with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article

Authors

1 M.Sc. Student, Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

2 Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

Abstract

Introduction
Rice is the fourth most consumed grain worldwide. In recent years, monitoring the area under rice cultivation; as a strategic crop in Gilan province has become more important because of the uncontrolled migration of residents of the southern provinces to it. Remote sensing is one of the practical tools to study the trend of changes in the area under cultivation of agricultural and horticultural products on a large scale and in a short time. This technique can help policymakers to make true and timely decisions. The aim of this study is to estimate the area under rice cultivation in Kiashahr county of Gilan province.
Materials and Methods
The images of the TM sensor of Landsat 5 satellite and the OLI sensor of Landsat 8 satellite were used to prepare land use maps. First, geometric and atmospheric corrections were made to the images. Then, supervised classification using the maximum likelihood algorithm was used to prepare land use maps for each year. Seven main classes/land covers, based on the available data of the area were determined: rice-land, semi-dense forest, sparse forest, built-up area (towns and other urbanized areas), waterbody, sandy area and other areas. Then, the area of each land use was calculated by GIS, and their changes were compared.
Results and Discussion
Overall accuracy and kappa coefficient of classification were 98.45% and 0.98 for 2000, 97.59% and 0.97 for 2010, and 98.72% and 0.98 for 2020, respectively. According to the results, rice land area decreased by 4.42% from 2000 to 2010. It also had a decrease of 2.64% between 2010 and 2020. In total, rice lands decreased by 6.94% between 2000 and 2020, so its area has decreased to 10311.69 hectares. This downward trend can be due to the conversion of rice land to the built-up area. The area of semi-dense forest decreased by 47.48% between 2000 and 2010, but its downward trend decreased to 26.36% between 2010 and 2020. In total, semi-dense forest area decreases by 61.32%, equal to 682.25 hectares over a period of 20 years. This is due to the uncontrolled cutting of trees and the change of land use from semi-dense forests to sparse forests and built-up areas. Also, during this period, built-up areas and sparse forests have grown by 67.94% and 18.73%, respectively. But, semi-dense forests, water bodies and sandy areas have decreased by 61.32%, 4.91% and 61.48%, respectively.
Conclusion
The reduction rate in the area of rice land and semi-dense forest classes between 2010 and 2020 was lower than the ten-year period before, which can be attributed to the adoption of restrictive laws and more inspections by relevant organizations. However, the downward trends in these land uses have continued over the past decade. Meanwhile, the increase of 67.94% of built-up lands indicates that the lost lands in the forest and rice land classes have been converted into the built-up area. The rate of land-use change in the built-up class has the highest rate among the studied classes. This result indicates the need for serious attention to land-use change in the rural area more than before. Another point is that there was a growth in sparse forests between 2000 and 2010, and then a reverse trend was observed between 2010 and 2020, which shows that in a period of 10 years, deforestation has taken place, and in 10 years later, the lands from these destructions have been converted to the built-up area. As a result, serious attention to natural resource organizations is necessary. It is considered that there was a deliberate destruction of forests over time with the aim of personal profit.

