An easy, accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler

Published Date
December 2016, Volume 27, Issue 6, pp 1361–1372

Original Paper

First Online: 

03 June 2016

DOI: 10.1007/s11676-016-0267-5

Cite this article as: 

Duarte, L. & Teododo, A.C. J. For. Res. (2016) 27: 1361. doi:10.1007/s11676-016-

Author 

• Lia Duarte
• Ana Cláudia Teododo

Abstract 

To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk (FFR) maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model (susceptibility map, hazard map, vulnerability map, economic value map, and potential loss map) and was produced according to Portuguese Forest Authority’s (AFN, Autoridade Florestal Nacional) rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category “very high” or “high” fire risk. The “very high” fire risk area is mainly classified as broad-leaved forest and has the steepest slopes (>15 %). The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads. In addition, 92.6 % of the “high” and “very high” risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS proprietary software. Although the results were obviously the same, the model developed here presents several advantages over the other two approaches. Besides being faster, it is easy to change the model parameters according to user needs (i.e., to the rules of different countries), and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena (e.g., floods, earthquakes, landslides). The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.

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