Vejetasyon İndeksleri, Ana Bileşenler Analizi ve Google Earth Engine Kullanılarak Tarımsal Alan Sınıflandırması: Söke/Aydın Örneği

Abstract

Arazi kullanım ve arazi örtüsü (AKAÖ) sınıflaması çevresel değişim ve bozunmanın dünya genelinde en çok kullanılan göstergelerinden biri olarak bilinmektedir. AKAÖ sınıflaması için çeşitli algoritmalar ve metotlar var olup, en önemli hususların başında sınıflama haritalarının güvenliği gelmektedir. Çalışma, Google Earth Engine (GEE) platform kullanılarak tarımsal sınıflama için en geliştirici metotu belirlemek için Sentinel-2 görüntülerinin original bantlarının yanında Normalize Edilmiş Farklılık Vejetasyon İndeksi (NDVI), Yeşil NDVI (GNDVI) ve Ana Bileşenler Analizi (ABA) ile üretilmiş AKAÖ haritalarının doğruluğunun değerlendirilmesi üzerine odaklanmıştır. Bunun yanında, seçilen alan içerisindeki kısa dönem AKAÖ değişimlerinin belirlenmesi amaçlanmıştır. Amaçlara ulaşabilmek için, farklı yılların aynı ayında alınmış olan bulutluluk oranı %10’ dan az olan görüntüler kullanılarak AKAÖ2018 ve AKAÖ2022 haritaları eldesi için Mayıs 2018 ve Mayıs 2022 için ortalama görüntüler oluşturulmuştur. Alan rassal orman (RO) algoritması ile zeytin (Z), ekili tarım (E), Dikili tarım (D), orman (O) doğal vejetasyon (DV), yerleşim alanı (Y) ve su yüzeyi (S) olmak üzere yedi ana sınıfa ayrılmıştır. AKAÖ haritalarının güvenilirlikleri sınıf alanı büyüklüğüne göre rastgele dağıtılmış control noktaları göz önünde bulundurularak değerlendirilmiştir. Sınıfların birbirlerine dönüşümleri belirlenmiştir.

Keywords

• Ana Bileşenler Analizi
• Google Earth Enine
• Sentinel-2
• sınıflama doğruluğu
• tarımsal alan
• vejetasyon indeksleri.

Agricultural Land Classification Using Vegetation Indices, PCA, and Google Earth Engine: Case Study of Söke/Aydın

Abstract

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

Keywords

• Agricultural Land Classification Using Vegetation Indices
• PCA
• and Google Earth Engine: Case Study of Söke/Aydın

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