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Mapping the risk of winter storm damage using GIS-based fuzzy logic

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Abstract

Abiotic and biotic factors that cause damage to forest trees also threaten the sustainability of forest resources. Although winter storms can be one of the most damaging forces, very few studies have focused on winter storm damage in Turkey. To prevent or minimize storm damage, we must evaluate the factors that influence the degree of damage and develop storm risk maps for the forested areas. Here, a GIS-based mathematical model (fuzzy logic) was used to develop such a risk map by considering risk factors such as tree species, tree age, crown density, site quality, topographical features (elevation, slope, aspect), climatic variables (wind, precipitation), and soil depth. The Alabarda Forest Enterprise Chief within the borders of Tavşanlı Forest Enterprise Directorate was selected as the study area due to high occurrence of storm damage in the region during winter 2015–2016. For model verification, the risk map of storm damage was compared to the actual areal distribution of storm damage reported by the Enterprise Directorate. The model based on fuzzy logic indicated that slightly more than half of the study area (52.49%) was under very low storm damage risk, 28.12% was under low risk and 19.19% was classified as high risk. A very small portion of the total study area was classified as very high risk. These results indicated a close relation with the storm damage map generated using Tavşanlı FED records. The results revealed that the most effective risk predictors for overall storm damage risk were wind direction and speed, followed by slope and site quality factors.

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Authors and Affiliations

  1. Department of Forest Engineering, Faculty of Forestry, 16310, Yıldırım, Bursa, Turkey

    Abdullah E. Akay & İnanç Taş

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  1. Abdullah E. Akay
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  2. İnanç Taş
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Correspondence to Abdullah E. Akay.

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Corresponding editor: Hu Yanbo.

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Akay, A.E., Taş, İ. Mapping the risk of winter storm damage using GIS-based fuzzy logic. J. For. Res. 31, 729–742 (2020). https://doi.org/10.1007/s11676-019-00904-1

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