Vol. 7, No. 3, p. 243252, September 2003
Development of GIS-based geological hazard information system and its application for landslide analysis in Korea
ABSTRACT: Techniques for geological hazard management, assess- ment and prediction must be developed for the prevention and mitigation of geological hazards. To enable this, data sets related to geological hazard prevention related must be constructed, analyzed and distributed to customers. For this, a spatial database (SDB) including geological hazards, basic maps, damageable objects, sat- ellite imagery, meteorological data and terrain analysis data was constructed using GIS, and to manage the SDB, a geological haz- ard information system (GHIS) was developed. The SDB is covering most area of South Korea and was formed at national, regional and medium scales separately in the form of index and adminis- trative district unit. Using the GHIS, the SDB can be selected according to scale, locality and different types of data, and can be edited and manipulated. For the application of the constructed SDB, landslide hazard was analyzed for the Janghung area, Korea.
Landslide susceptibility was analyzed using the landslide-occur- rence factors by frequency ratio model. For the verification, the result of the analysis was applied to study areas. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.
Key words: GIS, spatial database, geological hazard, information sys- tem, landslide
1. INTRODUCTION
In Korea, many people are harmed and much properties are damaged every year because of typhoons and heavy rains. Especially in the central and north part of Kyungi province of Korea, there were many disasters such as floods and landslides in 1996, 1998, 1999 and 2002. These phe- nomena are recurring, so the heavy rain is not just an atmo- spheric extraordinary phenomenon. Moreover, in the developments of the society there, the number of large construction activ- ities such as roads, railroads, nuclear facilities, dams, power stations, and petroleum facilities is rapidly increasing. Thus, systematic database (DB) construction of geological haz- ards and of geological hazard factors must be achieved for the assessment and prevention of those geological hazards.
The objective of this study is to construct a geological haz- ard related spatial database (SDB) and develop an informa- tion system for easy use of the DB.
The contents and scope of this study are to construct an SDB including such features as geological hazards, dam- ageable elements, basic maps, hydrological data, and satel-
lite images, to use a GIS (Geographic Information System) to apply the SDB to hazard assessment techniques, and to develop a geological hazard information system (GHIS).
GIS is a very useful tool for the input, management, analysis and output of spatial data such as topography, geology, soil and land cover. Consequently, GIS has been used recently in many fields and, in this project, GIS is used for SDB construction and in the development of the application and the information system. There are some studies for construction of information system using GIS (Gauna and Sozza, 1999;
Laitinen and Neuvonen, 2001; Oh, 2001; Donoghue and Mironnet, 2002). The difference of this study is to construct SDB and information system focus on geological hazard and related data.
Earthquake epicenter, date, magnitude and hazard maps were included in the SDB. The landslide locations in the pilot study area were detected and constructed for the DB by processing aerial photography and satellite imagery.
Map and damageable data were designed and made added to the SDB. Map data such as topographic maps, geological maps, soil maps, forest maps, mine maps and land cover maps were collected, entered, edited and processed for con- struction of the SDB. Damageable elements such as facil- ities and population were also collected, entered, edited and processed into the SDB. JERS and IRS satellite imagery was included, as well as meteorological data such as daily weather station precipitation and temperature, rainfall fre- quency maps, and probable maximum precipitations. Finally, terrain analysis data such as DEM, slope, aspect, curvature and hillshade maps were calculated from the topographic DB and DEM.
The constructed SDB is used for geological hazard man- agement and analysis. Except for the landslide and mine map DBs, the SDB covers most of South Korea. The SDB was made at the scales of 1:1,000,000, 1:250,000 and 1:50,000, and related to each map index and administrative district unit. The geological hazard information system (GHIS) was designed and developed for output from the SDB. The sys- tem consists of pull-down menus and icons for ease of use, and the SDB output is selected by scale, locality and kind of data. For application of the constructed SDB, landslide assessment was performed in the Janghung area, Korea.via the probability technique, which uses the results of the anal- Saro Lee*
Ueechan Choi
}
Geoscience Information Center, Geology & Geoinformation Division, Korea Institute of Geoscience &Mineral Resources (KIGAM), 30, Gajung-Dong, Yusung-gu, Daejeon 305-350, Korea
*Corresponding author: [email protected]
ysis of the relationship between landslide and factors such as topography, soil, forest and lineament in the Janghung area, Korea.
