
Vulnerability
Vulnerability
Assessment
Land
Vulnerability
Funtional
Vulnerability
Risk
Assessment
Figure 2.1: Criteria for risk assessment (Disaster Preparedness and Mitigation-2002)
2.2 Landslide Hazard mapping
2.2.1. Definition
Although by the term landslide is used for mass movements occurring along a well defined
slid ing surface, it has been used in literature as the most general term for all kinds of mass
movements, including some with little or no true sliding, such as rock- falls, topples, and debris
flows (Varnes, 1984) In this context, mass movement is used subsequently as a synonymous
term for landslide, similar to slope movement.
Zonation refers to the division of the land surface into areas and the ranking of these areas
according to degrees of actual or potential hazard. Hence landslide hazard zonation shows
potential hazard of landslides or other mass movements on a map, displaying the spatial
distribution of hazard classes. In general three basic principles or fundamental assumptions
guide all zonation studies (Varnes 1984).
Ø The past and the present are keys to the future: Natural slop failures in the future will
most likely occur where geologic, geomorphic and hydraulic situation have led to past
4
and present failures. Thus, we have the possibility to estimate the features of potential
future failure. The absence of past and present failures does not mean that failures will
not occur in the future.
Ø The main conditions that cause landslides can be identified: The basic cause of slope
failures can be recognized, are fairly well known from several case studies and the
effects of them can e rated or weighed. It is possible to map correlate the contributing
factors to each other.
Ø Degree of hazard can be estimated: If the condition and processes that promote
instability can be identified, it is often possible to estimate their relative contribution
and give them some qualitative or semi-quantitative measurement. Thus, a summery of
the degree of potential hazard in an area can be built up, which depends on the number
of failure including factors present, their severity, and their interaction.
2.2.2 Scale of mapping for landslide hazard zonation
There are several technique for landslide hazard zonation can be applied, making use of GIS.
Therefore the appropriate scale on which the data is collected and the result presented varies
considerably. More detailed hazard maps require more detailed input data. Thus the objective
of the analysis and the requires accuracy of the input data determine the scale.
The following scales of analysis have been differentiated for landslide hazard zonation
according to the definition by the International Association of Engineering geologists (1976):
§
National scale(<1:1,000,000)
§
Regional scale(1:100,000 – 1: 1,000,000)
§
Medium scale(1:25,000 – 1:100,000)
§
Large scale( 1:2,000 – 1:25,000)
2.3 Fundamental of Remote sensing
2.3.1 Concept of Remote Sensing
Remote sensing is defined as the science and technology by which the characteristics of the
objects of interest can be identified, measured or analyzed the characteristics of the objects
without direct contact.
Electromagnetic radiation, which is reflected or emitted from an object, is the usual source of
remote sensing data. A device, to detect the electro-magnetic radiation, reflected or emitted,
from an object is called a “remote sensor” or “sensor”. A vehicle to carry the sensor is called a
“platform”.
Remote sensing is classified into three types with respect to the wavelength regions.
Ø Visible and reflective Infrared remote sensing.
Ø Thermal infrared remote sensing.
Ø Microwave remote sensing.
5
2.3.2 Spectral Reflectance of Landcovers
Spectral reflectance is assumed to be different with respect to the type of landcover.
This is the principle that in many cases it allows the identification of landcovers with remote
sensing by observing the spectral radiance from s distance far removed from the surface.
Fig.2.2 shows three curves of spectral reflectance for typical land covers; vegetation, soil and
water. As seen in the figure, vegetation has a very high reflectance in the near infrared region,
though the re are three low minima due to absorption.
Soil has rather higher values for almost all spectral regions. Water has almost no reflectance in
the infrared region.
Figure 2.2: Spectral reflectance of vegetation, soil and water
Figure 2.2 shows two detailed curves of leaf reflectance and water absorption. Chlorophyll,
contained in a leaf, has strong absorption at 0.45 m and 0.67 m, and high reflectance at near
infrared (0.7-0.9 m). This results in a small peak at 0.5-0.6 (green co lor band), which makes
vegetation green to the human observer.
Near infrared is very useful for vegetation surveys and mapping because such a steep gradient
at 0.7-0.9 m is produced only by vegetation.
