برآورد شاخص‌های تنوع گونه‌ای در جنگل‌های هیرکانی با استفاده از داده‌های ماهواره سنتینل-2 (مطالعة موردی: جنگل خیرود، استان مازندران)

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه جنگلداری و اقتصاد جنگل، دانشکدة منابع طبیعی، دانشگاه تهران، کرج، ایران.

10.22059/jfwp.2023.362198.1261

چکیده

تنوع زیستی به‌عنوان یکی از نمایه ­های مهم پایداری جنگل، نقش مهمی در بررسی اثرات تغییرات اقلیمی بر بوم‌سازگان‌های جنگلی ایفا می‌کند. اندازه‌گیری تنوع درختان و درختچه‌ها در سطح جنگل، پیش‌نیازی برای نظارت و ارزیابی تغییرات تنوع زیستی است. سنجش از دور از جمله ابزارهای مناسب جهت جمع‌آوری داده‌ها برای برآورد تنوع گونه‌ای است. بدین‌منظور در پژوهش حاضر توانایی داده‌های سنجندة MSI ماهوارة سنتینل-2 مورد آزمون قرار گرفت. ابتدا در بخش­ های پاتم، نم­خانه، و گرازبن جنگل خیرود تعداد 75 قطعه‌­نمونه با ابعاد 20×20 متر پیاده‌سازی و مشخصات نوع، تعداد گونه‌ها در هر قطعه‌نمونة برداشت شدند. سپس شاخص‌های تنوع گونه‌ای بتا (جاکارد و سورنسن) برای هر یک از قطعه‌های نمونه در نرم‌افزار R محاسبه شدند. تصاویر سنتینل­-2 مربوط به تاریخ‌های 19 مرداد ماه (فصل تابستان) و 22 مهر ماه (فصل پاییز) سال 1400 دریافت شدند. پس از انجام پیش‌پردازش‌ها و اطمینان از کیفیت داده‌های ماهواره‌ای، پردازش‌های شامل تهیة شاخص‌های پوشش گیاهی، اعمال تجزیه مؤلفه‌های اصلی (PCA)، تبدیل تسلدکپ و محاسبة متغیرهای بافتی انجام شدند. همبستگی بین شاخص‌های تنوع گونه‌ای اندازه‌گیری شدة زمینی و متغیرهای طیفی و بافتی در سطح احتمال 95 درصد بررسی شد. به‌منظور مدل‌سازی از رگرسیون خطی چندمتغیره به روش گام‌به‌گام و جنگل تصادفی استفاده شد. نتایج تحلیل رگرسیون نشان دادند متغیرهای بافتی حاصل از تصویر فصل پاییز با ضریب تبیین برابر  0/383 و درصد خطای جذر میانگین مربعات معادل 36/57 مطلوب‌ترین عملکرد را در برآورد شاخص تنوع گونه‌ای سورنسن داشته است. به‌طور کلی، نتایج پژوهش حاضر بیان کرد تصاویر ماهواره‌ای سنتینل-2 عملکرد متوسطی در برآورد شاخص‌های تنوع گونه‌ای در سه بخش مورد مطالعة جنگل‌ خیرود دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of species diversity in the Hyrcanian forests using Sentinel-2 Data (Case study: Kheyrud forest, Mazandaran)

نویسندگان [English]

  • Azar Ghaisaryan
  • Parviz Fatehi
  • Vahid Etemad
Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

As a sustainable forest indicator, biodiversity plays a crucial role in understanding the effects of climate change on forest ecosystems. Measuring the diversity of trees and shrubs in forests is essential for monitoring and evaluating changes in biodiversity. Remote sensing (RS) is an effective tool for collecting such data. To estimate tree and shrub species diversity, we used Sentinel-2 data from August 10 and October 13, 2021. We measured 75 field plots with dimensions of 20 m × 20 m in the Patom, Namkhaneh, and Gorazban districts. In each field plot, the tree species and diameter at breast height of all trees with a diameter greater than 7.5 cm were recorded. We used the Jaccard and Sorensen indices in R software to calculate the beta diversity indices for each sample plot. Preprocessing steps were applied to the Sentinel2 data, and we then performed several spectral transformation approaches, that is, vegetation indices (VIs), principal component analysis (PCA), and Tasseled Cap, and generated texture variables. A vector map was used to extract the spectral and textural values corresponding to each field plot. Correlation analysis between the measured species diversity and spectral and textural variables was conducted at a 95% probability level. Multiple Linear Regression (MLR) analysis was performed using stepwise and Random Forest (RF) methods for modeling. Our regression analysis revealed that texture variables with a window size of 5×5 and spatial resolution of 10 m in Sentinel-2 summer images had the best performance in estimating the Sorensen diversity index( R2= 0.383 and RMSE%= 36.57). However, based on our results, we can conclude that the Sentinel-2 data has a moderate performance in estimating diversity in the Patom, Namkhaneh, and Gorazbon districts.

کلیدواژه‌ها [English]

  • Beta Species Diversity
  • Hyrcanian Forests
  • Multiple Linear Regression
  • Random Forest
  • Sentinel-2

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