Planetscope görüntüleri kullanılarak Tunçbilek açık ocak kömür madeni bölgesinde arazi kullanım ve örtü değişikliklerinin makine öğrenmesi tabanlı zamansal değişim tespiti

Öz

Bu çalışma, Batı Anadolu’da yoğun madencilik faaliyetlerinin yürütüldüğü bir bölge olan Kütahya’daki Tunçbilek açık ocak kömür madeni ve çevresindeki arazi kullanım ve örtü (LULC) değişimlerini incelemektedir. Yüzey madenciliği operasyonlarının uzun vadeli çevresel etkilerinin değerlendirilmesi ve sürdürülebilir arazi yönetiminin desteklenmesi açısından LULC dinamiklerinin anlaşılması büyük önem taşımaktadır. Bu amaçla, 2016 ve 2021 yıllarına ait yüksek çözünürlüklü PlanetScope uydu görüntüleri kullanılmış ve zamansal değişimlerin tespiti için Maksimum Olabilirlik Sınıflandırması (MLC) ile Destek Vektör Makineleri (SVM) olmak üzere iki denetimli makine öğrenme algoritması uygulanmıştır. Elde edilen sonuçlara göre, sınıflandırma doğruluğu açısından SVM, MLC’ye kıyasla daha yüksek performans göstermiştir. MLC için kappa katsayıları 2016 yılında 0.73, 2021 yılında ise 0,72 olarak belirlenirken; SVM için bu değerler sırasıyla 0.87 ve 0.84 olarak hesaplanmıştır. Özellikle orman ve ekili alan sınıflarında SVM, kullanıcı ve üretici doğruluklarında daha yüksek başarı elde etmiştir. 2016–2021 yılları arasında tarım alanlarında %6.83’lük bir artış, çıplak toprak alanlarında ise %7.9’luk bir azalma gözlemlenmiştir. Madencilik alanı ise yaklaşık %1.39 oranında genişlemiştir. Bu bulgular, LULC değişimlerinin izlenmesinde makine öğrenmesi tabanlı uzaktan algılama yöntemlerinin etkinliğini ortaya koymakta ve karmaşık, hassas peyzajlarda madencilik faaliyetlerinin çevresel etkilerinin daha iyi anlaşılmasına katkı sağlamaktadır.

Anahtar Kelimeler

• Değişim tespiti
• Makine öğrenmesi
• Maden sahası
• MLC
• Planet Scope
• SVM
• Uzaktan algılama

Machine learning-based temporal change detection of land use and land cover changes in the Tunçbilek open pit coal mine region using Planetscope imagery

Abstract

This study investigates land use and land cover (LULC) changes in the Tunçbilek open-pit coal mine and its surroundings, a region experiencing intense mining activity in western Türkiye. Understanding LULC dynamics is crucial for assessing the long-term environmental impacts of surface mining operations and supporting sustainable land management. High-resolution PlanetScope imagery from 2016 and 2021 was used in conjunction with two supervised machine learning algorithms Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) to detect temporal changes in six land cover classes. The results show that SVM outperformed MLC in classification accuracy. The kappa values for MLC were 0.73 (2016) and 0.72 (2021), whereas SVM achieved 0.87 and 0.84, respectively. SVM also provided higher user and producer accuracy rates, particularly for the forest and planted classes. Between 2016 and 2021, notable land cover transitions were observed, including a 6.83% increase in cultivated lands and a 7.9% decrease in barren land. The mining area itself expanded by approximately 1.39%. These results highlight the effectiveness of machine learning-based remote sensing methods in monitoring LULC changes and contribute to a better understanding of the environmental impacts of mining activities in complex and sensitive landscapes.

Keywords

• Change detection
• Machine learning
• Mining site
• MLC
• Planet Scope
• Remote sensing
• SVM

Kaynakça

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