نشریه محیط زیست طبیعی

بررسی رابطه دمای سطحی و الگوی مکانی سرزمین با بکارگیری مدل های رگرسیونی و سنجه های سیمای سرزمین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه تهران، دکترای محیط زیست

2 گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه تهران، ایران

چکیده

با توجه به آثار منفی افزایش دمای مناطق انسان ساخت روی مصرف انرژی و رفاه انسانی، جبران این اثرات منفی از طریق کنترل الگوی پوشش های سبز و اثر خنک کنندگی آن از اهمیت زیادی برخودار است. هدف از این مطالعه تعیین مناسب ترین رابطه رگرسیونی الگوی مکانی سرزمین با دمای سطحی شهرستان رشت بوده است و بدین منظور از سنجه های سیمای سرزمین به عنوان متغیرهای ورودی به مدل سازی استفاده شد. کاربری/ پوشش و دمای سطحی زمین با استفاده از طبقه بندی تصویر سنجنده های OLI/TIRS خرداد ماه سال 1397 ماهواره لندست 8 نقشه سازی شدند. سپس سنجه های ترکیب و پیکره بندی محاسبه و در نهایت مدل های رگرسیونی مختلف برازش یافته و با هم مقایسه شدند. نتایج نشان داد گرچه هر چهار مدل خطی، لگاریتمی، نمایی و توانی کارایی مناسبی در پیش بینی دمای سطحی از طریق سنجه های سیمای سرزمین دارند، ولی بیشترین کارایی در منطقه مطالعاتی مربوط به مدل توانی است و این موضوع متاثر از الگوی چیدمان پوشش زمین در منطقه است.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating the Relationship between Land Surface Temperature and Landscape Spatial Pattern by Using Regression Models and Landscape Metrics

نویسندگان [English]

  • Bahman Jabbarian Amiri 1
  • Seyed Sadeq Dezhkam 2

1

2 Department of Environment, Faculty of Natural Resources, University of Tehran, Iran

چکیده [English]

Due to the negative effects of built up area temperature on energy consumption and human welfare, it is important to mitigate these negative effects by controlling the spatial pattern of green cover and its cooling effect. Therefore, in order to reveal the type and shape of relationship between landscape spatial patterns with land surface temperature (LST) in Rasht, the statistical modeling approach and landscape metrics were used. Land use- land cover and LST mapping were performed using Landsat sensor imagery classification and finally calculated landscape metrics of entered into the modeling process. Validation results showed that although all four linear, logarithmic, exponential and power regression models have good performance in predicting LST through land cover metrics, the highest performance was related to power model. In addition, this models indicate LST is strongly correlated with near distance to green patch and near distance to water patch metrics.

کلیدواژه‌ها [English]

  • Land Surface Temperature
  • Landscape Pattern
  • Regression
  • Rasht County
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نشریه محیط زیست طبیعی
دوره 73، شماره 2
مرداد 1399
صفحه 285-298