Document Type : Original Article


Department of Natural Resources and Environment, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran


The increase in impervious surfaces due to the urbanization has caused many adverse effects on urban ecological systems, including the urban heat environmental risk. Revealing the relationship between land use/land cover composition and land surface temperature (LST) gives insight into how to effectively reduce the temperature in urban environments. In recent years, the expansion of Bushehr city has resulted in an increase in impervious surface, the decrease of green space and a significant change in the temperature. It is also essential to determine the influence of green spaces on the surrounding environment in urban planning of Bushehr.
Material and methods:
In order to reveal the comprehensive effects of green space on the urban thermal environment in the severely hot regions, this paper adopts remote sensing technology to extract and analyze green space, land surface temperature (LST), and land cover (LC) from the Landsat spectral imaging data (August 2018) with clear-sky conditions. A field survey was carried out in August 2018 and temperature and relative humidity was recorded for 13 selected green spaces in the Bushehr city. To analyze the field data, a multivariate linear regression model between temperature and relative humidity with three variables of direction, distance, and the size of green spaces was determined and interactions between variables were investigated. By applying a multivariate regression model, the relationship between temperature and relative humidity data was calculated for each point. Tukey test was carried out between the averages of temperature variations and relative humidity for each of the three variables at a 95% confidence level.
Results and discussion:
The results of the R2 correlation coefficient from the linear regression model between normalized difference vegetation index (NDVI) and land surface temperature equal to 0.72 indicated a high correlation between temperature and NDVI variations. The results of the temperature variation analysis with NDVI changes indicated that NDVI variations, which are actually plant frequency indices, were one of the most important factors in reducing temperature or in improving the most important ecological function of urban green space. The results of the recorded data at different intervals and directions from the green spaces indicated a gradual decrease in temperature and an increase in relative humidity by reducing the distance from the green spaces. Accordingly, the effect of green spaces on temperature and relative humidity was significant up to a distance of 60 meters (p≤0.05). The western direction had the lowest temperature and the highest relative humidity and the eastern direction had the highest temperature and the lowest relative humidity. The R2 correlation coefficient obtained from the linear model between temperature and relative humidity with the interactions between the three variables (direction, distance, and the size of green spaces) were also 0.88 and 0.95, respectively.
The results indicated that urban green space plays a significant role in improving the urban thermal environment. By using remote sensing technology and comparing the thermal environments, we conclude that the location and distance of urban green spaces affect the thermal pattern in an urban environment. We can establish certain rules on the distribution of the urban green space and the cooling ranges in hot seasons in the surroundings of urban green spaces


  1. Ca, V.T., Asaeda, T. and Abu, E.M., 1998. Reductions in air conditioning energy caused by a nearby park. Energy and Buildings. 29(1), 83-92.
  2. Cao, X., Onishi, A., Chen, J. and Imura, H., 2010. Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landscape and Urban Planning. 96(4), 224-231.
  3. Cavan, G., Lindley, S., Jalayer, F., Yeshitela, K., Pauleit, S., Renner, F., Gill, S., Capuano, P., Nebebe, A., Woldegerima, T., Kibassa, D. and Shemdoe, R., 2014. Urban morphological determinants of temperature regulating ecosystem services in two African cities. Ecological Indicators. 42, 43-57.
  4. Chandra, S., Sharma, D. and Dubey, S.K., 2018. Linkage of urban expansion and land surface temperature using geospatial techniques for Jaipur City, India. Arabian Journal of Geosciences. 11(2), 847-861.
  5. Cheng, K.S., Su, Y.F., Kuo, F.T., Hung, W.C. and Chiang, J.L., 2008. Assessing the effect of landcover changes on air temperatu× re using remote sensing images-A pilot study in northern Taiwan. Landscape and Urban Planning. 85(2), 85-96.
  6. Deng, Y., Wang, S., Bai, X., Tian, Y., Wu, L., Xiao, J., Chen, F. and Qian, Q., 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific Reports, 8(1), 1-12.
