Assessment of landscape patterns affecting land surface temperature in different biophysical gradients in Shenzhen, China

Transcription

1 Urban Ecosyst (2013) 16: DOI /s Assessment of landscape patterns affecting land surface temperature in different biophysical gradients in Shenzhen, China Miaomiao Xie & Yanglin Wang & Qing Chang & Meichen Fu & Minting Ye Published online: 17 July 2013 # Springer Science+Business Media New York 2013 Abstract The urban heat island (UHI) effect is one of the important ecological effects of urbanization. This study focuses on the different effects of landscape patterns on LST within different land covers. The land cover was measured by surface biophysical components, including vegetation fraction (VF) and impervious surface area (ISA), acquired by a linear spectral mixture model (LSMM). LST was derived from Landsat-5 TM thermal infrared (TIR) data using the generalized single-channel method. Landscape patterns were measured by landscape metrics, including the Shannon diversity index (SHDI), the aggregation index (AI), patch density (PD), and fractal dimension area-weighted mean index (FRAC_AM). Shenzhen, a rapidly urbanizing city in China, was taken as the case study area. Results showed that VF and ISA are more important than spatial patterns in determining LST. However, these effects change in densely covered areas. VF and LST are negatively correlated, with the inflection of the regression curves being 45 %. In areas with VF lower than 45 %, the correlation between LST and VF is monotonically linear. In areas with VF higher than 45 %, landscape patterns can act to decrease LST. The aggregation index (AI) and the largest patch index (LPI) can contribute to decreasing LST significantly. Impervious surfaces contribute to high temperature, and the inflection point of the regression curves is 70 %. In areas with ISA higher than 70 %, a fragmented pattern of impervious surfaces can M. Xie (*) : M. Fu School of Land Science and Technology, China University of Geosciences, Beijing , China xiemiaomiao@cugb.edu.cn M. Xie xmiaomiao@gmail.com Y. Wang College of Urban and Environmental Sciences, Peking University, Beijing , China Q. Chang Department of Ornamental Horticulture and Landscape Architecture, China Agriculture University, Beijing , China M. Ye Department of Geography, Michigan State University, East Lansing, MI 48824, USA

2 872 Urban Ecosyst (2013) 16: lower LST. These findings provide insights for planners into how the strategic use of landscape to mitigate UHI effects may vary for different land covers. Keywords Urban heat island. Land surface temperature. Vegetation. Impervious surface area. Landscape patterns. Landscape metrics Introduction The urban heat island (UHI) effect is closely related to urban ecosystems because it influences regional climate, energy consumption, atmospheric patterns, biodiversity, and residents health in urban areas (Saaroni et al. 2000; Voogt and Oke 2003). High temperatures cause lower atmospheric pressures in urban areas than in rural areas, resulting in a reflux of polluted air back into urban areas (Voogt and Oke 2003; Yuan and Bauer 2007). These pollutants, such as ozone, can also be produced by higher temperatures (Cardelino and Chameides 1990). High temperature together with polluted air increases the probability of endemic diseases, respiratory illnesses, and cardiovascular mortality in urban areas. Especially in tropical cities, heat waves are exacerbated by local climate change in urban areas (Patz et al. 2005). As the world s urban areas continue to grow rapidly, the resulting environmental problems will at some point affect more than half the world s population (Imhoff et al. 2010; UNFPA 2007). UHI can be characterized by land-surface temperature (LST), which can be derived from remotely sensed thermal infrared (TIR) data. LST is one of the indicators of surface-energy balance (Voogt and Oke 2003; Xian and Crane 2006; Weng 2009) which is sensitive to surface characteristics (Voogt 2002). High LST values result from increased area of impervious surfaces which absorb solar energy, reduced areas of vegetation and water, land-cover patterns, and high building densities (Patz et al. 2005; Fung et al 2009; Nichol 2005; Nichol et al. 2009). Under a constant meteorological situation, land cover and landscape patterns are the two main factors determining LST (Zhou et al. 2011; Lietal.2011). Past studies have explored the effects of land-cover types on LST, including the cooling effects of cropland, forests, and parks and the effects of built-up land on UHI expansion (Balling and Brazel 1988; Rothetal.1989; Streutker 2003). However, the characteristics of every land-cover type are heterogeneous, which causes variations in temperature. Some of the temperature variations can be between 10 C and 30 C in the same land-cover type (Saaroni et al. 2000; Amiri et al. 2009). To expand the understanding of the relationship between land cover and LST, two important biophysical components, impervious surface area (ISA) and vegetation fraction (VF), have been used to describe urban structure (Ridd 1995; Lietal.2011). Impervious surfaces include asphalt, cement, and other impermeable surfaces which cover the ground, such as building roofs, roads, and parking lots. Because impervious surfaces directly change the surface energy-exchange conditions and reduce the surface latent heat exchange, the urban climate can obviously be influenced by the amount of impervious surfaces. The expansion of ISA is closely related to the increase of surface temperature (Xian and Crane 2006; Yuan and Bauer 2007; Lietal.2011). Vegetation has proven to be one of the most important factors in reducing UHI effects at different scales (Gallo et al. 1993; Owenetal.1998; Raynoldsetal.2008; Wengetal.2004). Rather than just the

