نوع مقاله : مقاله علمی پژوهشی

نویسندگان

• حسن محمودزاده ¹
• آذر پویان جم ²
• فاطمه امان زاده ³

¹ دانشیار جغرافیا و برنامه ریزی شهری، گروه جغرافیا و برنامه ریزی شهری، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران

² دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی،گرایش مطالعات شهری و روستایی،دانشگاه تبریز

³ دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، گرایش مطالعات شهری و روستایی، دانشگاه تبریز

چکیده

وضعیت پراکندگی تابش‌های حرارتی و ارتباط آن با نوع کاربری‌های موجود در شناخت میکروکلیمای نواحی شهری دارای اهمیت زیادی می‌باشد. دمای سطح زمین یکی از پارامترهای مهم در مطالعات مربوط به تغییر اقلیم و برآورد بیلان تابش در مطالعات توازن انرژی می‌باشد. آگاهی از درجه حرارت سطح زمین جهت انجام فعالیت‌ها و مطالعات علوم زمین، از قبیل تغییرات محیط زیست جهانی و مخصوصا آب و هوای شهری، ضروری است. در این پژوهش برای تحلیل جزایر حرارتی از تصاویر 15/7/2018، 24/2/2019 لندست 8 با سنجنده (OLI و TIRS) و نقشه کاربری اراضی استفاده شده است. پس از اعمال پردازش روی تصاویر، برای محاسبه دمای سطح زمین از روش الگوریتم پنجره مجزا و برای طبقه‌بندی تصاویر از روش حداکثر احتمال استفاده شده است. الگوریتم پنجره مجزا یک ابزاری ریاضی است که با استفاده از اطلاعات زمینی، دمای روشنایی سنجنده حرارتی (TIRS)، قابلیت گسیلندگی زمین (LSE) و عامل پوشش گیاهی سبز جزء به جزء (FVC) به دست آمده از باند چند طیفی سنجنده OLI و دمای سطح زمین را برآورد می‌کند. تجزیه و تحلیل تصاویر در محیط نرم‌افزارهای ENVI 5.3 و ArcGIS 10.5 انجام گرفته است. نتایج در این پژوهش نشان داد که در فصل تابستان، پوشش گیاهی کم‌تراکم، مناطق مسکونی، گارگاهی و صنعتی با سطوح نفوذناپذیر مانند اسکلت فلزی، آسفالت-بتن و آجر-آهن دارای بیشترین درجه حرارت و در زمستان بالعکس به غیر از پوشش گیاهی کم‌تراکم، می‌باشند و در فصل تابستان و زمستان، پوشش گیاهی (فضای سبز، باغات و کشاورزی و باغ مسکونی) و مناطقی با سطوح آجر-چوب و خشت-چوب دارای کمترین درجه حرارت می‌باشند.

کلیدواژه‌ها

• جزایر حرارتی
• دمای سطح زمین
• الگوریتم پنجره مجزا
• لندست 8
• شهر ارومیه

موضوعات

• برنامه ریزی شهری

عنوان مقاله [English]

Calculation of land surface temperature and thermal island extraction using Landsat 8 satellite imagery and separate window algorithm in Urmia

نویسندگان [English]

• Hassan Mahmoudzadeh ¹
• azar puyanjam ²
• fatemeh amanzadeh ³

¹ Associate Professor of Geography and Urban Planning, Department of Geography and Urban Planning, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran

² MSc in Remote Sensing and Geographic Information System, Urban and Rural Studies, Tabriz University

³ MSc in Remote Sensing and Geographic Information System, Urban and Rural Studies, Tabriz University

چکیده [English]

Introduction
One of the emerging environmental hazards caused by the expansion of urbanization is the "thermal island" phenomenon, in which urban areas have a distinct climate compared to rural areas, and the city center has higher temperatures than its surrounding areas. This phenomenon occurs when a large percentage of natural surface coverings are destroyed and replaced by buildings, roads and other installations. The thermal island phenomenon has been studied and recorded in various cities around the world for more than 150 years. And it generally appears as the surface of the earth shifts from natural to non-perishable. Surface temperature is considered one of the most important parameters in identifying a city's climate that directly controls the effect of the city's heat island. And more recently, many regional studies, such as global climate change, hydrological and agricultural processes, urban land use and land cover, and soil moisture assessment, have been identified as important factors. Traditionally, urban heat islands have traditionally been studied using meteorological station data, or vehicle surveys, but today to reduce the weakness of these methods and to study them more closely, Satellite and remote sensing data are used more frequently because of more spatial resolution than terrestrial weather data. Remote sensing images, because of their wide coverage, timeliness and ability to obtain information in the thermal range of the electromagnetic spectrum, are a useful source of heat mapping and estimation of Earth's radiant energy.
Methodology
Split-Window algorithm is one of the most important methods for estimating surface temperature which is better than other methods for calculating surface temperature. An important feature of this algorithm is the elimination of atmospheric effects. Since this algorithm does not require accurate information on atmospheric profiles during satellite acquisition, it is widely used in several sensors to retrieve Earth's surface propagation capability. The sensors used in this algorithm include the Multi Spectral Sensor and the TIRS Thermal Sensor. The following are the cases: Due to the lack of a database to measure the Earth's surface propagation capability with Landsat 8 satellite images, the C coefficients through various numerical simulations It was obtained from atmospheric and surface conditions.In this study, Landsat 8 images with 7/15/2015 Landsat 8 (OLI and TIRS) images and land use maps were used to analyze the thermal islands. After processing the images, a separate window algorithm was used to calculate the surface temperature and the maximum likelihood method was used to classify the images. Discrete Window Algorithm is a mathematical tool that uses ground information, thermal sensor brightness temperature (TIRS), ground emission capability (LSE) and fractional green vegetation factor (FVC) obtained from OLI and temperature multispectral band. Estimates the surface of the earth. Image analysis was performed in ENVI 5.3 and ArcGIS 10.5 software environments.
Result and Discussion
Surface temperature is one of the main factors in the study of cities. Because only two or three degrees differs from the air temperature of the lower layers of the urban atmosphere, which is the center of the surface energy balance, which determines the climate between buildings and affects the comfort of urban dwellers. In the present study, preliminary processes such as radiometric, atmospheric and geometric corrections were carried out and then high atmospheric radii were converted to surface radiation and then calculated by vegetation index, vegetation fraction index, radiation power and water vapor column, temperature. Ground level in the study area was obtained using a separate window algorithm.
Conclusion
 The results of thermal extraction showed that maximum temperature was related to low density vegetation, residential, industrial, industrial, asphalt-concrete and brick-iron frameworks. Minimum temperatures are also visible in green, brick-wood and clay-wood. The results of this research for planners and experts at the regional level to obtain information on the status of land surface temperature and their relationship with land use can pave the way for management decisions to conserve natural and agricultural resources. It is suggested that due to the moderating role of vegetation, vacant land and the wilderness be changed to uses such as parks and landscapes, and in addressing other uses, the reasons for residential and industrial and workshop areas should be taken into account, and the surface temperatures of buildings most The city has its own surface area and has the highest amount of radiation reflection can be reduced by planting vegetation on the roofs of buildings known as green roofs. High resolution satellite images are also recommended for land use mapping.

کلیدواژه‌ها [English]

• Thermal Islands
• Ground Temperature
• Separate Window Algorithm
• Landsat 8
• Urmia City