مدل‌سازی عوامل موثّر بر توزیع مکانی و شدّت جزایر گرمایی شهر قم با استفاده از تبدیل تسلدکپ

جزایر حرارتی شهری در اقلیم‌های گرم و خشک تاثیرات نامطلوبی بر محیط زیست و سلامت انسان دارند. در نوشتار پیش رو، روشی برای بررسی عوامل تاثیرگذار بر جزایر حرارتی اقلیم فلات مرکزی ایران پیشنهاد شده است. در روش پیشنهادی، در گام اول پس از اعمال تصحیحات هندسی، رادیومتریک، اتمسفری و آماده‌سازی تصاویر ماهواره لندست8شامل سنجنده های olitirs، شاخص های تبدیل تسلدکپ ایجاد شد. در گام دوم با استفاده از الگوریتم پنجره مجزا دمای سطح زمین استخراج ‌شد. در گام سوم به‌منظور ارزیابی زیست محیطی جزایر حرارتی، شاخص واریانس پهنه حرارتی شهری در شش سطح طبقه‌بندی شد. درنهایت با استفاده از ضریب همبستگی بین شاخص‌های واریانس پهنه حرارتی شهری و تبدیل تسلدکپ ارتباط جزایر حرارتی با مناطق بایر، شهری، پوشش گیاهی و رطوبت ارزیابی شد. به‌منظور ارزیابی روش پیشنهادی شهر قم مورد مطالعه قرار گرفته است. نتایج نشان می دهد جزایر حرارتی ارتباط معکوس با میزان پوشش گیاهی (613/0)، آب و رطوبت (535/0) و با میزان خاک و مناطق مسکونی (709/0) ارتباط مستقیم دارند. با بررسی شاخص واریانس پهنه حرارتی شهری، مشخّص شد که میزان این شاخص در هسته شهر مورد مطالعه نسبت به حاشیه شهر کمتر است؛ ازجمله دلایل آن می توان به گستردگی شهر، عایق‌بندی سقف منازل مسکونی، افزایش تراکم پوشش گیاهی نسبت به حومه شهر، عبور رودخانه از هسته مرکزی شهر و وجود مناطق بایر، جاده‌های کمربندی، کارخانجاتو شهرک های صنعتی در حومه شهر اشاره کرد. نتایج نشان می‌دهد، روش پیشنهادی روشی کارآمد برای تحلیل عوامل تاثیرگذار بر پدیده جزایر حرارتی است.

Modelling the Effective Factors on Temporal and Thermal Island Distribution of Qom applying Tasseled Cap Transformation (TCP)

