Extraction of the city impervious surface and analysis of the heat island effect based on the multi-source and multi-temporal remote sensing image

Zongze He; Zhiyuan Hu; Fangtai Hu

doi:10.1117/12.2625431

6 December 2021 Extraction of the city impervious surface and analysis of the heat island effect based on the multi-source and multi-temporal remote sensing image

Zongze He, Zhiyuan Hu, Fangtai Hu

Author Affiliations +

Proceedings Volume 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021); 1208508 (2021) https://doi.org/10.1117/12.2625431
Event: 2021 International Conference on Green Communication, Network, and Internet of Things, 2021, Kunming, China

Abstract

Using remote sensing technology to extract urban impervious surface information has a wide range and high efficiency and plays an essential role in monitoring urban expansion and urban ecological environment change. Generally speaking, if a place is an impervious surface, its surface temperature would be higher. The traditional impervious surface information extraction is based on a single data source and a single-phase remote sensing image, and the error of the extraction results is large. Based on the multi-temporal Sentinel-2 remote sensing image and LJ1-01 nighttime light data in 2018, the impervious surface information was extracted in the main urban area of Qingdao. Combined with the surface temperature retrieved from Landsat OLI/TIRS data, the spatial relationship between the urban heat island effect and impervious surface coverage was analyzed. The results show that multi-temporal and multi-source data can accurately extract urban impervious surface information, and some regions between impervious surface coverage and urban heat island effect cannot show spatial correlation.

Citation Download Citation

Zongze He, Zhiyuan Hu, and Fangtai Hu "Extraction of the city impervious surface and analysis of the heat island effect based on the multi-source and multi-temporal remote sensing image", Proc. SPIE 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021), 1208508 (6 December 2021); https://doi.org/10.1117/12.2625431

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