Arazi Örtüsü Haritalamasında Farklı Makine Öğrenmesi Algoritmalarının Değerlendirilmesi: İzmir İli Örneği

Year 2023, , 105 - 117, 31.12.2023

Şevki Danacıoğlu

https://doi.org/10.17211/tcd.1296893

Abstract

Doğal kaynak yönetimi ve mekânsal planlama süreçlerinde ayrıntılı, güncel ve doğru bilgilere dayanan arazi örtüsü ve arazi kullanımı (AÖAK) durumunun tespiti önemli rol oynamaktadır. Ancak, bölgesel ölçekte arazi kullanım dinamiklerinin izlenmesini engelleyen veri işleme süreci ve depolama gereksinimi gibi bazı sınırlılıklar vardır. GEE, küresel ölçekte coğrafi verilerin işlenmesine olanak tanıyan açık kaynak kodlu, ücretsiz bir bulut platformdur. Bu araştırmanın amacı GEE üzerinde farklı makine öğrenmesi algoritmaları ile İzmir ili AÖAK haritasını elde etmek ve kullanılan sınıflandırma algoritmaların sonuçlarını karşılaştırmaktır. Araştırmada 2022 yılına ait 10m mekânsal çözünürlüğe sahip Sentinel-2 çok bantlı uydu görüntüleri ile çeşitli UA indeksleri kullanılmıştır. Araştırmada kullanılan geniş ölçekteki AÖAK sınıfları ‘Tarım Alanı’, ‘Orman Alanı’, ‘Beşeri Yüzeyler’, ‘Açık Yüzeyler’ ve ‘Su Yüzeyleri’ şeklinde belirlenmiştir. Çalışmada Sınıflandırma ve Regresyon Ağacı (SRA), Destek Vektör Makinesi (DVM), Rastgele Orman (RO) makine öğrenmesi algoritmaları kullanılmış ve her bir sınıflandırıcının Üretici Doğruluğu (ÜD), Kullanıcı Doğruluğu (KD) ve Genel Doğruluğu (GD) ile Kappa Katsayısı hesaplanmıştır. Sonuç olarak %97,2 GD ve Kappa değeri %95,7 olan RO sınıflandırma algoritması, en yüksek sınıflandırma doğruluğuna sahiptir. %96,1 GD ve %94,9 Kappa değeri ile DVM algoritması ikinci en yüksek sınıflandırma doğruluğuna sahip algoritma olmuştur. SRA algoritmasının GD %93,3, Kappa değeri ise %91.4 olarak hesaplanmıştır. Sonuç olarak RO yöntemi SRA ve DVM yöntemlerine göre daha iyi sonuç verdiği tespit edilmiştir. Diğer yandan sınıflandırma modellerinde özellikle açık yüzeyler ile beşeri yüzeyler ve çıplak tarım alanları arasındaki yansıma örtüşmesi bu sınıfların ayırt edilmesini güçleştirdiği görülmektedir.

Keywords

Arazi Örtüsü ve Arazi Kullanımı, Google Earth Engine, Makine Öğrenmesi

Supporting Institution

İzmir Bakırçay Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

KBP.2022.006

Evaluation of Different Machine Learning Algorithms for Land Cover Mapping: A Case Study of Izmir Province

Abstract

Detection of land use and land cover (LULC) based on detailed, current, and accurate information plays an important role in natural resource management and spatial planning processes. However, there are some limitations such as data processing and storage requirements that hinder monitoring of land use dynamics at the regional scale. GEE is an open-source, free cloud platform that enables processing of geographic data at the global scale. The aim of this research is to obtain the LCLU map of Izmir province using different machine learning algorithms on GEE and to compare the results of the classification algorithms used. Sentinel-2 multi-band satellite images with a spatial resolution of 10m for the year 2022 and various UA indices were used in the study. The broad-scale LCLU classes used in the study were determined as 'Agricultural Area', 'Forest Area', 'Human Surfaces', 'Open Surfaces' and 'Water Surfaces'. Classification and Regression Tree (CART), Support Vector Machine (SVM), and Random Forest (RF) machine learning algorithms were used in the study, and the Producer's Accuracy (PA), User's Accuracy (UA), Overall Accuracy (OA), and Kappa Coefficient of each classifier were calculated. As a result, the RF classification algorithm with a GD of 97.2% and a Kappa value of 95.7% had the highest classification accuracy. The SVM algorithm with a GD of 96.1% and a Kappa value of 94.9% was the second highest accuracy algorithm. The GD of the CART algorithm was calculated as 93.3% and the Kappa value was 91.4%. Therefore, it was found that the RF method produced better results than the CART and SVM methods. On the other hand, it is seen that the overlap of reflection between open surfaces and human surfaces and bare agricultural areas especially in classification models makes it difficult to distinguish these classes.

Keywords

Land Cover and Land Use, Google Earth Engine, Machine Learning, Izmir

Project Number

KBP.2022.006

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