The relationship between land surface temperature and NDVI withremote sensing: application to Shanghai Landsat 7 ETM + data

W. YUE{, J. XU*{, W. TAN{ and L. XU{{Department of Land Management, College of Environmental and Resource Sciences,

Zhejiang University, Hangzhou 310029, People’s Republic of China

{Department of Geography, Key Lab of GIScience, Ministry of Education, East China

Normal University, Shanghai 200062, People’s Republic of China

(Received 26 February 2005; in final form 8 August 2005 )

The relationship between land surface temperature (LST) and NormalizedDifference Vegetation Index (NDVI) associated with urban land-use type andland-use pattern is discussed in the City of Shanghai, China using data collectedby the Enhanced Thematic Mapper Plus (ETM + ) and aerial photographicremote sensing system. There is an apparent correlation between LST and NDVIfrom the visual interpretation of LST and NDVI contrasts. Mean LST andNDVI values associated with different land-use types are significantly different.Multiple comparisons of mean LST and NDVI values associated with pairings ofeach land-use type are also shown to be significantly different. The result of aregressive analysis shows an inverse correlation relationship between LST andNDVI within all land-use polygons, the same to each land-use type, butcorrelation coefficients associated with land-use types are different. An analysison the relationship between LST, NDVI and Shannon Diversity Index (SHDI)shows a positive correlation between LST and SHDI and a negative correlationbetween NDVI and SHDI. According to the above results, LST, SHDI andNDVI can be considered to be three basic indices to study the urban ecologicalenvironment and to contribute to further validation of the applicability ofrelatively low cost, moderate spatial resolution satellite imagery in evaluatingenvironmental impacts of urban land function zoning, then to examine theimpact of urban land-use on the urban environment in Shanghai City. Thisprovides an effective tool in evaluating the environmental influences of zoning inurban ecosystems with remote sensing and geographical information systems.

1. Introduction

Recent estimates indicate that over 45% of the world’s human population now livesin urban areas, with over 60% projected by 2030 (United Nations 1997). The globalrate of urbanization is expected to continue to accelerate in the near future, with theemergence of large urban agglomerations in developing countries (Berry 1990).As the size and number of urban agglomerations increase, so does the relativeimportance of the urban environment to the global population. Urban areas exertinfluences on the Earth system far disproportionate to their geographical extent

*Corresponding author. Email: jhxu@geo.ecnu.edu.cn

International Journal of Remote SensingVol. 28, No. 15, 10 August 2007, 3205–3226

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160500306906

and require special consideration in the context of a Digital Earth. Monitoringspatio-temporal changes in large urban/suburban areas will therefore becomeincreasingly important as the number and proportion of urban residents continue toincrease (Small 2001).

As the world’s cities continue to grow in both population and physical size, greaterconsideration of the manner in which rural lands are developed for urban andsuburban land use will become progressively more important (Vitousek et al. 1997).Removal of rural land-cover types, such as soil, water and vegetation, and theirreplacement with common urban materials, such as asphalt, concrete and metal, havesignificant environmental implications, including reduction in evapotranspiration,promotion of more rapid surface runoff, increased storage and transfer of sensibleheat, and reduction of air and water quality (Goward 1981; Owen et al. 1998; Wilsonet al. 2003). These changes, in turn, can have negative effects on landscape aesthetics,energy efficiency, human health and quality of living in urban environments(Mcpherson et al. 1997). These physical processes are difficult to monitor solely within situ instruments. Satellite-borne instruments can provide quantitative physical dataat high spatial or temporal resolutions. Visible and near-infrared remote sensingsystems have been used extensively to classify phenomena such as city growth, land-use/cover changes, vegetation index and population statistics.

Among the most widely studied consequences of converting rural land cover tourban and suburban development is the urban thermal environment, which ismanifested by higher surface and air temperatures within urban environments whencompared with surrounding rural areas (Landsberg 1981). The urban thermalenvironment is influenced by the physical characteristics of the land surface and byhuman socioeconomic activities. The thermal environment can be considered to bethe most important indicator for representing the urban environment. Vegetation isanother important component of the urban ecosystem that has been the subjectof much basic and applied research. Urban vegetation influences the physicalenvironment of cities through selective absorption and reflection of incidentradiation and regulation of latent and sensible heat exchange (Gallo et al. 1993,Carlson et al. 1994, Nichol 1996, Gillies et al. 1997, Owen et al. 1998). The presenceand abundance of vegetation in urban areas has long been recognized as a stronginfluence on energy demand and development of the urban heat island (Huang et al.1987). Urban vegetation abundance may also influence air quality and humanhealth. Changes in the built component of the urban environment are generallydocumented at various levels of detail but phenological changes in urban vegetationare not under direct human control and are not generally monitored over large areas(Wagrowski and Hites 1997).

