تهیۀ نقشۀ روشنۀ تاجی جنگل‌های خزری با استفاده از داده‌های پهپاد (مطالعۀ موردی: جنگل شصت‌کلاتۀ گرگان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری جنگلداری، دانشکدۀ علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 استاد گروه جنگلداری، دانشکدۀ علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

3 دانشیار گروه جنگلداری، دانشکدۀ علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

4 دانشیار گروه جنگل‌شناسی و اکولوژی جنگل، دانشکدۀ علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

10.22034/ijf.2022.301540.1801

چکیده

تهیۀ نقشۀ روشنه‌ها با استفاده از روش‌های دقیق و داده‌های مناسب برای درک بهتر ساختار جنگل و مدیریت آن ضروری است. هدف این پژوهش، بررسی و مقایسه روش‌های استخراج روشنه در تهیۀ نقشۀ روشنه‌های تاجی با استفاده از داده‌های پهپاد در بخشی از تودۀ مدیریت‌شدۀ طرح جنگلداری دکتر بهرام‌نیا (شصت‌کلاته) بوده است. مدل ارتفاعی تاج با استفاده از مدل رقومی سطح تاج حاصل از داده‌های پهپاد و مدل رقومی زمین حاصل از داده‌های لایدار استخراج شد. استخراج روشنه‌ها با روش‌های آستانه‌گذاری مدل ارتفاعی تاج (ارتفاع و شیب ثابت و ارتفاع نسبی) و طبقه‌بندی شیء‌پایۀ مدل ارتفاعی تاج و اورتوفتوموزائیک پهپاد انجام گرفت. نقشۀ واقعیت زمینی نقطه‌ای و محدوده‌ای با استفاده از برداشت زمینی و تفسیر بصری اورتوفتوموزائیک پهپاد تهیه شد. ویژگی‌های محدوده‌ای روشنه (مساحت، محیط و پیچیدگی شکل) محاسبه شد. صحت نقطه‌ای و تطابق هندسۀ محدوده‌ای روشنه‌های استخراجی با نقشۀ واقعیت زمینی ارزیابی شد. نتایج ارزیابی صحت نقطه‌ای نشان داد که به‌کارگیری روش آستانۀ شیب 60 درصد با صحت کلی 7/91 درصد و ضریب‌ کاپای 87/0 دارای بهترین نتیجه بوده است. در ارزیابی تطابق محدوده‌ای، بیشترین تطابق روشنه‌های استخراج‌شده با روشنه‌های واقعیت زمینی در آستانۀ ارتفاع نسبی و آستانۀ شیب 60 درصد به‌دست آمد. کمترین میانگین خطای برآورد پیچیدگی شکل روشنه، با آستانۀ شیب 70 درصد مدل ارتفاعی تاج (15/0) و کمترین میانگین خطای برآورد مساحت (76/33 متر مربع) و محیط (80/16 متر) در روش طبقه‌بندی شیءپایه مشاهده شد. به‌طور کلی چنانچه تطابق هندسی محدوده‌ای روشنه‌ها مدنظر باشد، روش طبقه‌بندی شیءپایه با صحت کلی (89 درصد)، می‌تواند روشنه‌هایی با تطابق مناسب و کمترین خطای برآورد محدوده ترسیم کند.

کلیدواژه‌ها


عنوان مقاله [English]

Canopy gap delineation using UAV data in a Hyrcanian forest (Case study: Shastklateh Forest)

نویسندگان [English]

  • Sh Amini 1
  • Sh Shataee Jouibary 2
  • M.H. Moayeri 3
  • R. Rahmani 4
1 Ph.D. Candidate of Forestry, Dept. of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran
2 Prof., Dept. of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran
3 Associate Prof., Dept. of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran.
4 4Associate Prof., Dept. of Silviculture and Forest Ecology, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran
چکیده [English]

Canopy gap delineation is essential for achieving a better comprehension of forest structure. This study aims to (a) extract canopy gaps using UAV data and (b) compare the performance of different canopy gap extraction methods in a managed stand in the northeast of Iran. A canopy height model (CHM) was produced by subtracting LIDAR digital terrain model from the UAV digital surface model. CHM classification performs to extract gaps by thresholding CHM (fixed height and CHM slope and relative height thresholds) and object-based classification on the UAV CHM and orthophoto. Ground truth is produced in the point and polygon forms through field measurements and visual interpretation of the UAV orthophoto. The geometry of the canopy gaps (Area, perimeter, and shape complexity) was calculated. Finally, the point and polygon base accuracy of delineated gaps assess for each of the methods. Point accuracy assessment suggests that 60% CHM slope produces the highest overall accuracy and Kappa coefficient of 91.7% and 0.874, respectively. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using relative height and 60% CHM slope thresholds. The lowest mean errors of GSCI produced by 70% CHM slope (0.15). Moreover, object-based classification showed the lowest mean error of area (33.76 m2) and perimeter (16.80 m). In conclusion, while area accuracy is considered the best fit of the delineated gap's geometry is gained by the object-based classification.

کلیدواژه‌ها [English]

  • Canopy Height Model
  • Object-based
  • Segmentation
  • Thresholding
  • UAV
 
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دوره 14، شماره 2
شهریور 1401
صفحه 135-154
  • تاریخ دریافت: 09 شهریور 1400
  • تاریخ بازنگری: 20 آبان 1400
  • تاریخ پذیرش: 22 آذر 1400
  • تاریخ اولین انتشار: 01 شهریور 1401