2. CONSTRUCTION OF A GEOLOGICAL HAZARD SDB AND ITS COMPONENTS
In this study, a geological hazard SDB was designed and constructed. The steps of the GIS database design, collec- tion and construction are shown in Figure 1. In the design stage, SDB input data entities were selected and classified as point, line or polygon types. Their attributes were assigned and the coverage area and input map scale were selected.
As required by the design, geological hazard data, geolog- ical hazard damage data entities, basic map data, meteoro- logical data, and satellite imagery were collected. Data were collected from hardcopy (paper) maps, digital maps and written reports. If the data were not digital they were dig- itized or scanned. The input method was selected based on efficiency of input. The methods were converting digital data and scanning hardcopy maps. If the collected data were CAD data, then they were converted into an ARC/INFO coverage. Constructing topology, editing digitized data, pro- jecting and transforming, entering attribute values and ver- ification were done after entering the map data. With regard to geographic coordinate systems, the Universal Transverse Mercator coordinate system was used. The status of the geological hazard SDB is shown in Table 1. For GIS soft- ware, ARC/INFO versions for UNIX and NT were used to design and construct the database. Among the many data structures, Coverages (vector data), GRIDs (raster data), and Images (BIL format, raster data) were used. Coverage data are used for representing geological hazards, damageable objects, and basic maps, except for the land cover map.
GRID data are used for representing meteorological data such as rainfall frequency maps and probable maximum
precipitation maps, and terrain analysis data except hill- shade imagery. Image data are used for representing satel- lite and hillshade imagery. The data types were selected on the basis of data efficiency and size.
After that database design, the GIS database was con- structed in accordance with the usual framework of data- base design. The SDB was classified to national (1:1,000, 000), regional (1:250,000) and medium (1:50,000 to 1:25, 000) scales, according to the original input data scales.
The geological hazard, damageable object, basic map, satellite image, and terrain analysis SDBs covered most of South Korea, but the landslide and mine map SDBs only covered the pilot study area because there were insufficient data to cover a larger area. The SDBs were constructed at 1:250,000 and 1:50,000 scales and related to map index and administrative districts for ease use and management.
2.1. Geological Hazard SDB
Geological hazard data such as earthquake, landslide, flood and land subsidence are needed to allow geological hazard distribution recognition and geological hazard anal- ysis. In this study, earthquake occurrence location, earth- quake hazard maps, and landslide occurrence locations were collected and included in the SDB. Earthquake occur- rence location is an essential component of earthquake his- tory for the recognition of spatial distribution and relationship analysis of faults. Earthquake occurrence location and earthquake hazard maps cover the entire Korea, but land- slide occurrence location maps only cover Yongin, Janghung and Boun in Korea (where landslides have occurred inten- sively). The geological hazard occurrence location data are essential for geological hazard analysis, but except for earthquake data, there is no organization that collects and manages geological hazard data systematically for the whole of Korea. Consequently, geological hazard data collection is difficult: geological hazard data for Korea must be collected and managed systematically.
2.2. Damageable Object SDB
The geological hazard damageable object SDB consists of people and facilities that can be damaged by geolog- ical hazard events. In this study, population and 70,000 facilities such as parks and resorts, traffic facilities, pub- lic facilities, educational, religious and industrial facilities were formed into an SDB. The population and facility SDB was provided by GEOWIN Int., then edited. The damageable object SDB is needed to assess the risk of geological hazards to people and facilities. If there are many people and facilities, a plan to counteract hazards must be setup, compared with the situation of few people and facilities.
Fig. 1. Database construction flow.
2.3. Basic Map SDB
Basic map data such as topographic maps, geologic maps, soil maps, forest maps, land cover maps, and mine maps were formed into an SDB. The Agency for Defense Devel- opment provided the topographic maps, the National Insti- tute of Agricultural Science and Technology provided soil
maps, and the Korea Forest Research Institute of Korea Forest Service provided the forest maps. These data were then edited and made into an SDB. The topographic DB contains administrative districts, roads, railroads, water fea- tures and contours. The topographic DB contains funda- mental data for mapping and the digital elevation model (DEM) needed for undertaking the terrain analysis and the Table 1. Status of SDB construction.
