Policies and strategies in incorporating and using available new ICT, including

ESCAP Technical Paper
Information and Communications Technology and
Disaster Risk Reduction Division
Policies and strategies in incorporating
and using available new ICT, including
space-based applications, in multisectoral regional cooperation for
resilient, inclusive and sustainable
development
Prepared by the Space Applications Section, Information and Communications Technology and
Disaster Risk Reduction Division, ESCAP
December 2014
Disclaimer: The designations employed and the presentation of the material in this paper do not imply the
expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status
of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or
boundaries. References and maps obtained from external sources might not conform to the UN Editorial guidelines.
The content in this document is the opinions and view points of the author's and not that of ESCAP or IDD and this
publication has been issued without formal editing.
Abbreviations
ADRC
ASEAN
CRED
CRISP
CSSTEAP
DaLA
DHN
DST
EM-DAT
EPEDAT
ESCAP
GIS
GISTDA
GLOF
GNSS
GPS
GRIP
GSDI
HFA
ITU
ISRO
JAXA
KARI
NARL
NDMO
NDMA
NETP
NGO
NOAA
NSDI
OCHA
OpenDRI
PDNA
RESAP
SAARC
SDGs
SOPAC
SPARRSO
SBTF
UN-GIMM
The Asian Disaster Reduction Center
Association of Southeast Asian Nations
Centre for research in Epidemiology of Disasters
Centre for Remote Imaging, Sensing and Processing
Centre for Space Science and Technology Education in Asia and the
Pacific
Damage and Loss Assessment
Digital Humanitarian Network
Department of Science and Technology
Emergency Events Database
Early Post-Earthquake Damage Assessment Tool
United Nations Economic and Social Commission for Asia and the
Pacific
Geographical Information System
Geo-Informatics and Space Technology Development Agency
Glacial Lake Outburst Flood
Global Navigation Satellite Systems
Global Positioning Systems
Global Risk Identification Program
Global Spatial Data Infrastructure
Hyogo Framework for Action
International Telecommunication Union
Indian Space Research Organization
Japan Aerospace Exploration Agency
Korea Aerospace Research Institute
National Applied Research Laboratories
National Disaster Management Organisation
National Disaster Management Agency/Authority
National Emergency Telecoms Plan
Non-Governmental Organisation
National Oceanic and Atmospheric Administration
National Spatial Data Infrastructure
The United Nations Office for the Coordination of Humanitarian Affairs
Open Data for Resilience Initiative
Post-Disaster Needs Assessment
Regional Space Applications Programme for Sustainable Development
South Asian Association for Regional Cooperation
Sustainable Development Goals
Applied Geoscience and Technology Division of the Secretariat of the
Pacific Community
Bangladesh Space Research and Remote Sensing Organization
Standby Volunteer Taskforce
United Nations initiative on Global Geospatial Information
Management
2
UN-SPIDER
UNDP
UNEP
UNITAR
UNOSAT
USGS
United Nations Platform for Space-based Information for Disaster
Management and Emergency Response
United Nations Development Programme
United Nations Environment Programme
United Nations Institute for Training and Research
The Operational Satellite Applications Programme of the United
Nations Institute for Training and Research
The United States Geological Survey
3
Table of Contents
Abbreviations .................................................................................................................................. 2
Table of Contents ............................................................................................................................ 4
List of Figures .................................................................................................................................. 5
List of Tables ................................................................................................................................... 5
1.
2.
3.
4.
5.
6
Introduction ............................................................................................................................. 6
1.1.
Disaster events ................................................................................................................. 6
1.2.
Economic loss ................................................................................................................... 7
1.3.
Disaster fatalities .............................................................................................................. 8
1.4.
ICT and space-based applications .................................................................................... 9
Policy approaches to building resilience ............................................................................... 11
2.1.
Understanding vulnerability and exposure .................................................................... 13
2.2.
Planning for disaster risk reduction ............................................................................... 14
Strategies for building resilience ........................................................................................... 16
3.1.
Geo-referenced information systems for disaster risk management............................ 17
3.2.
Rapid impact assessment ............................................................................................... 19
3.3.
Data needs...................................................................................................................... 21
3.4.
Challenges to using geo-referenced data ...................................................................... 26
Sources of information .......................................................................................................... 27
4.1.
Disaster databases ......................................................................................................... 27
4.2.
Hazard and risk data ....................................................................................................... 29
Building institutional capacity ............................................................................................... 30
5.1.
Identified priorities and requirements........................................................................... 30
5.2.
Capacity development ................................................................................................... 31
Regional and South-South cooperation ................................................................................ 33
References .................................................................................................................................... 35
4
List of Figures
Figure 1: Trends of occurrence of Natural Hazard ......................................................................... 7
Figure 2: Trends of occurrence of Natural Hazard Events by Sub-region (1973 – 2012) ............... 7
Figure 3: Economic losses from Natural Hazards by type (1973 – 2012) ....................................... 8
Figure 4: Economic losses from Natural Hazards ........................................................................... 8
Figure 5: Total Deaths from Natural Hazards by type (1973 – 2012) ............................................. 9
Figure 6: Total Deaths from Natural Hazards ................................................................................. 9
Figure 7: ICT sector’s adaptation framework: Resilient pathways ............................................... 11
Figure 8: Disaster Risk Management Framework ......................................................................... 12
Figure 9: Higher exposure corresponds to greater losses in Samoa ............................................ 13
Figure 10: Comparison of the cost–benefit ratios of improved land use planning, relocating
exposed settlements, and retrofitting and mitigation measures in Colombia ............................ 14
Figure 11: The percentage reduction of mortality in Colombia due to different risk reduction
strategies....................................................................................................................................... 15
Figure 12: Architecture and framework for Geo-DRM systems ................................................... 18
Figure 13: Interaction between geo-DRM and different types of users ...................................... 19
Figure 14: Rapid assessment using geospatial technologies ........................................................ 20
Figure 15: Risk information scale for policy planning and project implementation .................... 22
Figure 16: Integrating risk assessment from planning to investment for DRM ........................... 26
List of Tables
Table 1: Scales of hazard assessment, with indication of basic mapping units and the optimal
scale for different types of hazards .............................................................................................. 21
Table 2: Disaster Types and Data Needs....................................................................................... 23
Table 3: Spectral bands and temporal resolution of major satellites frequently used in DRM and
DRR ................................................................................................................................................ 25
Table 4: Global data sources for inventory of hazardous events, and hazard assessment ......... 29
5
1. Introduction
It is well known that the Asia-Pacific suffers the most from disasters due to the growing
population and economies becoming more exposed to disaster hazards. For decades,
international agreements have advocated building resilience to disasters, including the
implementation of disaster risk reduction strategies. Many countries have developed policy
instruments to address disaster risk reduction. However, the information, tools and
technologies necessary to develop and implement such policies and strategies in an informed
manner are not yet universally accessible and not being utilized to their full potential.
This paper discusses the status of availability of data and information which can support
informed policy making for disaster risk management. It builds on recommendations from
various intergovernmental and expert meetings organized by the Economic and Social
Commission for Asia and the Pacific (ESCAP), and includes not only statistics on disasters and
demographics, but the expanding use of ICT and space-related applications, which have
significantly increased the availability and accessibility of data, information, knowledge and
expertise.
Worldwide there has been a paradigm shift in disaster management moving away from
emergency response to a holistic view of disaster risk reduction and management.
Considerable progress in disaster risk management has been achieved in the Asia-Pacific region
over the past decade resulting in lower mortality risks from extreme weather-related hazards,
even though economic losses have been on an upward trajectory (UNESCAP, 2013). The lessons
learned since the establishment of the Hyogo Framework for Action (HFA) and MDGs is now
being reflected in negotiations of new international agreements in the beyond 2015
development agenda, such as HFA2 and the Sustainable Development Goals (SDGs).
