Land surface temperature estimating in urbanized landscapes using artificial neural networks

  • Mahsa Bozorgi
  • Farhad Nejadkoorki
  • Mohammad Bagher Mousavi


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.


Land surface temperature Scenario prediction Temperature estimating Artificial neural network Isfahan 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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© 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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