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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 14 Sayı: 3

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

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

Recep Uğur Acar(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, JEOLOJİ MÜHENDİSLİĞİ BÖLÜMÜ, MİNERALOJİ PETROGRAFİ ANABİLİM DALI)0000-0002-0420-6263
Enes Zengin(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ, MİMARLIK FAKÜLTESİ, ŞEHİR VE BÖLGE PLANLAMA BÖLÜMÜ, ŞEHİR VE BÖLGE PLANLAMA ANABİLİM DALI)0000-0002-5740-7763
Ali Samet Öngen(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, JEOLOJİ MÜHENDİSLİĞİ BÖLÜMÜ, UYGULAMALI JEOLOJİ ANABİLİM DALI)0000-0002-4019-7157
Pages
1001–1013
Publication Date
2025-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that SVM outperformed MLC in classification accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:The results show that SVM outperformed MLC in classification accuracy.
🔬Design:Modeling & Simulation
📍Scope:2016 Verileri
⚙️Variables:Değişim tespiti · Makine öğrenmesi · Maden sahası

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.

Özet

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.

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Cite This Article

Acar, R. U., Zengin, E., Öngen, A. S. (2025). Machine learning-based temporal change detection of land use and land cover changes in the Tunçbilek open pit coal mine region using Planetscope imagery. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 14 Sayı: 3, 1001–1013. https://doi.org/10.28948/ngumuh.1619090

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