EKOSİSTEM YÖNETİMİNDE YAPAY ZEKÂ DESTEKLİ COĞRAFİ MEKÂN ANALİZİ: MAKİNE ÖĞRENİMİ YÖNTEMLERİNİN ENTEGRASYONU

Yıl 2025, Cilt: 9 Sayı: 2, 560 - 584, 27.10.2025

Can Saygıner , Engin Nurlu

https://doi.org/10.32328/turkjforsci.1704059

Öz

Artan çevresel baskıların – özellikle iklim değişikliği, biyoçeşitlilik kaybı ve arazi bozulmasının – yarattığı karmaşıklık, ekosistem yönetiminde daha bütüncül ve bilimsel yaklaşımları zorunlu kılmaktadır. Bu çalışma, makine öğrenimi ile coğrafi mekân analizlerin bütüncül bir şekilde bir araya getirilmesinin, ekosistemlerin anlaşılması, izlenmesi ve etkin bir şekilde yönetilmesi açısından yenilikçi ve dönüştürücü bir yaklaşım sunduğunu ortaya koymaktadır. Makale, yakın dönemli araştırmalar ve örnek uygulamalar üzerinden, Rastgele Orman, Destek Vektör Makineleri, Yapay Sinir Ağları ve Derin Öğrenme modelleri gibi makine öğrenimi yöntemlerinin, CBS (Coğrafi Bilgi Sistemleri), uzaktan algılama ve uydu görüntüleme teknolojileriyle nasıl entegre edildiğini değerlendirmektedir. Ayrıca, yapay zekâ temelli ekolojik çözümler geliştirmede güçlü platformlar olan Python ve R dillerininin; veri ön işleme, tahmine dayalı modelleme, görselleştirme ve karar destek sistemlerindeki rollerine dikkat çekilmektedir. Seçilen on beş örnek uygulama, ormansızlaşma değerlendirilmeleri, biyoçeşitliliğin korunması, arazi kullanımı ve arazi örtüsü sınıflandırması ile ekosistem hizmetlerinin değerlendirilmesi gibi çeşitli ekolojik senaryolarda bu teknolojilerin başarılı kullanımını ortaya koymaktadır. Bu çalışmalar, ekosistem yönetiminde yöntemsel çeşitlilik, coğrafi kapsam ve uygulamaya dönük değer açısından örnek teşkil etmektedir. Ayrıca, inceleme; Google Earth Engine, TensorFlow gibi platformların entegrasyonunu, coğrafi mekân yapay zekâ iş akışlarında kullanılan araç setlerini ve yöntemsel gelişmeleri kapsamaktadır. Veri kalitesi, hesaplama yükü, model yorumlanabilirliği ve etik kaygılar gibi süregelen sorunlara dikkat çekilmekte; gelecekteki araştırma alanları ve disiplinlerarası iş birlikleri için olası yönelimler sunulmaktadır. Bu sentez, yapay zekâ ile coğrafi mekân analizlerinin birleşiminin, hızla değişen küresel koşullar karşısında uyarlanabilir, şeffaf ve etkin bir ekosistem yönetimini destekleme potansiyelini ortaya koymaktadır.

Anahtar Kelimeler

Coğrafi mekân analizi , makine öğrenimi , ekosistem yönetimi , uzaktan algılama , alan kullanımı ve arazi örtüsü değişimi

AI-DRIVEN GEOSPATIAL ANALYSIS IN ECOSYSTEM MANAGEMENT: INTEGRATION OF MACHINE LEARNING METHODS

Öz

The escalating complexity of environmental pressures, including climate change, loss of biodiversity, and land degradation, demands new and integrated, science-based solutions for managing ecosystems. In this study, it is explored how the integration of machine learning (ML) and geospatial analysis is a truly transformative approach to understand, monitor, and manage ecosystems. Through reviewing a wide spectrum of recent research and cases, the paper evaluates the integration of ML processes—e.g., Random Forest, Support Vector Machines, Artificial Neural Networks, and deep learning models—alongside geospatial technologies like GIS, remote sensing, and satellite image technology. Importantly, the research focuses on Python and R as strong programming platforms for developing ecological AI solutions and underscores their importance for data preprocessing, predictive models, visualization, and decision support. A select group of fifteen case studies illustrates successful applications across various ecological scenarios, including assessments of deforestation, biodiversity conservation, land use and land cover classification, and evaluation of ecosystem services. These articles were selected to illustrate methodological variety, geographic coverage, and applied relevance of advances in ecosystem management. Furthermore, the review covers significant methodological progress, toolkits, and platform integration (such as Google Earth Engine and TensorFlow), which are used across geospatial AI workflows. It also highlights ongoing issues—e.g., data quality, computational demand, model interpretability, and ethical considerations—and possible avenues for future research and interdisciplinary collaboration and outlines future directions to foster cross-disciplinary research and sustainable AI application. This synthesis illustrates the promise of the consolidation of AI and geospatial analysis to support adaptive, transparent, and efficient ecosystem management amid the rapid global change.

Anahtar Kelimeler

Geospatial analysis , machine learning , ecosystem management , remote sensing , land use and cover change

Kaynakça

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