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Land surface temperature estimating in urbanized landscapes using artificial neural networks

  • Mahsa Bozorgi
  • Farhad Nejadkoorki
  • Mohammad Bagher Mousavi
Article
  • 134 Downloads

Abstract

Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explore the non-linear relationships between urban LST and green cover spatial patterns derived from Landsat 8 OLI imagery. The model was calibrated using two sets of variables: Normalized Difference Built Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Furthermore, Compact Development Scenario (CDS) and Green Development Scenario (GDS) were defined. The results showed that GDS is more successful in mitigating urban LST (mean LST = 40.93) compared to CDS (mean LST = 44.88). In addition, urban LST retrieved from the CDS was more accurate in terms of ANOVA significance (sig = 0.043) than the GDS (sig = 0.010). The findings of this study suggest that developing green spaces is a key strategy to combat against the risk of LST concerns in urban areas.

Keywords

Land surface temperature Scenario prediction Temperature estimating Artificial neural network Isfahan 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abrahart, R., Kneale, P. E. & See, L. M. (2004) Neural networks for hydrological modeling. CRC Press.Google Scholar
  2. Afrakhteh, R., Asgarian, A., Sakieh, Y., & Soffianian, A. (2016). Evaluating the strategy of integrated urban-rural planning system and analyzing its effects on land surface temperature in a rapidly developing region. Habitat International, 56, 147–156.CrossRefGoogle Scholar
  3. Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P. & Schweik, C. M. (2002) A review and assessment of land-use change models: dynamics of space, time, and human choice. General Technical Report.  https://doi.org/10.2737/NE-GTR-297.
  4. Aronoff, S. (2005). Remote sensing for GIS managers. CA: Esri Press Redlands.Google Scholar
  5. Asadolahi, Z., Salmanmahiny, A., & Sakieh, Y. (2017). Hyrcanian forests conservation based on ecosystem services approach. Environmental Earth Sciences, 76.  https://doi.org/10.1007/s12665-017-6702-x.
  6. Asgarian, A., Amiri, B. J., & Sakieh, Y. (2015). Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystems, 18(1), 209–222.CrossRefGoogle Scholar
  7. Asgarian, A., Soffianian, A., & Pourmanafi, S. (2016). Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: a case of central Iran using multi-temporal Landsat 8 imagery. Computers and Electronics in Agriculture, 127, 531–540.CrossRefGoogle Scholar
  8. Batty, M. (2009). Urban modeling. International encyclopedia of human geography. Oxford: Elsevier.Google Scholar
  9. Cetin, M. (2015). Determining the bioclimatic comfort in Kastamonu City. Environmental Monitoring and Assessment, 187(10), 640.  https://doi.org/10.1007/s10661-015-4861-3.
  10. Çetin, M. (2016). Determination of bioclimatic comfort areas in landscape planning: a case study of Cide Coastline. Turkish Journal of Agriculture-Food Science and Technolog, 4(9), 800–804.CrossRefGoogle Scholar
  11. Cetin, M., Adiguzel, F., Kaya, O., & Sahap, A. (2016). Mapping of bioclimatic comfort for potential planning using GIS in Aydin. Environment, Development and Sustainability, 20(1), 361–375.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. Chen, Z., & Chen, J. (2006). Investigation on extracting the space information of urban land-use from high spectrum resolution image of ASTER by NDBI method. Geo-Information Science, 2, 026.Google Scholar
  14. Dezhkam, S., Amiri, B. J., Darvishsefat, A. A., & Sakieh, Y. (2014) Simulating urban growth dimensions and scenario prediction through sleuth model: a case study of Rasht County, Guilan, Iran. GeoJournal, 79(5), 591–604.Google Scholar
  15. Di Sabatino, S., Buccolieri, R., Pulvirenti, B., & Britter, R. (2007). Simulations of pollutant dispersion within idealised urban-type geometries with CFD and integral models. Atmospheric Environment, 41(37), 8316–8329.CrossRefGoogle Scholar
  16. Erell, E., & Williamson, T. (2006). Simulating air temperature in an urban street canyon in all weather conditions using measured data at a reference meteorological station. International Journal of Climatology, 26(12), 1671–1694.CrossRefGoogle Scholar
  17. Ford, A. (2000). Modeling the environment: an introduction to system dynamics modeling of environmental systems. International Journal of Sustainability in Higher Education, 1(1).Google Scholar
  18. Goodarzi, M. S., Sakieh, Y., & Navardi, S. (2017). Measuring the effect of an ongoing urbanization process on conservation suitability index: integrating scenario-based urban growth modeling with conservation assessment and prioritization system (CAPS). Geocarto International, 32, 834–852.  https://doi.org/10.1080/10106049.2017.1299799.CrossRefGoogle Scholar
  19. Grover, A., & Singh, R. (2015). Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): a comparative study of Delhi and Mumbai. Environments, 2, 125–138.CrossRefGoogle Scholar
  20. Gupta, N. (2013). Artificial neural network. Network and Complex Systems, 3(1), 24–28.Google Scholar
  21. Hare, M., Letcher, R., & Jakeman, A. (2003). Participatory modelling in natural resource management: a comparison of four case studies. Integrated Assessment, 4(2), 62–72.CrossRefGoogle Scholar
  22. Hasani, M., Sakieh, Y., Dezhkam, S., Ardakani, T., & Salmanmahiny, A. (2017). Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data. Environmental Monitoring and Assessment, 189, 163.  https://doi.org/10.1007/s10661-017-5883-9.CrossRefGoogle Scholar
  23. Iranian Bureau of Statistics (2011). Statistical yearbook of Isfahan province. URL: http://www.amar.org.ir/Default.aspx?tabid=667&fid=11275salname-02-98.pdf.
  24. Karlessi, T., Santamouris, M., Synnefa, A., Assimakopoulos, D., Didaskalopoulos, P., & Apostolakis, K. (2011). Development and testing of PCM doped cool colored coatings to mitigate urban heat island and cool buildings. Building and Environment, 46(3), 570–576.CrossRefGoogle Scholar
  25. Kim, J.-H., Gu, D., Sohn, W., Kil, S.-H., Kim, H., & Lee, D.-K. (2016). Neighborhood landscape spatial patterns and land surface temperature: an empirical study on single-family residential areas in Austin, Texas. International Journal of Environmental Research and Public Health, 13(9), 880.CrossRefGoogle Scholar
  26. Li, H. & Liu, Q. (2008) Comparison of NDBI and NDVI as indicators of surface urban heat island effect in MODIS imagery. International Conference on Earth Observation Data Processing and Analysis (ICEODPA). International Society for Optics and Photonics.  https://doi.org/10.1117/12.815679
  27. Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131, 14–37.CrossRefGoogle Scholar
  28. Lillesand, T., Kiefer, R. W. & Chipman, J. (2014) Remote sensing and image interpretation. Hoboken: Wiley. Google Scholar
  29. Nunes, J., Ferreira, J., Gazeau, F., Lencart-Silva, J., Zhang, X., Zhu, M., & Fang, J. (2003). A model for sustainable management of shellfish polyculture in coastal bays. Aquaculture, 219(1), 257–277.CrossRefGoogle Scholar
  30. Olgyay, V. (1973). Design with climate: bioclimatic approach to architectural regionalism. Princeton: Princeton University Press.Google Scholar
  31. Olgyay, V. (2015). Design with climate: bioclimatic approach to architectural regionalism. Princeton: Princeton University Press.CrossRefGoogle Scholar
  32. Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Sciences, 20, 125–145.CrossRefGoogle Scholar
  33. Qaid, A., Lamit, H., Ossen, D., & Shahminan, R. (2016). Urban heat island and thermal comfort conditions at micro-climate scale in a tropical planned city. Energy and Buildings, 133, 577–595.CrossRefGoogle Scholar
  34. Qudrat-Ullah, H., & Seong, B. S. (2010). How to do structural validity of a system dynamics type simulation model: the case of an energy policy model. Energy Policy, 38(5), 2216–2224.CrossRefGoogle Scholar