Keywords

Main Subjects

Open Access

©2021 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

  1. Ajith, K., Geethalakshmi, V., Ragunath, K., Pazhanivelan, S., & Panneerselvam, S. (2017). Rice Acreage Estimation in Thanjavur, Tamil Nadu Using Lands at 8 OLIIMAGES and GIS Techniques. International Journal of Current Microbiology and Applied Sciences, 6, 2327-2335. https://doi.org/10.20546/ijcmas.2017.607.275
  2. Alipour, F., Aghkhani, M., Abasspour-Fard, M., & Sepehr, A. (2014). Demarcation and estimation of agricultural lands using etm+ imagery data (case study: Astan ghods razavi great farm). Journal of Agricultural Machinery, 4, 244-254. (in Persian with English abstract). https://doi.org/10.22067/JAM.V4I2.34827
  3. Ansari Amoli, A., & Alimohammadi Sarab, A. (2011). Rice area estimation by using multi-temporal classification method and AVHRR data. Spatial Planning (Modares Human Sciences), 15, 1-16. (in Persian).
  4. Atzberger, C. 2013. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5, 949-981. https://doi.org/10.3390/rs5020949
  5. Bagan, H., & Yamagata, Y. 2012. Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, 127, 210-222. https://doi.org/10.1016/j.rse.2012.09.011
  6. Chauhan, S., Darvishzadeh, R., Boschetti, M., Pepe, M., & Nelson, A. (2019). Remote sensing-based crop lodging assessment: Current status and perspectives. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 124-140. https://doi.org/10.1016/j.isprsjprs.2019.03.005
  7. Darvishzadeh, R., Matkan, A. A., & Eskandari, N. (2011). Evaluation of ALOS-AVNIR2 spectral indices for prediction of rice biomass. Journal of Geographical Landscape, 6, 11-14. (in Persian).
  8. Dashti Marvili, M., Kamkar, B., & Kazemi, H. (2019). Detection of rice and soybean grown fields and their related cultivation area using Sentinel-2 satellite images in summer cropping patterns to analyze temporal changes in their cultivation area (Case study: four watershed basins of Golestan Province). Journal of Water and Soil Conservation (Journal of Agricultural Sciences and Natural Resources), 26, 151-167. (in Persian).
  9. FAO. 2019. Food and agriculture organization of the United Nations. FAOSTAT: Crops. http://www.fao.org/faostat/en/#data/QC.
  10. Godarzi Mehr, S., Abbaspour, R. A., Ahadnezhad, V., & Khakbaz, B. (2012). Comparison of support vector machine, neural network, and maximum likelihood methods for the separation of lithological units. Iranian Journal of Geology, 6, 75-92. (in Persian).
  11. Hopkins, P. F., Maclean, A., & Lillesand, T. (1988). Assessment of Thematic Mapper imagery for forestry applications under Lake States conditions. Photogrammetric Engineering and Remote sensing (USA).
  12. Izaddoost, H., Samizadeh, H., Rabiei, B., & Abdollahi, S. (2013). Evaluation of salt tolerance in rice (Oryza sativa) cultivars and lines with emphasis on stress tolerance indices. Cereal Research, 3, 167-180. (in Persian). https://doi.org/20.1001.1.22520163.1392.3.3.1.2
  13. Kazemi Posht Mousavi, H., Pirdashti, H. A., Bahmanyar, M. A., & Nasiri, M. (2007). Study the effects of nitrogen fertilizer rates and split application on yield and yield components of different rice (Oryza sativa) cultivars. Pajouhesh-va-Sazandegi, 20, 68-77. (in Persian).
  14. Khatami, R., Mountrakis, G., & Stehman, S. V. (2016). A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future Remote Sensing of Environment, 177, 89-100. https://doi.org/10.1016/j.rse.2016.02.028
  15. Li, C., Wang, J., Wang, L., Hu, L., & Gong, P. (2014). Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sensing, 6, 964-983. https://doi.org/10.3390/rs6020964
  16. Mather, P., & Tso, B. (2016). Classification methods for remotely sensed data. CRC press. https://doi.org/10.1201/9781420090741
  17. Mondal, S., Jeganathan, C., Sinha, N. K., Rajan, H., Roy, T., & Kumar, P. (2014). Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India. The Egyptian Journal of Remote Sensing and Space Science, 17, 123-134. https://doi.org/10.1016/j.ejrs.2014.09.002
  18. Naghinezhad, A. R., Saeidi Mehrvarz, S., Norouzi, M., & Faridi, M. (2006). Contribution to the vascular and bryophyte flora as well as habitat diversity of the boujagh national park, n. Iran. Rostaniha, 7, 83-105. (in Persian).
  19. Nuarsa, I., Nishio, F., & Hongo, C. (2010). Development of the empirical model for rice field distribution mapping using multi-temporal Landsat ETM+ data: case study in Bali Indonesia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, XXXVIII, part 8.
  20. Paul, G. C., Saha, S., & Hembram, T. K. (2020). Application of phenology-based algorithm and linear regression model for estimating rice cultivated areas and yield using remote sensing data in Bansloi River Basin, Eastern India. Remote Sensing Applications: Society and Environment, 19, 100367. https://doi.org/10.1016/j.rsase.2020.100367
  21. Prasad, A., Singh, R., Tare, V., & Kafatos, M. (2007). Use of vegetation index and meteorological parameters for the prediction of crop yield in India. International Journal of Remote Sensing, 28, 5207-5235. https://doi.org/10.1080/01431160601105843
  22. Richards, J. A., & Richards, J. (1999). Remote sensing digital image analysis. Springer.
  23. Sakamoto, T., Sprague, D. S., Okamoto, K., & Ishitsuka, N. (2018). Semi-automatic classification method for mapping the rice-planted areas of Japan using multi-temporal Landsat images. Remote Sensing Applications: Society and Environment, 10, 7-17. https://doi.org/10.1016/j.rsase.2018.02.001
  24. Shen, S., Yang, S., Li, B., Tan, B., Li, Z., & Le Toan, T. (2009). A scheme for regional rice yield estimation using ENVISAT ASAR data. Science in China Series D: Earth Sciences, 52, 1183-1194. https://doi.org/10.1007/s11430-009-0094-z
  25. Singha, M., Dong, J., Zhang, G., & Xiao, X. (2019). High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific data, 6, 1-10. https://doi.org/10.1038/s41597-019-0036-3
  26. Torbick, N., Chowdhury, D., Salas, W., & Qi, J. (2017). Monitoring rice agriculture across Myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sensing, 9, 119. https://doi.org/10.3390/rs9020119
  27. USGS. (2021). United States Geological Survey. USGS Earth Explorer. https://earthexplorer.usgs.gov/.
  28. Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402
  29. Yaghouti, H., Pazira, E., Amiri, E., & Masihabadi, M. H. (2018). Application of satellite imagery and remote sensing technology to estimate rice yield. Journal of Soil and Water Resources Conservation, 7, 55-68. (in Persian).
  30. Yang, C., Everitt, J. H., & Murden, D. (2011). Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75, 347-354. https://doi.org/10.1016/j.compag.2010.12.012
  31. Younesi, B., Ahmadi Sani, N., & Sharafi, S. (2019). Evaluation of IRS-P6 Images for Orchards Area Estimating. Remote Sensing & GIS, 11, 115-128. (in Persian). https://doi.org/10.52547/gisj.11.1.113
  32. Ziaeian Firouzabadi, P., Sayad Bidhendi, L., & Eskandari Noudeh, M. (2009). Mapping and acreage estimating of rice agricultural land using radarsat a satellite images. Physical Geography Research Quarterly, 41, 45-58. (in Persian).
CAPTCHA Image