ارزیابی توزیع مکانی دمای سطح زمین شهرستان بهبهان در سال‌های 1378 تا 1392 با کاربرد سنجش از دور حرارتی

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 کارشناس ‏ارشد سنجش از دور و GIS، دانشکدة علوم دانشگاه شهید چمران اهواز

2 استادیار گروه محیط زیست، دانشکدة محیط زیست و منابع طبیعی، دانشگاه ملایر

3 دانشجوی دکتری آمایش محیط زیست، دانشکدة منابع طبیعی و محیط زیست دانشگاه ملایر

4 دانشیار گروه سنجش از دور و GIS دانشگاه شهید چمران اهواز

چکیده

در عصر حاضر، گرم‏ترشدن محیط زیست شهری یکی از آثار ناآگاهانة توسعة شهری ناپایدار است که با کاهش پوشش گیاهی در ارتباط است. بنابراین، آگاهی از درجة حرارت سطح زمین برای اجرای مطالعات علوم زمین، از قبیل تغییرات محیط زیست جهانی و مخصوصاً آب و هوای شهری، ضروری است. بدین منظور، توزیع مکانی و تغییرات دمای سطح با توجه به نقشه‏های کاربری اراضی و شاخص پوشش گیاهی در شهرستان بهبهان تجزیه و تحلیل شده است. بدین منظور، از تصاویر ماهوارة لندست سنجنده‏های ETM+ و OLI استفاده شد. پس از تصحیحات هندسی و اتمسفری، تصاویر با استفاده از الگوریتم حداکثر احتمال طبقه‏بندی شد. همچنین، تغییرات دما با مدل LCM ارزیابی شد. نتایج نشان داد طبقة یک دمایی (دمای کمتر از 13 درجة سانتی‏گراد)، که خنک‏ترین پهنه‏هاست، در دو بخش کوهستانیِ شمالی و جنوبی بیشترین گسترش را دارد. در هستة شهرنشینی، طبقة یک دمایی در سال 1378 به‏صورت لکه‏هایی پراکنده منطبق بر پارک‏های شهری گسترده شده است و در سال 1392، برخلاف انتظار، با توجه به کاهش چشم‏گیر دما نسبت به سال‏های گذشته نواحی شهری در طبقة اول قرار گرفته است. روی‏هم‏گذاری نقشة هر کدام از طبقات دما به کل طبقات با نقشه‏های کاربری اراضی نشان داد مرکز شهر، بخش‏‏هایی از اراضی لخت، و بخش‏هایی از اراضی کشاورزی در غرب منطقه در طبقة اول قرار گرفته‏اند. همچنین، بیشتر اراضی لخت و کشاورزی و بخش‏های اندکی از مراتع در طبقة دوم، بیشتر اراضی مرتعی در طبقة سوم، و بخش‏های کمی از این اراضی در طبقة چهارم‏اند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of spatial distribution of earth surface temperature in Behbahan during 2000 _ 2014 period using thermal remote sensing

نویسندگان [English]

  • hossien aghdar 1
  • kamran shayesteh 2
  • Fatemeh Mohammadyari 3
  • kazem rangzan 4
1 Shahid Chamran University of Ahvaz
2 Assistant Professor
3
4 Associ profe
چکیده [English]

Extended abstract:
Introduction: At present, warmer urban environment is one of the Naagahanh unsustainable urban development is associated with a reduced vegetation. Therefore, knowledge of ground temperature for geosciences, such as changes in the global environment and especially urban weather, is necessary. temperature as one of the most significant climatic parameters can be one of the main factors is tropical in town planning That the leading type of dedicated facility in the city and even determines the structure, shape and texture of urban. Unbridled and unplanned growth of cities, especially big cities of the country are looking to increase their environmental degradation and increasing pollution has been followed. The indiscriminate construction process and reduce the space required for the development of green space as breathing lungs Dramatic changes in the micro-climate in cities, especially in large cities led. Today, because of the importance of thermal remote sensing for environmental studies, many researchers in basic research thermal remote sensing and sensors technology development and application of new thermal data necessary to know.
Methodology: The studied region is between 50 degrees and 19 minutes longitude to 50 degrees and 25 minutes eastern, 30 degrees and 45 minutes latitude to north 30 degrees and 32 minutes in zone 39 located. The highest slope is 69.87 and the lowest slope 1%, the minimum annual temperature 18.1 c° and the maximum annual temperature 32.37 °c. The space of area is 615.6 square kilometers and regional climate is dry based on Domarten method. In this study, spatial distribution and variation of surface temperature were analyzed base on land use maps and vegetation index in the city of BEHBAHAN. For this purpose, ETM+ and OLI images of Landsat satellite were used. After geometric and atmospheric Corrections, images were classified using Maximum Likelihood Algorithm, and temperature changes were evaluated by LCM model.

