تهیه نقشه رقومی کربن آلی ذخیره شده در خاک با استفاده از روش‌های یادگیری ماشینی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم ومهندسی خاک، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی ومنابع طبیعی دانشگاه تهران. کرج، ایران

2 استاد گروه علوم ومهندسی خاک، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی ومنابع طبیعی دانشگاه تهران. کرج، ایران

3 استاد گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی ومنابع طبیعی دانشگاه تهران. کرج، ایران

چکیده

بررسی ذخایر کربن آلی خاک  (SOCS) در زمین‌های کشاورزی و نقش عوامل مؤثر بر تغییرپذیری آن و مدل‌سازی رقومی برای پیش‌بینی سناریوهای احتمالی ذخایرکربن در آینده مهم است. هدف از این مطالعه بررسی تنوع مکانی و برآورد مقدار کربن آلی ذخیره در عمق 100 سانتی متری بر اساس دو نسل از مدل‌های یادگیری ماشین در بخشی از دشت قزوین است.  محتوای کربن آلی خاک، 211 نمونه خاک که اطلاعات آن از قبل جمع آوری شده و موجود بود استخراج گردید. از متغیرهای محیطی، 11 متغیر برپایه مدل رقومی ارتفاع و 25 شاخص طیفی مستخرج از تصاویر ماهواره‌های لندست 8 و سنتینل 2 با قدرت تفکیک مکانی 10 متر استفاده شد. علاوه بر این، مجموعه داده‌ها به دو بخش تقسیم شد: 70 درصد از داده‌ها به عنوان آموزش و 30 درصد از داده ها برای اعتبارسنجی مدل انتخاب شدند. جهت مدل‌سازی کربن ذخیره آلی در منطقه مورد مطالعه از دو مدل جنگل تصادفی (RF) و جنگل تصادفی کوانتایل  (QRF) استفاده شد. نتایج اعتبارسنجی نشان داد که استفاده از مدل QRF ضریب تعیین بالاتری نسبت به مدل RF دارد. با توجه به نتایج اهمیت نسبی متغیرهای محیطی، پارامترهای مدل رقومی ارتفاع و عمق دره نسبت به سایر متغیرها در مدل‌سازی فضایی SOCS اهمیت بیشتری دارند. به طور کلی، پیشنهاد می‌شود که در فرآیند مدل‌سازی ویژگی‌های ثانویه خاک، به بررسی مدل‌های هیبریدی پرداخته شود.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting and Mapping of Soil Organic Carbon Stock Using Machin Learning Algorithm

نویسندگان [English]

  • Seyyed Erfan Khamoshi 1
  • Fereydoon Sarmadian 2
  • Mahmoud Omid 3
1 Ph.D. Candidate, Department of Soil Science, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 Professor, Department of Soil Science, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
3 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]

 
Investigation of soil organic carbon stock (SOCS) in agricultural lands and the role of factors affecting its variability and digital modeling are important for predicting possible scenarios of future carbon stock. The purpose of this study was to investigate the spatial variability and to estimate SOCS at 0 to 100 cm depth based on two generation of machine learning approaches in a part of Qazvin plain. SOCS of about 211 legacy soil data were prepared. The environmental variables including 11 geomorphometric variables and 25 spectral indices with 10-meter spatial resolution were used. Further, the dataset was divided into two parts: 70% of data were chosen as training and 30% of data for model validation. Two algorithm were used for SOCS modeling in the study area. Validation results indicated that the QRF had a higher coefficient of determination than the RF. According to the results of the relative importance of environmental variables, DEM and Valley depth parameters are more important in the spatial modeling of SOCS than other variables. Generally, it is suggested to investigate hybrid models in the process of modeling secondary soil characteristics.

کلیدواژه‌ها [English]

  • Machine learning
  • Soil organic carbon Stock
  • remote sensing
  • Environmental covariates
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