Field validation of country-wide remote sensing based-land use classification in Kyrgyzstan

Abstract

Observing and monitoring land use, land-use change and forestry (LULUCF) trends has extensively been used remote sensing. Collect Earth, a free remote sensing tool, was used in Kyrgyzstan to assess the historical and present LULUCF trends in 2015 and 2019. However, it is quite difficult for users to classify land cover and determine changes in land use if no satellite images with sufficient temporal and spatial resolution are available. The unavailability of high/very high spatial and temporal resolution satellite images (7.2%) or the availability of low spatial and temporal resolution satellite images (7.8%) was the primary reason for mandatory field verification. A fieldwork was conducted to validate the remote sensing assessment in 2019. In total, 941 sample plots were visited, and 119 misclassified sample plots were detected during the field validation work. Hence, this article reports an updated version of LULUCF assessment in Kyrgyzstan. The database update resulted in the re-classification of 1073 sample plots in Kyrgyzstan. The results of the field validation showed that forestlands occupied 1.81 million ha (9%) of the total land in 2019, with a 5.33% uncertainty in Kyrgyzstan. However, it was 1.36 million ha based on the remote sensing study.

Keywords

• Collect Earth
• Field validation
• Land use
• Remote sensing
• Kyrgyzstan

Kırgızistan’da ülke çapında uzaktan algılama tabanlı arazi kullanım sınıflandırmasının saha doğrulaması

Abstract

Arazi kullanımı, arazi kullanım değişikliği ve ormancılık (AKAKDO) eğilimlerini gözlemlemek ve izlemek için uzaktan algılama yaygın olarak kullanılmaktadır. Ücretsiz bir uzaktan algılama yazılımı olan Collect Earth, Kırgızistan’da 2015 ve 2019 yıllarındaki tarihi ve mevcut AKAKDO eğilimlerini değerlendirmek için kullanılmıştır. Ancak, yeterli zamansal ve mekansal çözünürlüğe sahip uydu görüntüleri mevcut değilse, kullanıcıların arazi örtüsünü sınıflandırması ve arazi kullanımındaki değişiklikleri belirlemesi oldukça zordur. Yüksek/çok yüksek mekansal ve zamansal çözünürlüklü uydu görüntülerinin eksikliği (%7,2) ve düşük mekansal ve zamansal çözünürlüklü uydu görüntülerinin (%7,8) varlığı, zorunlu saha doğrulamasının birincil nedeni olmuştur. 2019 yılında, arazide seçilen örnek sahalar ziyaret edilerek bir saha çalışması yürütülmüştür. Toplamda 941 örnek saha ziyaret edilmiş ve arazi doğrulama çalışması sırasında 119 yanlış sınıflandırılmış örnek saha tespit edilmiştir. Bu nedenle, bu makale Kırgızistan’daki AKAKDO değerlendirmesinin güncellenmiş bir versiyonunu sunmaktadır. Veritabanı güncellemesi, Kırgızistan›daki 1073 örnek alanın yeniden sınıflandırılmasıyla sonuçlanmıştır. Saha doğrulama sonuçları, Kırgızistan’da ormanlık alanların 2019’da toplam arazinin 1,81 milyon hektarını (%9) kapladığını %5,33’lük bir belirsizlik ile göstermiştir. Halbuki, bu alan, uzaktan algılama çalışmasına göre 1,36 milyon ha olarak hesaplanmıştır.

Keywords

• Collect Earth
• Saha doğrulaması
• Arazi kullanımı
• Uzaktan algılama
• Kırgızistan

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