Dynamics of Land Surface Temperature in Response to Land-Use/Cover Change

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<ul><li><p>Dynamics of Land Surface Temperature inResponse to Land-Use/Cover Changegeor_686 23..36</p><p>XIAOLU ZHOU1* and YI-CHEN WANG21Department of Geography, National University of Singapore, 1 Arts Link, AS2 #03-01, Singapore,117570.2Department of Geography, National University of Singapore, 1 Arts Link, AS2 #03-05, Singapore,117570.*Corresponding author. Email: g0800627@nus.edu.sg; xiaoluzhou86@gmail.com</p><p>Received 21 July 2010; Revised 10 October 2010; Accepted 14 November 2010</p><p>AbstractIn this study, we employed Geographical Information Systems and remotesensing techniques to investigate the impact of land-use/cover change on landsurface temperature (LST) in a rapidly urbanisation city, Kunming in south-west China. Spatial patterns of LST and land use for 1992 and 2006 werederived from Landsat images to examine how LST responded to urban growth.Remote sensing indices were used to quantify land-use types and employed asexplanatory variables in LST modelling. The geographically weighted regres-sion (GWR), a location dependent model, was performed to explore the influ-ences of the spatially varied land-use conditions on the LST patterns. Resultsrevealed that rapid urbanisation in Kunming altered the local thermal environ-ment, particularly in increasing the LST in the zone surrounding the urban core.Remote sensing indices demonstrated that water and vegetation played animportant role in mitigating the urban heat island effect, while built-up andbarren land accounted for the increase in LST. The GWR improved thegoodness-of-fit for LST modelling and provided insights into the spatiallyvaried relationship between LST and land-use conditions.</p><p>KEY WORDS land-use/cover change; urban heat island; remote sensing; geo-graphical information systems; geographically weighted regression; Kunming</p><p>IntroductionRapid population growth and continuous exploi-tation of natural resources during the pastcentury have caused land-use/cover change(LUCC) worldwide. Urbanisation is occurring inmany countries, increasing the concentration ofpopulation in cities. The rate of urbanisation insome developing countries is remarkable overrecent decades because of the pursuit of fast eco-nomic development (Li et al., 2009). In China,the launch of economic reforms since the late1970s has largely accelerated the urbanisationprocess (Luo and Wei, 2009).</p><p>The process of urbanisation often replacesnatural vegetation and agricultural land withimpervious surfaces, such as buildings and roads(Thi Van and Xuan Bao, 2010). This trend hasproduced a series of environmental impacts onbiodiversity, local climate, hydrologic processes,and so forth (Streutker, 2002; Roy et al., 2009).Of these impacts, the urban heat island (UHI),the phenomenon where temperatures in urbancores are higher than they are in surroundingrural areas (Voogt and Oke, 2003), is commonlyassociated with cities. Because each land-use/cover type has its unique thermal, moisture, and</p><p>23Geographical Research February 2011 49(1):2336doi: 10.1111/j.1745-5871.2010.00686.x</p></li><li><p>optical spectral properties, LUCC will affectthe local thermal environment (Oke, 1982). Forexample, the expansion of the impervioussurface area alters the heat capacity and radiativeproperties of the land surface, largely reducingthe evapotranspiration in urban areas (Streutker,2002).</p><p>Prior studies on urban thermal environmentshave been based on air temperature obtainedfrom weather stations (Li et al., 2009). Althoughthese in situ data can provide accurate local tem-perature, they are normally costly for large-scaleanalyses and subject to private or governmentalrestrictions (Owen et al., 1998). Furthermore,data from weather stations are discrete points inspace, which hardly reflect the spatial variationof temperature caused by different land-use/cover types. An alternative to measuring urbanthermal environment is using land surface tem-perature (LST) because it is able to modulate theair temperature of the layer immediately abovethe earth surface and is a major parameterassociated with surface radiation and energyexchange (Voogt and Oke, 1998; Weng, 2009). Avariety of satellite sensors that capture thermalinfrared information have been available, such asLandsat Thematic Mapper (TM) and EnhancedThematic Mapper Plus, and Moderate-resolutionImaging Spectroradiometer, facilitating the in-vestigation of LST in high spatial and temporalresolutions.</p><p>Studies have investigated the relationshipbetween LST and LUCC (Chen et al., 2006;Xiao and Weng, 2007; Thi Van and Xuan Bao,2010) with two main foci. The first focus hasbeen on the comparison of the LST of differentland-use conditions. Multispectral techniqueshave been used because thermal and land-useinformation can be obtained simultaneously froma single sensor (Voogt and Oke, 2003). Analy-tical functions of Geographical InformationSystems (GIS), such as spatial overlay and imagedifferencing, coupled with biophysical para-meters of surface temperature and emissivityderived from remotely sensed images, haveeffectively unveiled the impacts of LUCCon thermal environment change (Chen et al.,2006).