پیش‌بینی عملکرد کلزا در مراحل مختلف رشد به‌وسیله تصاویر سنجنده OLI ماهواره لندست

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فن‌آوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران

2 دانشیار، گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران

3 استادیار پژوهش، موسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی

4 استادیار، گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران

چکیده

کلزا منبع روغن خوراکی است و کشت آن در ایران و جهان رو به رشد می­باشد. در زمینه تخمین عملکرد کلزا به­وسیله سنجش از دور تحقیقات کمی صورت گرفته است. در سال زراعی 96-95 با هدف پیش­بینی عملکرد کلزا به­وسیله ماهواره لندست 8، سنجنده OLI، سه مزرعه کشت این محصول مورد ارزیابی قرار گرفت. تصاویر ماهواره­ای در پنج مرحله قبل از گل­دهی، اوایل گل­دهی، اوج گل­دهی، رسیدگی سبز و رسیدگی خشک پردازش گردید و تعدادی از شاخص­های گیاهی براساس نسبت بین باندها استخراج گردید. محدوده شبکه­ای پیکسل­های مزارع تعیین گردید و برای افزایش دقت تعیین موقعیت پیکسل­ها در مزارع از سیستم موقعیت­یابی جهانی سینماتیک زمان واقعی (RTKGPS) استفاده گردید. نمونه­برداری از داخل پیکسل­های مزارع در هنگام برداشت انجام گردید و عملکرد دانه کلزا اندازه­گیری گردید. در مجموع از سه مزرعه مورد مطالعه 28 پیکسل برای پیاده­سازی مدل­های پیش­بینی و نیز اعتبارسنجی آنها اخذ شد. از مدل­های رگرسیونی خطی ساده و چند متغیره برای ارزیابی ارتباط بین عملکرد کلزا و شاخص­های گیاهی استفاده گردید. نتایج نشان داد براساس مدل رگرسیون خطی ساده، بین مراحل رشد، بالاترین ضریب تبیین (R2) در هر یک از شاخص­های گیاهی به یکی از دو مرحله اوج گل­دهی و رسیدگی سبز تعلق داشت. ضریب تبیین در تمام شاخص­های گیاهی، در مرحله قبل از گل­دهی ضعیف (پایین­تر از 10 درصد) و در دو مرحله اوائل گل­دهی و رسیدگی خشک نسبتاً متوسط (52-24 درصد) بوده است. براساس این مدل، در مرحله اوج گل­دهی شاخص تفاضل نرمال شده زردی (NDYI) با 67 درصد و در مرحله رسیدگی سبز شاخص نسبت پوشش گیاهی (RVI) با 64 درصد بالاترین ضریب تبیین را نسبت به سایر شاخص­های گیاهی کسب کرده­اند. با به­کارگیری مدل رگرسیون خطی چند متغیره گام به گام با چهار باند مرئی و مادون قرمز نزدیک به­عنوان ورودی، بهترین مدل پیش­بینی عملکرد کلزا در مرحله گل‌دهی با ضریب تبیین 78 درصد و میزان اعتبارسنجی 74 درصد به­دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of Canola Yield in Some of Growth Stages by Using Landsat Satellite, OLI Sensor

نویسندگان [English]

  • Naeim Loveimi 1
  • Asadollah Akram 2
  • Nikrooz Bagheri 3
  • Ali Hajiahmad 4
1 Ph.D. Student, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
2 Associate Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
3 Assistant Professor, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), karaj, Iran
4 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

Canola is a source of edible oil and its cultivation in Iran and the world is growing. Only few studies have been carried out by remote sensing for canola yield estimation,. In 2017-2018, in order to predict the canola yield by Landsat satellite, OLI sensor, three farms were evaluated. The satellite images were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturing, and some of vegetation indices were extracted based on the ratio of the bands. The pixel network of each farm was determined and the Real Time Kinematic Global Positioning System (RTKGPS) was used to increase the precision of pixels location in the farms. Sampling was done inside farms pixels during harvesting time and canola yield was measured. Totally, 28 pixels from three studied farms were used to develop and validate the predictive models. Simple and multivariate linear regression models were used to assess the relationship between canola yield and vegetation indices. The results showed that, on the basis of simple linear regression models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturing. The coefficient of determination in all vegetation indices was low in the before flowering stage (less than 10 percent) and relatively medium (24- 52 percent) in the early flowering and dry maturing stages. According to this model, the NDYI with 67 percent in the peak of flowering stage, and the RVI with 64 percent in the green maturing stage had the highest coefficient of determination compared to other vegetation indices. The stepwise multivariate linear regression models, with four visible and near infrared bands, resulted to the best yield predictive model in the peak of flowering stage, with 78 and 74 percent of coefficient of determination, for its implementation and validation, respectively.

کلیدواژه‌ها [English]

  • Yield Estimation
  • remote sensing
  • Vegetation index
  • NDYI
  • RVI
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