Original articleVariations in land surface temperature and cooling efficiency of green space in rapid urbanization: The case of Fuzhou city, China☆
Introduction
Urbanization is the population shift from rural to urban areas, and is often characterized by an intense concentration of the urban population, influx of human activities, extensively built-up land development, and a sharp decrease of blue-green space (Foley et al., 2005, Forman, 2016, Grimm et al., 2008). Urbanization around the world is continuing. The United Nations projected that nearly all-global population growth from 2016 to 2030 will be absorbed by cities. By 2050, about 64% of the developing world and 86% of the developed world will be urbanized, much of which will occur in Africa and Asia. In China, the 2015 urbanization rate was 56.10% such that the urban population is expected to increase by 70% in 2030 (Sun et al., 2016). The urbanization process also induces the replacement of natural and semi-natural land cover types into sealed impervious surfaces, modifications of the biophysical environment, and alterations of land surface energy processes (Fu and Weng, 2016, Voogt and Oke, 2003). The variation of thermal inertia and albedo depends on the types of land cover. As a consequence, rapid urbanization has caused the significant urban microclimate phenomenon—urban heat island (UHI)—which is one of the major concerns related to the urban thermal environment (Akbari and Kolokotsa, 2016, Oke, 2002).
Many studies have identified that physical processes, such as radiative fluxes, and land surface material characteristics (e.g., albedo, emissivity, thermal capacity, and conductivity) can explain the correlations between land cover types and temperature (Forman, 2014, Oke, 2002). For example, blue-green spaces and impervious surfaces are the most important elements in urban landscape for explaining the variation in heat distribution (Hamada and Ohta, 2010, Huang and Cadenasso, 2016, Ren et al., 2016). Land surface temperature (LST) values retrieved from airborne or satellite-borne remotely sensed thermal infrared imagery are regarded as a measurable indicator to simulate the air temperature (Foley et al., 2005; Ren et al., 2016, Schwarz et al., 2012, Voogt and Oke, 2003). The variation of LST and urban thermal patterns are interlinked with land cover types in different ways. For example, the spatial pattern of LST in an urban area is heterogeneous and is affected both directly and indirectly by the landscape composition and configuration. (Fu and Weng, 2016, Kuang et al., 2015, Monteiro et al., 2016, Sun and Chen, 2012).
The LST variation also depends on the spatial and temporal scale. Ma et al. (2016) pointed out that urban impervious surfaces and LST were positively correlated in summer daytime/nighttime and winter nighttime but negatively in winter daytime. Chen et al. (2017) found that LST diurnal variations increase with the urbanization index and that adverse effects are more substantial in the earlier stages of urbanization. The LST distribution is not only influenced by the physical elements of cities, but a relation between the social conditions of the population (e.g., income and education level) and the LST pattern has also been reported (Huang and Cadenasso, 2016). Some studies indicated that neighborhoods with low levels of income and education, as well as an aging population, often related to higher LST than other neighborhoods which can be explained by different backgrounds in the citizens’ ability to mitigate and adapt to the heat effect, such as the usage of air conditioners (Buyantuyev and Wu, 2010, Huang and Cadenasso, 2016, Klinenberg, 2015).
The benefits of urban green spaces have been assessed by many studies as an approach to mitigate the UHI effect. The studies proposed that urban green space has a positive effect on the adaptation of the thermal environment in cities by evapotranspiration and shading mechanisms provided by green vegetation (Jaganmohan et al., 2016, Santamouris, 2014, Žuvela-Aloise et al., 2016). The landscape composition, structure (e.g., size, shape, configuration), and vertical structure of green spaces also influence the urban cooling island (UCI) effect (Akbari and Kolokotsa, 2016, Bowler et al., 2010, Monteiro et al., 2016). Previous studies have investigated the relationships between green spaces and UHI/LST (Asgarian et al., 2015, Ren et al., 2016). In addition, some indexes such as the normalized difference vegetarian index (NDVI), leaf area index (LAI) and the sky view factor (SVF) were identified to have correlations with the green space (Kuang et al., 2015, Yan et al., 2014, Yu and Hien, 2006). However, with further understanding of the cooling effect of green spaces, some researchers noticed that the relationships between green space and LST are hard to support within urban planning and management practicality (Sun and Chen, 2012, Yu et al., 2015). Therefore, quantification of the cooling extent and intensity of green spaces has been addressed by many researchers. For example, Bowler et al. (2010) concluded from a meta-analysis that the mean reductions of urban temperature by green spaces are 0.94 °C during the day and 1.15 °C at night and that the cooling extents of green spaces are 500 m. However, in the case of Japan, a separate study showed that the cooling effect can exceed 300m, but that there is no cooling extent beyond 500 m (Hamada and Ohta, 2010). In a high-latitude city, Gothenburg, the maximum summer temperature difference between a park and a built-up area is 5.9 °C, and the cooling extents reached over 1.1 km from the park border (Upmanis et al., 1998), while in low latitude, the cooling extents of Mexico City reached over 2 km (Jauregui, 1991). A study by Oliveira et al. (2011) acknowledged that while the temperature reduction in Lisbon can reach 6.9 °C on a hot summer day, the cooling effect of green spaces also has a relationship with the different climate zones, which was also proven by other studies (Zhao et al., 2014).
