Gelişen teknolojiyle beraber diğer disiplinlerde olduğu gibi ormancılıkta da geleneksel uygulamaların daha ekonomik, etkin, hızlı ve kolay yapılabilmesi için yenilikçi yaklaşımların kullanımına talepler ve ihtiyaçlar artmaktadır. Özellikle son dönemde ortaya çıkan ormancılık bilişimi, hassas ormancılık, akıllı ormancılık, Ormancılık (Forestry) 4.0, iklim-akıllı ormancılık, sayısal ormancılık ve ormancılık büyük verisi gibi terimler ormancılık disiplinin gündeminde yer almaya başlamıştır. Bunların neticesinde de makine öğrenmesi ve son dönemde ortaya çıkan otomatik makine öğrenmesi (AutoML) gibi modern yaklaşımların ormancılıkta karar verme süreçlerine entegre edildiği akademik çalışmaların sayısında önemli artışlar gözlenmektedir. Bu çalışma, makine öğrenmesi algoritmalarının Türkçe dilinde anlaşılırlığını daha da artırmak, yaygınlaştırmak ve ilgilenen araştırmacılar için ormancılıkta kullanımına yönelik bir kaynak olarak değerlendirilmesi amacıyla ortaya konulmuştur. Böylece çeşitli ormancılık faaliyetlerinde makine öğrenmesinin hem geçmişten günümüze nasıl kullanıldığını hem de gelecekte kullanım potansiyelini ortaya koyan bir derleme makalesinin ulusal literatüre kazandırılması amaçlanmıştır.

In fighting against forest fires, it is crucial for the ground-based firefighting team to reach a fire area in critical response time in which the chance of controlling the fires is significantly high. Road networks are the key infrastructures that provide access to the forest areas for the protection of forest resources. In order to benefit from this important function of roads, especially in forested areas with high natural forest fire risk, they should be built in with adequate technical road standards since the low standards limit the fire truck speed that increases the arrival time of the firefighting team to the fire areas. Most of the forest roads in Turkey are Type-B secondary forest roads with low technical road standards (road width, curve radius, surface materials) that limit the speed of fire trucks. This paper aimed to evaluate the potential contribution of improving the standards of Type-B secondary forest roads in terms of increasing accessible forested areas in critical response time in the case of forest fire occurrence. The study area was Kahramanmaraş Forestry Enterprise Directorate (FED), where forests are sensitive to forest fires at the first degree. In the solution process, firstly, accessible forest areas by the firefighting teams (six teams) located in the study area according to the critical response time were determined by considering the existing road network in the study area. In the second scenario, the possible increase in the accessible forest areas with improved forest road standards and increased travel speed in forest roads was investigated. The results indicated that the areas that can be reached promptly by the firefighting teams in critical response time were 21% and 44% for considering existing roads and improved roads in the whole study area. On the other hand, the accessible forested areas in critical response time increased from 17% to 36% when standards of the forest roads were improved. It is indicated that improving road standards has a significant value to contribute the efficiency of firefighting activities if the practitioners implemented presented methodology.

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.

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.