Yazar "Ucar, Zennure" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Evaluating the Use of Smartphone Applications for Log Stacks Volume Measurement in Turkish Forestry Practices
    (Zagreb Univ, Fac Forestry, 2024) Ucar, Zennure; Eker, Remzi; Bilici, Ebru; Akay, Abdullah Emin
    With recent technological development, photo-optical measurement systems in mobile devices have been increasingly used for automatic wood volume estimation because of their ease of use and efficiency. This study aimed to evaluate the use of photo-optical mobile apps for measuring solid wood volume of the stack in Turkish forestry practices. For this study, 21 log stacks were measured using the traditional technique and two photo-optical mobile apps - iFovea Pro and Timbeter. A strong correlation was found between the traditionally measured solid wood volume of the stack and the volume estimated using both photo-optical apps, the number of logs in the stack, and the mean diameter of the stack. The estimated number of trees from the two apps and manual measurement were not statistically different. However, statistical differences were observed between all three measurement approaches for the mean diameter of the stack. Also, statistical test results indicated mixed results for estimated solid wood volume in the stack. In addition, the study tested whether both apps correctly measure the diameter of the logs in the stack. Thus, manually measured diameter of the randomly selected 50 trees within 21 stacks was compared to the log diameters measured automatically using both mobile apps. The results indicated no statistical difference between the three measurement approaches. The study results are promising for using photo-optical mobile apps in Turkish forestry in terms of transition to digital forestry. However, there are still opportunities to improve the capabilities of the method through further analysis of estimating stack volume using the image from both sides of the logs considering different quality and diameter classes with bark conditions.
  • Küçük Resim Yok
    Öğe
    Expanding the Accessible Forest Areas by Improving Forest Road Standards and Utilizing Mobile Fire-fighting Teams
    (2024) Kasap, Caner Yavuz; Akay, Abdullah Emin; Arıcak, Burak; Bilici, Ebru; Ucar, Zennure; Erkan, Neşat; Güney, Coşkun Okan
    In Turkiye, insufficient technical standards of the forest roads limit the speed of the fire truck, leading to increase in the arrival time of the initial response team to the fire areas. Improving forest road standards will increase the design speed and expand the accessible forest areas within the critical response time. In this study, the effect of improving forest road standards on expanding accessible forest areas was investigated. Considering the forest areas in Antalya Forestry Regional Directorate in Turkiye, accessible areas by the stationary initial response teams (103) and mobile teams (71) were determined from the existing road network, and then, the possible increase in the accessible forest areas was investigated when the road standards are improved. Within the scope of the study, the impact of mobile teams used in emergencies on forest areas reached during the critical response period was also evaluated. According to the results, in the scenario where current road standards and stationary teams were evaluated, it was determined that only 59.54% of the forest areas could be reached by initial response teams during the critical response time. When the road standards were improved, this rate increased to 71.69%. On the other hand, when the current road standards and stationary and mobile teams were evaluated together, it was determined that initial response teams could reach 70.40% of the forest areas during the critical response time, and if road standards were improved, this rate increased to 78.17%. Also, utilizing mobile teams increased the accessible forest areas within the critical response time by 9.03%. The results have shown that improvements in road standards and the presence of mobile teams have a very effective role in combating forest fires.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Precision Forestry Revisited
    (Mdpi, 2025) Vatandaslar, Can; Boston, Kevin; Ucar, Zennure; Narine, Lana L.; Madden, Marguerite; Akay, Abdullah Emin
    Highlights What are the main findings? Precision forestry has grown substantially since the early 2010s, driven by advances in UAV and LiDAR technologies. Nearly half of the reviewed studies focus on forest management and planning, with remote sensing platforms and sensors being the dominant tools. What are the implications of the main findings? Although data collection and analysis in forestry have advanced significantly, the translation of these tools into fully automated, integrated, and widely adopted practices is lagging. Geographic disparities and an aging, undertrained workforce continue to limit adoption, underscoring the need for updated forestry curricula and stronger industry-academia collaboration.Highlights What are the main findings? Precision forestry has grown substantially since the early 2010s, driven by advances in UAV and LiDAR technologies. Nearly half of the reviewed studies focus on forest management and planning, with remote sensing platforms and sensors being the dominant tools. What are the implications of the main findings? Although data collection and analysis in forestry have advanced significantly, the translation of these tools into fully automated, integrated, and widely adopted practices is lagging. Geographic disparities and an aging, undertrained workforce continue to limit adoption, underscoring the need for updated forestry curricula and stronger industry-academia collaboration.Abstract This review presents a synthesis of global research on precision forestry, a field that integrates advanced technologies to enhance-rather than replace-established tools and methods used in the operational forest management and the wood products industry. By evaluating 210 peer-reviewed publications indexed in Web of Science (up to 2025), the study identifies six main categories and eight components of precision forestry. The findings indicate that forest management and planning is the most common category, with nearly half of the studies focusing on this topic. Remote sensing platforms and sensors emerged as the most frequently used component, with unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) systems being the most widely adopted tools. The analysis also reveals a notable increase in precision forestry research since the early 2010s, coinciding with rapid developments in small UAVs and mobile sensor technologies. Despite growing interest, robotics and real-time process control systems remain underutilized, mainly due to challenging forest conditions and high implementation costs. The research highlights geographical disparities, with Europe, Asia, and North America hosting the majority of studies. Italy, China, Finland, and the United States stand out as the most active countries in terms of research output. Notably, the review emphasizes the need to integrate precision forestry into academic curricula and support industry adoption through dedicated information and technology specialists. As the forestry workforce ages and technology advances rapidly, a growing skills gap exists between industry needs and traditional forestry education. Equipping the next generation with hands-on experience in big data analysis, geospatial technologies, automation, and Artificial Intelligence (AI) is critical for ensuring the effective adoption and application of precision forestry.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.