Approaches to measuring the urban thermal environment include sampling of airand surface kinetic temperature at fixed locations, mobile sampling of air andsurface kinetic temperature from ground and airborne vehicles, and remotemeasurement of radiant temperature from airborne and space-borne platforms(Stone and Rodgers 2001). But remote sensing offers the most synoptic capabilities,at least for measurement of radiant temperature, because of the ability to collectmany samples over a broad area almost instantaneously. It can still be a useful toolfor gaining an overview of thermal variations within an urban ecosystem (Weng2003). As in the case of the urban thermal environment, remote sensing can be auseful tool for studying urban vegetation. In addition to providing measurements ofradiant surface temperature, remote sensing instruments collect measurements of

3206 W. Yue et al.

reflected energy in the red and near-infrared portions of the electromagneticspectrum that can be used to quantify the extent and changing conditions of urbanvegetation. The synoptic view of the urban mosaic provided by satellite and airbornesensors is an important complement to in situ measurements of physical,environmental and socioeconomic variables in urban settings (Small 2004).Compared with agricultural areas and sparsely populated regions, however,application of remotely sensed observations to studies of the urban environmenthas been rather limited. The Landsat ETM + sensor (Wilson 1988) is comparable tothe characteristic scale of urban land cover and is generally too coarse foridentification of individual structures. While this resolution has limited Landsat’suse for studies of the built urban environment, it may be sufficient to detectsignificant spatial and temporal variations in urban vegetation and surfacetemperature (Woodcock and Strahler 1987).

The objectives of this paper are (a) to present results of an analysis of therelationship between urban land surface temperature (LST) and NormalizedDifference Vegetation Index (NDVI) based on the texture of land use/cover; (b)to contribute to further validation of the applicability of relatively low cost,moderate spatial resolution satellite imagery in evaluating environmental impactsof urban land-use function zoning; and (c) to examine the impact of urbandevelopment on the urban environment in Shanghai City. The intention of thispaper is not to reiterate the well-established relationship between LST and NDVI,but rather to show how remote sensing technology can be used to obtain objectivemeasurements of the environmental influence of urban land-use zoning.

2. Data and methods

2.1 Study area

Shanghai is located on the east coast of China, with a latitude of 31u149 andlongitude of 121u299 and a population of 13.42 million and total area of 6340.5 km2.For more than 150 years, since ca 1850, Shanghai has been China’s biggest economiccentre (excluding Hong Kong). This pre-eminence reached its peak in the 1920s–1930s, when Shanghai became an important international city in East Asia withthriving global and local trade. Under Maoism (1949–1978) in China, it becamemore of an industrial production centre (Cai and Victor 2003). Its urbandevelopment has progressed at an unprecedented pace since the implementationof the economic reforms after 1978.

As shown in figure 1, within the research area, ten districts are considered to be thebuilt-up or core urban area, while six districts are considered to be the suburb, twomain natural rivers located within the Shanghai urban area are the Yangtze River andHuangpu River. The former is in the upper left of the research area and flows fromnorth-west to south-east. The latter, which flows from south to north, divides thewhole research area into two parts. The west of this river is called the Puxi area, whilethe east is called the Pudong area. Before the 1990s, the construction and developmentof Shanghai were focused mainly on the Puxi area. The land use/cover in the Puxi areaprogressed without a formal plan. Typically, most of the industries developed on thenorth coast of the research area or along Huangpu River for the convenience oftransportation. Since the early 1990s, urban planning has matured as the citydevelops. The east of the urban area, Pudong New District, has been undergoneaccelerated development. Some small factories were transplanted to the suburb, which

Land surface temperature and NDVI, Shanghai 3207

greatly changed the land use/cover of the total city. Like most Chinese cities, Shanghaiis experiencing a fast increase in both population and area. The urban sprawl ofShanghai occurs through encroachment into the adjacent agricultural and suburbanareas. Some decision-making factors have encouraged Metropolitan Shanghai tosprawl faster than other cities in China. Detecting the relationship between the urbanthermal environment and vegetation index and evaluating the environmentalinfluences of urban land use/cover are important elements in understanding,controlling and planning the urban ecosystem.

2.2 Satellite imagery pre-processing

A Landsat 7 ETM + image acquired in June 14, 2000 is served as the primary datasource for deriving LST and NDVI in the Shanghai urban ecosystem and as a basisfor analysis of the relationship between these variables under urban land-use/covercategories. The Landsat 7 satellite orbits the Earth at an altitude of ca 705 km,following a Sun-synchronous, near-polar path at an inclination of 98.22u from theEquator. This orbital pattern provides opportunities to collect imagery of urbanenvironments anywhere between 81u north and south latitudes. The temporalresolution of the sensor is 16 days. On the descending (N to S) daytime portion of anorbit, the satellite crosses the Equator at ca 10:00 a.m. local solar time, withincreasingly later local acquisition times north of the Equator. The Shanghai imageused in this research (centred at about 31.446uN, 121.536uE) was acquired atapproximately 10:30 a.m. The ETM + sensor is an imaging radiometer that collectsmeasurements of reflected and emitted energy from the Earth’s surface features ineight regions of the electromagnetic spectrum, ranging from visible to thermalinfrared at an 8-bit level. The information of these eight bands is summarized intable 1 (Landsat Project Science Office 2002).

Figure 1. Map of the study area.

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The data acquisition date has very clear atmospheric conditions and the imagewas acquired through the NRSCC National Remote Sensing Center of China,which corrected the radiometric and geometrical distortions of the images to aquality level of 1G before delivery. Homogeneous atmospheric conditions within theimage were assumed, so no atmospheric corrections were performed. The image wasthen further rectified to a Shanghai coordinate system (a modified TransverseMercator coordinate system), and was resampled using the nearest neighbouralgorithm with a pixel size of 60660m for all bands (the same as the thermal band).The resultant rms. error was found to be less than 0.5 pixels.

