تجزیه‌ و تحلیل آماری و پیش‌بینی تغییرات زمانی- مکانی جزایر حرارتی شهری با استفاده از داده‌های سنجش‌ از دور

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه سنجش از دور و سیستم اطلاعات جغرافیایی، مرکز سنجش از دور و GIS، دانشگاه شهید بهشتی، تهران، ایران

چکیده

سابقه و هدف: جزیره گرمایی شهری به‌عنوان یکی از اثرات توسعه شهرنشینی می‌تواند بر روی گیاهان و جانوران درگیر در اکوسیستم شهری و حومه‌ای، غلظت آلاینده‌ها، کیفیت هوا، مصرف انرژی و آب و همچنین سلامت و اقتصاد انسان تأثیر منفی بگذارد. بنابراین، تجزیه وتحلیل مکانی-زمانی تغییرات جزیره گرمایی شهری به­ عنوان رویکردی موثر برای درک تأثیر شهرنشینی بر اکوسیستم شهری و حومه‌ای در نظر گرفته شده است که می‌تواند از توسعه و برنامه‌ریزی شهری پایدار نیز حمایت کند. بر این اساس، این مطالعه یک رویکرد جدید برای شناسایی روند و پیش‌بینی الگوی تغییرات جزایرحرارتی شهری با استفاده از تجزیه و تحلیل آماری، آنتروپی شانون و آمار کای اسکور ارائه می‌کند.
مواد و روش‌ها: منطقه موردمطالعه در این تحقیق شامل شهر رشت و اطراف آن است که در شمال کشور ایران واقع است. این مطالعه با استفاده از تصاویر سنجش از دور از سال 1991 تا 2021 که توسط ماهواره لندست 5 و 8 با فاصله زمانی ثابت 10 سال جمع آوری شده است، اجرا شد. تمامی تصاویر مربوط به فصل تابستان است. برای انجام این مطالعه ابتدا پیش‌پردازش‌های موردنیاز همچون تصحیحات اتمسفری و رادیومتریکی بر روی تصاویر اعمال‌شده است سپس در گام دوم شاخص‌های بیوفیزیکی سطح منطقه از تصاویر ماهواره‌ای استخراج‌شده است. در گام سوم دمای سطح زمین نیز با استفاده از تصاویر ماهواره‌ای در سال 2021 محاسبه شد. در گام چهارم، رگرسیون خطی چند متغیره خصوصیات بیوفیزیکی سطح و دمای سطح زمین در سال 2021 اعمال شد و سپس از مدل سلول های خودکار - زنجیره مارکوف برای پیش‌بینی دمای سطح زمین برای سال 2031 استفاده شد. در نهایت الگوی تغییرات جزایر حرارتی شهر رشت با استفاده از تحلیل‌های آماری در جهات جغرافیایی مختلف و دوره‌های زمانی متفاوت مورد بررسی قرار گرفت.
نتایج و بحث: نتایج این مطالعه نشان داد که بیشترین همبستگی مثبت (R=0.89) بین شاخص NDBI و دمای سطح زمین بوده است. همچنین بیشترین همبستگی منفی (R = -0.81) بین شاخص سبزینگی و دمای سطح زمین و در نهایت کمترین همبستگی (R = 0.42) بین شاخص درخشندگی با دمای سطح زمین بود. پیش‌بینی دمای سطح زمین با استفاده از مدل رگرسیون چند متغیره و شاخص‌های بیوفیزیکی سطح حاکی از خطای پایین این مدل (RMSE=1.33K) برای پیش‌بینی دمای سطح زمین در سال 2021 است. این بدان معناست که مقادیر پیش بینی شده در سال 2021 به مقادیر واقعی نزدیک است و بنابراین می ­توان به این مدل برای پیش بینی دمای سطح زمین در سال 2031 اعتماد کرد. تجزیه و تحلیل آماری درباره الگوی تغییرات جزایر حرارتی مشاهده ‌شده و مورد انتظار نشان می‌دهد که میزان نرخ تغییرات برحسب زمان و مکان متفاوت بوده است و همچنین به صورت پیوسته از سال 1991 تا 2031 رو به افزایش است. علاوه بر این این تجزیه و تحلیل ­ها همچنین نشان داد که جزایر حرارتی شهر رشت از درجه آزادی بالا و درجه پراکندگی بالایی برخودار است. بنابراین درجه خوب بودن آن منفی است.
نتیجه‌گیری: الگوی تغییرات جزایر حرارتی از گذشته تا به زمان حال و پیش بینی آن در آینده نشان می ­دهد که وابستگی بالایی با الگوی تغییرات اراضی ساخته‌ شده دارد. در نتیجه با نظارت و کنترل مستقیم الگوی اراضی ساخته شده (همچون توسعه عمودی از طریق بام و دیوارهای سبز و مصالح ساختمانی با توان بازتابی بالا) و جلوگیری از ساخت و سازها در زمین­ های کشاورزی حاشیه شهر الگوی تغییرات جزایر حرارتی را کنترل نمود.

