بررسی و تفکیک جزیره حرارتی و گرمایش جهانی در دشت مشهد

جهان امروزه جریان بی‌سابقه‌ای از شهری ‌شدن را تجربه می‌نماید. با توسعه شهرنشینی، وسعت بیشتری از پوشش طبیعی مناطق کشاورزی و جنگل جای خود را به شهرها داده و معضلات اکولوژیستی شهری، منجمله ایجاد جزایر حرارتی، برای زیست‌گاه موجودات زنده فراهم آورده است. از طرفی گرم شدن زمین در سالیان اخیر بر اثر پدیده گرمایش جهانی نیز، که بر اثر فعالیت‌های انسانی ایجاد می‌شود، بر این مشکلات می‌افزاید. اما این دو منشاء یکسانی نداشته و تفکیک آن از طریق تفاضل مقادیر بدست آمده در شهر و نقاط مجاور، در مدیریت شهری لازم و ضروری است. پژوهش حاضر بر روی دشت مشهد، با استفاده از تصاویر ماهواره لندست 5، لندست 7 و لندست 8 بین سال‌های 1996 تا 2016 میلادی انجام گردید. در این مطالعه به کمک روش‌های مختلف تعیین دمای سطح (lst)، دمای پوشش گیاهی محاسبه شد. نتایج نشان داد که میانگین دمای سطح زمین (lst) منطقه مطالعاتی در روزهای تحت بررسی، به طور میانگین بین 9/17 تا 0/49 درجه سانتی‌گراد در مناطق مختلف آن دارای تغییرات زمانی و مکانی است. بیشترین و کمترین مقادیر میانگین دمای سطح زمین به ترتیب در کاربری‌های شهری و کوهستانی مشاهده شد. هم چنین، مناطق شهری دارای تفاوت محسوس دمای سطح زمین (lst) نسبت به سایر کاربری‌ها می باشد. نتایج استفاده از الگوریتم‌های مختلف محاسبه lst در دشت مشهد نشان داد که روش پنجره مجزا (sw) نسبت به سایر روش‌ها مقادیر بالاتری از lst را ارائه می‌دهد، این روند تقریباً در تمامی کلاس‌های کاربری اراضی در منطقه مطالعاتی دیده شد. در مناطق شهری، از بین روش‌های مختلف تعیین lst، بیشترین همبستگی‌ها بین دمای هوا در ایستگاه سینوپتیک مشهد و lst محاسباتی به روش تک کاناله یا sc دیده شد (r2=0.964). در مناطق کوهستانی و کشاورزی، نیز بیشترین همبستگی‌ها بین دمای هوا و lst محاسباتی به روش تک پنجره اصلاحی یا imw دیده شد (r2=0.951) و (r2=0.943).

Evaluation and Separation between Urban Heat Island and Global Warming in Mashhad Region

Introduction: In arid and semiarid regions such as Iran, water is the most important limiting factor in economic development, and its management is of high importance. In recent years, due to irrigation expansion, low productivity in agricultural sector, and the rainfall shortage, water resources have been adversely affected in Iran. Undoubtedly, global warming in arid and semiarid countries has increased the need for aquatic plants and the severity of drinking water shortages, making it more difficult to plan for limited resources. Studying the spatial and temporal changes of evapotranspiration is essential for the comprehensive planning of water management in Mashhad and providing an appropriate solution for optimal use of available water resources. However, spatiotemporal analysis of evapotranspiration regardless of the phenomenon of global warming and thermal island leads to unrealistic results. Therefore, the aim of this study was to address these shortcomings in previous studies in Mashhad. The specific objectives were: temporal analysis of evapotranspiration in the existing statistical period and estimation of annual evapotranspiration volume with respect to global warming, investigating the effect of global warming factors and thermal island on evapotranspiration and eventually water resources management in Mashhad. Materials and Methods: This study was carried out in Mashhad, city of Khorasan Razavi province with an area of 204 square kilometers, in northeastern Iran. Satellite imagery used for this research was a time series from Landsat 5 (TM sensor), Landsat 7 (ETM +) and Landsat 8  (OLI and TIRS sensors) from 1996 to 2016. The selected images for 2016 consisted of a time series of 13 images and a 16day interval. After receiving satellite imagery, the performance of atmospheric corrections was evaluated based on FLAASH and TAC methods for reflective and thermal bands, respectively. The radiometric correction of images and reflection calculation of reflection was also conducted for bands 4 and 5 (values of ρ) and radiations of thermal bands10 and 11 (Lsen values) in the ILWIS software environment. Then, the temperature of the vegetation was calculated using different methods of determining the surface temperature (LST). Result and Discussion: The results showed that, on average, NDVI values in urban, mountainous and agricultural classes were 0.39, 0.37, and 0.4, respectively. However, the lowest and largest absolute value of NDVI were, respectively, 0.29 and 0.82, both of which are obtained in agricultural lands. The mean land surface temperature (LST) was 34.2 °C during days, which had a time and spatial variation between 17.9 to 49.4 °C in different regions. The highest and lowest mean LST was observed in urban and mountainous applications, respectively. Urban areas also had a significant difference in LST compared to other land uses due to the type of land cover in urban areas (mainly asphalt, stone, brick, cement, iron, etc.) and activities such as vehicle traffic, smoke and heat from factories and industries. The SplitWindow (SW) method gave higher LST values compared with other methods. Then, the singlechannel (SC), Improved MonoWindow (IMW) and singlewindow (MW) methods provided lower amounts of LST. The same trend was observed in almost all land use classes in the study area. It was also found that in urban areas, the strongest correlation between air temperature and LST was calculated by applying SC (R2 = 0.937). In mountainous regions, the highest correlation between air temperature and computed LST was found for the IMW (R2 = 0.951). Similarly, in the agrorangeland areas, the highest correlation between air temperature and computed LST was obtained by IMW (R2 = 0.953). Conclusion: In the study area, the general trend of NDVI index was declining between 1996 and 2016. Reducing the percentage of vegetation cover in different sectors such as agriculture and rangeland, changing the type of vegetation (crop pattern) in agricultural sector and urban green spaces are the reasons for decreasing NDVI index in Mashhad region. The average LST was 34.2 °C in the days, which had a time and spatial variation between 17.9 to 49 °C in different regions. The maximum and minimum average LST was observed in urban and mountainous regions, respectively. The SW provided higher LST values compared to other methods. The SC, IMW and MW methods, however, provided lower LST values. The same trend was observed in almost all land use classes in the study area. It was also found that in urban areas, the highest correlation between air temperature and LST was found by using SC (R2=0.937). In mountainous regions, the strongest correlations between air temperature and LST was observed by using the Split Window Algorithm (SW) Improved MonoWindow (IMW) (R2=0.951). Similarly, in the agricultural and rangeland areas, the highest correlation between air temperature and LST was observed using the Split Window (SW) Improved MonoWindow (IMW) (R2 =0.953).