Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan

In this article, it was targeted to reveal the variations of NDVI which may represent the phenological stages of agricultural crops derived from Landsat 8 imagery from the start to end of growing seasons which eventually influence the final yields. An effective method was developed to map seasonal phenological variations of crops over large geographic regions using 16-day Landsat 30 m resolution NDVI time series data obtained from USGS. The Google Earth Engine (GEE) platform was used for processing the Landsat 8 data. The areas with cloud cover and cloud shadows were masked out, filled by no data and smoothing double logistic filter was fitted on the time series of the reflectance values. Phenological metrics extracted from the NDVI time series were obtained by the TIMESAT software. Seasonal data were extracted for growing seasons of the years of 2015 and 2016. The phenology maps were created for study area.

Landsat 8 Uydu Görüntü Uygulaması – Ürün Fenolojisinin Haritalanması İçin NDVI Zaman Serisi: Afganistan’ın Balkh ve Jawzjan Bölgeleri Örneği

Bu makalede tarımsal ürünlerin yetiştirme sezonu boyunca nihai verimini etkileyen fenolojik dönemleri temsil edebilecek Landsat 8 görüntüsünden elde edilen normalize edilmiş vejetasyon indeksi (NDVI) değişiminin ortaya konulması hedeflenmiştir. Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumundan (USGS) elde edilen Landsat 16-gün 30 m çözünürlüklü NDVI zaman serileri verisi kullanılarak geniş coğrafi alanlar üzerindeki ürünlerin mevsimsel fenolojik değişimlerini haritalama amacıyla etkin bir metot geliştirilmiştir. Landsat 8 verilerinin işlenmesi için Google Earth Engine (GEE) platformu kullanılmıştır. Bulut örtüsüne ve bulut gölgelerine sahip alanlar maskelenmiş, verilerle doldurulmamış ve yansıma değerlerinin zaman serisine çift lojistik filtresi uyarlanmıştır. NDVI zaman serilerinden elde edilen fenolojik metrikler TIMESAT yazılımı ile elde edilmiştir. 2015 ve 2016 yılı yetiştirme sezonu için mevsimsel veriler sağlanmış ve çalışma alanı için fenoloji haritaları oluşturulmuştur.

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Borini Alves D., Pérez-Cabello F., Rodrigues Mimbrero M., 2015. Land-use and land-cover dynamics monitored by NDVI multitemporal analysis in a selected southern amazonian area (Brazil) for the last three decades. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(7W3): 329– 335. https://doi.org/10.5194/isprsarchives-XL-7-W3-329-2015.

Central Statistic Organisation., 2012. Settled Population of Balkh province by Civil Division, Urban, Rural and Sex-2012-13: 21–22.

Eklundh L., Jönsson P., 2017. TIMESAT 3.3 with seasonal trend decomposition and parallel processing Software Manual. Lund and Malmo University, Sweden. Sweden: Lund and Malmo University, Sweden. Retrieved from http://www.nateko.lu.se/TIMESAT/ 2017- 05-29.

Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R., 2017. Remote sensing of environment google earth engine : Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031.

Govaerts B., Verhulst N., 2010. The normalized difference vegetation index (NDVI) Greenseeker (TM) handheld sensor: toward the integrated evaluation of crop management. Part A-Concepts and case studies, International Maize and Wheat Improvement Center 1–12.

Hamel S., Garel M., Festa-Bianchet M., Gaillard J. M., Côté S.D., 2009. Spring normalized difference vegetation index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates. Journal of Applied Ecology, 46(3): 582–589. https://doi.org/10.1111/j.1365-2664.2009.01643.x.

Jakubauskas M. E., Legates R., 2002. Crop identification using harmonic analysis of timeseries AVHRR NDVI data. Computers and Electronics in Agriculture, 37: 127–139. Retrieved from www.elsevier.com/locate/compag.

Li L., Friedl M.A., Xin Q., Gray J., Pan Y., Frolking S., 2014. Mapping crop cycles in China using MODIS-EVI time series. Remote Sensing, 6(3):2473–2493. https://doi.org/10.3390/rs6032473.

Osman J., Inglada J., Dejoux J., 2015. Assessment of a Markov logic model of crop rotations for early crop mapping. Computers and Electronics in Agriculture, 113: 234–243. https://doi.org/10.1016/j.compag.2015.02.015.

Pan Z., Huang J., Zhou Q., Wang L., Cheng Y., 2015. Mapping crop phenology using NDVI time-series derived from HJ-1 A / B data International Journal of Applied Earth Observation and Geoinformation Mapping crop phenology using NDVI time-series derived from HJ-1 A / B data. International Journal of Applied Earth Observations and Geoinformation, 34(February):188–197. https://doi.org/10.1016/j.jag.2014.08.011.

Qamer F. M., Shah S. N. P., Murthy M. S. R., Baidar T., Dhonju K., Hari B. G., 2014. Operationalizing crop monitoring system for informed decision making related to food security in Nepal. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(8): 1325–1330. https://doi.org/10.5194/isprsarchives-XL-8-1325-2014.

Sakamoto T., Yokozawa M., Toritani H., Shibayama M., Ishitsuka N., Ohno H., 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3–4): 366–374. https://doi.org/10.1016/j.rse.2005.03.008.

Ustuner M., Sanli F.B., Abdikan S., Esetlili M.T., Kurucu Y., 2014. Crop type classification using vegetation indices of rapideye imagery. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL: 195–198. Istanbul, Turkey: ISPRS Technical Commission VII Symposium. https://doi.org/10.5194/isprsarchives-XL-7-195-2014.

Van Leeuwen W.J. D., Orr B.J., Marsh S.E., Herrmann S.M., 2006. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sensing of Environment, 100(1): 67–81. https://doi.org/10.1016/j.rse.2005.10.002.

Wardlow B.D., Egbert S. L., (2008). Large-area crop mapping using time-series MODIS 250 m NDVI data : An assessment for the U . S . Central Great Plains. Remote Sensing of Environment, 112: 1096–1116. https://doi.org/10.1016/j.rse.2007.07.019.

Xijie L., 2013. Remote sensing, normalized difference vegetation index and crop yield forecasting. University of Illinois at Urbana-Champaign, 2013 Urbana,. Retrieved from https://www.ideals.illinois.edu/bitstream/handle/2142/46590/Xijie_Lv.pdf?sequence=1.

Xue J., Su B., 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 17. https://doi.org/10.1155/2017/1353691.

Zhang X., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., Huete A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471–475. https://doi.org/10.1016/S0034-4257(02)00135-9.

Zhao X., Wang X., Cao G., Chen K., Tang W., Zhang Z., 2017. Crop identification by using seasonal parameters extracted from time series landsat images in a mountainous agricultural county of eastern qinghai province , China. Agricultural Science, 9(4): 116–127. https://doi.org/10.5539/jas.v9n4p116.
Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi-Cover
  • ISSN: 2459-1580
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2015
  • Yayıncı: Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü