Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region

Remote Sensing of Environment 173 (2016) 145–155

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Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region Jian Peng ⁎, Pan Xie, Yanxu Liu, Jing Ma Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

a r t i c l e

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Article history: Received 22 November 2014 Received in revised form 17 November 2015 Accepted 23 November 2015 Available online 5 December 2015 Keywords: Urban thermal environment dynamics High temperature center Landscape composition Landscape configuration Beijing metropolitan region

a b s t r a c t Urbanization has eco-environmental consequences; among which are effects on the urban thermal environment, which have drawn extensive attention especially in metropolitan regions having intensive population and high building density. In this study, the variation of the thermal environment during the urbanization process from 2001 to 2009 in the Beijing metropolitan region was evaluated using the spatial Lorenz curve and distribution index. In addition, the effects of landscape composition and spatial configuration on the thermal environment were investigated using correlation analysis and piecewise linear regression. The urban heat island (UHI) was found to be much more significant in summer than in spring, autumn and winter. Furthermore, the magnitude of the urban thermal environment in Beijing has increased during the process of urbanization. The suburban areas of Beijing, including the new urban development zone and ecological conservation zone, have increased the magnitude of the thermal environment. However, the opposite effect has occurred in the center of Beijing, including the core functional zone and urban function extended zone. Landscape types such as built-up areas and barren land make the most significant contribution to the thermal environment, whereas ecological land plays a significant role in mitigating the UHI. When the coverage of ecological land exceeded 70% (25 km2) of the total land area, the cooling efficiency of this landscape type was relatively obvious, and the shape index and fragmentation index of landscape configuration both had a significantly positive correlation (0.594 and 0.510 Pearson's coefficients, respectively) with average land surface temperature. The Pearson's coefficient between the ecological land proportion and the average land surface temperature was 0.614 (P b 0.01); this value was higher than that for the effects of the spatial configuration, indicating that landscape composition affects the thermal environment more than does spatial configuration. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Urbanization is taking place at an unprecedented rate around the world, particularly in China. Between 1978 and 2012, the proportion of the Chinese population living in urban areas increased from 17.9% to 52.6%. In March 2014, the Chinese government released the National New-Type Urbanization Plan, which sets targets for China's urbanization ratio to reach 60% by 2020. By changing the material and energy flows, urbanization has transformed the natural ecosystem to a coupled human and natural system, which inevitably has resulted in various effects on the eco-environment, including biodiversity, energy flows, and biogeochemical cycles, as well as regional climate and human health (Gunawardhana, Kazama, & Kawagoe, 2011; Li, Li, Zhu, Song, & Wu, 2013; Luck & Wu, 2002; Mander, Kull, Tamm, Kuusemets, & Karjus, 1998; Peng, Liu, Liu, Wu, & Han, 2012; Saaroni, Ben-Dor, Bitan, & Potchter, 2000). The thermal environment is a significant part of the eco-environment, and it is a manifestation of the surface and atmosphere energy balance (Voogt & Oke, 2003; Weng, 2009; Xian & ⁎ Corresponding author. E-mail address: [email protected] (J. Peng).

http://dx.doi.org/10.1016/j.rse.2015.11.027 0034-4257/© 2015 Elsevier Inc. All rights reserved.

Crane, 2006). High temperatures in urban areas not only accelerate the formation of urban smog and polluted air, but also greatly increase energy consumption in summer, contributing to global warming (Kolokotroni, Zhang, & Watkins, 2007; Mihalakakou, Santamouris, Papanikolaou, Cartalis, & Tangrassoulis, 2004; Yuan & Bauer, 2007). A well-documented consequence of urban thermal environment change due to urbanization is the formation of the urban heat island (UHI), which refers to the phenomenon of urban temperatures being higher than those in rural surroundings (Taha, 1997; Voogt & Oke, 2003). UHIs are common in metropolitan regions due to the agglomeration of population, industry, building infrastructure, and transportation, all of which produce excessive heat that is emitted into the atmosphere (Buyantuyev & Wu, 2010). Land surface temperature (LST) is the most direct manifestation of the urban thermal environment. Remotely sensed LST records the radiation energy emitted from the landscapes of the ground surface, including vegetation, water bodies, barren land, building roofs and paved surfaces (Arnfield, 2003; Voogt & Oke, 2003). Therefore, the landscape pattern may potentially influence LST (Arnfield, 2003). In recent research, remotely sensed LST has been widely applied to study the spatial pattern of UHI and its relationship with landscape patterns, to provide a

