پیش بینی عملکرد محصول نیشکر با استفاده از پردازش سری زمانی تصاویر ماهواره ای لندست (مطالعه موردی: منطقه میان آب استان خوزستان)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مکانیزاسیون گروه مهندسی ماشین های کشاورزی دانشکده فناوری و مهندسی پردیس کشاورزی و منابع طبیعی دانشگاه تهران کرج ایران

2 استاد گروه مهندسی ماشین های کشاورزی دانشکده فناوری و مهندسی پردیس کشاورزی و منابع طبیعی دانشگاه تهران کرج ایران

3 استاد گروه علوم و مهندسی خاک دانشکده فناوری و مهندسی پردیس کشاورزی و منابع طبیعی دانشگاه تهران کرج ایران

4 استادیار گروه مهندسی ماشین های کشاورزی دانشکده فناوری و مهندسی پردیس کشاورزی و منابع طبیعی دانشگاه تهران کرج ایران

چکیده

پیش بینی مقدار محصول نیشکر، نقش کلیدی برای گستره وسیعی از کاربردها مانند مدیریت تولید نیشکر، آماده سازی کارخانه های فرآوری نیشکر و انبار و پیش فروش فرآورده های صنعتی دارد. این تحقیق مقایسه مدل سازی عملکرد نیشکر با روش پردازش سری زمانی شاخص سبزینگی تفاضلی نرمال شده (NDVI)، شاخص سبزینگی تفاضلی سبز نرمال شده (GNDV)  و شاخص سبزینگی ارتقاء یافته  (EVI)که از تصاویر ماهواره ای استخراج شده اند را انجام می دهد. برای اینکار از 474 تصویر ماهواره لندست 7 بدست آمده از آرشیو سازمان نقشه برداری آمریکا مربوط به ژانویه 2001 لغایت دسامبر 2017 استفاده شد. ابتدا داده های تصاویر به انعکاس بالای جو تبدیل و سپس پیکسل های تحت تاثیر آسمان ناصاف از جمله ابر، سایه، برف و یخ حذف گردید. در مرحله بعد میانگین شاخص های سبزینگی NDVI ،GNDVI و EVI منطقه مورد مطالعه برای هر تصویر محاسبه و سری زمانی هفتگی از میانگین شاخص های سبزینگی محاسبه گردید. مقدار تجمعی شاخص سبزینگی از هفته 15 لغایت 44 و میانگین عملکرد ساقه در هکتار مشاهده شده در یک مدل رگرسیونی بررسی شد. نتایج نشان داد شاخص سبزینگی NDVI   با 63/0R2= و ton/ha71/4RMSE= و شاخص سبزینگی GNDVI با 60/0R2= وton/ha93/4RMSE= رابطه خوبی با عملکرد ساقه در هکتار نیشکر دارند. با استفاده از مدل انتخاب شده عملکرد ساقه در هکتار مزارع کشت و صنعت میان آب در سال 1396به میزان ton/ha 35/86 پیش بینی شدکه ton/ha16/4 کمتر از مقدار مشاهده شده بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Sugarcane Yield Estimation Using LANDSAT Time-Series Imagery: (Case Study - MianAB Region in Khouzestan Province)

نویسندگان [English]

  • mostafa khosravirad 1
  • Mahmoud omid 2
  • fereydoun sarmadian 3
  • Soleiman Hosseinpour 4
1 Ph.D. Student of Agricultural Mechanization, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran
2 Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran
3 Professor, Department of Science and Soil Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran
4 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj: Iran
چکیده [English]

Prediction of sugarcane yield is very important for a wide range of applications like, sugarcane production management, preparation of sugarcane refineries and pre-sales and warehouse industrial products. In this study, a model based on time-series processing of vegetation indices, Normalized Difference Vegetation Index (NDVI), Green Normalized Differnce Vegetation Index (GNDVI) and Enhanced Vegetation Index (EVI) extracted from satellite images, were used to estimate sugarcane yield. Overall 474 Landsat 7 satellite images from January 2001 to December 2017 obtained from USGS (U.S. Geological Survey) were processed. At first the DN (Digital Number) of pixels were converted to TOA (Top of Atmosphere) reflectance and then the distorted pixels due to not clear sky such as cloud, shadow, snow and ice were eliminated. Consequently, the average of the vegetation indices values of study region for every images were computed. Then the weekly time-series of vegetation indices were calculated via interpolation. The accumulated vegetation indices values from 15th to 44 th week of year and average observed yields efficiency were evaluated by regression model. The result showed the NDVI and GNDVI vegetation indices with R2=0.63, RMSE=4.71 ton/ha and R2=0.60, RMSE 4.93 ton/ha, respectively, have good relations with sugarcane stem yield efficiency. The 2017 sugarcane yield of MianAB Sugarcane Agro-Industry Company efficiency was predicted as 86.35 ton/ha using the NDVI model which was 4.16 ton/ha less than observed value.

کلیدواژه‌ها [English]

  • Growth profile
  • Biomass
  • Vegetation index
  • Yield modelling
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