Browsing by Author "Göltas, M."

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ASSESSMENT OF THE MONTHLY FOREST FIRE DANGER POTENTIAL USING GIS-BASED ANALYTIC HIERARCHY PROCESS IN SOUTHWEST TÜRKÎYE
(2024.01.01) Göltas, M.; Ayberk, H.; Küçük,Ö.
Every year, more than ten thousand hectares of forest in Turkiye are affected by fires. The majority of forest fires occurs in the southern part of Turkiye, where conifer forests and maquis prone to fire are abundant. Forest fires can lead to the loss of human lives, properties and natural resources. Knowledge of Forest Fire Danger Potential (FFDP) is critical to protect lives, property, and natural resources from fire damage. We modeled and mapped FFDP with a GIS-based Analytic Hierarchy Process. The FFDP model was developed based on nine environmental factors that affect fire behavior, including maximum temperature, precipitation, wind speed, species composition, development stage, canopy cover, slope, aspect, and elevation. FFDP was mapped and thoroughly assessed. The results showed that FFDP was significantly correlated with maximum temperature, precipitation, and species composition. We found that the FFDP differed considerably on a monthly basis. Forest lands in the study area of 2% in May, 50% in June, 65% in July, 61% in August, 25% in September, and 0% in October belonged to the extreme danger class. For model evaluation, we compared fire locations from 2008 to 2018 with those on the FFDP maps and then controlled the actual number of fires in each category and its fire danger class. The dominant danger classes of the study area according to the months were: extreme class in June, July, and August (50%, 65% and 61%, respectively), high class in May and September (74% and 68%, respectively) and moderate class in October (82%). This danger classes were more affected by fires. We observed that FFDP changed significantly by month. The amount of burned area per fire was the highest in the extreme danger class in August and July (3.39 ha and 2.14 ha, respectively). The amount of burned area was higher in areas with extreme or high fire danger class. This study can guide fire organizations in pre -fire management planning, firefighting, and post -fire studies.
Forest fire occurrence modeling in Southwest Turkey using MaxEnt machine learning technique
(2024.01.01) Göltas, M.; Ayberk, H.; Kücük, O.
Climate anomalies and potential increased human pressure will likely cause the increase in frequency and damage of forest fires in the near future. Therefore, accurately and temporally estimating and mapping forest fire probability is necessary for preventing from destructive effects of forest fires. In this study, the forest fire occurrence in Southwestern Turkey was modeled and mapped with the maximum entropy (MaxEnt) approach. We used past fire locations (from 2008 to 2018) with environmental variables such as fuel type, topography, meteorological parameters, and human activity for modeling and mapping, using data that could be obtained quickly and easily. The performances of fire occurrence models was quite satisfactory (AUC: range from 0.71 to 0.87) in terms of the model reliability. When the fire occurrence models were analyzed in detail, it was seen that the environmental variables with the highest gain when used alone were the maximum temperature, tree species composition, and distance to agricultural lands. To evaluate the models, we compared the fire locations between 2019 and 2020 with those on reclassified fire probability maps. Fire location from 2019-2020 fit substantially within the model fire occurrence predictions since many fire points in high or extreme fire probability categories has been observed. The results of this study can be a guideline for the Mediterranean forestry that has consistently struggled the forest fires and attempted to manage effectively forest lands at fire risk.