Spatio-Temporal Modelling of the Urban Environment Quality

Document Type : Research Paper


1 MA in Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Iran

2 Assistant Professor of Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Iran

3 Assistant Professor of Environmental Planning, Management and Education, Faculty of Environment, University of Tehran, Iran


Fast growth of urban population and increasing demands for high living standards have intensified the pressure on natural resources and made it more difficult to answer every need. Regardless of the fact that environmental capacity, population and economy would affect the fundamental functions of the environment. Thus, analysis of the environmental quality can help us to understand the exact need for natural resources in any urban areas along with its economy and social development. The quality of urban environment is recognized as an indicator for assessing and measuring the degree of suitability in urban settlements. It is also a rate for meeting the needs of individuals and society which can be affected by several factors such as air, noise and etc. All these factors would vary by any changes in time and space. Previous studies mainly focused on spatial changes, but in this research we seek to consider seasonal changes in addition to spatial ones. We have also tried to use more complete set of indicators. Therefore, the main purpose of this study is to make a modeling of the quality of urban environment based on a set of spatio-temporal factors.
We have used satellite imagery and some geospatial data including NDVI maps, land surface temperature, land surface moisture, land surface albedo, solar radiation, air pollution, urban heat island, building height, population density, enhanced built-up and bareness index and also noise pollution. Landsat 8 (OLI) is used to calculate NDVI indices, land surface temperature, land surface moisture, land surface albedo, urban heat island, and enhanced built-up and bareness index. A Digital Elevation Model (DEM) has been used to extract solar radiation. Finally, we have used location based field data to enhance air pollution, building height, population density and noise pollution. Because of the uncertain nature of quality measurements, we have used Fuzzy logical approach to model the quality of urban environments. One of the most important fuzzy operators for overlaying the indices is the GAMMA. Gamma operator is the general mode of multiplication and addition. In other words, the gamma fuzzy function is the product of the algebraic multiplication of two functions of collect and multiply fuzzy. This function is the result of the compatibility between the incremental effect of the fuzzy sum function and the reduction effect of the fuzzy multiplication function. Therefore, districts of 3, 6 and 11 of Tehran municipality have been selected to be measured for the quality of urban environment in Tehran along a northern-southern line. 
Results and discussion
The results of this research have indicated that a northern-southern trend in the quality of urban environment which is reducing from north to south. The environmental quality conditions of the three defined urban areas are categorized into five classes of moderate, very good, good, very low and low. According to the results, region number three has a better environmental condition than the regions six and eleven. We can also realize that most of the selected indicators have represented seasonal changes within a year in the study area. This is due to the existence of more parks and less air pollution in the northern regions. The time intervals also show a better quality in spring and summer than in autumn and winter. To investigate seasonal changes, the total area of each class was compared in different seasons and the urban environmental qualities were devised into five categories: very good, good, medium, low and very low. In the spring, a large partial of the region has a modest and good quality, and a small part of it has a very good situation. In the summer, most of the areas have a middle class situation and a small part with a very low level. This indicates the region's good status on this season. In the fall, most of the area has the lowest quality and the minimum of it has a very good level that indicates the worst condition for the urban environmental quality. In the winter, the situation is a little better. Most parts of the area are in middle level and small parts of is the area are in the lowest class. Therefore, the quality of urban environments changes dramatically within a year. At the next step, we have studied the Pearson correlation coefficient of indicators. The results of the correlations showed that the greenness is the most effective indicator of quality in urban environments. One-At-A-Time (OAT) Sensitivity Analysis was used to analyse the sensitivity of the model. Given the fact that all the changes in model outcome are less than the total percentage of input change (30% increase) for all the variables, it can be concluded that the results of the gamma fuzzy model are reliable and not affected by one or more specific variables. 
According to an extensive review of the literature, this study has introduced a wide array of factors in both natural and artificial environments to assess the urban environmental quality (UEQ) of Tehran. It is hopeful that this study provides a useful basis for more researches in the field of UEQ by combining both natural and built-up parts of urban areas. Further work can focus on validation and verification of the UEQ indices in the future.


