مقایسه قابلیت داده‌های سنجنده های WorldView-2، Pleiades-2و IRS-LISS III در برآورد موجودی جنگل (مطالعه موردی: جنگل آموزشی پژوهشی دارابکلا- ساری)

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری

2 دانشیار دانشگاه کشاورزی ساری

3 عضو مرکز تحقیقات مرکز تحقیقات منابع طبیعی مازندران

چکیده

چکیده

سابقه و هدف: آگاهی از وضعیت مشخصه‌های کمی جنگل همانند موجودی سرپا، یکی از مهمترین اصول در برنامه‌ریزی و تصمیم‌گیری مدیریت جنگل می‌باشد. هدف از این مطالعه، مقایسه قابلیت داده‌های سنجنده‌های مختلف و روش‌های ناپارامتری در برآورد موجودی سرپای جنگل می‌باشد.

مواد و روش‌ها: منطقه مورد مطالعه سری یک جنگل دارابکلا در استان مازندران در جنوب شرق ساری است با مساحت 2612 هکتار که در حوزه آبخیز 74 اداره کل منابع طبیعی شهرستان ساری واقع شده است. با استفاده از روش نمونه برداری منظم -تصادفی با قطعات 10 آری با شبکه آماربرداری 330 در 500 متر ، 150 قطعه نمونه دایره ای برداشت گردید. پیش‌پردازش و پردازش‌های لازم همانند نسبت گیری، ایجاد شاخص‌های گیاهی و آنالیز بافت بر روی تصاویر ماهواره‌ای سه سنجنده WorldView-2، Pleiades-2 وIRS-LISS III انجام شد. سپس ارزش متناظر با قطعه نمونه ها از تمام باندها استخراج گردید. برای مدلسازی در این مطالعه از روش‌های مختلف رگرسیونی شامل واریانت های مختلف روش نزدیکترین همسایه، کرنل‌های مختلف روش ماشین بردار پشتیبان و روش جنگل تصادفی استفاده شد.
یافته‌ها: نتایج مربوط به مدلسازی موجودی سرپا با استفاده از روش ماشین بردار پیشتبان(SVM) نشان داد بهترین کرنل به ترتیب برای سنجنده worldview- 2،IRS-LISS III وPleiades-2 چند جمله ای، توابع پایه شعاعیRBF)) و چندجمله ای، با درصد مجذورمیانگین مربعات خطای 57/34، 5/49، 03/43 بود. در روش نزدیک ترین همسایه(KNN) بهترین واریانت برای سه سنجنده مذکور به ترتیب شبیشف(Chebychev)، شبیشف (Chebychev) و سیتی بلاک (City block) با درصد مجذورمیانگین مربعات خطای 18/41، 09/55 و 97/46 بود . در روش جنگل تصادفی درصد مجذورمیانگین مربعات خطا برای این سه سنجنده به ترتیب برابر با 33/31 ، 91/48 و 68/45 بود . نتایج نشان داد بهترین مدل برای برآورد موجودی سرپا، مربوط به الگوریتم جنگل تصادفی و داده‌های تصاویر WorldView-2 با درصد مجذور میانگین مربعات خطا برابر با 33/31 درصد و اریبی نسبی برابر با 8/2 درصد بود. دلیل بهتر بودن نتایج سنجنده World Veiw2 نسبت به سنجنده Pleiades وجود تعداد باند بیشتر و عرض کمتر باندها می‌باشد. زیرا هرچه تعداد باند بیشتر و عرض باند باریکتر باشد اطلاعات در باندهای مختلفی ذخیره می شوند و نسبت سیگنال به نویز افزایش می یابد در نتیجه آشکارسازی پدیده ها بهتر صورت می گیرد و دقت نتایج نیز بالاتر می رود.

نتیجه گیری: نتایج تفاوت زیادی بین الگوریتم‌های ناپارامتریک از نظر میزان درصد مجذور میانگین مربعات خطا نشان نداد ولی از نظر سنجنده تفاوت زیادی مشاهده گردید. نتایج کلی این مطالعه نشان داد سنجنده‌ها و روش‌های رگرسیونی مورد استفاده در این مطالعه، دارای قابلیت نسبتا مناسبی در برآورد موجودی جنگل می‌باشند. همچنین نتایج نشان داد علاوه بر قدرت تفکیک مکانی سنجنده ها، قدرت تفکیک طیفی آنها نیز تأثیر چشمگیری در بالا بردن دقت نتایج مدلسازی موجودی جنگل با استفاده از تصاویر ماهواره ای دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of WORLD VIEW2 , PLEIDES2 and IRS LISSIII satellites capability for estimating stand volume of forest ( case study: Darabkola Experimental Forest)

نویسندگان [English]

  • Vahideh Bahrami 1
  • Asghar Fallah 2
  • Ramzanali Khorami 3
چکیده [English]

Abstract

Background and objectives : Investigation on quantitative characteristics of forest such as Stand volume is one of the most important principles in planning and forest management decision. The aim of this study is comparison of various satellites data capability and non-parametric methods for estimating stand volume of forest.

Materials and methods : The studied area is district 1 Darabkola forest in Mazandaran province in southeast of Sari with 2612 hectares which is located in 74 basin of Sari natural recourses Department. Using systematic-random with 10 R.sample plots with 300m×500m sampling net system were measured150 circular sample plots. The necessary preprocessing and processing include ratio, vegetation index, Principal Component Analysis and texture analyse were done on WorldView-2، Pleiades-2 and IRS-LISS III imagery . For modeling in this study be used different regression methods include different variants of k-Nearest Niebuhr, kernel machine support vector and random forest .

Results : The results of modeling the stand volume using machine support vector showed that the best kernel in order for worldview- 2,IRS-LISS III and Pleiades-2 satellites was Polynomial,RBFand Polynomial with %RMSE equal to 34/57,49/5 and 43/03.The best variant in k-Nearest Niebuhr in order for said satellites was chebychev,chebychev and City block with %RMSE equal to 41/18,55/09 and 46/97. %RMSE in random forest method in order for said satellites was 31/33,48/91 and 45/68. Results showed random forest was the best model for estimation stand volume and WorldVeiw-2 satellite data has the best result with percent root mean square error and bias of estimation equal to 31.33 and 2.8 percent.Because of more bands and less width of them, WorldView-2 satellite has better outcomes than Pleiades-2 satellite; since if there are more bands and width of them is narrower, information can be saved in different bands and ratio of signal to noise will be increased. Therefore, phenomenon detects better and accuracy of outcomes increases.

Conclusion : The results did not show much difference between the non-parametric algorithms in terms of Percent Root Mean Square Error, but a large difference was observed in terms of sensors. Overall results of this study showed sensors and Regression methods used in this study have a relatively high capability in estimation of forest stand volume . The results also show in addition to the spatial resolution of satellites their spectral resolution has a significant impact on raising the accuracy of the forest stand volume modeling results using satellite images .

کلیدواژه‌ها [English]

  • World View 2
  • Pleiades
  • Nearest Neighbor
  • Support Vector Machine and random forest
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