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Determination of Quantitative Spectral Characteristics and Coverage Distribution of Cotton in Antalya Region Using Satellite Data

Year 2007, Volume: 20 Issue: 1, 1 - 10, 01.06.2007

Abstract

The aim of this study was to determine the spectral characteristics and distribution of the cotton growing areas in the West Mediterranean Region of Turkey by means of the digital data from the Landsat7 ETM Satellite. In this context, spectral characteristics of cotton in the predefined 128 test areas, spectral distinction graphs, histograms, equal probability ellipses and spectral differentiation levels were formed. Optimum band combination was found to be 4th, 5th and 2nd bands for determination of the cotton growing areas in this region. The research area was classified by using maximum likelihood method with this band combination. In conclusion, the cotton-grown areas could be defined by with 92.9% confidence in the West Mediterranean Region using by satellite data.

References

  • Altınbaş, Ü., Kurucu, Y. ve Bolca, M., 2002. Büyük Menderes Ovasındaki Toprak Taksonomik Birimleri ile Pamuk Bitki Örtüsünün Uzaktan Algılama Tekniği Kullanılarak Belirlenmesi Üzerine Araştırmalar. Teknolojisi 4. Sempozyumu, Kahramanmaraş Sütçü İmam Üniversitesi, 20-22 Eylül, Kahramanmaraş. Tarımsal Bileşim
  • Anonymous, 1999-2003. Cotton: Review of the World Situation. International Cotton Advisory Committee.
  • Blazquez, C.H., Nigg, H.N., Hedley, L.E., Ramos, L.E. and Simpson, S.E., 1996. Field assessment of a fiber optic spectral reflectance system. HortTechnology, 6: 73-76.
  • Colaizzi, P.D., Barnes, E.M., Clarke, T.R., Choi C.Y. and Waller P.M., 1999. Using multi-spectral reflectance of cotton canopies and volumetric soil moisture measurements for irrigation management. Presented at the 1999 ASAE International Meeting, Toronto, Ont. Paper No. 991132.
  • Craig, J.C. and Shih, S.F., 1998. The spectral response of stress conditions in citrus trees: development of methodology. Soil and Crop Science Society of Florida, 57: 16-20.
  • Curran, P.J., Dungan, J.L., Macler, B.A., Plummer, S.E., and Peterson, D.L., 1992. Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration. Remote Sensing of Environment, 39: 153-166.
  • Dawbin, K. W., and Evans, J. C., 1988. Large area crop classification in new South Wales Australia using Landsat data. International Journal of Remote Sensing, 9(2): 295-301.
  • Erdas Field Guide, 1997., Erdas Field Guide Fourth Edition, Revised and Expanded. ERDAS, Inc. 2801 Buford Highway, NE Atlanta, Georgia 30329-2137 USA, www.erdas.com.
  • Gabrıel, B.S., John, G.L., Andy, D.W. and Sue, E.N., 2000. Using high spatial resolution multi spectral data to classify corn and soybean crops. Photogrammetric Engineering and Remote Sensing, 66(3): 319-327.
  • Gausman, H.W., 1982. Visible light reflectance, transmittance, and absorptance of differently pigmented cotton leaves. Remote Sensing of Environment, 13: 233-238.
  • Gitelson, A.A. and Merzlyak, M.N., 1997. Remote estimating of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18: 2691-2697.
  • Guoxiang, L. and Dawei, Z., 1990. Estimating ISBN. 0-408-04767-4 pp. 137-147
  • Guyot, G., 1990. Optical Properties of Vegetation Canopies. Applications of Remote Sensing in Agriculture. [Edited by] J.A. Clark, M.D. Steven ISBN. 0-408-04767-4 pp. 19-43
  • Jensen, J.R., 1986. Introductory Digital Image Processing A Remote Sensing Perspective. Prentice-Hall, Englewood Cliffs, New Jersey, USA.
  • Leblon, B., 1997. Soil and Vegetation Optical Properties. Volume 4 Applications in Remote Sensing. http://research.umbc.edu/~tbenja1/leblon/frame9. html
  • Lee, K.Y., Liew, S.C., Kwoh, L.K., Nakayama, M., 2001. Land cover classification using NASA/JPL Polarimeric Synthetic Aperture Radar (POLSAR) Data. 22nd Asian Conference on Remote Sensing, SINGAPORE. http://www.crisp.nus.edu.sg/~acrs2001/sessions. html
  • Li, D., Di, K. and Li, D., 2000. Land use classification of remote sensing image with GIS data based on spatial data mining techniques. International Society for Photogrammetry and Remote Sensing XIXth Congress International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3., Amsterdam, p. 238-245. www.gitc.nl
  • Lydia, S., Ustin, S. L., Roberts, D. A., Gamon, J.A. and Penuelas, J., 2000. Deriving water content of chaparral vegetation from AVIRIS data REMOTE SENS. ENVIRON. 74: 570–581 ÓElsevier Science Inc., 2000 655 Avenue of the Americas, New York, NY 10010 www.elsevier.com/locate/sna
  • Maas, S.S., 1998. Estimating cotton canopy ground cover from remotely sensed scene reflectance. Agronomy Journal, 90: 384-388.
  • Mahey, R.K., Singh, R., Sidhu, S.S. and Narang R.S., 1989. Remote sensing assessment of water stress effects on wheat. 22nd Asian Conference on Remote Sensing. SINGAPORE.
  • http://www.crisp.nus.edu.sg/~acrs1989/sessions.html
  • Moran, M.S., Inoue, Y., and Barners, E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61: 319-346.
  • Rees, W.G., 1990. Topics in Remote Sensing Physical Principles of Remote Sensing. Cambridge University Press. Cambridge-UK
  • Penuelas, J., Gamon, J.A., Freeden, A.L., Merino, J. and Field, C.B., 1994. Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote Sensing of Environment, 48: 135-146.
  • Poul, J.C., John, A.K. and Geoffrey, M.S., 1997. Remote sensing the biochemical composition of a slash pine canopy, IEEE Transactions on Geoscience and Remote Sensing, 35: 415-420.
  • Soil Survey Staff, 1999.A Basic System of Soil Classification for Making and Interpreting Soil Surveys. NRCS, Washington DC., Agricultural Handbook, 436 p.
  • Sönmez, N.K. ve Sarı, M., 1999. Sayısal Uydu Verileri ile Batı Akdeniz Bölgesinde Buğday Bitkisinin Spektral Özelliklerinin ve Alansal Dağılımının Belirlenmesi. Turkish Journal of Agriculture and Forestry, 23(4): 929-934.
  • Rudorf, B.F.T and Batista G.T., 1991. Wheat yield estimation of the farm level using TM Landsat and agrometeorological data. International Journal of Remote Sensing, 12: 2477-2484.
  • Shimazaki Y. and Tateishi R., 2001. Land cover mapping using spectral and temporal linear mixing model at Lake Baikal Region. 22nd Asian Conference on Remote Sensing. SINGAPORE.
  • http://www.crisp.nus.edu.sg/~acrs2001/sessions.html
  • Zewen, L., Dong, J., Denghuai, L. and Cuizhi, Z., 1990. The Estimation of cotton-growing areas by remote sensing. Asian Conference on Remote Sensing ACRS. http://www.gisdevelopment.net /aars/acrs/1990/P/pp0010pf.htm
  • Zarco-Tejada, P.J., Pushnik, J.C., Dobrowski, S. and Ustin, S.L., 2003. Steady-State chlorophyll-a fluorescence detection from canopy derivative reflectance and double-peak red-edge effect. Remote Sensing of Environment, 84: 283-294.

Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi

Year 2007, Volume: 20 Issue: 1, 1 - 10, 01.06.2007

Abstract

Bu çalışmada Landsat-7 ETM uydusunun sayısal verileri kullanılarak Batı Akdeniz Bölgesinde üretimi yapılan pamuk bitkisinin spektral özelliklerinin ve alansal dağılımının belirlenmesi amaçlanmıştır. Bu amaca yönelik olarak yer gerçekleri bilinen toplam 128 test alanında, pamuk ve diğer örtü tiplerinin spektral karakteristikleri, histogramlar, spektral yansıtım eğrileri, eş olasılık elipsleri ve spektral ayırım düzeyleri şeklinde oluşturulmuştur. Elde edilen veriler ışığında test alanındaki pamuk bitkisinin diğer örtü tipleri ile karışmadan ayırt edilebileceği en uygun band kombinasyonu 4., 5. ve 2. bandlar olarak belirlenmiştir. Belirlenen bu band kombinasyonu ile ‘maximum likelihood’ yöntemine göre araştırma alanı sınıflandırılmıştır. Sonuç olarak, Batı Akdeniz Bölgesinde üretimi yapılan pamuk bitkisinin alansal dağılımının, uydu verileri kullanılarak %92,9’lık bir doğrulukla saptanabileceği belirlenmiştir

