Does urbanization increase diurnal land surface temperature variation? Evidence and implications
Introduction
Urbanization typically results in built-up environments containing large areas of impervious surfaces with increased thermal inertia. Urbanization impacts stormwater runoff, air quality and carbon concentration (American Forests, 2005), net primary productivity and soil respiration rates (Kaye, Mcculley, & Burke, 2005), and ambient air temperatures (Cheng, Su, Kuo, Hung, & Chiang, 2008). Researchers have developed different landscape metrics for quantitatively evaluating the effects of urbanization including changes in land-cover patterns, impacts on urban ecosystems, and deterioration of urban air quality (O’Neill et al., 1988; Plotnic, Gardner, & O’Neill, 1993; Riitters et al., 1995; Riitters, O’Neill, Wickham, & Jones, 1996). Several studies have also demonstrated that such landscape metrics can be derived using remote sensing images (Deng, Wang, Hong, & Qi, 2009; Griffith, Stehman, Sohl, & Loveland, 2003; Herold, Scepan, & Clarke, 2002). Most of such landscape metrics have been used to characterize changes in land-cover patterns and their effects on environmental and ecological regularities within a region. Frohn and Hao (2006) suggested that spatial resolution should be considered when applying metrics to quantify the spatial patterns of landscapes. Li, Zhou, and Ouyang (2013) showed that LST was negatively correlated with the percentage of green space cover in Beijing; however, the relationship varied depending on spatial resolution. Sun, Wu, Lv, Yao, and Wei (2013) demonstrated that landscape-level metrics could identify different types of urban growth in the city of Guangzhou, China.
Although numerous landscape metrics have been proposed, only a few of such metrics are suited to quantify the degree of urbanization. The most apparent characteristic of urbanization is the increase of built-up land cover. Therefore, the percentage of apartment buildings in total construction housing (Kouichi, 1987), road density and percentage of built-up area (Lee, Ding, Hsu, & Geng, 2004), and the proportion of impervious surfaces (Yuan & Bauer, 2007) were conveniently used as urbanization indices. However, modern urban planning often requires the establishment of large green spaces to alleviate the adverse effects of urbanization such as an increase in the ambient air temperature and deterioration of urban air quality. To reflect the effects of urban green spaces, a remote-sensing-based urbanization index that integrates the normalized difference vegetation index (NDVI) and coverage ratio of built-up land cover was developed and used to assess the degree of urbanization in different Asian cities (Guo, Wang, Cheng, & Shu, 2012; Hung, Chen, & Cheng, 2010).
One of the major concerns related to urbanization is its effect on the urban thermal environment. Atmospheric heat islands are normally measured by in situ air temperature sensors; by contrast, a surface urban heat island (SUHI) refers to the excess warmth of urban areas compared with the non-urbanized surroundings, and it is measured using LST levels observed by thermal infrared remote sensing (Yuan & Bauer, 2007). Voogt and Oke (2003) concluded that NDVI-based measures are favorable for describing the differences between urban and rural surface properties. Oke, Johnson, Steyn, and Watson (1991) addressed the roles of surface geometry and surface thermal properties in the creation of SUHIs and the importance of assessing these parameters in both urban and rural environments.
The intensity of the UHI effect is often measured as the difference between the temperature found in the center of an urban area and the background rural temperature (Atkinson, 2003: Hung, Uchihama, Ochi, & Yasuoka, 2006). The thermal inertia estimated using remote sensing thermal images has also been used in studies related to UHI effects (Chen, Du, & Dong, 2008; Nasipuria, Majumdar, & Mitra, 2006). The estimation of the thermal inertia of an earth surface object is dependent on two factors: the albedo and diurnal LST variation (Nasipuria et al., 2006). The apparent thermal inertia (ATI) is a measure of the proportion of radiation absorbed, divided by the subsequent increase in surface temperature ():where N is the scaling factor, C is a constant determined by the latitude and solar declination angle, and is the apparent albedo (Nasipuria et al., 2006). Therefore, the diurnal temperature variation in an object is dependent on its absorbed energy [i.e., ] and apparent thermal inertia. Objects possessing low thermal inertia and albedo (and, by implication, high absorptance) display considerable diurnal temperature variation. Both thermal inertia and albedo vary depending on the type of land cover, and therefore changes in land cover caused by urbanization have a direct impact on the diurnal temperature variation in an urban environment. Hence, the variation in diurnal LST is a primary characteristic of the effects of urbanization.
