Spatial Technology for Risk Management
Shattri MANSOR, Mohammed Abu SHARIAH, Lawal BILLA, Iwan SETIAWAN and
Fisal JABAR, Malaysia
Keywords: spatial technology, risk management, disaster, flood, forest fire, landslide
SUMMARY
Spatial information technology is a tool that supports for researching natural disaster risk
management programs of flooding, fire forest, and landslides. Flooding disaster management
provides a quick response to the rapid onset of disaster by Flood Early Warning System and
Flood Monitoring and Mitigation, hence the use of NOAA AVHRR and GMS data in order to
better mitigate and manage disasters. Developing a peat swamp forest fire disaster
management system, improve the existing method of forest fire hazard assessment and
dynamic distribution resource. The study integrates high spatial resolution remote sensor data
with Geographyical Information System (GIS) data and multi criteria analysis for developing
a methodology to model peat swamp forest fire disaster risk, to assist in providing decision
support systems for emergency operations and prevention action. Landslide is the result of
wide variety of processes, which included geological, geomorphological, and meteorological
factors such as lithology, structure, soil cover, slope aspect, slope inclination, elevation, and
rainfall. The spatial technology has the ability to assessment, estimation of landslide hazard
region by creating thematic maps and overlapping them to produce final hazard map, that
leads to instability in the region by classifying the region to three categories: low, medium,
and high risk.
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Spatial Technology for Risk Management
Shattri MANSOR, Mohammed Abu SHARIAH, Lawal BILLA, Iwan SETIAWAN and
Fisal JABAR, Malaysia
1. INTRODUCTION
Disaster risk management is the systematic management of administrative decisions,
organisation, operational skills and abilities to implement policies, strategies and coping
capacities of the society or individuals to lessen the impacts of natural and related
environmental and technological hazards (Strand 2003). Spatial technology make easier to
explore the world and the neighborhood you live in, share knowledge, find opportunities, and
make informed decisions. The development of spatial technologies has been driven by the
need for better decision making. Early innovators were motivated by the belief that experts in
a wide variety of fields could make better decisions if they had better tools for analyzing and
visualizing geographic data (Harrison, 2004).
Term "risk" is often confused with "hazard”. Risk is the probability or chance that hazard
posed. The Hazard is inescapable part of life (Smith 1996). The hazard is the potential;
disaster is the actual event (Drabek 1997). Disaster is the result of a hazard impacting a
community (Blanchard, 1999). Disaster is a source of danger whose evaluation encompasses
three elements; risk of personal harm, risk of property, risk of environmental damage and the
acceptability of the level degree of risk (Kovach 1995 and Smith 1996). Natural or
environmental disaster have the advantages of including both natural and man-made
dimensions, such as lithosphere disasters (landslide, subsidence, earthquake), atmospheric
disasters (rain, lighting, temperature), hydrosphere disasters (flooding, coastal erosion),
biologic disasters (forest fires and wildfire), and technological disasters (oil spills, transport
accidents, and failures of constructions), which causes substantial damage/pollute or
injury/death to civilian property or persons. The risk is the probability or chance that the
hazard posed. Consequently, it can be reduced by primarily preparing a suitable risk
management. Risk management is important in Protecting community and environment
safety, providing better information to make decisions, enabling better asset management and
monitory, and improving the perception of community for risks.
The goal of this paper is to dealing with the natural hazard (flood, forest fire, and landslide)
to reduce the impacts that can be taken prior to their occurrence, which adapts spatial
information technologies to support increased coordination among multiple programs of risk
management by examining the application of these technologies to the task of identifying,
analyzing, assessing, treating and monitoring.
2. RISK AND RISK MANAGEMENT
Risk arises out of uncertainty. It is an inherent part of existence and is the chance of
something happening as a result of a hazard or a disaster which will impact on community
and environment. It is measured in terms of the likelihood of it happening and the
consequences if it does happen that be tried to reduce the likelihood of risk effecting on
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community. The risk is the probability or chance that the hazard posed. Thus, it can be
minimised by initially preparing a suitable risk management.
