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Land surface temperature estimating in urbanized landscapes using artificial neural networks
- Bozorgi, Mahsa, Nejadkoorki, Farhad, Mousavi, MohammadBagher
- Environmental monitoring and assessment 2018 v.190 no.4 pp. 250
- Landsat, algorithms, analysis of variance, case studies, green infrastructure, issues and policy, land use planning, landscapes, neural networks, normalized difference vegetation index, risk, surface temperature, urban areas, urbanization
- 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.