</p><p>The second focus has been on modelling LSTbased on remote sensing indices. Instead ofdirectly using land-use types that are categoricaldata, remote sensing indices are employed toquantitatively represent land-use/cover types.For example, the Normalized Difference Vegeta-tion Index (NDVI) has been used to validate the</p><p>role of green space in mitigating UHI (Yuanand Bauer, 2007). The Normalized DifferenceBuilt-up Index (NDBI) (Zha et al., 2003) and theNormalized Difference Water Index (NDWI)(Gao, 1996) have been employed to representurban and water areas quantitatively. Althoughprior studies have adopted these indices to modelLST (Chen et al., 2006), only a few of them havecompared the modelling results of differentyears.</p><p>When modelling the LST, global regressionmodels, such as ordinary least squares regression(OLS), are commonly developed to estimate LSTbased on explanatory variables (Weng et al.,2004; Chen et al., 2006). These global modelsassume that the relationships between LSTand explanatory variables are spatially constant(Bagheri et al., 2009). However, the explanatoryvariables, such as land-use/cover types and theirchanges, often have various effects on LSTacross the space. Research is thus needed thatemploys localised statistical models in analysingthe relationship between LST and LUCC.</p><p>In this study, the effect of LUCC on LST in arapidly urbanisation city, Kunming, China, wasinvestigated. LST was derived from satelliteimages, and the change of the LST pattern inresponse to urbanisation was explored using GISand remote sensing techniques. Three remotesensing indices were compared in terms oftheir effectiveness in LST estimation. Effects ofspatial variation of land-use/cover distribution onLST were examined by comparing the OLS, aglobal model, with the geographically weightedregression (GWR), a location dependent method.Specifically, the study addressed three questions.First, how has urbanisation affected the localthermal environment? Second, how effective aredifferent remote sensing indices in LST estima-tion? Third, does the location dependent GWRmethod perform better in LST modelling than theOLS method?</p><p>Study areaThe study area, Kunming, is located from10210 to 10340E and 2423 to 2633Nin the north-central Yunnan province, China(Figure 1). It is situated on a plateau with eleva-tion ranging from 1500 m to 2800 m. The met-ropolitan area of Kunming is located in theDianchi basin with an elevation of 1890 m, sur-rounded by mountains to the north, east, andwest. The city is characterised by a subtropicalhighland climate with warm and humid summersand cold and dry winters. Mean annual precipi-</p><p>24 Geographical Research February 2011 49(1):2336</p><p> 2010 The AuthorsGeographical Research 2010 Institute of Australian Geographers</p></li><li><p>tation is around 1000 mm; mean annual sunexposure is about 2250 hours.</p><p>Kunming is the capital ofYunnan province andhas experienced rapid development in the lasttwo decades. The gross domestic product (GDP)increased from 16.9 billion ChineseYuan in 1992to 56.2 billion in 1998. From 1998 to 2008, theGDP nearly tripled, attaining 160.5 billionChinese Yuan (KBS, 2009). The fast economicgrowth is accompanied by rapid urbanisation,causing a great loss of green space in the subur-ban areas and an increase of the urbanised areafrom 184.4 km2 in 1992 to 257.8 km2 in 2005(Cai, 2007). The study area is within metropoli-tan Kunming, an area of 538.9 km2, consisting ofurban area (dominated by impervious land),urban fringe (combination of impervious landand agriculture land), and suburban area (domi-nated by forest land).</p><p>Materials and methods</p><p>DataRemotely sensed images and field survey datawere used in this study. The remotely sensedimages used were Landsat 5 TM images obtained</p><p>from the US Geological Survey website withstandard systematic corrections. Because of dataquality, availability, and comparability, twoscenes of images, dated 16 August 1992 and 19May 2006, were used in this study. The opticalbands of TM images (bands 15 and 7) havea spatial resolution of 30 m and the thermalband (band 6) has a spatial resolution of 120 m.Field survey data consisted of ground controlpoints collected using Global PositioningSystems (GPS) for geo-referencing the two TMimages and photographs for assisting in imageinterpretation.</p><p>Image preparationFigure 2 is the flow chart of the analysis. Forimage preparation, the GPS ground controlpoints were used to geo-reference and rectifythe two TM images to the Universal TransverseMercator coordinate system zone 48, using theWGS84 datum. The nearest neighbour methodwas employed to resample all the bands into30 m. The root mean square error was controlledwithin 0.5 pixels for these two images. Toremove the effect of cloud on land-use classifi-cation and LST derivation, cloud cover, which</p><p>Figure 1 Location of the study area, the Kunming metropolitan area, in Yunnan Province, China.</p><p>X.L. Zhou and Y.-C. Wang: Dynamics of Land Surface Temperature 25</p><p> 2010 The AuthorsGeographical Research 2010 Institute of Australian Geographers</p></li><li><p>accounted for less than 1% of the images, wasmasked out from the analysis. Atmospheric cor-rection was carried out in ENVI 4.6 using theFast Line-of-sight Atmospheric Analysis ofSpectral Hypercubes (FLAASH) atmosphericcorrection module to convert digital numbers(DN) into reflectance. FLAASH is an atmo-spheric correction tool based on the MODTRANradiation transfer codes and algorithms by whicha unique model can be computed for each image(ENVI, 2009). The process filters the interfer-ence from the path radiation, such as aerosol,dust particle, and water vapour.</p><p>Image classificationThe two TM images were classified into fiveland-use types, including built-up land, water,barren land, forest, and agriculture land, for theassessment of LST change in response to urbani-sation. This classification system was feasibleconsidering the medium resolution satelliteimages used. In addition, it conformed to theChinese land-use system (SAC, 2007), and aclassification system used in prior researchwhose study area shared the same characteristicsas Kunming city (Xiao and Weng, 2007).</p><p>The maximum likelihood classifier wasemployed. Statistics of training samples wereplotted to ensure the separability of differentclasses and comparability between two images(Jensen, 2005). Accuracy assessment was per-formed using the stratified random samplingmethod. Field survey assisted by photographsand knowledge of the area were used as refer-ences in the assessment. The overall accuracy forthe images of 1992 and 2006 was 83.7% and80.8%, respectively. The Kappa coefficients forboth images were above 0.78.</p><p>Derivation of LSTThe DN values of both Landsat images wereconverted into spectral radiance based on the fol-lowing formula:</p><p>Radiance gain DN offset= + (1)Where, gain and offset were derived from thehead files of images and the Landsat currentradiometric calibration coefficients (Chanderet al., 2009).</p><p>The retrieved radiance was converted intoat-satellite brightness temperature (BT) based</p><p>Landsatimage in</p><p>1992</p><p>Image preparation</p><p>Optical bands (15, 7) Thermal band (6)</p><p>Landsatimage in</p><p>2006</p><p>Classified images</p><p>Change detection</p><p>Spatial overlay (a)</p><p>Spatial overlay (b) Correlation andregression</p><p>Univariate regression</p><p>Multivariate regression</p><p>Derivation of remotesensing indices</p><p>Derivation of BT</p><p>Derivation of LST</p><p>OLS GWR</p><p>Figure 2 Flow chart of the analysis. Spatial overlay (a) is conducted using the land-use layer and the LST layer, while spatialoverlay (b) uses the land-use change layer and the LST layer. BT, brightness temperature; GWR, geographically weightedregression; LST, land surface temperature; OLS, ordinary least squares regression.</p><p>26 Geographical Research February 2011 49(1):2336</p><p> 2010 The AuthorsGeographical Research 2010 Institute of Australian Geographers</p></li><li><p>on the following equation, assuming that landcover had the same emissivity (Weng and Lu,2008):</p><p>T KK Lb</p><p>=</p><p>+( )2</p><p>11ln (2)</p><p>Where Tb is the at-satellite BT in Kelvin (K);Ll is the spectral radiance in Wm-2 sr-1 mm-1;K1 and K2 are the calibration constantswith K1 = 607.76 Wm-2 sr-1 mm-1 and K2 =1260.56 K.</p><p>Because the focus of the study, like most of theprior work (e.g. Weng et al., 2004; Chen et al.,2006; Li et al., 2009), was on the spatialvariation of surface temperature, the followingmethods were employed. To retrieve the LST, theat-satellite BT (Tb) needs to be corrected accord-ing to the real object properties. Therefore, theemissivity corrected surface temperature wascomputed following Artis and Carnahan (1982):</p><p>T TTs</p><p>b</p><p>b=</p><p>+ ( )1 ln (3)</p><p>Where Ts is the LST in K; l is the wavelength ofemitted radiance (l = 11.5 mm) (Markham andBarker, 1985); a equals 1.438 10-2 mK, calcu-lated as a = hc/s, with h as the Planck constant(6.626 10-34 Js), c as the velocity of light (2.998 108 m s-1), and s as the Boltzmann constant(1.38 10-23 J/K); e is the surface emissivityderived from NDVI (Artis and Carnahan, 1982;Roerink et al., 2000).</p><p>Derivation of remote sensing indicesThree remote sensing indices, NDVI, NDBI, andModified Normalized Difference Water Index(MNDWI) (Xu, 2006), were computed to char-acterise the land-use types of this study. TheNDVI is an index widely used to describe thegreenness of an area (Chen and Brutsaert, 1998),and is used to represent the extent of forest andagricultural land. The NDBI is an indicator ofurban areas, which can reveal the built-up andbarren land (Zha et al., 2003; Chen et al., 2006).The MNDWI is selected to represent water areasbecause water areas often reveal remarkabledifferences in thermal characteristics when mod-elling the urban thermal environment, and com-pared with NDWI, MNDWI can remove thebuilt-up land noise on water in the urban areas(Xu, 2008). These three indices are calculated asfollows:</p><p>NDVI R RR R</p><p>NIR RED</p><p>NIR RED=</p><p>+(4)</p><p>NDBI R RR R</p><p>MIR NIR</p><p>MIR NIR=</p><p>+(5)</p><p>MNDWI R RR R</p><p>GREEN MIR</p><p>GREEN MIR=</p><p>+(6)</p><p>Wher...</p></li></ul>

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