Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China

Transcription

1 Journal of Environmental Sciences 19(2007) Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China XIAO Rong-bo 1, OUYANG Zhi-yun 1,, ZHENG Hua 1, LI Wei-feng 1, SCHIENKE Erich W 2, WANG Xiao-ke 1 1. State Key Lab of Urban and Regional Ecology Environmental Sciences, Chinese Academy of Sciences, Beijing , China. ecoxiaorb@163.com 2. Department of Science and Technology Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA Received 1 March 2006; revised 8 May 2006; accepted 2 June 2006 Abstract Land surface temperature (LST), which is heavily influenced by urban surface structures, is a significant parameter in urban environmental analysis. This study examined the effect impervious surfaces (IS) spatial patterns have on LST in Beijing, China. A classification and regression tree model (CART) was adopted to estimate IS as a continuous variable using Landsat images from two seasons combined with QuickBird. LST was retrieved from the Landsat Thematic Mapper (TM) image to examine the relationships between IS and LST. The results revealed that CART was capable of consistently predicting LST with acceptable accuracy (correlation coefficient of 0.94 and the average error of 8.59%). Spatial patterns of IS exhibited changing gradients across the various urban-rural transects, with LST values showing a concentric shape that increased as you moved from the outskirts towards the downtown areas. Transect analysis also indicated that the changes in both IS and LST patterns were similar at various resolution levels, which suggests a distinct linear relationship between them. Results of correlation analysis further showed that IS tended to be positively correlated with LST, and that the correlation coefficients increased from to with increases in IS pixel size. The findings identified in this study provide a theoretical basis for improving urban planning efforts to lessen urban temperatures and thus dampen urban heat island effects. Key words: urban heat islands; urban land cover; normalized difference vegetation index (NDVI); climate mitigation; regression tree Introduction Land surface temperature (LST), which modulates the air temperature of the lowest layers of the atmosphere, is of prime importance to the urban environment because of its key role in the energy balance of the surface. LST not only helps to determine the internal climate among buildings, but also influences energy exchanges that affect the comfort of city dwellers (Voogt and Oke, 2003; Wang et al., 2004). Modification of land cover in urban areas has been shown to cause both local air and surface temperatures to rise several degrees higher than that of surrounding rural areas (Streutker, 2003). This effect, which is typically referred to as the urban heat island (UHI), has been documented for over 150 years. There are a variety of reasons for the urban/rural temperature variance: (1) changes in the physical characteristics of the surface (albedo, thermal capacity, heat conductivity), owing to the replacement of vegetation by asphalt and concrete; (2) the decrease of surface moisture available for evapotranspiration; (3) changes in the radiative fluxes and in the near surface Project supported by the Pilot Project of Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX32SW2424). *Corresponding author. zyouyang@rcees.ac.cn flow, resulting from the complicated geometry of streets and tall buildings; and (4) anthropogenic heat emissions (Dousset and Gourmelon, 2003). According to Streutker (2002), the most significant among these is the difference in the thermal properties of surface areas resulting from the changing character of the urban landscape. Consequently, a large number of studies have sought to analyze the relationship between surface temperature and land cover/land use. Unger et al. (2001) applied regression analysis to examine the influences of urban and meteorological factors on the surface air temperature in Szeged, Hungary. Dousset and Gourmelon (2003) investigated the effects of downtown surface physical properties, especially in business and industrial districts that display heat island effects larger than 7 C. Weng (2001) examined LST pattern and its relationship with land cover in the Zhujiang Delta, China. Work by Nichol (2005) also indicated that different land uses present different thermal behaviors between day and night. Voogt and Oke (1998) found strong directional variations in apparent surface temperature over each of three urban land-use areas (light industrial, residential, and downtown). These researches have contributed to our understanding of the thermal patterns created by individual land use/cover within a city such as parks, water, industrial