  35. Rouse Jr., J. W., Haas, R., Schell, J., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.Google Scholar
  36. Sakieh, Y., Salmanmahiny, A., Jafarnezhad, J., Mehri, A., Kamyab, H., & Galdavi, S. (2015). Evaluating the strategy of decentralized urban land-use planning in a developing region. Land Use Policy, 48, 534–551.CrossRefGoogle Scholar
  37. Sakieh, Y., Jaafari, S., Ahmadi, M., & Danekar, A. (2017a). Green and calm: modeling the relationships between noise pollution propagation and spatial patterns of urban structures and green covers. Urban Forestry & Urban Greening, 24, 195–211.  https://doi.org/10.1016/j.ufug.2017.04.008.CrossRefGoogle Scholar
  38. Sakieh, Y., Salmanmahiny, A., & Mirkarimi, S. H. (2017b). Tailoring a non-path-dependent model for environmental risk management and polycentric urban land-use planning. Environmental Monitoring and Assessment, 189, 91.  https://doi.org/10.1007/s10661-017-5796-7.CrossRefGoogle Scholar
  39. Shahmohamadi, P., Che-Ani, A., Ramly, A., Maulud, K. N. A., & Mohd-Nor, M. (2010). Reducing urban heat island effects: a systematic review to achieve energy consumption balance. International Journal of Physical Sciences, 5(6), 626–636.Google Scholar
  40. Shashua-Bar, L., & Hoffman, M. E. (2002). The Green CTTC model for predicting the air temperature in small urban wooded sites. Building and Environment, 37(12), 1279–1288.CrossRefGoogle Scholar
  41. Sherafati, S. H. A., Saradjian, M. R., & Niazmardi, S. (2013). Urban heat island growth modeling using artificial neural networks and support vector regression: a case study of Tehran, Iran. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W3, 399–403.CrossRefGoogle Scholar
  42. Short, N. M. & Stuart, L. M., Jr. (1982) The heat capacity mapping mission (HCMM) anthology. Washington, DC: Scientific and Technical Information Branch, National Aeronautics & Space Administration, 465 Google Scholar
  43. Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440.CrossRefGoogle Scholar
  44. Soffianian, A., Nadoushan, M. A., Yaghmaei, L., & Falahatkar, S. (2010). Mapping and analyzing urban expansion using remotely sensed imagery in Isfahan, Iran. World Applied Sciences Journal, 9(12), 1370–1378.Google Scholar
  45. Trajanov, A. (2011). Analysis of results of ecological simulation models with machine learning. Informatica: an International Journal of Computing and Informatics, 35(2), 285–286.Google Scholar
  46. USGS. (2016) Landsat 8 (L8) data users handbook. Department of the Interior U.S. Geological Survey. https://landsat.usgs.gov/sites/default/files/documents/Landsat8DataUsersHandbook.pdf
  47. 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.CrossRefGoogle Scholar
  48. Xiong, Y., Huang, S., Chen, F., Ye, H., Wang, C., & Zhu, C. (2012). The impacts of rapid urbanization on the thermal environment: a remote sensing study of Guangzhou, South China. Remote Sensing, 4(7), 2033–2056.CrossRefGoogle Scholar
  49. Yegnanarayana, B. (2009) Artificial neural networks. New Delhi: Prentice-Hall of India Pvt.Ltd. Google Scholar
  50. Zannetti, P. (1990) Air pollution modeling: theories, computational methods and available software. New York: Springer Science+Business Media.  https://doi.org/10.1007/978-1-4757-4465-1
  51. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.CrossRefGoogle Scholar
  52. Zhang, Y., Odeh, I. O., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264.CrossRefGoogle Scholar
  53. Zheng, Z., Fan, S., & Wang, Y. (2006). Effects of urban heat island on summer high temperatures in Beijing. Journal of Applied Meteorological Science, 17, 48–53.Google Scholar
  54. Zhou, W., Huang, G., & Cadenasso, M. L. (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and Urban Planning, 102(1), 54–63.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Environmental ScienceYazd UniversityYazdIran
  2. 2.Department of Computer EngineeringYazd UniversityYazdIran

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