Eurasian Journal of Soil Science

Volume 1, Issue 2, Sep 2012, Pages 116 - 126

Stable URL: http://ejss.fess.org/10.18393/ejss.2012.2.116-126
Copyright © 2012 The authors and Federation of Eurasian Soil Science Societies



A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique

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Bolca ,M., Kurucu,Y., Altınbaş,Ü., Esetlili,M., Özen,F., 2012. A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique. Eurasian J Soil Sci 1(2):116 - 126.
Bolca ,M.,Kurucu,Y.Altınbaş,Ü.Esetlili,M.,& Özen,F. A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique Eurasian Journal of Soil Science, DOI : 10.18393/ejss.2012.2.116-126
Bolca ,M.,Kurucu,Y.Altınbaş,Ü.Esetlili,M., and ,Özen,F."A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique" Eurasian Journal of Soil Science, DOI : 10.18393/ejss.2012.2.116-126
Bolca ,M.,Kurucu,Y.Altınbaş,Ü.Esetlili,M., and ,Özen,F. "A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique" Eurasian Journal of Soil Science, DOI : 10.18393/ejss.2012.2.116-126
M,Bolca .Y,Kurucu.Ü,Altınbaş.MT,Esetlili.F,Özen "A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique" Eurasian J. Soil Sci, vol., no., pp., DOI : 10.18393/ejss.2012.2.116-126
Bolca ,Mustafa ;Kurucu,Yusuf ;Altınbaş,Ünal ;Esetlili,M. ;Özen,Fulsen A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique. Eurasian Journal of Soil Science,. DOI : 10.18393/ejss.2012.2.116-126

How to cite

Bolca , M., Kurucu, Y., Altınbaş, Ü., Esetlili, M., T. Özen, F., T.2012. A study on the determination of electromagnetic reflection values of agricultural crop pattern to improve accuracy of land use map by remote sensing technique. Eurasian J. Soil Sci. 1(2): 116 - 126.

Author information

Mustafa Bolca , Ege University Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100, İzmir, Turkey
Yusuf Kurucu , Ege University Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100, İzmir, Turkey
Ünal Altınbaş , Ege University Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100, İzmir, Turkey
M. Esetlili , Ege University Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100, İzmir, Turkey
Fulsen Özen , Ege University Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100, İzmir, Turkey

Publication information

Issue published online: 25 Sep 2012
Article first published online : 16 Sep 2012
Manuscript Accepted : 11 Sep 2012
Manuscript Received: 12 May 2012

Abstract

With this study, using remote sensing technique, a data base which covers data on the electromagnetic energy reflections of various kinds of plants has been formed with the purpose of determining crop patterns. A 1/5.000 scale cadastral map was used as topographic map for the purpose of using remote sensing technique more effectively and sensibly for such crops as cotton, maize and sun flower of which the agriculture is exercised widely in Torbalı township and in this context in all the Aegean Region. In the current study, August 2001 dated Landsat 7 satellite images of the region were interpreted and ground realities and satellite images of the agricultural crops with high economic value which are widely cultivated in the region were overlapped and their values of reflection were determined. Images thus obtained were overlapped with 1/5.000 cadastre maps and product varieties could be determined at the basis of large section of a map, plot and parcel. Separately collaboration with technical personnel from the Directorate of Torbalı Township Agriculture was achieved in field and lab studies, and by transferring the data obtained into their computers, tangible steps were taken in the direction of applying technology at the basis of the Township. As a result, an important and basic database was formed that could be used for the payout of incentive premiums to the local organization for various crops or that could render functionality to the implementation of Agricultural policies based on record system.

Keywords

Agricultural crop pattern, GIS, Landsat 7 ETM, Remote sensing

Corresponding author

References

Adams, M.L, Norvell, W.A., Philpot, W.D., 1999. Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing 20, 3663–3675.

Alexander, R., Millington, A.C., 2000. Vegetation mapping: From patch to planet. Chichester John Wiley and Sons Ltd.

Anonymous, 1997. The Population Information Program Center for Communication Programs. The Johns Hopkins School of Public Health, USA.