Although the spatial patterns of LST and UHI general characteristics of urban-to-rural temperature differences have been extensively studied, a comprehensive understanding of how the rapid urbanization dynamic process affects land cover change (LCC), as well as LST changes, has not received considerable attention. Explicitly, many studies focus on either LCC or LST change while very few studies combine LCC and LST change and interactions or quantify the land cover transfer process associated LST changes. We still lack understanding of the key LCC and transfer process, which could significantly contribute to LST variation and aggravate the UHI effect. Additionally, an approach to optimize the most significant decreased land cover type (urban blue-green space) in order to maximize the cooling effect is also lacking. Therefore, the city of Fuzhou, which is located in one of the most dynamic urbanization regions in China, was selected as a case study. The land use transfer matrix (LUTM) method was used and the concept of cooling efficiency (CE) has been proposed, defined and calculated. We use our results to (1) quantitatively investigate LST variations resulting from LCC dynamics, as well as differences and correlations in the rapid urbanization process, and (2) explore how to minimize the negative effect and maximize the cooling efficiency by studying the most dramatically decreased land cover type. Moreover, (3) provide operable implications for the urban planner and decision maker.
Section snippets
Study area
Fuzhou (118°08′E ∼ 120°31′E, 25°15′N ∼ 26°29′N), the capital city of Fujian Province, is located in southeast China. The Greater Fuzhou metropolitan region has an area of 12,251 km2, including five urban districts (1026 km2) and eight suburban and rural counties (Fig. 1). Fuzhou has a humid subtropical climate, influenced by the East Asian Monsoon, with long, hot and humid summers and short, mild and dry winters. The annual mean temperature is 19.7 °C and the annual precipitation ranges from 796.5 mm
LCC assessment from 2000 to 2013
Overall, the results (Fig. 5) show that built-up lands rank No. 1 in terms of the size and percentage of land cover type from 2000 to 2013. The rapidly increasing proportion of built-up land corresponds to the context of rapid urbanization in Fuzhou. Meanwhile, the proportion of urban green spaces was reduced from 6984.45 ha (33.2%) to 2843.64 ha (13.5%) during 2000–2013, which is the most significant change in land cover type proportion. The percentage of water areas increased slightly from 2000
Impact of the LCC on variations of LST
Many studies has revealed that the LST patterns highly correlate to the distributions of urban green-blue spaces and impervious surfaces, as well as the spatial scale dependence, temporal variations, and socioeconomic conditions (Asgarian et al., 2015, Buyantuyev and Wu, 2010, Huang and Cadenasso, 2016, Ma et al., 2016, Zhou et al., 2011). Weng et al. (2007) identified that LST is positively related to impervious lands but negatively to the green spaces fraction in Indianapolis (USA). Sun and
Conclusion
This study investigated land cover changes as well as LST changes within the rapid urbanization process. The city of Fuzhou was selected as a case study to analyze the patterns, dynamics, and correlations of the LCC and LST change. We found that LST values increased with increasing proportion of built-up land and sharply decreasing the proportion of green space from 2000 to 2013. We also combined our findings with the LUTM method and identified that LST variation is not only associated with the
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The JSPS KAKENHI (Grant Number JP15KK0142) financed this study. The Chinese Scholarship Council (CSC) also supported this study.