ارزیابی مکانی شدت بیابان‌زایی نرماشیر با شاخص DDI

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، گروه جغرافیا، دانشگاه زنجان، زنجان، ایران

چکیده

بیابان‌زایی یکی از پدیده های مخرب زیست محیطی در سراسر جهان به شمار می آید. این تحقیق با هدف ترسیم درجه بیابان‌زایی در منطقه نرماشیر کرمان در سال 2023 با استفاده از ماهواره لندست 8 انجام شد. در ابتدا با استفاده از روش تبدیل کلاهک زنگوله ای TCT، سه شاخص روشنایی کلاهک زنگوله ای (TCB)، سبزی (TCG) و رطوبت (TCW) استخراج شد. در دومین مرحله، شاخص‌های تفاوت نرمال شده پوشش گیاهی (NDVI) و albedo برآورد گردید. در مرحله بعدی بر اساس ترکیب TCG-TCB، NDVI-albedo و TCW-TCB، تحلیل رگرسیون خطی انجام شد. نتایج نشان داد که همبستگی بالایی به میزان 77/0- بین TCW و TCB برقرار بوده و بین NDVI با albedo همبستگی بسیار ضعیفی معادل 25/0 ایجاد شده است. بیشترین همبستگی مثبت به میزان 49/0 در بین شاخص‌های TCW و TCG دیده می شود. با توجه به بیشترین مقادیر همبستگی و بر اساس روابط رگرسیونی بین TCW با TCB شاخصی برای درجه بندی بیابان‌زایی ایجاد شده و به پنج طبقه بیابان‌زایی خیلی کم، کم، متوسط، زیاد و خیلی زیاد تقسیم گردید. بر این اساس تنها 9/6 درصد از منطقه مورد مطالعه دارای شدت بیابان‌زایی خیلی زیاد بوده و 5/10 درصد از شدت بیابان‌زایی زیاد برخوردار هستند. دقت کلی این شاخص معادل 2/91 بوده است. این روش به علت سادگی و توانمندی بالا، از قابلیت بالایی در مدیریت و حفاظت از اراضی خشک و نیمه خشک برخوردار است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Desertification Assessment in Narmashir Region Using Desertification Degree Index (DDI)

نویسنده [English]

  • Mehdi Feyzolahpour
Assistant Professor, Department of Geography, University of Zanjan, Zanjan, Iran
چکیده [English]

Desertification is one of the most destructive environmental phenomena globally. Remote sensing data and techniques offer valuable information for desertification mapping and assessment. Desertification is a particularly grave environmental threat in Iran, especially in the southeastern parts of the country. This research was conducted to map the degree of desertification in the Narmashir region of Kerman province in 2023 using Landsat 8 satellite imagery. Initially, the Tasseled Cap Transformation (TCT) method was used to extract three indices: Tasseled Cap Brightness (TCB), Tasseled Cap Greenness (TCG), and Tasseled Cap Wetness (TCW). Next, the Normalized Difference Vegetation Index (NDVI) and albedo were estimated. Subsequently, linear regression analysis was performed on the combinations of TCG-TCB, NDVI-albedo, and TCW-TCB. The results showed a strong negative correlation between TCW and TCB and a very weak correlation between NDVI and albedo. The highest positive correlation was observed between TCW and TCG. Based on the highest correlation values and the regression relationship between TCW and TCB, a Desertification Degree Index (DDI) was created for desertification grading. The DDI was divided into five desertification classes: very low, low, medium, high, and very high. Based on this, only of the studied area exhibited very high desertification intensity, and showed high desertification intensity. The overall accuracy of this index was found to be. Due to its simplicity and high capability, this method is highly effective for managing and protecting arid and semi-arid lands.

کلیدواژه‌ها [English]

  • Tasseled Cap Transformation (TCT)
  • albedo
  • NDVI
  • TCW-TCB
  • Narmashir
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انتشار آنلاین از تاریخ 23 مهر 1404
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