نوع مقاله : علمی - پژوهشی
نویسندگان
1 کارشناس ارشد سنجش از دور، دانشکدۀ نقشهبرداری، دانشگاه صنعتی خواجه نصیرالدین طوسی
2 استاد گروه فتوگرامتری و سنجش از دور، دانشکدۀ نقشهبرداری، دانشگاه صنعتی خواجه نصیرالدین طوسی
3 استادیار گروه فتوگرامتری و سنجش از دور، دانشکدۀ نقشهبرداری، دانشگاه صنعتی خواجه نصیرالدین طوسی
4 کارشناس ارشد سنجش از دور و عضو سازمان فضایی ایران
چکیده
ایران یکی از کشورهای خشک و نیمهخشک بهشمار میرود که به خشکسالی دچار است. کمبود اطلاعات هواشناسی طولانیمدت در پهنۀ وسیعی از کشور یکی از بزرگترین مشکلات برای مشاهده و پیشبینی کوتاهمدت خشکسالی در ایران است. در این مقاله، با بهکار بردن روش ماشین بردار پشتیبان (SVM) و با استفاده از دادههای 42 ایستگاه سینوپتیک منتخب در ایران، عملکرد شاخصهای پوشش گیاهی طیفی پهنباند NDVI، NDVI-DEV، VCI و TCI در پیشبینی خشکسالی بررسی شد. بدین منظور، از شاخص خشکسالی (SPI) برای بیان خشکسالی استفاده شد که نشاندهندۀ شدت و دورۀ خشکسالی، از سال 1985 تا 2008 است. شاخصهای پوشش گیاهی یادشده از تصاویر سنجندۀ NOAA-AVHRR محاسبه و استخراج شدند. این شاخصها، بهصورت ورودی، به مدل SVM وارد شدند و مقادیر SPI را بهدست دادند. با این روش، شاخصهای TCI و NDVI، بهترتیب، دارای بالاترین و پایینترین همبستگی با شرایط خشکسالی شناخته شدند
کلیدواژهها
عنوان مقاله [English]
An Investigation of Remote Sensing Vegetation Indices Ability in Drought Condition Prediction in Iran
نویسندگان [English]
- H Heydari 1
- M.J Valadan Zouj 2
- Y Maghsoudi 3
- M.R Beheshtifar 4
1 M.Sc. of Geodesy and Geomatics Engineering Faculty, K.N.Toosi University of Technology
2 Professor, Dep. of Photogrammetry and Remote Sensing, College of Geodesy and Geomatics, K. N. Toosi University of Technology
3 Assistant prof., Dep. of Photogrammetry and Remote Sensing, College of Geodesy and Geomatics, K. N. Toosi University of Technology
4 M.Sc. of Remote Sensing and Member of Remote Sensing Centre, ISA, Tehran, Iran
چکیده [English]
Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices (SVIs) have been employed in order to drought modeling and its forecast. To this end, SPI drought indicator (standardized precipitation index) used to represent period of drought and its intensity. Some broad band spectral vegetation indices including Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were extracted by using NOAA-AVHRR satellite imagery. These indices entered to SVM classifier model to gain the SPI index as its result. After comparing the results, TCI was diagnosed as the best index to predict drought condition via 3 months SPI (trimester SPI).
کلیدواژهها [English]
- remote sensing
- Drought monitoring
- Spectral vegetation indices
- SPI
- SVM
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