Risk Management is a process consisting of welldefined steps which, when taken in sequence,
support better decision making by contributing to a
greater insight into risks and their impacts (Sai
Global 2003). It is as much about identifying
opportunities as it is about used to avoid, reduce or
control risks. The first step in the risk management
process is focused on the environment to establish
the boundaries in which risks must be managed and
guide decisions on managing risks, and develop risk
Figure 1: Risk management
evaluation criteria. The second step involves
identifying the risks which arise from aspects of the
environment that will be established from previous step to develop a complete inventory of
the risks and what each involves, by selecting suitable techniques to identify potential risks,
examining sources of possible risks, pose a major threat to community. Assess and analyse
the impact of the risks represent the third step, which involves deciding on the relationship
between the likelihood (frequency or probability) and the consequences (the impacts) of the
risks that be identified. The level of risk should be analysed in relation to what are currently
doing to control that risk. Control measures decrease the level of risk, but there may be
sufficient risk remaining for the risk to be considered with others. Risk evaluation will be
clarified the following as the activity of risk managing and its outcomes, the degree of control
over the risk, the potential and actual losses which may arise from the risk, and the benefits
and opportunities presented by the risk. the next step is to treat the risks that be decided as
unacceptable by identifying the options which could use to treat the risks, selecting the best
option in terms of its feasibility and cost effectiveness, preparing a risk treatment plan, and
implementing the risk treatment plan.
Typically, the disaster risk management system addresses the three distinct phases predisaster planning i.e. early warning, during disaster activities (=response) and post disaster
(includes recovery, relief, rescue and rehabilitation) (Narain, 2003) (Figure 1). The mitigation
of the effects of disasters requires relevant information regarding the hazard in real time. Also
the possible prediction and monitoring of the disaster requires rapid and continuous data and
information generation or gathering. Early warning in the disaster context implies the means
by which a potential danger is detected or forecast and an alert issued. There are three
abilities, which constitute the basis of early warning. The first, largely technical ability is to
identify a potential risk, or the likelihood of occurrence of a hazardous phenomenon that
threatens a vulnerable population. The second ability is that of identifying accurately the
vulnerability of a population to whom a warning needs to be directed. The third ability, which
requires considerable social and cultural awareness, is communication of information to
specific recipients about the threat in sufficient time and with a sufficient clarity so that they
take action to avert negative consequences. Establishment of a disaster early warning system
requires the development of both local and national risk information capabilities and use of
relevant technological applications for rapid and improved warnings. Satellites through their
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continues coverage of the globe, provide essential information that can lead to rapid and
effective detection and interpretation of many hazards (Ottichilo 2003).
Disaster estimating is the foundation in
urban risk management that the main aim of
risk management is to estimate and predict
the loss for the areas which possibly suffer
from disaster with the help of many means
of spatial information technique, as well as
to analyze the cost, which is possibly
produced in the course of carrying the
control schemes for disaster protection into
execution. Those historical and real-time
information can be been gathered by remote
sensing,
photogrammtry
and
aerial
photographs for determining zones of slope
instability, Earth-Observing Satellite Images
for mapping and monitoring of different
disasters, Meteo-Satellites for weather
conditions and flood hazards prediction and
Figure 2: Disaster cycle
monitoring, Satellite Radar Satellite for
hazard monitoring, or other methods, then been handled in Geographyical Information
System (GIS). It can provide important basis for the selection of the
control schemes in each decision-making stage.
3. SPATIAL TECHNOLOGY FOR FLOOD
RISK MANAGEMENT
The development of efficient flood management strategies involves a
comparative assessments of the potential financial benefit of flood
management scenarios by reducing the damaged to property and
infrastructure, displacement of communities and general loss of
income. Flooding is a natural phenomenon that regularly brings Figure 2: Flash flood
disruption and damage to different parts of world not excluding Malaysia 2003
Malaysia. Floods vary in scale from water running off a saturated hillside to large rivers
bursting their banks. The impacts of floods range from waterlogged fields and blocked roads
to widespread inundation of houses and commercial property and, occasionally, loss of life.
The consequences can be enormous, recent flash floods in Selangor and Kuala Lumpur of
Malaysia 2002 and again in 2003 resulted in extensive damage to property to the tune of over
300 million Ringgits (Figure 2).
3.1 Flood Early Warning System
In recent year, the advent of improve computing power and the introduction spatial
information technologies has made flood risk management that once seem an difficult task
due to the vast quantities of diverse information required a possibility. Remote sensing and
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in
GIS technique can be variously applied in the collection and processing of spatial and nonspatial data for flood risk management. The operational coupling of remotely sensed data
with hydrological oriented GIS has become a viable option in particular flood early warning
systems. Quantitative precipitation forecasting (QPF) through remote sensing with the
integration of hydrological data hydraulic modeling in the bid to implement a fully automated
flood simulation and forecasting system is drawing much attention in research circles.