1.1. Disaster events
Over the last four decades, the world witnessed a total of 9,812 significant natural hazard
events, of which 4,293, or 44 per cent, were reported in Asia and the Pacific. The most
frequently occurring hazards in the region are hydro-meteorological (UNISDR, 2012) affecting
more than 1.2 billion people since 2000 alone. (Theme Study – Building Resilience to Natural
Disasters and Major Economic Crisis (UNESCAP, 2013). Considering the occurrence of natural
hazards by type, floods and storms have been the most frequent, accounting for 67 per cent of
the events occurring between 1973 and 2012 (Figure 1). Across the various subregions, South
and Southwest Asia has experienced the largest number of natural hazard events over the last
four decades, followed by Southeast Asia and East and Northeast Asia (Figure 2).
6
Figure 1: Trends of occurrence of Natural Hazard
Source: UNESCAP: ESCAP Statistical Database; and EM-DAT: The OFDA/CRED International Disaster Database –
www.emdat.be [accessed on 15 September 2014].
Figure 2: Trends of occurrence of Natural Hazard Events by Sub-region (1973 – 2012)
Source: UNESCAP: ESCAP Statistical Database; and EM-DAT: The OFDA/CRED International Disaster Database –
www.emdat.be [accessed on 15 September 2014].
1.2. Economic loss
Over the past four decades, Asia-Pacific total economic losses due to disasters accounted for
$1.14 trillion, or 46 per cent of global losses. Four types of natural hazards (floods,
earthquakes, storms and tsunamis) were responsible for approximately 90 per cent of the total
economic losses in Asia and the Pacific (Figure 3). East and Northeast Asia alone suffered
approximately 70 per cent of the total economic losses of the region (Figure 4).
7
Figure 3: Economic losses from Natural Hazards by type (1973 – 2012)
Source: UNESCAP: ESCAP Statistical Database; and EM-DAT: The OFDA/CRED International Disaster Database –
www.emdat.be [accessed on 15 September 2014].
Figure 4: Economic losses from Natural Hazards
Source: UNESCAP: ESCAP Statistical Database; and EM-DAT: The OFDA/CRED International Disaster Database –
www.emdat.be [accessed on 15 September 2014].
1.3. Disaster fatalities
In addition, over 1.5 million people died in disasters during this period in Asia-Pacific. The
highest fatalities were caused by earthquakes (42 per cent) followed by storms, tsunamis and
floods (18.1 per cent, 16.3 per cent, and 11.9 per cent respectively) (Figure 5). Of all the
subregions, South and Southwest Asia suffered the most, having been hit by the most disaster
events (Figure 6).
8
Figure 5: Total Deaths from Natural Hazards by type (1973 – 2012)
Source: UNESCAP: ESCAP Statistical Database; and EM-DAT: The OFDA/CRED International Disaster Database –
www.emdat.be [accessed on 15 September 2014].
Figure 6: Total Deaths from Natural Hazards
Source: UNESCAP: ESCAP Statistical Database; and EM-DAT: The OFDA/CRED International Disaster Database –
www.emdat.be [accessed on 15 September 2014].
1.4. ICT and space-based applications
Over the last decade, numerous studies have attempted to take stock, understand the scope
and potential, and identify capacities and limitations, of the different aspects of technology and
disaster management. The demand and significance of space-based applications can be better
understood in today’s disaster management environment when considering the
recommendations of some of these studies. Retrospectively, much of this can be seen as an
increasing realization and the evolution of the use of space-based applications in disaster
management.
9
Over the years, identified priorities and gaps have shifted accordingly with the overall
development thinking and sectoral trends. Strategically, the need to integrate disaster
management into long-term sustainable development has always remained, but additionally
there is now a stronger focus on climate change adaptation. Within this context, new emerging
and compounded threats and shared burden of risk through strengthened public and private
commitments. Operationally, the trend has moved from the need to simply extend early
warning systems for multi-hazards, identifying national focal points and establishing multisectoral institutional links, to promoting wider cooperation within and across regions, countries
and provincial boundaries, the availability of integrated climate and socio-economic data
systems and broader and more seamless information and knowledge sharing networks. In
particular, the means of implementation was highlighted in the Rio+20 outcome as needing
greater emphasis. Among the draft SDGs, the means of implementation has one dedicated goal
alone, with key objectives including technology, capacity building and data, monitoring and
accountability. In this changing global policy environment much greater opportunities exist to
strengthen resilience and leverage innovative and emerging technologies.
The use of space-based applications for humanitarian response is already well established. Fully
integrating space-based applications into a broader disaster management context remains a
challenge. Considering space-based applications from an information management perspective,
the evolution and technological expectations of information management systems or
integrated and holistic technology systems, have significantly changed. Such information
management systems have now evolved to ultimately serve decision support. For instance, in
the United Kingdom (UK) it is straightforward to check the risk of flooding online using the
Environment Agency’s website. Maps are available to search by postcode or town name and
the risk is shown as a color code also indicating risk of extreme flooding with a probability of
once in 1000 years (UK Environment Agency Flood Maps). This is most evident in the concept
of ‘dashboard’ systems, which essentially provide a snapshot view for decision makers at
different levels within an organisational hierarchy. Within dashboard decision support systems,
decision makers get essential personalised information presented to them, which is appropriate
for their day-to-day and exceptional decision making needs.
Beyond this, ICT use to support decision making and development is growing exponentially and
adds value throughout all phases of a disaster (pre disaster, during the disaster and postdisaster). The growing use of technology in terms of early warning systems using a variety of
sensors; risk impact assessment using satellite data and imagery; and damage assessment using
data mining software and crowdsourcing are contributing towards sustainable developments further reducing the processing costs substantially*.
*
Space Report, 2014.
10
2. Policy approaches to building resilience
Certain resilience attributes have been identified by Ospina and Heeks (2010), as robustness,
self-organization, learning, redundancy, flexibility, diversity, rapidity and scale. Adapting
innovative and emerging ICT for Disaster Management involves the design of strategies that are
strengthened by resilience attributes, and that take place at various levels in response to
climate change impacts. The interaction between these components can provide the basis for
building the resilience approach to climate change adaptation as illustrated in the “ICT sector’s
adaptation framework: Resilient pathways”. http://www.itu.int/en/ITU-T/climatechange/
Documents/Publications/Resilient_Pathways-E.PDF
Figure 7: ICT sector’s adaptation framework: Resilient pathways
Source: Ospina and Heeks (2010).
Disaster risk management aims to reduce accumulated risk, prevent the creation of new risk
and strengthen resilience contributing to achieve sustainable development. UNISDR (2004),
ASEAN-UNISDR(2012) defines a disaster risk management framework to minimize
vulnerabilities and disaster risks within the broad context of sustainable development. The
framework comprises of the systematic process of using administrative decisions, organization,
operational skills and capacities to implement policies, strategies and coping capacities of the
society and communities to lessen the impacts of natural hazards and related environmental
and technological disasters. This comprises all forms of activities, including structural and nonstructural measures to avoid (prevention) or to limit (mitigation and preparedness) adverse
effects of hazards (UNISDR, 2004). The general risk management framework is composed of risk
assessment and risk reduction strategies with a central focus on risk analysis that includes
hazard identification, hazard assessment, exposure analysis, vulnerability assessment and risk
estimation (Figure 7). The framework helps facilitate risk sensitive decision making,
11
strengthens risk governance, offers solutions to combining structural and non-structural
measures that focuses on emergency preparedness (e.g. awareness raising, early warning
systems etc.), inclusion of risk information in long term (land use) planning and evaluation of
the most cost-effective risk reduction measures.
