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Mapping Wheat Growing Areas of Turkey by Integrating Multi-Temporal NDVI Data and Official Crop Statistics

Yıl 2017, Cilt: 26 Sayı: 1, 11 - 23, 29.06.2017
https://doi.org/10.21566/tarbitderg.323560

Öz



Wheat is
the most widely cultivated crop in the world providing critical food source of
most countries. It exceeds most of the grain crops in acreage and production because
of its ability to grow in wide range of climatic and geographic conditions. Timely
and reliable information on wheat acreages is essential for government services
in order to formulate their policies for planning of agricultural production
and monitoring their food supply.
Traditionally, agricultural
statistics is considered as the main source of such information
. Unfortunately, existing
statistical data of wheat acreages of Turkey, mostly dependent on farmers’
declarations, does not provide spatial information of where this crop
specifically is grown.
Satellite remote sensing technology can enable
the acquisition of such information indirectly with the use of ancillary data
of crop statistics. This study aims to determine wheat cultivation areas of
Turkey as percentage per unit area in a crop map by integrating time series satellite
NDVI imagery with the official crop statistics through regression analysis. The
regression results indicated that satellite data explained 95.8% of the
variability in official wheat crop statistics and actual wheat cropping areas were
significantly related to NDVI-based wheat classes. Validation of the produced wheat
map showed that there was good agreement between actual wheat fractions and
estimated NDVI-based wheat fractions explaining approximately 69% (Adj. R2) of the
total variability between them. This study suggests use of the methodology
employed here to governing bodies that need to identify and to map current wheat
cropping areas.




