Human-caused wildfire risk rating for prevention planning in Spain

12 pages, 4 figures.-- Online version published Aug 23, 2008.This paper identifies human factors associated with high forest fire risk in Spain and analyses the spatial distribution of fire occurrence in the country. The spatial units were 6,066 municipalities of the Spanish peninsular territory and Balearic Islands. The study covered a 13-year series of fire occurrence data. One hundred and eight variables were generated and input to a dedicated Geographic Information System (GIS) to model different factors related to fire ignition. After exploratory analysis, 29 were selected to build a predictive model of human fire ignition using logistic regression analysis. The binary model estimated the probability of high or low occurrence of forest fires, as defined by an ignition danger index that is currently used by the Spanish forest service (number of fires divided by forest area in each municipality). Thirteen explanatory variables were identified by the model. They were related to agricultural landscape fragmentation, agricultural abandonment and development processes. The prediction agreement found between the model binary outputs and the historical fire data was 85.3% for the model building dataset (60% of municipalities). A slightly lower predictive power (76.2%) was found for the validation data (the remaining 40%). The probabilistic output of the logistic was significantly related to the raw ignition index (Spearman correlation of 0.710) used by the Spanish Forest Service. Therefore, the model can be considered a good predictor of human-caused fire risk, aiding spatial decisions related to prevention planning in Spanish municipalities.This work was funded by the EC project ‘Forest Fire Spread and Mitigation (SPREAD), EC-Contract Nr. EVG1-CT-2001-00027, the Spanish project FIRERISK (AGL2000-0842-C04-01) and by a fellowship of the Spanish Ministry of Education and Science. The authors would like to thank the Spanish Forest Service, which provided the forest fires report database.Peer reviewe

Human-caused wildfire risk rating for prevention planning in Spain

12 pages, 4 figures.-- Online version published Aug 23, 2008.This paper identifies human factors associated with high forest fire risk in Spain and analyses the spatial distribution of fire occurrence in the country. The spatial units were 6,066 municipalities of the Spanish peninsular territory and Balearic Islands. The study covered a 13-year series of fire occurrence data. One hundred and eight variables were generated and input to a dedicated Geographic Information System (GIS) to model different factors related to fire ignition. After exploratory analysis, 29 were selected to build a predictive model of human fire ignition using logistic regression analysis. The binary model estimated the probability of high or low occurrence of forest fires, as defined by an ignition danger index that is currently used by the Spanish forest service (number of fires divided by forest area in each municipality). Thirteen explanatory variables were identified by the model. They were related to agricultural landscape fragmentation, agricultural abandonment and development processes. The prediction agreement found between the model binary outputs and the historical fire data was 85.3% for the model building dataset (60% of municipalities). A slightly lower predictive power (76.2%) was found for the validation data (the remaining 40%). The probabilistic output of the logistic was significantly related to the raw ignition index (Spearman correlation of 0.710) used by the Spanish Forest Service. Therefore, the model can be considered a good predictor of human-caused fire risk, aiding spatial decisions related to prevention planning in Spanish municipalities.This work was funded by the EC project ‘Forest Fire Spread and Mitigation (SPREAD), EC-Contract Nr. EVG1-CT-2001-00027, the Spanish project FIRERISK (AGL2000-0842-C04-01) and by a fellowship of the Spanish Ministry of Education and Science. The authors would like to thank the Spanish Forest Service, which provided the forest fires report database.Peer reviewe