Fındık ekili alanların uzaktan algılama ve makine öğrenme algoritmaları ile belirlenmesi; Piraziz, Giresun örneği
Determination of hazelnut cultivated areas using remote sensing and machine learning algorithms; the case of Piraziz, Giresun
Yazar: SEDA TERZİ TÜRK
Danışman: DOÇ. DR. FİLİZ BEKTAŞ BALÇIK
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Bilişim Uygulamaları Ana Bilim Dalı / Coğrafi Bilgi Teknolojileri Bilim Dalı
Derece: Yüksek Lisans, 2023
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry

Özet:
Tarımsal çalışmalar ve bu çalışmaların bir plan çerçevesinde yapılarak farklı ölçeklerde haritalarının üretilmesi ülkelerin ekonomik gelişiminde oldukça önemli rol oynar. Meyve ağaçlarından olan fındık bitkisi de dünya çapında popülerliği bilinen ve son yıllarda tarım tekniklerini geliştirerek büyük yatırımlar alan fındık üretimi birçok ülkede artmıştır. Küresel fındık üretiminde birinci sırada olan Türkiye, üretimin en elverişli alanlarda gerçekleşmesini ve talebe göre yönlendirilmesini düzenlemektir. Bu tür devlet düzenlemelerine göre hem potansiyel fındık alanları eğim, bakı gibi topoğrafya özelindeki kriterlere göre hem de mevcuttaki fındık ekili alanların tespiti uzaktan algılama (UA) teknolojileri gibi gelişmiş teknolojiler kullanılarak haritaları üretilebilir. Mevcuttaki tarım arazilerinin kullanımı hakkında bilgi edinmek için yersel çalışmalar arazinin yapısal özelliklerinden dolayı zahmetli, pahalı ve oldukça zaman alıcıdır. Bundan dolayı uydu görüntüleri arazi kullanımının belirlenmesinde sıklıkla kullanılmaktadır. En yaygın kullanımı görüntülerin sınıflandırılmasıdır. Arazi örtüsü ve arazi kullanımı (AÖ/AK) haritalarının üretiminde, literatür araştırmaları sonucunda, her ne kadar gelişmiş, yüksek çözünürlüklü uydu görüntüleri temin edilse de sınıflandırmada kullanılacak algoritma ve sınıflandırmaya destek verecek uzaktan algılama indekslerinin üretilen tematik haritanın doğruluğunun artmasında etkili olduğu görülmektedir.
Bu çalışmada,  iki farklı tarihli Sentinel-2 MSI uydu görüntüsü kullanılarak farklı sınıflandırma algoritmaları ve farklı bitki indekslerinin kullanımı ile fındık ekili alanların belirlenmesi ve fındık ekili alanların topografik özelliklerinin analiz edilmesi amaçlanmıştır. Çalışma bölgesi olarak Karadeniz Bölgesinde bulunan Giresun ilinin bir ilçesi olan Piraziz ilçesi seçilmiştir. Piraziz ilçesinin seçilmesinin sebebi, Türkiye'de Karadeniz hattı boyunca yetişmekte olan fındığın Ordu ve Giresun illerinde yoğunlaşmasıdır. Piraziz ilçesi de Giresun ili sınırında kalan, bölge halkının çoğunluğunun geçim kaynağı olarak fındık tarımı yaptığı bir ilçedir. 