Disaster Information Management
Disaster Risk Management
Risk Assessment
Exposure
Ri sk m on it or in g & u p d a tin g
Risk Estimation
Risk Evaluation
& Visualization
Non-structural
Structural
Risk transfer
Emergency planning
Awareness / training
Early warning
Landuse planning
EIA / SEA
Codes
Standard
Reinforcing
Protective measures
Risk Governance / risk communication
Vulnerability
Assessment
Pre- and Post-Disaster Data / Statistics
Geospatial Info (Vector/Raster/DEM)
Hazard Assessment
Cost – benefit Assessment
Risk Reduction, Management and Resilience
Figure 8: Disaster Risk Management Framework
Source: Concept from Cees J. van Westen, Geospatial Technologies for Natural Hazards.
12
2.1. Understanding vulnerability and exposure
Asia Pacific Disaster Report (2012) highlighted that one of the main drivers of risk is the
vulnerability and the growing socio-economic exposure to natural hazards. Understanding
vulnerability and exposure serves as key knowledge inputs to develop and implement
appropriate, cost effective risk reduction and prevention measures and to ‘act’ on managing
the risk. Exposure refers to the physical location of people or assets in comparison to a hazard,
such as an industrial estate located in a flood prone area or a city on a fault line, which would
make it more exposed to earthquakes. Vulnerability refers to the circumstances of people
which allows them to be positioned or more affected by a hazard. For example, often poorer
people can only afford to live in more hazardous areas such as on the edge of a river or in a
flood plain. When hit by a disaster or shock, their capacity to respond, cope and recover from
the event is often eroded.
The infrastructure sectors, particularly, have a high exposure as most of them are located on
marginal land such as flood plains, drought prone areas, seismic locations and multi-hazard
areas. As a result of the rapid development taking place in the recent years, there has been an
increase in the exposure of the infrastructures to disasters. The Great East Japan Earthquake
and devastating tsunami, the ensuing nuclear disaster which it provoked, and the Southeast
Asian floods were major contributors to the staggering $294 billion in losses from disasters
suffered by States in the region during 2011 (APDR, 2012). The case study of Samoa provides
good example to illustrate the interrelated correlation between hazard, exposure and hence
disaster losses (Figure 8) and also indicates that the regions with higher exposure are
characterized by maximum risk and incur the highest annual disaster losses.
Figure 9: Higher exposure corresponds to greater losses in Samoa
Source: Olivier Mahul, (World Bank, 2010) Disaster risk financing and insurance Understanding disaster risk for
better (global and local) financial decision making, Disaster Risk Financing and Insurance, FCMNB and GFDRR World
Bank, 5th Asian Ministerial Conference on Disaster Risk Reduction, Technical session: Local Risk Assessment and
Financing October 24, 2012.
13
2.2. Planning for disaster risk reduction
Good land use planning, incorporating resilience into building standards and identifying and
understanding the interdependencies between critical infrastructure are important disaster risk
reduction strategies. The Global Assessment Report (2011), based on the case study in
Colombia, suggests that land use planning and improved building standards generate the
largest ratio of benefits to costs (approximately 4 to 1) (Figure 10). These measures can be even
more attractive when taking into account the political and economic benefits of avoiding loss of
life and injury, decreasing poverty and increasing human development. Saving human lives, for
example, may be a more powerful incentive for DRM than pure cost-effectiveness. In Colombia,
better prospective and corrective investments in risk management led to significant reductions
in mortality (Figure 11). Therefore, careful consideration should be given to identifying suitable
and secure location while planning infrastructure. Regulations and guidelines on zoning and
land use planning are necessary to ensure the safety and stability of infrastructure. Avoiding
construction in vulnerable locations, realignment of coastal roads or shifting to higher locations,
provision of warning signs, and designation of emergency rescue routes are some of the
measures that could be adopted to enhance their resiliencies. The urban land use, addressing
disaster resilience across sectors and scales in Makati City, Philippines provides good example
(ESCAP Theme Study 2013).
Figure 10: Comparison of the cost–benefit ratios of improved land use
planning, relocating exposed settlements, and retrofitting and mitigation
measures in Colombia
Source: ESCAP based on ERN-AL, 2011 Probabilistic modelling of disaster risk at global level: Development of a
methodology and implementation of case studies, Phase 1A: Colombia, Mexico, And Nepal. Background paper for
the Global Assessment Report, 2011 on Disaster Risk Reduction.
14
Mortality
reduction
100%
80%
60%
40%
20%
0%
Without
improvement
Land Use
Relocation
Planning and
Design Strategy
Percentage of mortality reduction
Retrofitting and
mitigation
measures
Remianing risk
Figure 11: The percentage reduction of mortality in Colombia due to
different risk reduction strategies
Source: Global Assessment Report 2011, Chapter 5: Investing Today for Safer Tomorrow, Revealing Risk, Redefining
Development, UN Report 2011.
In addition, during a disaster some core systems and infrastructure is needed for emergency
relief and reconstruction. Hospitals, water supplies, transportation systems,
telecommunications and energy are key systems which are often interdependent. Failure of
one system can significantly affect many others. Though building resilience into these systems
can be costly initially, it has been estimated that the benefit repays the initial investment four
times over (ESCAP Theme Study, 2013).
For instance, The National Emergency Telecoms Plan (NETP), by International
Telecommunication Union (ITU) supports designing and formulating Standard Operating
Procedures, deploying telecommunication resources during emergency situations such as
satellite telecommunications equipment for voice and data services to support communication
needs in the field within the first 24 to 48 hours aftermath a disaster, human and institutional
capacity building, assisting countries to formulate policies and draft appropriate regulations for
emergency telecommunications, forging stakeholder partnerships as a form of resource
mobilization, etc. in many disaster hit countries. Such efforts help restore vital communication
links in terms of:
•
•
•
•
Coordinating rescue and relief operations;
Setting up telemedicine links between hospitals and medics in the field;
Providing call centers where disaster victims can contact their loved ones.
Coordinating infrastructure recovery/re-building operations.
The earthquake and tsunami that devastated Japan in March 2011, threw the country's rail
network into complete chaos. But the JR East's urgent earthquake detection and alarm system
15
(UrEDAS), made up of seismometers installed at 97 locations, sent an automatic stop signal to
the Shinkansen - Japan's high-speed bullet train - electric power transmission system, triggering
the emergency brake on 33 trains. As with the Shinkansen seismometer, when they detect
earthquake-induced tremors, they determine the expected effect of the earthquake and send
out warning signals to cut the power supply to the trains. Industry experts agree that critical
damage was averted due to the installation of such seismometers - the one at Shinkansen is
one of nine along the Pacific coast - alongside the completion of anti-seismic reinforcement
works such as quakeproof structures and anti-derailing systems that were undertaken based on
the experience of the 1995 Great Hanshin-Awaji and 2004 Niigata Chuetsu earthquakes.
Subsequently, planning for disaster risk reduction should incorporate the following actions:
•
•
•
•
•
•
•
Develop Standard Operating Procedures
Establish multi-disciplinary partnerships
Develop and use ICTs for disaster prediction, detection monitoring, and response
Design and develop early warning systems
Establish collaboration platforms to share information for better preparedness and
response
Strengthen Institutional Capacities through training
Build disaster management into development agendas to optimize the use of resources.
