قابلیت داده‌‌های سنجنده OLI ماهواره لندست 8 در برآورد زی‌‌توده روی زمینی توده‌‌های ممرز (Carpinus betulus) در جنگل خیرود

نوع مقاله: علمی- پژوهشی

نویسندگان

1 کارشناس ارشد جنگل‌‌داری، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

2 استاد، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

3 دانشجوی دکتری جنگل‌‌داری، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

DOR: 98.1000/1735-0883.1397.26.406.73.3.1602.1583
 
در پژوهش پیش‌رو به‌‌بررسی قابلیت داده‌‌های سنجنده OLI ماهواره لندست 8 به‌‌منظور برآورد زی‌‌توده روی زمینی توده‌‌های به‌نسبت خالص ممرز در بخشی از جنگل‌‌های هیرکانی پرداخته شد. برای مدل‌‌سازی و اعتبارسنجی نتایج، 55 قطعه‌‌نمونه به‌‌طور انتخابی تعیین شد و مقدار واقعی زی‌‌توده روی زمینی در آن­‌ها اندازه‌‌گیری شد. در هر قطعه‌نمونه، قطر برابر سینه تمام درختان قطورتر از 7/5 سانتی‌‌متر اندازه‌‌گیری شد. سپس با استفاده از جدول حجم محلی و چگالی بحرانی، زی‌‌توده روی زمینی این درختان محاسبه شد. پیش‌‌پردازش‌‌ها و پردازش‌‌های لازم بر روی داده‌‌های ماهواره‌‌ای انجام شد. مدل‌‌سازی ‌‌به‌روش‌های پارامتریک رگرسیون گام‌‌به‌‌گام، رگرسیون پس‌رو و روش‌‌های ناپارامتریک شبکه عصبی مصنوعی، k نزدیک‌‌ترین همسایه و جنگل‌‌تصادفی انجام شد. بررسی ضریب همبستگی پیرسون بین زی‌‌توده روی زمینی در قطعه‌نمونه­‌ها و ارزش‌‌های طیفی متناظر آن­‌ها در باندهای اصلی و محاسباتی نشان داد که باند مادون قرمز نزدیک، بیشترین مقدار همبستگی را با زی‌‌توده روی زمینی داشت (0/52-). از میان روش‌‌های پارامتریک، رگرسیون پس‌‌رو با ضریب تعیین تعدیل‌شده 0/295 و درصد مجذور میانگین مربع خطا 28/63 درصد و از میان روش‌‌های ناپارامتریک، شبکه عصبی مصنوعی با کمترین درصد مجذور میانگین مربع خطا (23/45 درصد) مناسب‌‌ترین عملکرد را برای برآورد زی‌‌توده روی ‌‌زمینی داشتند. نتایج این پژوهش را می‌‌توان ضعیف دانست. از این­‌رو، داده‌‌های سنجنده OLI ماهواره لندست 8 را فقط می‌‌توان در سطوح وسیع و به‌‌صورت کوچک‌مقیاس برای برآورد زی‌‌توده روی زمینی توده‌‌های ممرز به‌کار برد. البته برای کسب اطمینان از این نتیجه‌‌گیری کلی و تعمیم آن به جنگل‌‌های هیرکانی لازم است که مطالعات تکمیلی انجام شود.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the capability of Landsat 8 OLI data for estimation of aboveground woody biomass of common hornbeam (Carpinus betulus L.) stands in Khyroud Forest

نویسندگان [English]

  • Fardin Moradi 1
  • Ali Asghar Darvishsefat 2
  • Manouchehr Namiranian 2
  • Ghasem Ronoud 3
1 M.Sc. of Forestry, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Prof., Faculty of Natural Resources, University of Tehran, Karaj, Iran
3 Ph.D. Student, Faculty of Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

In this research, capability of Landsat 8 OLI was studied for estimation of aboveground biomass in pure stands of the common hornbeam (Carpinus betulus L.) in Hyrcanian forests of Iran. In order to obtain in situ aboveground biomass, diameters at breast height (DBH) of all trees greater than 7.5 cm were measured in 55 sample plots. Then, in situ aboveground biomass was calculated using local volume table and specific gravity in each plot. About 70 percentages of in situ measurements (40 sample plots) were used for modeling aboveground biomass based on Landsat 8 OLI data using different methods of stepwise regression, backward regression, artificial neural network, k-nearest neighbor and random forest. Validation of the models was done using 30 percentages of in situ measurements (15 sample plots). Based on the Pearson correlation coefficient, near-infrared band showed the highest correlation with aboveground biomass (0.52). Backward regression with adjusted R2 of 0.295 and RMSE% of 28.63%, and artificial neural network with RMSE% of 23.45% showed the best performance among parametric and non-parametric methods, respectively. Based on the results, Landsat 8 OLI data seems suitable for aboveground biomass estimation in pure stands of the common hornbeam only over large areas and small scale. Although more investigations are required to verify and generalize the results to the entire Hyrcanian forests of Iran.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • biomass
  • Hyrcanian forests
  • k-nearest neighbor
  • modeling
  • random forest
  • satellite image
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