Figure 3: Data and Tools Integration for Flood Early Warning System
Figure 3 describes data and tools integration flood early warning system in the Langat river
catchment in Malaysia. Ideally actual rain fall data gathered in real-time should be suitable
this, but in other to increase the lag time of possible flood it was important develop methods
to estimate and forecast precipitation hence the use NOAA AVHRR and GMS data (Figure
4). The incorporation of Mike 11 hydraulic model together with telemetry rain gauges and
flow sensors provide real-time simulation and flood control measures such as reservoir
capacity, river flow and levels. The role of the GIS in the outlined framework is multifaceted
(Lanza et. al. 1993), being essentially the operational platform for the handling of vast data
required and also for the implementation of a fully automated rainfall- runoff and flood
modeling. The forecasting systems further provide a The forecasting systems further provide
consequently flood mitigation should not only encompass the traditional engineering, sociocommunity and infrastructure development but also broadly involve an integrated planning
and management approach through the enhancement of public administration, watershed
management, land use planning and prioritization, not forgetting the control of land
degradation and reforestation, and basis for warning local authorities and the affected
population of the expected levels and extent of flood inundation, basis for warning local
authorities and the affected population of the expected levels and extent of flood inundation.
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3.2 Flood Monitoring and Mitigation
The effective planning of flood risk management
strategies and preparedness for the safety of
communities living in flood prone areas requires
knowledge of past floods and the ability to
anticipated impending flooding. Generally experts
of the agreement that flood risk management
strategies
should
involve:
hydrological
assessments, monitoring networks and information
systems, Flood risk and damage assessment, realtime flood forecasting and operational water
management systems, river hydraulics and
morphology, and land use and climate change
studies.
Figure 4.Low cloud clusters on AVHRR over the
south of peninsular Malaysia (Billa et al 2003)
4. SPATIAL TECHNOLOGY FOR FOREST FIRE MANAGEMENT
Forest fire is defined as an uncontained and freely spreading combustion which consumes the
natural fuels of either a forest or a plantation or a grassland or a shifting cultivation area that
is, duff litter, grass, dead branch wood, snags, logs, stumps, weeds, brush foliage, and to a
limited degree, green trees (Ketpraneet et.al. 1991).
4.1 Fire Hazard Model Early Warning System
A wildfire risk model can serve as an early warning system to predict the severity level of
future fire risks which significant for wildfire prevention and fighting strategies. Developing
a GIS-based wildfire hazard modeling which aim to identify geographic locations with the
highest wildfire hazards and risks. Fire hazard is defined as a fuel condition or state that may
result in an undesired wildfire event. Risk is defined as the probability of an event occurring.
For example, dense housing within a high wildfire hazard area may have a higher probability
or risk burning than home s within a patchy fuel complex (Sampson et al. 1998). In the
literature the words hazard and requirement have different meanings. Hazard includes both
risk and danger component (risk is associated to prevention and ignition, danger corresponds
to spread and fighting actions) (Wybo, et al 1995).
A fire risk model will be developed by combining the point data such as weather data and the
surface data such as satellite data. We introduce the fire risk model to the forest fire early
detection system which is working at present in order to offer the information that is useful
for deciding on the fighting priority in the fire prevention planning in the targeted area. For
the disaster mitigation, information of the present condition and prediction of the situation are
necessary.
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4.2 Fire Hazard Models
Fire hazard model is developed based on Chuvieco and Congalton (1989) that considers the
influence of several factors in forest fire. The methodology can be modified for classifying
the fire hazard areas assesses for each pixel according to the following variables: Forest
species, land use type, elevation, slope, aspect, and distance to roads.
