Spatial Variability and Temporal Heterogeneity of Surface Urban Heat Island Patterns and the Suitability of Local Climate Zones for Land Surface Temperature Characterization

Abstract

This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

Variabilities of Land Surface Temperature and Frontal Area Index Based on Local Climate Zone

“…A method with a combination of artificial visual interpretation and machine learning (e.g. random forest algorithm) was employed [42]. A flowchart of LCZ classification was shown in Fig.…”

“…The LCZ system is capable of explaining the LST variability of different types [40] and the relationship between built lands and LST [41]. For 10 built LCZ types, the highest temperature usually occurs in the heavy industrial land in cities of different sizes [41] [42] or the large low-rise areas in Nanjing [43], within tropical, temperate and cold climate regions of the globe [44]. Nevertheless, compact and high-rise pattern contributes to an LST increase [22], and some studies [23] indicated that scattered high-rise buildings can facilitate LST reduction.…”

Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

“…The percentages of overall accuracy and Kappa coefficient of LULC classification maps in 2000, 2011 and 2020 are 98.12%, 87.97%, 91.37% and 89.68%, 91.25%, and 90.18%, respectively. The percentages of overall accuracy and Kappa coefficient for all years are greater than 87.97%, indicating that the classification shows high accuracy [50,51].…”

“…The percentages of overall accuracy and Kappa coefficient of LULC classification maps in 2000, 2011 and 2020 are 98.12%, 87.97%, 91.37% and 89.68%, 91.25%, and 90.18%, respectively. The percentages of overall accuracy and Kappa coefficient for all years are greater than 87.97%, indicating that the classification shows high accuracy [50,51]. On the whole, the vegetation area is showing a continuous decreasing trend, the built-up area is continuously expanding, and the water body and bare land area are also showing a continuous decreasing trend.…”

Estimation of China’s Contribution to Global Greening over the Past Three Decades

“…For about three decades, from 1982 to 2009, the global leaf area index (LAI) trend was estimated at 0.068 ± 0.045 m 2 m −2 yr −1 (mean ± standard deviation, 1 sigma) [1]. This generally increasing trend since 1982 would have shifted urban energy balance and regulated land-atmosphere interactions, including sensible heat fluxes, evapotranspiration, carbon dioxide (CO 2 ) exchange between land and atmosphere, and other trace gases and aerosols [2][3][4]. Furthermore, the changes in LAI since the 1980s are likely to have affected land-surface boundary conditions and influenced the surface albedo, roughness, and even the dynamics of the terrestrial water cycle [5].…”