Urban heat islands in hot and dry climates have adverse effects on the environment and human health. In this study, a method has been proposed to investigate the factors affecting the heat islands of Iran’s central plateau climate. In the first step, after applying geometric, radiometric, atmospheric corrections and preparing Landsat 8 satellite images, including OLITIRS sensors, Tasseled Cap transformation is created. In the second step, the surface temperature of the earth is extracted using Split window algorithm. In the third step, in order to evaluate the heat islands, the Urban Thermal Field Variance Index is classified into six levels. Finally, using the correlation coefficient between TCT and Urban Thermal Field Variance Index indicators, the relationship of heat islands with the desert, urban areas, vegetation, and humidity is evaluated. In order to evaluate the proposed method, the city of Qom has been studied. The results of the proposed method show that heat islands are inversely related to the amount of vegetation (0.613), water and humidity (0.535) and directly related to the amount of soil and desert areas (0.709). Examining the Urban Thermal Field Variance Index, it was shown that the rate of this index in the core of the studied city is less than the outskirts of the city which can be due to the expansion and dispersion of the city, insulation of the roofs of residential houses, increasing the density of vegetation in the suburbs, river crossing through the city center, the presence of barren areas, ring roads, factories and industrial towns in the suburbs cited. The results reveal that the proposed method is an efficient method to analyze the factors affecting the phenomenon of heat islands.Extended Abstract1IntroductionIncreasing temperature changes in urban areas accelerate the production of toxic gases from compounds between different oxides (including NO and NO2), changes in climate patterns and an increase in stress for people. In addition, lowering the city’s temperature is associated with energy consumption, which can reduce the city’s air quality. Therefore, first it is necessary to identify the factors affecting the creation of urban heat islands (UHI) and then to consider ways to reduce its effects. The study of thermal islands has been done by meteorological data and traditionally by Manley. Since then, new horizons have been created using remote sensing to observe and analyze the factors affecting heat islands on a global scale. Previous research has proved that there is a strong correlation between uniform differential indexes of plants and uniform differentiated urban indices with changes in surface temperature.2Materials and MethodsTasseled Cap Transformation (TCT) is an efficient tool to compress multispectral data. It is now more useful in remote sensing than principal component analysis because it can compress multispectral data into multiple bands commensurate with the associated physical properties and thus provide a better and more comprehensible understanding of land use to classify land cover.  The most important of these indicators are as the following.• Lighting to identify phenomena such as barren soil and residential areas• Greenery to identify vegetation• Humidity to identify water and moistureAfter correcting the images of Landsat 8 satellite, the surface temperature will be determined using these images in order to study the thermal islands. In fact, infrared and thermal remote sensing images are a good sources of information to prepare water and land surface thermal maps due to their wide coverage. Ground surface temperature is an important indicator in the study of ground energy balance models on a regional and global scale since meteorological stations only measure temperature information for specific points. The SplitWindow Algorithm (SWA) is one of the suitable methods to determine the land surface temperature. Land surface temperature is one of the most important products that can be measured by remote sensing sensors. Basically, one of the measurements of thermal distance measurement is the preparation of surface temperature maps of land; one method of calculating it is to use a SWA. This algorithm is more accurate than other methods to calculate the surface temperature of the earth. An important feature of this algorithm is the elimination of atmospheric effects. In order to evaluate the effect of UHI on the quality of urban life, Temperature Humidity Index (THI), Physiological Equivalent Temperature (PET), WetBulb Globe Temperature (WBGT) and Urban Thermal Field Variance Index (UTFVI) can be used. UTFVI observes a desired degree of temperature relative to the existence of the phenomenon of UHI on the quality of the urban environment in terms of level of monitoring.3Results and DiscussionIn this study, Landsat 8 satellite images have been used to determine and evaluate the important factors affecting thermal islands. First, the necessary corrections are made on the satellite images, then in the first step, TCT are used to determine the moisture, vegetation and soil of the study area. In the second step, the land surface temperature is determined using a SWA. The findings from the second step showed that the surface temperature in the central part of the city is lower than the desert edge of the city due to less barren areas, denser vegetation and river crossing through the city center. In the third step, UTFVI is classified using the threshold to evaluate the thermal islands of Qom. Then, in order to analyze and compare TCT that indicate moisture, vegetation, soil and residential areas with thermal islands and surface temperature, the correlation matrix between the indices was calculated. The findings reveal that the indices of thermal islands and surface temperature around the city of Qom are higher than the central core; and is inversely related to greenness.It was expected that residential areas in the city center would have higher temperatures than the central outskirts of the city by absorbing infrared waves due to the presence of concrete materials in the structures; However, due to the scattering and expansion relative to the density of residential areas, the almost equal height of residential areas and the integrated white roof insulation of these areas, these surfaces act as an almost integrated surface reflecting infrared waves, increasing albedo and much less absorption. As a result, contrary to expectations, in total, the average temperature of the surface and the area of ​​thermal islands in the central level of the city is less than the urban outskirts of the city limits. Therefore, it is expected that if the necessary measures are not taken to reduce the thermal islands on the relatively desert edge of the city, the size of these islands will increase and cover more areas of the city center.4ConclusionThe findings from this study reveal that greenness and wetness indices are strongly correlated with land surface temperature but in the opposite direction. Moreover, the amount of UTFVI in the central core of the study area is less than the outskirts of the city which can be due to different reasons; the radiant insulation of the roof of residential houses, due to the high density of the central core and the arrangement of the city core, creates an almost smooth and integrated surface that in addition to not absorbing heat from the sun reflects a very high percentage of sunlight resulting in lower land temperature and UHI.The high density of buildings in the central core of the city and also their relatively integrated height has led to the formation of alleys that can reduce the temperature of the city and thus reduce the number of UHI in the central core compared to the suburbs.