Araştırma Makalesi
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BBCH Ölçeğinde Sentinel-2 Görüntüleri ile Ayçiçeği ve Buğday Bitkilerinin Vejetatif Aşamalarının İzlenmesi

Yıl 2021, Cilt: 38 Sayı: 1, 46 - 52, 30.04.2021
https://doi.org/10.13002/jafag4681

Öz

Günümüzde tarım arazilerinin azalması ve artan nüfus sayısı ile artan yiyecek talebi sebebiyle tarımsal ürünlerin haritalanması, incelenmesi ve takibi önem arzetmektedir. Uzaktan algılama tekniği ile birlikte tarımsal ürünlerin büyük ve küçük ölçekte takibini yapmak hızlı ve daha ekonomik olmuştur. Bu araştırmanın amacı, Tokat ilinde başlıca tarım ürünü olan buğday (Triticum aestivum L.) ve ayçiçeği (Helianthus annuus L.) bitkilerinin uydu görüntüleri yardımıyla üretilmiş farklı bitki indeksleri ile mahsüllerin vejetatif evrelerini izlemektir. Vejetatif evreler izlenirken Sentinel-2 uydu görüntülerinden üretilen Bitki İndeksleri (Vegetation Indices) kullanılmıştır. Normalize edilmiş fark bitki örtüsü indeksi (Normalized Differential Vegetation Index), Normalize edilmiş fark bitki örtüsü indeksi kırmızı-kenar (Normalized Differential Vegetation Index red-edge), Yeşil normalize edilmiş fark bitki örtüsü indeksi (Green Normalized Difference Vegetation Index) ve Normalize Fark Bitki örtüsü indeksi (Normalized Different Index) kullanılmıştır. Vejatatif evrelerin takibinde Biologische Bundesanstalt, Bundessortenamt and Chemical industry ölçeği kullanılmıştır. Çalışmanın sonucunda, Buğday ve ayçiçeği bitkisi için en yüksek yansıtım değerleri çiçeklenme döneminde görülmüştür. Sentinel-2 uydusunun zamansal çözünürlüğünün 5 gün olmasından dolayı Ocak ayından Eylül ayına kadar iki bitki için de her evrede görüntü elde edilmiştir. Bölgede üretimi fazla olan iki bitkinin dört farklı indekse göre yansıtım değerleri elde edilmiştir