The following is the model to identify the fire risk area based on the fire affecting factors:
P=
0.1 AVI + 0.046 AVII + 0.022 AVIII + 0.010 AVIV + 0.004 AAI + 0.01 AAII + 0.024
AIII + 0.156 VF.I + 0.094 VF.II + 0.078 VF III + 0.062 VF.IV + 0.047 VF.V + 0.039 VF.VI +
0.000 VF.VII + 0.04 DI + 0.014 DII + 0.014 DII + 0.027 DIII + 0.082 DIV + 0.123 DV +
0.004 EI + 0.007 EII + 0.034 EIII + 0.014 EIV
Where:
AV = Distance to roads variable
AA = Distance to rivers variable
VF = Forest species variable
D = Slopes variable
E = Aspect variable
(I…..VII = Subclasses of each variable)
Logistic Regression (Myung-Hee Jo et al.1999)
Zi = 3.754 + 0.231 (slope) + 0.324 (elevation) + 0.165 (aspect) + 0.328 (stream) + 0.195
(forest type) + -0.017 (agricultural pattern) + -0.128 (Urban) + 0.030 (road) + 0.872 (rainfall)
+ 0.652 (Sunshine) + 0.713 (moisture).
Pi = exp(Zi) / (1 + exp(Zi))
Darmawan Model (2000), all the variable have the same weight
FT + EL + GR + AS + BR
Model 2. Fuel type derived from land use/cover has a weight twice higher than the other
variables
20FT + 10 EL + 10 GR + 10 AS + 10 BR
Model 3. Fuel type derived from land use/cover has a weight higher followed by gradient
30 FT + 20 EL + 10 GR + 10 AS + 10 BR
Where FT = Fuel type
EL = Elevation
GR = Gradient
AS = Aspect
BR = Buffer road.
It has been known that a basic factor affecting spread of forest fire as follow: fuel type, fire
behavior and human activities (Wirawan, 2000). Therefore three groups of factors were
identified: land use/cover related to fuel type, terrain related to fire behavior, and road
related to human activities. Castro and Chuveico (1998) divided the factor into Human Risk
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Index (HRI) and Topography Index (TI). They included several human activities related to
fire risk such as agricultural practices, accessibility for recreational use and proximity to
urban areas. Fire risk relative importance of six factors i.e. slope, topography, soils,
vegetation, hydrographic and land use, stream and aspect. The potential factors including
aspect, elevation, slope, stream, vegetation, which can have an effect on forest fire will be
extracted for probability analysis.
Topography affects the amount of solar radiation an area receives. Topography can modify
wind speeds and direction and create wind eddies. Important factors associated with
topography include Aspect, elevation and steep slopes.
Aspect is the direction the slope faces-its exposure in relation to the sun. Fire condition will
vary dramatically according to aspect. Southern exposures receive maximum solar and wind
influences, while the northern slopes receive the least. Generally eastern aspects receive early
heating from the sun and early slope winds, while western aspects receive late heating and
transitional wind flows. Aspect is related to the amount of sunshine. In general, the cases of
forest fire occur in area of south more than in the area of north because a southern exposure
has higher burning point. Actually, more than 40% of forest fire happened in aspect of south
area while doesn’t happen in other area.
Slope is critical factor in fire behavior and aspect is clearly related to insolation and air
humidity. Typically, in the temperate zones of the Northern Hemisphere, south-facing slopes
receive more solar radiation than north-facing slopes. Therefore, south-facing slope are
hotter, drier and pose greater fire risks. More than 60% of forest fire happened in between a
slope of zero and a slope of twenty degrees and in aspect of south and southern west (Hee jo,
et al 2000). 65% cases in entire forest fire occurred in between a slope of zero and 20
degrees. The rate of forest fire decrease remarkably as slope increase (Hee jo, et al 2000).
Slope increase fire risk: as surface’s slope increase as well.
Elevation influence vegetation compositions, fuel moisture and air humidity. More than 90%
cases of forest fire happened at 100m above the sea level. Most these disasters take place in
lower area above the sea level. Fire less severe at higher elevation due to higher rainfall. Two
factors to consider are (1) elevation above sea level and (2) elevation changes in relation to
surrounding topography features. It has been reported that fire behavior trends to be less
severe at higher elevation due to high rainfall. Step gradient increased the rate of fire spread
because of more efficient convective preheating and ignition, and gradient facing to the east
receives more ultraviolet during the day, as consequence east aspect drier faster (e.g.
Chuviecio and Congalton, 1989). Terrain (elevation, gradient and aspect) was derived from
digital elevation model (DEM).
Stream is regarded as an important role not only to extinguish forest fire but also to provide
moisture towards plants. The area far from stream has higher dangerous factors.
Hydrographic features help to detain and distinguish fire, by serving as break-lines and water
resources. Roads can be immediate factor to forest fire because there are human beings (Hee
jo, et al 2000).