Kaynakça

  • Anonymous, 2002. http://pekko.geog.umd. edu/usda/test. (Date of Access 15.01.2016)
  • Anonymous, 2015. http://faostat3.fao.org. (Date of Access, 14.02.2016)
  • Anonymous, 2016. http://www.gadm.org. (Date of Access, 17.01.2016)
  • Bossard M., Feranec J., Otahel J., 2000. CORINE Land Cover technical guide-addendum 2000, Technical report No 40, European Environment Agency, Copenhagen
  • Brand S. and Malthus T.J., 2004. Evaluation of AVHRR NDVI for monitoring intra-annual and inter-annual vegetation dynamics in a cloudy environment (Scotland, UK). Proceedings of the XXth ISPRS Congress, Commission-II. Istanbul, Turkey, July 12–23, 2004 pp. 477-482
  • Carroll M., Townshend J., Dimiceli C., Noojipady P., Sohlberg R., 2009. A New Global Raster Water Mask at 250 Meter Resolution. International Journal of Digital Earth. (Volume 2 number 4)
  • Carroll M.L., Dimiceli C.M., Sohlberg R.A., Townshend J.R.G., 2004. 250m MODIS Normalized Difference Vegetation Index, 250ndvi28920033435, Collection 4, University of Maryland, College Park, Maryland, Day 289, 2003
  • De Bie C.A.J.M., Khan M.R., Toxopeus A.G., Venus V., Skidmore A.K., 2008. Hypertemporal image analysis for crop mapping and change detection. Proceedings of the XXI congress: Silk road for information from imagery: The International Society for Photogrammetry and Remote Sensing, 3-11 July, Beijing, China. Comm. VII, WG VII/5. Beijing: ISPRS, 2008. pp. 803-812
  • De Bie C.A.J.M., Khan M.R., Smakhtin V.U., Venus V., Weir M.J.C., Smaling E.M.A., 2011. Analysis of multi - temporal SPOT NDVI images for small - scale land - use mapping. International Journal of Remote Sensing, 32 (21):6673-6693 Campbell J.B. 1996. Introduction to Remote Sensing. 2nd edition. Guilford Press, New York, 622 p
  • Goward S.N. and Huemmrich K.F., 1992. Vegetation canopy PAR absorptance and the Normalized Difference Vegetation Index: an assessment using the SAIL model. Remote Sensing of Environment, 39: 119–140
  • Groten S.M.E. and Ocatre R., 2002. Monitoring the length of the growing season with NOAA. International Journal of Remote Sensing, 23(14): 1271-1318
  • Gumma M.K., Uppala D., Mohammed I.A., Whitbread A.M., Mohammed I.R., 2015. Mapping Direct Seeded Rice in Raichur District of Karnataka, India. Photogrammetric Engineering and Remote Sensing, 81(11):873-880
  • Guo W.Q., Yang T.B., Dai J.G., Shi L., Lu Z.Y., 2008. Vegetation cover changes and their relationship to climate variation in the source region of the Yellow River, China, 1990-2000. International Journal of Remote Sensing, 29, 2085-2103
  • Hill M.J. and Donald G.E., 2003. Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series. Remote Sensing of Environment, 84: 367-384
  • Jansen L.J.M. and Di Gregorio A., 2003. Land-use data collection using the ‘‘land cover classification system’’ (LCCS): results from a case study in Kenya. Land Use Policy, 20 (2):131–148
  • Jönsson P. and Eklundh L., 2004. TIMESAT – a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30: 833–845
  • Kim S.R., Prasad A.K., El-Askary H., Lee W.K., Kwak D.A., Lee S.H., Kafatos M., 2014. Application of the Savitzky-Golay Filter to Land Cover Classification Using Temporal MODIS Vegetation Indices. Photogrammetric Engineering and Remote Sensing, 7(11):675-685
  • Khan M.R., De Bie C.A.J.M., Van Keulen H., Smaling E.M.A., Real R., 2010. Disaggregating and mapping crop statistics using hypertemporal remote sensing. International Journal of Applied Earth Observation and Geoinformation, 12, 36-46
  • Nguyen T.T.H., De Bie C.A.J.M., Amjad A., Smaling E.M., Chu T.H., 2012. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam through hyper-temporal SPOT NDVI image analysis. International Journal of Remote Sensing, 33 (2):415–434
  • Oetter D.R., Cohen W.B., Berterretche M., Maiersperger T.K., Kennedy R.E., 2000. Land cover mapping in an agricultural setting using multi-seasonal Thematic Mapper data. Remote Sensing of Environment, 76, 139–155 Reed B.C., Brown J.F., Vander Zee D., Loveland T.R., Merchant J.W., Ohlen D.O., 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5):703–714
  • Roerink G.J., Menenti M., Verhoef W., 2000. Reconstructing cloud free NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9): 1911–1917
  • Shao Y., Lunetta R.S., Ediriwickrema J., Liames J.S., 2010. Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data. Photogrammetric Engineering and Remote Sensing, 76(1):73-84
  • Swets D.L., Reed B.C., Rowland J.R., Marko S.E., 1999. A weighted least-squares approach to temporal NDVI smoothing. Proceedings of the ASPRS Annual Conference, 21-27 May 1999; Portland, Oregon, USA, pp.526-536
  • Tucker C.J. and Sellers P.J., 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11):1395–1416
  • Unal E., Mermer A., Yıldız H., 2014. Assessment of rangeland vegetation condition from time series NDVI data. Journal of Field Crops Central Research Institute, 23 (1):14-21
  • Wardlow B.D. and 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
  • Verberien S., Eerens H., Piccard I., Bauwens I., Van Orshoven J., 2008. Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium. International Journal of Applied Earth Observation and Geoinformation, 10, 486–497
  • Viovy N., Arino O., Belward A., 1992. The best index slope extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing, 13(8):1585–1590
  • Wood S., Sebastian K., Scherr S.J., 2000. Pilot Analysis of Global Ecosystems—Agroecosystems. World Resources Institute, Washington, DC, USA: International Food Policy Research Institute, p.125

Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması

Yıl 2017, Cilt: 26 Sayı: 1, 11 - 23, 29.06.2017
https://doi.org/10.21566/tarbitderg.323560

Öz



Buğday dünya genelinde tarımı en yaygın
yapılan tarım ürünüdür ve birçok ülke için ana besin kaynağı olarak
görülmektedir. Geniş iklimsel ve coğrafi koşullar altında yetişebilme
özelliğinden dolayı, buğdayın üretim miktarı ve yetiştirme alanı diğer tahıl
ürünlerinden daha fazladır. Buğday tarımı yapılan alanlarla ilgili olarak
güncel ve güvenilir bilgiye erişim, ülkelerin tarımsal üretimlerini planlamaya
ve üretim alanlarını gözlemlemeye yönelik politikaların geliştirilmesinde büyük
önem arz etmektedir. Tarım istatistikleri geleneksel olarak bu tür bilgilerin
ana kaynağı olarak öngörülse de, ülkemizde olduğu gibi çiftçi beyanına bağlı tarım
istatistikleri maalesef hangi tarım ürününün hangi mekânsal konumda
yetiştirildiği bilgisini sunmamaktadır. Uzaktan algılama teknolojisi, tarım
istatistiklerini yardımcı veri kaynağı şeklinde kullanarak bu tür bilgiyi
üretmemize imkân sağlamaktadır. Bu çalışmanın amacı, zaman serisi NDVI verileri
ve resmi tarım istatistiklerini regresyon analizi ile entegre ederek Türkiye
buğday alanlarını birim alanda yüzde değer olarak belirlemek ve
haritalandırmaktır. Regresyon analizi sonuçlarına göre; NDVI uydu verisi, resmi
buğday istatistiklerindeki değişkenliğin %95.8’ni açıklayabilmektedir ve gerçek
buğday parselleri istatistiksel olarak NDVI verisinden üretilen buğday
sınıfları ile önemli derecede ilişkilidir. Regresyon modeli ile elde edilen
buğday haritasının doğruluk analizine göre, gerçek buğday alan yüzdeleri ile
NDVI verisinden üretilen buğday alan yüzdeleri arasındaki ilişki %69 R2 düzeyindedir. Bu
çalışmada kullanılan yöntem, buğday üretimi yapılan parselleri belirlemek isteyen
kurumlar için tavsiye edilebilir niteliktedir.




