برآورد عملکرد محصولات کشاورزی با استفاده از تصاویر سری زمانی سنتینل‌ـ2 (مطالعة موردی: شهرستان زنجان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد سنجش ‌از دور و سیستم اطلاعات جغرافیایی، واحد الکترونیکی دانشگاه آزاد اسلامی، تهران

2 استادیار پژوهشگاه هوافضا، وزارت علوم تحقیقات و فنّاوری، تهران

چکیده

اساس برنامه‌ریزی و مدیریت صحیح داشتن آمار و اطلاعات دقیق و به‌هنگام است. یکی از مهم‌ترین آمار و اطلاعات بخش کشاورزی میزان تولید سالیانة هر محصول یا سطح زیرکشت است. یکی از ابزارهایی که در کمترین زمان و با هزینة پایین و دقت مناسب، می‌تواند سطح زیرکشت محصولات را محاسبه کند دانش و فنّاوری سنجش ‌از دور است. در این تحقیق، از دو روش طبقه‌بندی شبکة عصبی مصنوعی و ماشین بردار پشتیبان استفاده‌ شده و سطح زیرکشت محصولات غالب منطقه، شامل هشت کلاس، از تصاویر سری زمانی سنتینل‌ـ2 برآورد شده است. براساس نتایج به‌دست‌آمده، صحت کلی هریک از روش‌های شبکة عصبی مصنوعی و ماشین بردار پشتیبان به‌ترتیب برابر با 97.74 و 97.96%، با ضریب کاپای 0.97 برای برآورد سطح زیرکشت محصولات بوده است. بنابراین، نتایج این دو روش مورد قبول واقع شده است. با توجه به صحت کلی، می‌توان نتیجه گرفت که دو روش طبقه‌بندی نتایج تقریباً یکسانی در منطقه دارد. علاوه‌براین، طبق نتایج صحت کاربر، می‌توان بیان کرد درمورد چهار محصول یونجه، برنج، پیاز و خربزه، عملکرد روش ماشین بردار پشتیبان بهتر از روش شبکة عصبی است و درمورد گندم دیم و آبی، جارو و مرتع، روش شبکة عصبی بهتر از ماشین بردار پشتیبان در منطقه عمل می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of Agricultural Crop Yield Using Sentinel-2 Images (Case Study: Zanjan City)

نویسندگان [English]

  • Seyed Ahmad Mousavi 1
  • milad janalipour 2
  • Nadia Abbaszadeh Tehrani 2
1 M.Sc. Student of Remote Sensing and Geographic Information System, Islamic Azad University, Tehran
2 Assistant Prof. of Aerospace Research Institute, Ministry of Science, Research and technology, Tehran
چکیده [English]

The basis for proper planning and management is to have accurate and timely statistics and information. One of the most important statistics of the agricultural sector is the annual production rate of each crop, which also depends on the area under cultivation of crop and its efficiency. One of the tools that can calculate the area under cultivation in the least time, with low cost and with high accuracy is remote sensing science and technology. In this study, two classification methods including artificial neural networks and support vector machine with different kernels are used and the area under cultivation of major crops in the region consisting of 8 classes is estimated. According to the results, the overall accuracy of the artificial neural networks and support vector machine was 97.74% and 97.96% with a kappa coefficient of 0.97 for both methods, indicating that both methods are good for separation and classification of agricultural lands in the area. Based on the overall accuracy, it can be concluded that the two methods of classification have almost the same results in the region. Also, based on the results of the user's accuracy for the four crops including Alfalfa, Rice, Onion and Melon, the support vector machine method performs better than the neural network method and also for dry and water Wheat, Sorghum, and Pasture, the neural network method out performs the support vector machine in the region.

کلیدواژه‌ها [English]

  • Remote Sensing
  • Classification method
  • Artificial Neural Network
  • Support Vector Machine
  • Sentinel-2

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