تهیة نقشة تیپ جنگل با استفاده از تصاویر ابرطیفی پریسما در جنگل خیرود

نوع مقاله : مقاله پژوهشی

نویسندگان

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

2 گروه مدیریت منابع جنگلی، دانشکدة جنگلداری، دانشگاه کشاورزی کراکوف، کراکوف، لهستان.

10.22059/jfwp.2024.381635.1312

چکیده

آگاهی از تیپ جنگل یکی از موارد ضروری در برنامه ­ریزی و مدیریت پایدار جنگل بوده، ولی تهیة نقشة آن به روش‌های میدانی زمان­بر و پرهزینه است. از این ­رو، سنجش‌از‌دور به ­ویژه استفاده از داده‌های ابرطیفی همانند ماهوارة پریسما با 243 باند طیفی، می ­تواند به ­عنوان جایگزینی مناسب تلقی شود. پژوهش پیش‌رو به ارزیابی قابلیت این تصاویر براساس روش­ های ماشین بردار پشتیبان (SVM)، نقشه‌بردار زاویة طیفی (SAM) و جنگل تصادفی (RF) در بخش گرازبن جنگل خیرود پرداخته است. نقشة واقعیت زمینی در 131 قطعه‌نمونه با ابعاد 45×45 متر به‌طور میدانی با روش کوچلر و براساس فراوانی 100 اصله از قطورترین درختان تهیه و پنج تیپ شامل راش خالص، راش آمیخته، راش-ممرز، ممرز-راش و ممرز آمیخته مشخص شد. تصحیح هندسی تصاویر، رفع نویز و نرم­سازی منحنی طیفی انجام و سپس اثر انحنای طیفی تصحیح شد. الگوریتم آشکارسازی ناهنجاری­ها بر روی تصویر اعمال و  باندهای دارای نویز و محدوده­ های جذب آب حذف شدند. سپس برروی 103 باند طیفی باقی­مانده، الگوریتم حداقل سهم نویز و شاخص خلوص پیکسل برای یافتن پیکسل­ های خالص طیفی اجرا و در طبقه­ بندی بکار گرفته شدند. همچنین، شاخص­های NDVI، ReNDVI،MTCI ، NDIred و RTVI محاسبه و به مجموعه باندی اضافه شدند. الگوریتم ­های­ SVM و RF به‌ترتیب با صحت کلی 53/09و 43/19و ضریب کاپای 0/38 و 0/25 و استفاده از باندهای حاصل از الگوریتم حداقل سهم نویز، بهترین نتایج را نشان دادند. به‌طور کلی، نویز به نسبت زیاد داده ­های پریسما و رفتار طیفی نزدیک تیپ­ های مورد مطالعه به‌رغم انجام پردازش ­ها و بکارگیری الگوریتم ­های طبقه ­بندی مناسب، مانع از کسب نتایج رضایت‌بخش شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Forest type mapping using PRISMA imagery in the Kheyrud forest

نویسندگان [English]

  • Marjan Firoozy Nejad 1
  • Parviz Fatehi 1
  • Ali Asghar Darvishsefat 1
  • Vahid Nasiri 2
1 Department of Forestry and Forest Economics, Faculty of Natural Resources, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
2 Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Krakow, Poland.
چکیده [English]

Mapping forest types is essential for sustainable management, however, field methods are time-consuming and costly. Therefore, Modern remote sensing, especially hyperspectral imagery like PRISMA with 243 spectral bands, is valuable for mapping Hyrcanian mixed forests. However, its accuracy and capabilities must be assessed. In this study, the capability of PRISMA data was assessed using SVM, SAM, and RF classification approaches to create forest type maps for the Gorazbon district of Kheyrud Forest. A ground truth map consisting of 131 field sample plots, each measuring 45 × 45 m, was generated using Kuchler's abundance method based on the 100 trees with the largest diameters. Five forest types were identified: pure beech, mixed-beech, beech-hornbeam, hornbeam-beech, and mixed hornbeam. Geometric correction, smoothing and noise filtering was applied, then, Illumination correction was performed. Anomalies were detected using the RX algorithm. The minimum noise fraction (MNF) transformation and pixel purity index (PPI) were applied to the remaining 103 spectral bands and then used in the classification process. Additionally, indices such as NDVI, ReNDVI, MTCI, NDIred, and RTVI were incorporated into the classification process. The results showed that the SVM and RF algorithms achieved overall accuracies of 53.09% and 43.19%, with kappa coefficients of 0.38 and 0.25, respectively. The best combination of input data was derived from the spectral bands obtained through the MNF transformation. Based on the findings, high noise in PRISMA imagery and the similar spectral behavior of forest types in this region hindered the species discrimination and classification performance.

کلیدواژه‌ها [English]

  • Forest type
  • Hyperspectral images
  • MNF
  • PRISMA
  • SVM
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