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پیشبینی عملکرد کلزا در مراحل مختلف رشد بهوسیله تصاویر سنجنده OLI ماهواره لندست | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 9، دوره 50، شماره 1، فروردین 1398، صفحه 101-113 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2018.256567.665058 | ||
| نویسندگان | ||
| نعیم لویمی1؛ اسداله اکرم* 2؛ نیکروز باقری3؛ علی حاجی احمد4 | ||
| 1دانشجوی دکتری گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فن‌آوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 2دانشیار، گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 3استادیار پژوهش، موسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی | ||
| 4استادیار، 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 | ||
| تخمین عملکرد؛ سنجش از دور؛ شاخص گیاهی؛ NDYI؛ RVI | ||
| عنوان مقاله [English] | ||
| Prediction of Canola Yield in Some of Growth Stages by Using Landsat Satellite, OLI Sensor | ||
| نویسندگان [English] | ||
| Naeim Loveimi1؛ Asadollah Akram2؛ Nikrooz Bagheri3؛ Ali Hajiahmad4 | ||
| 1Ph.D. Student, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Associate Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran | ||
| 3Assistant Professor, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), karaj, Iran | ||
| 4Assistant 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 | ||
| مراجع | ||
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