3 Urban Ecosyst (2013) 16: abundance of vegetation, the research scale, the study area, and the vegetation status also impact the correlation between vegetation abundance and LST (Weng et al. 2004). Besides biophysical components, landscape spatial patterns, including patch size and shape and spatial configuration, are important causative factors of LST (Yue and Xu 2007; Zhang et al. 2009; Zhou et al. 2011; Li et al. 2011). On the urban scale, there are significant relationships between landscape metrics and LST (Zhang et al. 2009; Li et al. 2011). However, a study in the Gwynns Falls watershed showed that the impact of spatial configuration on LST is less important than that of land-cover features (Zhou et al. 2011). The relationship between landscape metrics and LST is dependent on different land-cover features, study scales, and variations among regions (Li et al. 2010, 2011; Su et al. 2012). The impact of spatial patterns on LST is closely related to land-cover features. The main objective of this study is to define the interactions between vegetation, impervious surface area, and landscape metrics influencing LST. The thresholds of the influences of biophysical components on LST and the functioning of landscape patterns were also explored by comparing the correlation coefficients of LST and landscape metrics for different biophysical components. Shenzhen, a rapidly urbanizing area in China, was chosen as the case study. Study area and methods Study area and data description The study area, Shenzhen, is located on the Pearl River Delta in southeastern China and has an area of 195,284 ha. The area has a southern subtropical monsoon climate, and its mean annual temperature is 22.4 C, with a mean annual rainfall of 1,948 mm. The rainy season in Shenzhen is April to September. Vegetation is transitional between tropical and subtropical flora. Shenzhen was the first Special Economic Zone in China. As its economy has developed quickly, this area has experienced rapid urbanization in the last two decades, changing from a traditional agricultural region to a fast-developing urban region. A previous study showed that in 2005, approximately 39.3 % of the total land was urban area, up from only 3.06 % in 1986 (Li et al. 2005). Two Landsat-5 TM images acquired in 2005 (path121/row44, path122/row44) were used for landscape classification and surface-temperature calculation in this study. Because the UHI effects in Shenzhen are obvious in fall and winter (Zhong 1996) and the atmospheric situation is stable, data for November 23, 2005, a day with very clear atmospheric conditions and no wind, were chosen. The two images were geometrically rectified to the local coordinate system of Shenzhen using 50 ground control points symmetrically distributed across the images. A nearestneighbor method was used for resampling when conducting rectification, with an error of less than one pixel. The digital numbers (DN) of bands 1 through 5 and band 7 were converted to exo-atmospheric reflectance using the method provided by Chander and Markham (2003). The exo-atmospheric reflectance data were then used for calculation of biophysical components. Surface temperature retrieval The surface temperature was characterized using the infrared thermal band of Landsat TM 5 and the generalized single-channel method (Jiménez-Muñoz and Sobrino 2003). This