کلیدواژه‌ها

عنوان مقاله [English]

Statistical analysis and predicting spatio-temporal variations of urban heat islands using remote sensing data

نویسندگان [English]

  • Keyvan Ezimand
  • Hossein Aghighi
  • Davod Ashourloo
  • Alireza SHakiba
Department of Remote Sensing and GIS, Remote Sensing and GIS Center, University of Shahid Beheshti, Tehran, Iran
چکیده [English]

Introduction: The urban heat island (UHI) as a climatic effect of urbanization can negatively impact the flora and fauna involved in urban and suburban ecosystems, the presence of pollutants, air quality, energy and water consumption, as well as human health and economy. Therefore, spatiotemporal analysis of the urban heat island changes has been considered an effective approach to understanding the impact of urbanization on the urban and suburban ecosystem, which also can support sustainable urban development and planning. Accordingly, this study contributes a novel approach to identifying the trend and predicting the pattern of UHI changes using statistical analysis, Shannon's entropy and chi-score statistics.
Material and methods: The study area of this research is the city of Rasht and its surroundings, a region located in the north of Iran. This research was implemented using remote sensing images from 1991 to 2021 that were collected by LANDSAT 5 and 8 with a fixed time interval of 10 years. All images were captured in the summer. In order to conduct this research in the pre-foresight stage, first, the required preprocessing, including atmospheric and radiometric corrections applied to the satellite images. Then, the surface biophysical characteristics of the study area were extracted from the satellite images. In the third step, the land surface temperature was computed using satellite images in 2021. In the fourth step, Multivariate linear regression between surface biophysical characteristics and the land surface temperature in 2021 was applied and then the cellular automata-Markov chain model was utilized to predict the land surface temperature for 2031. Finally, the pattern of changes in the urban heat island of Rasht city was investigated using statistical analysis in different geographic directions and different time periods.
Results and discussion: The results of this study indicate that the highest positive correlation (R=0.89) was between NDBI and LST. Moreover, the highest negative correlation (R=-0.81) was between the greenness and LST. Our results also showed that the lowest correlation (R=0.42) was between the brightness and LST. The predicted LST corresponding to surface biophysical characteristics using a multivariate linear regression model illustrates the low error of this approach (RMSE=1.33K) in 2021. This means that the predicted values in 2021 are close to the real values, and therefore, this model can be trusted to predict LST in 2031. Statistical analysis of the patterns of observed and expected changes in UHI clearly illustrated that Rasht urban expansion and the UHI expansion will consistently continue to increase from 1991 to 2031. However, the expansion rate changes over time and space. Moreover, these analyses also showed that the UHI of Rasht city has a high degree of freedom and a high degree of sprawl. Thus, and as a result, its degree of goodness is negative.
Conclusion: The pattern of UHI changes is highly dependent on the pattern of built-up land changes: as a result, sustainable development, resilience and environmental protection of Rasht requires direct monitoring and control of the pattern of urban growth, such as preventing changes in built-up areas and agricultural lands in suburban areas by incorporating a vertical form of development as well as constructing green roofs and walls and using high-reflectance building materials.

کلیدواژه‌ها [English]

  • Remote sensing data
  • cellular automata-Markov chain modeling
  • Statistical analysis
  • urban heat island (UHI)

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فصلنامه علوم محیطی
دوره 21، شماره 3
1402
صفحه 89-112

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