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

Year 2019, Volume: 5 Issue: 1, 49 - 62, 30.05.2019
https://doi.org/10.28979/comufbed.557792

Abstract

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.

References

  • 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) Green-seeker (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 time-series 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 Obser-vation and Geoinformation Mapping crop phenology using NDVI time-series derived from HJ-1 A / B data. International Journal of Applied Earth Observations and Geoin-formation, 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. Opera-tionalizing crop monitoring system for informed decision making related to food securi-ty 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 Envi-ronment, 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 Pho-togrammetry, 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. Re-mote 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 fore-casting. 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 agricul-tural county of eastern qinghai province , China. Agricultural Science, 9(4): 116–127. https://doi.org/10.5539/jas.v9n4p116.

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

Year 2019, Volume: 5 Issue: 1, 49 - 62, 30.05.2019
https://doi.org/10.28979/comufbed.557792

Abstract

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.

References

  • 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) Green-seeker (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 time-series 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 Obser-vation and Geoinformation Mapping crop phenology using NDVI time-series derived from HJ-1 A / B data. International Journal of Applied Earth Observations and Geoin-formation, 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. Opera-tionalizing crop monitoring system for informed decision making related to food securi-ty 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 Envi-ronment, 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 Pho-togrammetry, 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. Re-mote 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 fore-casting. 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 agricul-tural county of eastern qinghai province , China. Agricultural Science, 9(4): 116–127. https://doi.org/10.5539/jas.v9n4p116.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Abdul Walid Salik This is me 0000-0001-7836-2368

Ersin Karacabey 0000-0003-4166-1553

Publication Date May 30, 2019
Acceptance Date May 22, 2019
Published in Issue Year 2019 Volume: 5 Issue: 1

Cite

APA Salik, A. W., & Karacabey, E. (2019). Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(1), 49-62. https://doi.org/10.28979/comufbed.557792
AMA Salik AW, Karacabey E. Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi. May 2019;5(1):49-62. doi:10.28979/comufbed.557792
Chicago Salik, Abdul Walid, and Ersin Karacabey. “Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan”. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5, no. 1 (May 2019): 49-62. https://doi.org/10.28979/comufbed.557792.
EndNote Salik AW, Karacabey E (May 1, 2019) Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 1 49–62.
IEEE A. W. Salik and E. Karacabey, “Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan”, Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 5, no. 1, pp. 49–62, 2019, doi: 10.28979/comufbed.557792.
ISNAD Salik, Abdul Walid - Karacabey, Ersin. “Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan”. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5/1 (May 2019), 49-62. https://doi.org/10.28979/comufbed.557792.
JAMA Salik AW, Karacabey E. Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2019;5:49–62.
MLA Salik, Abdul Walid and Ersin Karacabey. “Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan”. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 5, no. 1, 2019, pp. 49-62, doi:10.28979/comufbed.557792.
Vancouver Salik AW, Karacabey E. Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2019;5(1):49-62.

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