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Soil types affect the amount of moisture retained in the soil profile. If more moisture is
available, the risk of fire is reduced.
Different types of land use must be considered because some areas, such as campgrounds,
have greater potential for fire to ignite due to human activities. Vegetation density must also
be considered. An area’s vegetation must be considered because some vegetative types are
more flammable than others are, thereby increasing fire risk.
Higher density, the more fuel there is to burn, thus increasing fire risk. Fuel, which is
composed of the amount of precipitation, the humidity of air, the direction of the wind and
temperature, is very related to season and time (Hee jo, et al 2000). Wildland fuels are critical
elements in many wildlend fire planning and management activities. Fuels represent the
organic matter available for fire ignition and combustion, and they represent the one factor
relating to fire that humans can control (Rothermel 1972, Albini 1976, Salas and Chuvieco
1994). Fire managers need to spatially describe fuel characteristics across many spatial sacles
to aid in fire management decision-making (Mutch et al. 1993, Covington et al. 1994, Ferry
et al. 1995, Leenhouts 1998). A spatial description of fuels is fundamental to assessing fire
hazard and risk across a landscape so management projects can be prioritized and designed.
Accurate, spatially explicit fuels data have become increasingly important as land
management agencies embrace prescribe fire as a viable treatment alternative to reduce the
potential for severe fires over large land areas.
4.3 Model and System Development
A research is aimed at developing a peat swamp forest fire disaster management system at
Pekan District, southern of Pahang, Peninsular Malaysia, which may improve the existing
method of forest fire hazard assessment and dynamic resource distribution. The study
integrates high spatial resolution remote sensor data with GIS data and multi criteria analysis
for developing a methodology to model peat swamp forest fire disaster risk using GIS and
user interface with Multi Criteria Analysis (MCA) to verify this model with occurrence fire in
the study site, which may assist in providing decision support systems for emergency
operations and prevention action (Figure 5).
A mosaic of two scenes Landsat TM image was used for land use classification of this study
area using RS software. Slope, elevation and aspect as well road network were derived from
digital topographic map. 3D and spatial analysis will be used for classifying and reclassifying
factors in a spatial data. Flow chart of the study framework is depicted in Figure 6.
A GIS-based fire hazard model will be applied to determine the severity level of wildfire
hazard zone in terms of wildfire vulnerability mapping by assessing the relative importance
between wildfire factors and the location of fire ignition. A DEM dataset will be used in
order to derive slope and aspect dataset. All of dataset will be converted into grid cell size in
Arc View grid format. Multi Criteria Analysis (MCA) will be employed to determine the
rank of fire hazard area. The roads network, elevation, slope and aspect were obtained from
digital topographic map. The elevation, slope and aspect map was obtained in 2D vector
format, and was then converted to 3D, to generate the Digital Terrain Model (DTM). These
themes slope elevation, and aspect, will be reclassified using spatial analyst, into four classes
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that indicated the hazard value. After the same processes is repeated for the other fire hazard
factors, these fire hazard layers can be overlaid onto one final map yielding the Final Fire
Hazard map.
Main Menu
Fire Hazard Menu
Figure 5: User interface GIS-Grid based forest fire hazard mapping (Setiawan 2004)
Variables Inputting/Processing
Model Developing and Assessment
Imagery
Data
Image
Processing:
Enhancement
Filtering
Unsupervised
Classification
Supervised
Classification
Accuracy
Assessment
Forest
Type and
Land use
map
Overlaying
Fuel Type
map
Actual Burn
Scar Map
Scoring
Road
Topography
map
3D Analysis
Spatial
Analyst
AHP
Elevation
Slope
Weighting
GISBased
Fire Risk
Model
Model
Assessment
3D
Tools
Visualization
Existing
Models
Aspect
User Interface
Fire Risk
Assessment
System
System Developing
Figure 6: Data and Tools Integration for forest fire operational assessment system.
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5. SPATIAL TECHNOLOGY FOR LANDSLIDES RISK MANAGEMENT
Landslides risk management deals with situations that occur prior to, during, and after the
landslide in order to reduce or avoid the human, physical and economic losses suffered by
individuals, by the society, and by the country at large. Landslides management framework
includes: information on prediction and warning, risk assessment or vulnerability analysis of
landslide occurrence, and rehabilitation plans for each event. From natural hazards point of
view landslide risk; the chance of landslide to happen that, will have an impact on human
objectives or properties. It is measured in terms of consequence and probability. Recent
advances in IT, RS and GPS have resulted in the creation of powerful tools, which empower
to deal spatially with landslides disasters. These tools have used for dividing the above
elements in to more specified objects for detailed study in sequential management scheme.
