نوع مقاله : علمی - پژوهشی
نویسنده
دانشگاه خواجه نصیرالدین طوسی
چکیده
تجمع گازهای گلخانه‌ای در اتمسفر، مهم‌ترین عامل افزایش دمای کرة زمین از نیمة دوم قرن بیستم به بعد، شناخته شده است. به‌دام‌انداختن کربن در جنگل‌ها و میان درختان راه‌حلی عملی، کارآمد و ارزان برای کاهش سطح دی‌اکسید‌کربن در اتمسفر است. بنابراین اندازه‌گیری زیست‌توده در بررسی تغییرات آب‌وهوایی و چرخة کربن جهانی اهمیت ویژه‌ای دارد. در پژوهش حاضر روشی بر پایة تبدیلات موجک به‌منظور تخمین زیست‌توده در منطقه‌ای جنگلی با درختان پهن‌برگ در شمال ایران ارائه شده است. تبدیلات مختلف موجک (تبدیلات دوبعدی گسسته) روی تصویر رادار با روزنة مجازی سنجندة ALOS PALSAR اعمال شدند و ضرایب به‌دست‌آمده به‌عنوان داده‌های جداگانه ذخیره شدند. میزان همبستگی هریک از پارامترهای محاسبه‌شده با مقدار زیست‌توده به‌وسیلة آنالیز رگرسیون چندگانه بررسی شد. نتایج نشان دادند که ضرایب به‌دست‌آمده از تبدیل موجک Db2 در مقایسه با سایر تبدیلات، همبستگی بیشتری با مقدار زیست‌توده دارند. در تجزیة یک‌مرحله‌ای، مقدار همبستگی با زیست‌توده تقریباً 5/0 و در تجزیة دومرحله‌ای تصاویر، مقدار همبستگی به‌دست‌آمده برای تصویر مایکروویو به بیش از 75/0 ارتقا پیدا کرد. پژوهش حاضر نشان داد که استفاده از تبدیلات موجک می‌تواند روش مناسبی برای تخمین زیست‌توده ـ به‌ویژه در مناطقی با ساختار پوشش گیاهی پیچیده ـ باشد. کلیدواژهها: تصاویر آلوس پالسار، تبدیل موجک، زیست‌تودة جنگل، آنالیز رگرسیون چندگانه.
کلیدواژهها
عنوان مقاله [English]
Estimation of Forest Biomass Using SAR Data
چکیده [English]
The increasing concentration of greenhouse gases has been identified as a main cause of increase of global mean temperatures since the mid-20th century. The effect of human-induced climate change could be unprecedented and far-reaching. Carbon sequestration into trees and forests is an effective and inexpensive way for mitigating the CO2 level in the atmosphere. Hence, accurate measurement of biomass will be of great importance to global carbon cycle and climate change. This study performed a wavelet-based forest aboveground biomass estimation approach in a temperate deciduous forest, Kheyroud Kenar forest in north part of Iran. Wavelet analysis, specifically two-dimensional discrete wavelet transform (DWT) was applied to ALOS PALSAR images to obtain wavelet coefficients (WCs), which were correlated with forest inventory data using multiple linear regression analysis to investigate the relationship. The results indicate that Db wavelet coefficients correlate better with field biomass data than other parameters. For the first level of the decomposition, the correlation coefficient is 0.5 while for second level, the overall R value increased up to 0.75. This study demonstrates that wavelet-based biomass estimation could be a very promising approach for providing better biomass estimation; however, further research is needed for identifying robust wavelet coefficients and optimizing procedures. Keywords: ALOS PALSAR, Wavelet analysis, Forest biomass, Multiple regression analysis.
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