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Journals / International Journal of Engineering and Geosciences / 2026 / Volume: 11 Issue: 1

Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021)

Başak Kafes Demirci(YILDIZ TECHNICAL UNIVERSITY)0000-0003-0966-4260
Osman Salih Yılmaz(CELÂL BAYAR ÜNİVERSİTESİ)0000-0003-4632-9349
Füsun Balık Şanlı(YILDIZ TECHNICAL UNIVERSITY)0000-0003-1243-8299
Pages
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Publication Date
2025-10-01
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AI Summary & Key Takeaways

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

According to the classification results, the initial stage achieved overall accuracy ranging from 79.97% to 90.63%, with an average of approximately 85.00%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The study investigates changes in the İğneada Longoz forests in Kırklareli Province, Türkiye, between 2000 and 2021 using Landsat-7 and Landsat-8 satellite imagery.
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Key Findings & Data:To better distinguish longoz forests and other forested areas from surrounding features, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated into the classification alongside standard spectral bands.
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Conclusion & Impact:According to the classification results, the initial stage achieved overall accuracy ranging from 79.97% to 90.63%, with an average of approximately 85.00%.
🔬Design:Modeling & Simulation
📍Scope:2000 Verileri
⚙️Variables:İğneada Longoz · Google Earth Engine · NDVI

Abstract

The study investigates changes in the İğneada Longoz forests in Kırklareli Province, Türkiye, between 2000 and 2021 using Landsat-7 and Landsat-8 satellite imagery. Classification was performed with the random forest (RF) machine learning algorithm and remote sensing (RS) techniques on the Google Earth Engine (GEE) platform. To better distinguish longoz forests and other forested areas from surrounding features, the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated into the classification alongside standard spectral bands. In the first stage of the classification process, the study area was categorized into seven classes, identifying all forest areas. In the second stage, the area was further divided into nine classes, isolating longoz forests from other forest types. Additionally, NDVI and EVI time series analyses were conducted to evaluate forest phenology. According to the classification results, the initial stage achieved overall accuracy ranging from 79.97% to 90.63%, with an average of approximately 85.00%. The Kappa statistics varied between 0.721 and 0.877. In the second stage, the overall accuracy ranged from 75.92% to 90.04%, while the Kappa statistic was between 0.649 and 0.866.

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Cite This Article

Demirci, B. K., Yılmaz, O. S., Şanlı, F. B. (2025). Investigation of Longoz Forests with Remote Sensing Techniques on Google Earth Engine Platform: The Case of İğneada Longoz Forests (2000–2021). International Journal of Engineering and Geosciences, Volume: 11 Issue: 1. https://doi.org/10.26833/ijeg.1650786

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