Kaynakça

  • Anonymous, 2002. http://pekko.geog.umd. edu/usda/test. (Date of Access 15.01.2016)
  • Anonymous, 2015. http://faostat3.fao.org. (Date of Access, 14.02.2016)
  • Anonymous, 2016. http://www.gadm.org. (Date of Access, 17.01.2016)
  • Bossard M., Feranec J., Otahel J., 2000. CORINE Land Cover technical guide-addendum 2000, Technical report No 40, European Environment Agency, Copenhagen
  • Brand S. and Malthus T.J., 2004. Evaluation of AVHRR NDVI for monitoring intra-annual and inter-annual vegetation dynamics in a cloudy environment (Scotland, UK). Proceedings of the XXth ISPRS Congress, Commission-II. Istanbul, Turkey, July 12–23, 2004 pp. 477-482
  • Carroll M., Townshend J., Dimiceli C., Noojipady P., Sohlberg R., 2009. A New Global Raster Water Mask at 250 Meter Resolution. International Journal of Digital Earth. (Volume 2 number 4)
  • Carroll M.L., Dimiceli C.M., Sohlberg R.A., Townshend J.R.G., 2004. 250m MODIS Normalized Difference Vegetation Index, 250ndvi28920033435, Collection 4, University of Maryland, College Park, Maryland, Day 289, 2003
  • De Bie C.A.J.M., Khan M.R., Toxopeus A.G., Venus V., Skidmore A.K., 2008. Hypertemporal image analysis for crop mapping and change detection. Proceedings of the XXI congress: Silk road for information from imagery: The International Society for Photogrammetry and Remote Sensing, 3-11 July, Beijing, China. Comm. VII, WG VII/5. Beijing: ISPRS, 2008. pp. 803-812
  • De Bie C.A.J.M., Khan M.R., Smakhtin V.U., Venus V., Weir M.J.C., Smaling E.M.A., 2011. Analysis of multi - temporal SPOT NDVI images for small - scale land - use mapping. International Journal of Remote Sensing, 32 (21):6673-6693 Campbell J.B. 1996. Introduction to Remote Sensing. 2nd edition. Guilford Press, New York, 622 p
  • Goward S.N. and Huemmrich K.F., 1992. Vegetation canopy PAR absorptance and the Normalized Difference Vegetation Index: an assessment using the SAIL model. Remote Sensing of Environment, 39: 119–140
  • Groten S.M.E. and Ocatre R., 2002. Monitoring the length of the growing season with NOAA. International Journal of Remote Sensing, 23(14): 1271-1318
  • Gumma M.K., Uppala D., Mohammed I.A., Whitbread A.M., Mohammed I.R., 2015. Mapping Direct Seeded Rice in Raichur District of Karnataka, India. Photogrammetric Engineering and Remote Sensing, 81(11):873-880
  • Guo W.Q., Yang T.B., Dai J.G., Shi L., Lu Z.Y., 2008. Vegetation cover changes and their relationship to climate variation in the source region of the Yellow River, China, 1990-2000. International Journal of Remote Sensing, 29, 2085-2103
  • Hill M.J. and Donald G.E., 2003. Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series. Remote Sensing of Environment, 84: 367-384
  • Jansen L.J.M. and Di Gregorio A., 2003. Land-use data collection using the ‘‘land cover classification system’’ (LCCS): results from a case study in Kenya. Land Use Policy, 20 (2):131–148
  • Jönsson P. and Eklundh L., 2004. TIMESAT – a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30: 833–845
  • Kim S.R., Prasad A.K., El-Askary H., Lee W.K., Kwak D.A., Lee S.H., Kafatos M., 2014. Application of the Savitzky-Golay Filter to Land Cover Classification Using Temporal MODIS Vegetation Indices. Photogrammetric Engineering and Remote Sensing, 7(11):675-685
  • Khan M.R., De Bie C.A.J.M., Van Keulen H., Smaling E.M.A., Real R., 2010. Disaggregating and mapping crop statistics using hypertemporal remote sensing. International Journal of Applied Earth Observation and Geoinformation, 12, 36-46
  • Nguyen T.T.H., De Bie C.A.J.M., Amjad A., Smaling E.M., Chu T.H., 2012. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam through hyper-temporal SPOT NDVI image analysis. International Journal of Remote Sensing, 33 (2):415–434
  • Oetter D.R., Cohen W.B., Berterretche M., Maiersperger T.K., Kennedy R.E., 2000. Land cover mapping in an agricultural setting using multi-seasonal Thematic Mapper data. Remote Sensing of Environment, 76, 139–155 Reed B.C., Brown J.F., Vander Zee D., Loveland T.R., Merchant J.W., Ohlen D.O., 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5):703–714
  • Roerink G.J., Menenti M., Verhoef W., 2000. Reconstructing cloud free NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9): 1911–1917
  • Shao Y., Lunetta R.S., Ediriwickrema J., Liames J.S., 2010. Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data. Photogrammetric Engineering and Remote Sensing, 76(1):73-84
  • Swets D.L., Reed B.C., Rowland J.R., Marko S.E., 1999. A weighted least-squares approach to temporal NDVI smoothing. Proceedings of the ASPRS Annual Conference, 21-27 May 1999; Portland, Oregon, USA, pp.526-536
  • Tucker C.J. and Sellers P.J., 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing, 7(11):1395–1416
  • Unal E., Mermer A., Yıldız H., 2014. Assessment of rangeland vegetation condition from time series NDVI data. Journal of Field Crops Central Research Institute, 23 (1):14-21
  • Wardlow B.D. and 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
  • Verberien S., Eerens H., Piccard I., Bauwens I., Van Orshoven J., 2008. Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium. International Journal of Applied Earth Observation and Geoinformation, 10, 486–497
  • Viovy N., Arino O., Belward A., 1992. The best index slope extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing, 13(8):1585–1590
  • Wood S., Sebastian K., Scherr S.J., 2000. Pilot Analysis of Global Ecosystems—Agroecosystems. World Resources Institute, Washington, DC, USA: International Food Policy Research Institute, p.125
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Ediz Ünal Bu kişi benim