To extract the surface temperature of three steps:
A: Convert the digital number to radiation
Number of ETM digital conversion of radiation to be used the following formula:
L=LMIN+(LMAX-LMIN)/(QCALMAX-QCALMIN )QCAL-QCALMIN
Where
QCALMIN: The Digital Numeric Value
QCALMAX: Most digital value
QCAL: Digital at the pixel number
LMIN: Spectral Band 6 in the amount of zero emission value DN (W m-2 sr-2 μm-1)
LMAX: radiation value of 6 band in the amount in the DN 255 (W m-2 sr-2 μm-1)
Values LMIN (2.200) and LMAX (10.55) of the extracted image file and Heather in the relationship.
B: Converted spectral radiance temperature blackbody
TB = K2/(Ln(K1/L+1))
ETM using thermal-band data from Planck equation, the temperature blackbody radiation (TB) in which the radiation is assumed to be one, to be converted.
Where
TB: Satellite effective temperature in Kelvins
K1: The first calibration constant equal to 666.09 W m-2 sr-2 μm-1
K2: The first calibration constant equal to 1282.71
L: Spectral radiance sensor (W m-2 sr-2 μm-1)
C; Be corrected radiation
Correction can restore correct radiation surface temperature as well as the quality of information obtained through thermal infrared data is effective. One of the operational and applied to obtain the radiation is (threshold method NDVI) Through the red and near infrared bands were obtained
NDVI = (float(b4)-float(b3))/(float(b4)+float(b3))
Where b4 b3 band 4 and band 3. Finally, after correcting the thermal image and calculate emissivity, land surface temperature is calculated using Equation 6.
LST = TB/([1+[(λ*(TB/p))]*Lnε])
Where
TB: Brightness temperature
λ: Radiance emitted wavelength (μm 5/11)
p= k / hc
Where
h: Planck's constant
c; speed of light
k: Stefan-Boltzmann constant
ε: Emissivity
Results and discussion: The results showed that class 1 of temperature (Temperatures lower than 13°c), which indicates the coolest areas, has the most expansion in northern and southern mountains. In the urban core, this class of temperature was expanded as scattered spots according to urban parks in 2000, but on the contrary in 2014, urban areas were located in the first class of temperature because of significant temperature reduction compared to previous years. Overlaying the map of each Temperature class with land use maps showed that downtown and some parts of bare and agricultural lands in the west areas were located in the first temperature class, most bare and agricultural lands and some parts of pastures were located in second class, most of pasture lands in third class, and a little part of them were located in the fourth class. After the preparation of the temperature map, it attempted to detect the changes and review the changes during the studied period with the model LCM, which is extensively used in IDRISI Tiga software. These changes include reductions, increments and net changes for each class, and transition from one floor to another. The highest temperature drop was observed in the third floor with an average temperature of 16 ° C and the highest increase in the second floor with an average temperature of 14 ° C. This means that in 2013, in the western regions of the area (third floor), the air temperature was reduced and, naturally, the area of this class was lower than in 2000, and reduced area as shown in Fig. 7 and the results are added to the second floor temperature. In fact, the displacement of the area between the second and third temperature classes has occurred, and in general, the temperature has cooled down to 2000.
Conclusion: The severity of the impact of human activity on the environment of cities, as concentrated areas of human use of the environment, depends heavily on the distance from urban centers. The farther away from urban centers and rural areas closer to the effects of human activities on the environment and reduced for certain changes. NDVI increase is the increase in the prevalence of vegetation and land cover is more homogeneous and homogeneous. but NDVI decrease introduce more varied (water, outdoor, bare soil, construction, etc.) and more heterogeneous mosaic of land.

کلیدواژه‌ها [English]

