تحلیل زمانی – مکانی جزیرۀ حرارتی شهر مشهد با توجه به گسترش شهر و تغییرات کاربری - پوشش زمین

نوع مقاله: پژوهشی - کاربردی

نویسندگان

1 استاد سنجش از دور و سیستم‌های اطلاعات جغرافیایی دانشگاه تهران

2 دانشجوی کارشناسی ارشد سنجش از دور و سیستم‌های اطلاعات جغرافیایی دانشگاه تهران

چکیده

افزایش دمای سطح زمین و شکل‌گیری جزایر حرارتی در کلان‌شهرها به یکی از معضلات زیست‌محیطی تبدیل شده است که در نتیجۀ گسترش برنامه‌ریزی‌نشده این شهرها پدید آمده است. امروزه استفاده از تصاویر ماهواره‌ای در مطالعات محیط زیست شهری به دلیل فراهم آوردن دید یکپارچه و کاهش هزینه و زمان انجام مطالعات، رشد فزاینده‌ای داشته است. هدف از این مطالعه بررسی تغییرات دمایی و گسترش جزیرۀ حرارتی در شهر مشهد به دنبال گسترش این شهر در دهه‌های اخیر است. در این تحقیق با استفاده از تصاویر چندزمانۀ لندست (TM 1987، ETM+ 2000 و OLI/TIRS 2014) و روش حد آستانه NDVI و قانون پلانک برای تصاویر TM و ETM+ و الگوریتم دو پنجره برای تصاویر OLI/TIRS، دمای سطح زمین استخراج شد. سپس دمای سطح زمین نرمال گردید و رابطۀ آن با کسر پوشش گیاهی و تغییرات کاربری - پوشش زمین بررسی شد. نتایج نشان داد که حدود 2500 هکتار از اراضی کشاورزی و فضای سبز به کاربری ساخته‌شده تبدیل شده که تقریباً برابر با افزایش مساحت طبقۀ دمایی بسیار گرم بوده است. این موضوع نشان می‌دهد که کاهش پوشش گیاهی، مهم‌ترین عامل در گسترش جزایر حرارتی شهر مشهد بوده است. بررسی تعییرات دمای سطح زمین و جزیرۀ حرارتی نشان داد با از بین رفتن پوشش گیاهی داخل و اطراف شهر طبقۀ دمایی بسیار خنک (29- 25 درجۀ سانتیگراد)، جای خود را به طبقۀ دمایی متوسط (37 – 33 درجه) داده است. همچنین با دستکاری دامنه‌های اطراف شهر، دمای آنها افزایش یافته و به طبقۀ دمایی گرم (41 – 41 درجه) و بسیار گرم (45 – 41 درجه) تغییر یافته‌اند. نتایج تغییرات توزیع دمای سطح نشان داد که چندین خوشۀ دمایی در شمال غرب، جنوب و جنوب غرب مشهد توسعه یافته‌اند. این تغییرات در شمال غرب به دلیل دستکاری اراضی متصل به شهر برای آماده‌سازی این اراضی برای گسترش آتی شهر و در جنوب به دلیل توسعۀ فرودگاه بوده است. همچنین دامنه‌های رو به آفتاب در غرب و جنوب غرب به دلیل دستکاری این دامنه‌ها گرم‌تر شده‌اند. علاوه بر این، تبدیل اراضی کشاورزی در شرق و شمال شرق باعث تغییر دما از طبقۀ دمایی خیلی خنک و خنک (33 - 29 درجه) به متوسط و تا حدودی گرم شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Spatial- Temporal Analysis of Urban Heat- Island of Mashhad City due to Land Use/ Cover Change and Expansion

نویسندگان [English]

  • Seyyed Kazem Alavipanah 1
  • Sirous Hashemi Darrehbadami 2
  • Ali Kazemzadeh 2
1 Professor, Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran
2 M.A. in Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran
چکیده [English]