Main Subjects

  1. امینی فسخودی، عباس، 1385، ارزیابی واحدهای تصمیم‌گیری با استفاده از مدل برنامه‌ریزی اولویت‌بندی فازی گروهی، مجلة پژوهشی دانشگاه اصفهان (علوم انسانی)، دورة بیستم، شمارة 1، صص، 230-211.
  2. بحرینی، حسین و منوچهر طبیبیان، 1377، مدل کیفیت محیط‌زیست شهری، فصلنامة محیط‌شناسی، سال بیست و چهارم، شمارة 21، صص 41-56.
  3. تقوایی، مسعود، شیخ بیگلو، رعنا و لیلا اسحاق دواتگر، 1389، بررسی و تحلیل آلودگی‌های ناشی از مشاغل شهر اصفهان، فصلنامة محیط‌شناسی، سال سی و ششم، شمارة 56، صص 111-122.
  4. حسینی، حاتم، 1383، درآمدی بر جمعیت‌شناسی اقتصادی-اجتماعی و تنظیم خانواده، چاپ دوم، انتشارات دانشگاه بوعلی سینا، همدان.
  5. سیف‌الدینی، فرانک و حسین منصوریان، 1390، تحلیل الگوی تمرکز خدمات شهری و آثار زیست‌محیطی آن در شهر تهران، فصلنامة محیط‌شناسی، سال سی‌و‌هفتم، شمارة 60، صص 53-64.
  6. طرح جامع تهران، 1385، مرکز مطالعات و برنامه‌ریزی شهر تهران، شهرداری تهران.
  7. ‌ کاویانی، محمدرضا و بهلول علیجانی، 1382، مبانی آب و هواشناسی، چاپ نهم، انتشارات سمت، تهران.
  8. متکان، علی‌اکبر و همکاران، 1388، سنجش کیفیت مکان‌های شهری، با استفاده از روش‌های ارزیابی چند متغیره در GIS (مورد مطالعه: شهر تهران). فصلنامة سنجش‌ازدور و GIS ایران، سال یکم، شمارة 4، صص 1-20.
  9. شاکری، اقبال و امید صمدی، ۱۳۸۵، بلندمرتبهسازی پاسخی برای کاهش مناطق متراکم و فرسودة شهری، سیزدهمین کنفرانس مهندسی عمران سراسر کشور.

10. Amini Faskhoodi, A., 2006, Evaluation of Decision Making Units Using Group Fuzzy Prioritization Planning Model, Research Journal of Isfahan University (Humanities), Vol. 20, No. 1, PP. 211-230. (In Persian)

11. Bahraini, H., and Tabibian, M., 1998, Urban Environmental Quality Model, Journal of Environmental Studies, Vol. 24, No. 21, PP. 41-56. (In Persian)

12. Taghvaie, M., Shaykh Bayglou, R., and Eshagh Davatgar, L., 2011, Analysis of Pollutions Resulted from Jobs in Isfahan City, Journal of Environmental Studies, Vol. 36, No. 56, PP. 111-122. (In Persian)

13. Hoseini, H., 2004, Introduction to Socio -Economic Demography and Family Planning, Vol. 2, University of Bu Ali Sina Publication, Hamadan. (In Persian)

14. Seifolddini, F., and Mansourian, H., 2012, Pattern of Urban Services Concentration and Its Environmental Impacts on Tehran City, Journal of Environmental Studies, Vol. 37, No. 60, PP. 53-64. (In Persian)

15. Plan, T. M., 2006, Tehran Urban Research and Planning Center, Municipality of Tehran. (In Persian)

16. Kavyani, M., and Alijani, B., 2002, The Foundations of Climatology, 9 th Vol., SAMT Publication, Tehran. (In Persian)

17. Matkan, A. et al., 2010, Measuring the Quality of Urban Places by Using MulticriteriaEvaluation Method in GIS (Case Study: Tehran City), Journal of Remote Sencing and GIS, Vol. 1, No. 4, PP. 1-20. (In Persian)

18. Akbari, H., Menon, S., and Rosenfeld, A., 2009, Global Cooling: Increasing World-Wide Urban Albedos to Offset CO2, Climatic Change, Vol. 94, No. 3, PP. 275-286. (In Persian)

19. Shakeri A., and Samadi, O., 2006, Tall Building Response to Reduce Dense and Old Areas of Urban, Thirteenth Conference of Civil Engineering, Iran.

20. Almusaed, A., 2011, The Urban Heat Island Phenomenon Upon Urban Components, In Biophilic and Bioclimatic Architecture, Springer London, PP. 139-150. ‌

21. Arnold Jr, C. L., and Gibbons, C. J., 1996, Impervious Surface Coverage: The Emergence of a Key Environmental Indicator, Journal of the American Planning Association, Vol. 62, No. 2, PP. 243-258.‌

22. As-Syakur, A. R. et al., 2010, Studi Perubahan Penggunaan Lahan Di DAS Badung, Bumi Lestari, Vol. 10, No. 2, PP. 200-207.