5.1 Landslides Risk Management Scheme
The value of a landslide risk assessment has always been and remains primarily dependent on
the extent that the hazards are recognized and understood, Powell (2000).
Introduction of risk management scheme as the major strength of the landslide management
system can be understood as answering the following questions:
−
What might happen? (Recognition)
−
How likely is it? (Probability)
−
What damage or injury might result? (Consequence)
−
How important is it? (Assessment)
−
What can be done about it? (Recovery)
The previous questions can be answered through the following scheme components:
zonation, assessment, and monitoring.
5.1.1 Zonation
Landslide Hazard Zonation (LHZ) refers to “the division of a land surface into homogeneous
areas or domains and their ranking according to degrees of actual or potential hazard caused
by mass movement” (Varnes, 1984).
The first task to identify various terrain factors which rule the stability of slope. Under the
present study an attempt has been made to prepare a landslide hazard zonation map based on
the combination of data acquired from various geological, geo-morphological and land
use/cover thematic maps, which includes Tectonic Setting, Geological Setting, Slope
Material(Weather Ability and Homogeneous), Discontinuity (Faults ,Joints, Number of Major
Joint Sets and Spacing), Slope Dimensions (Slope, Aspect, Aperture , Persistence, and
Height), Hydrologic Conditions (Rainfall and Water Content, Previous Instability, Land Use ,
Land Cover, and Drainage Pattern. Most of the previous parameters can be extracted from
remote sensing data with field check. These parameters can be converted to digital format in
GIS environment. Using special algorithm landslides hazard zonation map will be produced.
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Remote sensing data and GIS techniques have been used to create thematic maps and
overlapping them to produce final hazard map that leading to instability in the region by
classifying the region to three categories: low, medium, and high risk using the integration of
GIS with remotely sensed data at Pos Slim- Cameron Highlands region, Peninsular Malaysia
(Figure 7).
5.1.2 Assessment
To judge or decide the potential, value, type or significance of a citrine slope stability.
Assessment depends on internal slope factors while zonation done through the outer
parameters. That’s why the assessment still depends more on field work in compare with
zonation which can be done through remote sensing data. Slope Assessment System (SAS)
was developed to evaluate the risk of cut slopes that used for East-West highway alignments
in Peninsular Malaysia. This system based on risk hazard value (G-Rating) and potential
instability (PI).
G-Rating used 13 geological parameters that proposed by experts engineers: geology,
weather ability, joints, aperture, persistence, orientation, spacing, number of major joint sets,
faults, hydrologic conditions, rainfall, slope height, and previous instability slopes. The
geometrical characteristics and discontinuity of the slope mass are shown and analyzed using
Schmidt net, to PI carried out using a technique which proposed by Markland (1972).
According to SAS a 70 cut slope along the second stage of east-west highway have been
assessed to three categories: no risk, low risk, medium risk and high risk. The comparison
between the SAS out put and field check shows a high accuracy. Later on SAS converted to
Expert System which can assess, categorize and give recommendations to any cut slope.
5.1.3 Monitoring
According to pervious phases, slopes with a high potential to slide will be put under
surveillance. There are two main techniques for landslides monitoring. First, is in site using
geotechnical or surviving tools, while the second implemented using remote sensing
techniques.
Synthetic Aperture Radar (SAR) data with differential informatory will be choose for
monitoring Pos Slim- Cameron Highlands region, Peninsular Malaysia (30X30 km2)
according to Table (1), For high risk slope category one of geotechnical techniques will be
used according to slope morphology and significance.