3. Strategies for building resilience
With the available technologies from sensors that gather infrastructure conditions to geospatial
located digital pictures, the question is whether cities can process and use this data to make
sustainable planning decisions. There is a need to create synergies between geographic and
statistical information. For instance, National Institute of Statistics and Geography (INEGI) a
member of the United Nations initiative on Global Geospatial Information Management (UNGGIM) Americas, has a mandate to publically provide all kind of maps, geospatial information,
economic information, pricing index surveys etc., making it an SDI, further creating an
environment to incorporate the Caribbean countries. Currently, there are 24 countries in UNGGIM Americas and 11 countries in the Caribbean.
Crowdsourcing or collaborative mapping have been successfully implemented using existing
portals and mobile mapping applications in recent times. For instance, the Map the
Neighbourhood in Uttarakhand (MANU) multi-institutional programme had been initiated by
the Department of Science and Technology (DST), Government of India, primarily to map the
extent of devastation and damage associated with this extreme rainfall event in the Char-Dham
(Gangotri, Yamunotri, Badrinath and Kedarnath) and Pinder Valley areas, covering about 8000
km2. Similar crowdsourcing technology had also been used in the Philippines. The day before
Typhoon Haiyan made landfall, the UN Office for the Coordination of Humanitarian Affairs
(OCHA), also partner of NetHope, put out an urgent call to the Digital Humanitarian Network
(DHN) to ask volunteers to sift through social media and help digitally map the impact in the
Philippines. The Standby Volunteer Task Force (SBTF) responded to the appeal, jumpstarting a
16
MicroMapper project dedicated to tagging reports to help OCHA assess the typhoon's damage.
Images were tagged from 'mild' to 'severe' and then geo-located to create maps of the disaster
damage. Big data has huge capabilities with its own challenges. National ICT policy makers
should incorporate and build capacity in terms of open data policy, sharing and availability.
According to the Geospatial World Forum, 2014, experts equivocally agreed that geospatial
technology is ubiquitous but there is still a waning gap between the demand and supply of
geospatial technologies across the world which needs to be addressed effectively. The future
depends on developing and practicing new policies and strategies in incorporating and using
available new ICT, including space based applications, in multi-sectoral regional cooperation for
resilient, inclusive and sustainable development.
3.1. Geo-referenced information systems for disaster risk
management
Disaster management requires a multi-disciplinary approach, collating and consolidating
information from various sources. Incorporating location-based data into existing disaster
information can provide a major advantage in making informed decisions ultimately, saving
more lives. Disaster information management essentially deals with bringing in the knowledge
components in order to understand the risk, communicate the risk and ‘act’ on the risk
reduction. The information needs to capture comprehensively the hazard, vulnerability,
exposure and its interaction in the form and manner that the decisions are taken based on such
information. In the pre-disaster phase, this can involve mapping and monitoring the hazards,
exposure, vulnerability and risk. In the post-disaster phase, disaster information management
involves damage and loss assessment including disaster loss databases.
A geo-referenced information systems for disaster risk management (Geo-DRM) portal
combines socio-economic data with satellite imagery to ensure that the right information is
available to the right person at the right time when a disaster strikes. This not only provides the
basis for in-country efforts to digitize disaster data and information in the form of sharable
layers and integrated maps, but also provides a centralized repository of socioeconomic and
baseline data for different disaster contexts and risk modeling of scenarios. This can provide a
highly effective tool for supporting evidence-based decision making for disaster preparedness
and rapid analysis/impact assessment. Figure 12 provides an overview of the architecture and
framework of a typical Geo-DRM system.
17
Figure 12: Architecture and framework for Geo-DRM systems
Geo-referenced data is simply data concerning a location. When structured within a geographic
information system (GIS), this information can be used to analyze complex situations, including
circumstances important in preparing for, and responding to, natural disasters. A geoportal
provides online access to geographic information that is layered with different types of
information from various sources, providing a more holistic image of an environment or
situation. GIS applications allow for the modelling of real-world scenarios with the added ability
to customize the context. This allows for sharing information in the form of layers and
integrated maps. It also provides a centralised repository of baseline data for different disaster
contexts and risk modelling of scenarios. Figure 13 shows the ways in which different types of
users can interact with a Geo-DRM system.
18
Figure 13: Interaction between geo-DRM and different types of users
3.2. Rapid impact assessment
Post-disaster damage and loss assessments have been conducted by affected countries for
emergency response, recovery and reconstruction. However, often, due to a lack of
standardized methodologies, these assessments are not precise nor multi-sectoral. The UN
Global Assessment Report 2013 observes that direct disaster losses are at least 50 per cent
higher than internationally reported figures. The quality of timely and multi-sectoral
assessments contribute substantially to recovery and reconstruction, which is an important
window for the ex-post investments towards mainstreaming disaster risk reduction into
sustainable development. The UN ECLAC Damage and Loss Assessment (DaLA) based PostDisaster Needs Assessment (PDNA) serves as an important tool for the valuation of physical
damages and economic losses to support the financing needs for recovery and reconstruction.
With the standardized methodology adopted by several development partners, the PDNA
enables sector-wise damage and loss assessment and helps the affected countries to mobilize
the financial resources for recovery and reconstruction, including assistance from donors and
development partners.
While PDNA missions are always led by the affected countries, a lack of institutional capacity
has been the constraining factor in adopting and institutionalizing the DaLA methodology into
the national disaster damage and loss assessment system. Furthermore, in recent times, there
is an emerging trend in performing rapid assessment of disaster impacts by downscaling PDNA
and attaching priority to the context specific limited sectors for assessment. The rapid
assessment is also driven by advances in science, technology and innovation, particularly space
applications, GIS, statistical time-series analysis/simulations and semi-empirical loss assessment
models, such as ShakeCast and Early Post-Earthquake Damage Assessment Tool (EPEDAT) for
potential earthquake damage assessment. Using smart tools and techniques, the rapid
19
assessment needs to be designed in a manner where it contributes substantially to the PDNA
process in case it is taken up by the government and development partners at a later date.
Geospatial information systems and satellite imagery in disaster risk reduction have been
realized to have huge potential and hold the key to developing new methods, tools and
modules of rapid assessment. One of the key issues for all actors involved in post disaster need
assessment activities is to obtain timely, relevant, and accurate information regarding
geographic location and spatial extent of areas affected by a natural disaster, including
preliminary damage estimation to human and physical assets. This information should be
collected immediately after a major disaster, possibly prior the PDNA mission in order to plan
and to prioritize the strategic collection of sectoral information needed for the recovery
planning process. The use of satellite imagery can be a very cost-effective way to collect such
information and often, in the immediate aftermath of a major disaster, represents the only
source of synoptic information available within affected remote and less developed areas.
Recent trends suggest that disaster hit countries in the Asia-Pacific region, are now conducting
rapid assessments to plan the recovery and reconstructions measures, for instance, the
Damage and Needs Assessment for Pakistan Floods 2010, Rapid Needs Assessment for Thailand
Floods 2011, Rapid Assessment for Uttarakhand Flash Floods 2013 and Rapid Assessment for
Typhoon Haiyan 2014.
Figure 14: Rapid assessment using geospatial technologies
20
The process of Rapid Impact Assessment includes analyzing the exposed assets based on the
baseline information, coming up with disaster footprints based on user driven damage extent
maps, grading the damage and spatial distribution and finally coming up with the impact
assessment of priority sectors.
3.3.
Data needs
While risk information is critical, its scale varies vastly from planning to implementing disaster
risk reduction policies. The key challenges lie in having a set of ‘actionable’ risk information
addressing both planning and implementation issues. Although it is possible to visualize and
analyze geospatial data in many scales, in practice the scale of input decides the scale of
analysis. Table 1, provides an overview of different scales and approaches for hazard
assessment with different satellite data varying from a range of being publically available to
commercially available satellite data. There are number of factors that play an important role in
deciding the scale of hazard risk assessment such as the aim of hazard assessment, the type of
hazard, the size and characteristics of the study area, the available data and resources, and the
required accuracy.