2.3 Derivation of LST

According to Gibbons and Wukelic (1989), the ETM + thermal band has a spatialresolution of 60m and a noise level equivalent to a temperature difference of 0.5uC.In addition, air temperature impacts differ diurnally between urban and rural areas(Yang et al. 1984). The local time of satellite overpass was in the morning (ca 10:30hours), so that the chance for detecting a weak Urban Heat Island (UHI) ismaximized. According to Professors Zhou (East China Normal University) andYang (South China Normal University), who have been studying the UHI ofShanghai and Guangzhou for decades using weather station and in situ data, theUHI air temperature difference peaked at 6.8uC in winter and at 4.8uC in summerduring 1984 in Shanghai (Zhou and Su 1994), the air temperature difference betweenthe urban and rural values peaks at 21:00 hours and goes down gradually until itreaches a minimum at 14:00 hours at Guangzhou (Yang et al. 1984). Since ETM +does not provide day and night infrared images of the same day, this research doesnot include the impact of the diurnal heating cycle on the urban heat islands (Weng2003).

Digital numbers (DN) in each band of the Level 1G ETM + image used in thisresearch were converted to physical measurements at sensor radiance (Ll) using aformula that accounts for the transformation function used to convert the analoguesignal received at the sensor to DN stored in the resulting image pixels(equation (1)):

Ll~gain|DNzoffset !1"

Where Ll is at sensor radiance, gain is slope of the radiance/DN conversionfunction, DN is digital number of a given pixel, and offset is the intercept of theradiance/DN conversion function (Landsat Project Science Office 2002). Gain and

Table 1. Characteristics of the Enhanced Thematic Mapper Plus (ETM + ) bands.

Band

Spatialresolution

(m)

Lowerlimit(mm)

Upperlimit(mm)

Bandwidth(nm)

Bitsperpixel Gain Offset

1 28.50 0.45 0.52 70 8 0.786274521 26.19999982 28.50 0.53 0.61 80 8 0.817254878 26.00000003 28.50 0.63 0.69 60 8 0.639607867 24.50000004 28.50 0.75 0.90 150 8 0.939215686 24.50000005 28.50 1.55 1.75 200 8 0.128470589 21.00000006 57.00 10.40 12.50 2100 8 0.066823533 0.000000007 28.50 2.10 2.35 250 8 0.044243138 20.34999998 14.25 0.52 0.90 380 8 0.786274521 26.1999998

Land surface temperature and NDVI, Shanghai 3209

offset values are supplied in metadata accompanying each ETM + image, and theDN to radiance formula (as with all formulae presented in this paper) can beimplemented easily using map algebra functions of a raster-based GeographicInformation System (GIS). ERDAS Imagine 8.5 was used as the primary imageprocessing and GIS tool in the current project.

The ETM + Band 6 image can then be converted from spectral radiance to aphysically useful variable. This is the effective at-satellite temperature of the viewedEarth-atmosphere system under an assumption of unity emissivity and using pre-launch calibration constants supplied by the Landsat Project Science Office (2002).The conversion formula is:

TB~K2= ln K1=Llz1! "! " !2"

Where TB is the radiant surface temperature (K), K2 is the calibration constant 2(1282.71), K1 is the calibration constant 1 (666.09), and Ll is the spectral radiance ofthermal band pixels. (Landsat Project Science Office 2002).

The temperature values obtained above are referenced to a black body. Therefore,corrections for spectral emissivity (e) become necessary according to the nature ofthe land cover. The emissivity of a surface is controlled by such factors as content,chemical composition, structure and roughness (Snyder et al. 1998). For vegetatedsurfaces, emissivity can vary significantly with plant species, areal density andgrowth stage (Snyder et al. 1998). In the mean time, emissivity is a function ofwavelength, commonly referred to as spectral emissivity (Weng et al. 2004).Estimation of emissivities for ground objects from passive sensor data has beenmeasured using different techniques. However, almost all methods need a land-cover classification. This paper pays more attention to the environmental influenceof urban land use, thus giving a land classification according to land-use function.The emissivity is then corrected only according to the nature of the land surfacevegetation cover. Vegetated areas were given a value of 0.95 and non-vegetatedareas 0.92 (Nichol 1994). The emissivity-corrected land surface temperatures (LST)were computed as follows (Artis and Carnahan 1982, Weng et al. 2004):

LST~TB

1z lTB=r! "lne!3"

where l is the wavelength of emitted radiance (for which the peak response andthe average of the limiting wavelengths (l511.5) will be used), r5h6c/s(1.43861022mK), s is Boltzmann’s constant (1.38610223 JK21), h is Planck’sconstant (6.626610234 Js), and c is the velocity of light (2.9986108m s21).