4 874 Urban Ecosyst (2013) 16: method needs total water vapor as a parameter and can obtain high accuracy with a root mean square deviation lower than 1 K (Sobrino et al. 2004). The first step was to calculate the brightness temperature (T k ) from digital number (DN) data using the following formula based on Planck s radiation function: T k ¼ K 2 ln K 1 þ 1 L λ L λ ¼ GAINS DN þ BIASES Formula 1:K 1 = w*m 2 sr 1 μm 1 and K 2 = K. L λ is the at-sensor radiance calculated by DN, and the GAINS and BIASES values are derived from the image header files. The unit of T k is K. LST was retrieved by the generalized single-channel method using a number of parameters, including at-sensor radiance, brightness temperature, radiation wavelength, and emissivity (Jiménez-Muñoz and Sobrino 2003) (Fig. 1). The emissivity can be calculated by the NDVI (Normalized Difference Vegetation Index) method (Jiménez-Muñoz and Sobrino 2006; Valor and Caselles 1996). The parameters used in the generalized single-channel method were calculated using the functions for total atmospheric water vapor content derived from in-situ meteorological data (Jiménez-Muñoz and Sobrino 2003; Jiménez- Muñoz and Sobrino 2006; Li et al. 2010). Biophysical components of land cover In this study, ISA and VF were derived by a linear spectral mixture model (LSMM) based on a vegetation impervious soil (V-I-S) conceptual model that can supply the proportions of Fig. 1 LST of Shenzhen on November 23, Several heat islands are designated as a, b, c, and d, representing the manufacturing areas in Songgang and Shajing, Baoan Airport, industrial areas in Qianhai, and Yantian Port

5 Urban Ecosyst (2013) 16: impervious surface and vegetation in every pixel (Wu and Murray 2003; Wu 2004; Xian and Crane 2006; Yuan and Bauer 2007). Ridd (1995) developed a V-I-S conceptual model to describe the biophysical components of an urban surface. In this conceptual model, each pixel is composed of vegetation components, impervious surface components, and soil components. The pure pixels with only one component are called end-members (Roberts et al. 1998). In this study, the LSMM was used to obtain VF and ISA for each pixel (Wu and Murray 2003;Wu2004). LSMM is a simple but efficient way to retrieve land-cover characteristics with a precision greater than 90 % (Wu 2004; Yuan and Bauer 2007). The reflectance of mixed pixels is the weighted sum of reflectance for each component, as shown in the following formula: R iλ ¼ Xn K¼1 f ki C kλ þ ε iλ : Formula 2: Let R iλ be the reflectance of the i-th pixel, and let C kλ be the reflectance of end-member k in wave band λ. The area proportion of end-member k in pixel i is denoted as f ki, with error ε iλ. To perform measurements with higher precision, the LSMM is usually calculated after pure pixels have been derived to obtain the reflectance. In this study, the minimum noise fraction (MNF) transformation was used to reduce data redundancy and inter-band correlation before extracting the end-members. The pixel purity index (PPI) calculation was used to choose the pure pixels from the three principal components determined by the MNF transformation using an iterative algorithm. The end-members were selected by the N-dimensional visualizer based on the MNF transformation and PPI calculation in ENVI 4.3. Subsequently, the spectrumseparated results, including VF and ISA were calculated by linear spectral unmixing in ENVI 4.3 (Fig. 2). The accuracy was assessed using impervious-surface and vegetation-cover classifications from a 3-m resolution aerial photo. One hundred sampling regions, each with a size of 300 m 300 m, were chosen by random sampling. The impervious surface and vegetation cover were classified by visual interpretation in the sampling regions to calculate the proportions of impervious surface and vegetation. The results show that the root mean square error of ISA was and the root mean square error of VF was Analysis of landscape patterns Landscape patterns impact ecological processes (Turner 2005), and energy transmission is one of the most important ecological processes in urban areas. A number of landscape metrics were selected to characterize landscape diversity, patch shape, and spatial configuration, including the Shannon diversity index (SHDI), the aggregation index (AI), patch density (PD), and fractal dimension area-weighted mean index (FRAC_AM). SHDI was used to describe the diversity at the landscape level. The AI and PD were chosen to describe spatial configuration characteristics in terms of aggregation and fragmentation (York et al. 2011). The largest patch index (LPI) represented the dominance of landscape, while FRAC_AM described the shape complexity of patches. The landscape metrics were calculated based on landscape classification maps including eight land-cover types: cropland, orchard land, forest land, developed land, water, swamp land, unused land, and grassland. The supervised maximum likelihood classification was used to obtain the landscape map from Landsat-5 TM data (Fig. 3). The accuracy of the classification result was evaluated by the overall Kappa index as 0.801, which was acceptable for the urban

