Abstract

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.

Highlights

  • Rapid urbanization changes land surface characteristics tremendously and causes significant increases in urban temperatures compared to the rural surroundings, which is known as the urban heat island (UHI) effect

  • We identified the pseudo-invariant feature (PIF) based on the normalized difference of vegetation index (NDVI) and normalized difference building index (NDBI) of the reference and target images

  • This study validated the effectiveness of the pseudo-invariant feature-based linear regression model (PIF-LRM) in temporally normalizing

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Summary

Introduction

Rapid urbanization changes land surface characteristics tremendously and causes significant increases in urban temperatures compared to the rural surroundings, which is known as the urban heat island (UHI) effect. Previous studies have identified urban expansion as one of the major drivers of surface temperature increases Most of these studies have pointed out that (1) urban areas covered with buildings, roads, and other manmade features are warmer than the surrounding rural areas with natural land cover types; (2) hot areas are getting hotter, and areas with high temperatures tend to expand spatially [8,9,10,11]. Most studies attribute these patterns to an extensive replacement of natural areas by impervious urban land cover. A thorough understanding of the coupled impacts of urban expansion and urban greening on the urban temperature is of great importance for better urban planning and management

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