2.4 Derivation of NDVI

Since the urban thermal environment is related to the reduction of evapotranspira-tion from the surface vegetation cover, it is useful to recognize the relationshipbetween surface vegetation cover and water availability. Recent research has shownthat NDVI is a good indicator of surface radiant temperature (Lo et al. 1997).NDVI for the city of Shanghai is therefore computed from the Landsat ETM +image data using the following formula:

NDVI~Band 4# Band 3

Band 4zBand 3!4"

3210 W. Yue et al.

Where Band 3 is the red band (0.63–0.69 mm) and Band 4 is the near-infrared (0.63–0.69 mm) of the ETM + image data.

2.5 Land-use data and computation of landscape index

Urban land-use/cover data in the analysis are formatted polygon coverage by on-screen digitizing from high-resolution (,0.5m) aerial photography in 2000.According to the characteristics of urban land use/cover, combined with theeconomic, cultural and ecological function, this research divided urban land use/cover in Shanghai into ten general categories (table 2). The spatial distribution ofthese land-use/cover categories is described in figure 2.

In order to describe the spatial pattern quantitatively, a Landscape Ecology indexis introduced: Shannon’s Diversity Index (SHDI) (Turner and Gardner 1991).

SHDI~{Xm

i~1

Pilog2Pi !5"

Where Pi is the ratio of land-use/cover type i within the total area and m is thenumber of land-use types. From equation (5), SHDI will clearly depend onlandscape classification. In this paper, in order to disclose the environmentalinfluence of urban land-use types and the spatial pattern, the landscape is classifiedaccording to urban land-use function. Therefore, the SHDI in the research reflectsthe spatial pattern character of the urban landscape.

In order to disclose the difference in urban land-use spatial pattern and itsinfluence on environment, a 1 km61 km moving window is used to compute theSHDI within the total area, with the aid of FRAGSTAT Landscape Ecologysoftware, obtaining 600 SHDI samples. The relationship among LST, NDVI andSHDI within every moving window is then analysed.

Table 2. Land-use/cover key for Shanghai city, China.

Land-use type Basically includes:

Numberof

polygons

% of totalresearcharea

Industrial Light, heavy and warehouse 1503 18.67Transportation Highways, railways, main streets, air

field, bridges, parking lots, main ports75 7.40

Commercial andServices

Retailing, offices, government institutions,private institutions

1230 5.82

Residential Old high density dwellings, new styledwellings, villas and country dwellings

3833 27.29

Park and green land Park, green land 631 4.47Agriculture Cropland, orchard, livestock land etc. 1132 25.44Water bodies Rivers and streams, ponds and lakes,

wetland (no vegetation visible)492 5.37

University Variety of land-use types of highereducation institutions includingclassrooms, offices, dormitories,

gymnasiums and parking structures

32 0.65

Central businessdistrict (CBD)

Central business district –core activities ofall types with a variety of related land uses

12 0.90

Vacant or building Vacant land, on building land 660 3.98

Land surface temperature and NDVI, Shanghai 3211

3. Analysis and discussion

3.1 Visual interpretation of LST and NDVI contrasts

To improve understanding of the LST and NDVI pattern, the LST and NDVIimages of Shanghai are displayed simultaneously with the aid of ERDAS imageprocessing software. Figures 3(a) and (b) provide an informative visual depiction ofthe spatial pattern of the urban thermal environment and vegetation cover withinShanghai city. In the LST image of Shanghai, the LST of the urban centre is higherthan in the suburb. It is clear that higher temperature is associated with the urbanbuildings and major transportation arteries, composed of non-porous materials,such as metal, asphalt and concrete (Lo et al. 1997). In contrast, the temperature ofwater bodies, crop, park and green land is lower. Yangtze River in the north-easternpart of the research area shows a cooler region. The urban–rural contrast indicatesthe exit of urban heat islands. The values are the opposite in the NDVI image(figure 3(b)). Built-up or core urban areas have low values and the Yangtze andHuangpu Rivers also have low values due to the absence of vegetation. High valuesare detected within the areas of cropland, park and green land as a result ofrelatively high levels of green biomass.

In order to disclose the variance of LST and NDVI in different directions, thepixel values of LST and NDVI are derived based on W–E (figure 4(a)) and N–S(figure 4(b)) profiles.

The W–E profile starts with the suburb of Jiading district, through the OutlineRoad, Suzhou River, Changfeng Park, People Square, Huangpu River, PudongNew District, east Outline Road and ends at the East Sea. As for the LST, the effectof urban thermal environment can be detected obviously by the phenomenon that

Figure 2. Spatial pattern of land use/cover in Shanghai city.

3212 W. Yue et al.

LST values are relatively higher in the built-up or core urban area than in thesuburbs. As for NDVI, values in the built-up or core urban area are far lower thanin the suburbs except for some parks and green lands. Comparing LST with NDVI,one finds that the two have totally oppposing changing trends. The N–S profilebegins at the Yangze River, passed through the Donghai Dockyard, Jiangwan greenland, Fudan University, Huangpu River, Hunan (south of Shanghai) IndustryDistrict, south Outline Road and arrives at the suburb: Minhang District. The linebasically goes through the built-up or core urban area and suburbs, such as Lujiazuiand Minhang District. The effect of UHI and NDVI are also obvious in figure 4.

The two profiles show that the peaks of the LST are usually the areas whereindustries and manufacturing factories are located, while the troughs are mostlywhere the waters (rivers) and green lands (parks) are found. The peaks of NDVIappear at urban green land, park, university and cropland. Generally, LST and

Figure 3. (a) Land surface temperature (LST) image of the study region; (b) NormalizedDifference Vegetation Index (NDVI) image of the study region. Red lines represent N–S andW–E profiles across the research area.

