• Reza Delirhasannia 1

  • Parisa Rezazade 2

  • Fatemeh Mikaeili 3

  • Saeed Samadianfard 4

  1. 1 Asoc. Professor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran
  2. 2 M.Sc., Alumnus, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
  3. 3 M.S student, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
  4. 4 Assist. Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

Abstract

The increase in population has intensified the process of land use in different parts of the world. The purpose of the present research was to investigate the changes in the use of irrigated and rainfed cultivation with an emphasis on the water needs of plants in the Sarab Plain located in East Azarbaijan Province, Iran. Based on this, in order to discover the changes created in the study area, the images of TM, ETM and OLI sensors of Landsat satellite in 1996, 2005 and 2021 were processed and classified after geometric and atmospheric corrections. Using the overall accuracy test and Kappa statistic, the accuracy of production maps was determined. The classification results showed that the Support vector machine method has a higher accuracy than the neural network method with an average total accuracy of 93.37% and an average kappa of 91.33%. During the years of 1996-2021, the area of irrigated cultivation increased by 24.2% and the area of rainfed cultivation decreased by 2.7%. Moreover, the volume of water required for the five major products of the region was calculated using Cropwat software and it was found that the volume of water consumption has increased in the period of 1996-2021.

Keywords

Subjects

 Irrigation

Ahmadi, S., & Hasani, H. (2023). Fusion of spectral and spatial information for agricultural crop classification in multi-temporal Sentinel images (Case Study: Qorveh County), Remote Sens.GIS, 15(1), 39- 61. DOI: 10.52547/gisj.15.1.39 [In Persian].
Akbari, M. (2013). Estimation of Cropped Area and Cropping Intensity using Remote Sensing Data, Water Res. Agri., 27)1(, 77- 88. DOI: 10.22092/jwra.2013.128813[In Persian].
Al-taani, A., Al-husban, Y., & Farhan, I. (2021). Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an Governorate, Jordan. The Egypt. J. Remote Sens. Space Sci., 24(1), 109- 117. DOI: 10.1016/j.ejrs.2020.01.001
Anonymous (2022). Annual agricultural statistics. Information Technology and Relations of the Ministry of Agriculture-Jihad. Available on: https://www.eaj.ir
Deilami, B. R., Ahmad, B. B., Saffar, M. R., & Umar, H. Z. (2015). Review of change detection techniques from remotely sensed images. Res. J. Appl. Sci. Eng. Technol., 10(2), 221-229.‏ DOI: 10.19026/rjaset.10
Dixon, B., & Candade, N. (2008). Multispectral land use classification using neural networks and support vector machines: one or the other. Int. J. Remote Sens, 29(4), 1185-1206. DOI: 10.1080/01431160701294661
Farzadmehr, J., & Tabaki Bajestani, K. (2018). Capability of Landsat 8 satellite images to estimate the area under cultivation of saffron (case study: city of Torbat Heydarieh), Saffron Agronom. Technol., 6(1), 49- 60. DOI: 10.22048/jsat.2017.48518.1194 [In Persian].
Haque, M. I., & Basak, R. (2017). Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. Egypt. J. Remote Sens. Space Sci., 20(2), 251-263. DOI: 10.1016/j.ejrs.2016.12.003
Jafari Sayadi, F. (2016). The use of remote sensing in estimating the area under cultivation and the amount of water consumed by rice. Dissertation of Master's course in Irrigation and Drainage, Sari University of Agricultural Sciences and Natural Resources, Gorgan.
Jahanbakhshi, A., & Kheiralipour, K. (2020). Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit. Food Sci. Nutrit., 8(7), 3346- 3352. DOI: 10.1002/fsn3.1614
Lillesand T. M, & Kiefer, R. W. (2000). Remote Sensing and image interpretation. Fourth Edition, John Wiley and Sons, Inc., New York, pp. 724.
Malmiran, H. (2001). Digital processing of satellite images. Translation and compilation, publications of the Geographical Organization of the Ministry of Defense and Armed Forces Support.
Meshesha, T. W., Tripathi, S. K., & Khare, D. (2016). Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa watershed northern central highland of Ethiopia. Model. Earth Syst. Environ., 2(4), 168. DOI: 10.1007/s40808-016-0233-4
Mohammadpour, P., Arjmandi, R., Hasani, A. H. & Ghoddousi, J. (2022). Classification and Assessment of the land use changes using Landsat satellite imagery (Case Study: Rey Plain). Human Environ., 20(3), 279- 297. DOI: 20.1001.1.15625532.1401.20.3.19.2 [In Persian].
Mousavi, S.M., Egdernezhad, A. & Sepehri, S. (2023). Determining the appropriate amount of irrigation water for wheat, barley, potatoes and sugar beet in chaharmahal and bakhtiari province using aquacrop and the concept of virtual water. Iran. J. Irrig. Drain., 1(17), 25- 41. DOI: 20.1001.1.20087942.1402.17.1.3.7 [In Persian].
Mosayebi, M., & Maleki, M. (2014). Change detection in land use using remote sensing data and GIS (Case study: Ardabil county). J. RS  GIS, 5(1), 75- 86. [In Persian].
Parsinejad, M., Raja, O., & Chehrenegar, B. (2022). Practical analysis of remote sensing estimations of water use for major crops throughout the Urmia Lake basin. Agri. Water Manage, 260, 107232. DOI: 10.1016/j.agwat.2021.107232
Yousefi, S., Tazeh, M., Mirezee, S., Moradi, H. R., & Tavanger, S. H. (2011). Comparsion of different classification algorithms in satellite imagery to produce land use maps (Case study: Noor city). J. RS.  GIS, 2(2), 15-25.