Table 1: Scales of hazard assessment, with indication of basic mapping units and the optimal scale for
different types of hazards
(EQ = Earthquakes, DR= Droughts, FL= Floods, VO= Volcanoes, WS = windstorms, CO=Coastal,
LS= Landslides, WF= Wildfire).
Indicated in the applicability: (*** = highly applicable, ** = moderately applicable, * = Less
applicable)
Scale
Level
Mapping
Scale
(million)
Spatial
Resolution
Satellite
Data
Area
covered
(km2)
EQ
DR
FL
VO
WS
CO
LS
WF
Global
Global
<1:5
1-5 km
NOAAAVHRR
148
million
*
**
*
*
**
*
*
*
Very
Small
Continental/
large
countries
1-5
1
MODIS,
SPOT
5-20
million
**
***
**
*
***
**
*
*
Small
National
0.1-1
0.1-1 km
METOPAVHRR,
TERRA
30-600
million
***
***
***
*
***
***
*
**
Regional
Provincial
0.05 -0.1
100 m
Landsat,
HJ-1A,
ASTER
100010000
***
**
***
**
***
***
**
***
Medium
Municipal
0.0250.05
10 m
THEOS,
ALOS,
100
**
**
***
***
**
**
***
**
21
Large
Community
>0.025
1-5 m
IKONOS,
Quickbird
Worldview 1-2,
Geo Eye1
10
**
*
***
***
*
*
***
*
Source: Geospatial technologies for Natural Hazards Assessment and Disaster Risk Management (Westen, 2010).
Figure 15 provides different risk information scales for policy planning and project
implementation based on the aim of the hazard assessment. The requirement of authorities
may wary depending on the level and scale of a project, for instance, identification of areas
affected at the country level, project development and planning at the regional level or
conducting a feasibility study at the district level.
Figure 15: Risk information scale for policy planning and project implementation
Source: Derived from APDIM EGM presentation, Delhi, 2014.
While geo-spatial data on a 1:50,000 scale does help broad level hazard zonation, vulnerability
and risk assessment calls for 1:10,000 and 1: 2,000 scale data in order to develop an effective
disaster management information system.
The information system is to be dynamically linked to decision support with query shell and
associated analytical tools. The database may reside with different Ministries/agencies. It is
22
important to mirror those data of multiple scales to the end users – who are mostly National
Disaster Management Organisations (NDMO)/Agencies (NDMA).
Table 2: Disaster Types and Data Needs
Disaster
Type
Flood
Remote Sensing
Data
Regional Mapping/
Monitoring:
MODIS, NOAA
District Level:
Landsat, SPOT, IRS-1C,
Resourcesat, Theos
In case of clouds:
Radar (ERS, JERS,
RADARSAT)
Urban Flooding:
Geoeye, Digital Globe
Drought
Regional Mapping/
Monitoring:
MODIS, NOAA
District Level:
Landsat, SPOT, IRS-1C,
Resourcesat, Theos
For Soil Moisture:
Radar (ERS, JERS,
RADARSAT)
Earthquake
Weather Satellite:
GOES (Geostationary
Operational
Environmental
Satellites), METEOSAT
(METerologicalSATellite)
, GMS (Europe
Geostationary
Meterological) , INSAT
(Indian National
Satellite)
District Level:
Landsat, SPOT, IRS-1C,
GIS Data
Statistical/
Demographic
Data
Ancillary Data
for modeling
and early
warning
Landuse/ Landcover/
Topographic:
Rivers/ Streams,
reservoirs, lakes, ponds,
Soil Type, Contour
Maps, DEM, Admin
boundary, Roads,
Railways,
Airports/helipads,
Seaports, Shelter places
(hospitals/ religious
places, academic
buildings etc),
Agriculture, Forest,
Urban.
Climate:
Rain Fall, Temperature,
Landuse/ Landcover/
Topographic:
Rivers/ Streams,
reservoirs, lakes, ponds,
Soil Type, Contour
Maps, DEM, Admin
boundary, Roads,
Railways,
Airports/helipads,
Seaports, Agriculture
Population, House
Types, No. of
houses, Households,
Income level
Hydraulic data,
river bed
roughness,
Sediment grain
size, Hydraulic
calculations,
Surface roughness,
Maximum water
levels in Dams,
Water
management plan,
Base flow,
Population,
Population density,
Avg Family size,
Plant water stress,
Drought & Non
Drought periods
data at local scale,
Water
management plan
Sources of Food,
Food transportation
methods,
Ecology, Crop
parameter
Climate:
Humidity, Rainfall,
Temperature,
Evaporation, Soil
moisture, reservoirs,
Admin boundary,
Geologic:
Geology, Geostructural,
Population, House
Types, No. of
23
Resourcesat, Theos
For large scale:
High Resolution during
earthquake or for
damage assessment but
NOT for monitoring
Landslide
SPOT-5, ASTER, IRS-ID,
Aerial Photographs,
High resolution –
Geoeye, Quickbird
Volcanic eruptions
points,
Landuse/ Landcover/
Topographic:
Rivers/ Streams,
reservoirs, lakes, ponds,
Soil Type, Contour
Maps, DEM, Admin
boundary, Roads,
Railways,
Airports/helipads,
Seaports, Agriculture,
Forest, Urban.
Facilities:
Shelter places
(hospitals/ religious
places, academic
buildings etc),
Rescue points, Health
facilities
Landuse/ Landcover/
Topographic:
Rivers/ Streams,
reservoirs, lakes, ponds,
Soil Type, Contour
Maps, DEM, Admin
boundary, Roads,
Railways,
Airports/helipads,
Seaports, Agriculture,
Forest, Urban.
houses, Households,
Avg Family size
Population, House
Types, No. of
houses, Households,
Avg Family size
Rainfall,
Population, House
Types, No. of
houses, Households,
Avg Family size
Historical cyclone
data
Slope, Aspect, Flow
direction
Previous landslide
hazard maps.
Lithology, Lineament,
Settlement, Rescue
points, Health facilities
Cyclone
Regional
Mapping/Monitoring:
MODIS
Meteorological /
Weather Satellite:
INSAT, GMS (Europe
Geostationary
Meteorological), GOES,
MTSAT, HIMAWARI,
Wind-Cloud 2,4,GOMS,
COMS, PCW
Cyclone Dataset,
Admin boundary maps,
Rivers,
Evacuation centers,
Hospital, Academic
Buildings
Transportation network
24
Various kind of satellite imagery are available from course resolution to high spatial resolution.
