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RESEARCH ARTICLE
Contents Vol 16(3)

Integrating new methods and tools in fire danger rating

Christos Vasilakos A , Kostas Kalabokidis B D , John Hatzopoulos A , George Kallos C and Yiannis Matsinos A
+ Author Affiliations
- Author Affiliations

A Department of Environmental Studies, University of the Aegean, University Hill, 81100 Mytilene, Greece.

B Department of Geography, University of the Aegean, University Hill, 81100 Mytilene, Greece.

C Department of Applied Physics, National and Kapodistrian University of Athens, Bldg PHYS-V, 15784 Athens, Greece.

D Corresponding author. Email: kalabokidis@aegean.gr

International Journal of Wildland Fire 16(3) 306-316 https://doi.org/10.1071/WF05091
Submitted: 3 October 2005  Accepted: 17 November 2006   Published: 3 July 2007

Abstract

Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.

Additional keywords: geo-informatics, GIS, natural hazards, neural networks, wildfires.


Acknowledgements

Funding for the present research was provided by the European Union within the RTD project ‘Automated Fire and Flood Hazard Protection System/AUTO-HAZARD PRO’ (EVG1-CT-2001–00057). The authors thank their colleagues at the Geography of Natural Disasters Laboratory at the University of the Aegean, and the Greek authorities (the Mytilene–Lesvos Fire Department and Forest Service, and the General Secretariat for Civil Protection) for their support and cooperation. We also acknowledge and appreciate the peer review on previous drafts by three anonymous reviewers of the journal and Dr Peter F. Moore of GHD, Australia.


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