Next Article in Journal
Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China
Next Article in Special Issue
Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China
Previous Article in Journal
Urban Land-Cover Classification Using Side-View Information from Oblique Images
Previous Article in Special Issue
Time-Series Analysis Reveals Intensified Urban Heat Island Effects but without Significant Urban Warming
Open AccessArticle

Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

1
School of Life Sciences, Nanjing University, Nanjing 210023, China
2
Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Copenhagen 1958, Denmark
3
School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
4
Jiangsu Institute of Urban Planning and Design, Nanjing 210036, China
5
College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(3), 391; https://doi.org/10.3390/rs12030391
Received: 31 December 2019 / Revised: 17 January 2020 / Accepted: 20 January 2020 / Published: 26 January 2020
Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25–5 km) and spatial extents (20–60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect. View Full-Text
Keywords: land surface temperature; urban heat island; natural surface; human activity; point of interest; multi-scale analysis; scaling land surface temperature; urban heat island; natural surface; human activity; point of interest; multi-scale analysis; scaling
Show Figures

Graphical abstract

MDPI and ACS Style

Luan, X.; Yu, Z.; Zhang, Y.; Wei, S.; Miao, X.; Huang, Z.Y.X.; Teng, S.N.; Xu, C. Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants. Remote Sens. 2020, 12, 391.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop