Tài liệu Landslide hazard and risk assessment for road network using rs and gis a case study of xin man district, vietnam

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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
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