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scientific basis for the mitigation of UHI as well as for urban planning and management (Cao, Onishi, Chen, & Imura, 2010; Cheng, Wei, Chen, Li, & Song, 2015; Huang, Zhou, & Cadenasso, 2011; Li, Zhou, Ouyang, Xu, & Zheng, 2012; Weng, Liu, Liang, & Lu, 2008; Zhang, Zhong, Feng, & Wang, 2009; Zhou, Huang, & Cadenasso, 2011). Landscape composition and spatial configuration are two fundamental aspects of landscape pattern (Gustafson, 1998; Li et al., 2012; Turner, 2005; Zhang, Wu, Grimm, McHale, & Buyantuyev, 2013; Zhou et al., 2011). Landscape composition is typically described by the landscape types and the proportion of each landscape type; landscape configuration focuses on describing spatial characteristics of individual areas and the spatial relationships among the multiple areas (Gustafson, 1998). Landscape composition, which can manifest surface cover characteristics and abundant landscape types, significantly affects the urban thermal environment (Buyantuyev & Wu, 2010; Xiao et al., 2008; Zhou, Qian, Li, Li, & Han, 2014). Previous research has primarily focused on the effects of landscape type and landscape proportion on the thermal environment (Buyantuyev & Wu, 2010; Frey, Rigo, & Parlow, 2007; Gallo et al., 1993; Owen, Carlson, & Gillies, 1998; Weng, 2009; Weng, Lu, & Schubring, 2004; Xie, Wang, Chang, Fu, & Ye, 2013; Zhou et al., 2014). Natural landscape elements, such as forests, water bodies, and wetlands, are generally accepted as playing a significant role in alleviating high temperature. On the contrary, urban landscape elements, such as built-up areas and barren land, increase temperature in the thermal environment (Balling & Brazel, 1988; Roth, Oke, & Emery, 1989; Streutker, 2003). Many studies have shown that there is a positive correlation between the proportion of green space or water bodies in a landscape and their cooling effects (Sun & Chen, 2012; Sun, Chen, Chen, & Lü, 2012; Zhang et al., 2009; Zhou et al., 2011). Together with correlation analyses, there is a significant research trend in defining the threshold size of landscape units at which they begin to have effects on the thermal environment (Tan & Li, 2013). Rather than just considering the relationship between landscape composition and the thermal environment, there is an increasing interest in the effects of the spatial configuration of landscapes on the thermal environment. These studies mainly have explored the cooling effects of water bodies or green space, and many studies have demonstrated that the spatial configuration of landscapes significantly affects the magnitude of LST (Li et al., 2011, 2012; Sun & Chen, 2012; Sun et al., 2012; Zhang, Wu, & Chen, 2010; Zhou et al., 2011). However, these studies primarily have focused on a discrete area within the landscape only and examined the effects of the area's size and shape, rather than the spatial arrangement in the landscape. Few studies have taken both landscape composition and spatial configuration into account and quantitatively explored their combined impacts on the thermal environment (Zhou et al., 2011). As one of the largest cities in China, Beijing has experienced rapid urbanization, and it has one of the highest UHI intensities in the world (Cai, Du, & Xue, 2011; Kuang et al., 2015; Memon, Leung, & Liu, 2009; Sun, Lü, Chen, Yang, & Chen, 2013; Zhang, Xie, Gao, & Yang, 2014; Zhang et al., 2009). Taking the Beijing metropolitan region as a study case, this study investigated the dynamics of the thermal environment and the influence of landscape patterns, considering both landscape composition and spatial configuration. The research aims were: (1) to explore the dynamics of Beijing's urban thermal environment in the urbanization process from 2001 to 2009, especially focusing on the spatial heterogeneity between the functional zones; (2) to investigate the effects of landscape type and landscape proportion on the urban thermal environment and try to find the threshold of landscape proportion at which the ecological land can significantly mitigate the UHI; (3) to examine the effect of landscape configuration, including the shape characteristic and spatial arrangement feature, on the urban thermal environment; and (4) to compare the effects of landscape composition and configuration on the urban thermal environment.

2. Methodology 2.1. Study area Beijing is situated in north China at latitude from 39°28′N to 41°05′N and longitude from 115°25′E to 117°30′E. As the nation's capital, Beijing has an area of 16,808 km2 and a permanent population of 19.62 million (as of 2010, according to the Sixth Census of China). Except for the southeast of the city, where a plain slopes slightly to the Bohai Rim, the other sides of the city are surrounded by mountains. Beijing is characterized by a warm temperature zone and has a typical continental monsoon climate with four distinct seasons, including a hot and rainy summer and a cold and dry winter. The annual average temperature in Beijing is 12.3 °C and annual precipitation is approximately 572 mm. As China's political and cultural center, as well as its international exchange center, Beijing experienced rapid urbanization after the reform and opening up policy in 1978. Its population increased from 8.72 million in 1987 to 19.62 million in 2010, and the urbanization ratio, measured as the percentage of urban population, increased from 55% to 86% over this period. This urbanization was highlighted in 2001, when Beijing successfully bid for the 2008 Olympic Games. With the successful preparation and hosting of the Olympics, Beijing stepped into the developed stage of urbanization. This study was conducted using data for the years 2001 to 2009, a period that is a typical representation of the developed stage of urbanization. According to the latest administrative divisions, Beijing can be divided into four functional zones (Fig. 1), including: (1) the core functional zone (I-CFZ), i.e., the downtown area of the whole city, including the Dongcheng and Xicheng Districts; (2) urban function extended zone (II-UFEZ), including Haidian, Chaoyang, Fengtai and Shijingshan Districts; (3) new urban development zone (III-NUDZ), including Changping, Shunyi, Fangshan, Tongzhou and Daxing Districts; and (4) ecological conservation zone (IV-ECZ), including Miyun, Yanqing, Huairou, Pinggu and Mentougou Districts. The core functional zone is the traditional inner city, mainly featuring the political and cultural functions of Beijing. The urban function extended zone, which is often identified as part of the city center, is the accumulated area of hightechnology industries, universities, and educational institutions. The new urban development zone mainly includes the modern manufacturing or agricultural industries, and the ecological conservation zone is Beijing's ecological barrier and water source, which plays a significant role in sustainable development. 2.2. Land surface temperature retrieval To eliminate the unknown impact of atmospheric effects and solar geometry, eight remotely sensed images were used as the source of data in this study. The images were taken during highly clear atmospheric conditions in spring (May 27, 2001 and May 17, 2009), summer (August 31, 2001 and September 22, 2009), autumn (October 31, 2000 and October 27, 2010) and winter (January 3, 2001 and January 25, 2009). The images were obtained from the remote sensing data sharing website (http://ids.ceode.ac.cn/) maintained by the Earth Observation and Digital Earth Science Center of the Chinese Academy of Sciences. The land surface temperature (LST) was retrieved from the Landsat 5 satellite thermal infrared band (10.40–12.50 μm) with a resolution of 120 m. The LST maps were retrieved using the following steps. First, the digital number (DN) of the thermal infrared band was converted to top-of-atmosphere (TOA) radiance (Lλ) using Eq. (1) (Landsat Project Science Office, 2009). Lλ ¼ gain þ DN  offset