Classification Sub Classification Data Type Scale Region
Geological hazard data
Earthquake occurrence location Point (Vector) National scale Korea
Earthquake risk map Polygon (Vector) National scale Korea
Landslide Line (Vector) Medium scale Pilot areas
Geological hazard damageable object data
People Polygon (Vector) Small scale
Medium scale South Korea
Facility Line (Vector)
Polygon (Vector)
Small scale
Medium scale South Korea
Basic map data
Topographic map
Administrative district Water
Road Rail road Contour
Polygon (Vector) Line (Vector) Line (Vector) Line (Vector) Line (Vector)
National scale Small scale Medium scale
South Korea
Geological map Polygon (Vector)
National scale Small scale Medium scale
South Korea
Soil map Polygon (Vector) Small scale
Medium scale South Korea
Forest map Polygon (Vector) Small scale
Medium scale South Korea
Land cover map GRID (Raster) Medium scale South Korea
Mine map Line (Vector) Medium scale Pilot area
Meteorological data
Station location Point (Vector) National scale South Korea
Precipitation, Evaporation, Temperature, Humidity, Amount of sunshine, Sunshine time, Amount of cloud, Wind direction
Text South Korea
Rainfall frequency map GRID (Raster) National scale
(1 km × 1 km) South Korea Probable maximum precipitation map GRID (Raster) National scale
(1 km × 1 km) South Korea
Satellite data
JERS satellite image Image (Raster) Medium scale
(18 m × 18 m) South Korea
IRS satellite image Image (Raster) Medium scale
(5 m × 5 m) South Korea
Terrain analysis data
Altitude map (DEM) GRID (Raster) Medium scale
(30 m × 30 m) South Korea
Slope GRID (Raster) Medium scale
(30 m × 30 m) South Korea
Aspect GRID (Raster) Medium scale
(30 m × 30 m) South Korea
Curvature GRID (Raster) Medium scale
(30 m × 30 m) South Korea
Hillshaded image Image (Raster) Medium scale
(30 m × 30 m) South Korea (National scale: 1:1,000,000, small scale: 1:250,000, medium scale: 1:25,000 to 1:50,000)
geological hazard analysis. The geological DB which con- tains the lithology is needed to analyze landslide and land subsidence. The soil DB which contains the textures, mate- rials, drainage, effective thickness and topography of soil, is needed for analyzing landslides, land subsidence, flood, and liquefaction. The forest DB map which contains the type, age, diameter and density of forest trees, is needed for ana- lyzing landslides and floods. The land cover DB is need for analyzing floods, because of the runoff difference between different land covers. There are many abandoned mines in Korea; the mine DB is needed for land subsidence analysis.
2.4. Meteorological SDB
The meteorological SDB includes information such as daily precipitation, temperature, humidity, sunshine dura- tion, amount of cloud, wind direction for each meteorolog- ical station and administrative district. The data were downloaded from the Korea Meteorological Administration and the Rural Development Administration web site and made into a DB. The 49 rainfall frequency maps, and 20 probable maximum precipitation maps (Ministry of Construction and Transportation, 1988) were made into an SDB. The rainfall frequency map represents rainfall frequency during 30 minutes, 1 hour, 2 hours, 3 hours, 6 hours, 12 hours, or 24 hours, and with reappearance periods of 2 years, 5 years, 10 years, 20 years, 50 years, 100 years or 200 years. The theoretical probable maximum precipitation (PMP) is the greatest depth of precipitation for a given duration that is physically possible over a given storms area at a particular geographical location at a certain time of year. The mete- orological SDB is essential data for geological hazard analysis such as floods and landslides that are caused by heavy rain.
2.5. Satellite Image SDB
IRS (Indian Remote Sensing Satellite) and JERS (Japanese Earth Remote Sensing) satellite imagery were made into an SDB. The SDB is used for topographic recognition, geo- logical structure detection, and landslide location recogni- tion. Especially the high-resolution imagery (IRS) was used for detection of landslide locations. JERS satellite imagery was bought, georectified, and made into an SDB; IRS sat- ellite imagery was donated by KEOC (Korea Earth Obser- vation Center), georectified and
made into an SDB.
2.6. Terrain Analysis SDB
Using the topographic contour data, a DEM was calcu- lated and used to create an SDB consisting of DEM, alti- tude, slope, aspect, curvature, and hillshade imagery. The maps were acquired by terrain analysis of a DEM, which is the altitude of the topography. Slope is the maximum rate of
change of topography and aspect is the direction of the steepest down-slope of topography. The hillshaded imagery means the hypothetical illumination of a topographic sur- face, and the curvature map is the curvature between topo- graphic cells. The terrain analysis SDB is needed for the analysis of topographic recognition, landslides, and floods.