2 No. 2 Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China 251 complexes, tree cover, and so on. However, traditional urban land use/cover classification schemes can only discover the inter-class thermal characteristics, but provide little information on intra-class differences. Impervious surfaces (IS), as one of the most important land cover types and characteristic of urban/suburban environments, are known to effect urban surface temperatures by altering the sensible and latent heat fluxes that exist within and between the urban surface and boundary layers (Yang et al., 2003a). However, few studies have examined the effect IS spatial structure has on the relationships between IS and thermal effects within strictly urban areas. The urban IS refers to any nonporous land cover that prevents water from infiltrating into sub-surface layers, e.g., buildings, roads, parking lots, sidewalks, and other built surfaces (Yang et al., 2003a). In addition to its consideration as an indicator for identifying spatial extent, intensity, and type of urban land use/cover changes (Xian and Crane, 2005; Yang et al., 2003b; Yang and Liu, 2005), IS has also been identified as a key environmental indicator of urban land use and water quality (Civco et al., 2002). To examine the relationships between IS and LST, the percentage of IS must first be estimated. In recent years research has increased in the use of classification and regression tree model (CART) technology to map subpixel impervious surfaces (Xian and Crane, 2005; Yang et al., 2003b). The process uses high-resolution imagery as a source of training data for representing the urban land-cover heterogeneity, with medium-resolution Landsat imagery used to extrapolate IS over large-scale areas. The main advantage of the regression tree algorithm is that it can account for non-linear relations between predictive and target variables, and thus allowing both continuous and discrete variables to be used as input (predictive) data. In this paper, we derived the IS distribution from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (ETM+) data with CART, and synthesized its impacts on surface temperature in Beijing, China. As LST was calculated from satellite sensor, this study focused on the surface temperature heat island, not the air temperature heat island. In so doing, we hoped to identify a method for estimating IS using Landsat TM/ETM+ and high resolution imagery; to quantify the spatial configuration of IS in Beijing; and to examine the relationships between IS and LST at various resolutions. 1 Study area and data resources Beijing, which covers approximately km 2 and has a total population of more than 13 million, is the political capital of China. Over the past two decades, Beijing has undergone intense urbanization that has seriously impacted the urban thermal environment. Heat island effects in Beijing have been very evident since 1961, when the average daily temperature in the city was determined to be 4.6 C higher than that in the suburbs (Song and Zhang, 2003). Our study area, which includes the whole of the Central Urban District, four near suburban districts, and parts of six other suburban districts and counties in the Beijing municipality, covers km 2 (24.3%) of the greater Beijing Municipality area. Beijing s development pattern is a typical concentric expansion, showing a ringshaped pattern as you move from the inner city to the outskirts. In order to reveal the spatial configuration of IS at different stages of urbanization, we divided the study area into six zones based on the location of the city s ring-roads (Fig.1). Cloud free Landsat TM and ETM+ images were acquired to retrieve urban IS on two separate dates, August 31, 2001, and May 22, Considering the data availability and reducing the errors caused by shadows, a summer QuickBird (July 5, 2002) was utilized to derive a training/test dataset (Table 1). Fig. 1 Location of the study area. Table 1 Summary of the images used in this study Date Type of image Spatial resolution (m) No. of bands Sun elevation (degree) Sun azimuth (degree) Aug. 31, 2001 TM 30, 90 a May 22, 2002 ETM+ 15 b, 30, 60 c July 5, 2002 QuickBird 0.64 d, 2.88 e a The thermal band has a resolution of 90 m; b the panchromatic band has a resolution of 15 m; c the thermal band has resolution of 60 m; d the panchromatic has resolution of 0.64 m; e the multi-spectral band has resolution of 2.88.

3 252 XIAO Rong-bo et al. Vol Methodology 2.1 Image preprocessing The 2002 ETM+ image was registered to 1:10000 topographic maps of Beijing (produced by the Beijing Institute of Surveying and Mapping) and Transverse Mercator georeferenced. The other TM image was then co-registered to the ETM+ image, and with the resulting root mean square (RMS) values ranging from 0.13 to 0.24 pixels. Each image was then radiometrically corrected according to methods described in Chander and Markham (2003). The QuickBird image was also rectified to topographic maps with the RMS error of 0.23 pixels. 2.2 Estimation of impervious surfaces The process of mapping impervious surfaces involved: (1) developing a training/validation dataset using Quickbird imagery, (2) selecting and sampling the input predictives; (3) building and assessing a regression tree model, and (4) finalizing spatial modeling and mapping of IS (Fig.2). QuickBird imagery has the highest-resolution of any commercially available remote sensing satellite imagery ranging from 0.61 to 2.88 m. A pan-sharpened image with 0.64-m resolution and compiled from a resolution merge with a multispectral image was used as the base for data calibration. In order to account for the spectral and spatial variability of IS, a large number of training datasets were collected within the study area. Areas where there were observable land-cover change differences between the QuickBird and the TM/ETM+ imagery were excluded from the training/testing data set. An unsupervised classification with the ISODATA (Iterative Self-Organizing Data Analysis) algorithm was conducted to classify the QuickBird image, resulting in five land cover types as impervious surfaces, water, vegetative areas, bare soil, and shadows. A