ð1Þ

In Eq. (1), gain is the rescaled gain (value = 0.055) and offset refers to the rescaled bias (value = 1.18243).

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Fig. 1. Location of the Beijing metropolitan region, China. Panel A presents the location of Beijing in China. Panel B shows Beijing's four functional zones, including the core functional zone, urban function extended zone, new urban development zone, and ecological conservation zone. Each functional zone consists of several districts.

The final expression for the emissivity is given by Eq. (6).

Second, the TOA radiance (Lλ) was converted to surface-leaving radiance (LT) through an atmospheric correction process using Eqs. (2)–(6) (Barsi, Schott, Palluconi, & Hook, 2005; Yuan & Bauer, 2007; Zhou et al., 2014).

ε ¼ 0:02644F v þ 0:96356

Lλ −Lμ −τð1−εÞLd LT ¼ τε

The third step in retrieving LST data was to transform the surface leaving radiance (LT) to at-satellite brightness temperature (TB), assuming that the earth surface is a black body using Eq. (7) (Chander & Groeneveld, 2009).

ð2Þ

In Eq. (2), Lμ is the upwelling radiance, Ld is the downwelling radiance, τ is the atmospheric transmission, and ε is the emissivity of the surface. Lμ, Ld and τ were calculated using the web-based atmospheric correction tool (available at http://atmcorr.gsfc.nasa.gov/) developed by Barsi et al. (2005). The term ε was calculated using the Normalized Difference Vegetation Index (NDVI) Thresholds Methods (Eq. (3)) (Li et al., 2011; Sobrino, Jiménez-Muñoz, & Paolini, 2004). ε ¼ ε v F v þ εu ð1−F v Þ þ dε

ð3Þ

In Eq. (3), εv is the emissivity of vegetation, and εu is the emissivity of urban surface. According to previous studies, the emissivity of vegetation is typically 0.99, and the emissivity of urban surface is typically 0.92 (Artis & Carnahan, 1982; Li et al., 2011; Nichol, 1998, 2009). The term Fv refers to the vegetation fraction, which can be calculated using Eq. (4) (Carlson & Ripley, 1997). In Eq. (4), NDVImax = 0.5 and NDVImin = 0.2.  Fv ¼

NDVI−NDVI min NDVI max −NDVI min

K  2  K1 ln þ1 LT

ð7Þ

In Eq. (7), K1 and K2 are the pre-launch calibration constants. For Landsat 5 TM, K1 = 607.76 W m2 sr−1 μm−1 and K2 = 1260.56 K. Finally, the at-satellite temperature (TB) was corrected for varied emissivity (ε) of different landscapes, and the emissivity-corrected LST was computed using Eq. (8) (Artis & Carnahan, 1982). LST ¼

TB 1 þ ðλ  T B =ρÞ ln ε

ð8Þ

In Eq. (8), λ is the wavelength of emitted radiance (11.457 μm for Landsat 5 TM band 6), ρ = 1.438 × 10−2 mK, and ε is the surface emissivity, which was calculated using Eq. (6).

2 ð4Þ

The term dε (Eq. (3)) includes the geographical distribution of the natural surface as well as internal reflections, and can be calculated using Eq. (5) (Sobrino et al., 2004). dε ¼ ð1−εu Þð1− F v ÞFεv

TB ¼

ð6Þ

ð5Þ

In Eq. (5), F is the shape factor, whose mean value is 0.55, assuming different geographical distributions (Sobrino, Caselles, & Becker, 1990).

