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Article Dans Une Revue Sustainable Cities and Society Année : 2023

Spatial prediction of the urban inter-annual land surface temperature variability: An integrated modeling approach in a rapidly urbanizing semi-arid region

Résumé

The inter-annual land surface temperature (LST) is a meteorological indicator of urban environments, affecting energy consumption and quality of life. In this study, the annual LST variability (ALSTV) of a rapidly urbanizing region in Iran including Karaj, Shahriar and Mohammad-Shahr cities and their surrounding suburbs was esti-mated using Landsat-8 images. Pixel-based image classification and object-based segmentation techniques were employed to extract built-up patches and spectrally homogeneous regions. Using the multiple regression analysis, the ALSTV of built-up patches was modeled as a function of their structure and the spatial characteristics of other land-use patches located in their respective segments (as neighborhood factors). A Cellular Automata model was utilized to simulate the expansion of built-up area up to 5% and, therefore, estimate their future ALSTV. The mean ALSTV was highest in urban areas (33.25 +/- 7.41 degrees C), while the lowest mean value of 24.12 +/- 3.24 degrees C was in green covers. The ALSTV of built-up patches were positively associated with their area and the percentage and size of neighborhood built-up patches, while negatively with the percentage of neighborhood green patches. To create a thermally comfortable landscape, additional green patch allocation and preventing excessive urban patch growth are required in all large built-up segments.
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Dates et versions

hal-04090069 , version 1 (05-05-2023)

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Zahra Mokhtari, Majid Amani-Beni, Ali Asgarian, Alessio Russo, Salman Qureshi, et al.. Spatial prediction of the urban inter-annual land surface temperature variability: An integrated modeling approach in a rapidly urbanizing semi-arid region. Sustainable Cities and Society, 2023, 93, pp.104523. ⟨10.1016/j.scs.2023.104523⟩. ⟨hal-04090069⟩
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