Aragon, R., Oesterheld, M., 2008. Linking vegetation heterogeneity and functional attributes of temperate grasslands through remote sensing. Applied Vegetation Science 11, 117−130.

Arkoç, V., 1996. General Information about Torbalı. Sepici Holding Company, İzmir.

Barbosa, P.M., Casterad, M.A., Herrero, J., 1996. Performance of several Landsat 5 TM image classification methods for crop extent estimates in an irrigation district. International Journal of Remote Sensing 17, 3665–3674.

Bastiaanssen, W.G.M., Molden, D.J., Makin, I.W., 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management 46, 137–155.

Casasnovas, J.M., Montero, A.M., Casterad, M.A., 2005. Mapping multi-year cropping patterns in small irrigation districts from time-series analysis of Landsat TM images. European Journal of Agronomy 23(2), 159-169.

Cerqueira Leite, P.B., Feitosa, R.Q., Formaggio, A.R., Pedro da Costa, G.A.O., Pakzad, K., Del’Arco Sanches, I., 2011. Hidden Markov Models for crop recognition in remote sensing image sequences. Pattern Recognition Letters 32, 19-26.

Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sensing of Environment 76, 349–359,

Delli, G., 1998. Estimation of Poplar Grove Areas and its yields. A.G.M-Ankara, I.O.A-Italy.

Dinçer, M., 1993. Geology of Çakırbeyli village-Torbalı. Deparment of Geology Engineering of Dokuz Eylül University, İzmir-Turkey.

Doğan, H.D., 2001. The remote sensing & GIS studies in agricultural areas. 1st Symposium of Global Space Activities & Potential in Turkey. Ankara.

Eastman, J.R., 2002. Idrisi32 Version I32.22 On-line help. Clark Labs, Clark University, Worcester, MA.

Ercan, M., Uluer, K., Selek, F., 2002. Determination poplar grove areas by using remote sensing data in Adapazarı and Düzce Plain. The research center of forest ministry of Turkey. Technical Bulletin No:192, İzmit-Turkey.

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahles, A.H, Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C., 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment 83, 287– 302.

Geomedia Professional Version 5.00, Copyright (1996-2002), Intergraph Corporation, Huntsville, Alabama 35894-0001

Hansen, M., Dubayah, R., DeFries, R., 1996. Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing 17, 1075– 1081.

Hansen, M. C., DeFries, R.S., Townshend, J.R.G., Sohlberg, R., 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing 21, 1331– 1364.

Hansen, P.M., Schjoerring, J.K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86, 42–55.

Huang, W., Wang, J., Wang, Z., Zhaochun, J., Liu, L., Wang, J., 2004. Inversion of foliar biochemical parameters at various physiological stages and grain quality indicators of winter wheat with canopy reflectance. International Journal of Remote Sensing 25, 2409–2419.

Image Analyst User Guide, 1997. Intergraph Corp., Manager Mapping Sciences Documentation, Mail Stop IW17 B2-Dept. 3620, One Madison Industrial Park, Huntsville, AL, USA.

Jakubauskas, M.E., Legates, D.R., Kastens, J.H., 2002. Crop identification using harmonic analysis oftime-series AVHRR NDVI data. Kansas Applied Remote Sensing (KARS) Program, 2335 Irving Hill Road, University of Kansas, Lawrence, KS 66045, USA.

Karagüllü, E., Sherill, D., Harrison, J., 1998. Management planning of information technology, the project of development GIS applications and design of database. The Research center of Remote sensing and GIS, Agricultural Ministry of Turkey, Ankara.

Mart´ın-Ord´onez,T., Casterad, M.A., Herrero, J., 2000. Three years of mapping irrigation water in the Flumen irrigation district, Spain. In: Casanova, J.L. (Ed.), Remote Sensing in the 21st Century: Economic and Environmental Applications. Balkema, Rotterdam, pp. 191–194.

Meteoroloji Bölge Müdürlüğü, 1990. Meteorological data reports about Torbalı Province. Poligon- İzmir, Turkey.

MicroStation 95 Version 05, 1995. Bentley Systems, Incorporated, 690 Pennsylvania Drive, Exton, PA 19341, USA.