2.3. Interpretation of land cover Six major landscape types—forest, farmland, urban green space, water body, built-up area, and barren land—were extracted from the satellite images. Forest is the area characterized by tree cover, mainly including natural and semi-natural forestlands, shrub lands and mixed forests. Farmland is characterized by vegetation that has been planted for the production of food, and this category includes paddy fields, vegetable fields, and dry land. Urban green space refers to the woodlands and gardens that are distributed around buildings. Water body refers to all areas of open water, including lakes, reservoirs, rivers, streams,

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and ponds. Built-up area is defined as the area that has a high percentage of constructed materials, including residences, commercial centers, industrial zones, railways, highways, and expressways. Barren land refers to land without vegetation cover, mainly including exposed soil and landfill sites. In this study, forest, farmland, urban green space, and water bodies were combined into a category called “ecological land”. High resolution historical images from Google Earth™ were used as the reference layers to assess the classification accuracy. A total of 134 random points were generated on both Landsat summer images for comparing classification data and reference data. The interpretation accuracy was 87.31% (August 31, 2001) and 84.33% (September 22, 2009), respectively, which indicated that the classification process was reliable. 2.4. High temperature center and distribution index The urban thermal environment intensity was divided into four categories, i.e. “high”, “sub-high”, “sub-low”, and “low”, according to the Jenks natural breaks classification method in ArcGIS™, in which classes were established among the largest breaks in the data array (Smith, 1986; Weng et al., 2008). The “high” level, which has the highest average LST, was defined as the high temperature center (HTC). A distribution index (DI) was constructed (Eq. (9)) to describe the contribution made by a spatial unit, such as a district, to the thermal environment of the whole city. DI ¼

Shi Sh = Si S

ð9Þ

In Eq. (9), i refers to a spatial unit, Si is the area of this spatial unit, Shi is the HTC area in this spatial unit, Shi/Si refers to the HTC proportion in the spatial unit, Sh is the total HTC area in Beijing, S is the total area of Beijing, and Sh/S means the area proportion of HTC in Beijing. If DIN 1, the distribution frequency of HTC in the spatial unit i is higher than that in the entire city, which means this spatial unit makes a positive contribution to Beijing's thermal environment (i.e., contributes to warming). If DIb1 the spatial unit makes a negative contribution to the thermal environment (i.e., contributes to cooling). 2.5. Quantification of landscape configuration The landscape shape characteristic and spatial arrangement feature are two components of spatial configuration (Gustafson, 1998). The shape index was used to indicate the shape characteristic of the ecological land, and the fragmentation index was used to indicate the spatial arrangement feature of the ecological land. Shape index and fragmentation index were calculated using Eqs. (10) and (11) (Peng et al., 2010): C ¼ L=A

ð10Þ

F ¼ N=A

ð11Þ

In Eqs. (10) and (11), C is the shape index of ecological land, F is the fragmentation index of ecological land, L is the perimeter of ecological land, N is the total number of ecological land parcels, and A is the total area of ecological land. 3. Results 3.1. Urban thermal environment dynamics 3.1.1. LST variation As the acquisition time and meteorological conditions are not exactly the same for the eight Landsat 5 images, the pixel values of the retrieved LST maps were standardized using the maximum difference normalization method before further analysis. The standardized LST

maps (Fig. 2) well illustrate the spatial patterns of the thermal environment in four seasons in 2001 and 2009. The UHI effect, which refers to the phenomenon of urban temperatures being higher than those in rural surroundings (Taha, 1997; Voogt & Oke, 2003; Zhou et al., 2014), was more significant in summer than in other seasons. In 2001, there was an obvious high temperature area assembling in the city center (core function zone and urban function extended zone). By 2009, however, the UHI had spread to the new urban development zone and ecological conservation zone, which was also the trend of the city expansion. In spring, autumn and winter, however, there was no significant sign of the UHI on the LST maps, and the LST value of the city center was very similar to that of the arable land around it. Thus, it is difficult to distinguish the urban development trend using the LST pattern in spring, autumn and winter. The significant UHI in summer has been noticed by previous studies (Memon et al., 2009; Sun et al., 2013; Zhou et al., 2014), and summer has been identified as the best season in which to examine the thermal environment and urban expansion (Yuan & Bauer, 2007). This rule has also been found in Beijing, where the UHI is much more significant in summer than in other seasons (Wang, Wang, & Wang, 2007; Yang, Zhao, Shen, Hai, & Fang, 2010). Taking account of the significant UHI and its obvious adverse impact on human sense of comfort in summer than in other seasons (Memon et al., 2009), the subsequent part of the study focused on the urban thermal environment dynamics and associated landscape pattern factors in the summer season. Fig. 3 shows the area proportions for the four LST categories (high, sub-high, sub-low, and low) in Beijing in summer. Between the years 2001 and 2009, there was an obvious increase in the proportion of high level area (from 13.45% to 17.86%) and sub-high level area (from 25.54% to 31.00%), whereas the proportion of the low level area declined sharply (from 24.30% to 13.96%). These changes indicate an adverse effect on the thermal environment through the urbanization process from 2001 to 2009. In different functional zones, the percentage of area in the high level LST category showed an obvious decline in the direction from the city center to the suburban areas, whereas the percentage of low level area exhibited the opposite trend. In the core functional zone, the high level temperature was the dominant thermal environment category, occupying more than 92% and 83% of the whole zone in 2001 and 2009, respectively. In the ecological conservation zone, the proportion of high level temperature comprised only 3.63% in 2001 and 9.97% in 2009. The low level temperature category was dominant in the ecological conservation zone, with 38.68% and 18.33% of the area in 2001 and 2009, respectively. In contrast, there were few low level thermal distributions in the core functional zone. 3.1.2. High temperature center variation The high level thermal category of the LST map in Fig. 2 is defined as the High Temperature Center (HTC). The distribution and migration of HTCs during the urbanization process was examined. In 2001, the HTCs of Beijing were assembled around the core functional zone and formed a monocentric pattern of HTC distribution. The HTCs were mainly distributed in the core functional zone and urban groups of the urban function extended zone, including Xiyuan group in Haidian, Nanyuan group in Fengtai, and the Jiuxianqiao and Dingfutuan groups in Chaoyang. However, a small number of HTCs were evident in the new urban development zone, mainly distributed in the international airports in the towns of Shunyi and Nankou in Changping. A few HTCs were also distributed in the ecological conservation zone. However, unlike the monocentric distribution pattern in 2001, there was a trend for HTC to diffuse outward from the city center to the suburban areas through the urbanization process, forming a polycentric pattern of HTC distribution in 2009. The core functional zone and urban function extended zone continued to be dominated by HTC area in 2009; however, the magnitude of this area sharply declined from the 2001 level. In contrast, in the new urban development zone and ecological conservation zone, a series of discrete HTCs formed due to the development of