6 876 Urban Ecosyst (2013) 16: Fig. 2 VF (Vegetation fraction) and ISA (percentage of impervious surface area) in Shenzhen as derived from Landsat TM images scale (Landis and Koch 1977). Landscape metrics were calculated using the moving window method at the landscape level using Fragstats 3.3 (McGarigal et al. 2002; Riittersetal.1995). The moving window was a square 120 m on a side, which fitted the resolution of the LST map. The continuous surfaces of landscape metrics were sampled randomly using 21,744 points, which was 1 % of the total pixels, for further statistical analysis in SPSS.

7 Urban Ecosyst (2013) 16: Fig. 3 Eight types of land cover in Shenzhen, China. Landscape metrics were calculated based on this landscape map Correlation and regression analysis between biophysical components and LST The biophysical components of land cover, landscape metrics, and LST values were derived from maps using 21,744 random samples, which was 1 % of the total pixels, for further statistical analysis in SPSS. By comparing statistical values, including maximum values, minimum values, mean values, and standard deviations, the statistical characteristics of the samples were found to be consistent with the values in a portion of the whole image (Table 1). This meant that the sample values could be used for statistical analysis. The Pearson correlation was first calculated to assess the associations between LST and variables representing land-cover and landscape metrics. A negative correlation means a reducing effect on LST, and a positive correlation means that the biophysical component can enhance LST. Regression models were then used to test the relationships between LST and the biophysical components. To determine the thresholds of effects of biophysical components on LST, the VF and ISA variables were divided into 100 steps at 1 % intervals (Li et al. Table 1 Statistical characteristics of biophysical component values in the set of random samples were found to be consistent with those of whole images Indices Extent Maximum value Minimum value Mean value Std ISA Whole image 100 % % % Samples % 0.02 % % % VF Whole image 100 % % 22.2 % Samples 100 % % 22.2 %

8 878 Urban Ecosyst (2013) 16: ; Yuan and Bauer 2007; Chen et al. 2006). The mean temperature of each step was imported to the regression model. If the threshold was not clear, the variables were generalized into fewer steps, e.g., 20 steps at 5 % intervals. By comparison of regression models and data scatters, the inflection points of relationships between LST and biophysical components could be found to define the gradients. Assessment of the effect of landscape patterns for various biophysical gradients For various biophysical component gradients, the correlations between landscape metrics and LST, which can be used to compare the effects of landscape patterns on LST for a number of biophysical gradients, were calculated. An impact coefficient for metrics (IC k ) was constructed (see formula below). The larger the absolute value of the IC metric, the more important is the effect on LST in the biophysical gradient. IC VF is the ratio of coefficients between landscape metrics and LST in different VF gradients, and IC ISA is the ratio of coefficients between landscape metrics and LST in different ISA gradients. IC k ¼ Coefficient i.coefficient global Formula 3: k indicates the kind of biophysical component. Coefficient i is the Pearson correlation coefficient between landscape metrics and LST on gradient i, and Coefficient global is the correlation coefficient between landscape metrics and LST in the whole sample. Results Correlation between LST and land-cover variables The average temperatures varied among different land-cover types. In 2005, the lowest temperature in the study area was 7.92 C, and the highest temperature was C (Fig. 1). Developed land accounted for the largest area in Shenzhen among the eight landcover types (Fig. 3), and the mean temperature of the developed land was the highest at 19.3 C, 3.18 C higher than the lowest-temperature land-cover type (water) (Table 2). The heat islands were located in the industrial areas and transportation centers (e.g., the manufacturing areas in Songgang and Shajing, Baoan Airport, industrial areas in Qianhai, and Yantian Port, as indicated by a, b, c, and d in Fig. 1). Unused land and grassland had the second and third highest mean temperatures respectively. The temperatures of swamp land and forest land were relatively lower, but still slightly higher than water. The temperatures of cropland and orchard land were in the mid-range and were C and C respectively. By Pearson correlation analysis, the correlation coefficient between VF and LST was found to be , which indicates the cooling effect of vegetation. Correlation analysis showed that pixels with higher ISA are warmer than others. On the overall level, the correlation analysis showed that the relationship between landscape metrics and LST was significant at the 0.05 level (Table 3). LST was positively correlated with SHDI, PD, and FRAC_AM, with correlation coefficients of 0.193, 0.174, and respectively (Table 3). The correlation coefficient between FRAC_AM and LST was the largest. LST was negatively correlated with AI and LPI, with correlation coefficients of and respectively (Table 3).