Altitude is used in flooded area calculation; slope is essen- tial for soil erosion and landslide analysis; and aspect and curvature relate to water flow and are therefore needed for flood and landslide analysis. The hillshade imagery is needed for three-dimensional displays of topography and for topographic recognition.
3. DEVELOPMENT OF A GEOLOGICAL HAZARD INFORMATION SYSTEM (GHIS)
A geological hazard information system (GHIS) was developed to use the constructed SDBs. The system devel- opment environment was ArcView 3.2 and the development language was Avenue (included in ArcView). Consequently, all of the functionality of ArcView can be used. Avenue offers an easy and powerful development environment. Avenue uses a graphical user interface and allows the development of a user-friendly application. Moreover, it is a scripting lan- guage that can integrate different programming languages such as ESRI Arc Macro Language (AML), Microsoft Visual Basic, and AutoLISP by Autodesk. Thus, the geological hazard spatial information system was developed and operated under Windows 95/98 or WINDOWS NT. Because running the Avenue requires ArcView, the information system needs ArcView to run.
The geological hazard spatial information system has many functions such as SDB display, retrieval, identifica- tion, edit, and help. The information system consists of view, table, chart, and a layout environment like in ArcView, but the information system is operated mainly in the view envi- ronment. The view environment is composed of pull-down menus and icons for ease of use. The pull-down menu con- sists of 14 main menu items (Table 2). The main menu items have several submenus. The ‘Data Management’ menu has data management related functions and the ‘Selection Scale/Type’ menu allows the selection of scale and region types. The ‘Geological hazard DB’ menu allows the dis- play and retrieval of the geological hazard DB and the
‘Damageable object DB’ menu allows the display and retrieval of population, and facility. The ‘Basic map DB’
menu allows the display and retrieval of many kinds of maps and the ‘Satellite Image DB’ menu displays the sat- ellite imagery data. The ‘Meteorological DB’ provides meteorological related DB data. In additon the ‘Terrain analysis DB’ provides terrain analysis results. The icons have many kinds of functions such as open, save, print, zoom, pan, and identify. The detail menus and functions are shown in Table 2.
Table 2. Function of Geological Hazard Information System (GHIS).
Data Management
Close Close All
Set Working Directory Save Project
Save Project As Print
Print Setup Export
Manage Sources Import Data Export Data Exit
Closes the active and all view, table, chart, layout, or script Closes the all view, table, chart, layout, or script
Sets the working directory Saves the project file to same name Saves the project file to new name Print the view contents
Setup printer
Exports the view or layout you are working on to a file Allows you to delete, copy, and rename data sources
Converts ASCII, IEEE floating-point, DEM, and DTED files to grid data Converts grid data sets to either an ASCII or IEEE floating-point file format Ends your ArcView session
Selection Scale/Type
National Scale Small Scale Medium Scale
Set data selection in national scale (1:1,000,000)
Set data selection in small scale (1:250,000) and map index or administrative district Set data selection in medium scale (1:50,000−25,000) and map index or administrative district
Geological Hazard DB
Earthquake Epicenter Hazard Map
Display the earthquake epicenter in current view Display the earthquake hazard map in current view
Landslide
Location Susceptibility Possibility Risk
Display the landslide location in current view
Display the landslide susceptibility map in current view Display the landslide possibility map in current view Display the landslide risk map in current view Damageable
DB
Population Large Facility Small Facility
Display the population per administrative district Display the large facility that is polygon type Display the small facility that is point type
Basic Map DB
Boundary Topographic Map Geological Map Soil Map Forest Map Land Use Map Mine Map
Display the boundary map
Display topographic map that consist of contour, road, drainage and administrative district Display geological map that consist of lithology in current view
Display soil map that consists of texture, material, drainage, topographic type, and thickness Display forest map that consists of type, age, diameter, and density in current view Display land use map in current view
Display mine map in current view Satellite Image
DB
JERS IRS
Display JERS satellite image in current view Display IRS satellite image in current view Meteorological
DB
Station
Rainfall Frequency Map Probable Maximum Precipitation Map
Display meteorological station in current view Display rainfall frequency map in current view
Display probable maximum precipitation map in current view
Terrain Analysis DB
DEM Slope Map Aspect Map Hillshaded Image Curvature Map
Display DEM that is altitude map in current view
Display slope map that is maximum rate of change in current view