Kaynakça

  • Alexandridis TK, Oikonomakis N, Gitas IZ, Eskridge KM, Silleos NG (2014). The performance of vegetation indices for operational monitoring of CORINE vegetation types, International Journal of Remote Sensing, 35:9, 3268-3285.
  • Belgiu M, Csillik O (2018). Sentinel-2 cropland mapping using pixel-based and object based time-weighted dynamic time warping analysis. Remote Sens. Environ. 204,509–523.
  • Claverie M, Demarez V, Duchemin B, Hagolle O, Ducrot D, Marais-Sicre C, Dejoux J.-F, Huc M, Keravec P, Béziat P, Fieuzal R, Ceschia E, Dedieu G (2012). Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data, Remote Sens. Environ., 124, 844-857
  • Dereli MA (2019). Sentinel-2A Uydu Görüntüleri ile Giresun İl Merkezi için Kısa DönemArazi Örtüsü Değişiminin Belirlenmesi, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19, 361-368. (In Turkish)
  • ESA (2019). European Space Agency, Missions, Sentinel-2. https://sentinel.esa.int/web/sentinel/missions/sentinel-2 (Accessed to web: 07.10.2019)
  • Frampton WJ, Dash J, Watmough G, Milton EJ (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J. Photogramm. Remote Sens.82, 83–92.
  • Ghosh P, Mandal D, Bhattacharya A, Nanda MK, Bera S (2018). Assessing Crop Monitoring Potential of Sentinel-2 in A Spatio-Temporal Scale. ISPRS TC V Mid-term Symposium Geospatial Technology – Pixel to People 20–23 November 2018, Dehradun, India, Vol. 42 227-231.
  • Gitelson AA, Merzlyak M (1994). Spectral Reflectance Changes Associated Withautumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. J. Plant Physiol 143: 286-292.
  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ 58: 289– 298.
  • Herbei MV, Sala F (2015). Use landsat image to evaluate vegetation stage in sunflower crops. AgroLifeScientific Journal 4(1): 79-86.
  • Iqbal J, Read JJ, Whisler FD (2013) Using remote sensing and soil physical properties for predicting the spatial distribution of cotton lint yield. Turkish Journal of Field Crops 18(2):158-165.
  • Marino S, Alvino A (2019). Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices. Agronomy, 9, 226.
  • Maier U (2001). Growth stages of mono- and dicotyledonous plants - BBCH Monograph. 2nd Edition, Federal Biological Research Centre for Agriculture and Forestry, Basel, Switzerland, P:158.
  • Oğuz, İ. (1993). Köy Hizmetleri Tokat Araştırma Enstitüsü arazisinin toprak etüd, haritalanması ve sınıflandırılması, Gaziosmanpaşa Üniversitesi yayınları, Yüksek Lisans Tezi (basılmamış), Tokat (In Turkish).
  • Rouse J, Haas R, Schell J, Deering D (1974). Monitoring Vegetation Systems in the Great Plains With ERTS. In: Third ERTS Symposium 10–14 December 1974, Washington, USA,1, 48-62.
  • Shahrokhnia MH, Ahmadi SH, (2019). Remotely Sensed Spatial and Temporal Variations of Vegetation Indices Subjected to Rainfall Amount and Distribution Properties. SpatialModeling in GIS and R for Earth and Environmental Sciences. Elsevier, 21-53.
  • Shamal SAM, Weatherhead K (2014). Assessing spectral similarities between rainfed and irrigated croplands in a humid environment for irrigated land mapping. Outlook on Agriculture, 43(2), 109-114.
  • Ozdogan M,Yang Y, Allez G, Cervantes C (2010). Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sensing. 2, 2274–2304.
  • Tokat Governorship (2019). Tokat'ta Toprak, Tarım, Su, Coğrafya,Turizm ve Dahası... http://www.tokat.gov.tr/tokatta-tarim-toprak-ve-turizm (Accessed to web: 04.10.2019).
  • TUIK (2019), Turkish Statistical Institute, http://www.tuik.gov.tr/PreTablo.do?alt_id=1001 (Accessed to web: 07.10.2019).
  • Walsh SO, Shafian S (2018). Assessment of red-edge based vegetatİon Indices derived from unmanned arial Vehicle for plant nitrogen content Estimation. 14th International Conference on Precision Agriculture, June 24 – June 27, Montreal, Quebec, Canada.
  • Zhang Z, Liu M, Liu X, Zhou GA (2018). New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice. Sensors 18, 2172.

Monitoring Vegetative Stages of Sunflower and Wheat Crops with Sentinel-2 Images According to BBCH-Scale

Yıl 2021, Cilt: 38 Sayı: 1, 46 - 52, 30.04.2021
https://doi.org/10.13002/jafag4681

Öz

Nowadays, due to the decrease in agricultural land and increasing population and food demand, mapping, examination and monitoring of agricultural products are important. With the remote sensing technique, it has been faster and economical to monitor agricultural products on a large scale. The aim of this study, is to monitor the vegetative stages of wheat (Triticum aestivum L.) and sunflower (Helianthus annuus L.) which are the main agricultural products in Tokat province of Turkey using different vegetation indices produced by satellite images. Vegetation Indices produced from multi-temporal Sentinel-2 satellite images were used for monitoring vegetative stages, namely, Normalized Differential Vegetation Index, Normalized Differential Vegetation Index red-edge, Green Normalized Difference Vegetation Index and Normalized Different Index. Biologische Bundesanstalt, Bundessortenamt and the Chemical Industry scale were used in the monitoring of vegetative stages for both plants. Since the temporal resolution of the Sentinel-2 satellites were 5 days, images were obtained at each stage of two plants from January to September. The reflectance values of two plants in all stages were obtained according to four different indices. As a result of the study, the highest reflectance values for wheat and sunflower plants were observed during flowering period.

