بررسی امکان تهیه نقشه پراکنش توده‌های زیرآشکوب شمشاد خزری (Buxus hyrcana) با تصاویر طیفی فصل خزان ماهواره آیکونوس (مطالعه موردی: ذخیره گاه شمشاد خیبوس– انجیل سی، مازندران)

نوع مقاله: علمی- پژوهشی

نویسندگان

1 دانشجوی دکتری جنگل‌داری، دانشکده کشاورزی و منابع طبیعی، دانشگاه لرستان، خرم‌آباد، ایران

2 استاد، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

3 استادیار، دانشکده کشاورزی و منابع طبیعی، دانشگاه لرستان، خرم‌آباد، ایران

4 دکتری جنگل‌داری، سازمان فضایی ایران، کرج، ایران

چکیده

با توجه به اهمیت شمشاد خزری (Buxus hyrcana ) به‌عنوان گونه همیشه‌سبز و زیراشکوب جنگل‌های هیرکانی و ضرورت شناخت سطح پراکنش آن، در پژوهش پیش‌­رو قابلیت تصویر فصل خزان چندطیفی ماهواره آیکونوس در تهیه نقشه پراکنش شمشاد در بخشی از منطقه حفاظت‌شده جنگلی خیبوس- انجیل­‌سی مازندران بررسی شد. تطابق هندسی تصاویر آیکونوس با استفاده از تصویر پانکروماتیک ماهواره Pleiades از همین منطقه که پیشتر با استفاده از نقاط کنترل زمینی و ارتفاعی برداشتی توسط سامانه موقعیت‌­یاب جهانی تفاضلی زمین‌مرجع شده بودند، با RMSE کمتر از یک پیکسل انجام شد. نقشه واقعیت زمینی نمونه‌­ای با سه طبقه غیرجنگل، جنگل پهن­‌برگ بدون زیرآشکوب شمشاد و جنگل پهن­‌برگ دارای زیرآشکوب شمشاد به کمک DGPS برداشت و تهیه شد. پس از ایجاد شاخص‌های گیاهی، تفکیک‌­پذیری طبقات با معیار واگرایی تبدیل‌شده با استفاده از 75 درصد از واقعیت زمینی به‌عنوان نمونه‌های تعلیمی بررسی شد. طبقه­‌بندی نظارت‌شده با الگوریتم­‌های مختلف پارامتریک (حداکثر احتمال، فاصله ماهالونوبیس، حداقل فاصله، متوازی­‌السطوح) و ناپارامتریک (ماشین ­بردار­ پشتیبان) روی باندهای اصلی و بهترین ترکیب باندی انجام شد و صحت نتایج طبقه­بندی با 25 درصد نمونه‌های نقشه واقعیت زمینی ارزیابی شد. نتایج نشان داد که نتیجه طبقه­‌بندی با الگوریتم ماشین بردار پشتیبان هم با باندهای اصلی و هم با بهترین ترکیب باندی دارای بهترین صحت کلی و ضریب کاپا (به‌ترتیب 97/87 درصد و 0/96) در مقایسه با دیگر الگوریتم‌های مورد استفاده بود. نتایج نشان داد که تصویر چند­طیفی فصل خزان ماهواره آیکونوس قابلیت زیادی در تهیه نقشه پراکنش شمشاد داشت و کارایی الگوریتم ناپارامتریک ماشین بردار پشتیبان در مقایسه با الگوریتم‌های دیگر بیشتر بود.

کلیدواژه‌ها


عنوان مقاله [English]

Capability investigation on spectral images of Ikonos from leaveless season for Box (Buxus hyrcana Pojark.) understory distribution mapping in the Hyrcanian forest (Case study: Khiboos-Anjilsi Buxus reserved area, Mazandaran)

نویسندگان [English]

  • Rouhollah Esmaili 1
  • Shaban Shataee Joibari 2
  • Javad Soosani 3
  • Hamed Naghavi 3
  • Farrokh Poorshakori 4
1 Ph.D. Student Forestry, Faculty of Agriculture and Natural Resources, Lorestan University, Khoram Abad, Iran
2 Prof., Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
3 Assistant Prof., Faculty of Agriculture and Natural Resources, Lorestan University, Khoram Abad, Iran
4 Ph.D. Forestry, Iranian Space Agency, Karaj, Iran
چکیده [English]

As one of the most important understory evergreen species in Hyrcanian forests of Iran, information on the distribution of  Box  (Buxus Hyrcana Pojark.) are essential for both forest research and practice. Here, the capability of very high spatial resolution IKONOS satellite imagery acquired in leaf-off condition was tested for mapping Box distribution in a part of Khiboos-Anjili forest reserve in Mazandaran province. The IKONOS imagery was geometrically corrected with a georefrenced panchromatic Pleaides scene, which was orthorectified using 3D ground control points obtained using differential GPS (RMSE less than one pixel). Reference data samples from three classes of non-forested area, deciduous stands without Box understory and deciduous stands with Box understory were recorded using DGPS-supported field survey. By means of a number of vegetation indices, classes seperabilities were evaluated on main and synthetic image channels by partitioning 75% training area and transformed divergence. IKONOS image was classified using both main and best-selected bands and a number of nonparametric (Maximum Likelihood, Mahalonobis distance, Minimum distance to mean and Paralell piped) and parametric (Suport Vector Machine) classifiers. Then the classified images were assessed using 25 percent of unused sample points. Results of validation using the 25% left-out test data showed the highest performance by SVM algorithm compared to other algorithms, with overall accuracy and Kappa coefficient of 97.87% and 0.96, respectively. The results also showed the potential of IKONOS imagery from leaf-off season has to map Box trees in understory layer.

کلیدواژه‌ها [English]

  • Hyrcanian forests
  • Kappa coefficient
  • parametric and non-parametric algorithms
  • Suport Vector Machine
  • vegetation index

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