Display aspect map that is a the steepest down-slope direction in current view Display hillshaded image that is the hypothetical illumination of a surface in current view Display curvature map that is curvature between cell in current view
Edit
Cut Themes Copy Themes Delete Themes Undo Edit Cut Graphic Copy Graphic Delete Graphic Paste
Select All Graphics
Deletes the active theme(s) from the Table of Cont to clipboard Copies the active theme(s) in your view to the clipboard Deletes the active theme(s) from the Table of Cont Performs an Undo
Cuts the selected graphics or features to the clipboard Copies the selected graphics or features onto the clipboard Deletes currently selected graphics or features
Pastes data that has been copied or cut onto the clipboard Selects all the graphics that have been drawn on a view
4. CASE STUDY: LANDSLIDE SUSCEPTIBILITY ANALYSIS
4.1. Study Area and Method
For the application of the constructed SDB and the geo- logical hazard information system (GHIS), landslide hazard were analyzed for the Janghung, Korea. In order to achieve
this, landslide hazard analysis techniques were verified in the study area using frequency ratio model.
There have been many studies in landslide hazard eval- uation using GIS. Guzzetti et al. (1999) summarized many landslide hazard evaluation studies. Recently, there are studies for landslide hazard evaluation using GIS such as Gokceoglu et al. (2000), Luzi et al. (2000), Randall et al.
(2000), Rautelal and Lakheraza (2000), Baeza and Coromi- Table 2. (continued).
View
View Properties Add Theme New Theme
Themes On Themes Off Themes On Themes Off Layout Menu Choice TOC Style
Zoom To Full Extent Zoom In
Zoom Out
Zoom To Active Themes Zoom To Selected Find
Lets you review and change the properties of the view you are working on Lets you add one or more themes to the current view from existing data sources Creates a new, empty theme in your view
Make all themes in a view visible or make all themes in a view invisible Make all themes in a view visible or make all themes in a view invisible Puts the view you are working on into a layout
Shows a dialog where you can change the style of the TOC Zooms to the full extent of all the themes in a view
Zooms in on the center of a view or a layout by a factor of 2.0 Zooms out from the center of a view or a layout by a factor of 2.0
Zooms to the spatial extent of the geographic features in the active theme(s) Zooms to the spatial extent of the currently selected features
Finds a feature in a view, table, or chart that has the attribute value you type in
Theme
Theme Properties Starting Edit Save Edits Theme
Lets you review and change the properties of the active theme Use this option to make the active theme editable
Allows you to save all edits made to the theme during the current edit session Save Edits As Theme
Convert To Shape file Cover to Grid Save Data Set Edit Legend Hide Show Legend Auto Levels Remove Labels Open Theme Table Query Builder Clear Selected Features Edit Theme Expression
Allows you to save your edits out to a new shape Converts the active theme into an ArcView shape file
Converts the selected features of each active theme to a grid theme
Saves the data source associated with a grid theme as a permanent data source Lets you change how the active theme is displayed
Lets you hide the legend of the active theme(s) in your view's Table of Contents Set rules for labeling the features of the active theme
Use this to remove the labels from the active theme(s) Use this to open the attribute table for the active theme(s)
Lets you query data according to tabular attributes by building a query expression This control deselects any selected features in the active theme(s)
Brings up the map query builder or map calculator associated with a grid theme
Graphic
Graphic Properties Text Properties Size and Position Align
Bring To Front Send To Back Group On A View Ungroup On A View
Lets you review and change the properties of the active graphic
This dialog is where you change how new text and label objects will appear Let you change the size and position of graphics of the selected graphics Aligns the selected graphics in the view or layout
Brings a selected graphic to the front of other graphics Puts a selected graphic behind other graphics Groups selected graphics into a single graphic
Ungroup the selected, previously grouped graphic into individual graphics
Windows
Tile Cascade Arrange Icons Show Symbol Window
Arranges windows as non-overlapping tiles Arranges windows
Arranges iconified windows Shows the symbol window
Help
ArcView Help
Information system help About Information System
Displays the dialog for browsing and searching ArcView's help system
Displays the dialog for browsing and searching geological hazard spatial information system
Provides information about geological hazard spatial information system
nas (2001), Temesgen et al. (2001), Donati and Turrini (2002), Parise et al. (2002), and Rece and Capolongo (2002). They have applied probabilistic and statistical method to landslide hazard mapping. Especially, in Korea, there are some studies of landslide hazard evaluation with GIS for the same study area (Lee and Min, 2001; Lee et al., 2002a, 2002b).