9 Urban Ecosyst (2013) 16: Table 2 Mean temperature variation between land-cover types. Developed land had the highest spatial mean surface temperature Land cover types Area percentage Spatial mean ( C) Standard deviation ( C) Minimum LST ( C) Maximum LST ( C) Cropland 1.22 % Orchard land % Forest land % Developed % land Water 4.75 % Swamp land 1.20 % Unused land 9.61 % Grass land 5.21 % Inflection points of relationships Effect of vegetation on LST The regression model showed that mean LST decreases as VF increases (Fig. 4). For the 1 % interval, the R 2 of the regression was Considering the impact of vegetation on LST, the regression equation was LST= VF. Taking C as the initial temperature, LST will decrease by 0.23 C when VF increases by 10 %. The linear regressions at 5 % and 10 % VF intervals predicted LST more accurately, with R 2 values of and respectively. However, in areas with high VF, both very low and high LST values occurred (Fig. 4). The scatter illustrations at 5 % and 10 % VF interval show that there is a point of inflection on the regression curve (Fig. 4). The inflection point of the relationship between VF and LST is at 45 % VF. The effects of vegetation on surface temperature are different on either side of the inflection point. When VF is smaller than 45 %, the slope of the regression curve is greater (Fig. 4). The cooling rate is greater when VF is lower than 45 % than overall. Effect of impervious surfaces on LST The Pearson correlation analysis showed that there is a positive correlation between impervious surfaces and LST, but the regression model showed that this relationship is not monotonic. At both 1 % and 5 % ISA intervals, it was ineffective to describe the relationship Table 3 Correlation coefficients of landscape metrics and LST on the overall level (including all samples) Biophysical components and landscape metrics VF ISA FRAC_AM AI LPI PD SHDI Pearson correlation a a b b b b b a Correlation is significant at the 0.01 level (two-tailed) b Correlation is significant at the 0.05 level (two-tailed)

10 880 Urban Ecosyst (2013) 16: LST = VF R² = Mean LST( ) Mean LST( ) VF (1% interval) ISA (1% interval) LST = VF R² = LST = ISA R² = Mean LST( ) Mean LST( ) VF (5% interval) ISA (5% interval) LST= VF R² = LST = ISA R² = Mean LST( ) Mean LST( ) VF (10% interval) ISA (5% interval, ISA<70%) Fig. 4 Regression relationships between LST (land-surface temperature) and VF (vegetation fraction) and ISA (percentage impervious surface area) by an overall linear regression model (Fig. 4). The correlations with ISA below 70 % and above 70 % are obviously different (Fig. 4). When ISA is less than 70 %, the relationship between ISA and LST is linear. When ISA reaches 70 % or more, both very high and low temperatures are observed. Therefore, the samples were separated by the 70 % inflection point. When ISA is less than 70 %, the surface temperature and ISA are positively linearly correlated (Fig. 4). In this range, surface temperature is enhanced significantly by an increase