Table 3 shows the temporal frequency and spectral bands of major satellites used in different
type of disasters in addition to their respective scales:
Table 3: Spectral bands and temporal resolution of major satellites frequently used in DRM and DRR
Map Scale
Satellite
1:1000, 000, 1:500, 000
NOAA
MODIS
Spatial
Resolution
(m)
1000
250
500
1000
Number
of Bands
Range of
Bands
Revisit Period
(days)
5
2
5
12
2 times /day
2 times / day
1:50,000
Landsat-ETM
15
30
1
7
1:25,000
SPOT
1:2000
IRS –
CARTOSAT 2
IRS –
RESOURCESA
T-1
5
10
1
1
3
1
0.58-12.5 m
620-876 nm
459-2155 nm
405 – 965
nm,
1.360-14.865
m
PAN
Visible, NIR,
TIR
Pan
Visible, NIR
PAN
5.6
1
PAN
5
23.5
70
4
4
Visible, NIR
Visible, NIR
24
15
30
90
3
6
5
0.52-0.86 m 21
1.60-2.43 m
8.12-11.65
m
17
1:10,000
1:25,000
1:100,000
1:25, 000
IRS –
RESOURCESA
T-2
ASTER
16
4
Cases in Indonesia and Japan illustrate that at some point, many countries have used DRM
investments based on risk-sensitive spatial plans and land use strategies to accomplish equally
important development objectives. However, the success of these DRM-driven investments
depends on the initial identification of the joint values, which can be realized through risksensitive decision-making. These beneficial attributes can be clarified by effective risk
communication extended throughout planning processes and across individual development
sector interests. To encourage a change in the mindset of decision-makers about how DRM
investments are perceived, it is crucial that there is a clear understanding of risks and the
unavoidable link they provide between disasters and development. Looking at the diverse
25
mapping scale of disaster risk information, there is a need to integrate risk assessment from the
planning to the implementation phase (Figures 16). Although challenges exist, the national and
regional level, small scale data are generally free and publically available whereas for projectbased community level data needs, large scale data are only available through commercial
satellites.
Figure 16: Integrating risk assessment from planning to investment for DRM
3.4. Challenges to using geo-referenced data
The key challenge today is to find ways to integrate the information available from various
sources into national datasets and into the spatial data infrastructures of a country. Some of
the critical gaps identified based on the availability of data for effective disaster management
are the following:
a) Most countries do not have a commonly agreed system of data collection with agreed
definitions, standards, and formats.
b) Most public sector organizations which collect data on disasters do not make these data
available in the public domain.
c) The data collection and analysis process is very often influenced by extraneous
considerations and political compulsions.
d) The damage and loss assessment figures keep changing due to the lack of appropriate
checks and balances within the system.
e) Even among data bases like CRED’s Emergency Events Database (EM-DAT), DesInventar,
Sigma and NATCAT, very often there is a lack of consensus and consistency in the
26
numbers of disasters, lives lost, number of people affected and economic damage
caused by the disasters.
f) There is a need to standardize and incorporate the inputs from the private and
volunteered geographic information (VGI) community, and to leverage the increasing
amounts of crowd sourced information, in order to maximize the value available from
these various datasets (UN-GGIM, 2013).
g) As the obsolescence rate is quite high, it is very crucial to integrate recent disaster data
as each disaster possesses unique characteristics based on the period and extent.
Hazard and risk assessment requires a multitude of data, coming from different sources.
Therefore it is important to have a strategy on how to make data available for risk
management. Spatial risk information requires the organization of a spatial data infrastructure,
where through the internet, basic geospatial data can be shared among different technical and
scientific organizations involved in hazard and risk assessment. A spatial data infrastructure is
the foundation or basic framework (e.g. of a system or organization) with policies, resources
and structures to make spatial information available to decision makers when they need it,
where they need it and in a form where they can use it (almost) immediately.
The United Nations Spatial Data Infrastructure promotes the development of a framework for
sharing, processing, applying, and maintaining spatial data sets within an environment of
agreed technologies, policies, and standards. The convergence with the Global Spatial Data
Infrastructure (GSDI) and National Spatial Data Infrastructure (NSDI) in member countries. and
the sharing of maps with stakeholder groups through inter-operable formats, would be
extremely useful for improving the effectiveness of humanitarian organizations in emergency
response as well as in prioritizing underserved areas and remote settlements.
The World Bank has launched the Open Data for Resilience Initiative (OpenDRI) Field Guide
(GFDRR, 2014), which sets basic standards for open source creation and communication of
disaster and climate change information. The Guide draws on a number of projects under the
OpenDRI platform, including Genode, an open source tool for managing and visualizing
geospatial data. InaSAFE, is another project developed in collaboration with Australia and
Indonesia, combines community-sourced mapping projects and hazard data, to create rapid
disaster impact assessments. These tools empower both governments and communities to
develop mitigation strategies for storms, flooding, droughts and other natural hazards.
4. Sources of information
4.1. Disaster databases
A comprehensive dataset on the incidence of disasters, their effect upon people, livelihood, and
economic, social and environmental implications is the prerequisite to understand all the
components – hazard, vulnerability, exposure and risk. There are now a number of
organizations that collect disaster related data with different scales and objectives:
27





EM-DAT at the Centre for Research on the Epidemiology of Disasters (CRED) with the
criteria of at least 10 casualties, 100 or more affected, a declaration of emergency or call
for external assistance (Guha-sapir, Hoyois, & Below, 2013).
Munich Re database for natural catastrophes NatCatSERVICE on material and human
loss events worldwide (Munich Re, 2014) and a similar disaster event database (SIGMA)
maintained by Swiss Re - these data are not publicly available.
GLIDEnumber (2010) at Asian Disaster Reduction Center (ADRC) with the specific feature
that each disaster receives a unique identifier and a number of relevant attributes.
http://glidenumber.net/
Disaster loss database (DesInventar (2010)), which allows local authorities,
communities, and Non-Govenmental Organisations (NGO) to collect disaster
information at a local level. The disaster loss database is available online.
The Global Risk Identification Program (GRIP) and the Centre for research in
Epidemiology of Disasters (CRED) have initiated a service, called DisDAT, which brings
together all publicly available disaster databases from different countries (GRIP, 2010).
There are several challenges involved in using the disaster data from the sources mentioned
above for hazard and risk assessment. They suffer from problems in standardizing the
information, as it is collected from a variety of sources. Data collected by insurance companies
suffer the problem that they are collected for particular purposes, and are related to the
coverage of the insurance premiums, which may bias the values and the events that are
reported. Disaster information collected at the local level (e.g. DesInventar) is more complete
as it includes also small magnitude/high frequency events, but the coverage of such database is
limited worldwide. Learning from DesInventar's experience, Indonesia’s initiative on the
development of a disaster loss database with the assistance through the United Nations
Development Programme (UNDP), namely the Disaster Data and Information of Indonesia
(DiBi), is a good example of organizing a disaster database system to suit government
requirements. It’s a comprehensive disaster loss database that is being used to guide the
ongoing process for developing a national DRR plan and for monitoring the impact of crisis on
poverty at the community level.
The importance of establishing a compatible disaster loss database is also to facilitate
developing evidence-based DRR strategies and plans. It’s important to (i) develop a core set of
disaster statistics with the appropriate domain and themes containing the comprehensive
categories of statistics, (ii) ensure the collection and analysis of sex, age and disability
disaggregated data to increase knowledge and understanding of the underlying risks and social
vulnerabilities and (iii) use nationally reliable and internationally comparable historical loss and
damage data. In this context, UNDP has worked on operationalizing the national disaster and
loss databases in Indonesia, Sri Lanka, Nepal, the Islamic Republic of Iran, Orissa and Tamil
Nadu states in India. UNDP has also worked towards establishing databases as a tool to monitor
disaster risk and prepare disaster management plans, and as criteria for allocation of funds,
based on levels of risks in Cambodia, Myanmar, Viet Nam and Lao People's Democratic
Republic. The institutional capacity development to scale-up such efforts will be an important
challenge in the coming years.
28
4.2. Hazard and risk data
The Global Assessment Report (2013), while reviewing all available data globally, highlighted
the interactive Risk Viewer, the national disaster loss database platform DesInventar, and the
global risk database Preview as important repository for risk information available in the public
domain.
Risk Data Viewer: A joint effort by leading scientific institutions, governments, UN agencies and
development banks, the private sector and non-governmental organizations. This interactive
Risk Viewer provides the global risk data from the Global Assessment Report presented in an
easily accessible manner (http://risk.preventionweb.net)
National Disaster Loss Data Collection Initiative: National disaster loss databases have been
built over the past years, starting with the pioneer work of Latin American countries, which has
been more recently followed by Asian and African countries. The majority of these databases
have been built with the support of the United Nations (UNISDR and UNDP) using the
DesInventar methodology and open software tools (http://www.desinventar.net).