23. Brunsell, N. A., and Gillies, R. R., 2003, Length Scale Analysis of Surface Energyfluxes De-Rived From Remote Sensing, Journal of Hydrometeorology, Vol. 4, No. 6, PP. 1212–1219.

24. Carp, F. M., Zawadski, R. T., and Shokrkon, H., 1976, Dimensions of Urban Environmental Quality, Environment and Behavior, Vol. 8, No. 2, PP. 239-264.

25. Chen, Q., Ren, J. Li., Z., and Ni, C., 2009, Urban Heat Island Effect Research in Chengdu City Based on Modis Data, Proceedings of 3rd International Conference On Bioinformatics and Biomedical Engineering. ICBBE, Beijing, China, PP. 1–5.

26. Farcas, F., 2008, Road Traffic Noise: A Study of Skåne Region, Sweden, (Doctoral Dissertation, Msc Thesis, Linköping University, Sweden).‌

27. Faryadi, S., and Taheri, S., 2009, Interconnections of Urban Green Spaces and Environmental Quality of Tehran, International Journal of Environmental Research, Vol. 3, No. 2, PP. 199-208.‌

28. Fisher, P., Abrahart, R. J., and Herbinger, W., 1997, The Sensitivity of Two Distributed NonPoint Source Pollution Models to the Spatial Arrangement of the Landscape, Hydrological Processes, Vol. 11, No. 3, PP. 241-252.

29. Fu, P., and Rich, P. M., 2000, The Solar Analyst 1.0 User Manual, Helios Environmental Modeling Institute.

30. Gurram, M. K., 2016, Urban Environmental Quality Assessment at Ward Level Using AHP Based GIS Multi-Criteria Modeling–A Study on Hyderabad City, India, Asian Journal of Geoinformatics, Vol. 15, No. 3, PP.16-29.‌

31. Hayati, H., and Sayadi, M. H., 2012, Impact of Tall Buildings in Environmental Pollution, Environmental Skeptics and Critics, Vol. 1, No. 1, PP. 8-11.‌

32. Hofmann, M. et al., 2012, Perceptions of Parks and Urban Derelict Land by Landscape Planners and Residents, Urban Forestry and Urban Greening, Vol. 11, No. 3, PP. 303-312.‌

33. Jacobson, M. Z., and Ten Hoeve, J. E., 2012, Effects of Urban Surfaces and white Roofs on Global and Regional Climate, Journal Of Climate, Vol. 25, No. 3, PP. 1028-1044.‌

34. Joseph, M., Wang, F., and Wang, L., 2014, GIS-Based Assessment of Urban Environmental Quality in Port-Au-Prince, Haiti, Habitat International, Vol. 41, PP. 33-40.‌

35. Kaili, D., 2003, Fuzzy Evaluation of Urban Environmental Quality: Case Study Wuchang Wuhan (Doctoral Dissertation, Master’s Thesis, International Institute For Geo-Information And Earth-Observation, Enschede, The Netherlands).‌

36. Kaufmann, R. K. et al., 2007, Climate Response to Rapid Urban Growth: Evidence of a Human-Induced Precipitation Deficit, Journal of Climate, Vol. 20, No. 10, PP. 2299-2306.

37. Kim, R., and Van Den Berg, M., 2010, Summary of Night Noise Guidelines for Europe, Noise and Health, Vol. 12, No. 47, PP. 61-63.

38. Li, Z. L., 2013, Satellite-Derived Land Surface Temperature: Current Status and Perspectives, Remote Sensing of Environment, Vol. 131, PP. 14-37.‌

39. Liang, S., 2001, Narrowband to Broadband Conversions of Land Surface Albedo I: Algorithms, Remote Sensing of Environment, Vol. 76, No. 2, PP. 213-238.

40. Lilburne, L., and Tarantola, S., 2009, Sensitivity Analysis of Spatial Models, International Journal of Geographical Information Science, Vol. 23, No. 2, PP. 151-168.

41. Malczewski, J., 1999, GIS and Multicriteria Decision Analysis, John Wiley and Sons.

42. MHRC, 2017, Mercer Human Resource Consulting, Quality of Living Global City Rankings Mercer Survey.

43. Nichol, J., and Wong, M. S., 2005, Modeling Urban Environmental Quality in A Tropical City, Landscape and Urban Planning, Vol. 73, No. 1, PP. 49-58.