11 Urban Ecosyst (2013) 16: in ISA. The regression equation is LST= ISA (Fig. 4). When the initial temperature is C, other factors being constant, each 10 % increase in ISA can increase the surface temperature by C. This warming effect was much greater than the warming effect of impervious surfaces on the overall level. Effects of landscape patterns in VF and ISA gradients On the overall level, the Pearson correlation analysis showed that there are significant correlations between landscape metrics and LST. However, the effects on LST of landscape patterns are not as important as those of land-cover features (Table 3). IC was used to investigate the effects of landscape metrics on LST for various land-cover gradients. Except for FRAC_AM and AI in the areas with ISA greater than 70 %, the landscape metrics have a stronger effect on LST when VF is greater than 45 % or ISA is greater than 70 % (Table 4). When the vegetation cover was greater than 45 %, the influence of landscape pattern on surface temperature was more significant, and the correlation coefficients were greater than that at the overall level (Table 4). Increases in SHDI, PD, and FRAC_AM enhanced temperature increases, while AI and LPI had a negative effect on surface temperature. SHDI values that indicated a diverse landscape, in areas with dense vegetation cover and a diversity of landscape, showed that vegetated landscapes and other landscapes had a more staggered distribution. Greater heterogeneity makes it easier to transmit energy along the temperature gradient, and therefore the surface temperature was influenced by energy transmission. AI characterizes the aggregative distribution of vegetation; adjacent pixels with vegetation cover are more similar when AI is greater. The correlation coefficient between AI and LST is 0.311, showing that large aggregations of vegetation cover reduce the surface temperature. Corresponding to the degree of fragmentation, vegetation patches with high PD values are divided by urban construction. The correlation coefficients for PD and LST were positive, and IC VF for PD was greater than one (Table 4), which means that landscape fragmentation is a significant factor affecting temperature in areas with high VF. LPI and LST showed a typical negative correlation, in which increased dominance of vegetation patches enhanced the cooling effect of vegetation. Larger values of FRAC_AM correspond to complexity of vegetation patch shapes, which is associated with greater contact between the vegetation patch and the surrounding landscape, which can promote energy transmission efficiency. By this means, the surface temperature in the vegetation patch is increased, and the correlation between FRAC_AM and LST is positive. Table 4 Correlations between landscape pattern metrics and surface temperature with VF greater than 45 % or ISA greater than 70 %. The comparison between certain areas (VF>45 % or ISA>70 %) and overall correlation is shown as IC VF and IC ISA Metrics VF>45 % ISA>70 % IC VF IC ISA SHDI a a AI a a PD a a LPI a a FRAC_AM a a Correlation is significant at the 0.01 level (two-tailed)

