The comparison of remote sensing indices in the classification of barley and wheat crops

Year 2024, Volume: 20 Issue: 3, 171 - 197, 30.12.2024

Aykut Durgut , Sarp Korkut Sümer , Emre Özelkan

Abstract

The rapid population growth and global climate change in the world bring about issues such as food scarcity and hunger. As in many countries, Turkey is also experiencing an increase in the per capita demand for cereals. In our country, wheat occupies the first rank and barley the second rank in terms of the area planted with cereals. Determining the production quantity of critical crops such as wheat and barley in advance, ensuring no loss in their production, and achieving maximum yield have significant impacts on both national and international economic planning and food security. Remote sensing vegetation indices are used to determine and monitor wheat and barley fields. However, since wheat and barley are similar crops and their growing periods are close, it becomes challenging to distinguish them using remote sensing methods. In this study, wheat and barley fields in the provinces of Balıkesir (Edremit and İvrindi districts), Çanakkale (Ezine, Gelibolu, and Gökçeada districts), Manisa (Turgutlu district), Samsun (Yakakent district), Kayseri (İncesu district), Eskişehir (Sivrihisar district), and Yozgat (Sorgun district) were investigated using remote sensing methods in the years 2022 and 2023. The images of the selected fields were obtained between October 1, 2022, and July 31, 2023, from the Landsat 9 OLI-2/TIRS-2 and Sentinel-2 MSI satellites via the cloud-based Google Earth Engine (GEE) platform. From the obtained images, vegetation indices such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), SAVI (Soil Adjusted Vegetation Index), GCI (Green Chlorophyll Index), GLI (Green Leaf Index), GARI (Green Atmospherically Resistant Index), DVI (Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), RDVI (Ratio Vegetation Index), TGI (Transformed Vegetation Index), VARI (Visible Atmospherically Resistant Index), MCARI (Modified Chlorophyll Absorption Ratio Index), TVI (Triangular Vegetation Index), NDRE (Normalized Difference Red Edge Index), RECI (Red Edge Chlorophyll Index), and CVI (Chlorophyll Vegetation Index) were generated. This study, which includes a comparative analysis of 17 different indices from two different satellites, aimed to determine which index is the most suitable for distinguishing wheat from barley. As a result, it was found that in the differentiation of wheat and barley fields, the GARI index in June from the Landsat 9 OLI-2/TIRS-2 satellite (f=4.98, p=0.03) and the GLI index in May from the Sentinel 2 satellite (f=624.2, p=0.00) were effective.

Keywords

Vegetation indices, Wheat, NDVI

Thanks

Bu makale, Çanakkale Onsekiz Mart Üniversitesi, Lisans Üstü Eğitim Enstitüsü'nde, Tarım Makinaları ve Teknolojileri Mühendisliği Anabilim Dalı doktora programında yürütülmekte olan " Uzaktan Algılama ve Makine Öğrenmesi Yöntemleri Kullanılarak Ürün Rekolte Tahmini" başlıklı Doktora Tezi kapsamında hazırlanmıştır.

Arpa ve buğday bitkilerinin sınıflandırılmasında uzaktan algılama indislerinin karşılaştırılması

Abstract

Dünyadaki hızlı nüfus artışı ve küresel iklim değişikliği beraberinde gıda kıtlığı ve açlık gibi sorunlar getirmektedir. Dünya'da birçok ülkede olduğu gibi Türkiye’de kişi başına hububat ihtiyacı artmaktadır. Ülkemizde hububat yetiştirilen alanların büyük bölümünde birinci sırada buğday, ikinci sırada ise arpa yer almaktadır. Buğday ve arpa gibi kritik ürünlerin üretim miktarının önceden belirlenmesi, bitkinin üretiminde kayıp olmaması ve en yüksek verim elde edilebilmesi, ulusal ve uluslararası ekonomik planlamayı ve gıda güvenliğini etkilemektedir. Arpa ve buğday üretim alanlarının belirlenmesi ve izlenmesi için uzaktan algılama bitki indeksleri kullanılabilmektedir. Ancak buğday ve arpanın benzer bitkiler olması ve yetişme zamanlarının da yakın olması uzaktan algılama yöntemleri ile ayrıştırılmasını güçleştirmektedir. Bu çalışmada, Balıkesir ili Edremit ve İvrindi ilçeleri, Çanakkale ili Ezine, Gelibolu ve Gökçeada ilçeleri, Manisa ili Turgutlu ilçesi, Samsun ili Yakakent ilçesi, Kayseri ili İncesu ilçesi, Eskişehir ili Sivrihisar ilçesi, Yozgat ili Sorgun ilçesinde 2022 ve 2023 yıllarında buğday ve arpa ekilen üretim alanları uzaktan algılama yöntemleri ile incelenmiştir. Belirlenen tarlaların 01 Ekim 2022 ve 31 Temmuz 2023 tarihleri arasındaki görüntüleri bulut tabanlı Google Earth Engine(GEE) platformu ile Landsat 9 OLI-2/TIRS-2 ve Sentinel-2 MSI uydularından elde edilmiştir. Elde edilen görüntülerden NDVI (Normalleştirilmiş Fark Bitki İndeksi), EVI (Geliştirilmiş Bitki İndeksi), LAI (Yaprak Alan İndeksi), SAVI (Toprak Düzeltilmiş Bitki İndeksi), GCI (Yeşil Klorofil İndeksi), GLI (Yeşil Yaprak İndeksi), GARI (Yeşil Atmosfer Dirençli İndeks), DVI (Fark Bitki İndeksi), GNDVI (Yeşil Normalleştirilmiş Fark Bitki İndeksi), RDVI (Oranlı Bitki İndeksi), TGI (Dönüştürülmüş Bitki İndeksi), VARI (Görünür Atmosfer Dirençli İndeks), MCARI (Değiştirilmiş Klorofil Emilim Oranı İndeksi), TVI (Üçgen Bitki İndeksi), NDRE (Normalleştirilmiş Fark Kızıl Kenar İndeksi), RECI (Kızıl Kenar Klorofil İndeksi), CVI (Klorofil Bitki İndeksi) bitki indeksleri üretilmiştir. 2 farklı uydudan 17 farklı indisin karşılaştırmalı analizini içeren bu çalışmada, arpa ile buğdayı ayırt edebilecek en uygun indisin hangisi olduğu araştırılmıştır. Çalışma sonucunda buğday ve arpa ekilen tarlaların ayrımında t-testi, Anova ve Lojistik Regresyon analizleri sonucunda Landsat 9 OLI-2/TIRS-2 uydusunda Haziran ayında GARI indeksi (f=4.98, p=0.03), Sentinel 2 uydusunda Mayıs ayında GLI indeksi(f=624.2, p=0.00) etkili olduğu belirlenmiştir.

Keywords

Bitki indisleri, Buğday, NDVI

Thanks

Bu makale, Çanakkale Onsekiz Mart Üniversitesi, Lisans Üstü Eğitim Enstitüsü'nde, Tarım Makinaları ve Teknolojileri Mühendisliği Anabilim Dalı doktora programında yürütülmekte olan " Uzaktan Algılama ve Makine Öğrenmesi Yöntemleri Kullanılarak Ürün Rekolte Tahmini" başlıklı Doktora Tezi kapsamında hazırlanmıştır.

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