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بررسی قابلیت و حساسیت سنجی شاخص‌های طیفی ماهواره‌ای در پهنه‌بندی شدت آتش‌سوزی مناطق جنگلی (مطالعۀ موردی: جنگل‌کاری عرب داغ–گلستان) | ||
| نشریه جنگل و فرآورده های چوب | ||
| دوره 73، شماره 1، خرداد 1399، صفحه 97-110 اصل مقاله (1.39 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jfwp.2020.295878.1065 | ||
| نویسندگان | ||
| محمدواثق الحاجی خلف1؛ شعبان شتایی* 2؛ رقیه جهدی3 | ||
| 1دانشجوی کارشناسی ارشد علوم و مهندسی جنگل- مدیریت جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران. | ||
| 2استاد گروه جنگلداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران. | ||
| 3استادیار علوم و مهندسی جنگل، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران. | ||
| چکیده | ||
| تهیۀ نقشۀ دقیق شدت آتش‌سوزی برای مدیریت ریسک آتش در اکوسیستم‌های جنگلی حائز اهمیت است. شاخص‌های طیفی از سنجنده‌های نوری به‌عنوان یکی از باندهای قابل قبول برای طبقه‌بندی و نشان دادن تفاوت طیفی طبقات مختلف پوشش گیاهی شناخته شده است. در این تحقیق قابلیت مجموعه‌ای از شاخص‌های استخراج‌شده از تصاویر ماهواره‌های Sentinel-2 و Landsat-8 با اندازۀ تفکیک مکانی مختلف برای تهیۀ نقشۀ دقیق شدت آتش‌سوزی با استفاده از الگوریتم جنگل تصادفی در منطقۀ دچار آتش‌سوزی سال 1397 جنگلکاری‌های عرب‌داغ استان گلستان بررسی شد. بعد از پیش‌پردازش‌های لازم، شاخص‌های تک و دوزمانۀ مناسب از تصاویر سنجنده‌های تحت بررسی ایجاد شد. مقادیر شاخص بهینه برای باندها در فضای دوبعدی قبل و بعد از آتش‌سوزی برای بررسی حساسیت این باندها به تغییرات اتفاق‌افتاده درون طبقات آتش‌سوزی محاسبه شد. بهترین نتیجه مربوط به باندهای NIR-SWIR2 با مقدار شاخص بهینۀ 77/0 برای سنجندۀ Sentinel-2 و 68/0 برای سنجندۀ Landsat8-OLI به‌دست آمد. براساس مقادیر شاخص بهینه، بهترین شاخص‌ها انتخاب شد و مقادیر این شاخص‌ها پس از آتش‌سوزی و همچنین شاخص‌های دوزمانه (قبل و بعد آتش‌سوزی) استخراج شدند. نقشۀ واقعیت زمینی نمونه‌ای طبقات شدت آتش‌سوزی با استفاده از روش نمونه‌گیری انتخابی با بازدید میدانی از طبقات شدت دچار آتش‌سوزی در منطقه تهیه شد. طبقه‌بندی با شاخص‌های مختلف با الگوریتم جنگل تصادفی انجام گرفت و نتایج با نقشۀ واقعیت زمینی نمونه‌ای ارزیابی شد. بهترین نتیجه با تلفیق شاخص‌ها از همۀ باندهای استخراج‌شده از سنجندۀ Landsat8-OLI به روش شاخص دوزمانه با ضریب کاپای 96/0 به‌دست آمد. | ||
| کلیدواژه‌ها | ||
| الگوریتم جنگل تصادفی؛ تصاویر ماهواره‌ای؛ شاخص طیفی دوزمانه؛ شاخص بهینه؛ شدت آتش‌سوزی | ||
| عنوان مقاله [English] | ||
| Ability and Sensitivity Study of Spectral Indices for Wildfire Severity Mapping (Case Study: Arabdagh-Golestan Reforestations) | ||
| نویسندگان [English] | ||
| Mhd.Wathek Alhaj Khalaf1؛ Shaban Shataee2؛ Roghaye Jahdi3 | ||
| 1M.Sc. Student, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I.R. Iran | ||
| 2Prof., of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I.R. Iran | ||
| 3Assist. Prof., of Forest Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, I.R. Iran | ||
| چکیده [English] | ||
| Fire severity mapping is very important for managing the fires in forest ecosystems. The extraction of spectral indices from optical sensors is recognized as one of the most effective bands for the classification of vegetation classes. In this study, the ability and sensitivity of some spectral indices extracted from Sentinel-2 and Landsat 8-OLI images with different spatial resolutions have been investigated for fire severity mapping using the Random Forest algorithm in a burned area located in the reforested area of Arabdagh, Golestan province. After necessary preprocessing on the bands, the appropriate mono and bi-temporal spectral vegetation indices were created. The optimal index values for bands in the bi-spectral spaces pre/post-fire were calculated to evaluate the sensitivity of bands to the changes occurring within the fire classes. The best results were obtained for the NIR-SWIR2 bands with an optimal index value of 0.77 for Sentinel-2 and 0.67 for Landsat8-OLI. The best indices were selected based on values of optimality index. The values of these indices were calculated after the fire as well as the differential (pre/post-fire) ones. The ground truth of fire severity classes map was prepared by a selective sampling method through field surveying. The classification was done with different indices by random forest (RF) algorithm and the results were assessed by the ground truth points. The result showed that the best results were obtained for a combination of many differential indices from all bi-bands of Landsat 8-OLI with kappa coefficient (0.96). | ||
| کلیدواژه‌ها [English] | ||
| Bi-Spectral indices, optimality, random forest algorithm, satellite images, wildfire severity | ||
| مراجع | ||
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