12 882 Urban Ecosyst (2013) 16: By comparing the correlation coefficients of landscape metrics with LST in areas with various impervious surface gradients, the functioning of impervious landscape patterns can be inferred. When ISA is greater than 70 %, IC ISA showed that the correlations between SHDI, AI, PD, LPI, and LST are statistically different from those at the overall level and that the relationships are different from those on the overall level (Table 4). Shape complexity (represented by FRAC_AM) has little influence on LST when the percentage of impervious surface reaches 70 % or more. SHDI and PD are negatively correlated with LST in areas with ISA higher than 70 %, with the coefficients being and (Table 4). This shows that in a fragmented and diverse pattern, energy exchange with the surrounding areas is more frequent, resulting in a decrease in temperature on the impervious surface. AI and LPI are positively correlated with LST, with coefficients of and (Table 4). Increasing the aggregation and dominance of impervious patches enhanced the warming effect of the impervious surface. Discussion Effect of biophysical components The temperature range and standard deviation in this study illustrated that great differences exist in a particular land-cover type, in addition to the variations among different land-cover types. Similar results have been shown in recent studies (Amiri et al. 2009; Saaroni et al. 2000). For example, the difference between maximum temperature and minimum temperature in built-up areas is close to 10 C in Tel Aviv (Saaroni et al. 2000). Compared with discrete data on land-cover types, the biophysical components of land cover are more detailed and specific and can better explain spatial variations in surface temperature. In this study, vegetation and impervious surfaces were used as the biophysical components of land cover in a rapidly urbanizing area. Vegetation was considered to be the most important factor in reducing temperature (Gallo et al. 1993; Weng et al. 2004). It helps to moderate the urban climate by shading, evapotranspiration, and lower surface emissivity (Breuste and Qureshi 2011). This study showed that an increase in VF contributes to a drop in temperature, especially in areas with relatively low vegetation abundance. In well-vegetated areas, the baseline temperature is low, and the effect of vegetation in decreasing temperature would not be as obvious as in areas of lower VF or NDVI. Chen et al. (2006) showed a linear positive correlation between NDVI and temperature for NDVI values of 0.6 or more. This study has shown that the linear regression model is not effective for describing the relationship between VF and LST for VF values greater than 45 % (Fig. 4). This means that in highly vegetated areas, improving VF can reduce temperature, but the effect will be insignificant. The surface temperature is more influenced by other factors. Other studies have indicated that the observed cooling effects of vegetation can be influenced by study scale, vegetation type, and climate features (Weng et al. 2004; Mahmood et al. 2006). In this study, which compared the correlation coefficients of landscape metrics with surface temperature in areas with different vegetation gradients, the effects of landscape patterns can be deduced. In many past studies, ISA has proved to be a good indicator for explaining surface thermal characteristics influenced by human activity (Imhoff et al. 2010; Xian and Crane 2006; Yuan and Bauer 2007). The Pearson correlation analysis in this study also showed that there is a significant positive relationship between ISA and LST. Studies in Shanghai and the Twin Cities Metropolitan Area showed a very good linear relationship between ISA and LST

13 Urban Ecosyst (2013) 16: (Yuan and Bauer 2007; Li et al. 2011). However, in this study, the relationship between ISA and LST is not linear when ISA reaches 70 % or more. One of the important differences between this study and past studies is the urban structure. Shenzhen is a multiple-center urban area, which is quite different from Shanghai and the Twin Cities (Yuan and Bauer 2007; Li et al. 2011). During the urbanization process, urban areas expanded quickly, and the urban structure is very complex. These factors may impact the effects of impervious surfaces on LST. Future research could improve comparison studies between different urban structures. This study focuses on the effects of landscape patterns in different gradients of impervious surface areas. Impact of landscape patterns on LST The UHI effect is suitable for an ecological-effects study on an urban scale, representing absorption and release of surface energy as well as energy transmission (Quattrochi et al. 2000). According to landscape ecological theory, energy flows are due to energy variations between adjacent landscapes (Forman 1995). The efficiency of heat flow is influenced by the spatial pattern of landscape elements. Patch size and shape, building shapes, and spatial configuration are the important pattern characters influencing surface energy transmission (Zhou et al. 2011; Weng 2009). A number of landscape metrics were used in this study to describe landscape patterns, including landscape diversity, spatial configuration of landscapes, and shape characteristics of patches. This and previous studies have shown that landscape pattern has an influence on LST (Li et al. 2011; Zhang et al. 2009; Zhou et al. 2011; Li et al. 2012). However, the effect is limited, being less than that of biophysical components (Zhou et al. 2011). On the landscape level, considering all random samples in this study, it can be concluded that the diversity, fragmentation, and complexity of the landscape contributes to increases in LST, while the aggregation patterns of the landscape configuration can reduce LST in Shenzhen because forest landscape, which has a cooling effect on LST, is the most aggregative landscape in Shenzhen (Tables 2 and 3). An aggregation pattern of highly vegetated landscape can reduce LST (Table 4). This result is consistent with the findings of a recent study in Nanjing, China (Zhang et al. 2009). The IC index showed that the effects of landscape pattern on surface temperature are different for different impervious surface gradients and different vegetation gradients. One of the important objectives of this study is to find the areas where LST is more sensitive to landscape metrics. Results suggest that the functioning of landscape patterns on LST is stronger in areas which are relatively more homogeneous, such as those with vegetation cover higher than 45 % or impervious surface greater than 70 % (Table 4), while the percentages of vegetation cover and impervious surface influence less on LST, representing inflections on the relationships between VF, ISA and LST. The areas with VF higher than 45 % are usually forest landscapes in Shenzhen. In these areas, the aggregation pattern creates obstructions which prevent the surface from absorbing energy from energy sources such as downtown, industrial areas, residential centers, and others. Therefore, the surface temperature of vegetation with an aggregated and dominant pattern is lower than in a more fragmented pattern. On the contrast, in areas with high ISA coverage, high SHDI values reveal the staggered distribution pattern of an impervious landscape, corresponding to a heterogeneous distribution of surface energy (Table 4). In a diverse landscape, heated plots in the impervious surface are separated, and therefore the surface temperature is decreased. Fragmentation of an impervious landscape makes energy transportation efficient from impervious surfaces to adjacent areas, and therefore the correlation between LST and PD is negative (Table 4).

