Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China

In this study, relationships between land use/land cover (LULC) types and land surface temperature (LST) patterns in Shenyang, China were investigated using Landsat 8 image. Typical characteristics of LST in summer time and temperature variations over all LULC types were firstly examined. Afterwards, spatial distribution of LST in radial and circumferential directions was characterized based on profile and concentric zonal analysis, in which contributions of LULC types to LST were studied. In addition, models that can effectively predict LST distribution were developed based on multiple linear regression method (MLRM) and the partial least squares regression method (PLSRM). Results indicate that (1) LST of the study area mainly ranges from 32 degrees C to 41 degrees C, where building and road are main drivers leading to hot temperatures. (2) Temperature profiles vary greatly with LULC types, while urban expansion along central axis causes to horizontal arrangement of building and road, resulting in hot but small-fluctuated LST temperature in 0 and 180. (3) Mean and standard deviation of LST in concentric zones are 36.0 +/- 4.2 degrees C, and LST over all LULC types follows the following pattern: building > road >bare land > agricultural land > green land > water bodies. Due to large proportions of building and road, Buffer 3, 4 and 5 undergo the highest temperature. Although green land and water bodies show significant capability to mitigate UHI effects, cooling effects are unapparent when their sizes are small. (4) Both models developed on MLRM and PLSRM have high degree of accuracy, while the latter is more reasonable for different coefficients can respectively indicate negative and positive roles of LULC types in affecting surface temperature. In general, this study can assist urban planners and policy makers to understand relationships between local LULC types versus LST patterns, and can help them predict UHI patterns along rapid urban expansion, and then make rational urban planning decisions for UHI mitigation. (C) 2017 Elsevier B.V. All rights reserved.