Crop Cover Fraction Estimation Based On Digital Images from 2014-2016: A Case Study of Kadirli in Osmaniye Province

Pinar KARAKUS; Hakan KARABÖRK

Makale detayları

Crop Cover Fraction Estimation Based On Digital Images from 2014-2016: A Case Study of Kadirli in Osmaniye Province

Yazarlar:

Pinar KARAKUS | Hakan KARABÖRK

Yayıncı:

Osman ORHAN

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Turkish Journal of Remote Sensing 2020 Vol.2 Issue 2, pp.50-57

Belge Tipi:

Makale

Yayın Tarihi:

12/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000715548

Sıklık:

İki Yıllık

eISSN:

2687-4997

Özet:

 


Crop cover fraction is commonly used to define ecosystem change and vegetation quality. In 2014, 2015 and 2016, color images were taken in approximately 90 sample fields at intervals of one to two weeks. Images were gathered in April, May, June and July. These 4 months means the growth period from planting until the harvesting. In this way, plant phenology was studied closely. Two approaches were used to estimate crop cover fraction in two crop types in this study. In first method, the images were transformed from the RGB color space to the HSI color space. Object-based classification was used to separate the images as the green vegetation and the non-green part. In the second method, The Green Crop Tracker software is used. The Green Crop Tracker is an applicable alternative to ground-based methods. In this approach, both the loss of time and the loss of labor is less than object-based classification. Results from the green Crop Tracker software and object based classification were compared during the growing seasons in 2014, 2015 and 2016 high correlation was obtained between these two methods (for 2014 R²=0.89, for 2015 R²=0.87, for 2016 R²=0.90).     

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Makale detayları

Crop Cover Fraction Estimation Based On Digital Images from 2014-2016: A Case Study of Kadirli in Osmaniye Province

Yazarlar: Pinar KARAKUS | Hakan KARABÖRK

Yayıncı: Osman ORHAN

Ana Başlıklar: Uygulamalı fizik

Dergi: Turkish Journal of Remote Sensing 2020 Vol.2 Issue 2, pp.50-57

Belge Tipi: Makale

Yayın Tarihi: 12/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000715548

Sıklık: İki Yıllık

eISSN: 2687-4997

Sayı

Yazarlar:

Pinar KARAKUS | Hakan KARABÖRK

Yayıncı:

Osman ORHAN

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Turkish Journal of Remote Sensing 2020 Vol.2 Issue 2, pp.50-57

Belge Tipi:

Makale

Yayın Tarihi:

12/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000715548

Sıklık:

İki Yıllık

eISSN:

2687-4997