References

  • Altınbaş, Ü., Kurucu, Y. ve Bolca, M., 2002. Büyük Menderes Ovasındaki Toprak Taksonomik Birimleri ile Pamuk Bitki Örtüsünün Uzaktan Algılama Tekniği Kullanılarak Belirlenmesi Üzerine Araştırmalar. Teknolojisi 4. Sempozyumu, Kahramanmaraş Sütçü İmam Üniversitesi, 20-22 Eylül, Kahramanmaraş. Tarımsal Bileşim
  • Anonymous, 1999-2003. Cotton: Review of the World Situation. International Cotton Advisory Committee.
  • Blazquez, C.H., Nigg, H.N., Hedley, L.E., Ramos, L.E. and Simpson, S.E., 1996. Field assessment of a fiber optic spectral reflectance system. HortTechnology, 6: 73-76.
  • Colaizzi, P.D., Barnes, E.M., Clarke, T.R., Choi C.Y. and Waller P.M., 1999. Using multi-spectral reflectance of cotton canopies and volumetric soil moisture measurements for irrigation management. Presented at the 1999 ASAE International Meeting, Toronto, Ont. Paper No. 991132.
  • Craig, J.C. and Shih, S.F., 1998. The spectral response of stress conditions in citrus trees: development of methodology. Soil and Crop Science Society of Florida, 57: 16-20.
  • Curran, P.J., Dungan, J.L., Macler, B.A., Plummer, S.E., and Peterson, D.L., 1992. Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration. Remote Sensing of Environment, 39: 153-166.
  • Dawbin, K. W., and Evans, J. C., 1988. Large area crop classification in new South Wales Australia using Landsat data. International Journal of Remote Sensing, 9(2): 295-301.
  • Erdas Field Guide, 1997., Erdas Field Guide Fourth Edition, Revised and Expanded. ERDAS, Inc. 2801 Buford Highway, NE Atlanta, Georgia 30329-2137 USA, www.erdas.com.
  • Gabrıel, B.S., John, G.L., Andy, D.W. and Sue, E.N., 2000. Using high spatial resolution multi spectral data to classify corn and soybean crops. Photogrammetric Engineering and Remote Sensing, 66(3): 319-327.
  • Gausman, H.W., 1982. Visible light reflectance, transmittance, and absorptance of differently pigmented cotton leaves. Remote Sensing of Environment, 13: 233-238.
  • Gitelson, A.A. and Merzlyak, M.N., 1997. Remote estimating of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18: 2691-2697.
  • Guoxiang, L. and Dawei, Z., 1990. Estimating ISBN. 0-408-04767-4 pp. 137-147
  • Guyot, G., 1990. Optical Properties of Vegetation Canopies. Applications of Remote Sensing in Agriculture. [Edited by] J.A. Clark, M.D. Steven ISBN. 0-408-04767-4 pp. 19-43
  • Jensen, J.R., 1986. Introductory Digital Image Processing A Remote Sensing Perspective. Prentice-Hall, Englewood Cliffs, New Jersey, USA.
  • Leblon, B., 1997. Soil and Vegetation Optical Properties. Volume 4 Applications in Remote Sensing. http://research.umbc.edu/~tbenja1/leblon/frame9. html
  • Lee, K.Y., Liew, S.C., Kwoh, L.K., Nakayama, M., 2001. Land cover classification using NASA/JPL Polarimeric Synthetic Aperture Radar (POLSAR) Data. 22nd Asian Conference on Remote Sensing, SINGAPORE. http://www.crisp.nus.edu.sg/~acrs2001/sessions. html
  • Li, D., Di, K. and Li, D., 2000. Land use classification of remote sensing image with GIS data based on spatial data mining techniques. International Society for Photogrammetry and Remote Sensing XIXth Congress International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3., Amsterdam, p. 238-245. www.gitc.nl
  • Lydia, S., Ustin, S. L., Roberts, D. A., Gamon, J.A. and Penuelas, J., 2000. Deriving water content of chaparral vegetation from AVIRIS data REMOTE SENS. ENVIRON. 74: 570–581 ÓElsevier Science Inc., 2000 655 Avenue of the Americas, New York, NY 10010 www.elsevier.com/locate/sna
  • Maas, S.S., 1998. Estimating cotton canopy ground cover from remotely sensed scene reflectance. Agronomy Journal, 90: 384-388.
  • Mahey, R.K., Singh, R., Sidhu, S.S. and Narang R.S., 1989. Remote sensing assessment of water stress effects on wheat. 22nd Asian Conference on Remote Sensing. SINGAPORE.
  • http://www.crisp.nus.edu.sg/~acrs1989/sessions.html
  • Moran, M.S., Inoue, Y., and Barners, E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61: 319-346.
  • Rees, W.G., 1990. Topics in Remote Sensing Physical Principles of Remote Sensing. Cambridge University Press. Cambridge-UK
  • Penuelas, J., Gamon, J.A., Freeden, A.L., Merino, J. and Field, C.B., 1994. Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote Sensing of Environment, 48: 135-146.
  • Poul, J.C., John, A.K. and Geoffrey, M.S., 1997. Remote sensing the biochemical composition of a slash pine canopy, IEEE Transactions on Geoscience and Remote Sensing, 35: 415-420.
  • Soil Survey Staff, 1999.A Basic System of Soil Classification for Making and Interpreting Soil Surveys. NRCS, Washington DC., Agricultural Handbook, 436 p.
  • Sönmez, N.K. ve Sarı, M., 1999. Sayısal Uydu Verileri ile Batı Akdeniz Bölgesinde Buğday Bitkisinin Spektral Özelliklerinin ve Alansal Dağılımının Belirlenmesi. Turkish Journal of Agriculture and Forestry, 23(4): 929-934.
  • Rudorf, B.F.T and Batista G.T., 1991. Wheat yield estimation of the farm level using TM Landsat and agrometeorological data. International Journal of Remote Sensing, 12: 2477-2484.
  • Shimazaki Y. and Tateishi R., 2001. Land cover mapping using spectral and temporal linear mixing model at Lake Baikal Region. 22nd Asian Conference on Remote Sensing. SINGAPORE.
  • http://www.crisp.nus.edu.sg/~acrs2001/sessions.html
  • Zewen, L., Dong, J., Denghuai, L. and Cuizhi, Z., 1990. The Estimation of cotton-growing areas by remote sensing. Asian Conference on Remote Sensing ACRS. http://www.gisdevelopment.net /aars/acrs/1990/P/pp0010pf.htm
  • Zarco-Tejada, P.J., Pushnik, J.C., Dobrowski, S. and Ustin, S.L., 2003. Steady-State chlorophyll-a fluorescence detection from canopy derivative reflectance and double-peak red-edge effect. Remote Sensing of Environment, 84: 283-294.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Agricultural Engineering
Journal Section Articles
Authors