For the first step of this study, the study areas, Janghung in Korea, was selected. Then landslide locations, topogra- phy, soil, forest, and land cover databases were used from geological hazard information system (GHIS) for landslide analysis. The scale of the database was 1:5,000-scale topo- graphic maps, 1:25,000 or 1:50,000-scale soil maps, and 1:25,000-scale forest maps and land cover map. Using the landslide locations and the constructed spatial data-sets, a frequency ratio model was applied and landslide suscepti- bility map was made. Then, the susceptibility map was ver- ified using existing landslide location.
The study area, Janghung, lies between latitudes 37o43'N and 37o46'N, and longitudes 126o56'E and 127o01' E, and covers an area of 40.74 km2. The study area is in the north- western part of the Kyonggi gneiss complex, which is mainly composed of gneisses. In 1998, there were serious landslide damages in the Janghung area and the landslide locations were detected from IRS (Indian Remote Sensing) and field survey. The landslides were mainly soil slide and the land- slides occurred where the maximum daily rainfall is 208.5 mm (Fig. 2).
The frequency ratio that is used in this study is ratio of probability that is occurrence probability to not-occurrence probability in certain attribute. In the case of landslide, if we set the landslide occurrence event to B and certain fac- tors’ attribute to D, the frequency ratio in D is ratio of con- ditional probability. So, the ratio is higher than 1, the higher relationship between landslide and the certain factors’
attributed and the ratio is lower than 1, the lower relation- ship between landslide and the certain factors’ attribute.
4.2. Application and Verification of Landslide Suscepti- bility Mapping
Using the probability-frequency method, the spatial rela- tionship between the location of landslides and each land- slide-related factor was derived. The factors such as altitude, slope, aspect and curvature from the topographic database, soil texture, material, drainage, effective thickness, and topography from the soil database, forest type, forest diam- eter, and forest density from the forest map, and land cover data from Landsat TM image, were extracted from the SDB. Using the landslide locations and the constructed spa- tial data sets, a landslide analysis method was applied and verified. For this, the calculated and extracted factors were converted to a grid (ARC/INFO GRID type). Then, using frequency ratio model, the spatial relationships between the landslide location and each landslide-related factor were
derived such as Table 3. The ratings were used for calcu- lating the landslide susceptibility index and mapping. So, the ratio of each factors’ type or range were summed to cal- culate the landslide susceptibility index, as shown in Equa- tion (1)
LSI (Landslide Susceptibility Index) =ΣFR (1) (where FR = Frequency ratio of each factors’ type or range) The relation analysis is the ratio of the area where land- slides occurred to the total area, so a value of 1 means an average value. If the value is greater than 1, it shows a higher correlation; if lower than 1, a lower correlation. The landslide-hazard map was made using the LSI value index for the interpretation that is shown in Figure 3. The index is classified by equal areas and grouped into five classes for visual and easy interpretation.
For the verification of landslide susceptibility calculation methods, two basic assumptions are needed. One is that Fig. 2. Case study areas and landslide location with hillshaded map.
Table 3. Rating of each factor’s range and type.
