Details

Autor(en) / Beteiligte

• Ezimand, Keyvan
• Aghighi, Hossein
• Ashourloo, Davod
• Shakiba, Alireza

Titel

The analysis of the spatio-temporal changes and prediction of built-up lands and urban heat islands using multi-temporal satellite imagery

Ist Teil von

• Sustainable cities and society, 2024-04, Vol.103, p.105231, Article 105231

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •The NBCI was developed based on remote sensing data to detect built-up land.•The prediction of UHI and built-up lands presented using CA-Markov.•The relations between UHI and built-up are investigated using statistical models.•The built-up lands changes have direct and strong effects on the changes of UHI. Irregular and rapid shifts in built-up lands are among the prime causes of increased Land Surface Temperature (LST) and the subsequent formation of Urban Heat Islands (UHIs). Timely and accurate delineation of built-up lands is fundamental to the understanding of LST variation and environmental changes; however, most of the built-up land maps have coarse resolutions. The present study attempts to develop a combinational index representing the biophysical characteristics of the land surface using exponential functions and Landsat time-series images for accurate built-up land maps on a fine scale. The developed index was used along with the Otsu threshold to categorize different built-up from 2001 to 2021 and then utilized as an input to the CA-Markov model to predict built-up lands in 2031. The corresponding relations between changes in built-up lands and its effects on LST and UHI were also analyzed via partial correlation analysis and bivariate LISA. The accuracy assessments of the proposed model for 2001–2021 revealed an accuracy value of 78 for the period (2001–2021) and a value of 77 when the CA-Markov model was utilized for 2021. The correlation values connecting the built-up land status and LST were also significantly strong and positive (0.86–0.89). The apparent similarities and patterns of clusters linking the built-up lands to UHI were also verified by global bivariate Moran's I index of greater than 0.328, showing how an increase in built-up lands leads to increasing the UHI. These findings are helpful for urban construction planning to minimize the negative environmental impacts of urbanization.