Preprint Article Version 1 This version is not peer-reviewed

Correlation Between Impervious Surface and Surface Temperature Change in Typical Urban Agglomerations-Xuzhou City Example

Yandong Gao
ORCID logo ,
Huiqin Liu
* ,
Hua Zhang
,
Nanshan Zheng
,
Shijin Li
ORCID logo ,
Shubi Zhang
,
Di Zhang
,
Zhi Li
,
Chao Yan
ORCID logo
Version 1 : Received: 15 October 2024 / Approved: 15 October 2024 / Online: 15 October 2024 (20:41:08 CEST)

How to cite: Gao, Y.; Liu, H.; Zhang, H.; Zheng, N.; Li, S.; Zhang, S.; Zhang, D.; Li, Z.; Yan, C. Correlation Between Impervious Surface and Surface Temperature Change in Typical Urban Agglomerations-Xuzhou City Example. Preprints 2024, 2024101214. https://doi.org/10.20944/preprints202410.1214.v1 Gao, Y.; Liu, H.; Zhang, H.; Zheng, N.; Li, S.; Zhang, S.; Zhang, D.; Li, Z.; Yan, C. Correlation Between Impervious Surface and Surface Temperature Change in Typical Urban Agglomerations-Xuzhou City Example. Preprints 2024, 2024101214. https://doi.org/10.20944/preprints202410.1214.v1

Abstract

The distribution of impervious surface is one of the important assessment indicators for evaluating the quality of urban ecological environment. Taking Xuzhou city as an example, we study the influence of the spatial distribution of impervious surfaces on urban surface temperature, using Landsat 8 remote sensing imagery and nighttime lighting data to extract the impervious surface information from 2013 to 2022 and carry out surface temperature inversion. The study area was divided into built-up and non-built-up areas using nighttime lighting data and the relevant indices were used to establish interest zones and finally the extraction of impervious surfaces was completed using supervised classification methods. The experimental results show that the kappa coefficient is not less than 0.8562, which makes the result more reliable. By extracting the impervious surface of Xuzhou City, it was found that the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014-2016 and 2019-2021, and slowed down in 2017-2018 and 2021-2022. The expansion of impervious surface was found to be one of the reasons for the increase of urban surface temperature through stacked analysis and linear regression analysis.

Keywords

impervious surface; land surface temperature; supervision and classification; spatio-temporal variation

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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