Topography
Slope (degree) Rating
Forest
1st age (1~10 years old) 2.13
0~5 0.00 2nd age (11~20 years old) 1.26
6~10 0.11 3rd age (21~30 years old) 0.91
11~15 0.31 4th age (31~40 years old) 0.00
16~20 0.58 5th age (41~50 years old) 0.00
21~25 1.04 Wood density Rating
26~30 1.68 Non forest area 1.04
31~35 2.48 Loose 0.00
36~40 2.79 Moderate 0.66
41~90 1.95 Dense 1.43
Curvature Rating
Soil
Soil drainage Rating
Concave 1.01 Poorly drained 0.37
Flat 0.89 Moderately well drained 1.09
Convex 1.04 Well drained 0.00
Aspect Rating Excessively drained 1.27
Flat 0.00 Soil material Rating
S 0.74 Colluyium 0.00
SE 0.34 Valley alluvium 0.00
SW 0.74 Rock residuum 1.11
W 1.64 Valley alluvial colluviums 1.09
NW 1.43 Soil texture Rating
E 1.55 Low humic gley soil and alluvial soils 0.00
N 0.76 Humic gley soils and alluvial soils 0.00
NE 0.46 Regosols 1.09
Topographic Type Rating Lithosols 1.21
Lower hilly area and hilly area 0.31 Red-yellow podzolic soils and lithosols 0.00 Piedmont slope area 1.05 Regosols and red-yellow podzolic soils 0.00
Valley area 0.00 Rock 0.61
Mountainous area 1.38 Effective thickness Rating
Rock 0.61 0~20 cm 0.61
Forest
Forest type Rating 21~50 cm 1.27
Non forest area 0.00 51~100 cm 0.33
Mixed broad-leaf tree 0.97 101~150 cm 0.00
Needle and broad 0.57
Lineament
Distance from Lineament Rating
Artificial chestnut tree 0.00 0~100 m 1.60
Korea nut pine 2.35 101~200 m 0.12
Larch 0.00 201~300 m 0.00
Rigida pine 0.90 301~400 m 0.00
Pine 0.00 401~500 m 0.00
Field 0.00 501 m < 0.00
Cultivated land 0.00
Land cover
Land cover Rating
Wood diameter Rating Water 4.65
Non forest area 0.00 Urban 0.00
Very small diameter 2.13 Forest 1.14
Small diameter 1.01 Grass 0.27
Medium diameter 0.00 Agriculture 1.04
Wood age Rating Barren 0.45
Non forest area 0.00
landslides are related to spatial information such as topog- raphy, soil, forest, geology and land cover. The other is that future landslides will be precipitated by a specific impact factor such as rainfall or earthquake (Chung and Fabbri, 1999). In this study, the two assumptions are satisfied because the landslides are related to the spatial information and the landslides were precipitated by one cause, heavy rainfall in the of the study area.
The landslide susceptibility analysis result verified using the landslide locations for the same study areas using the landslide locations. The verification method was performed
by comparison of existing landslide data and landslide sus- ceptibility analysis results for the Janghung of the study area. The comparison results are shown in Figure 4 as a line graph. The success rates in Figure 4 illustrate how well the estimators perform with respect to the landslides used in constructing those estimators (Chung and Fabbri, 1999). To obtain the relative ranks for each prediction pattern, the cal- culated index values of all cells in the study area were sorted in descending order. Then the ordered cell values were divided into 100 classes, with accumulated 1% intervals. The above procedure also was adapted for the landslide occurred cells by comparing the 100 classes obtained with the distribution on the study area. In Figure 4, in the case of between the 90 and 100% (10%; x axis) class that is highest index value contains 38% (y axis) of the landslides in study area in suc- cess rate. A 0−20% class (20%; x axis) contain 64% (y axis) and 0−30% class (30%; x axis) contain 81% (y axis) of the landslides in study area.
5. DISCUSSION AND CONCLUSIONS
For prevention and mitigation of natural hazards, tech- niques for hazard assessment and prediction must be devel- oped and contour maps must be prepared. To achieve this, hazard prevention related data must be constructed, ana- lyzed and distributed to customers. Consequently, hazard DB construction, supply and utilization are fundamental work to be done.
In this study, earthquake epicenter, date, magnitude and hazard maps were formed into SDBs. For the landslide DB construction and susceptibility assessment, landslide loca- tions of Yongin, Janghung and Boun areas where landslides had occurred were detected. Basic map data and damage- Fig. 3. Landslide susceptibility maps by frequency ratio model.
Fig. 4. Illustration of cumulative frequency diagram showing land- slide susceptibility index rank (x-axis) occurring in cumulative percent of landslide occurrence (y-axis).
able object data were designed and made into an SDB using GIS. Topographic maps, geological maps, soil maps, forest maps, hydrological data, satellite imagery, and damageable object data were all collected, processed and made into an SDB. The constructed SDB covered most territory of South Korea and was formed at national (1:1,000,000), small (1:250,000) and medium (1:25,000−1:50,000) scales. By using GIS, a geological hazard prevention spatial information sys- tem was designed and developed for retrieval of the SDB.
