Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. (March 2021)

• Record Type: Journal Article
• Authors: Sekertekin, Aliihsan; Zadbagher, Elaheh
• Is Part Of: Ecological indicators. Volume 122(2021)
• Publication Date: 2021-03
• Subjects: Land Surface Temperature (LST) -- Surface Urban Heat Island (SUHI) -- Markov chain method -- Multiple regression -- Landsat -- Simulation; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.107230
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: LST image for 2021 was projected using NDBI and NDVI. Indices were simulated for 2021 using Markov Chain method. Regression model was applied to obtain LST image 2021. The impervious area was the main driving force for the distribution of SUHI effect. If the urbanization rate continues, SUHI distribution would increase dramatically. Abstract: This study aims to simulate the future form of Land Surface Temperature (LST) distribution and Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. For this purpose, Landsat-5 and Landsat-8 satellite data acquired in 2006, 2011 and 2016, respectively, were utilized, and Zonguldak, a province of Turkey, was selected as a test site. In the study, LST image for 2021 was projected using spectral indices, namely Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Actually, the correlation analysis between four spectral indices (NDVI, SAVI, MNDWI and NDBI) and LST was conducted. However, NDBI and NDVI were determined as highly correlated indices with LST having correlation coefficient 0.89 and −0.90, respectively, thus MNDWI and SAVI were not considered for the further steps. A Multiple Linear Regression (MLR) analysis was carried out between these two indices (NDBI and NDVI) and LST to extract a mathematical model for LST retrieval. In order to obtain the future form of LST image (2021), these indices were simulated for 2021 using Markov Chain method and then regression model was applied. Before generating LST image for 2021, LST for 2016 image was simulated and compared with the existing 2016 LST image using Kappa index of agreement technique to evaluate the performance. The Kappa value was obtained as 0.73 which is reasonable for future simulation. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The obtained results showed that the distribution of SUHI effect was highly related to impervious area and if the urbanization rate in the study area continues as it is, this expansion will cause to a dramatic increase in SUHI distribution unless any proper urban planning model is considered by decision makers.