Land surface temperature and NDVI, Shanghai 3213

NDVI show an obvious inverse correlation. On the other hand, the changing trendsof LST or NDVI are closely related to the urban shape and urban sprawl ofShanghai.

Figure 4. (a) W–E profiles across the research area and (b) LST and NDVI variance alongthe N–S profiles in figures 3(a) and (b).

3214 W. Yue et al.

3.2 Difference in mean LST and mean NDVI by land-use/cover types

To understand the relationship between LST and land use/cover better and, thus,vegetation abundance indicators (NDVI), the thermal environment and green spacesignature of each land-use/cover type must be investigated. A zonal summary GISoperation by superimposing land-use/cover image with images of LST and NDVI(figures 2, 3(a), (b)) is used to derive measurements of central tendency anddispersion of LST and NDVI associated with different land-use/cover types. Theresult of the GIS zonal statistics is shown in figure 5.

Figures 5(a) and (b) depict mean LST and NDVI values associated with differentland-use/cover types, with error bars representing ¡1 standard deviation of eachtype. From figure 5, as might be expected, the industrial land-use/cover type showsthe highest mean LST value because of a large amount of human-induced heat beingreleased and because of the close urban centre. Commercial or Services and CBDland-use/cover types also show higher LST, which, together with the industrial type,form one of the UHI effects. At the same time, the park or green land types exhibit

Figure 5. Mean values of (a) LST and (b) NDVI associated with land-use/cover type.

Land surface temperature and NDVI, Shanghai 3215

the highest mean NDVI value because of the predominance of trees, grass and othervegetated land-use/cover types. Greater vegetation cover in the park or green landtype suggests comparatively higher rates of evapotranspiration and favouring oflatent over sensible heat exchange between surface and atmosphere as comparedwith more developed places (Wilson et al. 2003). The lowest mean LST land-use/cover type, however, is neither park/green land nor water bodies. It appears in theagriculture type of land use/cover. This is because all the parks and green lands andmost of the water bodies are spread within the urban area and are highly affected bythe UHI and ambient land-use/cover types. Meanwhile, agriculture land-use/covertypes are mainly far from the urban centre, with a low influence from the UHI andwith relatively homogeneous land surface material, producing the lowest mean LST.As for mean NDVI, the industrial, transportation, Commercial or Services, CBDand water bodies land-use/cover types lack vegetation, which results in a low meanLST. More vegetation appears in the universities and vacant or building areas,leading to higher mean NDVI values.

From figure 5, it can be seen clearly that mean LSTs and mean NDVIs havedistinct differences with certain different land-use/cover types. For example, for LSTthere are the differences between the industrial, Commercial or Services land-use/cover types and the park and green land, agriculture and water bodies land-use/cover types; so as for NDVI. However, these results do not provide enoughinformation on which specific pairing(s) differ. To gain further insight, a series ofpost hoc multiple comparison tests using Tamhance’s T2 (a modified test for Studentt) were conducted to disclose the significance of differences in mean LST and NDVImeasurements associated with each possible pairing of land-use/cover types.

Tamhane’s T2 post hoc tests are similar to the Student’s t-test, but they decreasethe chance of Type I error for multiple tests by adjusting a (which represents asignificant level) as a function of sample size and number of tests completed. Thesetests are appropriate in situations where the variance and/or sample size of groups isunequal (Hochberg and Tamhane 1987). Table 3 shows results of post hoc testsassociated with bivariate combinations of land-use/cover types for LST and NDVI,respectively. Values with an asterisk indicate cases where the mean differenceexceeded the critical value at a50.0001, indicating a statistically significantdifference in mean values associated with a given pairing.

With the aid of the Statistical Package for the Social Sciences (SPSS) statisticalsoftware, the results of multiple comparisons of mean LST and NDVI associatedwith land-use/cover types are derived and displayed in table 3. Within the 45 possiblecombinations of all land-use/cover types, 29 pairings show significantly differentmean values of LST and 34 pairings exhibit significantly different mean NDVI.Within not-significant difference pairings, there are 16 pairings for mean LST, forexample, university and industrial, CBD and industrial, agriculture and transporta-tion, etc. Similarly, mean NDVI associated with the industrial is not significantlydifferent from transportation, university and CBD, while transportation is notsignificantly different from residential, university and vacant or building. Since theland surfaces of industrial, CBD, transportation and residential areas are composedmostly of non-porous material, such as asphalt, concrete and metal (Carlson 1986,Kim 1992), their mean LST and mean NDVI do not have significant difference atthe a50.0001 significant level. Except for water bodies, the mean LST valueassociated with agriculture is significantly different from other land use/cover types.The conclusion can be drawn that the differences in mean LSTs associated with all

3216 W. Yue et al.

Table 3. Tamhane’s T2 multiple comparison table of mean differences for land-use/cover types

Industrial TransportationCommercialand services Residential

Park orgreen land Agriculture

Waterbodies University CBD

(a) LSTTransportation 1.7489*Commercialand services

20.5131* 22.2621*

Residential 0.8206* 20.9284* 1.3337*Park or green land 0.4153* 21.3337* 0.9284* 20.4053*Agriculture 1.8802* 0.1313* 2.3934* 1.0596* 1.4650*Water bodies 1.7907* 0.0418 2.3038* 0.9701* 1.3754* 20.0895University 20.0347 21.7836* 0.4785 20.8552 20.4499 21.9149* 21.8254*CBD 20.5086 22.2575* 0.0046 21.3292 20.9238 22.3888* 22.2993* 20.4739Vacant orbuilding

0.6260* 21.1230* 1.1391* 20.1946 0.2107 21.2543* 21.1647* 0.6606 1.1345

(b) NDVITransportation 20.0361Commercialand services

0.0298* 0.0659*

Residential 20.0436* 20.0075 20.0734*Park or green land 20.1326* 20.0965* 20.1624* 20.0890*Agriculture 20.1405* 20.1044* 20.1703* 20.0969* 20.0079Water bodies 20.1023* 20.0661* 20.1320* 20.0586* 0.0304* 0.0383*University 20.0397 20.0036 20.0695* 0.0039 0.0929* 0.1008* 0.0625*CBD 0.0815 0.1176* 0.0517 0.1251* 0.2141* 0.2220* 0.1838* 0.1212*Vacant orbuilding

20.0391* 20.0030 20.0689* 0.0045 0.0935* 0.1014* 0.0631* 0.0006 20.1206*

Lan

dsurface

temperature

andNDVI,Shangha

i3217

land-use/cover types are large between vegetated and unvegetated surfaces; so it isbetween the urban centre and urban outer areas. For example, park or green landscattering in the urban centre contributes a lot to the reduction of the UHI effectand improving the urban thermal environment, while, except for park or green land,the mean NDVI value associated with agriculture was significantly different fromother land-use/cover types. This reveals that the NDVI between agriculture andpark/green land does not have a distinct difference. However, due to the spatiallocation (park or green land in the UHI centre while agriculture occurs in the UHIfringe) and the different influence from the UHI effect, the mean LSTs between thetwo land-use/cover types do have a distinct difference. The conclusion can be drawnthat the thermal radiation of the urban land surface is changed by the transportationof a quantity of heat. Meanwhile, among the land-use/cover types with similar landsurface characteristics, such as agriculture and park/green land, CBD andresidential, the NDVI differs a lot. The reasons for this can be explained via thedifferent land surface characteristics of the land use/covers and their different spatialpatterns. The relationship between different spatial pattern of land use/covers andLST, NDVI will be discussed in §3.4.

3.3 The relationship between LST and NDVI by land-use/cover types

In order to evaluate the influences of urban land use/cover accurately, a regressionanalysis between mean LST and mean NDVI associated with land-use/cover type ismade. There are a lot of references in the literature discussing the relationshipbetween temperature and NDVI. Weng et al. (2004), for example, discussed therelationship between LST and NDVI using seven land-use/cover types (commercial,industrial land, residential, cropland, grassland, pasture, forest and water) atdifferent scales; all the results exhibited significant inverse correlation. Lo et al.(1997), using high-resolution thermal infrared imagery, disclosed a strong negativecorrelation between NDVI and radiant temperature of residential, agricultural andvacant/transitional land-cover types in Huntsville, indicating that the irradiance of aland-cover type is influenced greatly by the amount of vegetation present. Sobrinoand Raissouni (2000) used the inverse relationship between NDVI and radiantsurface temperature measurements collected in multi-temporal Advanced Very HighResolution Radiometer (AVHRR) imagery for characterizing land-cover dynamicsin Morocco. Wilson et al. (2003) found that relationships between radianttemperature and NDVI were significant for the city as a whole and within allindividual zoning categories from the point of view of evaluating environmentalinfluences of zoning in urban ecosystems.

A lot of research has disclosed that temperature and NDVI values have a positivecorrelation associated with water bodies, such as rivers, lakes and ponds; theirvalues appear to be low. Therefore, excluding pixels that correspond to water bodieswill evidently increase the accuracy of regression for mean LST and NDVI values byland-use/cover types. The result of regression analysis between LST and NDVI forall polygons (excluding water bodies) in Shanghai is shown in figure 6. Each pointrepresents the mean LST and NDVI value linked to one land-use/cover polygon andthe black line is the result of linear simulation; regression function is also exhibited.Regression analysis result shows that there is significant inverse correlation betweenmean LST and NDVI values for the land-use/cover polygon. The correlationcoefficient is significant at the 0.01 level (2-tailed). It is suggested that morevegetation cover (greater NDVI) in some land-use/cover areas can cause

3218 W. Yue et al.

correspondingly higher rates of evapotranspiraton and promote the latent andsensible heat exchange between the land surface and atmosphere when comparedwith little vegetation cover, such as in further developed areas.

Moreover, regression analysis between LST and NDVI of every polygon was alsomade within land-use/cover types excluding water bodies. The results are shown intable 4. Where Y is mean LST associated with land-use/cover type, X is NDVIassociated with land-use/cover type. Besides the regression function, the number ofsamples in the regression of every land-use/cover type, the Pearson Correlationcoefficient and sigma are shown. The CBD, with only 12 samples, is not generallyrepresentative of the true CBD. The highest coefficient of regression functionappears in the agriculture area and the lowest in the residential area, i.e. withincreasing NDVI, the LST of the agriculture area decreases the slowest, while that ofthe residential area decreases more quickly. The correlation coefficient test was thenperformed. It can be seen that the correlation coefficient of the university area issignificant at the 0.05 level and the other land-use/cover types are significant at the0.01 level. There is no correlation at all for agriculture, because the recently plantedpaddy fields – the dominant form of agriculture – show a reflectance feature withwater and vegetation on 14 June, 2000.

The results of regression analysis exhibit that LST and NDVI have significantinverse correlation, so either Shanghai is taken as a whole research object (figure 6)including all polygons as samples (except for water bodies) or different land-use/cover types are taken as a research object. The comparison of coefficients ofregression function discloses that the LST of the residential, industrial, CBD,commercial and services and transportation areas are more fiercely affected by

Figure 6. Scatter plot linear regression of mean LST and NDVI by every land-use/coverpolygon.

Land surface temperature and NDVI, Shanghai 3219

NDVI. The distribution of green lands around these land-use/cover types mightreduce urban temperature and improve the urban thermal environment moreeffectively.

3.4 The feature space of LST, NDVI and SHDI

From the analysis above, it can be concluded that LST is related closely to thecharacteristics of the land surface. The abundance of vegetation on the land surfaceand LST have a significant inverse correlation. However, the different materials ofland surface result in the different radiation and transportation of quantity of heat.For different land-use/cover types, the number of the types, its proportion andspatial structure evidently affect urban LST. Therefore, the landscape index, SHDI,is introduced to analyse the relationship between diversity of urban land use/coverand LST and NDVI. SHDI is an integrated description of the proportion of land-use/cover type and area. Within a certain extent, the value of SHDI is determined bytwo factors, one is the number of land-use/cover types and the other is its evennessof composition (McGarigal and Marks 1995). That is to say, under the condition ofthe same extents, the more even the distribution of proportion of land-use/coverareas, the higher the SHDI. While under the condition of the same evenness, themore the types are within an extent, the higher is the SHDI. Thus, SHDI can beregarded as an integrated description of abundance of land-use/cover types andcomplexity of its proportion. On the other hand, SHDI is associated closely withland-use spatial pattern. Generally, the more complex the land-use/cover types, thegreater the fragmentations and, therefore, the higher the SHDI. As for Shanghai,since the land-use/cover type is mainly agriculture outside of the Outline Road andis relatively simple, the area inside the Outline Road was chosen as the area in whichto study the environmental effect of urban land use/cover. First, the SHDI ofdifferent areas in the land-use map was computed by 1 km61 km moving windows(Zhang et al. 2004), then zonal statistics of LSI and NDVI were carried out with thesame moving windows. Within the Outline Road, the total number of 1 km61 kmmoving windows is 649. The calculation mentioned above is done with FRAGSTATS

Table 4. Linear regression and correlation coefficients for the relationship between LST andNDVI by land-use/cover types.

Land-use typeRegressionfunctions n r R2 Sig.

CBD Y5211.562X + 300.41 12 20.7737 0.5986 0.00315018 **University Y524.073X + 301.3 29 20.4351 0.1893 0.018341941 *Industrial Y5214.324X + 300.86 1431 20.7226 0.5221 2.09876102231 **Park andgreen land

Y525.984X + 301.21 613 20.5242 0.2747 1.47339610244 **

Transportation Y529.589X + 299.76 55 20.5812 0.3378 3.2710161026 **Residential Y5215.036X + 300.66 3794 20.7753 0.6011 0 **Agriculture Y523.426X + 299.59 1108 20.1897 0.0360 1.96234610210 **Commercialand services

Y529.742X + 301.13 1202 20.6457 0.4169 1.01136102142 **

Vacant orbuilding

Y528.762X + 299.95 9581 20.6502 0.42270

**

*Correlation is significant at the 0.05 level (2-tailed).**Correlation is significant at the 0.01 level (2-tailed).

3220 W. Yue et al.

and ARCGIS software. The relationship between SHDI and mean LST and NDVIwas then analysed by SPSS and the results were tested.

Mean values of LST, NDVI and SHDI were calculated using 1 km61 km movingwindows, respectively. The results are displayed in figures 7(a)–(c). The valueincreases as the colour changes from green to red. The dark red line represents theOutline Road, while the dark blue line is the Inner Ring Road. The blue polygonrepresents the Huangpu River. Shanghai is considered to be the most influentialindustrial and commercial city in the neoteric history of China. The Puxi Districthas been developed for hundreds of years but Pudong New District began itsdevelopment in the early 1990s. There are still a lot of agricultural areas between theOutline Road and the Inner Ring Road. Therefore, the pattern of urbandevelopment is obviously anisomerous. In addition, as a newly developing district,the shape, construction materials and spatial pattern of Pudong New District differa lot from those of Puxi. The spatial pattern of this development and land-use/covertype is shown in figures 7(a)–(c).