The system consists of pull-down menus and icons and the SDB can be retrieved by scale, locality and kind of data.
For the landslide susceptibility analysis, frequency ratio model was applied and verified for the study area of Jan- ghung, Korea, using the geological hazard information sys- tem (GHIS). The relationship analysis between landslide and factors was performed. Then, using the relationships, the landslide susceptibility map made and was verified by calculating the correlation observed between landslide occurrence location and the landslide susceptibility map. Generally, the verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide location.
The probability method−frequency ratio model - is some- what simplistic, but the process of input, calculation and output could be understood easily. Moreover, the large amount of data can be processed in the GIS environment quickly and easily. The constructed SDB can be used in other studies. The SDB can be applied to earthquake, land- slide, flood and land subsidence analysis. Furthermore, the SDB can be used to provide digital map data, detection of geological structures, analysis of soil and groundwater pol- lution potential and estimation of soil loss. Thus, the devel- oped information system can be used for landslide assessment and management by administrative organizations and insti- tutions relevant to landslides. Because the constructed SDB is at the national scale, small scale and medium scale, the scales are suited to regional hazard assessment but not suited to individual facility assessment. A more detailed SDB must be constructed for individual facility assessment.
REFERENCES
Baeza, C. and Corominas, J., 2001, Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms, 26, 1251−1263.
Chung, C.F. and Fabbri, A.G., 1999, Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering &
Remote Sensing, 65, 1389−1399.
Donati, L. and Turrini, M.C., 2002, An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina;
Perugia, Italy). Engineering Geology, 63, 277−289.
Donoghue, D. and Mironnet, N., 2002, Development of an integrated geographical information system prototype for coastal habitat monitoring. Computers & Geosciences, 28, 129−141.
Gauna, I. and Sozza, A., 1999, The geographic information system of the Turin City Council on the Internet. Computers, Environment and Urban Systems, 23, 485−494.
Gokceoglu, C., Sonmez, H. and Ercanoglu, M., 2000, Discontinuity controlled probabilistic slope failure risk maps of the Altindag (set- tlement) region in Turkey. Engineering Geology, 55, 277−296.
Guzzetti, F., Carrarra, A., Cardinali, M. and Reichenbach, P., 1999, Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomor- phology, 31, 181−216.
Laitinen, S. and Neuvonen, A., 2001, BALTICSEAWEB: an informa- tion system about the Baltic Sea environment. Advances in Envi- ronmental Research, 5, 377−383.
Lee, S., Choi, J. and Min, K., 2002a, Landslide susceptibility analysis and verification using the Bayesian probability model. Environ- mental Geology, 43, 121−130.
Lee, S., Chwae, U. and Min, K., 2002b, Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea. Geomorphology, 46, 149-162.
Lee, S. and Min, K., 2001, Statistical analysis of landslide suscepti- bility at Yongin, Korea. Environmental Geology, 40, 1095−1113.
Luzi, L., Pergalani, F. and Terlien, M.T.J., 2000, Slope vulnerability to earthquakes at subregional scale, using probabilistic techniques and geographic information systems. Engineering Geology, 58, 313−336.
Ministry of Construction and Transportation, 1988, Korean rainfall frequency map and probable maximum precipitation map. Gwacheon, 71 p.
Oh, K., 2001, Landscape information system: A GIS approach to managing urban development. Landscape and Urban Planning, 54, 79−89.
Parise, M. and Randall, W.J., 2000, A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earth- quake. Engineering Geology, 58, 251−270.
Randall, W.J., Edwin, L.H. and John, A.M., 2000, A method for pro- ducing digital probabilistic seismic landslide hazard maps. Engi- neering Geology, 58, 271−289.
Rautelal, P. and Lakheraza, R.C., 2000, Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). International Journal of Applied Earth Observation and Geoinformation, 2, 153−160.
Rece, A. and Capolongo, D., 2002, Probabilistic modeling of uncer- tainties in earthquake-induced landslide hazard assessment.
Computers & Geosciences, 28, 735−749.
Temesgen, B., Mohammed, M.U. and Korme, T., 2001, Natural Haz- ard Assessment Using GIS and Remote Sensing Methods, with Particular Reference to the Landslides in the Wondogenet Area.
Ethiopia, Physics and Chemistry of Earth (C), 26, 665−615.
Manuscript received May 21, 2003 Manuscript accepted August 15, 2003