Kaynakça

  • Alexandridis TK, Oikonomakis N, Gitas IZ, Eskridge KM, Silleos NG (2014). The performance of vegetation indices for operational monitoring of CORINE vegetation types, International Journal of Remote Sensing, 35:9, 3268-3285.
  • Belgiu M, Csillik O (2018). Sentinel-2 cropland mapping using pixel-based and object based time-weighted dynamic time warping analysis. Remote Sens. Environ. 204,509–523.
  • Claverie M, Demarez V, Duchemin B, Hagolle O, Ducrot D, Marais-Sicre C, Dejoux J.-F, Huc M, Keravec P, Béziat P, Fieuzal R, Ceschia E, Dedieu G (2012). Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data, Remote Sens. Environ., 124, 844-857
  • Dereli MA (2019). Sentinel-2A Uydu Görüntüleri ile Giresun İl Merkezi için Kısa DönemArazi Örtüsü Değişiminin Belirlenmesi, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19, 361-368. (In Turkish)
  • ESA (2019). European Space Agency, Missions, Sentinel-2. https://sentinel.esa.int/web/sentinel/missions/sentinel-2 (Accessed to web: 07.10.2019)
  • Frampton WJ, Dash J, Watmough G, Milton EJ (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J. Photogramm. Remote Sens.82, 83–92.
  • Ghosh P, Mandal D, Bhattacharya A, Nanda MK, Bera S (2018). Assessing Crop Monitoring Potential of Sentinel-2 in A Spatio-Temporal Scale. ISPRS TC V Mid-term Symposium Geospatial Technology – Pixel to People 20–23 November 2018, Dehradun, India, Vol. 42 227-231.
  • Gitelson AA, Merzlyak M (1994). Spectral Reflectance Changes Associated Withautumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. J. Plant Physiol 143: 286-292.
  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ 58: 289– 298.
  • Herbei MV, Sala F (2015). Use landsat image to evaluate vegetation stage in sunflower crops. AgroLifeScientific Journal 4(1): 79-86.
  • Iqbal J, Read JJ, Whisler FD (2013) Using remote sensing and soil physical properties for predicting the spatial distribution of cotton lint yield. Turkish Journal of Field Crops 18(2):158-165.
  • Marino S, Alvino A (2019). Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices. Agronomy, 9, 226.
  • Maier U (2001). Growth stages of mono- and dicotyledonous plants - BBCH Monograph. 2nd Edition, Federal Biological Research Centre for Agriculture and Forestry, Basel, Switzerland, P:158.
  • Oğuz, İ. (1993). Köy Hizmetleri Tokat Araştırma Enstitüsü arazisinin toprak etüd, haritalanması ve sınıflandırılması, Gaziosmanpaşa Üniversitesi yayınları, Yüksek Lisans Tezi (basılmamış), Tokat (In Turkish).
  • Rouse J, Haas R, Schell J, Deering D (1974). Monitoring Vegetation Systems in the Great Plains With ERTS. In: Third ERTS Symposium 10–14 December 1974, Washington, USA,1, 48-62.
  • Shahrokhnia MH, Ahmadi SH, (2019). Remotely Sensed Spatial and Temporal Variations of Vegetation Indices Subjected to Rainfall Amount and Distribution Properties. SpatialModeling in GIS and R for Earth and Environmental Sciences. Elsevier, 21-53.
  • Shamal SAM, Weatherhead K (2014). Assessing spectral similarities between rainfed and irrigated croplands in a humid environment for irrigated land mapping. Outlook on Agriculture, 43(2), 109-114.
  • Ozdogan M,Yang Y, Allez G, Cervantes C (2010). Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sensing. 2, 2274–2304.
  • Tokat Governorship (2019). Tokat'ta Toprak, Tarım, Su, Coğrafya,Turizm ve Dahası... http://www.tokat.gov.tr/tokatta-tarim-toprak-ve-turizm (Accessed to web: 04.10.2019).
  • TUIK (2019), Turkish Statistical Institute, http://www.tuik.gov.tr/PreTablo.do?alt_id=1001 (Accessed to web: 07.10.2019).
  • Walsh SO, Shafian S (2018). Assessment of red-edge based vegetatİon Indices derived from unmanned arial Vehicle for plant nitrogen content Estimation. 14th International Conference on Precision Agriculture, June 24 – June 27, Montreal, Quebec, Canada.
  • Zhang Z, Liu M, Liu X, Zhou GA (2018). New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice. Sensors 18, 2172.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Ömer Gökberk Narin 0000-0002-9286-7749

Ömer Faruk Noyan Bu kişi benim 0000-0001-5451-6297

Saygın A Abdikan Bu kişi benim 0000-0002-3310-352X

Yayımlanma Tarihi 30 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 38 Sayı: 1

Kaynak Göster

APA Narin, Ö. G., Noyan, Ö. F., & Abdikan, S. A. (2021). Monitoring Vegetative Stages of Sunflower and Wheat Crops with Sentinel-2 Images According to BBCH-Scale. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 38(1), 46-52. https://doi.org/10.13002/jafag4681