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Fig. 2. The spatial distribution of seasonal standardized land surface temperature (LST) in the Beijing metropolitan region in 2001 and 2009. The LST maps were retrieved from band 6 of Landsat 5 images with a resolution of 120 m. Areas for which the standardized LST value is lower (i.e., nearer 0) are cooler than those for which LST is higher (i.e., nearer 1).

new city areas. These include the Yanqing basin and Liangxian suburb of Fangshan, as well as newly developed towns in Huairou and Miyun. Table 1 shows the areal variation of HTCs in the four functional zones in 2001 and 2009. The total area of HTCs in Beijing increased from 2257.31 km2 to 2996.87 km2, which accounted for 13.43% and 17.83% of Beijing's total area in 2001 and 2009, respectively. There was a sharp decrease in the proportions of area comprised by HTCs in two zones between 2001 and 2009: the proportion of HTC area decreased by 26.27% in the core functional zone and by 11.34% in the urban function extended zone. However, an increase in the proportion of HTC area was evident in the new urban development zone (3.73%) and the ecological conservation zone (7.52%).

The Lorenz curve is commonly used to describe the inequality in the distribution of income among social classes or other population units. In this study, a spatial Lorenz curve was used to examine the spatial heterogeneity in distribution and variation of HTCs in Beijing (Fig. 4). The horizontal axis of Fig. 4 represents the cumulative percentage of 16 districts in Beijing, and the vertical axis represents the cumulative percentage of the HTC proportion in each district. The “standard line”, which is the diagonal in Fig. 4, indicates an equal distribution of HTC proportion in each district. The closer the spatial Lorenz curve is to the standard line, the more equally distributed are the HTCs. On the contrary, a departure from the standard line represents an inequality in the HTC distribution. Fig. 4 shows that the spatial Lorenz curve in 2009 was much

Fig. 3. The proportions of area in four land surface temperature categories (high, sub-high, low and sub-low) in the Beijing metropolitan region and four functional zones (CFZ, core functional zone; UFEZ, urban function extended zone; NUDZ, new urban development zone; and ECZ, ecological conservation zone).

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Table 1 Area ratio variation of high temperature centers in four functional zones between 2001 and 2009. Zone⁎

2001

2009

Change

I-CFZ II-UFEZ III-NUDZ IV-ECZ Beijing

92.07% 56.41% 17.15% 3.61% 13.43%

65.80% 45.07% 20.88% 11.13% 17.83%

−26.27% −11.34% 3.73% 7.52% 4.40%

⁎ CFZ, core functional zone; UFEZ, urban function extended zone; NUDZ, new urban development zone; and ECZ, ecological conservation zone.

closer to the standard line than that in 2001, which indicates that there was a more equal distribution of HTCs in Beijing's 16 districts in 2009 than in 2001. The distribution index (DI) was used to manifest the heterogeneity and variation in regional contributions in Beijing's thermal environment (Fig. 5). Except for the ecological conservation zone, the other three zones all had DI values larger than 1, which means that these areas made a larger contribution to the thermal environment than the average contribution. The core functional zone had the highest DI value (6.86 and 3.69 for 2001 and 2009, respectively), followed by the urban function extended zone (4.20 and 2.53) and new urban development zone (1.28 and 1.17). However, the ecological conservation zone had DI values much lower than 1 in both years (0.27 and 0.62 for 2001 and 2009, respectively). Because the ecological conservation zone contributed the least to the thermal environment of Beijing, this zone played a significant role in mitigating the regional high temperature. All other zones had a detrimental impact on the thermal environment. From 2001 to 2009, all functional zones except ECZ had DI values much higher than Beijing's average standard, but all exhibited a decline in DI values through the urbanization process. Although its contribution to the thermal environment was the smallest of the four zones, the ecological conservation zone, exhibited an increasing trend in DI value. This manifests a narrow trend in the contribution difference among functional zones to the whole region. There was also a great difference between the DI values of different districts. Taking 2001 for example, the largest DI value was 6.86 in the