binary map was then generated by recoding the impervious surfaces as 1 on the land cover map and all other areas as 0. The resulting classification accuracy was greater than 85% under standard procedures described in Congalton (1991). A grid-network file with a grid system identical to the Landsat TM/ETM+ imagery was created (Yang and Liu, 2005), where each cell covered a ground area of 900 m 2 (30 m 30 m). All 0.64-m pixels within one 30 m 30 m grid cell classified as impervious surfaces on the binary map were enumerated to determine the percentage of IS for each grid cell (Yang, 2006) sampling grids were randomly selected and divided into training and test datasets. A number of predictive variables (e.g., normalized difference vegetation index (NDVI), brightness, greenness) have proven useful in establishing meaningful statistical models to estimate IS (Smith, 2000; Yang et al., 2003a; Yang and Liu, 2005). In this study, TM and ETM+ were acquired for two different seasons to capture vegetation dynamics over a growing season and to maximize the potential for land cover separability discrimination. In addition to the reflective bands from TM and ETM+, NDVI, and the first three components of the Tasseled Cap transformation (Brightness, Greenness, Wetness), additional band ratios calculated from the radiance values of TM/ETM+ bands 1 and 5 were added as possible soil moisture indicator to help in discriminating between concrete and exposed soil (Smith, 2000). Cubist ( was used to develop the regression tree model with the training data (percentage of impervious surfaces developed from Quickbird) serving as the dependent variable. Based on an accuracy assessment in identifying the percentage of impervious surfaces, the best model was selected and then applied to the whole study area. 2.3 Derivation of land surface temperature There are three types of methods which have been developed to retrieve LST from at-sensor and auxiliary data: single-channel method, split-window technique, and multi-angle method. Because the last two methods require at least two channels, single-channel method is the only method that can be applied to the Landsat platform, with one thermal channel (Sobrino et al., 2004). Traditionally, the main disadvantage of single-channel method is that some atmospheric parameters need to be considered, usually by a complicated procedure of radiosounding. In this study, LST were derived from the corrected TM (August 31, 2001) TIR band ( m) by using the method described in Chander and Markham (2003), which does not require atmospheric parameters and is used widely. First, the digital numbers were transformed into absolute radiance using L λ = (L max L min )/255 DN+ L min (1) Fig. 2 The flowchart of research methodology on this study. where L λ is the spectral radiance, L min and L max (mw/(cm 2 sr µm)) are spectral radiances for each band at digital numbers 0 and 255, respectively. For TM 5, L min and L max were the values and in (mw/(cm 2 sr µm)), respectively (Chander and Markham, 2003). The next step was to convert the spectral radiance into a satellite brightness temperature (i.e., blackbody tempera-

4 No. 2 Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China 253 ture, T B ) under the assumption of uniform emissivity using the following conversion formula: T B = K 2 ln(k 1 /L λ + 1) where T B is effective at-satellite temperature in Kelvin, K 2 and K 1 are calibration constants in Kelvin, and L λ is spectral radiance at the sensor s aperture. For TM, K 1 = and K 2 = mw/(cm 2 sr µm). The resulting temperature values are therefore referenced to a black body. The emissivity corrected land surface temperatures (LST) were computed according to Artis and Carnahan (1982) and Weng et al. (2004) as: LST= T B 1+(λ T B /ρ)lnε where λ=11.5 µm (Markham and Barker, 1985), ρ= mk, andεis land surface emissivity, which was obtained using NDVI Threshold Methods (Sobrino et al., 2004) as follows:ε=ε soil, when NDVI<0.2;ε = ε veg, when NDVI>0.5; ε = ε veg P v + ε soil (1 P v ), when 0.2 NDVI 0.5. whereε soil is soil emissivity,ε veg is vegetation emissivity, P v is the vegetative proportion obtained according to Carlson and Ripley (1997) as: ( ) 2 NDVI NDVImin P v = (4) NDVI max NDVI min where NDVI max = 0.5, NDVI min = 0.2. Here, soil and vegetation emissivities were estimated as 0.97 and 0.99, respectively (Li et al., 2004). NDVI is calculated from the pixel values of the Landsat TM as: NDVI= ρ(band 4) ρ(band 3) ρ(band 4)+ρ(band 3) whereρis band reflectivity. Reflectivity is then calculated as (Chander and Markham, 2003): ρ P = π L λ d 2 E 0 cosθ z (6) (2) (3) (5) whereρ P is unitless planetary reflectance, d is the earthsun distance in astronomical units, E 0 is mean solar exoatmospheric irradiance, andθ z is solar angle at zenith. The parameters were obtained from the literature (Chander and Markham, 2003), except forθ z, which was retrieved from the TM header file. 2.4 Analysis of relationship between IS and LST In order to describe the spatial trends of IS and LST in different directions, four transects were drawn through the center of the city using Tiananmen Square as the center for the N-S profile (0 azimuth) and constructing a new transect every 45 (Fig.3c). Then we conducted a neighborhood statistical analysis (supported by ArcView) to act as a moving window that could scan across the 30-m IS grid map to get a sense of the scale effects involved in the IS and LST relationship. Mean values were calculated for 2 2, 4 4, 8 8, 16 16, and neighborhoods and corresponding to widths of 60, 120, 240, 480, and 960 m respectively. Based on these grid maps, sample points were selected every 30 m from the urban center to rural areas along all four transects to study spatial change along the urban-to-rural gradients. Finally, the area wide relationship was investigated through correlation analysis (pixel to pixel) using the Spatial Modeler in ERDAS. 