Çalışmanın ilk aşamasında, Haziran ve Ekim aylarına ait Sentinel-2 MSI uydu görüntüleri temin edilmiştir. Sınıflandırma için çalışma bölgesinde fındık, orman, tarım/mera, şehir yapısı, endüstriyel ve ticari alan, yol, su yüzeyi olmak üzere 7 AK/AÖ kategorisi belirlenmiştir. Sentinel-2 MSI uydu bantları arasında hem korelasyona bakılarak hem de farklı bant kombinasyonları denenerek bant2, bant3, bant4, bant8a, bant11 ve bant12 orijinal bant olarak belirlenmiştir. Ayrıca, Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI), Yeşil Bant Normalize Edilmiş Fark Bitki Örtüsü İndeksi (GNDVI) ve Normalize Edilmiş Fark Kırmızı Kenar İndeksi (NDVIre) gibi çeşitli bitki indeksleri hesaplanmış ve bu indeksler, orijinal bant verilerine ayrı ayrı eklenerek rastgele orman (RO) ve destek vektör makineleri (DVM) algoritmaları kullanılarak iki farklı tarihte sınıflandırma işlemi gerçekleştirilmiştir. Bu sınıflandırma işlemleri sonucunda elde edilen 20 senaryoya ait genel sınıflandırma doğrulukları hesaplanmış ve bu sınıflandırma doğruluk değerleri arasındaki farkların anlamlılığı McNemar's testi ile istatistiksel olarak analiz edilmiştir. 
Hesaplanan indeksler, sınıflandırma doğruluk değerlendirme sonuçları analiz edildiğinde, Ekim ayında fındık ekili alanların belirlenmesinde en iyi sonuçlar elde edilmiştir. Rastgele orman algoritması ile yapılan sınıflandırmalarda genel sınıflandırma doğrulukları destek vektör makinelerine göre daha iyi sonuçlar vermiştir. Topografik özelliklerin analizi sonucunda fındık ekili alanların en fazla 300-600 metre arasında, %20-30 eğiminde ve doğu cephesinde bulunduğu belirlenmiştir.

Abstract:
Agricultural studies and the generation of maps at different scales through planned agricultural activities play a crucial role in the economic development of countries. As an example of such studies, the hazelnut plant has gained popularity worldwide among fruit trees and has received significant investments in recent years due to advancements in agricultural techniques. Consequently, hazelnut production has increased in many countries. Turkey ranks first in global hazelnut production and aims to regulate production in the most suitable areas and align it with market demands. According to such government regulations, potential areas for hazelnut cultivation can be mapped based on topographic criteria such as slope, aspect, and advanced technologies like remote sensing (RS) for the identification of existing hazelnut cultivated areas. However, ground-based studies conducted to obtain information about the use of current agricultural land can be laborious, costly, and time-consuming due to the structural characteristics of the terrain. Therefore, satellite imagery is frequently employed in determining land use. Image classification is the most commonly used method in this regard. Literature surveys indicate that the accuracy of thematic maps depicting land cover and land use (LC/LU) can be enhanced by utilizing advanced and high-resolution satellite imagery, as well as remote sensing indices that support the classification algorithm. 
In this study, two different dates of Sentinel-2 MSI satellite images, various classification algorithms, and vegetation indices were used for the determination of hazelnut cultivation areas and the analysis of their topographic characteristics. Vegetation indices are widely used measurements in agricultural and ecological analyses, providing valuable information about plant growth, health status, and vegetation cover. The vegetation indices used in this study were derived from processing the spectral data obtained from Sentinel-2 MSI satellite images. These indices facilitated the monitoring and evaluation of hazelnut tree growth, as well as the detection and assessment of vegetation density. Sentinel-2 MSI satellite images have proven to be highly useful in land cover classification. The high resolution, broad spectral range, and repeated observations of satellite images have made them effective tools for determining vegetation and soil properties. Using Sentinel-2 MSI satellite images, this study successfully identified hazelnut cultivation areas and conducted an analysis of agricultural land use.
Piraziz district, located in the Giresun province of the Black Sea Region in Turkey, was selected as the study area. Piraziz is a region where hazelnuts, which grow along the Black Sea coast in Turkey, are concentrated. Hazelnuts are an important agricultural product in this region due to favorable climatic conditions and soil properties. Piraziz district is widely known for hazelnut cultivation, thanks to its climate and soil structure. The majority of the local population is engaged in hazelnut production, making a living from hazelnut farming.
The aim of this study is to identify hazelnut cultivation areas and analyze their topographic characteristics in the Piraziz district. The growing conditions of hazelnuts, including climate, soil composition, and slope, are of critical importance for hazelnut production. Therefore, analyzing topographic features assists in determining the areas where hazelnut trees thrive under optimal conditions, leading to the highest yield. Moreover, the utilization of Sentinel-2 MSI satellite images in delineating the study area has provided a significant advantage in land cover classification, resulting in accurate and reliable identification of hazelnut cultivation areas.
