مدلسازی اندوخته کربن روی‌ زمینی جنگل‌های زاگرس با استفاده از داده‌های زمینی و تصاویر ماهواره لندست 8

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشکده منابع طبیعی و علوم دریایی دانشگاه تربیت مدرس

2 عضو هیات علمی

3 دانشیار دانشگاه گرگان

4 تربیت مدرس

چکیده

سابقه و هدف: اهمیت اطلاع از اندخته کربن روی زمینی جنگل برای مدیریت جنگل در سطح محلی، مدیریت اراضی در سطوح منطقه‌ای و گزارش انتشار کربن در سطوح ملی و بین‌المللی مهم است. به همین سبب یافتن رو‌ش‌های کم‌هزینه و سریع برای برآورد اندوخته کربن در محدوده‌های وسیع به یک ضرورت تبدیل شده است. بر این اساس، در سال‌های اخیر ارزیابی قابلیت داده‌های منابع مختلف سنجش‌ازدوری در برآورد اندوخته کربن روی زمینی جنگل‌ها مورد بررسی قرار گرفته است. در این تحقیق قابلیت تصاویر ماهواره لندست 8 برای برآورد اندوخته کربن توده‌های شاخه‌زاد بلوط جنگل های زاگرس بررسی گردید. همچنین کارایی چهار روش مدل‌سازی ناپارامتری شامل جنگل تصادفی، شبکه‌های عصبی مصنوعی، کوبیست و رگرسیون اسپلاین تطبیقی چندگانه بررسی شد.
مواد و روش‌ها: منطقه مورد مطالعه در بخشی از جنگلهای زاگرس و در استان کرمانشاه در دو منطقه سرفیروزآباد (جنگل شدیداً دست‌خورده) و گهواره (جنگل با حداقل دست‌خوردگی) انجام گرفت. تعداد 124 قطعه نمونه با ابعاد 30×30 متر در یک شبکۀ آماربرداری 200×200 متر در دو منطقه مورد بررسی با استفاده از روش منظم با شروع تصادفی برداشت، و با استفاده از روابط آلومتریک مختص گونه بلوط، مقدار کربن روی زمینی در این نمونه‌ها محاسبه شد. برای مدل‌سازی اندوخته کربن روی زمینی با استفاده از داده-های سنجش‌ازدوری، از متغیرهای مختلف استخراج‌شده از تصاویر لندست 8 مانند مقدارهای‌ باندهای، نسبت‌گیری‌های باندی، شاخص‌های گیاهی، تجزیه مؤلفه‌های اصلی و تبدیل تسلدکپ به‌عنوان متغیر مستقل و مقدارهای محاسبه‌شده اندوخته کربن روی -زمینی در قطعه نمونه‌های برداشت شده به‌عنوان متغیر وابسته استفاده شد. ارزیابی صحت نتایج چهار روش ناپارامتری مدل‌سازی جنگل تصادفی، شبکه‌های عصبی مصنوعی، کوبیست و رگرسیون اسپلاین تطبیقی چندگانه مورد استفاده در این پژوهش با استفاده از اعتبارسنجی متقابل و به روش Leave-one-out صورت گرفته و از معیارهای ارزیابی ضریب تبیین، ریشه میانگین مربعات خطا و اریبی استفاده شد.
یافته‌ها: نتایج نشان داد دقت برآورد اندوخته کربن در منطقه کمتر دستخورده بیشتر از منطقه دست‌خورده است. مقایسه نتایج روش‌های مدل‌سازی مورد استفاده در این مطالعه نشان داد که اختلاف قابل ‌توجهی در بین نتایج روش‌های به‌ کار گرفته شده وجود ندارد و استفاده از روش‌های مختلف تاثیر چندانی در بهبود نتایج نداشت. علاوه‌براین، استفاده از کل نمونه‌های در یک مدل بدون تفکیک نمونه‌ها براساس منطقه مورد مطالعه سبب افزایش صحت برآوردها در منطقه دست‌خورده شد.
نتیجه‌گیری کلی: نتایج برآوردها با ضریب تبیین بیشتر از 7/0 و درصد ریشه میانگین مربعات خطا نزدیک به 32% در مجموع هر دو منطقه مورد بررسی، بیانگر توانایی نسبتا مناسب تصاویر لندست 8 و روش‌های ناپارامتری در کمی کردن اندوخته کربن در جنگل‌های زاگرس با صرفه‌جویی در وقت و هزینه است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling aboveground carbon stock of Zagros forests using field data and Landsat 8 imagery

نویسندگان [English]

  • Amir Safari 1
  • Hormoz Sohrabi 2
  • Shaban Shataee 3
  • Galil Alavi 4
1
2
3
4
چکیده [English]

Abstract
Background and objectives: Information on aboveground carbon (AGC) is important for managing forests at local level, land management at regional levels, and carbon emissions reporting at national and international levels; therefore, there is a critical need for low-cost and time-saving approaches for quantifying of AGC. According to this, the estimation of aboveground carbon stock from remotely-sensed data has currently attracted a lot of attention. We assessed the capability of Landsat 8-derived spectral variables for AGC estimates in Zagros coppice oak forests by four non-parametric modeling including: random forest (RF), Cubist, Multivariate adaptive regression spline (MARS) and artificial neural networks (ANNs)
Materials and methods:
The study was carried out in part of Zagros forest, in Kermanshah Province. The values of aboveground carbon (AGC) terrestrial references was determined using field measurement data collected in two sites, Gahvareh (very low degraded (LD) site) and SarfiruzAbad (highly degraded (HD) site). Totally, 124 plots (30×30 meters) surveyed in two studied sites by the systematically-gridded plot design and AGC was calculated by developed species-specific allometric equations for Brant oak trees. For modeling AGC using the remotely-sensed data, we used different Landsat 8 derived variables such as single raw bands, simple band ratios, vegetation indices, principle component analysis and tasseled cap as independent variables and calculated AGC values in plots as dependent variable. The assessment of accuracies of four used non-parametric modeling methods: RF, Cubist, MARS and ANNs and was evaluated by “Leave-one-out” cross validation via criteria such as coefficient of variation (R2), root mean square error (RMSE) and bias.
Results: The results showed the accuracy of AGC estimates in LD site was better than HD site. The comparison of used modeling methods revealed that there are not significant difference in performances and accuracies of used non-parametric approaches. In additional, using the total plots from two test sites in one model caused the increase the results for HD site estimates.
Conclusion: Results showed R2 and relative RMSE values of approximately 0.7 and 32% cross-validated (combined two studied sites) for modeling AGC using Landsat derived variables, which reveals the high potential of Landsat 8 images and non-parametric modeling methods for quantifying AGC in cost and time saving approach for Zagros forests.
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کلیدواژه‌ها [English]

  • Artificial neural networks
  • Cubist
  • Multivariate Adaptive Regression Splines
  • Random Forest
  • Remote sensing
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