  7. Di Leo, N., Escobedo, F.J. and Dubbeling, M., 2016. The role of urban green infrastructure in mitigating land surface temperature in Bobo-Dioulasso, Burkina Faso. Environment, Development and Sustainability. 18(2), 373-392.
  8. Eludoyin, A.O., Omotoso, I., Eludoyin, O.M. and Popoola, K.S., 2019. Remote Sensing Technology for Evaluation of Variations in Land Surface Temperature, and Case Study Analysis from Southwest Nigeria. In Geospatial Challenges in the 21st Century (pp. 151-170): Springer, Germany.
  9. Gorse, C., Parker, J., Thomas, F., Fletcher, M., Ferrier, G. and Ryan, N., 2019. The Planning and Design of Buildings: Urban Heat Islands-Mitigation. In Industry 4.0 and Engineering for a Sustainable Future (pp. 211-225). Springer, Germany.
  10. Guo, L., Liu, R., Men, C., Wang, Q., Miao, Y. and Zhang, Y., 2019. Quantifying and simulating landscape composition and pattern impacts on land surface temperature: a decadal study of the rapidly urbanizing city of Beijing, China. Science of The Total Environment. 654, 430-440.
  11. Hami, A., Abdi, B., Zarehaghi, D. and Maulan, S.B., 2019. Assessing the thermal comfort effects of green spaces: A systematic review of methods, parameters, and plants’ attributes. Sustainable Cities and Society. 49, 101634.
  12. Hart, M.A. and Sailor, D.J., 2009. Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island. Theoretical and Applied Climatology. 95(3-4), 397-406.
  13. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83(1-2), 195-213.
  14. Jenerette, G.D., Harlan, S.L., Buyantuev, A., Stefanov, W.L., Declet-Barreto, J., Ruddell, B.L., Myint, S.W., Kaplan, S. and Li, X., 2016. Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA. Landscape Ecology. 31(4), 745-760.
  15. Kabisch, N., Strohbach, M., Haase, D. and Kronenberg, J., 2016. Urban green space availability in European cities. Ecological Indicators. 70, 586-596.
  16. Kaplan, G., Avdan, U. and Avdan, Z.Y., 2018. Urban heat island analysis using the landsat 8 satellite data: A case study in Skopje, Macedonia. In Multidisciplinary Digital Publishing Institute Proceedings. 2(7), 358-367.
  17. Keeley, M. and Benton-Short, L., 2019. Urban Green Space. In Urban Sustainability in the US (pp. 239-279). Springer, Germany.
  18. Li, Z.L., Tang, B.H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, Isabel F. and Sobrino, J.A., 2013. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment. 131, 14-37.
  19. Liu, Y., Peng, J. and Wang, Y., 2017. Diversification of land surface temperature change under urban landscape renewal: a case study in the main city of Shenzhen, China. Remote Sensing. 9(9), 1-19.
  20. Meng, H., Jing, L. and Xin, H., 2019. The Influence of underlying surface on land surface temperature--a case study of urban green space in Harbin. Energy Procedia. 157, 746-751.
  21. Murphy, D.J., 2007. The relation between land-cover and the urban heat island innortheastern Puerto Rico. International Journal of Climatology. 31(8),1222-1239.
  22. NASA, 2008. Landsat 8 science data users handbook. Science 186. Available online at:
  23. Norton, B. A., Coutts, A.M., Livesley, S.J., Harris, R.J., Hunter, A.M. and Williams, N.S., 2015. Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and Urban Planning. 134, 127-138.
  24. Owen, T., Carlson, T. and Gillies, 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.
  25. Peng, J., Ma, J., Liu, Q., Liu, Y., Li, Y. and Yue, Y., 2018. Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective. Science of the Total Environment. 635, 487-497.
  26. Peng, J., Xie, P., Liu, Y. and Ma, J., 2016. Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment. 173, 145-155.
  27. Rani, M., Kumar, P., Pandey, P. C., Srivastava, P.K., Chaudhary, B., Tomar, V. and Mandal, V.P., 2018. Multi-temporal NDVI and surface temperature analysis for Urban Heat Island inbuilt surrounding of sub-humid region: a case study of two geographical regions. Remote Sensing Applications: Society and Environment. 10, 163-172.