Oldeland, J., Dorigo, W., Lieckfeld, L., Lucieer, A., Jürgens N., 2010. Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery. Remote Sensing of Environment 114, 1155–1166

Panigrahy, S., Sharma, S.A., 1997. Mapping of crop rotation using multidate Indian remote sensing satellite digital data. ISPRS Journal of Photogrammetry and Remote Sensing 52, 85–91.

Panigrahy, S., Chakraborty, M., 1998. An integrated approach for potato crop intensification using temporal remote sensing data. ISPRS Journal of Photogrammetry and Remote Sensing 53, 54–60.

Paoli, H., Volante, J., Fern´andez, D., Noe, Y., 2003. Analisis de la rotaci on de cultivos en la region NOA por sistemas de informaci on geografica: campana agricola 2000–2001. Informe de la campana agricola 2000–2001, Instituto Nacional de Tecnologıa Agropecuaria (INTA)—Estaci on Experimental Agropecuaria de Salta, Salta, Argentina, 7 pp.

Pinter, P.J., Ritchie, J.C., Hatfield, J.L., Hart, G.F., 2003. The agricultural research service’s remote sensing program: an example of interagency collaboration. Photogrammetric Engineering & Remote Sensing 69, 615–618.

Raupenstrauchk, J.D., Selige, T.M., 1998. Detection of crop rotation using satellite remote sensing for nutrient balance models and risk assessment. In: Gudmandsen (Ed.), Future Trends in Remote Sensing. Balkema, Rotterdam, pp. 139–143.

Ren, J., Chen, Z., Zhou, Q., Tang, H., 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation 10(4), 403–413.

Roy, P.S., Tomar, S., 2001. Landscape cover dynamics pattern in Meghalaya. Indian Institute of Remote Sensing (IIRS), NRSA, Dept. of Space, 4-KalidasRoad, P.B. 135, Dehra Dun, 248001 India.

Schuerger, A.C., Capelle, G.A., Di Benedetto, J.A., Mao, C., Thai, C.M., Evans, M.D., Richards, J.T., Blank, T.A., Stryjewski, E.C., 2003. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in Bahia grass (Paspalum notatum Flugge). Remote Sensing of Environment 84, 572–588.

Şimşek, C. , 1998. Hydrogeology of Torbalı province (MSc. Thesis). Dokuz Eylül University, Institute of Natural and Applied Science, Buca/İzmir.

Tuğaç, M.G., Torunlar, H., Peşkircioğlu, M., 2001. Creating agricultural database and Land use planning by using Geographical information system. Institute of Field Crops, Agriculture Ministry, Ankara-Turkey

Wardlow, B.D., Egbert, S., 2002. Discriminating cropping patterns for the U.S. Central Great Plains region using time-seriesMODIS 250-meter NDVI data – Preliminary results. Proceedings of the ISPRS Commission I: “Integrated remote sensing at the global, regional and local scale”, vol. XXXIX part I, 12 pp. Denver, CO, USA.

Wiegand, C.L., Richardson, A.J., Kanemasu, E.T., 1979. Leaf area index estimates for wheat from Landsat and their implications for evapotranspiration and crop modeling. Agronomy Journal 71, 336–342.

Zak, M.R., Cabido, M., 2002. Spatial patterns of the Chaco vegetation of central Argentina: Integration of remote sensing and phytosociology. Applied Vegetation Science 5, 213−226.

Abstract
With this study, using remote sensing technique, a data base which covers data on the electromagnetic energy reflections of various kinds of plants has been formed with the purpose of determining crop patterns. A 1/5.000 scale cadastral map was used as topographic map for the purpose of using remote sensing technique more effectively and sensibly for such crops as cotton, maize and sun flower of which the agriculture is exercised widely in Torbalı township and in this context in all the Aegean Region. In the current study, August 2001 dated Landsat 7 satellite images of the region were interpreted and ground realities and satellite images of the agricultural crops with high economic value which are widely cultivated in the region were overlapped and their values of reflection were determined. Images thus obtained were overlapped with 1/5.000 cadastre maps and product varieties could be determined at the basis of large section of a map, plot and parcel. Separately collaboration with technical personnel from the Directorate of Torbalı Township Agriculture was achieved in field and lab studies, and by transferring the data obtained into their computers, tangible steps were taken in the direction of applying technology at the basis of the Township. As a result, an important and basic database was formed that could be used for the payout of incentive premiums to the local organization for various crops or that could render functionality to the implementation of Agricultural policies based on record system. 