  • Evaluation of Changes
  • Earth's surface temperature
  • NDVI
  • thermal remote sensing
  • Behbahan City
  1. خلیل ولی‏‏زاده، کامران؛ غلام‏نیا، خلیل؛ عینالی، گلزار و موسوی، سید محمد، 1396. برآورد دمای سطح زمین و استخراج جزایر حرارتی با استفاده از الگوریتم پنجرة مجزا و تحلیل رگرسیون چندمتغیره (مطالعة موردی شهر زنجان)، نشریة پژوهشوبرنامه‏ریزیشهری، س ۸، ش 30، صص ۳۵-۵۰.
  2. شکیبا، علیرضا؛ ضیائیان فیروزآبادی، پرویز؛ عاشورلو، داوود و نامداری، سودابه، 1388، تحلیل رابطة کاربری و پوشش اراضی و جزایر حرارتی شهر تهران، مجلة سنجش از دور و GIS ایران، س ۱، ش 1، ۳۹-56.
  3. علوی‏پناه، کاظم، ۱۳۸۵، سنجشازدورحرارتیوکاربردآندرعلومزمین، تهران: انتشارات دانشگاه تهران.
  4. ملک‏پور، پیمان؛ طالعی، محمد؛ رضایی، یوسف و خوش‏گفتار، مهدی، 1389، بررسی درجة حرارت سطح زمین و ارتباط آن با کلاس‏های پوشش- کاربری زمین شهری با استفاده از دادة سنجندة ETM+ مطالعة موردی (شهر تهران)، همایش ملی ژئوماتیک.
  5. هاشمی، محمود؛ علوی‏پناه، کاظم و دیناروندی، مرتضی، 1392، ارزیابی توزیع مکانی دمای سطح زمین در محیط زیست شهری با کاربرد سنجش از دور حرارتی، مجلة محیطشناسی، س ۳۹، ش ۱، ۸۱-99.
    1. AlaviPanah, K., 2005, Thermal Detection and Application in Earth Sciences, Tehran University Press, (in Persian).
    2. Crooks, K.R.; Suarez, A.V. and Bolger, D.T., 2004, Avian assemblages along a gradient of urbanization in a highly fragmented landscape, Biological Conservation, Vol. 115, PP. 451-462.
    3. Dousset, B. and Gourmelon, F., 2003, Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS J. Photogram. Remote Sens, Vol. 58, PP. 43-54.
    4. Forman, R.T. and  Godron, T.M., 1986, Landscape Ecology, Wiley, New York, NY, 619 pp.

10. Gallo, K.P., 1993, The use of a vegetation index for assessment of the urban heat islandeffect, International Journal of Remote Sensing, Vol.14, No. 11, PP. 2223-2230.

11. Geist, H. and Lambin, E.F., 2001, What drives tropical deforestation? LUCC Report Series No.4, LUCC International Project office, University of Louvain.

12. Gingrich, S.E. and Diamond, M.L., 2001, Atmospherically derived organic surface films along an urban–rural gradient. Environ. Sci. Tech. Vol. 35, PP.4031-4037.

13. Goward, S.N.; Cruickshanks, G.D. and Hope, A.S., 1985, Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape, Remote Sensing of Environment, Vol. 18, PP.137-146.

14. Guhathakurta, S. and Gober, P., 2007, The impact of the Phoenix urban heat island on residential water use. Journal of the American Planning Association, Vol.73, No. 3, PP. 317-329.

15. Hahs, A. K. and Mcdonnell, M.J., 2006, Selecting independent measures to quantify Melbourne’s urban– rural gradient. Landscape Urban Plan. Vol. 78, No. 4, PP. 435-448.

16. Harrington, L. P., 1977, The role of urban forests in reducing urban energy consumption, editedby Proceedings of the Society of American Foresters, pp 60-66.

17. Hashemi, M.; Alavi panah, K. and Dinarvandi, M., 2013, Evaluation of spatial distribution of surface temperature in urban environment Application of Thermal Detection, Journal of Environmental Studies, Vol. 39, No. 1, PP. 81-99 (in Persian).

18. Huang, Y. J., 1987, The potential of vegetation in reducing summer cooling loads inresidential buildings, Journal of Climate and Applied Meteorology, Vol. 26, PP. 1103-1116.

19. Khalil Valizadeh, K.; Gholamnia, Kh.; Einali, G. and Mosavi, S.M., 2017, Estimation of ground temperature and thermal islands extraction using a separate window algorithm and multivariate regression analysis (case study of Zanjan city), Journal Urban research and planning, V. 8, NO. 30, pp: 35-50 (in Persian).

20. Khan, S.M. and Simpson, R.W., 2001, Effect of a heat island on the meteorology of a complex urban airshed, Boundary-Layer Meteorology, Vol. 100, No. 3, PP. 487-506.

21. Khandelwal, S.; Goyal, R.; Kaul, N. and Mathew, A., 2017, Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science.

22. Lambin, E. F., 2000, Land cover assessment and monitoring, in WILEY, J.: Encyclopedia of Analytical Chemistry.

23. Landsberg, H. E., 1981, The Urban Climate; Academic Press: New York, NY, USA, PP. 84-89.

24. Liu, L. and Zhang, Y., 2011, Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Journal Remote Sens Vol. 3, PP. 1535-1552.

25. Lu, Y.; Feng, P.; Shen, C. and Sun, J., 2009, Urban Heat Island in Summer of Nanjing Based on TM Data. In Proceedings of 2009 Joint Urban Remote Sensing Event, Shanghai, China, 20–22 May , PP. 1-5.

