Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

Youshui Zhanga, Inakwu O.A. Odehb,  ,   and Chunfeng Hana

aCollege of Geography, Fujian Normal University, Fuzhou 350007, China

bFaculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW 2006, Australia

Received 1 August 2008;  accepted 10 March 2009.  Available online 9 April 2009. 

Abstract

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.

Keywords: Urban heat island; Land surface temperature; Impervious surface area; NDVI; NDBI

Article Outline

1. Introduction2. Methods2.1. Study area and data2.2. Image pre-processing2.3. Derivation of LST, NDVI and NDBI from TM and ETM+ imageries2.3.1. LST2.3.2. NDVI and NDBI2.4. The derivation of urban percent ISA3. Results and discussion

3.1. Land surface imperviousness and LST3.2. Relationship among imperviousness, NDVI, NDBI and LST3.3. Relationship between UHI and LULC patterns4. ConclusionsReferences

1. Introduction

Changes in land cover (which is the biophysical attributes of the earth's surface) and land use (the utilization of land for a given human purpose) have been reported to be the main driver of environmental change, while climatic change and climate variability may influence land-use preferences differently in different parts of the world (Brunsell, 2006). Therefore accurate and up-to-date information on land cover and the state of the environment is critical to environmental monitoring, management and planning (Assefa, 2004). However, the interaction between land-use/land-cover (LULC) change and the spatial–temporal climatic variability is poorly understood which requires the development of new models linking the climate variability with the changes in land use and land cover, especially at the urban–rural regional scale.

Recent developments in remote sensing and image analysis have identified land surface temperature (LST) as one of the key parameters controlling the physical, chemical and biological processes at the interface between the Earth and the atmosphere. It is an important factor for the study of urban climate ([Voogt and Oke, 2003] and [Small, 2006]). LST has been shown to be an effective means of partitioning latent heat fluxes and thus surface radiant temperature response as a function of varying surface soil water content and vegetation cover (Owen et al., 1998). These findings have encouraged investigations of the relationship between LST and vegetation abundance (e.g., [Gallo and Owen, 1998a], [Gallo and Owen, 1998b], [Weng, 2001] and [Weng et al., 2004]). Urbanization and industrialization can lead to modification of land surface and near-surface atmospheric conditions, which in turn could cause change in thermal properties of urban areas causing them to be warmer than the surrounding non-urbanized areas. This phenomenon is called urban heat island (UHI), which is mainly caused by replacement of vegetated areas by non-evaporating and impervious materials such as asphalt and concrete ([Dousset and Gourmelon, 2003], [Kim, 1992] and [Ruiliang et al., 2006]). The UHI phenomenon can influence the radiative fluxes in the near-surface flow because in urban areas, the higher level of sensible heat fluxes is caused by LULC changes caused by the removal of the original vegetated areas that were characterized by lower heat fluxes. Furthermore, urbanization generally leads to reduced evapotranspiration and more rapid runoff of rainwater.

The monitoring of UHI phenomenon and the physical processes associated with it have traditionally been conducted by ground-based observations taken from fixed thermometer networks or by traversing a targeted area with a thermometer mounted on vehicles ([Voogt and Oke, 2003] and [Weng et al., 2004]). However, the advent of remote sensing technology has made it possible to study UHIs using satellite remote sensing data, especially thermal data taken by either satellite-borne or air-borne sensors. Rao (1972) was the first to demonstrate the possibility of identifying urban areas based on the analyses of thermal infrared data acquired by a satellite sensor. Following this, Gallo et al. (1995) reviewed the validity and utility of UHI derived from the satellite-acquired imagery. (Gallo and Owen, 1998a) and (Gallo and Owen, 1998b) and (Streutker, 2002) and (Streutker, 2003) derived land surface temperature and evaluated the UHI phenomenon using National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer (NOAA AVHRR) data for regional-scale urban

temperature mapping. Similar studies by Chen et al. (2006) andWeng (2001) used Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) thermal infrared (TIR) data to assess the local patterns of UHI. Generally, with increasing availability of thermal remote sensing data from Landsat Thematic Mapper, Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), and the NOAA AVHRR, research of LST, using thermal images, has been a topic of great interest in the remote sensing community for the past three decades. The application of low spatial resolution remotely sensed data is requisite for understanding the affects of UHI on climatic patterns. However, understanding the thermal response of individual land covers within a city is equally valuable, as radiative transfer over such intensively developed environment varies significantly over a short space due to the diversity of urban land covers and their respective physical properties (Renee et al., 2006).