  28. Ren, Y., Qu, Z., Du, Y., Xu, R., Ma, D., Yang, G., Shi, Y., Fan, X., Tani, A. and Guo, P., 2017. Air quality and health effects of biogenic volatile organic compounds emissions from urban green spaces and the mitigation strategies. Environmental Pollution. 230, 849-861.
  29. Spangenberg, J., Shinzato, P., Johansson, E. and Duarte, D., 2019. Simulation of the influence of vegetation on microclimate and thermal comfort in the city of São Paulo. Revista da Sociedade Brasileira de Arborização Urbana. 3(2), 1-19.
  30. Şimşek, Ç.K. and Ödül, H., 2019. A method proposal for monitoring the microclimatic change in an urban area. Sustainable Cities and Society. 46, 101407.
  31. Sun, S., Xu, X., Lao, Z., Liu, W., Li, Z., Garcia, E.H., He, L. and Zhu, J., 2017. Evaluating the impact of urban green space and landscape design parameters on thermal comfort in hot summer by numerical simulation. Building and Environment. 123, 277-288.
  32. Syariz, M.A., Lin, B.Y., Denaro, L.G., Jaelani, L.M., Van Nguyen, M. and Lin, C.H., 2019. Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 147, 56-64.
  33. Taleai, M. and Yameqani, A.S., 2018. Integration of GIS, remote sensing and Multi-Criteria Evaluation tools in the search for healthy walking paths. KSCE Journal of Civil Engineering. 22(1), 279-291.
  34. Thanh Hoan, N., Liou, Y.A., Nguyen, K.A., Sharma, R., Tran, D.P., Liou, C.L. and Cham, D., 2018. Assessing the effects of land-use types in surface urban heat islands for developing comfortable living in Hanoi City. Remote Sensing. 10(12), 1-20. doi: 10.3390/rs10121965.
  35. Tran, D.X., Pla, F., Latorre-Carmona, P., Myint, S.W., Caetano, M. and Kieu, H.V., 2017. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing. 124, 119-132.
  36. Vargas-Hernandez, J.G., Pallagst, K. and Zdunek-Wielgołaska, J.A., 2019. Urban Green Spaces for Sustainable Community Development: A Strategic Management Approach. In Optimizing Regional Development Through Transformative Urbanization (pp. 271-287): IGI Global, USA.
  37. Wang, S., Ma, Q., Ding, H. and Liang, H., 2018. Detection of urban expansion and land surface temperature change using multi-temporal landsat images. Resources, Conservation and Recycling. 128, 526-534.
  38. Weng, Q., Lu, D. and Schubring, J., 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. 89(4), 467-483.
  39. Xu, X., Sun, S., Liu, W., Garcia, E. H., He, L., Cai, Q., Xu, S., Wang, J. and Zhu, J., 2017. The cooling and energy saving effect of landscape design parameters of urban park in summer: a case of Beijing, China. Energy and Buildings. 149, 91-100.
  40. Yahia, M.W., Johansson, E., Thorsson, S., Lindberg, F. and Rasmussen, M.I., 2018. Effect of urban design on microclimate and thermal comfort outdoors in warm-humid Dar es Salaam, Tanzania. International Journal of Biometeorology. 62(3), 373-385.
  41. Yang, J., Sun, J., Ge, Q. and Li, X., 2017. Assessing the impacts of urbanization-associated green space on urban land surface temperature: a case study of Dalian, China. Urban Forestry & Urban Greening. 22, 1-10.
  42. Yang, Y.J., Gao, Z., Shi, T., Wang, H., Li, Y., Zhang, N., Zhang, H. and Huang, Y., 2019. Assessment of urban surface thermal environment using MODIS with a population-weighted method: a case study. Journal of Spatial Science. 64(2), 287-300.
  43. Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z. and Sobrino, J., 2019. Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sensing. 11(1), 1-36.
  44. Zölch, T., Maderspacher, J., Wamsler, C. and Pauleit, S., 2016. Using green infrastructure for urban climate-proofing: an evaluation of heat mitigation measures at the micro-scale. Urban Forestry & Urban Greening. 20, 305-316.