Because of the water content in a leaf, there are two absorption bands at about 1.5 m and 1.9
m. This is also used for surveying vegetation vigor.
6
Figure 2.3: Spectral reflectance of a green left
2.3.3 Description of the data set-Landslide image
Database of remote sensing is used in this thesis is Landsat 7 ETM. The application of satellite
data has increased enormously in the past decade. After the initial low-spatial resolution
images of the LANDSAT MSS ( which were about 60 by 80 m), LANDSAT now has a
significant improve in its characteristics with thematic mapper (TM) images. It has a spatial
resolution image of the 30 m and excellent spectral resolution. Landsat TM provides sevens
bands to cover the entire visible, near infrared and middle infrared portions of the spectrum,
with one additional band providing a lower resolution of the thermal infrared (table 2-1).
Landsat satellite orbits are arranged to provide good coverage of a large portion of the earth’s
surface. The satellite passed over each location every 18 days, offering a theoretical temporal
resolution of 18 days.
Table 2.1: Landsat 7 ETM image characteristic
Band
Spectral range(µm)
Spatial resolution(m)
1
0.45-0.52
30
2
0.52-0.60
30
3
0.63-0.69
30
4
0.76-0.90
30
5
1.55-1.75
30
6
10.4-12.5
60
7
2.08-2.35
30
Pan
0.50-0.90
15
7
2.3.4 Image interpretation
Image interpretation is defined as the extraction of qualitative and quantitative information in
the form of a map, about the shape, location, structure, function, quality, condition,
relationship of and between objects, etc. by using human knowledge or experience.
Information extraction in remote sensing can be categorized into four types which are as
follows:
Classification is a type of categorization of image data using spectral, spatial and temporal
information.
Change detection is the extraction of change between multi- date images.
Extraction of physical quantities corresponds to the measurement of temperature, atmospheric
constituents, and elevation and so on from spectral.
Identification of specific features is the identification, for example, of disaster, lineament and
other feature etc.
Figure 2.4 show a typical flow of the image interpretation process:
Preparation
Pre-works
Image reading
Image measure
Image analysis
Thematic Map
Figure 2.4: The image interpretation processing
2.3.5 Image Processing System
The remotely sensed data are usually digital data. Together information from that we
need data processing. The processes are given in the chronological order:
Ø Input data.
8
Ø
Ø
Ø
Ø
Reconstruction/ correction.
Transformation.
Classification.
Out put.
Figure 2.5 shows the data flow in remote sensing.
Reflection/Radiation of
Electromagnetic Radiation
Camera
Scanner
A/D conversion using a
film scanner etc.
Primary Processing D/D
conversion.
Digital Image
Reconstruction / Correction
Transformation
Classification
Transformed
image
Classified
image
Mapping
Hard/Soft copy
Database
Figure 2.5: Data Flow in Remote sensing
9
2.3.6 Geometric Correction
Geometric correction is undertaken to avoid geometric distortions from a distorted image, and
is achieved by establishing the relationship between the image coordinate system and the
geographic coordinate system using calibration data of the sensor, measured data of position
and attitude, ground control points, atmospheric condition etc.
The steps to follow for geometric correction are as follows:
Input image
(1) Selection of method
a. Systemetic correction.
b. Non-systematic correction.
c. Combined method.
(2) Determination of parameters.
(3) Accuracy check
(4) Interpolation and resampling
Figure 2.6 : The flow of geometric correction
There are three methods of geometric correction as mentioned below:
-Systemmetic correction.
-Non-systemmatic correction.
-Combined method.
2.3.7 Registration and Rectification
Refael C. Gonzalez Rechard E. Woods (1993) explained that the another important application
is the image registration or finding correspondence between two images. The procedure for
image registration is the same as the method just illustrated for geometric correc tion.
However, the emphasis is on transforming an image so that it will correspond with another
image of the same science but viewed perhaps from other prospective.
2.3.8 Classification
Classification of remotely sensed data is used to assign corresponding levels with respect to
groups with homogeneous characteristics, with the aim of discriminating multiple objects from
each other within the image.
10
- Xem thêm -