3 Results and discussion 3.1 Spatial characteristics of IS Results from the regression tree models, as well as a visual inspection, indicated that IS estimation was accurate, with a correlation coefficient of 0.94 and average error of 8.59% (Fig.3c). Green spaces with low impervious values were found outside of 5th ring-road, while red spaces indicating high impervious values were clearly visible in the urban areas. The city center, satellite cities, airports and major highways were found to have the highest IS values, while forests, farmlands, and gardens had the lowest IS values. To capture the synoptic features of Beijing s IS, we grouped those ten equal categories (Table 2). The average Fig. 3 Spatial distribution of land surface temperature, NDVI and IS.

5 254 XIAO Rong-bo et al. Vol. 19 Table 2 Area proportion of IS categories in different regions IS (%) Zone 1 (%) Zone 2 (%) Zone 3 (%) Zone 4 (%) Zone 5 (%) Zone 6 (%) Total (%) Average IS (%) IS value for the total study area was 20.80% with standard deviation of However, in most grids (58.60%) the average IS was less than 10%. IS percentage values in different regions also varied dramatically ranging from 67.32% in Zone 1 to 9.32% in Zone 6. The amount of impervious surfaces within urban area is considered a significant environmental indicator of watershed health, water quality, and overall ecosystems well being (Arnold and Gibbons, 1996). It is significant that an increased proportion of impervious surfaces cause both quality and quantity of the stormwater runoff, leading to degraded stream and watershed systems, an increased quantity of stormwater for stream systems to absorb, sedimentation, and an increased pollution load carried by the stormwater (Brabec et al., 2002). It can be a future study direction to use IS value as an indicator for water quality and urban hydrological modeling. For example, the average IS value within the 5th ring-road in Beijing exceeded that of the total study area, the threshold of severe ecological effects on urban ecosystem, by 30% (Civco et al., 2002). This threshold is critical for many ecological processes, especially occurring in the urban watershed ecosystem. Therefore, in order to ensure the health of urban ecosystem, it is very urgent and strongly encouraged to maintain and conserve non-impervious surfaces and open spaces in the city center of Beijing. 3.2 Spatial pattern of LST and NDVI Across the entire study area, the shapes of LST patterns were generally concentric, increasing from the outskirts towards the inner urban areas. LST values ranged from 13.3 to 40.0 C, with a mean of 23.4 C and a standard deviation of 4.1 C (Fig.3a). Some hot spots, or so-called urban heat islands, could be easily identified. Interestingly, the most evident UHI did not occur in the Central of Business District (CBD). It was distributed to the south of the central city within an area bounded by the 4th ring road and the dry Yongding river bed in the southwest part of the city, which is the largest bare land area in Beijing. As might be expected, LST levels for some satellite cities and airports were also higher than their surroundings. There were also many smaller UHIs along highways in the northwest, east, and southern parts of the city. Correspondingly, vegetated urban parks in city center, such as Temple of Heaven (Tian Tan), the Forbidden City, and the Yuyuantan Garden were cooler than their surrounding built environments. Fig.3b shows the distributional NDVI for Beijing, with values ranging from to 0.795, a mean value of 0.405, and a standard deviation of Highly vegetated areas had correspondingly high NDVI values, which was usually found in forest and farmland outside of 5th ring road. By contrast, the urban areas as well as bare soil surface areas had relatively lower NDVI values. 3.3 Relationship between IS and LST Thermal signatures of IS categories Overall, average LST increased from to C with the increasing of IS percentage from 0 10% to 90% 100% (Table 3). This implies that urban development brought up LST by an average of 9.18 C by replacing nature environment (forest, water, and pasture) with impervious surfaces such as stone, metal, and concrete. The standard deviation value of LST decreased with growth of IS, indicating that thermal signature were getting more homogeneous with the increase of IS. Thermal signature homogeneity was mainly influenced by land cover types. For example, both forest and nature bare soil had low IS values, but their LST appeared very differently: the former generally show a low LST, because dense vegetation can reduce amount of heat stored in the soil and surface structures through transpiration, whereas the latter display a high LST. NDVI value and its standard deviation decreased with the increase of IS, indicating that higher IS areas were mainly covered by little vegetation, Table 3 Mean LST and NDVI of the IS categories IS (%) LST a ( C) NDVI a IS (%) LST a ( C) NDVI a ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±0.06 a Mean±SD.

