تأثیر طرح‌های متفاوت نمونه‌برداری خوشه‌ای در برآورد مشخصه‌های کمّی جنگل‌های زاگرس،‌ با استفاده از تصاویر سنجندة سنتینل‌- 2

نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشگر پسادکتری جنگل‌داری، دانشکدة منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 استاد گروه جنگل‌داری، دانشکدة منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

3 محقق، دانشگاه کشاورزی و منابع طبیعی سوئد، اومئو، سوئد

4 استادیار گروه جنگل‌داری، دانشکدة علوم کشاورزی و منابع طبیعی، دانشگاه لرستان

چکیده

گردآوردی اطلاعات میدانی دقیق، به‌منظور مدیریت پایدار مناطق جنگلی، مستلزم صرف زمان و هزینة بالایی است؛ بنابراین استفاده از روش‌های نمونه‌برداری و تصاویر ماهواره‌ای جایگزین مناسبی برای این کار خواهد بود. هدف پژوهش حاضر تأثیر طرح‌های گوناگون نمونه‌برداری خوشه‌ای در برآورد مشخصه‌های کمّی جنگل‌های سامان عرفی اولادقباد شهرستان کوهدشت، استان لرستان، با استفاده از تصاویر سنجندة سنتینل‌ـ 2 است. به‌منظور برآورد مشخصه‌های مورد بررسی، 150 خوشه در قالب شش طرح (مثلث، مربع، ستاره‌ای 1، خطی، اِل‌شکل و ستاره‌ای 2) در منطقه‌ای به مساحت تقریبی 4500 هکتار ایجاد شد. هر خوشه‌ شامل چهار ریزقطعه‌نمونه، با مساحت هفتصد مترمربع (شعاع ریزقطعه‌نمونة دایره‌ای برابر با پانزده متر و فاصلة بین ریزقطعه‌نمونه‌ها از هم، شصت متر) بود. سپس در هر ریزقطعه‌نمونه‌، مشخصه‌های تعداد و مساحت تاج درختان اندازه‌گیری شد. پس از پیش‌پردازش و پردازش‌ تصاویر (تجزیة مؤلفة اصلی، آنالیز بافت و ایجاد شاخص‌های گیاهی)، ارزش‌های رقومی متناظر با قطعات نمونة زمینی از باندهای طیفی استخراج و به‌منزلة متغیرهای مستقل، در نظر گرفته شد. مدل‌سازی با استفاده از روش‌های ناپارامتریک جنگل تصادفی، ماشین‌بردار پشتیبان و نزدیک‌ترین همسایه انجام شد. نتایج نشان داد میانگین تراکم در هکتار 51 اصله و سطح تاج‌پوشش 3294 مترمربع در هکتار است. نتایج اعتبارسنجی نشان داد که درمورد هر دو مشخصة تراکم و سطح تاج‌پوشش، الگوریتم جنگل تصادفی به‌همراه طرح‌های نمونه‌برداری خطی و ستاره‌ای 2، به‌ترتیب با درصد مجذور میانگین مربعات خطا 00/46 و 44/10 و اریبی (02/0- و 82/2%)، عملکرد بهتری در مدل‌سازی داشته‌ است. به‌طورکلی نتایج اعتبارسنجی مشخص کرد استفاده از طرح‌های متفاوت نمونه‌برداری خوشه‌ای، روش‌های مدل‌سازی ناپارامتریک جنگل تصادفی و تصاویر سنجندة سنتینل‌ـ 2 کارآیی بهتری در برآورد مشخصة تاج‌پوشش دارد اما، در مقابل، عملکرد مناسبی در برآورد تعداد در هکتار را نداشته است.

کلیدواژه‌ها

عنوان مقاله [English]

The Effect of Different Cluster Sampling Schemes in Estimating the Quantitative Characteristics of Zagros Forests Using Sentinel 2 Sensor Images

نویسندگان [English]

  • Nastaran Nazariani 1
  • Asghar Fallah 2
  • Habibolah Ramezani Moziraji 3
  • Hamed Naghavi 4
  • Hamid Jalilvand 2
1 Postdoctoral Researcher of Forestry, Dep. of Forestry, Faculty of Forestry, Sari Agricultural Sciences and Natural Resources University
2 Prof. of Dep. of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources
3 Researcher, Swedish University of Agriculture and Natural Resources, Umeau, Sweden
4 Assistant Prof., Dep. of Forestry, Faculty of Agricultural Sciences and Natural Resources, Lorestan University
چکیده [English]

Gathering accurate information for statistics requires high cost and precision. The time factor is also one of the important issues that should be seriously considered in statistics. Therefore, the use of sampling methods and satellite images will be a good alternative for this purpose. In the present study, the aim of the effect of different cluster sampling schemes in estimating the quantitative characteristics of the traditional forests of Olad Ghobad in Koohdasht township, Lorestan province using Sentinel 2 sensor images. To estimate the studied characteristics, 150 clusters in the form of six designs (triangular, square, star 1, linear, L-shaped, star 2) were implemented in the region. Then, in each subplot, the characteristics of the number and area of the tree canopy were measured. Afterimage preprocessing and appropriate image processing (principal component analysis, texture analysis, and different spectral ratios to create important plant indices), the corresponding digital values of the ground sample plots are extracted from the spectral bands and used as independent variables. Modeling was performed using nonparametric methods of random forest, support vector machine, and nearest neighbor. The results showed that the average density per hectare was 51 and the canopy area was 32.94%. The diagram of the mean squares of the error of the training and test data against the number of trees for the characteristic number per hectare and canopy showed that the optimal number of trees was obtained at approximately 75 and 350 points. The results of validation according to the percentage of squared mean squared error showed that for both density and canopy surface characteristics of random forest algorithm with linear and double star sampling designs with the squared percentage of mean squared error respectively (46.00%) and (10.44%) and Bias (-0.02%, 2.82%) along with cluster sampling designs linear and double star, respectively, had better performance in modeling. In general, the results showed that the use of different cluster sampling schemes, nonparametric modeling methods, and Sentinel2 sensor images can better performance estimate the quantitative characteristics of Zagros forests.

کلیدواژه‌ها [English]

  • Cluster sampling
  • Non-parametric method
  • Olad Ghobad forest
  • Random forest
  • Remote sensing

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