city center, while the smallest was 0.15 in Yanqing, located in the ecological conservation zone. However, districts in the same functional zone had a similar difference in DI values as that of the city center, in which the core functional zone had the highest DI value, followed in decreasing magnitude by that of the urban expansion zone and then the urban development zone. The eco-conservation zone had the smallest DI value. These results show that the division of the functional zones is reasonable from the perspective of their effects on the thermal environment. 3.2. Effects of landscape patterns on LST 3.2.1. Landscape type Landscape classification maps were used to extract the LST of corresponding landscape types. Due to the difference in capability of solar energy reflection and absorption of landscape types, a significant difference in the average LST of landscape types was observed (Table 2). Similar results have been shown in recent studies (Amiri, Weng, Alimohammadi, & Alavipanah, 2009; Feng, Zhao, Chen, & Wu, 2014; Saaroni et al., 2000). In 2009, barren land and built-up area registered the highest average LST (29.38 °C and 29.15 °C, respectively), followed by urban green space (25.89 °C) and farmland (25.53 °C). Forest had a relatively low average LST of 24.16 °C; even lower was that of water body, which was only 23.36 °C (or 5.79 °C lower than that of builtup area). The high average LST in built-up areas was mainly related to large impervious ground surface as well as to intensive human activity and extensive energy consumption (Feng et al., 2014; Imhoff, Zhang, Wolfe, & Bounoua, 2010; Xian & Crane, 2006; Yuan & Bauer, 2007). The evapotranspiration, shading, and lower surface emissivity of vegetation, as well as the high reflection character of water, resulted in low average LST values for both water bodies and forest (Breuste & Qureshi, 2011; Cao et al., 2010; Weng et al., 2004). The contribution that landscapes made to the thermal environment of the whole region, which can be manifested by the DI value, also varied. The barren land and built-up area showed apparently high DI values in 2009; 4.21 and 4.01, respectively. These two landscape types shared DI values that are much higher than 1, which implied a strengthening in the thermal environment magnitude from 2001 to 2009. Except for

Fig. 4. Spatial Lorenz curve in Beijing metropolitan region in 2001 and 2009.

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Fig. 5. Distribution index of high temperature centers for districts in the Beijing metropolitan region in 2001 and 2009. CFZ, core functional zone; UFEZ, urban function extended zone; NUDZ, new urban development zone; and ECZ, ecological conservation zone.

barren land and built-up area, however, the other landscape types, including the urban green space, farmland, forest, and water body, each exhibited a DI value less than 1, which means these landscape types contributed to mitigating the urban high temperature. The landscape type that made the least contribution to the whole region was water body (DI value of 0.02), followed by forest (DI value of 0.42), and farmland (DI value of 0.62). It should be pointed out that urban green space, which has a general cooling effect on the environment, registered a relatively high DI value of 0.96, which was nearly equal to the average value for the whole city. This result occurred mainly because the majority of urban green space in Beijing is dispersed in the areas that have a high intensity of buildings. For example, the urban green space in the core functional zone and urban function extended zone accounted for 39.2% of the total area, while the ecological conservation zone accounted for only 2.1%, thus masking the positive cooling effects of urban green space under the perspective of the whole region.

3.2.2. Landscape composition Landscape types that have a DI value less than 1, including water bodies, forest, farmland and urban green space, all have a positive effect on mitigating the region's high temperature. These four landscape types were regarded as ecological land in this study, which means they had a cooling effect for the entire city. Furthermore, 6-km × 6-km grids were used to examine the cooling effects of the proportion of the ecological land to the thermal environment. The relationship between the proportion of ecological land and the LST is shown in Fig. 6. The horizontal axis in Fig. 6 represents the percentage of ecological land in a 6-km × 6-km grid, and the vertical axis represents the average LST value of each corresponding grid. Generally, with the growth in the proportion of ecological land, the average LST decreased. Piecewise linear regression was used to identify the percentage of green space above which the cooling effect caused a dramatic difference in the LST trend; this “threshold” percentage was 0.71325, and

