DSS for managing forest fire casualties

General System description

System name: Decision support system for managing forest fire casualties

Brief overview

This system provides a series of spatial software tools for the assessment of the propagation and combating of forest fires.

Scope of the system

The system integrates GIS technologies (Arc/Info, ArcView, ArcSpatial Analyst, and Arc Avenue) under the same data environment and utilises a common user interface to produce an integrated computer system based on semi-automatic satellite image processing (fuel maps), socio-economic risk modelling and probabilistic models that would serve as a useful tool for forest fire prevention, planning and management. It can assist emergency assessment, management and combating of fire incidents, providing real time up-to-date accurate information on the position and evolution of the fire.

The "Decision support system for managing forest fire casualties" consists of the eight following modules:

• The Data Acquisition (DA) ,
• a satellite imagery importation module,
• a Fuel Mapping (FM) module,
• a Scenarios Generation (SG) module,
• a Socio-Economic Risk characterization module (SRM),
• a Probabilistic Planning (PP) module,
• a Valuation (VAL) module, and
• a User Interface (UI) module.

System origin

• It was developed near 2006 by a team of Greek researchers conducted by Marc Bonazountas, Despina Kallidromitou, Pavlos Kassomenos and Nikos Passas.
• It was tested in a real forest fire event in the island of Evoia, Central Greece, and revealed results close to the relevant authorities' expectations.

Support for specific issues

This system is addressed to fire fight and prevention.

Support for specific thematic areas of a problem type

• Conservation
• Transportation
• Policy/intervention alternatives

Capability to support decision making phases

• Intelligence

  Forest fire risk and management is maybe the principal concern in Mediterranean countries, specially in Greece.

• Design

  DSSs have shown to be useful tools in the analysis and management of forest fire events.

• Choice

  The implemented models enable a more wise decision-making.

• Monitor

  The system was tested in a real forest fire event in the island of Evoia.

Data and data models

Typical spatial extent of application

The system was desugned to fe used in a regional level.

Forest data input

Data consists of satellite images in the visible part of the solar spectrum from LANDSAT and SPOT satellites, and meteorological data from monitoring networks operating in the area of the application. The Scenarios Generation module requires information about historical databases used to generate data relative to fire starting points, forest fuel moisture contents, wind speed and direction, and availability of existing fire fighting infrastructures and resources, considering possible changes along the defined time period. Socio-economic variables as local permanent population, tourists, domestic animals, houses, type and height of vegetation are also needed by the Socio-Economic Risk characterization module.

Type of information input from user (via GUI)

Some other inputs are required, e.g., in order to obtain geographical distributions of fuel availability it is required to introduce a relationship table between vegetation classes and parametrised, fuel availability models.

Models

Forest models

Fire simulations are based on a very detailed forest fire spread engine that calculates fire propagation and fire characteristics for every cell.

Social models

There is a socio-economic risk module that characterises and analyses the socio-economic risk in the area under study ^[1]

Decision Support

Definition of management interventions

Fire prevention treatments scheduling, fire fighting infrastructures and resources allocation, and fire risk management.

Typical temporal scale of application

This aims to be used at an operational level.

Types of decisions supported

• Management level
  • administrative decisions
  • operating control decisions
• Management function
• planning decisions
  • organizing decisions
  • command decisions
  • control decisions
  • coordination decisions
• decision making situation
  • unilateral
  • collegial

Decision-making processes and models

• Logic modeling
• Simulation (with and without stochasticity)
• Multiple criteria/ranking

Output

Types of outputs

The outputs are shown as tables, charts and maps designed in the form of reports and cartographic representations.

Spatial analysis capabilities

Spatial behaviour of fire, fire fighting infrastructures and resources allocation, and fire risk are analysed.

Abilities to address interdisciplinary, multi-scaled, and political issues

It address the support of fire prevention and management administrative decisions.

System

System requirements

• Operating Systems: MS Windows OS
• Other software needed: This desktop DSS requires the installation of some other software like ArcGIS packages for data analysis, and ERDAS IMAGINE and Microsoft Excel for data transformation.
• Development status

Architecture and major DSS components

It is based on Visual C++ computer language.

Usage

Research and government level.

References

Cited references

1. ↑ BONAZOUNTAS M., KALLIDROMITOU D., KASSOMENOS P. A. et PASSAS N. (2005): Forest Fire Risk Analysis, Human and Ecological Risk Assessment, 11(3), 617-626.

External resources

BONAZOUNTAS, M., D. KALLIDROMITOU, P. KASSOMENOS et N. PASSAS (2007): A decision support system for managing forest fire casualties. Journal of Environmental Management, 84, 412–418.