1. Introduction
Increase in the land surface temperature and the formation of heat islands in the metropolis areas has become one of the environmental problems emerged by unplanned expansion of cities. Many factors are involved in the creation of urban heat islands. These factors can generally be divided into two groups of controlled and uncontrolled. In addition, the controllable and uncontrollable factors can be classified as another. The factors with temporary impact such as wind speed and cloud cover, the variables with constant and stable impact such as green space, building materials and sky view and, finally, the factors with periodic or cyclical impact such as solar radiation and heat sources are caused by human activities. Generally, the heat generated in the surface is resulted from the sun in the form of solar radiation, large industries and factories, cars, air ventilation systems and other sources related to the human activities (Rizwan et al., 2008: 122). Nowadays, use of satellite imagery in urban environment studies has been growing rapidly due to integrated vision and reduction in the cost and time of the studies. Land surface temperature is one of the most important variables measured using these images. Thermal images are widely used to evaluate the urban heat island. More research is focused on the earth surface temperature patterns and their relation to biophysical characteristics of urban areas, especially with vegetation index (Sobrino & Raissouni, 2000: 353; Weng et al., 2004: 467) and changes in land use/cover (Xiao & Moddy, 2005: 237; Amiri et al., 2009: 2006; Weng et al., 2009: 467).
Expansion of the city of Mashhad in the past two decades had augmented numerous environmental problems such as air pollution and urban heat island. These problems led to a decline in air quality and the health of urban life and the risk of diseases such as asthma, insomnia and other respiratory diseases. Hence, the need for environmental research and urban planning in the city has been doubled. Therefore, this research using multi-temporal Landsat images (TM, ETM+ and OLI) investigate spatial-temporal distribution of the urban heat-island variations due to changes in the urban development of Mashhad, vegetation, land use/ cover, simultaneously.
Methodology
In this study, Landsat images of TM (1/5/1987), ETM + (28/5/2000) and sensor OLI (11/5/2014) was used. The Mashhad topographic map at scale 1:25,000 was used for Geometric correction with RMSE error less than 0.5 pixels. We also used FLASH algorithm for the atmospheric correction on the images. To extract Land Surface Temperature from Landsat images, the raw values (DN) were converted into Spectral Radiance image. Spectral Radiance of thermal bands then was converted into brightness temperature. This is by assuming the fact that the brightness temperature of the black body (emissivity= 1) is calculated and contains the effects of the atmosphere (absorption and emission). Brightness temperature using sensor’s calibration coefficients is obtained from the following equation:
 
where T is effective at-sensor brightness temperature [K], K2 Calibration constant 2 [K], K1 Calibration constant 1 [W/(m2 sr μm)], L𝜆 Spectral radiance at the sensor's aperture [W/(m2 sr μm)]. Obtaining land surface temperature is required to know about land surface emissivity (LSE). To obtain the emissivity, hybrid threshold NDVI and image classification method was used. With obtaining the values of the emissivity, the temperature of the surface for TM and ETM+ images can be calculated from the following equation (Artis & Carnahan, 1982):
 
where Ts land surface temperature, T brightness temperature, ε emissivity power, λ wavelength of radiance, α=hc/k (h=Planck's constant; c=Velocity of light) (k=Boltzmann constant). To obtain the land surface temperature from Landsat 8 thermal data (TIRS), we also used split windows algorithms. Finally, thermal image didn’t take simultaneous and heterogeneous classes of images due to changes in urban land use.
Results and Discussion
The results showed that urban sprawl into agricultural areas can reduce green space and that this was the main factor in the surface temperature rise and expansion of urban heat islands in the city of Mashhad. However, global warming, industrial development of the city, and urban traffic (due to the expansion of the city) are increasing the temperature of the thermal expansion of the islands. Thus, the results indicated that about 2,500 hectares of agricultural land and green spaces are changed into built-up land use. This shows that the decrease in vegetation cover is the most important factor in development of the heat islands in Mashhad. With the loss of vegetation cover in and around the city, very cool temperature class (25-29°C) has been replaced by normal temperature class (33-37°C). Furthermore, by manipulating the surrounding areas of the city, their temperature is increased and transformed into the hot and very hot temperature classes.
Conclusion
The results of surface temperature distribution changes indicated that several temperature clusters in the North West, South and South West of Mashhad have been developed. In the north west this development is because of manipulation of the land connected to the city and in the south the development is mainly because of airport's development. The sunny slopes in the West and South West are because of manipulation of these domains. In addition, the conversion of agricultural lands into urban land uses in the east and north east can alter the very cool and cool temperature classes into normal and hot temperatures classes. The results also showed that the use of multi-temporal satellite data and conventional methods can represent the combined heat islands (assessment of spatial – temporal changes of the distribution of urban heat islands into changes in the development of urban, heat islands relationship with vegetation and land use/cover). These changes in the urban surface areas can reveal  all aspects of the formation and expansion of urban heat islands.

