نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی‌ ارشد، گروه علوم محیط زیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران (خوزستان)، اهواز، ایران.

2 دانشیار، گروه جغرافیا و برنامه‌ریزی شهری، دانشگاه شهید چمران اهواز، اهواز، ایران.

3 دانش‌آموخته کارشناسی ارشد، گروه جغرافیا و برنامه‌ریزی شهری، دانشگاه شهید چمران اهواز، اهواز، ایران.

4 دانش‌آموخته 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

عنوان مقاله [English]

Estimation of Agricultural Cultivation Area by Landsat 8 Satellite Images (Case study: Shushtar Province)

نویسندگان [English]

  • Mohammad Abiyat 1
  • Saeid Amanpour 2
  • Mahmud Abiyat 3
  • Majedeh Abiyat 4

1 Former M.Sc. Student, Department of Environmental Sciences, Islamic Azad University, Tehran (Khuzestan) Science and Research Branch, Ahvaz, Iran.

2 Associate Professor, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Former M.Sc. Student, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

4 Former 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
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