A Modeling of Urban Heat Island using Artificial Neural Network and Remote Sensing in Tehran Artificial Neural Network and Remote Sensing Section Original Research

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Published Nov 28, 2023
https://doi.org/10.47164/ijngc.v14i4.1314
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Volume 14, Issue 4, November 2023

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Zahra Azizi
Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resource and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
Navid Zoghi
Graduated M.Sc. Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
Saeed Behzadi
Assistant Professor, Department of Surveying and Geomatics Engineering, Shahid Rajaei Teacher Training University, Tehran, Iran

Abstract

The Urban Heat Island phenomenon happens due to the differences in the thermal behavior between urban and rural areas which many factors such as vegetated, water, impervious and built-up areas could affect this phenomenon. Urban Heat Island consists of three types: Canopy heat island, Boundary heat island, and surface heat island. In this study, the surface type of urban heat island is analyzed. In this paper, 13 TM/ETM+ images have been obtained from 1990 to 2015(an image biennially). Urban Heat Islands effects are much more severe in summer; therefore, all images have been taken in summer. NDVI, IBI, albedo, and also land surface temperature were derived from images. Various neural network topologies have been used to identify the best model for predicting the urban heat island intensity. The LST of 2016 has been considered as validation data, thus the best result from fitting structures was obtained from Cascade which the Bayesian Regularization was its training algorithm (R-squared=0.62, RMSE=1.839 K).


 

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Author Biography

Navid Zoghi, Graduated M.Sc. Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran

Graduated M.Sc. Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran

How to Cite
Azizi, Z., Navid Zoghi, & Saeed Behzadi. (2023). A Modeling of Urban Heat Island using Artificial Neural Network and Remote Sensing in Tehran: Artificial Neural Network and Remote Sensing. International Journal of Next-Generation Computing, 14(4). https://doi.org/10.47164/ijngc.v14i4.1314
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References

  1. CHAKRABORTY, S. D., KANT, Y. & MITRA, D. 2015. Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data. Journal of environmental management, 148, 143-152. DOI: https://doi.org/10.1016/j.jenvman.2013.11.034
  2. CHEN, X.-L., ZHAO, H.-M., LI, P.-X. & YIN, Z.-Y. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote sensing of environment, 104, 133-146. DOI: https://doi.org/10.1016/j.rse.2005.11.016
  3. COAKLEY, J. 2003. Reflectance and albedo, surface. Academic. DOI: https://doi.org/10.1016/B0-12-227090-8/00069-5
  4. DEMUTH, H. B., BEALE, M. H., DE JESS, O. & HAGAN, M. T. 2014. Neural network design, Martin Hagan.
  5. ELMAN, J. L. 1990. Finding structure in time. Cognitive science, 14, 179-211. DOI: https://doi.org/10.1207/s15516709cog1402_1
  6. ESTOQUE, R. C. & MURAYAMA, Y. 2015. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205-217. DOI: https://doi.org/10.1016/j.ecolind.2015.03.037
  7. GAO, B.-C. 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58, 257-266. DOI: https://doi.org/10.1016/S0034-4257(96)00067-3
  8. GARCÍA, P. & PÉREZ, E. 2016. Mapping of soil sealing by vegetation indexes and built-up index: A case study in Madrid (Spain). Geoderma, 268, 100-107. DOI: https://doi.org/10.1016/j.geoderma.2016.01.012
  9. GOBAKIS, K., KOLOKOTSA, D., SYNNEFA, A., SALIARI, M., GIANNOPOULOU, K. & SANTAMOURIS, M. 2011. Development of a model for urban heat island prediction using neural network techniques. Sustainable Cities and Society, 1, 104-115. DOI: https://doi.org/10.1016/j.scs.2011.05.001
  10. HUETE, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25, 295-309. DOI: https://doi.org/10.1016/0034-4257(88)90106-X
  11. KIKON, N., SINGH, P., SINGH, S. K. & VYAS, A. 2016. Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustainable Cities and Society, 22, 19-28. DOI: https://doi.org/10.1016/j.scs.2016.01.005
  12. 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. DOI: https://doi.org/10.1016/j.rse.2012.12.008
  13. LIU, H., TIAN, H.-Q., LIANG, X.-F. & LI, Y.-F. 2015. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Applied Energy, 157, 183-194. DOI: https://doi.org/10.1016/j.apenergy.2015.08.014
  14. LO, C. P., QUATTROCHI, D. A. & LUVALL, J. C. 1997. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing, 18, 287-304. DOI: https://doi.org/10.1080/014311697219079
  15. MAKOTO, K., SANATH, J. & JUJI, T. 1996. Relation between social and environmental conditons in colombo sri lanka and the urban index estimated by satellite remote sensing data. Proceedings of International Archives of Photogrammetry and Remote Sensing, 31, 321-326.
  16. OKE, T. R. 1973. City size and the urban heat island. Atmospheric Environment (1967), 7, 769-779. DOI: https://doi.org/10.1016/0004-6981(73)90140-6
  17. PAPANTONIOU, S. & KOLOKOTSA, D.-D. 2016. Prediction of outdoor air temperature using neural networks: Application in 4 European cities. Energy and Buildings, 114, 72-79. DOI: https://doi.org/10.1016/j.enbuild.2015.06.054
  18. ROUSE JR, J. W., HAAS, R., SCHELL, J. & DEERING, D. 1974. Monitoring vegetation systems in the Great Plains with ERTS.
  19. ŞAHIN, M. 2012. Modelling of air temperature using remote sensing and artificial neural network in Turkey. Advances in Space Research, 50, 973-985. DOI: https://doi.org/10.1016/j.asr.2012.06.021
  20. WANG, S., MA, Q., DING, H. & LIANG, H. 2016. Detection of urban expansion and land surface temperature change using multi-temporal landsat images. Resources, Conservation and Recycling.
  21. XU, H. 2008. A new index for delineating built‐up land features in satellite imagery. International Journal of Remote Sensing, 29, 4269-4276. DOI: https://doi.org/10.1080/01431160802039957
  22. YI, J. & PRYBUTOK, V. R. 1996. A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environmental pollution, 92, 349-357. DOI: https://doi.org/10.1016/0269-7491(95)00078-X
  23. 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, 583-594. DOI: https://doi.org/10.1080/01431160304987
  24. ZHANG, J., WANG, Y. & LI, Y. 2006. A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & geosciences, 32, 1796-1805. DOI: https://doi.org/10.1016/j.cageo.2006.05.001
  25. P. Jiang, X. Liu, H. Zhu, Y. Li Features of urban heat Island in mountainous Chongqing from a dense surface monitoring network Atmosphere (2019), 10.3390/atmos10020067 DOI: https://doi.org/10.3390/atmos10020067