44. Nichol, J., and Wong, M. S., 2009, Mapping Urban Environmental Quality Using Satellite Data and Multiple Parameters, Environment and Planning B: Planning and Design, Vol. 36, No. 1, PP. 170-185.‌

45. Parkes, A., Kearns, A., and Atkinson, R., 2002, What Makes People Dissatisfied With Their Neighbourhood, Urban Studies, Vol. 39, No. 13, PP. 2413-2438.‌

46. Pauleit, S., Ennos, R., and Golding, Y., 2005, Modeling the Environmental Impacts of Urban Land Use and Land Cover Change—A Study in Merseyside, UK, Landscape And Urban Planning, Vol. 71, No. 2, PP. 295-310.‌

47. Rose, L., and Devadas, M. D., 2009, Analysis of Land Surface Temperature and Land Use/Land Cover Types Using Remote Sensing Imagery–A Case in Chennai City, India, In the Seventh International Conference on Urban Climate, Yokohama, Japan, Vol. 29, No. 7, PP. 1-4.

48. Rouse Jr, J. et al., 1974, Monitoring Vegetation Systems in the Great Plains with ERTS.‌

49. Saaty, T. L., 2004, Decision Making the Analytic Hierarchy and Network Processes (AHP/ANP), Journal of Systems Science and Systems Engineering, Vol. 13, No. 1, PP. 1-35.

50. Sayadi, M. H. 2012, Evaluation of Noise Pollution in the Schools of Birjand City and Its Administrative Solutions, In 2011, Journal of Occupational Health and Epidemiology, Vol. 1, No. 3, PP. 132-138.‌ (In Persian)

51. Snedecor, G. W., and Cochran, W. G., 1989, Statistical Methods, 8 thedn, Ames: Iowa State Univ. Press Iowa.‌

52. Streit, G. E., and Guzmán, F., 1996, Mexico City Air Quality: Progress of an International Collaborative Project to Define Air Quality Management Options, Atmospheric Environment, Vol. 30, No. 5, PP. 723-733.

53. Vafai, F., Hadipour, V., and Hadipour, A., 2013, Determination of Shoreline Sensitivity to Oil Spills by Use of GIS and Fuzzy Model, Case Study the Coastal Areas of Caspian Sea in North of Iran, Ocean and Coastal Management, Vol. 71, PP. 123-130.‌ (In Persian)

54. Van Kamp, I. et al., 2003, Urban Environmental Quality and Human Well-Being: Towards a Conceptual Framework and Demarcation of Concepts; A Literature Study, Landscape and Urban Planning, Vol. 65, No. 1, PP. 5-18.

55. Van Poll, R., 1997, The Perceived Quality of the Urban Residential Environment, A Multi-Attribute Evaluation, Ph.D. Thesis, Groningen: University Of Groningen.‌

56. Wang, F. et al., 2015, An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval From Landsat 8 Thermal Infrared Sensor Data, Remote Sensing, Vol. 7, No. 4, PP. 4268.

57. WHO, 1998, WHOQOL Measuring Quality of Life. 15. Division of Mental Health and Prevention of Substance Abuse.

58. Woodcock, C. E., and Gopal, S., 2000, Fuzzy Set Theory and Thematic Maps: Accuracy Assessment and Area Estimation, International Journal of Geographical Information Science, Vol. 14, No. 2, PP. 153-172.‌

59. Xu, H.  et al., 2011, Spatial and Temporal Analysis of Urban Heat Island Effects in Chengdu City by Remote Sensing, In Geoinformatics, 2011 19th International Conference on IEEE, PP. 1-5.‌

60. Yanjun, L., and Ying, W., 2011, Study on Resource-Environment Response to the Rapid Urban Expansion of China, Energy Procedia, Vol. 5, PP. 2549-2553.

61. Yin, X., 1999, Bright Sunshine Duration in Relation to Precipitation, Air Temperature and Geographic Location, Theoretical and Applied Climatology, Vol. 64, No. 2, PP. 61-68.

62. Zhang, Y.  et al., 2015, Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data, Remote Sensing, Vol. 7, No. 8, PP. 10737-10762.

Volume 51, Issue 1
April 2019
Pages 229-247
  • Receive Date: 05 January 2018
  • Revise Date: 18 November 2018
  • Accept Date: 18 November 2018
  • First Publish Date: 21 March 2019