In contrast to the UHI effect, few studies have focused on diurnal temperature variations in urban environments (Argüeso, Evans, Fita, & Bormann, 2014; Georgescu, Moustaoui, Mahalov, & Dudhia, 2011; Qiao, Tian, & Xiao, 2013; Wouters, De Ridder, Demuzere, Lauwaet, & van Lipzig, 2013). Patterns of land-cover change, including the composition of vegetation, water, and built-up, are complicated, and characterizing how they are linked with changes in diurnal LST variation is difficult. For example, the instantaneous field of view of most space-borne thermal sensors covers a substantial mix of surface elements. Therefore, the effect of urbanization on diurnal LST variation is scale-dependent and involves a certain degree of uncertainty because of the combined effect of different land-cover types within the spatial scale of interest. In addition, most studies on urban temperature environments have focused on air temperatures or UHI effects (Argüeso et al., 2014, Qiao et al., 2013; Qu, Wan, & Hao, 2014). Nevertheless, it is still unclear how urbanization-induced landscape changes affect diurnal LST variation and what such changes in urban diurnal LST variation imply. To the best of our knowledge, no study to date has focused on changes in diurnal LST variation in urban environments. Therefore, the objectives of the current study were threefold: (1) to quantitatively investigate landscape changes in the study area by using a remote-sensing-based urbanization index; (2) to assess the effect of urbanization on diurnal LST variation and investigate its implications; and (3) to evaluate the effectiveness of the urbanization index in characterizing such effects, taking into account possible uncertainties.
Section snippets
Study area and data
Fig. 1 shows the analytical framework of this study. A portion of the Taipei Metropolitan Area (Fig. 2) comprising the old city center in the west (Region A), a region of early expansion in the center (Region B), and a newly developed industrial-business-residential complex region in the east (Region C) was selected for this study. Fig. 2 also shows three subregions (S1, S2, and S3) selected to demonstrate changes in landscape patterns (Section 4). The Keelung River, one of the major rivers of
Results
The confusion matrices of the LULC classification results established on the basis of selected training pixels are summarized in Table 2. Most of the classification accuracy rates were higher than 80%, with those of the built-up and water body classes exceeding 90%. The grass/crop class had lower accuracies, and the woods and the grass/crops were the most likely to be mistaken for each other because of their similar spectral response patterns. The empirical probability density functions of the
Quantifying landscape changes by urbanization indices
Beginning in 1991, several major projects and policies having profound effects on landscape patterns in Taipei were implemented. Such projects and policies are summarized as follows:
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Channelization of the Keelung River
The Keelung River, one of the major rivers of northern Taiwan, flows through the northern part of Taipei. The Taipei City Government and the Water Resources Agency (WRA) of Taiwan started the Keelung River Channelization Project (KRCP) in November 1991. Engineering projects within
Conclusions
We investigated the impact of urbanization on urban landscape patterns and diurnal LST variation in Taipei by using SPOT and MODIS satellite images captured over a period of 17 years. An urbanization index at 1-km spatial resolution was adopted as a quantitative measure of urbanization-induced changes in landscape patterns. Changes in the urbanization indices in different subregions within the study area and the effects of urbanization on the diurnal LST variation were assessed. A few
Acknowledgments
We acknowledge the use of the MODIS LST images provided through NASA’s website. We are also grateful to the editor and three anonymous reviewers for their insightful and constructive comments which led to significant improvements of this paper.
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