Keywords: Agricultural crop pattern, GIS, Landsat 7 ETM, Remote sensing

References

Adams, M.L, Norvell, W.A., Philpot, W.D., 1999. Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing 20, 3663–3675.

Alexander, R., Millington, A.C., 2000. Vegetation mapping: From patch to planet. Chichester John Wiley and Sons Ltd.

Anonymous, 1997. The Population Information Program Center for Communication Programs. The Johns Hopkins School of Public Health, USA.

Aragon, R., Oesterheld, M., 2008. Linking vegetation heterogeneity and functional attributes of temperate grasslands through remote sensing. Applied Vegetation Science 11, 117−130.

Arkoç, V., 1996. General Information about Torbalı. Sepici Holding Company, İzmir.

Barbosa, P.M., Casterad, M.A., Herrero, J., 1996. Performance of several Landsat 5 TM image classification methods for crop extent estimates in an irrigation district. International Journal of Remote Sensing 17, 3665–3674.

Bastiaanssen, W.G.M., Molden, D.J., Makin, I.W., 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management 46, 137–155.

Casasnovas, J.M., Montero, A.M., Casterad, M.A., 2005. Mapping multi-year cropping patterns in small irrigation districts from time-series analysis of Landsat TM images. European Journal of Agronomy 23(2), 159-169.

Cerqueira Leite, P.B., Feitosa, R.Q., Formaggio, A.R., Pedro da Costa, G.A.O., Pakzad, K., Del’Arco Sanches, I., 2011. Hidden Markov Models for crop recognition in remote sensing image sequences. Pattern Recognition Letters 32, 19-26.

Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sensing of Environment 76, 349–359,

Delli, G., 1998. Estimation of Poplar Grove Areas and its yields. A.G.M-Ankara, I.O.A-Italy.

Dinçer, M., 1993. Geology of Çakırbeyli village-Torbalı. Deparment of Geology Engineering of Dokuz Eylül University, İzmir-Turkey.

Doğan, H.D., 2001. The remote sensing & GIS studies in agricultural areas. 1st Symposium of Global Space Activities & Potential in Turkey. Ankara.

Eastman, J.R., 2002. Idrisi32 Version I32.22 On-line help. Clark Labs, Clark University, Worcester, MA.

Ercan, M., Uluer, K., Selek, F., 2002. Determination poplar grove areas by using remote sensing data in Adapazarı and Düzce Plain. The research center of forest ministry of Turkey. Technical Bulletin No:192, İzmit-Turkey.

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahles, A.H, Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C., 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment 83, 287– 302.

Geomedia Professional Version 5.00, Copyright (1996-2002), Intergraph Corporation, Huntsville, Alabama 35894-0001

Hansen, M., Dubayah, R., DeFries, R., 1996. Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing 17, 1075– 1081.

Hansen, M. C., DeFries, R.S., Townshend, J.R.G., Sohlberg, R., 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing 21, 1331– 1364.

Hansen, P.M., Schjoerring, J.K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86, 42–55.

Huang, W., Wang, J., Wang, Z., Zhaochun, J., Liu, L., Wang, J., 2004. Inversion of foliar biochemical parameters at various physiological stages and grain quality indicators of winter wheat with canopy reflectance. International Journal of Remote Sensing 25, 2409–2419.

Image Analyst User Guide, 1997. Intergraph Corp., Manager Mapping Sciences Documentation, Mail Stop IW17 B2-Dept. 3620, One Madison Industrial Park, Huntsville, AL, USA.

Jakubauskas, M.E., Legates, D.R., Kastens, J.H., 2002. Crop identification using harmonic analysis oftime-series AVHRR NDVI data. Kansas Applied Remote Sensing (KARS) Program, 2335 Irving Hill Road, University of Kansas, Lawrence, KS 66045, USA.

