Determination of Potato Crop Cultivation in Hamedan Province, Using time series Satellite Images IRSP6
Subject Areas : Land use planningAli shahbazi 1 , Loghman khodakrami 2 , kamran nasirahmadi 3
1 - Department of natural resources
2 - Faculty of Technology, Koya University
3 - Faculty of Chemical and Industrial Engineering
Keywords: Rremote sensing, Fuzzy Classification Method, cultivated, SAVI, NDVI ,
Abstract :
The aim of this study is to detect and quantify the cultivated area of potato fields in Hamadan Province using remote sensing methods and a time series of satellite photos. As a result, Awifs time-series imaging was used to determine the potato cropping area. For this purpose, pictures were taken at three different times when the potato plant turned green and yellow. Processing such as preparation, atmospheric and geometric correction, vegetation index, and unsupervised classification were performed on the images using appropriate training sites for supervised classification. Following the integration of these two layers, the studied area under the cropping map was prepared using the phase classification method. Additionally, by using the vegetation indices NDVI and SAVI, the area under cropping for the three main crop yields is determined first using the threshold level technique and in three temporal intervals. The kapa coefficient for potato under cropping area determined by phase classification, NDVI, and SAVI was 90, 87, and 85%, respectively. In 1998, the potato cropping area was determined to be 38740, 36728, and 36614 acres, respectively. This study clearly shows that the phase classification method and Awif data time series can be used to recognize and estimate potato under cropping area with acceptable precision and that vegetation indices distinguish potato under cropping area faster.
1. Abdalah Zadeh, M. And Nasiri, M. B. (2008). Determination of potato cultivated in the city using a series Borujen When images IRSP6. National symposium of Geomatic 2008. Survey Organisation, Tehran.
2. Alavi Panah, S. K. (2003). Remote sensing applications in geosciences. Tehran University Press.
3. Ashvrlv, M., A. Mhmddy, AS, Rezaeian, P. and Vashvrlv, D. (2006). Application of linear analysis in the diagnosis of separating wheat from other Products on satellite images. Journal of Environmental Sciences. (2), 101-116.
4. Barrett, E.C. and L.F. Curtis. (1992). Introduction to environmental remote sensing. Routledge.
5. Chen, C. F., Son, N. T., & Chang, L. Y. (2012). Monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam using time-series MODIS data. Advances in Space Research, 49(2), 292-301.
6. Curran, P. (Translation reza haer). (1995). Principles of remote sensing. Iranian Remote Sensing Center. Omid Publications, Tehran.
7. Guide, E. F. (2010). Technical documentation. ERDAS Inc.
8. Farn Chen, C. and Y. Tan Li. (2000). Supervised Classification of Multi-Temporal Remote Sensing Images, Int: 20 Th Asian Conferences on Remote Sensing.
9. http://www.sarmayeh.net/ShowNews.php?7063, Download in 28/6/2009.
10. French, A. N., Hunsaker, D. J., Sanchez, C. A., Saber, M., Gonzalez, J. R., & Anderson, R. (2020). Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agricultural Water Management, 239, 106266.
11. Jensen, J. R. (1996). Introductory digital image processing: a remote sensing perspective (No. Ed. 2). Prentice-Hall Inc.
12. Jeong, S., Ko, J., & Yeom, J. M. (2022). Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Science of the Total Environment, 802, 149726.
13. Hasim, S., & Bhar, K. K. (2020). Seasonal Cropping Pattern Extraction Using NDVI from IRS LISS-III Image of Kangsabati Commanded Area. Procedia Computer Science, 167, 900-906.
14. Hu, Q., Sulla-Menashe, D., Xu, B., Yin, H., Tang, H., Yang, P., & Wu, W. (2019). A phenology-based spectral and temporal feature selection method for crop mapping from satellite time series. International Journal of Applied Earth Observation and Geoinformation, (80), 218-229.
15. Khodakarami, L. (2022). Determination of Potato Crop Cultivation in Hamedan Province, Using time series Satellite Images IRSP6.
16. Khvajhaldyn, S.J. and Pvrmnafy, S. (2007). Determine the level of marginal paddy Zayandehrod River in Isfahan Region with satellite sensor digital data of IRS. Journal of Science and Technology of Agriculture and Natural Resources.Isfahan University of Technology. (1), 513-527.
17. Khvajhaldyn, S.J. (1997). The role of remote sensing of natural resources in sustainable agricultural development and use of these data in planning industrial agriculture. Proceedings of Seminar on Role of Technology in Agricultural Development, Scientific Research & Publications in cooperation with the Town Publishing Mani, Isfahan.
18. Mohan, B. K., Madhavan, B. B., & Gupta, U. D. (2000). Integration of IRS-1A L2 data by fuzzy logic approaches for landuse classification. International journal of remote sensing, 21(8), 1709-1723. 19. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (1994). Remote sensing and image interpretation. John Willey & Sons. Inc, United States of America.
20. Manfron, G., Delmotte, S., Busetto, L., Hossard, L., Ranghetti, L., Brivio, P. A., & Boschetti, M. (2017). Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France. International journal of applied earth observation and geoinformation, 57, 190-201.
21. Ranjbar, H., & Honarmand, M. (2004). Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification. International Journal of Remote Sensing, 25(21), 4729-4741.
22. Richards, J. A., & Richards, J. A. (1999). Remote sensing digital image analysis (Vol. 3, pp. 10-38). Berlin: springer.
23. Salimi, S. and Kazemi, F. (2009). Application of satellite images IRS in order to map the distribution of rice in the city Marvdasht. (2008). National symposium of Geomatic2008. Survey Organisation, Tehran.
24. Joshi, P. K., Roy, P. S., Singh, S., Agarwal, S., & Yadav, D. (2002). Biome level characterization (BLC) of western India-a geospatial approach. Tropical ecology, 43(1), 213-228.
25. Sarvyy, S. and Nasiri, A. (2002). Using remote sensing technology in the preparation of statistics and maps of land unse rice cultivation in the north (city of Amol and Babol). National symposium of Geomatic2002. Survey Organisation, Tehran.
26. Sawasawa, H. (2003). Crop yield estimation: Integrating RS, GIS, and management factors. International Institute for Geo-information Science and Earth Observation, Enschede The Netherlands. 27. Tso, B. and P. Mather. (2001). Classification Methods for Remotely Sensed Data.Taylor & Francis, UK.
28. Turker, M., & Arikan, M. (2004). Field-Based Crop Mapping through Sequential Masking Classification of Multi-temporal LANDSAT-7 ETM+ Images in Karacabey, Turkey. Int. Arch. Ph. RS, 35, 192-197.
29. Wang, F. (1990). Fuzzy supervised classification of remote sensing images IEEETransactions on Geosciences and Remote Sensingm. (28), 194-201.
30. Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote sensing of environment, 108(3), 290-310.
31. Wright, G. G., & Morrice, J. G. (1997). Landsat TM spectral information to enhance the land cover of Scotland 1988 dataset. International journal of remote sensing, 18(18), 3811-3834.
32. Zhtabyan, GH.R. and Tabatabai, M. R. (1999). Studying the process of desertification using satellite imagery processing and geographic information system (GIS). Iranian Journal of Rang and Desert. (2), 57-67.