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Land Use Risk
Map
Slope
Risk
Map
Aspect Risk
Map
Height Risk
Map
Final Risk
Map
Figure 7: GIS data base and final risk map in Pos-Slim Cameron Highlands, Malaysia (Syed Omar at
el 2004)
Location
Type
Techniques
Accuracy
Installation
Cost
Operating
Cost
Durability
Maintenance
Requirements
In Site
Surveying
Geotechnical
Strain meter EDM1
Tensio meter GPS2
Tilt meter
Triangulation
Geophone
Geometric
Leveling
Millimeters
Millimeters
Remote
Optical
Radar
Arial Photography HSMR4
SAR5
Satellite Images3
Lidar Imaging
High
High
CentimeterMillimeters
Medium-Low
CentimeterMillimeters
Low
High
Medium
Low
Low
Low
High
Medium
Medium
High
Low
High
Low
Table 1: Landslides Monitoring Techniques
1
Electronic Distance Measurement
Global Position System
3
High Spatial Resolution
4
High wall Stability Monitoring Radar
5
Synthetic Aperture Radar
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6. CONCLUSION
Spatial information technology is useful in dealing with natural hazards to support increased
coordination among multiple programs of risk management by examining the application of
these technologies to the task of identifying, analyzing, assessing, treating and monitoring.
The spatial technology can be used for provision of rapid and continuous data for flood
forecasting and environmental monitoring, for landslide hazard zoning and assessment; and
forest fire fighting and monitoring.
REFRENCES
Albini, F.A. (1976). Estimating Wildfire Behaviour and Effects, General Technical Report
INT-30, USDA Forest Service, Intermountain Forest and Range Experiment Station.
Alexandar, D. (2000). Confronting Catastrophe: New Perspectives on Natural Disasters,
Terra Publishing, England.
Billa, L., Shattri, M., Rodzi, A., Noardia, A., (2003). An Approach to Flood Early Warning
Using RS and GIS in: Proceeding of Advanced Technology Congress, ATC 20-21 May
2003. CD
Blanchard, B. W. (1999). Appendix: Hazard and Disaster Definitions, URL:
http://166.112.200.141/emi/hazards
Castro, R. and Chuvieco, E. (1998). ''Modelling Forest Fire Danger From Geographic
Information Systems''. Geocarto International, 13, 15-23,
Chuvieco, E. and Congalton, R.G. (1989). Application of remote sensing and geographic
information system to forest fire hazard mapping. Journal of Remote Sensing
Environment. 29, 147-159.
Covington, W.W., Everett, R.L., Steele, R., Irwin, L.L., Daer, T.A., and Auclair. (1994).
Historical and anticipated changes in forest ecosystems of the inland west of the United
States. Journal of Sustainable Forestry. 2(1/2):13-63.
Drabek, T. (1997). Multi Hazard Identification and Risk Assessment. Washington, D.C.:
FEMA.
Darmawan, M., Masamu, A., and Satoshi, T. (2000). Forest Fire Hazard Model Using
Remote Sensing and Geographic Information Systems: Toward Understanding of Land
and Forest Degradation in Lowland areas of East Kalimantan, Indonesia. URL:
http://www.crisp.nus.edu.sg/~acrs2001/pdf/234darmawan.pdf
Ferry, G. W., R. G. Clark, R. E. Montgomery, R. W. Mutch, W. P. Leenhouts, and G.
Thomas Zimmerman. (1995). Altered fire regimes within fire-adapted ecosystems.
Pages 222-224 in E.T. LaRoe, G. S. Farris, C. E. Puckett, P. D. Doran, and M. J. Mac,
editors. Our living resources. U.S. Department of the Interior, National Biological
Service, Washington, D.C., USA.
Harrison, J. (2004), Disaster Management, URL: http://www.geoplace.com/gr
Hee Jo. M., Bo Lee. M., Young Lee. S., Jo. Y., and Ryul Baek. S. 2000. The Development of
Forest Fire Forecasting System using Internet GIS and Satellite Remote Sensing. Asian
Conference
on
Remote
Sensing
http://www.gisdevelopment.net/aars/acrs/2000/ps3/ps310.shtml
Ketpraneet, S. ad et. al. 1991. Forest Fire and Effects of Forest Fire on Forest System in
Thailand Kasetsart Uiversity, Bagkok, Thailand
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Kovach, R. L. (1995). Earth Fury: An Introduction to natural Hazards and Disasters New
Jersey, Prentice Hall.
Lanza, L., Conti, M and La Barbera, P. (1993) Automated modeling of flash floods in
Mediterranean areas: the September 1992 event s over the Liguria region. Proc. Of XII
IASTED Int. Conf. on ‘ Modeling, Identification and Control’ Innsbruck, Austria, 1517 Febuaury 1993, p. 99-102
Leenhouts, B. (1998). Assessment of Biomass Burning in the Conterminous United States,
URL: http://www.consecol.org/vol2/iss1/art1/
Markland, J.T. (1972): A useful technique for estimating the stability of rock slopes when the
rigid wedge sliding type of failure is expected. Imperial College Rock Mechanics
Research, Report No. 19.
