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    Estimating the economic value of timber products potentially saved from wildfires by improving forest road standards
    (Taylor & Francis Inc, 2024) Erkan, Nesat; Akay, Abdullah E.; Bilici, Ebru; Ucar, Zennure; Guney, C. Okan
    The aim of this study was aimed to estimate the economic value of timber products from the forest stands that are potentially saved from wildfires after improving road standards. The study was implemented in the Alanya Forest Enterprise Directorate (FED) in the Mediterranean city of Antalya, Turkey. In the solution process, the possible increase in the accessible forest areas with improved forest road standards was investigated by using GIS-based network analysis methods. In the next step, the timber production in the forest areas potentially saved from the wildfire was calculated based on parameters such as site index, rotation period, and stand structure. Then, the economic value of timber product types was calculated using market prices. The results indicated that increasing the design speed on improved forest roads reduced the arrival time of firefighting teams to the forests, which consequently increased the accessible forest areas within the critical response time. It was found that the accessible forest areas within the critical response time increased from 47,231 hectares to 59,354 hectares when standards of the forest roads were improved. This saved 12,123 hectares of additional forest area from the wildfire in the Alanya FED. The cost of road improvement activities was estimated at US$ 2,286,998. It was calculated that the total timber products obtained from the forest area potentially saved was about 94,721 m3, and worth US$7,545,579 at market prices. The results can be used by policymakers in determining the potential investments in improving forest road standards to enhance the efficiency of firefighting activities.
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    Long-Term Impacts of Conifer Afforestation on Forest Floor Development and Soil Properties in Herbaceous Rangelands Under Semi-Arid and Sub-Humid Climates
    (Wiley, 2025) Kaya, Abdulgaffar; Gokbulak, Ferhat; Saglam, Reyhan; Erkan, Nesat
    This study examined the effects of afforestation on selected hydro-physical and chemical soil properties, forest floor development, and its chemical content following the conversion of herbaceous vegetation-covered rangelands into coniferous forests with Cedar of Lebanon (Cedrus libani) and Austrian pine (Pinus nigra) in Elaz & imath;& gbreve;, T & uuml;rkiye, under semi-arid and sub-humid climatic conditions over approximately 60 years. The research also examined the forest floor characteristics developed in these afforested areas. Afforestation with P. nigra did not significantly affect soil hydro-physical properties in either climate. However, C. libani resulted in notable improvements, especially under sub-humid conditions. In these areas, field capacity increased from 18.6% to 23%, permanent wilting point moisture from 12.05% to 14.29%, and available water capacity from 6.58% to 8.72%. Bulk density decreased from 1.10 to 0.99 g/cm3, enhancing porosity, aeration, water retention, and reducing erosion sensitivity. In contrast, C. libani had negative effects under semi-arid conditions, increasing bulk density (1.16-1.28 g/cm3) and reducing moisture retention. Chemical changes were limited overall. In semi-arid areas, C. libani reduced calcium and sodium, while P. nigra lowered nitrogen content. Both species increased magnesium in sub-humid areas while decreasing carbon and nitrogen levels. The forest floor in the afforested areas was very thin and weakly developed. While the leaf layer was present in all plots, the humus or fermentation layers were sometimes absent. In conclusion, C. libani showed more positive impacts on topsoil's hydro-physical and chemical properties than P. nigra, particularly under sub-humid conditions, supporting its potential for afforestation in similar climate conditions.
