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برآورد سطح زیرکشت محصولات کشاورزی با استفاده از تصاویر ماهواره لندست 8 (مطالعه موردی: شهرستان شوشتر) | ||
| به زراعی کشاورزی | ||
| مقاله 15، دوره 24، شماره 2، تیر 1401، صفحه 465-479 اصل مقاله (1.15 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jci.2021.322146.2537 | ||
| نویسندگان | ||
| محمد عبیات* 1؛ سعید امانپور2؛ محمود عبیات3؛ ماجده عبیات4 | ||
| 1دانش آموخته کارشناسی ارشد، گروه علوم محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران (خوزستان)، اهواز، ایران. | ||
| 2دانشیار، گروه جغرافیا و برنامه‌ریزی شهری، دانشگاه شهید چمران اهواز، اهواز، ایران. | ||
| 3دانش‌آموخته کارشناسی ارشد، گروه جغرافیا و برنامه‌ریزی شهری، دانشگاه شهید چمران اهواز، اهواز، ایران. | ||
| 4دانش‌آموخته کارشناسی ارشد، گروه جغرافیا و برنامه ریزی شهری، دانشگاه شهید چمران اهواز، اهواز، ایران. | ||
| چکیده | ||
| تصاویر ماهوارهای، از قابلیت بالایی جهت برآورد سطح زیرکشت محصولات کشاورزی برخوردارند. هدف این مطالعه، شناسایی سطح زیرکشت محصولات غالب در شهرستان شوشتر با استفاده از تصاویر لندست 8 طی دوره رشد در سال 1398 میباشد. با روشهای طبقهبندی حداکثر احتمال و ماشین بردار پشتیبان در رویکرد اول و استفاده از شاخص گیاهی NDVI در رویکرد دوم، محصولات زراعی در مراحل مختلف رشد و با توجه به تقویم زراعی آنها، نقشه الگوی کشت محصولات این منطقه نگاشته شد. جهت بررسی صحت نتایج، نقشههای تولیدشده با دادههای مرجع موردبررسی قرار گرفت. از آمار جهاد کشاورزی استان خوزستان در سال 1398 نیز برای ارزیابی نتایج استفاده شد. نتایج نشان داد که ضریب کاپا و صحت کلی در روش حداکثر احتمال به‌ترتیب 90 و 80 درصد، در روش ماشین بردار پشتیبان به‌ترتیب 92 و 90 درصد و در روش استفاده از شاخص NDVI، به‌ترتیب 95 و 93 درصد محاسبه شد. براساس نتایج، سطح زیرکشت گندم، جو، برنج و ذرت، در روش حداکثر احتمال، در مقایسه با آمار جهاد کشاورزی به‌ترتیب خطایی برابر 6/12، 4/16، 7/8 و 6/6 درصد و در روش ماشین بردار پشتیبان به‌ترتیب خطایی برابر 1/10، 3/8، 1/5، 2/7 درصد داشته است. اما استفاده از شاخص NDVI به‌عنوان بهترین روش برآورد سطح زیرکشت در منطقه، در مقایسه با آمار جهاد کشاورزی به‌ترتیب دارای خطایی برابر 4/2، 5/1، 3/4 و 6/4 درصد بوده که نشاندهنده قابلیت بالای شاخصهای گیاهی در برآورد سطح زیرکشت محصولات با توجه به مرحله فنولوژی آنها میباشد. | ||
| کلیدواژه‌ها | ||
| الکوی کشت؛ تصاویر ماهوارهای؛ طبقهبندی؛ NDVI؛ شوشتر | ||
| عنوان مقاله [English] | ||
| Estimation of Agricultural Cultivation Area by Landsat 8 Satellite Images (Case study: Shushtar Province) | ||
| نویسندگان [English] | ||
| Mohammad Abiyat1؛ Saeid Amanpour2؛ Mahmud Abiyat3؛ Majedeh Abiyat4 | ||
| 1Former M.Sc. Student, Department of Environmental Sciences, Islamic Azad University, Tehran (Khuzestan) Science and Research Branch, Ahvaz, Iran. | ||
| 2Associate Professor, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 3Former M.Sc. Student, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 4Former M.Sc. Student, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| چکیده [English] | ||
| Satellite images have a high capability for estimating the area under agricultural crops. The aim of this study was to identify the area under dominant crops such as in Shushtar Province using Landsat 8 satellite images during the growing season during 2019. With Maximum Probability technique and Support Vector Machine in the first approach and using NDVI index in the second approach, crops in different growing seasons and according to their calendar, a cropping pattern map was drawn. In order to evaluate the accuracy of the results, the generated maps with reference data were examined. Agricultural Jihad statistics of Khuzestan were also used. The results showed that Kappa coefficient and overall accuracy were calculated as 90% and 80% in the Maximum Probability technique, 92% and 90% in the Support Vector Machine and 95% and 93% in the NDVI, respectively. Based on the results, the cultivation area of wheat, barley, rice, and corn, in the Maximum Probability technique, in comparison with the statistics of Agricultural Jihad, had an error of 12.6, 16.4, 8.7 and 6.6%, respectively and in the Support Vector Machine had an error of 10.1, 8.3, 5.1 and 7.2%, respectively. However, using the NDVI index as the best approach for estimating the cultivation area in this region, in comparison with the statistics of Agricultural Jihad, has an error of 2.4, 1.5, 4.3 and 4.6%, respectively, which indicates the high capability of vegetation indices to estimate the Cultivation Area, According to their phenological stage. | ||