De Bie Kees Bu kişi benim

Yayımlanma Tarihi 29 Haziran 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 26 Sayı: 1

Kaynak Göster

APA Ünal, E., & Kees, D. B. (2017). Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, 26(1), 11-23. https://doi.org/10.21566/tarbitderg.323560
AMA Ünal E, Kees DB. Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi. Haziran 2017;26(1):11-23. doi:10.21566/tarbitderg.323560
Chicago Ünal, Ediz, ve De Bie Kees. “Zaman Serisi NDVI Verileri Ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması”. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi 26, sy. 1 (Haziran 2017): 11-23. https://doi.org/10.21566/tarbitderg.323560.
EndNote Ünal E, Kees DB (01 Haziran 2017) Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi 26 1 11–23.
IEEE E. Ünal ve D. B. Kees, “Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması”, Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, c. 26, sy. 1, ss. 11–23, 2017, doi: 10.21566/tarbitderg.323560.
ISNAD Ünal, Ediz - Kees, De Bie. “Zaman Serisi NDVI Verileri Ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması”. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi 26/1 (Haziran 2017), 11-23. https://doi.org/10.21566/tarbitderg.323560.
JAMA Ünal E, Kees DB. Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi. 2017;26:11–23.
MLA Ünal, Ediz ve De Bie Kees. “Zaman Serisi NDVI Verileri Ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması”. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, c. 26, sy. 1, 2017, ss. 11-23, doi:10.21566/tarbitderg.323560.
Vancouver Ünal E, Kees DB. Zaman Serisi NDVI Verileri ve Resmi Tarım İstatistikleri Kullanarak Türkiye Buğday Alanlarının Haritalandırılması. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi. 2017;26(1):11-23.