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Land cover Classification and Crop Estimation using Remote Sensing Techniques

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Najam Aziz, Nasru Minallah, Ahmad Junaid
10.5120/ijca2016912259

Najam Aziz, Nasru Minallah and Ahmad Junaid. Land cover Classification and Crop Estimation using Remote Sensing Techniques. International Journal of Computer Applications 155(2):1-6, December 2016. BibTeX

@article{10.5120/ijca2016912259,
	author = {Najam Aziz and Nasru Minallah and Ahmad Junaid},
	title = {Land cover Classification and Crop Estimation using Remote Sensing Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {2},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume155/number2/26574-2016912259},
	doi = {10.5120/ijca2016912259},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Due to the rapid growth of population the food need also increases which is the center of focus for various researchers and governments. For this purpose crop information system has been made, the aim of crop information system is to monitor the crop health and estimate the needs for the next four to five years. Geo graphic information system plays an important role in crop estimation and identification. GIS uses remote sensing technique to identify various crops and their yield. In this paper novel approaches are used for the identification and estimation of tobacco. SPOT 5 imagery having resolution of 2.5m is used for the estimation and identification of tobacco. For post processing, statistics like kappa coefficients and Receiver operating curves are utilized. This study mainly focuses on the Mardan region in KPK Pakistan. Classification is done for four categories, these categories are then classified using state of the art machine learning classifiers and the accuracy of these various classifiers has been compared.

References

  1. Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1), 26-33.
  2. Abtew, W., & Melesse, A. (2013). Crop Yield Estimation Using Remote Sensing and Surface Energy Flux Model. In Evaporation and Evapotranspiration (pp. 161-175). Springer Netherlands.
  3. Ferencz, C., Bognar, P., Lichtenberger, J., Hamar, D., Tarcsai†, G., Timar, G., ... & Ferencz, O. E. (2004). Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25(20), 4113-4149.
  4. Rodriguez, J. C., Duchemin, B., Watts, C. J., Hadria, R., Garatuza, J., Chehbouni, A., ... & Er-Raki, S. (2003, July). Wheat yields estimation using remote sensing and crop modeling in Yaqui Valley in Mexico. In Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International (Vol. 4, pp. 2221-2223). IEEE.
  5. Kamble, B., Kilic, A., & Hubbard, K. (2013). Estimating crop coefficients using remote sensing-based vegetation index. Remote Sensing, 5(4), 1588-1602.
  6. Singh, R. K., & Irmak, A. (2009). Estimation of crop coefficients using satellite remote sensing. Journal of irrigation and drainage engineering, 135(5), 597-608.
  7. Svotwa, E., Masuka, A. J., Maasdorp, B., Murwira, A., & Shamudzarira, M. (2013). Remote sensing applications in tobacco yield estimation and the recommended research in Zimbabwe. ISRN Agronomy, 2013.
  8. Baez-Gonzalez, A. D., Kiniry, J. R., Maas, S. J., Tiscareno, M. L., Macias, C. J. J. L., Mendoza, J. L., ... & Manjarrez, J. R. (2005). Large-area maize yield forecasting using leaf area index based yield model. Agronomy Journal, 97(2), 418-425.
  9. Gausman, H. W., Escobar, D. E., & Rodriguez, R. R. (1973). Discriminating among plant nutrient deficiencies with reflectance measurements. Annual Review of Crop Physiology, 244.
  10. Hunsaker, D. J., Pinter Jr, P. J., & Kimball, B. A. (2005). Wheat basal crop coefficients determined by normalized difference vegetation index. Irrigation Science, 24(1), 1-14.
  11. Bastiaanssen, W. G. M. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of hydrology, 229(1), 87-100.
  12. Irmak, S. (2010). Nebraska water and energy flux measurement, modeling, and research network (NEBFLUX). Transactions of the ASABE, 53(4), 1097-1115.
  13. Allen, R. G., Pereira, L. S., Howell, T. A., & Jensen, M. E. (2011). Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agricultural Water Management, 98(6), 899-920.
  14. Bausch, W. C., & Neale, C. M. U. (1990). Spectral inputs improve corn crop coefficients and irrigation scheduling. Transactions of the ASAE, 32(6), 1901-1908.
  15. Jayanthi, H., Neale, C. M., & Wright, J. L. (2001). Seasonal evapotranspiration estimation using canopy reflectance: a case study involving pink beans. IAHS PUBLICATION, 302-305..
  16. Irmak, A., Ratcliffe, I., Ranade, P., Hubbard, K., Singh, R. K., Kamble, B., & Kjaersgaard, J. (2011). Estimation of land surface evapotranspiration with a satellite remote sensing procedure.
  17. Benedetti, R., & Rossini, P. (1993). On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing of Environment, 45(3), 311-326.
  18. Choudhury, B. J., Ahmed, N. U., Idso, S. B., Reginato, R. J., & Daughtry, C. S. (1994). Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote sensing of environment, 50(1), 1-17.
  19. Irmak, A., & Kamble, B. (2009). Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation. Irrigation science, 28(1), 101-112.
  20. Hubbard, K. G., & Siva Kumar, M. V. K. (2001). Automated weather stations for applications in agriculture and water resources management.
  21. Allen, R. G., Clemmens, A. J., Burt, C. M., Solomon, K., & O’Halloran, T. (2005). Prediction accuracy for projectwide evapotranspiration using crop coefficients and reference evapotranspiration. Journal of Irrigation and Drainage Engineering, 131(1), 24-36.
  22. Banerjee, S., Chatterjee, S., Sarkar, S., & Jena, S. (2016). Projecting Future Crop Evapotranspiration and Irrigation Requirement of Potato in Lower Gangetic Plains of India using the CROPWAT 8.0 Model. Potato Research, 1-15.
  23. 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(2), 85-91.
  24. Lisita, A., Sano, E. E., & Durieux, L. (2013). Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data. International journal of remote sensing, 34(15), 5409-5428.
  25. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation. John Wiley & Sons.

Keywords

Remote sensing, Receiver operating curves, kappa coefficient, SVM, Maximum likelihood, Minimum distance, Mahalanobis distance.