Orman Ekosistemlerinin Biyofiziksel Unsurlarının Modellenmesinde Yapay Zeka: Fırsatlar ve Zorluklar

Öz

Ormanların biyofiziksel unsurları, ağaç sağlığı, biyoçeşitlilik ve ekosistem hizmetleri için hayati öneme sahiptir ve etkileri iklim değişikliği ve küresel çevre dinamiklerinden giderek daha fazla etkilenmektedir. Orman ekosistemlerinin biyotik ve abiyotik bileşenleri arasındaki etkileşimleri anlamak, ağaç kütlelerinin geniş mekansal yayılımı, büyüklüğü ve erişilemezliği ile birlikte, toprak, hidroloji, rölyef, meteorolojik koşullar ve diğer ekosistem bileşenleri de dahil olmak üzere çeşitli faktörler arasındaki genellikle karmaşık ilişkiler nedeniyle zorluklar ortaya koymaktadır. Bu inceleme, orman biyofiziksel unsurları alanına bir giriş niteliğindedir ve çok faktörlü bağlantılar ve köklerin ve vasküler sistemlerin toprak kalitesine verdiği tepkiler gibi önemli zorlukları özetlemektedir. Bu etkileşimlerin incelenmesini karmaşıklaştıran çevresel dinamiklerle ilgili ortaya çıkan sorunları vurgulamaktadır. Ayrıca, makale, orman ekosistemlerinin biyotik ve abiyotik bileşenleri arasındaki bağlantıları modellemede yapay zekanın (YZ) uygulamasını incelemektedir. Bu, bu karmaşık ekolojik ilişkileri daha iyi anlamamızı sağlamak için çeşitli mekansal ve zamansal ölçeklerde kullanılan uzaktan algılama, görüntüleme, coğrafi veri analizi ve kartografik yöntemler gibi gelişmiş tekniklerin entegrasyonunu içermektedir. Ormancılık sistemlerinden alınan dersler, ormancılık için araçları ve tuzakları vurgulayarak, makine öğrenimi (ML) ile desteklenen yorumlanabilir modellerin, ekolojik bağlamın entegrasyonunun ve rastgele orman (RF) ve destek vektör makineleri (SVM) gibi çeşitli algoritmaların doğrulanmasının gerekliliğini ortaya koymaktadır. Yapay zekanın karmaşık orman ekosistemlerinin izlenmesi için uygulanabilir içgörüler ve ölçeklenebilir çözümler sağlamasını sağlamak için koordineli veri altyapılarının şart olduğu sonucuna varıyoruz.

Anahtar Kelimeler

• Python
• veri modelleme
• veri analizi
• yapay zeka
• çevresel izleme

Artificial Intelligence for Modelling Biophysical Elements of Forest Ecosystems: Opportunities and Challenges

Abstract

Biophysical elements of forests are essential for tree health, biodiversity, and ecosystem services, with their effects increasingly influenced by climate change and global environmental dynamics. Understanding the interactions between biotic and abiotic components of forest ecosystems poses challenges due to the large spatial extent, size, and inaccessibility of tree massifs, along with the often complex relationships among various factors, including soil, hydrology, relief, meteorological conditions, and other ecosystem components. This review serves as an introduction to the field of forest biophysical elements, outlining significant challenges such as multifactorial links, and the responses of roots and vascular systems to soil quality. It highlights emerging issues related to environmental dynamics that complicate the study of these interactions. Furthermore, the paper explores the application of artificial intelligence (AI) in modeling the connections between biotic and abiotic components of forest ecosystems. This involves the integration of advanced techniques such as remote sensing, imaging, geospatial data analysis, and cartographic methods, which are employed across various spatial and temporal scales to enhance our understanding of these complex ecological relationships. Lessons from silviculture systems highlight tools and pitfalls for forestry, emphasising the necessity for interpretable models backed by machine learning (ML), the integration of ecological context, and validation of various algorithms such as random forest (RF) and Support vector machines (SVM). We conclude that coordinated data infrastructures are essential for ensuring that AI provides actionable insights and scalable solutions for monitoring complex forest ecosystems.

Keywords

• Python
• data modelling
• data analysis
• artificial intelligence
• environmental monitoring

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