In comparison with thermal remote sensing of natural and agricultural surfaces, thermal remote sensing of urban areas has been slow to advance beyond qualitative description of thermal patterns and simple correlation analysis (Ruiliang et al., 2006). Recently, Voogt and Oke (2003) reviewed most of previous research on UHI study and listed three themes of research: examination of the spatial structure of urban thermal patterns and their relation to urban surface characteristics (e.g., [Balling and Brazel, 1988] and [Dousset and Gourmelon, 2003]); thermal remote sensing for urban surface energy balances (e.g., [Assefa, 2004] and [Kim, 1992]); and study on the relation between atmospheric heat islands and surface urban heat islands (e.g., [Ben-Dor and Saaroni, 1997] and [Caselles et al., 1991]). The main objective of this study is to address the first theme.

Many of the previous remote sensing studies of the urban environment used Normalized Difference Vegetation Index (NDVI) as a major indicator of urban climate ([Lo et al., 1997], [Gallo and Owen, 1999] and [Yuan and Bauer, 2007]). However, NDVI is subject to seasonal variations which may influence the results of land surface UHI analysis. Moreover, the relationship between NDVI and LST is well known to be nonlinear, due to the predominantly bare ground surfaces which tend to exhibit larger variation in surface radiant temperature than the densely vegetated LULC types ([Price, 1990], [Gillies and Carlson, 1995], [Owen et al., 1998] and [Chen et al., 2006]). The variability and nonlinearity suggest that NDVI alone may not be sufficient to quantitatively study UHI. The intensity of UHI is related to the spatial extent and composition of vegetation and built-up areas and their temporal changes. Quantitative studies of the relationship between LULC patterns and LST are important for land-use management and planning. Furthermore while NDVI has been used for the estimation of vegetation productivity and rainfall in semi-arid areas ([Chen et al., 2004]and [Wang et al., 2004]), the Normalized Difference Built-up Index (NDBI) has been developed for the identification of urban and built-up areas (Zha et al., 2003). It is therefore possible that the utilization of both NDVI and NDBI as surrogates of LULC can reveal the relationships between different indices such as NDVI, NDBI, and land surface temperature in UHI studies.

Impervious surfaces, defined as land-cover types that impede water infiltration are primarily associated with transportation (streets, highways, parking lots and sidewalks) and building rooftops. In remote sensing, classified impervious surface area (ISA) has been used to quantify and map the degree of urbanization and extent of urban land use ([Yuan and Bauer, 2007], [Xian and Crane, 2006] and [Civco et al., 2002]). With increased concern regarding global climate change, it is important to analyse the relationship between the LST and percent ISA in an urbanized environment as an alternative approach to the study of urban expansion. Compared to the NDVI, the percent ISA is more stable and less affected by seasonal changes

in landscape conditions, which means that percent ISA may provide an additional metric for the analysis of LST and urban thermal patterns.

The aims of this study are to investigate the relationships of LST with NDVI, and those of NDBI with percent ISA using Landsat TM and ETM+ data obtained for the city of Fuzhou in south-eastern China; and to quantitatively compare the patterns and intensity of UHI with LULC types. In order to generate verifiable estimates of change in urban extent, the LULC types were quantitatively determined by implementing sub-pixel percent imperviousness estimation and selecting certain threshold values of percent ISA. In developing this method of quantifying the urban LULC types and associated surface thermal distribution using remote sensing data, it is envisaged that the method could be used for other similar geographical regions in China and indeed elsewhere.

2. Methods

2.1. Study area and data

The study area is Fuzhou City, located in the southeast coast of China (Fig. 1). With a population of over 5.75 million, Fuzhou is the major coastal city located between Hong Kong and Shanghai. The city is on a subtropical plain sandwiched between the Gu and Qi mountains with potential for expansion in all directions. Like many other Chinese cities, the population of Fuzhou is rapidly increasing leading to increased urban expansion. This urban growth is encroaching into the adjacent agricultural and other non-urban land. With sweltering summer and mild winter, the city has several advantages that make it suitable for our study. It is characterized by a diversity of land-cover types transversed by the Min River. The city is also characterized by high-, medium- and low-density urban developments in the central portion and several rural land-cover types – predominantly agricultural fields, forests, water and bare land in the surrounding landscapes. The built environment consists of buildings and roofs made up of concrete, brick tiles and metal plates, and majority of the roads are covered by asphalt and concrete. The city is therefore ideally suitable for the analysis of UHI phenomenon due to its diversity of land-cover types and the rapid urbanization.

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Fig. 1. Location map of study area showing the aerial photograph image.

To quantitatively derive LST and compute UHI intensity, Landsat 5 TM image (acquired on June 15, 1989) and Landsat 7 ETM+ image (acquired on March 4, 2001) were used. While bands 1–5 and 7 images have nominal spatial resolution of 30 m, the thermal infrared band (band 6) has 120 m spatial resolution for TM image and 60 m for ETM+ image. In addition, an IKONOS image (acquired on October 29, 2000) with 1 m spatial resolution and aerial photographs (acquired on May 20, 1988) with 2 m spatial resolution, rectified to the Universal Transverse Mercator (UTM) coordinate system, were used to calculate the percent ISA. Land-cover

classification of the remote sensing imageries was carried out using ancillary data obtained from 1:10,000 scale digital topographic maps.

2.2. Image pre-processing

To analyse the changes in temperature in relation to LULC types, the bi-temporal TM/ETM+ images were geo-referenced to a common UTM coordinate system based on the rectified high-resolution IKONOS image, aerial photograph and the 1:10,000 scale topographic maps. The RMSE of rectification is less than 0.3 pixels (≈9 m). Using the radiometric correction method of Schroeder et al. (2006), the original digital numbers of bands 1–5 and 7 images were converted to at-satellite radiance, at-satellite reflectance, and further converted to surface reflectance. While bands 1 through 5 and band 7 are at a spatial resolution of 30 m, the thermal band (band 6) comes at an original spatial resolution of 120 m for TM and 60 m for ETM+. In order to carry out further analysis on a common spatial resolution, bands 1–5 and band 7 of both Landsat imageries were resampled onto 120 m using the cubical convolution algorithm.

2.3. Derivation of LST, NDVI and NDBI from TM and ETM+ imageries

2.3.1. LST

LST is the radiative skin temperature of the land surface which plays an important role in the physics of the land surface through the process of energy and water exchanges with the atmosphere. The derivation of LST from satellite thermal data requires several procedures: sensor radiometric calibrations, atmospheric and surface emissivity corrections, characterization of spatial variability in land-cover, etc. As the near-surface atmospheric water vapour content varies over time due to seasonality and inter-annual variability of the atmospheric conditions, it is inappropriate to directly compare temperature values represented by the LST between multiple periods. Therefore the focus here is on the UHI intensity and its spatial patterns across the study region. UHI intensity is estimated as the difference between the peak temperatures (LST) of the urban area and the background non-urban temperatures (Chen et al., 2006). This UHI effect can be determined for the individual thermal images and then compared between two or more periods. However, before we compute UHI effect, we must first derive the LST based on different methods for TM and ETM+ images.

As described above the TM and ETM+ thermal infrared band (10.4–12.5 μm) data were used to derive the LST. Yuan and Bauer (2007) proposed a method of deriving LST in three steps: Firstly, the digital numbers (DNs) of band 6 are converted to radiation luminance or top-of-atmospheric (TOA) radiance (Lλ, mW/cm2 sr) using:

(1)

where b6 is the pixel digital number for band 6, Lmax = 1.896 (mW/cm2 sr), and Lmin = 0.1534 (mW/cm2 sr). In the case of Landsat 7 ETM+ image, TOA is derived by:

(2)

where QCALmin = 1, QCALmax = 255, and Lmax = 17.04 W/(m2 sr μm), and Lmin = 0.

Secondly, the TOA radiance is converted to surface-leaving radiance by removing the effects of the atmosphere in the thermal region. An atmospheric correction tool – MODTRAN 4.0 for the thermal band of Landsat sensors was applied. This tool uses the MODTRAN radiative transfer code and a suite of integration algorithms to estimate three parameters – atmospheric transmission, and upwelling and downwelling radiance – which enable the calculation of the surface-leaving radiance – LT or the radiance of a blackbody target of kinetic temperature T, in the form of (Eq. (3)):

(3)

where TOA is the radiance derived for the instrument, Lμ is the upwelling or atmospheric path radiance, Ld is the downwelling or sky radiance, τ is the atmospheric transmission, and   is the emissivity of the surface specific to the target type. Radiance values are in units of W/(m2 sr μm) and the transmission and emissivity is unitless. The emissivity could be based on the land-cover classification (Yuan et al., 2005) or the emissivity values as derived by Snyder et al. (1998).

Lastly, the radiance (LT) is converted to surface temperature (LST) using the Landsat specific estimate of the Planck curve (Eq. (4)) (Chander and Markham, 2003):

(4)

where LST is the temperature in Kelvin (K), K1 is the pre-launch calibration constant in W/(m2 sr μm) and K2 is another pre-launch calibration constant in Kelvin. For Landsat 5 TM, K1 = 607.76 W/(m2 sr μm) and K2 = 1260.56 K; for Landsat 7 ETM+, K1 = 666.09 W/(m2 sr μm) and K2 = 1282.71 K.

The LST image from the thermal band of ETM+ image (band 6) with original spatial resolution of 60 m was resampled to 120 m using the nearest neighbour algorithm to match the pixel size of the LST image from TM image.

2.3.2. NDVI and NDBI

The NDVI and NDBI indices were required to characterize the LULC types and to explore the quantitative relationships between LULC types and UHI. NDVI (Eq. (5), which has generally been used to express the density of vegetation (Purevdorj et al., 1998) is of the form

(5)

where ρ3 is the reflectance value of red band (band 3) and ρ4 is the reflectance value of near-infrared band (band 4), both of the Landsat images. The NDVI values range from −1 to 1, with positive values indicating vegetated areas and negative values signifying non-vegetated surface features.

Another index used in this study that is sensitive to the built-up area is NDBI (Zha et al., 2003), derived as

(6)

where b5 and b4 are the respective digital numbers of mid-infrared band (band 5) and near-infrared band (band 4) of the Landsat images.

In this study both the NDVI and NDBI were used to differentiate the LULC types by setting the appropriate threshold values. It is worth noting that these indices may have different range of values for different land-cover types, depending on the study regions, image acquisition time and different conditions of atmosphere and precipitation.

2.4. The derivation of urban percent ISA

Several studies have shown ISA to be highly correlated with urban land-cover types and their spatial patterns ([Jennings et al., 2004] and [Xian and Crane, 2005]). However, the identification of impervious surfaces is a challenge because of the complexity of urban and suburban landscapes and the limitation of remotely sensed data in terms of spectral and spatial resolutions. The heterogeneity of urban landscapes and the difficulty in selecting training samples are major causes of LULC misclassification, as the impervious surfaces consist of a mixture of different surfaces. Recently, research in impervious surface identification has moved toward per-pixel image classification, decision tree modelling, and sub-pixel classification (Lu and Weng, 2006). Sub-pixel percent ISA can be used to characterize urban LULC at resolutions smaller than the pixel size of the target imagery, and thus enhances the quantification of heterogeneous of urban LULC. While high-resolution imagery can be used to identify urban land-cover heterogeneity, the medium-resolution Landsat imagery could be used to extrapolate ISA over large regions (Xian and Crane, 2006).

In this study, the percent ISA was used as an indicator of urban spatial extent and level of development. To accurately estimate urban surface types, the sub-pixel imperviousness detection method was used to spatially quantify the urban LULC types following the steps illustrated in Fig. 2. In the case of Landsat ETM+ obtained in 2001, this method classified urban and non-urban areas of the 1 m resolution IKONOS image (produced by the fusion of IKONOS 1 m high-resolution panchromatic image and 4 m multi-spectral images, acquired in 2000) based on unsupervised classification, and further classified urban areas by thresholding. The ISA result was used to calculate the percent ISA based on the 120 m vector data and then convert to 120 m raster percent ISA image for each pixel of LST derived from 2001 ETM+ imagery. In the case of 1989 TM imagery, the 2 m resolution aerial photograph, acquired in May 1988, was utilized to extract urban areas and then converted to 120 m raster percent ISA image. In the case of rural areas for which there was no high-resolution IKONOS imagery or aerial photographs, we used the relationships of urban percent ISA and NDBI (derived from TM/ETM+ using Eq. (6)) to obtain percent ISA images through thresholding of NDBI values. The results of these analyses are two 120 m resolution images of percent ISA representing the general characteristics of rural–urban landscape mix for 1989 and 2001, respectively.

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Fig. 2. Flow chart showing the steps for deriving percent impervious surface area (ISA) and associated land surface temperature (LST).

3. Results and discussion

3.1. Land surface imperviousness and LST

The digital remote sensing method described here provides not only a measure of the magnitude of surface temperatures, but also the spatial extent of the surface UHI effects. The percent ISA, which was estimated within a continuous range of between 0% and 100%, was mapped for the region. As illustrated by the imperviousness maps in Fig. 3, the higher percent ISA threshold values captured almost all of the developed land including low-, medium-, and high-density residential areas, as well as the central business districts (CBD). Pixels were classified as urban at various levels of development when the percent ISA is equal to or greater than 10% and those pixels of less than 10% were classified as non-urban. Thus the urban development densities were defined by the ISA threshold values as 10–30% for low-density; 31–50% for medium-density; and >50% for high-density. Such detailed information on urban land-cover types also reveals CBD and urban residential areas with varying densities and patterns, rural developed centres and relatively undeveloped areas.

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Fig. 3. Spatial distribution patterns of land surface temperature (LST) for different percent impervious surface area (ISA) from the TM image acquired on June 15, 1989 (a, c, e, g) and ETM+ image acquired on March 4, 2001 (b, d, f, h).

The spatial patterns of LST estimated for the different categories of percent ISA are shown in Fig. 3. While Fig. 3g and h indicates relatively high ISA percentages and associated LST values, Fig. 3e, f and c, d are respectively indicative of medium and medium low percent ISA and associated LST. However, the lowest of the percent ISA and associated LST appear to be restricted to the rural areas (Fig. 3a and b). In both 1989 and 2001, the spatial extent and distribution patterns of LST show increasing values from natural landscape to the high density CBD where LULC types are predominantly made of concrete, stone, and metal. They also show a general UHI effect with a variety of thermal distributions in the urban area (yellow and red patterns in Fig. 3g and h), compared with other LULC types (Fig. 3a–e). The “hot spots”, observed by both satellite sensors, are more pronounced in downtown CBD in 1989 (Fig. 3g). As urbanization expanded, new “hot spots” have appeared in the western, southern and south-eastern outskirt of the city by 2001 (Fig. 3h). The thermal gradient from the centre to the edge of the city is from the hot (high percent ISA) to warm (medium percent ISA) to cool (low percent ISA). The results show that the spatial distribution patterns of the UHIs have changed from a scattered pattern in 1989 to a more contiguous pattern in 2001, which is indicative of urban filling and expansion.

Table 1 shows the areal extent of the different categories of percent ISA in Fuzhou city. Overall the areal extent of percent ISA >10% is 763.94 km2 in 1989, increasing to approximately 1213.64 km2 in 2001. This increase occurred more in the categories of 31–50% ISA and >50% ISA, which are more than compensated by the decrease in areal extent of the <10% ISA category. The largest increase of about 89.0% had occurred in the category of 31–50% ISA, which means that the medium density urban development had occurred quite significantly within 12 years.

Table 1.

The spatial extent (km2) of each category of percent ISA in 1989 and 2001 and change in spatial extent between the two periods.

Year/category of percent ISA <10% ISA 10–30% ISA 31–50% ISA >50% ISA

2001 (km2) 530.71 352.71 498.38 362.55

1989 (km2) 980.41 297.69 263.69 202.56

Difference (km2) −449.7 55.02 234.69 159.99

Percent change from 1989 to 2001 −45.9 18.5 89.0 79.0

The mean and standard deviation (SD) of LST for each imperviousness category are shown in Table 2. Generally, the high-density urban area (percent ISA categories >50% ISA) have higher mean LST for 1989, but in 2001 the mean LST is nearly the same as that of the medium- and high-density urban areas with the value slightly over 289 K. The mean LST values for relatively developed areas (percent ISA >10%) in both years are about 2–3 K higher than those of rural areas (percent ISA <10%). In comparative terms, the increase is probably because high-

density urban development areas were, on average, much larger in 1989 (mean LST of about 2.8 K) than in 2001 (2.46 K). This means that there is some homogenisation and expansion of urban development over the study region in 2001 compared to 1989. This is further supported by the comparative SD of LST for the percent ISA categories for each year (Table 2). The SDs of LST are much larger in 1989 compared to 2001, although this difference in SD of LST could also be attributed to seasonal fluctuations of LST, as mean LST and its SD in early spring (March 2001) would be expected to be less than mean LST in summer (June 1989). Explained in different context, the SDs of LST are generally larger for urban areas than the rural areas, indicating that the urban landscapes would have experienced wider variation in LST than the natural vegetation because of mix of LULC types and different building structures and construction materials. This explains the thermal heterogeneity that characterizes these areas.

Table 2.

Mean LST of categories of percent ISA and associated standard deviation (SD) in 1989 and 2001.

Category of percent ISA <10% ISA 10–30% ISA 31–50% ISA >50% ISA

Mean 2001 LST (K) 287.21 287.79 289.21 289.67

SD of 2001 of LST (K) 1.30 1.40 1.33 1.45

Mean 1989LST (K) 297.78 298.40 299.20 300.58

SD of 1989 LST (K) 1.32 1.63 2.16 1.96

The SDs of LST are relatively small for the low-density residential areas because of homogeneity of construction types contributing to low LST variation in these areas. Theoretically, the SD for urban areas should be smaller for a given TM image than obtained for ETM+ image because LST values were calculated from the original 120 m resolution thermal band 6 of Landsat TM, whereas the LST values obtained from the Landsat ETM+ thermal band were upscaled from an original 60–120 m. The LST values for ETM+ thermal band should have relatively large variations because finer (60 m) resolution imagery was upscaled to 120 m, and thus could have captured more detailed variability of the LST. However, probably because the percent ISA reflects seasonal UHI fluctuations, the SDs for the urban areas are smaller for ETM+ image (acquired in early spring) than in TM image (acquired in summer).

3.2. Relationship among imperviousness, NDVI, NDBI and LST

The land surface or near land surface temperature can be affected by the nature of land surface cover, ranging from the bare ground to vegetation cover types of variable density. It is well known that NDVI can be used as a surrogate for the density and vigour of vegetation. However, in this study NDVI was used to analyse the land-cover types as classified from the Landsat imagery and to estimate the emissivity of the surface ([Yuan et al., 2005] and [Snyder et al., 1998]). To better understand the relationships among NDVI, percent ISA (the different ISA categories) and LST, we analysed the mean NDVI for each percent ISA category. Sample points from LULC types: built-up, forest, cropland and bare land (the water body was excluded

from sampling) were used to investigate the relationships of LST with NDVI and with percent ISA (Fig. 4). Fig. 4a and b indicates the relationship between LST and NDVI is weak and nonlinear, variable and strongly affected by season, especially as the non-vegetated locations experience a wider variation in surface radiant temperature than densely vegetated locations. As Fig. 4a and b shows a triangular spread of scatterplots of NDVI versus LST can be observed in both cases. This similar to the work reported elsewhere ([Nemani et al., 1993], [Assefa, 2004] and [Yuan and Bauer, 2007]). An apparent weak nonlinearity in these relationships is less so in spring (March 2001 than in summer (June 1989)) which suggests that NDVI alone may not be suitable for quantitative study of UHI. However, Fig. 4c and d illustrates a better linear relationship between LST and imperviousness or percent ISA, although less so for 2001, which means that imperviousness, as estimated by UHI, could be used to study the spatial–temporal variation of LST.

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Fig. 4. Scatterplots of land surface temperature (LST) versus normalized difference vegetation index (NDVI): (a) June 1989, (b) March 2001, in comparison to scatterplots of LST versus percent impervious surface area (ISA): (c) June 1989 and (d) March 2001.

The mean NDVI for Fuzhou in 1989 and 2001 and associated SDs for different percent ISA categories are shown in Table 3. Generally, urban areas exhibit smaller NDVI values than non-urban areas, with consistent decrease in the mean NDVI as the percent imperviousness (ISA) increases. It is interesting to note that all of the percent ISA categories of urban areas have negative NDVI in the summer of 1989 in comparison to positive values during spring of 2001. This explains the fact that there was less vegetation cover interspersed within the developed areas in 2001 in comparison to 1989. Indeed there is a consistent decline in NDVI with increase level of urban development in both cases. The SD of NDVI for the urban areas, especially for the highest density ISA category (>50% ISA), is higher than that for the fully vegetated rural areas (<10% ISA) because vegetation landscapes in urban areas are interspersed with the variegated developed urban structures.

Table 3.

Mean NDVI for the categories of percent ISA and associated standard deviation (SD) in 1989 and 2001.

Percent ISA <10% ISA 10–30% ISA 31–50% ISA >50% ISA

NDVI (2001) 0.15 −0.01 −0.03 −0.09

SD (2001 NDVI) 0.09 0.07 0.08 0.10

NDVI (1989) 0.25 0.04 0.01 −0.11

SD (1989 NDVI) 0.05 0.06 0.14 0.20

Fig. 5 depicts the plots of the respective differences in the mean LST and NDVI for percent ISA <10% against the respective means of NDVI and LST for the other three percent ISA categories. In both cases (Fig. 5a and b) high average NDVI is shown to have lowered the mean LST by nearly 4 K in 1989 and 3 K in 2001. The dense natural vegetation canopy in rural areas reduces the surface radiant temperature leading to relatively low LST values. As ISA increases from medium to high urban development density, mean NDVI gradually decreases with concomitant increase in LST gradually. Such a strong negative relationships between LST and NDVI have been reported in other studies in thermal remote sensing for urban and rural environments ([Dousset and Gourmelon, 2003], [Gallo and Tarpley, 1996], [Lo et al., 1997] and [Wilson et al., 2003]). The basis of these relationships is that higher levels of latent heat fluxes are more representative of areas characterized by significant vegetation cover (e.g., forest and grass-land areas in this study) in comparison to areas with sparse or no vegetation cover and low surface moisture availability (such as built-up and bare-soil types). The latter developed surfaces are where significant sensible heat exchange occurs ([Ruiliang et al., 2006] and [Wilson et al., 2003]). Gallo and Owen (1999) evaluated the seasonal trends in temperature and NDVI and found that differences in NDVI and satellite-based surface temperature accounted for 40% of the variation in urban–rural temperature differences.

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Fig. 5. (a) The 1989 loss in mean normalized difference vegetation index (NDVI) and gain in mean land surface temperature (LST) of other percent ISA categories from natural landscapes (percent impervious surface area (ISA) ≤10%), as a function of other percent ISA categories (with percent ISA >10%); (b) The 2001 loss in mean NDVI and gain of LST of other percent ISA categories from natural landscapes (percent ISA ≤10%), as a function of other percent ISA categories (with percent ISA >10%).

To further investigate these relationships, a zonal analysis was carried out to evaluate the mean LST at the increment of percent ISA from 0% to 100%, and at the increment of the NDVI from −1 to 1. Fig. 6a and b shows relatively strong linear relationships (average r2 ≈ 0.68) between the mean LST and percent imperviousness for both 1989 and 2001, suggesting that the

variations in LST can be well accounted for by percent ISA, especially during the summer period – June 1989 (Fig. 6a). On the other hand, the associations between the mean LST and mean NDVI are not straightforward but weak (Fig. 6c and d) for both years. In order to quantitatively analyse the relationship between the density of built-up area and its temperature, the average values of NDBI and corresponding temperature were used. The scatterplots of mean NDBI and corresponding LST are shown in Fig. 7. The results indicate a statistically significance correlation (P = 0.05; r2 = 0.87 for 1989 and 0.75 for 2001) between NDBI and LST, and the resulting regression equation could be used to study the impact of built-up area on LST. It thus demonstrate the usefulness of NDBI for the study of LST, and hence imperviousness of LULC types.

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Fig. 6. Linear plots of mean land surface temperature (LST) versus percent impervious surface area (ISA): (a) 1989, (b) 2001; and plots of mean LST versus normalized difference vegetation index (NDVI): (c) 1989 and (d) 2001.

Full-size image (19K) High-quality image (158K)

Fig. 7. Linear plots of mean normalized difference built-up index (NDBI) versus mean land surface temperature (LST): (a) 1989 and (b) 2001.