14 884 Urban Ecosyst (2013) 16: Scale effects are a basic part of landscape ecology theory, and determining an appropriate scale for understanding the relationship between pattern and process is an important issue (Wu and Hobbs 2002). In this study, to minimize the amount of data missing on the urban edge and to satisfy the resolution of the thermal data, 120 m was chosen as the scale of the moving window. In a future study, the scale effects of the relationships between landscape metrics and LST should be researched to improve the prediction of urban temperature changes (Zhou et al. 2011). Implementation for urban planning UHI is one of the effective responses to extreme climatic events and is an important aspect of achieving urban sustainability (Patz et al. 2005). Enlarging vegetated areas and reducing impervious surfaces are the two main solutions. However, in rapidly urbanizing areas, expansion of impervious areas is an established trend. One way to mitigate the UHI effect is by increasing vegetation cover and albedo, but this strategy is a tradeoff requiring greater water use, especially in arid regions (Patz et al. 2005). The first step in alleviating UHI effects through landscape planning is figuring out where spatial patterns influence LST most strongly. In such areas, altering landscape patterns will more efficiently mitigate UHI effects. Although LST is not sensitive to landscape metrics on the overall level, relationships between LST and landscape metrics are significant in areas with a high VF or ISA. Aggregation and large patch size contribute to a reduction of UHI effects in areas with higher vegetation cover. The fragmentation of high-density vegetation weakens the cooling effects of vegetation patches on LST. In areas with a high density of ISA, landscape fragmentation reduces the positive effects on surface temperature, while the aggregation of impervious surfaces enhances UHI effects. Because the expansion of impervious surface is an inevitable trend in urbanizing regions, the ideal approach to reducing UHI effects is to optimize the diversity of landscape patterns in urban areas. Conclusions Biophysical components significantly influence LST in Shenzhen, China. Impervious surfaces are associated with high surface temperature, while vegetation is related to low surface temperature. The relationships between LST and vegetation or impervious surfaces have obvious inflection points. In different biophysical gradients, the correlation between LST and various landscape pattern metrics, including the AI, LPI, SHDI, PD, and FRAC_AM. The negative correlation between surface temperature and VF is significant, with an inflection point at 45 %. In areas with VF greater than 45 %, the aggregation pattern and the large patch area of the vegetation landscape contribute to decreasing surface temperature, while SHDI, PD, and FRAC_AM can reduce the effect on vegetation of decreasing temperature. An increase in ISA contributes to high surface temperature, especially for areas with ISA less than 70 %. When ISA is greater than 70 %, surface temperature is significantly influenced by landscape patterns. The aggregated pattern and the dominance of impervious surface enhance the temperature increase. In areas with more impervious surfaces, reducing the size of impervious patches, increasing the dominance of small vegetation patches, and enhancing landscape diversity can work efficiently to decrease surface temperature. These findings could help to create different strategies to mitigate UHI effects in different biophysical gradients.

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