برآورد ترسیب کربن و تنفس خاک جنگل با استفاده از مدل‌های مبتنی بر یادگیری ماشین در جنگل‌های شرق استان مازندران

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استاد، گروه علوم و مهندسی جنگل، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

2 دانش‌آموخته دکتری جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایر‏ان

3 دانشجوی دکتری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران‏

4 دانشجوی کارشناسی ارشد جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

چکیده

در این پژوهش مدل‌های مبتنی بر یادگیری ماشین (رگرسیون-خطی، k-نزدیک­ترین همسایه، ماشین بردار پشتیبان، جنگل تصادفی) برای برآورد ترسیب کربن و تنفس خاک در جنگل­های شرق استان مازندران ارزیابی شدند. پس از مشخص شدن نقاط نمونه­برداری، در هر یک از قطعات نمونه، قطر و ارتفاع درختان اندازه­گیری و زی­توده روی-زمینی درختان با استفاده از مدل‌های آلومتریک جنگل هیرکانی محاسبه شد. نمونه خاک از عمق صفر تا 20 سانتی­متر تهیه و تنفس خاک با دستگاه CO2-port اندازه­گیری شد. تنفس خاک با استفاده از متغیرهای وزن مخصوص ظاهری، درصد رطوبت، درصد اجزای بافت، دمای خاک، نیتروژن کل، فسفر و پتاسیم قابل‌جذب، درصد کربن و زی­توده درختان برآورد شد. ترسیب کربن خاک با کمک متغیرهای دما و رطوبت خاک و زی­توده درختان برآورد شد. مدل جنگل تصادفی (47/10RMSE= و 82/0R2=) و ماشین بردار پشتیبان (77/0RMSE= و 90/0R2=) به­ترتیب بالاترین عملکرد در برآورد ترسیب کربن و تنفس خاک داشت. متغیر رطوبت خاک در برآورد ترسیب کربن (مدل جنگل تصادفی) و تنفس خاک (مدل ماشین بردار پشتیبان) دارای بالاترین اهمیت نسبی بود. با توجه به نتایج به­دست­آمده می­توان با داشتن زی­توده روی-زمین درختان و ویژگی­های اولیه خاک، مقدار ترسیب کربن و تنفس خاک را در جنگل با دقت مناسب برآورد کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of carbon sequestration and forest soil respiration using machine learning ‎‎models in Eastern Forests of Mazandaran Province

نویسندگان [English]

  • Seyyed Mohammad Hojati 1
  • Mahya Tafazoli 2
  • Maryam Asadian 3
  • Ali Baluee 4
1 Professor, Department of Forest science and engineering, Sari Agricultural Sciences and Natural Resources ‎‎University, Sari, I. R. Iran‎
2 PhD in Forest Soil Science, Department of Forest science and engineering, Sari Agricultural Sciences and ‎‎Natural Resources University, Sari, I. R. Iran‎
3 PhD Student in Forestry, Department of Forest science and engineering, Sari Agricultural Sciences and ‎‎Natural Resources University, Sari, I.R. Iran‎
4 MSc Student in Forestry, Department of Forest science and engineering, Sari Agricultural Sciences and ‎‎Natural Resources University, Sari, I. R. Iran‎
چکیده [English]

In this study, machine learning based models (linear regression, k-nearest neighbor, support vector regression, random forest) were evaluated to estimate carbon sequestration and soil respiration in the forests of eastern Mazandaran province. After identifying the sampling points, in each plot, the diameter and height of the trees were measured and the above-ground biomass of the trees was calculated using allometric models of Hyrcanian forest. Soil samples were taken from 0-20 cm depth and soil respiration was measured by CO2-port. Soil respiration was estimated using bulk density, moisture content, texture, soil temperature, total nitrogen, available phosphorus and potassium, organic carbon and above-ground trees biomass. Soil carbon sequestration was estimated using soil temperature and moisture and above-ground tree biomass. Random forest model (RMSE = 10.47 and R2 = 0.82) and support vector machine (RMSE = 0.77 and R2 = 0.90) had the highest performance in estimating carbon sequestration and soil respiration, respectively. Soil moisture had the highest relative importance in estimating carbon sequestration (random forest model) and soil respiration (support vector machine model). According to the obtained results, it is possible to estimate the amount of carbon sequestration and soil respiration in the forest with appropriate accuracy by having the above-ground biomass of trees and basic soil characteristics.

کلیدواژه‌ها [English]

  • Climate change
  • Random Forest
  • Support vector regression
  • Forest soil properties
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