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    Mapping the probability of Forest fire in the Mediterranean region of Türkiye using the GIS-based fuzzy-AHP method
    (Taylor & Francis Inc, 2025) Ucar, Zennure; Guney, Coskun Okan; Akay, Abdullah E.; Bilici, Ebru; Erkan, Nesat
    Forest fires have increased in frequency, intensity, and extent significantly worldwide due to climate change and human activities, particularly in the Mediterranean region. Fire-prone areas should be determined primarily to take precautions against forest fires and reduce their ecological, economic, and social impact. This study aimed to determine the spatial distribution of forest fire probability for the Antalya Regional Directorate of Forestry (RDF), highly vulnerable to forest fires. The forest fire probability map was generated using the GIS-based Fuzzy-AHP method, prioritizing decision criteria, including stand characteristics, topographic features, meteorological parameters, and proximity to anthropogenic structures. The results indicated that the most important factors influencing the fire were tree species, development stage, and proximity to road networks. The generated map showed that 45.82% of the forests in Antalya RDF were in the very high class, while 15.82% were in the high-level class. The fire probability map, validated using the Area Under Curve (AUC) method, offered promising and acceptable results above 0.7. The Fuzzy-AHP method, when integrated with GIS techniques, effectively predicts fire probability levels in fire-sensitive forests. This method will empower fire managers to develop and implement strategies that enhance forest fire resilience by predicting areas with high fire probability.
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    Using Machine Learning Algorithms to Predict Forest Fire Probability in Mediterranean Region of Türkiye
    (Aves, 2025) Bektas, Aybike Goksu; Karas, Ismail R.; Akay, Abdullah Emin; Guney, Coskun Okan; Ucar, Zennure; Bilici, Ebru; Erkan, Nesat
    Determining the forest fire probability levels by analyzing the main fire factors can provide forest managers with the basis for making critical decisions on issues such as fire prevention strategies, fuel management, fire safety measures, emergency planning, and placement of firefighting teams. The main fire influencing factors, including vegetation factors, topographical factors, climate factors, and proximity to some features such as roads and residential areas, have been considered to generate forest fire probability maps. The machine learning (ML) algorithms have become an effective tool in predicting forest fire probability. This study aimed to generate a forest fire probability map by using two commonly used ML models, logistic regression (LR) and support vector machines (SVMs), integrated with Geographical Information System (GIS) techniques. The study was implemented in & Scedil;elale Forest Enterprise Chief (FEC) located in the Mediterranean city of Antalya in T & uuml;rkiye. In the study, the fire influencing factors were tree species, crown closure, tree stage, slope, aspect, and distance to roads. The forest fires that occurred from 2001 to 2021 in & Scedil;elale FEC was considered in the training stage of the models. The accuracy of the fire probability maps was verified using the area under curve (AUC) value. As a result of performing the ML models, estimations were made for 47 086 points on the map which were categorized into five fire probability levels (very high, high, medium, low, and very low). The results showed that the accuracy of the fire probability map generated by the LR model was better (AUC = 0.845) than the accuracy of map generated by the SVM model (AUC = 0.748). According to the probability maps, more than half of the forests had very high/high fire probability levels in the study area.
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    Valuing Improved Firefighting Access for Wildfire Damage Prevention in Mediterranean Forests
    (Mdpi, 2025) Akay, Abdullah Emin; Erkan, Nesat; Bilici, Ebru; Ucar, Zennure; Guney, Coskun Okan
    To effectively combat wildfires, ground teams must reach the fire site via road network within critical response time. However, low-standard forest roads can reduce firetruck speeds and delay fire response times. This study aimed to investigate how improving road standards affects firefighting access within critical response time and contributes to reducing timber losses. This study was conducted in Antalya, the city most affected by wildfires in T & uuml;rkiye. In the study, highly fire-prone forests were first identified based on a fire probability map of Antalya, developed through a GIS-based MCDA model incorporating the Fuzzy-AHP method. Then, the highly fire-prone forests and their corresponding timber volume were determined. Finally, the economic value of timber saved from fire and the present net value of total road costs were determined. As a result of improving forest roads, the forest areas that could be reached in time increased by 11.04%, making an additional 81,867.53 hectare of highly fire-prone forests accessible. The amount and economic value of timber products saved in this area were 971,195.55 m3 and 37,689,301, respectively. The cost of improved roads was 37,386,622 while the resulting positive net economic value of 302,679 indicates that investing in forest roads improvements is a viable option.