Abstract
The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.
Highlights
The rapid and often uncontrolled growth of urbanization and built-up development over the past years has caused a large number of environmental, climatic, and socio-economic problems at local, regional, and global scales [1,2,3,4]
The results suggest that the increase in built-up lands and the decrease in vegetation cover due to anthropogenic activities caused an increase in surface temperature and expansion of the area affected by surface urban heat islands (SUHIs)
To evaluate and compare spectral indices used for daytime SAHII (DSAHII) modelling, 14 cities in Europe and the United States of America (USA) with different conditions were selected
Summary
The rapid and often uncontrolled growth of urbanization and built-up development over the past years has caused a large number of environmental, climatic, and socio-economic problems at local, regional, and global scales [1,2,3,4]. Several studies have investigated the impact of SUHIs on urban flora [11], climate [12], pollutant concentrations [13], air quality [14,15], human health and heat-related deaths [16], global warming [17], thermal comfort [18,19], energy consumption [20], and socioeconomic and environmental impacts [21]; SUHIs play a large role in the quality of urban life [22,23] Due to these negative effects and considering that rapid population growth is expected in the near future, it will become increasingly important to monitor, predict, and recognize SUHI patterns to improve the quality of urban life [24,25,26,27]. Based on the effect on the surface energy balance, the factors contributing to SUHI formation in the city can be grouped into five main sets of factors: (a) anthropogenic heat enhancers, (b) evaporation reducers, (c) heat storage enhancers, (d) net radiation enhancers, and (e) convection reducers [31,34]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call