6 No. 2 Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China 255 whereas lower ones were mainly covered density forest or grass Relationship between IS and LST Because similar patterns of IS and LST showed on these four transects, here we only presented the results crossing the city from south to north to illustrate the distribution of IS and LST at all spatial resolution. In Fig.4, the IS values showed a somewhat symmetric pattern along the transect: shifting from low to high and low again. Between 0 and 15 km and between 50 and 65 km, most of the IS values were lower than 40%. By contrast, IS value was consistently higher than 60% between 15 and 50 km. The spatial distribution of LST showed some similarities with IS patterns, which indicated distinct linear relation between each other. From the city center and outward (both to the south and to the north), there existed a significant temperature gradient, which was described as UHI effects. Numerous peaks, valleys, plateaus, and basins in Fig.4a f indicated the heterogeneous features of land surface temperature over the space. This phenomenon was also observed in other cities (Weng et al., 2004). The curves generated by using different window sizes exhibited qualitatively similar patterns. Without the smoothing effect of the larger window size, however, the small window resulted in considerable fluctuation in the values of IS and LST. This demonstrate that with increasing of grain size, spatial heterogeneity of urban impervious surfaces diminishes gradually, which may be viewed as an example of land patterns changes with effect of scale (Luck and Wu, 2002). The relationships between IS and LST were investigated through correlation analysis (pixel to pixel). Table 4 shows the Person s correlation coefficients for the different resolutions. The significance of each correlation coefficient was determined by using a one-tail student s t-test. From Table 4 we can see that IS tended to be positively correlated with LST at all resolution levels. The good relationship observed between LST and IS confirms that the method for estimating IS is accurate. The main reason for this correlation is that vegetation or water covering and an increased rate of evapotranspiration in the non-impervious surfaces, which could contribute to decreasing surface temperature. The highest correlation (0.925) was found at 960 m, while the lowest correlation (0.807) was observed at 30 m. This showed an overall increase in the correlation coefficient between the two variables when the pixel size is degraded, which is expected when the variance of a heterogeneous area is smoothed by increasing the pixel size. Nevertheless, this effect might not be expected under a more homogenous area. Therefore, more analyses for future are warranted in which the smoothing effects of Table 4 Correlation coefficients between IS and LST at various resolution levels Resolution level (m) LST/IS Significant at 0.05 level. Fig. 4 Change in IS and LST pattern along the south-north transect for the different resolutions.

7 256 XIAO Rong-bo et al. Vol. 19 pixel degradation are compared among heterogeneous and homogeneous areas. 4 Conclusions In this study, the percentage of IS at the sub-pixel was estimated through regression tree algorithm based on Landsat TM/ETM+ and Quickbird imagery. Results demonstrated that this method was capable of predicting with consistent and acceptable accuracy provided with the correlation coefficient of 0.94 and the average error of 8.59%. Spatial patterns of IS exhibited changing gradients across the various urban-rural transects, with LST values showing a concentric shape that increased as you moved from the outskirts towards the downtown areas. Transect analysis also indicated that the changes in both IS and LST patterns were similar at various resolution levels, which suggests a distinct linear relationship between them. IS tended to be positively correlated with LST, and the correlation coefficients increased from to with increases in IS pixel size. 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