represented approximately 25 km2 in area. The best-fit regression (R2 = 0.342, P b 0.05) showed that when the proportion of the ecological land was below 70%, the cooling efficiency was 0.32 °C for every 10% (3.6 km2 in area) increase in the ecological land area proportion. However, when the ecological land proportion was above 70%, the cooling efficiency was 1.06 °C for every 10% increase in the ecological land area proportion (R2 = 0.377, P b 0.05). These results show that in the Beijing metropolitan region, the proportion of ecological land had a significant cooling effect that increased dramatically when the percentage of ecological land exceeded 70%. 3.2.3. Landscape configuration To isolate the landscape composition effects, grid samples that had an ecological land percentage greater than 70% were selected to examine the configuration impact. The shape index and the fragmentation index were both positively correlated to the LST as shown in Figs. 7 and 8. The Pearson's correlation coefficient was 0.594 (R2 = 0.3526, P b 0.01) between LST and the shape index, and 0.510 (R2 = 0.2605, P b 0.01) between LST and the fragmentation index. As the values of the shape index and fragmentation index increased, LST also increased. The proportion of ecological land was used to indicate the landscape composition impacts on the thermal environment. The shape index and fragmentation index were used to indicate the impacts of shape characteristics and spatial arrangement. With a Pearson coefficient of 0.614 (R2 = 0.3778, P b 0.01), the area percentage of ecological land had the most significant positive correlation with LST, followed by the shape index (Pearson coefficient = 0.594, R2 = 0.3526, P b 0.01) and the fragmentation index (Pearson coefficient = 0.510, (R2 = 0.2605, P b 0.01). These results show that landscape composition was a more important factor influencing LST than the configuration feature. When considering only the effect of landscape configuration, the shape characteristic was more important than the spatial arrangement feature. 4. Discussion 4.1. Sensitivity of landscape composition and LST correlations to grid size

Table 2 Average land surface temperature (LST) and distribution index (DI) for each landscape type in 2009. Landscape type

LST (°C)

DI

Urban green space Water body Farmland Forest Barren land Built-up area

25.89 23.36 25.53 24.16 29.38 29.15

0.96 0.02 0.62 0.42 4.21 4.01

Some justifications for the use of a 6-km × 6-km grid size in this study are appropriate. Because the pixel size of Landsat TM imagery is 120 m × 120 m, the side length of any grid based on this imagery should be an integer multiple of 120 m. In addition, the grid size could not be too small for the regional scale of this study. Consequently, an analysis was performed to examine the effect of grid sizes having side lengths ranging from 1.2 km to 12 km at 1.2-km increments, resulting in as many as 10,890 grid samples (for the 1.2-km × 1.2-km grid). The sensitivity of four variables to changes in grid size was examined by plotting

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Fig. 6. Relationship between the percentage of ecological land and land surface temperature (LST) in 2009.

the strength of linear regressions (R2 values) between each variable and LST as a function of grid size (Fig. 9). The four variables were ecological land proportion; ecological land proportions that were, respectively, smaller than and greater than the threshold described in Section 3.2.2; and the value of ecological land proportion when a threshold appeared. As shown in Fig. 9(a), with the increase of grid size, the R2 between ecological land proportion and LST increased as grid size increased, with an obvious turning point occurring at a grid side length of 6 km. When the grid side length was less than 6 km, the rate at which R2 increased was relatively fast; as the grid side length increased beyond 6 km, the rate of increase in R2 slowed and approached zero. Fig. 9(b) shows that when the grid side length was less than 6 km, R2 between ecological land proportion smaller than the threshold value and LST fluctuated and became independent of grid size at side lengths greater than 6 km. The fluctuation state may have been due to the shortage of sample points representing ecological area that was smaller than the threshold proportion determined in Section 3.2.2. As shown in Fig. 9(c), R2 was independent of grid size when the grid side length was between 6 km and 8.4 km, as well as between 9.6 km and 12 km. Fig. 9(d) shows that when the grid side length was less than 6 km, the threshold proportion

decreased rapidly as grid side length increased, but became independent of grid size when the grid side length was larger than 6 km. All four relationships plotted in Fig. 9 indicated that a turning point was reached at a grid side length of 6 km; grid side lengths larger than this threshold did not improve any of the relationships. Thus, in this study 6 km × 6 km was determined to be the suitable grid size. 4.2. Correlations between urban development and thermal environment dynamics This study showed that different landscape types have different effects on the regional thermal environment. Built-up area and barren land increased the temperature of the thermal environment, whereas the ecological land (including the forest, water bodies, farmland and urban green space) mitigated the high temperature of the region. As the ecological land percentage in the region increased, the LST declined. These findings reasonably explain the variation in the urban thermal environment of Beijing from 2001 to 2009. The landscape changes between 2001 and 2009 mainly manifested in a 7.21% increase in the built-up area, and a 4.93% decrease in the

Fig. 7. Relationship between the shape index of ecological land and land surface temperature (LST) in 2009.

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Fig. 8. Relationship between the fragmentation index of ecological land and land surface temperature (LST) in 2009.

area of urban green space, which increased the temperature of the thermal environment in the whole region. In this time period, the built-up area in the new urban development zone and the ecological conservation zone increased by 12.29% and 9.81%, respectively, leading to a rapid increase in the number and areal extent of HTCs in these zones. In the core functional zone, however, the construction land decreased by 0.74%, while there was growth of 2.93% and 0.56% for urban green space and water bodies, respectively. During the process of urbanization from 2001 to 2009, the government implemented general planning for Beijing from 2006 to 2020.

Plans include the migration of several industry and commercial lands from the core functional zone to the surrounding suburban areas. In preparation for the 2008 Olympic Games, many polluting industries, including the Shougang Iron & Steel Group Co. Ltd., were moved out from the center of Beijing. Therefore, the HTCs moved from the core functional zone and urban function extended zone to the new urban development zone and ecological conservation zone. Meanwhile, the construction and maintenance of the waters and parks in the center of the city were also significant factors in mitigating high temperature in the core functional zone.