| کلیدواژه‌ها [English] | ||
| Classification, Cultivation pattern, NDVI, Satellite imagery, Shushtar | ||
| مراجع | ||
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Aszadeh Tehrani, N., Seyed Ahmad, M., & Janalipour, M. (2020). Estimation of wheat area cultivation using Sentinel 2 satellite images (Case study: Sojasroud region, Khodabandeh city, Zanjan province) .Journal of Environmental Research and Technology, 5(7), 77-90. (In Persian). Ahmadi Naddoshan, M., Soffianian, A., & Khajedin, S. (2010). Land Cover Mapping of Arak City Using Artificial Neural Network and Maximum Likelihood Classifiers. Physical Geography Research Quarterly, 41(69), 83-98. (In Persian). 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(2), 244-254. (In Persian). Arekhi, S., & Fathizad, H. (2015). Evaluating the Efficiency of Four Artificial Neural Network Methods in Preparing Land Cover/Land Use Map Using ETM+ Data. Geography and Development Iranian Journal, 12(37), 133-146. Arekhi, S., & Adibnejad, M. (2011). Efficiency assessment of the Support Vector Machines for land use classification using Landsat ETM+ data (Case study: Ilam Dam Catchment). Iranian Journal of Rangeland and Desert Research, 18(3), 420-440. Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40years. Remote sensing of Environment, 12و: 210-222. Caren, C.D., David, J.M., & Volker, C.R. (2001). Phonological difference in tasseled cap indices improves deciduous forest classification. Remotesensing of Environment, 80, 460-472. Chuanga, W.C., Lina, C.Y., Chiena, C.H., & Choub, W.C. (2011). Application of Markov-chain model for vegetation restoration assessment at landslide areas caused by a catastrophic earthquake in Central Taiwan. Ecological Modelling, 222, 835-845. Dixon, B., & Candade, N. (2018). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, 29(4), 1185-1206. Entezari, A., Zandi, R., & Khosravian, M. (2019). Evaluation of spatial variations of vegetation and surface temperature using Landsat and midsize images, case study: Fars Province, 1967-2017. Watershed Engineering and Management, 11(4), 929-940. (In Persian). Feizizadeh, B., Didehban, K., & Gholamnia, K. (2016). Extraction of Land Surface Temperature (LST) based on Landsat Satellite Images and Split Window Algorithm Study area: Mahabad Catchment. Scientific- Research Quarterly of Geographical Data (SEPEHR), 25(98), 171-181. (In Persian). Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., & Kussul, O. (2014). Efficiency assessment of using satellite data for crop area estimation in Ukraine, International Journal of Applied Earth Observation and Geoinformation, 29, 22-30. Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Rao, M.N., Mohammed, I.A., & Whitbread, A.M. (2016). Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250m time-series data, International Journal of Digital Earth, 9(10), 981-1003. Hopkins, P.F., Maclean, A.L., & Lillesand, T.M. (2005). Assessment of thematic mapper imagery for forestry applications under Lake States conditions. Photogrammetric Engineering and Remote Sensing, 54(1), 61-68. Huang, C. L., Davis, S., & Townshend, J.R.G. (2002). An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23(4), 725-749. Jain, M., Mondal, P., Galford, G.L., Fiske, G., & Fries, R.S. (2017). An approach to map winter cropped area of smallholder farms across large scales using MODIS imagery. Remote Sensing, 9(6), 566. Jeffreys, H. (1946). An invariant form for the prior probability in estimation problems. In: Proceedings of the Royal Society of London a: mathematical, physical and engineering sciences, The Royal Society, 1007, 453-461. Joshi, PK., Roy, P., Singh, S., Agrawal, S., & Yadav, D. (2006). Vegetation cover mapping in India using multi-temporal IRS Wide Field Sensor (WiFS) data. Remote Sensing of Environment, 103(2), 190-202. Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359. Keuchel, J., Naumann, S., Heiler, M., & Siegmund, A. (2003). Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sensing of Environment, 86(4), 530-541. King, L., Adusei, B., Stehman, S.V., Potapov, P.V., Song, X.P., Krylov, A., Bella, C.D., Loveland, T.R., Johnson, D.M., & Hansen, M.C. (2017). A multi-resolution approach to national-scale cultivated area estimation of soybean, Remote Sensing of Environment, 195, 13-29. Knorn, J., Rabe, A., Radeloff, V.C., Kuemmerle, T., Kozak, J., & Hostert, P. (2009). Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sensing of Environment, 113(5), 957-964. Kumar, P., & Jeganathan, C. (2017). Monitoring Horizontal and Vertical Cropping Pattern and Dynamics in Bihar over a Decade (2001-2012) Based on Time-Series Satellite Data. Journal of the Indian Society of Remote Sensing, 45(3), 485-502. Lobell, D.B., Asner, G.P., Ortiz-Monasterio, J.I., & Benning, T.L. (2003). Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems & Environment, 94, 205-220. Mather, P. M., & Tso, B. (2009). Classification methods for remotely sensed data second edition, CRC Press. New York. Nateghi, S., Nohegar, A., Ehsani, A.H., & Bazrafshan, O. (2018). Evaluation of vegetation changes based upon vegetation indices using remote sensing. Iranian Journal of Range and Desert Research, 24(4), 778-790. Moradi, A.R., Jafari, M., Arzani, H., & Ebrahimi, M. (2016). Assessment of land use changes into dry land using satellite images and Geographical information system (GIS). RS & GIS Techniques for Natural Resources, 7(1), 89-100. Mousavi, S. A., Abbaszadeh Tehrani, N., & Janalipour, M. (2021). Estimation of the train wheat under cultivation area using Sentinel-2 satellite images (Case study: Sojasroud region, Khodabandeh city, Zanjan province). Journal of Environmental Research and Technology, 5(7), 17-28. Mushtaq, A.G., & Asima, N. (2014). Estimation of apple orchard using remote sensing and agro- meteorology land based observation in Pulwama district of Kashmir valley, International Journal of Remote Sensing and Geoscience, 3(6), 21-26. Nateghi, S., Nohegar, A., Ehsani, A., & Bazrafshan, O. (2017). Evaluating the vegetation changes upon vegetation index by using remote sensing. Iranian Journal of Range and Desert Research, 24(4), 778-790. (In Persian). Nguyen, D. B., & Wagner, W. (2017). European rice cropland mapping with Sentinel-1 data: The Mediterranean region case study, Water, 9(6), 392. Petropoulos, G.P., Kontoes, C., & Keramitsoglou, I. (2011). Burnt area delineation from a unitemporal perspective based on Landsat TM imagery classification using support vector machines. International Journal of Applied Earth Observation and Geoinformation, 13(1), 70-80. Rahimzadegan, M., & Pourgholam, M. (2017). Identification of the area under cultivation of Saffron using Landsat-8 temporal satellite images (Case study: Torbat Heydarieh). Journal of RS and GIS for Natural, 7(4), 97-115. (In Persian). Riahi, V., Zeaiean Firouzabadi, P., Azizpour, F., & Darouei, P. (2019). Identification and investigation of the area under cultivation in Lenjanat using Landsat 8 satellite images. Researches in Geographical Sciences. 19(52), 147-169. (In Persian). Sadoghi, H., Rajaee, T., & Rouhani, N. (2021). Identification and Investigation of Changes in Area of Hoseynabade Mishmast Village Using Satellite Images. JWSS, 24(4), 239-254. (In Persian). Schowengerdt, R. A. (2007). Remote Sensing: Models and Methods for Image Processing. Academic Press, United States. Yadav, I.S., Rao, N.S., Reddy, B.M.C., Rawal, R.D., Srinivasan, V.R., Sujatha, N.T., Bhattacharya, C., Rao, P.P.N., Ramesh, K.S., & Elango, S. (2002). Acreage and production estimation of mango orchards using Indian remote Sensing (IRS) satellite data, Scientia Horticulturae, 93(2), 105-123. | ||
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