Karagüllü, E., Sherill, D., Harrison, J., 1998. Management planning of information technology, the project of development GIS applications and design of database. The Research center of Remote sensing and GIS, Agricultural Ministry of Turkey, Ankara.

Mart´ın-Ord´onez,T., Casterad, M.A., Herrero, J., 2000. Three years of mapping irrigation water in the Flumen irrigation district, Spain. In: Casanova, J.L. (Ed.), Remote Sensing in the 21st Century: Economic and Environmental Applications. Balkema, Rotterdam, pp. 191–194.

Meteoroloji Bölge Müdürlüğü, 1990. Meteorological data reports about Torbalı Province. Poligon- İzmir, Turkey.

MicroStation 95 Version 05, 1995. Bentley Systems, Incorporated, 690 Pennsylvania Drive, Exton, PA 19341, USA.

Oldeland, J., Dorigo, W., Lieckfeld, L., Lucieer, A., Jürgens N., 2010. Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery. Remote Sensing of Environment 114, 1155–1166

Panigrahy, S., Sharma, S.A., 1997. Mapping of crop rotation using multidate Indian remote sensing satellite digital data. ISPRS Journal of Photogrammetry and Remote Sensing 52, 85–91.

Panigrahy, S., Chakraborty, M., 1998. An integrated approach for potato crop intensification using temporal remote sensing data. ISPRS Journal of Photogrammetry and Remote Sensing 53, 54–60.

Paoli, H., Volante, J., Fern´andez, D., Noe, Y., 2003. Analisis de la rotaci on de cultivos en la region NOA por sistemas de informaci on geografica: campana agricola 2000–2001. Informe de la campana agricola 2000–2001, Instituto Nacional de Tecnologıa Agropecuaria (INTA)—Estaci on Experimental Agropecuaria de Salta, Salta, Argentina, 7 pp.

Pinter, P.J., Ritchie, J.C., Hatfield, J.L., Hart, G.F., 2003. The agricultural research service’s remote sensing program: an example of interagency collaboration. Photogrammetric Engineering & Remote Sensing 69, 615–618.

Raupenstrauchk, J.D., Selige, T.M., 1998. Detection of crop rotation using satellite remote sensing for nutrient balance models and risk assessment. In: Gudmandsen (Ed.), Future Trends in Remote Sensing. Balkema, Rotterdam, pp. 139–143.

Ren, J., Chen, Z., Zhou, Q., Tang, H., 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation 10(4), 403–413.

Roy, P.S., Tomar, S., 2001. Landscape cover dynamics pattern in Meghalaya. Indian Institute of Remote Sensing (IIRS), NRSA, Dept. of Space, 4-KalidasRoad, P.B. 135, Dehra Dun, 248001 India.

Schuerger, A.C., Capelle, G.A., Di Benedetto, J.A., Mao, C., Thai, C.M., Evans, M.D., Richards, J.T., Blank, T.A., Stryjewski, E.C., 2003. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in Bahia grass (Paspalum notatum Flugge). Remote Sensing of Environment 84, 572–588.

Şimşek, C. , 1998. Hydrogeology of Torbalı province (MSc. Thesis). Dokuz Eylül University, Institute of Natural and Applied Science, Buca/İzmir.

Tuğaç, M.G., Torunlar, H., Peşkircioğlu, M., 2001. Creating agricultural database and Land use planning by using Geographical information system. Institute of Field Crops, Agriculture Ministry, Ankara-Turkey

Wardlow, B.D., Egbert, S., 2002. Discriminating cropping patterns for the U.S. Central Great Plains region using time-seriesMODIS 250-meter NDVI data – Preliminary results. Proceedings of the ISPRS Commission I: “Integrated remote sensing at the global, regional and local scale”, vol. XXXIX part I, 12 pp. Denver, CO, USA.

Wiegand, C.L., Richardson, A.J., Kanemasu, E.T., 1979. Leaf area index estimates for wheat from Landsat and their implications for evapotranspiration and crop modeling. Agronomy Journal 71, 336–342.

Zak, M.R., Cabido, M., 2002. Spatial patterns of the Chaco vegetation of central Argentina: Integration of remote sensing and phytosociology. Applied Vegetation Science 5, 213−226.



Eurasian Journal of Soil Science