Preview: The PREVIEW Global Risk Data Platform is a multiple agency effort to share spatial
data information on global risk from natural hazards. Users can visualize, download or extract
data on past hazardous events, human and economic hazard exposure and risk from natural
hazards. It covers tropical cyclones and related storm surges, drought, earthquakes, biomass
fires, floods, landslides, tsunamis and volcanic eruptions (http://preview.grid.unep.ch/). Table
A3.1, provides hazard specific datasets available in the public domain.
ASEAN Online Southeast Asia Disaster Map (OSA-Map): An integrated Knowledgebase of
Disaster Data, Risk, Modelling, Monitoring, & GIS for Association of Southeast Asian Nations
(ASEAN) Disaster Risk Reduction for ASEAN member Countries established at the ASEAN
Humanitarian Assistance Centre.
Disaster AWARE of Pacific Disaster Centre (PDC) provides easy access to hazard and risk
information and analytical products for multi-hazard monitoring, early warning, and decision
support, which may be customized to provide early warning, multi-hazard monitoring, impact
modeling, and information sharing capabilities.
Pacific Disaster Net: One of the premier information portals for DRM in the Pacific and
providing more than 10,000 documents dating back to 1595 and a range of information services
to support disaster management efforts (www.pacificdisaster.net).
Table 4: Global data sources for inventory of hazardous events, and hazard assessment
Hazard Type
Historic Events
Hazards
Cyclones
UNEP/GRID-Europe, based on various raw UNEP/GRID Europe
29
data sources
Cyclone storm surges
UNEP/ GRID-Europe, based on cyclones- UNEP/GRID -Europe
wind data
Droughts
UNEP/ GRID-Europe, based Climate International Research Institute
Research Unit(CRU) precipitation data
for Climate Prediction (IRI),
Columbia University
Earthquakes
United States Geological Survey (USGS) UNEP/GRID-Europe, USGS, and
ShakeMap Atlas
GSHAP (Global Seismic Hazard
Assessment Project)
Fires
European Space Agency (ESA-ESRIN) and IONA Fire Atlas
World Fires Atlas Program (ATSR)
Floods
Dartmouth Flood Observatory (DFO)
UNEP/GRID-Europe
Landslides
Not available
Hotspot project, International
Centre for Geohazards (ICG/NGI)
Tsunamis
National Geophysical Data Center (NGDC) Norwegian
Tsunami database, NOAA
Institute (NGI)
Volcanoes
Smithsonian Institute Volcanoes of the
world
Geotechnical
Source: Used in Preview project (UNEP/DEWA/GRID, 2010).
While these datasets available in the public domain are extremely valuable, there is a need to
customize and integrate them appropriately with operational national systems for more
effective utilization. Several countries have enhanced their capacity in disaster information
management taking advantage of using these opportunities. For example, the collaboration
between the Pacific Disaster Center and the National Disaster Warning Center of Thailand has
helped in strengthening the multi-hazard warning capacity and access to risk information in a
more comprehensive manner.
5. Building institutional capacity
5.1. Identified priorities and requirements
A survey conducted by ESCAP in March 2012†, found that skills relating to the handling of GIS
data and satellite data were inadequate or missing in many ESCAP member States, particularly
†
Analytical Overview of Unmet Data Needs and Critical Gaps to Establish a Geo-referenced Disaster Management
Information System/Platform in the High Risk Developing Countries of Asia and the Pacific, ESCAP, March 2012
30
for technical skills and data management skills. Recommendations were made to develop the
capacity of technical people dealing with geospatial data in:
a) Primary data acquisition using Global Positioning Systems (GPS)/ Global Navigation
Satellite Systems (GNSS)
b) Collecting and mapping attribute for GIS
c) Data input in the system – digitization
d) Data input in geodatabase (tabular) of GIS
e) Linking spatial and non-spatial geodatabases
f) Geo-referencing of satellite data with maps and other satellite products
g) Development of hierarchical database for GIS analysis
h) Development of metadata
i) Data management – storage/ archival/ retrieval/ security.
In addition to this, basic capacity to work on GIS software and systems is also necessary in order
to produce information for DRM and DRR actions. Therefore, capacity development was also
required in map abstraction, data analysis using statistical tools, geometric analysis and overlay
operations.
5.2. Capacity development
In line with these identified priorities and requirements, ESCAP has been supporting
governments build their capacity to utilize various data, including space-derived information,
for the purpose of disaster risk reduction, giving high priority to capacity-building programmes.
Over the past two years, a series of workshops and training courses on space technology and
GIS applications for effective disaster risk reduction have been conducted, especially in high-risk
developing countries that lack the capacity to access space technologies and GIS applications.
The training courses and workshops covered flood-risk mapping, modelling and assessment,
regional and sub-regional geo-referenced information for disaster management and satellite
imagery for disaster management in the Pacific, and the use of satellite data for drought
monitoring and early warning, amongst others. These capacity-building programmes benefitted
more than 300 governmental officials, researchers and managers from RESAP member States
between 2013 and 2014.
Specific examples include the ESCAP/UN-SPIDER Capacity Building Programme “Space
Technology for Flood Hazard Mapping, Flood Forecast and Rapid Mapping in Bangladesh” held
in May 2013. The training course was held in The Bangladesh Space Research and Remote
Sensing Organization (SPARRSO) and attended by 20 officials from 17 government
departments. In addition, ESCAP, UN-SPIDER and the Centre for Space Science and Technology
Education in Asia and the Pacific (CSSTEAP), which is the node of RESAP training networks,
jointly organized an International Training Course on Flood Risk Mapping, Modelling and
Assessment Using Space Technology at Dehradun, India, in July 2013. Participants from flood
prone countries including Cambodia, Indonesia, Myanmar, Philippines, Sri Lanka, and Viet Nam
were trained by experts in flood risk management. The purpose of the training programme was
to provide understanding of remote sensing and GIS applications in disaster risk reduction and
31
rapid response mapping, mainly for managing flood disaster.
The Regional Training Workshop on Applications of Space Technology for Disaster Risk
Management and Sustainable Development and Stakeholders Meeting on Regional Cooperative
Mechanisms on Space Applications in the Asia-Pacific Region, were held in Hong Kong, China in
August 2013. Participants from Bangladesh, China, Malaysia, Mongolia, Nepal, Pakistan,
Philippines, Sri Lanka, Vanuatu and Viet Nam as well as the Pacific Islands Telecommunications
Association (PITA), participated in the workshop and stakeholder meeting.
In 2013, the secretariat explored more support from the RESAP member countries,
governments and potential donors. A Capacity Building Programme on Space Technology and
GIS Applications for Disaster Risk Reduction was jointly organized by ESCAP, the Korea
International Cooperation Agency (KOICA) and UNITAR/UNOSAT in October to November 2013.
The programme aimed to enhance the capacity of selected developing countries on space
technologies and GIS applications for disaster risk reduction/management, particularly focusing
on challenges of drought management in those countries.
