Abstract
ABSTRACTThis study was done to evaluate the efficiency of three methods to predict the high-risk areas for fire in District Three of Neka Zalemroud forests located in Mazandaran Province, Iran. The fuzzy analytic hierarchy process (fuzzy AHP) and the spatial correlation method were used to model fire risk in the study area. The Dong model was used to provide the fire risk map. Following the construction of fire risk maps using three methods, the map of actual fires was overlaid to validate the used methods. Then the area of the high-risk and very high-risk classes of each fire risk map within the perimeters of actual fires was calculated. Results showed that the high-risk areas in the fire risk map prepared by the fuzzy AHP and spatial correlation methods closely follow the actual fires. On the other hand, the high-risk areas in the fire risk map prepared by the Dong model showed only a moderate agreement with actual fire areas. The final results showed that the fuzzy AHP model (accuracy 0.8) and the spatial correlation model (accuracy 0.92) have the strongest ability to predict the fire high-risk areas in Hyrcanian forests of Iran, relative to the Dong model (accuracy 0.51).
Highlights
Wildland fires have increased in the forests and rangelands of arid and semi-arid areas in recent years (Eskandari & Chuvieco 2015)
Results of similar studies done in the Baihe forest region of China by the Dong model showed that 80% of high-risk areas were located in the burned area (Dong et al, 2005), our results show that the Dong model has more efficiency for evaluating fire risk potential in Chinese forests than in Iranian Northern forests
Based on the results of the fuzzy AHP and spatial correlation methods, most of the study area has very high and high risk for forest fire, meaning that, District Three of Neka Zalemroud (DTNZ) forests will very likely be exposed to fires in the future
Summary
Wildland fires have increased in the forests and rangelands of arid and semi-arid areas in recent years (Eskandari & Chuvieco 2015). Fire can have many negative impacts on forest ecosystems and human communities, especially when fire occurs at a frequency or severity for which the ecosystem is not adapted. In these situations, fire can result in damage to ecosystem properties or forest resources. Prediction of potential future fire occurrence and recognition of high-risk areas in the forests through selection of the best method or model can be help to conserve these valuable ecosystems. Some studies have used Dong models to predict fire high-risk areas in the forests (Dong et al 2005; Erten et al 2005; Eskandari et al 2013a), whereas other researchers have applied analytic hierarchy process (AHP) or fuzzy sets to model forest fire risk
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