Forest fire prevention: a GIS tool for fire-fighting planning and management Enrico Marchi, Enrico Tesi & Niccolò Brachetti Montorselli Department of Forest Science and Technology in Forestry (DISTAF), via S. Bonaventura, 13 – 50145 Firenze (Italy).
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Claudio Conese, Laura Bonora & Maurizio Romani Institute of Biometeorology (CNR-IBIMET), via Madonna del Piano,10 – 50019 – Sesto Fiorentino, Firenze(Italy),
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Keywords: forest fire, hazard, model, fire-fighting, GIS. ABSTRACT: For the organization of efficient and effective firefighting activities an understanding of the potential ignition risk level and the allocation of the means in a territory is needed. The IBIMET, Institute of the National Research Council and the DISTAF, Department of Environmental Science and Technology in Forestry of the University of Florence, has been charged by the Tuscany Region administration to develop a methodology which analyses the variables that influence the probability of a forest fire occurrence, and classify the structures for the fighting activities, starting from the historical and present data, collected by the region itself, with an ability to update this territorial information archives. The final result has been the development of two different indices : the GRI (Global Risk Index) and the ODIF (Operational Difficulty Index in Fire Fighting). The Global Risk Index is developed by processing different parameters, such as meteorological data, DTM (digital terrain model), vegetation inventory, road network, urban areas and the ignition points. The analysis of these parameters, generates two sub indices: a Static Hazard and a Dynamic Hazard, that are mathematically merged to obtain the GRI. The Operational Difficulty Index in Fire Fighting includs all the factors affecting fire fighting activities by air and by ground (shapes of the different infrastructures are used: public and forest roads, fire fighting centres, helicopter bases, water sources, administrative boundaries etc.) and suggests the extinction efficiency of forest fires organization in a given area. These indices are then combined in a numerical matrix that generate the Final Risk Index. To better met the requirements of the region, the model has been developed in raster format which can be superimposed on different scales of topographic maps. The indices are still in an experimental phase. They were tested in some restricted area of Tuscany but will be fully operational by the end of the year 2008. The specific importance of this work is that all the analysis and classification performed are based on an input dataset from the standard archives of a public administration, the Tuscany Region in this case. As a result the program represents an “easy to use” GIS tool which can help to better understand fire behaviour and to plan a prevention activity.
1 INTRODUCTION Different methodologies are commonly used to compute forest fire risk indices (Chuvieco et al., 1999). These indices quantify the level of risk, usually at a local scale, sometimes at a national level or even at a larger scale (San-Miguel-Ayanz, 2002; San-Miguel-Ayanz et al., 2003). Forest fire risk indices point out stable conditions that favour fire occurrence and behaviour (static indices) or focus on determining the probability of forest fire ignition and the capability of fire spread (dynamic indices) (Sebastian-Lopez et al., 2000). Forest fires are strictly related to land use and vegetation characteristics of the area where ignition can occur. Ignition probability depends on a very large number of parameters, which should be analyzed simultaneously; the user-friend level of the nowadays software GIS permit this analysis, making operational methodologies as that proposed in the present work. The objective is to compute the Global Risk Index (GRI) and the Operational Difficulty Index in Fire Fighting (ODIF), developing a decision support instrument to organize the forest fire fighting services of the Tuscany Region. 2 THE STRUCTURE OF THE GLOBAL RISK INDEX The forest fire phenomenon is strictly related to many parameters, which should be considered and analysed simultaneously. The present model takes into account the most important parameters that characterize the Mediterranean ecosystems and affect the wild fires. The inputs of the system are listed in the first line of the diagram: the DTM (digital terrain model) used was elaborated at 90m, forestry regional inventory at 400m and meteorological parameters derived from the national meteorological station networks. The regional ignition point database is used to evaluate the social component.
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Scheme 1 Structure of the fire risk model
Static hazard All factors that do not change or change very slowly in time are grouped together. This hazard is divided in two components: The Intrinsic Factor and The Infrastructural Factor. The first one considers morphological features slope, aspect, land use and vegetation cover; all these will be classified in five classes. slope >45% 45%-25% 25%-15% 15%-5%