In 2014, a number of training programmes focused on building the knowledge and institutional
capacity on space technologies and GIS along with greater access to geospatial data. The
assistance resulted in the successful establishment and operation of national geo-portals and
enhancement of capacities in the areas of disaster risk mapping, modelling and impact
assessment, and geospatial data processing techniques. In addition, the ASEAN workshop on
the ‘Development of Mechanisms for Acquisition and Utilization of Space-Based Information
during Emergency Response’ was organized in 2014 by the AHA Centre, UN-SPIDER, ESCAP and
Australia. Participants together identified requirements and criteria for developing a potential
mechanism for ASEAN countries to acquire and utilize space-based information during
emergency response. Participants focused on strengthening the use of existing international
mechanisms, such as the International Charter and Sentinel Asia, and addressing the gaps in
acquiring and utilizing satellite data for smaller scale disasters and non-emergency needs and
uses.
Based on the survey of the needs of the RESAP members on capacity development, the
secretariat supported CSSTEAP, which is the RESAP education and training node, in organizing a
specialized training on Microwave Remote Sensing (SAR) and its Applications, in Dehradun,
India, in May 2014. A workshop was also held in Mongolia on Disaster Risk Management and
Assessment through the Applications of Space Technology and GIS - Focus on Urban Flooding,
Steppe and Forest Fire, and Earthquakes. Participants from various national and provincial
offices relating to disaster risk reduction, response and early warning attended the meeting,
along with international experts to discuss the opportunities in utilizing space technology and
GIS applications for disaster risk reduction in Mongolia. Finally, ESCAP conducted the “Capacity
Building Training on Applications of GIS and Geospatial Data Management for Disaster Risk
Reduction in the Philippines,” in Quezon, Philippines in May 2014, which provided knowledge
and skills on how to process satellite imagery, analyze disaster hazards and assess disaster
32
impacts by using open-source software such as QGIS and CAPRA ‡
ESCAP has been promoting the use of online geo-referenced information systems for disaster
risk management (Geo-DRM) since 2012, for countries with special needs (LDCs, LLDCs and
SIDS). Assistance on the establishment and use of Geo-DRM portals was provided to the pilot
countries of Bangladesh, the Cook Islands, Fiji, Kyrgyzstan, Mongolia and Nepal. Many positive
achievements have been observed, which were highlighted during the final project meeting
held in Bangkok, Thailand in June 2014. For example, the Cook Islands have now successfully
formed a GIS taskforce consisting of GIS experts from other ministries. They officially launched
their portal in August 2014 after fully mapping the island of Aitu. Mongolia has established their
Geo-DRM portal and has created layers using natural disaster information. It is using the portal
for mapping fodder resources, groundwater, land use, ecosystems, provincial borders, forests,
soil, grasslands and special protected areas. Mongolia is currently utilizing natural and manmade disaster data and will connect the portal to the emergency operation and early warning
center of NEMA. Nepal has formally launched their portal and all stakeholders are using the
system and continually uploading disaster related data. They are in the process of using the
system for wider disaster management planning.
Establishing such Geo-DRM portals has been identified as a need by national authorities and
agencies surveyed in each of the countries. The Geo-DRM portals have been positioned within
the appropriate in-country national authorities, so that they can provide a centralized, credible
and inclusive space for collecting, analyzing and disseminating disaster related data, coupled
with satellite imagery and socioeconomic information. During these tasks ESCAP further
connected ministries and agencies working within similar fields and coordinated with on-going
in-country efforts, through existing United Nations and inter-agency initiatives already in place.
Other countries that have requested the secretariat to provide technical support to establish
such Geo-DRM portals including Afghanistan, Bhutan, Cambodia, Kiribati, Lao People’s
Democratic Republic, the Maldives and Myanmar. A list of the activities to establish Geo-DRM
portals in ESCAP pilot countries, since the 17th session of the ICC on RESAP, have been
highlighted below.
6. Regional and South-South cooperation
Regional cooperation mechanisms, in utilizing space applications, are playing a critical role in
providing access to near real-time satellite imagery before, during and after the on-set of
disasters. These include ESCAP’s RESAP as well as Sentinel Asia. Additionally, regional and
South-South cooperation has been the key enabler for sharing information and building the
capacity for disaster information management. For example, regional inter‐governmental
organizations (IGO), such as the ASEAN, SAARC, and Applied Geoscience and Technology
Division of the Secretariat of the Pacific Community (SOPAC) have initiated several programmes
‡
The CAPRA (Probabilistic Risk Assessment) Program is an initiative that aims to strengthen the institutional
capacity for assessing, understanding and communicating disaster risk, with the ultimate goal of integrating disaster
risk information into development policies and programs.
33
that address the disaster information issues.
Through the RESAP network, ESCAP member States can request, share and access satellite
derived products and services, provided by RESAP members and ESCAP’s strategic partnership
with the Operational Satellite Applications Programme (UNOSAT) of the United Nations
Institute for Training and Research (UNITAR). These products include scenes of near real-time
satellite imagery and archived satellite imagery as well as damage maps for disaster impact
assessment and emergency response. Images are provided in the spirit of regional cooperation
and with the understanding that disaster mitigation is a public concern that benefits the region
as a whole.
The Regional Drought Mechanism is a good example of how regional cooperation can support
developing countries utilize space-based data, even when they have no space programme of
their own. It is currently supported by two regional service nodes (China and India) both of
which are functioning to provide space-based data, products, and capacity building for effective
drought monitoring and early warning. Upon the request of member States, experts from the
Regional Service Nodes and ESCAP work with a national implementation team to determine the
drought conditions in the country, the gaps and needs for information and institutional capacity
building, and the type of models and indices that would be most appropriate for the country’s
environmental and agricultural conditions.
Sentinel Asia is a collaborative initiative set-up in 2005 between disaster management agencies
and space agencies, to apply remote sensing and GIS technologies to support disaster
management in the Asia- Pacific region. § It comprises satellite data providers in Asia and the
Pacific. The main activities of Sentinel Asia include: emergency satellite earth observation upon
request, in case of major disasters; focus on specific disasters, through working group activities;
and capacity and human resources development for improving disaster management.
§
Asia-Pacific Regional Space Agency Forum (APRSAF) - http://www.aprsaf.org/initiatives/sentinel_asia/
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References
APDR. (2012). Reducing Vulnerability and Exposure to Disasters.
ASEAN-UNISDR. (2012). From Risk to Resilience : ASEAN Strategy on Disaster Risk Assessment.
ERN-AL. (2011). PROBABILISTIC MODELLING OF NATURAL RISKS AT THE GLOBAL LEVEL : THE
HYBRID LOSS EXCEEDANCE CURVE, (February).
GFDRR. (2014). Open Data for Resilience Initiative Field Guide.
Global Assessment Report. (2011). Revealing Risk , Redefining Development.
Guha-sapir, D., Hoyois, P., & Below, R. (2013). Annual Disaster Statistical Review 2013 The
numbers and trends.
Munich Re. (2014). TOPICS GEO : After the floods, Natural Catastrophe, Analyses, assessmnets,
positions.
The World Bank Group. (2009). Why is South Asia Vulnerable to Climate Change ?, 1–3.
UNESCAP. (2013). Building Resilience to Natural Disaster and Major Economic Crises.
UN-GGIM. (2013). Future trends in geospatial information management : the five to ten year
vision, (January).
UNISDR. (2004). Living with Risk : A Global review of disaster reduction initiatives (Vol. I).
UNISDR. (2012). The Role of Hydrometeorological Services in Disaster Risk Management.
UNISDR. (2013). Background Paper prepared for the Global Assessment Report on Disaster Risk
Reduction.
Westen, C. J. Van. (2010). Remote Sensing and GIS for Natural Hazards Assessment and Disaster
Risk Management, 1–61.
World Bank. (2010). Natural Hazards , UnNatural Disasters The Economics of Effective
Prevention.
http://www.munichre.com/en/reinsurance/business/non-life/natcatservice/index.html
http://www.swissre.com/rethinking/climate_and_natural_disaster_risk/
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