Güzelhisar Havzasında Endüstriyel Gelişmenin Arazi Kullanımı ve Arazi Örtüsü Özellikleri Üzerindeki Etkisinin Bulut Tabanlı Makine Öğrenme Teknikleri ile Değerlendirilmesi

Year 2023, , 135 - 150, 30.06.2023

Şevki Danacıoğlu , Hüseyin Can Öngül

https://doi.org/10.51800/ecd.1224255

Abstract

Endüstriyel faaliyetin varlığı, kentsel büyümenin ana itici gücüdür ve istihdam fırsatları yaratarak bölgenin sosyoekonomik durumunu etkilemektedir. Arazi Örtüsü ve Arazi Kullanımı (AÖAK), ekolojik koşullar, jeolojik ve jeomorfolojik özellikler, bitki örtüsü özellikleri gibi biyotik ve abiyotik faktörler ile sosyoekonomik yapı tarafından etkilenmektedir. AÖAK değişimlerini, bunların yoğunluğunu, değişim yönünü, etkenlerini ve izlemek, sürdürülebilir kalkınma planlaması için önemli bilgiler sağlamaktadır. Uzaktan Algılama (UA), bölgesel ve küresel AÖAK bilgisi elde etmek için en ekonomik ve uygulanabilir yaklaşım olarak kabul edilmektedir.. Çalışmanın amacı Güzelhisar Havzasında sanayi faaliyetlerinin AÖAK durumu üzerindeki etkisini araştırmaktır. Bu bağlamda uydu görüntüleri kullanarak makine öğrenme algoritması ile 1995-2022 yıllarına ait AÖAK durumu tespit edilmiştir. Sınıflandırmada AÖAK sınıfları ‘Su Yüzeyi’, ‘Orman Alanı’, ‘Tarım Alanı’, ‘Açık Yüzey’ ve ‘Beşeri Yüzey’ olarak belirlenmiştir. Araştırmada 30 m çözünürlüğü ile LANDSAT uydu görüntüleri kullanılmıştır. Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI), Toprakla Düzeltilmiş Bitki Örtüsü İndeksi (SAVI), Normalize Edilmiş Fark Su İndeksi (NDWI), Normalize Edilmiş Açık Yüzey İndeksi (NBLI), Çıplak Toprak İndeksi (BSI), Normalize Edilmiş Fark Yerleşim Alanı İndeksi (NDBI) indeksleri 1995 ve 2022 yılları için hesaplanarak doğruluğu artırmak amacıyla kullanılmıştır. Uydu görüntülerinin sınıflandırmasında Rastgele Orman (RF) makine öğrenme algoritması tercih edilmiştir. Görüntülerin elde edilmesinde ve sınıflandırma işlemlerinde Google Earth Engine (GEE) platformu kullanılmıştır. Sınıflandırma doğruluğu hata matrisi, kullanıcı doğruluğu, üretici doğruluğu, genel doğruluk ve Kappa Katsayısı ile hesaplanmıştır. Sonuç olarak araştırma sahasında beşeri yüzeylerde önemli miktarda artış meydana gelirken, tarım alanlarında ve açık yüzeylerde azalma olduğu tespit edilmiştir. Beşerî yüzeylerdeki artış miktarı dikkate alındığında bölgede sanayi faaliyetlerine bağlı istihdam potansiyelinin kentleşme üzerindeki etkisini göstermektedir. Araştırma kapsamında GEE platformunun yetenekleri, makine öğrenmesine dayalı sınıflandırma algoritması, sınıflandırma süreçleri ve elde edilen bulguların değerlendirilmesine kadar olan tüm süreç performansları değerlendirilmiştir. Bu açıdan çalışmanın tüm sonuçları, gelecekte yapılacak çalışmaların geliştirilmesi, ayrıca UA ve Coğrafi Bilgi Sistemleri araştırmalarında açık veri kaynaklarının ve bulut tabanlı platformların yaygınlaşması açısından önem arz etmektedir.

Keywords

Güzehisar Havzası, Uzaktan Algılama, Google Earth Engine, Arazi Kullanımı ve Arazi Örtüsü, Makine Öğrenmesi

Assessing Industrial Development Influence on Land Use and Land Cover Change Detection in Güzelhisar Basin with Cloud-Based Machine Learning Techniques

Abstract

Industrial activity is the main driving force behind urban growth, influencing the socioeconomic status of an area by creating employment opportunities. Land use and land cover (LULC) are influenced by various factors such as ecological conditions, geological and geomorphological features, vegetation characteristics, and socioeconomic structure. Monitoring LULC changes, their intensity, direction, and underlying causes provides valuable knowledge for sustainable development planning. Remote sensing (RS) is widely considered the most cost-effective and practical approach for obtaining regional and global LULC information. The aim of this study is to investigate the impact of industrial activity on LULC in the Güzelhisar Basin. Using satellite imagery and machine learning algorithms, the LULC status from 1995 to 2022 was determined. The LULC classes were classified as 'Water Surface', 'Forest Area', 'Agricultural Area', 'Bare Surface', and 'Built-Up Area'. The research utilized LANDSAT satellite images with a 30-meter resolution. To enhance accuracy, various indices including the NDVI, SAVI, NDWI, NBLI, BSI, and NDBI were calculated for the years 1995 and 2022. The Random Forest (RF) machine learning algorithm was employed for satellite image classification. The Google Earth Engine (GEE) platform was utilized for image acquisition and classification. Classification accuracy was evaluated using the Error Matrix, User's Accuracy, Producer's Accuracy, Overall Accuracy, and Kappa Coefficient. The findings indicate a significant increase in built-up areas and a decrease in agricultural and bare areas within the survey area. This demonstrates the impact of industrial operations on urbanization, considering the amount of increase in anthropic surfaces. The study thoroughly evaluates the capabilities of the GEE platform, machine learning-based classification algorithm, and the entire process from image classification to the assessment of obtained findings. These findings are crucial for future studies and the broader implementation of open data sources and cloud-based platforms in RS and Geographic Information Systems research.

Keywords

Google Earth Engine, Remote Sensing, Random Forest, Land Use and Land Cover Change, Güzelhisar Basin

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