MIKE 11 User Guide ( 2003) A Modeling System for Rivers and Channels, DHI water and
environment Agern Alle DK-2970 Horsholm Denmark
Mutch, R. W., Arno, S. F., Brown, J. K., Carlson, C. E., Ottmar, R. D., & Peterson, J. L.
(1993). Forest health in the Blue Mountains: a management strategy for fire-adapted
ecosystems (Gen. Tech. Rep. No. PNW-GTR-310). USDA Forest Service, Pacific
Northwest Res. Sta., Ogden, UT.
Myung-Hee Jo* Myung-Bo Lee**, Si-Young Lee**, Yun-Won, Jo* Seong-Ryul Baek*.
1999. The Development of Forest Fire Forecasting System using Internet GIS and
Satellite
Remote
Sensing.http://www.gisdevelopment.net/aars/acrs/2000/ps3/ps310.shtml
Narain, A. (2003). Disaster management system: an integrated approach using satellite
communication,
education
and
remote
sensing,
URL:
home.iitk.ac.in/~ramesh/narain.doc
Powell, G. (2000): Discussion “Landslide Risk Management Concepts And Guidelines”.
Australian Geomechanics, Volume 35, No 1, March 2000, Pp 49-52.
Ottichilo, W. K. (2003), USE OF SPACE TECHNOLOGY IN DISASTER RISK
ASSESSMENT AND MONITORING URL: www.ewc2.org/upload/downloads/
Ottichilo2003AbstractEWC2.doc
Rothermel, R. C. (1972) A mathematical model for predicting fire spread in wildland fuels.
Research Paper INT-115. Ogden, UT: US Department of Agriculture, Forest Service,
Intermountain Forest and Range Experiment Station, pp. 1-40
Sai Global (2003), Risk Management, URL: http://www.riskmanagement.com.au/
Salas, J. and E. Chuvieco. (1994). Geographic information systems for wildland fire risk
mapping. Wildfire 3(2), 7-13.
Sampson, N., Neuenschwander, L. F., and K. C. Harkins. 1998. Developing a wildfire
strategic assessment for the North Zone of the Idaho Panhandle National Forests. The
Sampson Group, Inc., Alexandria, VA-USA.
Setiawan,I., Mahmud, A., Mansor, S., Mohd Shariff, A. R.. and Nuruddin, A. A. (2004), User
Friendly GIS-Grid Based Forest Fire Hazard Mapping System (un publish)
Smith, K. (1996), Environmental Hazard, 2nd ed. New York, Routledge.
Strand H., Wilhelmsen, L. and Gleditsch, N.P. (2003). Armed Conflict Dataset Codebook,
PRIO: Oslo.
Syed Omar, S.; Jeber, F.; and Mansor, S. (2004). GIS/Rs for Landslides Hazards Zonation in
Pos Slim, Peninsular Malaysia, Prevention and Management International Journal, Vol
13 No 1, URL:
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http://caliban.emeraldinsight.com/vl=2479264/cl=92/nw=1/rpsv/dpm.htm
Varnes, D.J. (1984): Landslide hazard zonation: a review of principles and practice, Natural
Hazards 3, UNESCO: 63p
Wirawan,
N.,
(2000). Factors Promoting the Spread of Fire.
URL:
http://www.unu.edu/unpress/unupbooks/80815e/80815Eoq.html
Wybo, J., Gouma, V., Guarnieri, F. and Richard B. (1995) ‘Forest fire danger assessment:
combination of methods for efficient decision making’, The International Emergency
Management and Engineering conference, TIEMEC’94, 18-21 April, Hollywood
Beach, Florida, pp.344–349. Extended version in (1998) IEEE Transactions on
Engineering Management, Vol. 45, No. 2, pp.127–131
CONTACTS
Shattri Mansor, Mohammed Abu Shariah, Lawal Billa, Iwan Setiawan and Fisal Jabar
Spatial & Numerical Modeling Laboratory,Institute of Advanced Technology (ITMA)
University Putra Malaysia (UPM)
43400 Serdang
Selangor
MALAYSIA
Email: shattri@putra.upm.edu.my , upm_itma@yahoo.com
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