M. Sarı This is me

N. K. Sönmez This is me

M. Yıldıran This is me

Publication Date June 1, 2007
Published in Issue Year 2007 Volume: 20 Issue: 1

Cite

APA Sarı, M., Sönmez, N. K., & Yıldıran, M. (2007). Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi. Akdeniz University Journal of the Faculty of Agriculture, 20(1), 1-10.
AMA Sarı M, Sönmez NK, Yıldıran M. Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi. Akdeniz University Journal of the Faculty of Agriculture. June 2007;20(1):1-10.
Chicago Sarı, M., N. K. Sönmez, and M. Yıldıran. “Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin Ve Alansal Dağılımının Uydu Verileri Ile Belirlenmesi”. Akdeniz University Journal of the Faculty of Agriculture 20, no. 1 (June 2007): 1-10.
EndNote Sarı M, Sönmez NK, Yıldıran M (June 1, 2007) Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi. Akdeniz University Journal of the Faculty of Agriculture 20 1 1–10.
IEEE M. Sarı, N. K. Sönmez, and M. Yıldıran, “Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi”, Akdeniz University Journal of the Faculty of Agriculture, vol. 20, no. 1, pp. 1–10, 2007.
ISNAD Sarı, M. et al. “Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin Ve Alansal Dağılımının Uydu Verileri Ile Belirlenmesi”. Akdeniz University Journal of the Faculty of Agriculture 20/1 (June 2007), 1-10.
JAMA Sarı M, Sönmez NK, Yıldıran M. Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi. Akdeniz University Journal of the Faculty of Agriculture. 2007;20:1–10.
MLA Sarı, M. et al. “Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin Ve Alansal Dağılımının Uydu Verileri Ile Belirlenmesi”. Akdeniz University Journal of the Faculty of Agriculture, vol. 20, no. 1, 2007, pp. 1-10.
Vancouver Sarı M, Sönmez NK, Yıldıran M. Pamuk Bitkisinin Kantitatif Yansıma Özelliklerinin ve Alansal Dağılımının Uydu Verileri ile Belirlenmesi. Akdeniz University Journal of the Faculty of Agriculture. 2007;20(1):1-10.