Fig. 9. Sensitivity of correlations between landscape composition and land surface temperature (LST) to grid size. Panel (a) presents the variation in R2 between LST and ecological land proportion. Panel (b) presents the variation in R2 between LST and ecological land proportion that was smaller than the threshold. Panel (c) presents the variation in R2 between LST and ecological land proportion greater than the threshold. Panel (d) presents the variation in the value of ecological land proportion when a threshold appeared.

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4.3. Implications for urban landscape management This study examined the relationship between LST and landscape composition as well as between LST and spatial configuration. The finding that both the composition and configuration of landscape significantly affected the magnitude of the thermal environment provides important insights into ecological design and management in urban planning. The LST differs according to landscape types, and the proportion of landscape types is the most significant factor affecting LST. The builtup area and barren land have the highest average LST, while the ecological land (including forest, water bodies, farmland, and urban green space) help cool the region. Thus, the cooling effects increased as the areal percentage of ecological land in the region increased. More ecological lands, rather than additional built-up areas, should be planned in the city to mitigate the UHI phenomenon. A threshold was found in the cooling efficiency attributable to ecological land. When the percentage of ecological land exceeded 70% (approximately 25 km2 in area), a remarkable increase in cooling efficiency was observed compared to the cooling effect when the percentage of ecological land was less than 70%. The reason for the difference lies in the heterogeneity of ecological land, which consists of urban green space, forest, farmland, and water bodies. The forest distribution is mainly concentrated in the west and north of Beijing, with few other landscape types there, and forests often make up most (70% or more) of the ecological land. However, other components of ecological land (urban green space, farmland, and water bodies are mainly scattered around the construction land in the southeast and in the center of Beijing city. These three landscape components do not account for very much area (typically much less than 70%) within a grid. The enhanced cooling efficiency is obvious when the ecological land proportion reaches 70% due to the concentration of forest land. However, the cooling efficiency of ecological land is lower when this feature occupies less than 70% of total area because of the scattered distribution of urban green space, farmland and water bodies. Ecological lands can be regarded as cooling islands for the whole city and should be protected, especially for their significant role in mitigating the thermal environment. The shape characteristic and spatial arrangement of the ecological land also are important influences on the regional thermal environment. The shape index has a positive correlation with the LST, which means the more complex the ecological land shape, the higher the regional temperature. The fragmentation index also has a positive correlation with LST, indicating that the more fragmented the ecological land, the higher the regional temperature. These results offer guidance in urban green space planning. For example, a more simple and regular shape and more compact, less dispersed location of the ecological land can be favorable in mitigating regional high temperature. 5. Conclusions The thermal environment effect is a critical eco-environmental consequence of urban landscape change. Understanding thermal environment dynamic characteristics and their link with landscape composition and configuration is critically important to designing effective approaches in mitigating UHI in the urbanization process. Recent studies mainly have focused on the effects of landscape type and composition on the thermal environment, yet few have taken the quantitative analysis of both landscape composition and configuration into consideration. This study examined the variation of the thermal environment during the urbanization process from 2001 to 2009 in the Beijing metropolitan region, as well as the landscape composition and configuration impacts on the urban thermal environment. The following conclusions are justified by the results of this study. The UHI in the region is much more significant in summer than in spring, autumn and winter, and Beijing's thermal environment shows

an increase through the urbanization process. Beijing's suburban areas, including the new urban development zone and ecological conservation zone, increased in thermal environment magnitude, whereas the center of Beijing, including the core functional zone and urban function expansion zone, showed a decrease. Both the landscape configuration and the spatial configuration affect the magnitude of the thermal environment, whereas the composition feature is more important in determining the region's LST. The built-up area and barren land make the greatest contribution to increasing the thermal environment, whereas ecological land plays a significant role in mitigating the UHI, especially when its proportion reached 70% of the total area, clearly showing that this type of landscape feature imparts a cooling effect on the local environment. Increasing the amount of ecological land in the Beijing region will help to mitigate the UHI effects; those ecological lands that have an area larger than 25 km2 have an obvious high cooling efficiency for the region, and thus should have special protection. The spatial configuration of ecological land, including shape characteristics and spatial arrangement, is an important determinant in the effectiveness with which this landscape type mitigates UHI effects. Reasonable design and appropriate spatial arrangement of ecological land can significantly decrease the magnitude of thermal environment effects. For example, a relative simplification in the shape and enhancement of the interconnectedness of the ecological land can decrease the magnitude of LST. However, this research was conducted only in one metropolitan region in Beijing and included only the urbanization process from 2001 to 2009. Whether these conclusions can be adapted to other metropolitan regions and different urbanization processes should be further explored. More accurate landscape interpretation and LST retrieval maps based on high resolution images are required for further study, and a multi-scale study is also recommended (Li et al., 2012). Moreover, to explore the impacts of landscape composition and configuration on the urban thermal environment, this study took only the ecological land into consideration. Further study should focus on the interaction, as well as adjacency effects, among different landscape types.

Acknowledgments This research was financially supported by the National Natural Science Foundation of China (no. 41130534).

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