From figure 7(a), it can be seen that UHIs are distributed mainly in the PuxiDistrict, especially the old residential areas and CBDs in the centre and industrialareas in the north and west. In Pudong New District, however, only a few windowswith high temperature along the Huangpu River can be detected. Figure 7(b),illustrating the spatial distribution of NDVI with moving windows, shows theinverse to figure 7(a). Windows with high NDVI mainly distribute in the areasbetween the Outline Road and the Inner Ring Road in the Pudong New District andnear the Outline Road in the north-west of the research area. Those areas with highNDVI are mainly agricultural areas. Within the urban area, windows with highNDVI values can be detected only in areas with abundant vegetation, such as NewJiangwan green land and Xujiahui Park. The distribution of SHDI is clearlydifferent from the above two cases. The windows with high SHDI are distributed intwo areas, one is around the urban CBD and the other is in the eastern and south-eastern parts, between the Outline Road and Inner Ring Road. The former has thehighest land-value and the most even combination of area of land-use/cover types.The latter is now the foreland of urban expansion and includes all of the land-use/cover types (Zhang et al. 2004).

In order to disclose the influence of the combination and distribution of land use/cover on LST quantifiably, regression analysis of mean LST and SHDI in differentmoving windows is made after eliminating the effect of spatial self-correlationthrough comparing the autocorrelation among different moving window sizes andselecting a perfect moving window size. Figure 8(a) shows every moving window andits corresponding SHDI and mean LST by scattered points. The black linerepresents the result of linear regression. From figure 8(a), the positive correlationbetween mean LST and SHDI can be detected by 1 km61 km moving windows.LST increases as SHDI increases and this quantitative relation can be seen fromfigure 8(b). By the significant test of correlation coefficient, R is significant at the0.01 levels. This reveals that, in addition to the land surface materials, thecombination and spatial distribution of land-use/cover types also affect the valueand spatial pattern of LST. In contrast to the influence of NDVI, the more land-use/cover types and the more even the distribution, the higher the SHDI and LST, i.e.those areas experiencing a greater human influence have relatively higher LSTs andlower NDVIs. However, it should be noted that the conclusion is drawn to a certainextent and it may differ with different extents.

Land surface temperature and NDVI, Shanghai 3221

Figure 7. Spatial pattern of (a) mean LST, (b) mean NDVI and (c) mean SHDI by1 km61 km moving windows within the Outline Road of Shanghai city.

3222 W. Yue et al.

Furthermore, an analysis of SHDI andNDVIwasmade. Since NDVI is determinedby the abundance of vegetation and is influenced less by ambient land-use/cover types,the correlation between NDVI and SHDI is weak. R (the correlation coefficient) is20.3555 (table 5). The result of regression analysis is shown in figure 8(b). Itscorrelation is obviously weaker than that of SHDI and LST. Only the agriculture andparks/green lands have high NDVI. Thus, the higher the SHDI, the lower the NDVI.As a whole, SHDI and NDVI show an inverse correlation by 1 km61 km movingwindows and this correlation is significant at the 0.01 level.

Figure 8. Scatter plot linear regression of (a) mean LST and SHDI and (b) mean NDVI andSHDI by every moving window.

Land surface temperature and NDVI, Shanghai 3223

4. Conclusions

Within the Shanghai urban ecosystem, except for some parks with high NDVIvalues, the built-up or core urban areas have relatively low NDVI values and littlevegetation. Some values can even be detected in the centre of the urban area. Thespatial distribution of NDVI is opposite to that of LST. In the analysis of differentland-use/cover types, the results indicate that different land-use/cover types havesignificantly different effects on LST and NDVI as measured by the ETM + sensorin the Shanghai urban ecosystem. Comparisons of mean LST and NDVI values byindividual pairings of land-use/cover types also suggest that the majority can bedifferentiated on the basis of both LST and NDVI. Regarding the total polygons ofland-use/cover types in the research area, the mean LST and NDVI within everypolygon show a clear inverse correlation. This correlation also exists within everyland-use/cover type. In certain land-use/cover types, the correlation coefficient ismore significant than that of the total polygon level. The results reveal that theurban LST is affected mainly by the land surface materials and has a close relationto the abundance of vegetation. The spatial distance from the UHI centre is anotherimportant factor influencing the LST in some areas.

LST and SHDI have obvious positive correlation by 1 km61 km movingwindows and LST increases as SHDI increases. In contrast, NDVI and SHDIshow an inverse correlation and this correlativity is obviously weaker than that ofSHDI and LST. The conclusion can be drawn that the urban ecosystem of Shanghaidowntown, with a strong human influence, has relatively higher LST and lowerNDVI. From the analysis above, LST, SHDI and NDVI are considered to be threebasic indices to estimate the urban ecological environment and there existscorrelativity among the three indices. Hierarchies of the urban ecosystem can beevaluated by the spatial characteristics of SHDI–LST–NDVI. This provides aneffective tool in evaluating environmental influences of zoning in urban ecosystemsusing remote sensing and GIS.

AcknowledgementThe authors wish to thank the anonymous reviewers for their constructivecomments and suggestions. Further acknowledgement goes to the NationalNatural Science Foundation, China for financial support (Grant No. 40171069).

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Table 5. Correlation coefficients between SHDI and LST or NDVI by each 1 km61 kmmoving window.

Correlation coefficient test

LST NDVI

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N 649 649

**Correlation is significant at the 0.01 level (2-tailed).

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