3.3. Relationship between UHI and LULC patterns

From the above analysis, it can be seen that NDVI, NDBI and band DNs can be used to classify LULC types (water, bare land, built-up, forest, cropland), and that NDBI is more suitable for the quantitative study of land-cover types and associated surface temperature than NDVI. We further explored how different ISA categories can be used to study UHIs. To study the effect of human activities on global warming, it is necessary to study the temporal change in temperature, represented by UHIs, vis-à-vis LULC. Our study here indicates that the identification of ISA categories mimic the increase in relative extent of the medium-density residential area from 15.1% in 1989 to 28.6% in 2001. There is an areal increase in high-density residential areas as a proportion of the total area, which varies from 11.6% in 1989 to 20.8% in 2001. Similarly there is an increase in low-density residential areas varying from 17.1% in 1989 to 20.2% in 2001. This increase over the 12-year period is more due to urban expansion in the semi-rural areas in the vicinity of Fouzhu city. In contrast, the non-urban areas, as a proportion of the total study area declined during the same period: from 56.2% to 30.4%. Evidently, this

change in LULC has altered the land surface temperature in the region. Due to high heat capacity of water, temperature variation over water is less variable, and hence the temperature difference between percent ISA and waters can be used as the measure of the UHI intensity. We represent the mean UHI intensity for each period by calculating the difference in LST between different ISA categories and water (Table 4).

Table 4.

The mean difference between LST values of each category of percent ISA and water.

LST of water (K) Mean difference (K)

<10% ISA 10–30% ISA 31–50% ISA >50% ISA

2001 284.10 1.81 2.69 3.11 3.58

1989 295.30 2.48 3.10 3.40 5.28

It has been known that the UHI intensity can vary with the change of seasons, and that agricultural activities would influence the UHI interpretation of LULC types ([Weng, 2001] and [Chen et al., 2006]). In this study we observed an increasing trend of intensity of UHI (represented by LST) from 1989 to 2001 (Table 2 and Table 4). Using this information, we can estimate how changes in LULC may have contributed to the change in regional temperature. We then estimated the contribution to the net increase or decrease in temperature, which we termed as dT, by each percent ISA category to the regional temperature dynamics (Table 5). The estimated dT is the difference between the means of LST of each ISA category to the mean LST of two periods (1989 and 2001). The mean LST for the two dates is 293.14 K. It is concluded that urbanization has led to a significant temperature increase, which conforms to the changes of the total contributions of ISA category.

Table 5.

Contribution to LST by each category of percent ISA to the local temperature.

dTProportion of ISA category in 1989

1989 Contributiona

Proportion of ISA category in 2001

2001 Contributiona

<10% ISA −1.24 0.562 −0.725 0.304 −0.392

10–30% ISA −0.49 0.171 0.079 0.202 0.093

31–50% ISA 0.18 0.151 0.124 0.286 0.235

>50% ISA 1.03 0.116 0.173 0.208 0.31

dTProportion of ISA category in 1989

1989 Contributiona

Proportion of ISA category in 2001

2001 Contributiona

Total 1 −0.349 1 0.246

New mean LST 292.791 293.386a Note that ‘New mean LST’ here is the sum of the contribution to the mean LST value of 293.14 K taken from LST of 1989 and 2001.

4. Conclusions

In this study we investigated the relationships among the LST, percent ISA, NDVI and NDBI in Fuzhou city. The results indicate percent ISA is an accurate indicator of UHI effects with strong linear relationships between LST and percent ISA in June 1989 and March 2001. However, the relationship between LST and NDVI suffers perhaps due to seasonal effect. Detailed analysis of relationships between LST and percent ISA shows that variations in surface temperature could be better accounted for by differences in imperviousness than by the commonly used NDVI. This implies that percent ISA can be used to analyse LST quantitatively for UHI studies validated by NDBI.

All the analyses in this paper were based on the interpretation of remote sensing images, and the results showed that remote sensing images are ideal for analysing UHI. In future studies, there is the need to focus on: (i) the impact of the distribution pattern of different LULC types on UHI; (ii) more accurate estimation of the variable conditions of LULC types; (iii) comparison of UHIs estimated for cities of different sizes under different climatic conditions; and (iv) multi-temporal studies of UHIs of a single city over four seasons’ using different satellite data.

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