کلیدواژه‌ها [English]

  • Land surface temperature
  • land use/ cover changes
  • Mashhad
  • split-window algorithm
  • urban heat islands
علوی‌پناه، سیدکاظم؛ (۱۳۸۵). سنجش از دور حرارتی و کاربرد آن در علوم زمین، انتشارات دانشگاه تهران.

----------؛ (1392). کاربرد سنجش از دور در علوم زمین (علوم خاک)، انتشارات دانشگاه تهران، چاپ چهارم.

صادقی‌نیا، ع.؛ علیجانی، ب.؛ ضیاییان، پ.؛ (1391). تحلیل فضایی زمانی جزیرۀ حرارتی کلان‌شهر تهران با استفاده از سنجش از دور و سیستم اطلاعات جغرافیایی، جغزافیا و مخاطرات محیطی، شمارۀ چهارم، زمستان 1391.

فاطمی، سید باقر؛ رضایی، یوسف؛ (1389). مبانی سنجش از دور، انتشارات آزاده، چاپ دوم.

Amiri, R., Weng, Q., Alimohammadi, A., & Alavipanah, S. K. (2009). Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12), 2606-2617.

Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113(5), 893-903.

Lenney, M. P., Woodcock, C. E., Collins, J. B., & Hamdi, H. (1996). The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment, 56(1), 8-20.

Mackey, C. W., Lee, X., & Smith, R. B. (2012). Remotely sensing the cooling effects of city scale efforts to reduce urban heat island. Building and Environment, 49, 348-358

Liu, L., & Zhang, Y. (2011). Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3(7), 1535-1552.

Owen, T. W., Carlson, T. N., & Gillies, R. R. (1998). An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing, 19(9), 1663-1681.

Rajeshwari, A., & Mani, N. D. Estimation of land surface temperature of dindigul district using landsat data.

Rizwan, A. M., Dennis, L. Y., & Liu, C. (2008). A review on the generation, determination and mitigation of Urban Heat Island. Journal of Environmental Sciences, 20(1), 120-128.

Skokovic.D, Sobrino.J.A, Jimenez-Munoz.J.C, Soria.G, Julien.Y, Mattar.C and Jordi Cristobal,

2014 Calibration and Validation of Land Surface Temperature for Landsat 8 – TIRS Sensor”, Land product Validation and Evolution, ESA/ESRIN Frascati (Italy), pp 6

 

Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of environment, 79(2), 213-224.

 

Smith, C., & Levermore, G. (2008). Designing urban spaces and buildings to improve sustainability and quality of life in a warmer world. Energy policy, 36(12), 4558-4562.

Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, 21(2), 353-366.

Sobrino.J.A, Reillo.S, Cueca.J and Prata.A.J, “Algorithms for Estimating Surface Temperature from ASTR-2 Data”,

 

Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates.Remote sensing of environment, 86(3), 370-384.

Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335-344.

Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sensing of Environment, 89(4), 467-483.

Weng, Q., Liu, H., & Lu, D. (2007). Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban ecosystems, 10(2), 203-219.

Xu, H., Chen, Y., Dan, S., & Qiu, W. (2011, June). Spatial and temporal analysis of urban heat Island effects in Chengdu City by remote sensing. In Geoinformatics, 2011 19th International Conference on (pp. 1-5). IEEE.

Xiao, J., & Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2), 237-250.

Zhao, S., Qin, Q., Yang, Y., Xiong, Y., & Qiu, G. (2009). Comparison of two split-window methods for retrieving land surface temperature from MODIS data. Journal of earth system science, 118(4), 345-353.