شناسایی و برآورد عملکرد مزارع برنج با استفاده از تصاویر ماهوارهای و تکنیکهای سنجش‌ازدور (مطالعۀ موردی: استان کندز، افغانستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه جغرافیا، دانشگاه یزد، یزد، ایران

2 کارشناس ارشد سنجش ‌از دور و GIS، گروه جغرافیا، دانشگاه یزد، یزد، ایران

3 استاد گروه جغرافیا، دانشگاه یزد، یزد، ایران

4 دکتری مدیریت مناطق خشک و بیابانی، دانشگاه یزد، یزد، ایران

چکیده

بررسی سطح زیر کشت و برآورد میزان تولید محصولات کشاورزی، ازجمله برنج، تا حد زیادی می‌تواند باعث تأمین امنیت غذایی، تحلیل وضعیت محصولات کشاورزی و درنتیجه توسعۀ پایدار کشورهای درحال‌توسعه شود. در این پژوهش، با استفاده از تصاویر ماهوارۀ سنتینل-2،  به برآورد سطح زیرکشت و عملکرد برنج در استان کندز، کشور افغانستان در سال زراعی 2020 پرداخته شد. با به‌کارگیری سری ‌زمانی شاخص NDVI، مراحل فنولوژی گیاه برنج به‌دست آمد و پارامترهای فنولوژی (SoS و EoS) با استفاده از روش حداکثر تفکیک استخراج شد. سپس برای شناسایی و تعیین سطح زیرکشت مزارع برنج از روش طبقه‌‌بندی شیءگرای مبتنی‌‌بر فنولوژی استفاده شد. در این روش از سه نوع دادۀ میزان بازتابش باندهای انعکاسی، شاخص پوشش ‌‌گیاهی NDVI و پارامترهای فنولوژی به‌عنوان داده‌های کمکی استفاده شد. برآورد عملکرد با استفاده از روش تجربی تحلیل رگرسیون بین شاخص‌‌های گیاهی سنجش‌‌ازدوری (مانند: NDVI و LAI)  و داده‌های حاصل از برداشت زمینی انجام گرفت. برای ارزیابی صحت طبقه‌‌بندی و میزان عملکرد برآوردشده، از داده‌های مرجع، مانند نقاط برداشت میدانی و نقشه‌‌های پوشش اراضی سال‌های قبل استفاده شد. نتایج این تحقیق نشان داد که روش طبقه‌بندی شیءگرای مبتنی بر فنولوژی با دقت کلی 5/91 درصد و ضریب کاپا 87/0، روش دقیقی برای شناسایی مزارع برنج به شمار می‌رود. همچنان روش تجربی مبتنی بر تحلیل رگرسیون داده‌های زمینی و سنجش‌ازدوری با ضریب تعیین 86/0 و ضریب همبستگی پیرسون برابر با 92/0 دقت بالای آن را در برآورد عملکرد مزارع برنج نشان داد. صحت عملکرد برآوردشده در این پژوهش با مقایسۀ عملکرد واقعی (داده‌‌های برداشت میدانی) در 27 نقطۀ کنترلی ارزیابی شد. برای این کار از آزمون همبستگی پیرسون استفاده شد. این آزمون نشان داد بین عملکرد واقعی و عملکرد برآوردشده رابطۀ مثبت و بسیار قوی وجود دارد (000/0=P، 27=N و 929/0=R2).

کلیدواژه‌ها


عنوان مقاله [English]

Rice Paddies Mapping and Yield Estimating using Satellite Images and Remote Sensing Techniques (Case study: Kunduz province, Afghanistan)

نویسندگان [English]

  • Hamid Reza Ghafarian Malamiri 1
  • Mohammad Arif Saberi 2
  • Gholam ali Mozaffari 3
  • Fahime Arabi ali abad 4
1 Associate Professor of Geography, University of Yazd, Yazd, Iran
2 M. Sc of Remote Sensing and GIS, Department of Geography, University of Yazd, Yazd, Iran
3 Professor of Geography, University of Yazd, Yazd, Iran
4 Ph.D of Management of Dry and Desert Areas, University of Yazd, Yazd, Iran
چکیده [English]

Investigating the area under cultivation and yield estimation of agricultural products like rice; can greatly ensure food security, analyze the status of agricultural products and, finaly, sustainable development of developing countries. This study used Sentinel-2 satellite images, to estimate the area under cultivation and the yield of rice paddies in Kunduz province, Afghanistan in the 2020 crop year. Using the time series of NDVI index, the phenology stages of rice plants were obtained and the phenology parameters (SoS and EoS) were extracted using the maximum resolution method. Then, object-oriented classification method based on phenology was used to identify and determine the under-cultivated area of rice fields. In this method, three types of data of reflectivity of reflective bands, NDVI vegetation index and phenology parameters were used as auxiliary data. Yield estimation was done using the experimental method of regression analysis between remote sensing plant indices and the data obtained from ground harvesting. Also, the experimental method based on the regression analysis of ground data and distance measurement with the coefficient of determination of 0.86 and the Pearson correlation coefficient of 0.92 showed its high accuracy in estimating the yield of rice fields. The accuracy of the estimated performance in this research was evaluated by comparing the actual performance (field harvest data) in 27 control points. For this purpose, Pearson's correlation test was used. This test showed that there is a positive and very strong relationship between actual performance and estimated performance (P=0.000, N=27 and R2=0.929).

کلیدواژه‌ها [English]

  • Sentinel 2
  • Object-Based Classification
  • Phenology
  • Regression Analysis Vegetation Indices
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