These comprehensive analyses serve as a valuable source of information for farmers, researchers, and decision-makers involved in hazelnut production. Farmers engaged in the agricultural sector can utilize the insights derived from these analyses to facilitate agricultural planning, enhance productivity, and develop sustainable farming practices. Additionally, researchers and decision-makers focusing on hazelnut production can utilize the findings of these analyses to identify the most suitable regions for hazelnut cultivation and shape agricultural policies.
In the initial phase of this study, Sentinel-2 MSI satellite images for June and October were acquired. Seven land cover/land use categories were defined for classification in the study area, including hazelnut cultivation, forest, agricultural/pasture, urban areas, industrial and commercial areas, roads, and water surfaces. Among the satellite bands to be used for classification, band 2, band 3, band 4, band 8a, band 11, and band 12 were selected as the original bands based on correlation analysis and trials with different band combinations. Additionally, various vegetation indices such as Normalized Difference Vegetation Indices (NDVI), Green Normalized Difference Vegetation Indices (GNDVI), and Normalized Difference Red Edge Indices (NDVIre) were calculated, and these indices were individually added to the original band data. Classification processes were then carried out using Random Forest (RF) and Support Vector Machines (SVM) algorithms for the two different dates. Random Forest is a widely used method in machine learning and classification problems, which involves an ensemble of multiple decision trees. Each decision tree works independently on the training dataset by performing random sampling and random feature selection. This allows each tree to make its own prediction, and the results are combined to determine the best prediction. Random Forest algorithm is known for its robustness to dataset complexity and noise, and it offers the advantage of operating efficiently on large datasets. On the other hand, Support Vector Machines (SVM) is a machine learning method used for pattern recognition and classification problems. SVM creates a separation plane in space to separate data points into different classes, aiming to maximize the maximum marginal distance between classes. The SVM algorithm is based on an optimized equation to find the best separation plane in the hypothesis space used for classification. SVM is known for its ability to generate a decision boundary with high classification accuracy and it can work effectively with small datasets as well. In this study, hazelnut cultivation areas were classified using the RF and SVM algorithms. While the RF algorithm creates independent decision trees by performing random sampling and random feature selection, the SVM algorithm uses an optimized equation to determine the best separation plane in order to separate data points in space. Both algorithms have different features and advantages, and they were compared in this study for the classification of hazelnut cultivation areas.
In the study, McNemar's test was used for comparing the classification accuracies. McNemar's test is a statistical test used to assess whether classification results are significantly different. This test analyzes the differences between classification results using a contingency table. McNemar's test was applied to the obtained 20 different scenarios. This analysis plays a significant role in comparing the methods used for hazelnut cultivation area classification and determining the best-performing method.
According to the calculated vegetation indices and the evaluation of classification accuracy, the best results for identifying hazelnut cultivation areas were obtained in October. The classifications performed using the Random Forest algorithm achieved higher overall classification accuracies compared to Support Vector Machines. However, it was observed that the vegetation indices used in June also contributed positively to the classification process.
The conducted analysis of topographic features has revealed the characteristics of the areas where hazelnuts normally grow. According to these analyses, hazelnut cultivation areas are typically found within an elevation range of 300-600 meters. Additionally, these areas exhibit slopes ranging from 20% to 30% and are predominantly oriented towards the east. These topographic characteristics provide valuable information for identifying the regions where hazelnut trees can grow under optimal conditions and achieve optimum yields.
These detailed analyses provide an important source of information for the development of strategies and the identification of hazelnut cultivation areas for those involved in hazelnut production. In particular, farmers in the agricultural sector can benefit from these analysis results for agricultural planning and increasing productivity. Additionally, researchers and decision-makers focusing on hazelnut production can evaluate these findings for the optimization of sustainable agricultural practices and hazelnut cultivation.