26. Maimaitiyiming, M.; Ghulam, A.; Tiyip, T.; Pla, F.; Latorre-Carmona, P.; Halik, M.; Sawut, M. and Caetano, M., 2014, Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 89, PP. 59-66.

27. Malekpour, P., Taleai, M., Rezaee, Y. and Khoshgoftar, M., 2010, Survey of Ground Surface Temperature and its Relationship with Classes of Usable Land Coverage Using Sensor Data ETM+ (Case study: Tehran), Geomatics National Conference (in Persian).

28. Mcdonnell, M. J. and Pickett, S.T.A., 1990, Ecosystem structure and function along urban–rural gradients: an unexploited opportunity for ecology. Ecology, Vol. 71, PP. 1232-1237.

29. Mcdonnell, M. J., 1997, Ecosystem processes along an urban-to-rural gradient. Urban Ecosys. Vol.1, PP. 21-36.

30. Mcintyre, N. E., 2001, Ground arthropod community structure in a heterogeneous urban environment. Landsc. Urban Plann, PP. 52-57.

31. Oke, T. R., 1982, The energetic basis of the urban heat island, Quarterly Journal of the Royal Meteorological Society, Vol.108, PP. 1-24.

32. Pouyat, R.V.; Mcdonnell, M. J. and Pickett, S.T.A., 1995, Soil characteristics in oak stands along an urban– rural land-use gradient. J. Environ. Qual. Vol. 24, PP. 516-526.

33. Roth, M.; Oke, T. R., and Emery, W. J., 1989, Satellite derived urban heat islands from three coastal cities and the utilization of such data in urban climatology, International Journal of Remote Sensing.

34. Rouse, J. W.; Haas, R. H.; Schell, J. A. and Deering, D.W, 1973, Monitoring vegetation systems in the great plains with ERTS, Third ERTS Symposium, NASA SP-351 I, PP. 309-317.

35. Senanayake, I. P.; Welivitiya, W.P. and Nadeeka, P.M., 2013, Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data. Journal Urban Climate, PP. 19-35.

36. Shakiba, A.; Zieaian Firouzabadi, P.; Ashorlo, D. and Namdari, S., 2010 Analysis of the relationship between land use and land cover and thermal islands in Tehran, Iranian Remote Sensing&GIS, Vol. 1, No. 1, PP. 39-56 (in Persian).

37. Sobrino, J. A.; Caselles, V. and Becker, F., 1990, Significance of the Remotely Sensed Thermal Infrared Measurements Obtained Over a Citrus Orchard, ISPRS.

38. Sobrino, J.A.; Jiménez, M. and Paolinib, C. J., 2004, Land surface temperature retrieval from LANDSAT TM5, Remote Sensing of Environment, Vol. 90, PP. 434-440.

39. Streutker, D. R. A., 2002, Remote sensing study of the urban heat island of Houston, Texas, Int. J. Remote Sens, Vol. 23, PP. 2595-2608.

40. Sun, Q.; Tan, J. and Xu, Y., 2010, An ERDAS image processing method for retrieving LST and describing urban heat evolution: A case study in the Pearl River Delta Region in South China, Environ. Earth Sci. Vol. 59, PP. 1047-1055.

41. Tan, J. and Cherkauer, Keith A., 2013, Assessing stream temperature variation in the Pacific Northwest using airborne thermal infrared remote sensing, Journal of Environmental Management, Vol. 115, PP. 206-216.

42. U.S. EPA, 2007, Basic Information about Heat Island. Available online from following website: http://www.epa.gov/heatisland/about/index.html.

43. Voogt, J.A. and Oke, T. R., 2003, Thermal remote sensing of urban climates, Remote sensing of environment. Vol. 86, No. 3, PP. 370-384.

44. Wagrowski, D. M., and Hites, R., 1997, Polycyclic aromatic hydrocarbon accumulation in urban, suburban and rurual vegetation, Environmental Science & Technology, Vol. 31, No. 1, pp. 279-282.

45. Wear, D.; Turner, M. and Naiman, R., 1998, Land cover along an urban–rural gradient: Implications for water quality. Ecol. Appl. Vol. 8, PP. 619-630.

46. Weng, Q. and Schubring, j., .2004, Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. Vol. 89, pp. 467–483.

47. Xian, G. and Crane, M., 2006, An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data, Remote Sensing of Environment, Vol. 104, No. 2, PP. 147-156.

48. Xiao, R., 2007, Spatial Pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China, Journal of Environ. Science, Vol.19, PP. 250-256.

49. Zhan, Q.; Meng, F. and Xiao, Y., 2015, Exploring the relationships of between land surface temperature, ground coverage ratio and building volume density in an urbanized environment. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40, No. 7, PP. 255.