monitoring land surface temperature relationship to land use/land cover from satellite imagery in...

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Monitoring land surface temperaturerelationship to land use/land coverfrom satellite imagery in MaraqehCounty, IranBakhtiar Feizizadeh a b , Thomas Blaschke b , Hossein Nazmfar c ,Elahe Akbari a d & Hamid Reza Kohbanani aa Department of Physical Geography, Center for Remote Sensingand GIS , University of Tabriz , Iranb Department of Geoinformatics , University of Salzburg , Austriac Department of Geography and Urban Planning , University ofMohaghegh Ardabili , Irand Department of Physical Geography , University of HakimSabzevari , IranPublished online: 17 Oct 2012.

To cite this article: Bakhtiar Feizizadeh , Thomas Blaschke , Hossein Nazmfar , Elahe Akbari& Hamid Reza Kohbanani , Journal of Environmental Planning and Management (2012):Monitoring land surface temperature relationship to land use/land cover from satelliteimagery in Maraqeh County, Iran, Journal of Environmental Planning and Management, DOI:10.1080/09640568.2012.717888

To link to this article: http://dx.doi.org/10.1080/09640568.2012.717888

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Monitoring land surface temperature relationship to land use/land cover

from satellite imagery in Maraqeh County, Iran

Bakhtiar Feizizadeha,b*, Thomas Blaschkeb, Hossein Nazmfarc, Elahe Akbaria,d andHamid Reza Kohbanania

aDepartment of Physical Geography, Center for Remote Sensing and GIS, University of Tabriz,Iran; bDepartment of Geoinformatics, University of Salzburg, Austria; cDepartment of

Geography and Urban Planning, University of Mohaghegh Ardabili, Iran; dDepartment ofPhysical Geography, University of Hakim Sabzevari, Iran

(Received 15 January 2012; final version received 30 July 2012)

North-western Iran experiences high surface temperatures – a situation that islikely to become increasingly severe due to both climate change and thegrowing area of sealed surfaces as a result of socio-economic development.Land surface temperature (LST) is a key parameter with respect to land useand land cover (LULC). In this study, the Surface Energy Balance Algorithmfor Land (SEBAL) method has been applied to Landsat Enhanced ThematicMapper (ETMþ) imagery for Maraqeh County in north-western Iran (EastAzerbaijan Province), in order to model the spatial variation of LST and todetermine its quantitative relationship with LULC. The LST was found to below for orchards and water bodies, while pasture lands and areas currentlyunder cultivation had moderate LSTs. The results suggest that LST can besignificantly increased by urbanisation, desertification, and any other processesthat result in an increase in non-vegetated surfaces. High LST values werefound to be associated with rural and urban settlements, and also with severalbare areas of exposed soil, while the maximum LST values were associatedwith areas of rock outcrop. Our results indicate that LST has an inverserelationship with moisture content and biomass.

Keywords: land surface temperature; SEBAL; land use/land cover; Landsat;Maraqeh County

1. Introduction

To date, the effects of global warming in northern Iran have not been the subject ofintensive study, but awareness is gradually increasing in the areas of policy anddecision making. On a global scale, it is widely acknowledged that land use/coverchanges, the expansion of urban and agricultural areas, and deforestation are majortriggers for changes to regional and local temperature regimes. Urban areas and, inparticular, large metropolitan areas, generally have higher temperatures than theirsurroundings as a result of the greenhouse effect due to increasing use of fossil fuelsfor transportation, commerce, manufacturing and household purposes, and alsobecause of the large areas of sealed surfaces.

*Corresponding author. Email: [email protected]

Journal of Environmental Planning and Management

2012, 1–26, iFirst article

ISSN 0964-0568 print/ISSN 1360-0559 online

� 2012 University of Newcastle upon Tyne

http://dx.doi.org/10.1080/09640568.2012.717888

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Land surface temperature (LST) is an important factor in studies of globalchange and heat balance, and may serve as a proxy for climate change (Srivastavaet al. 2009). It is an important factor in terms of the controlling of most physical,chemical and biological processes of the Earth (Alavipanah et al. 2007). Anunderstanding of LSTs is important to a range of issues in the Earth sciences that arecentral to urban climatology, global environmental change and human-environmentinteractions (Mallick et al. 2008). Various types of land use have been found to affectthe LST and may be used as possible indicators of LST trends (Weng et al. 2007,Dontree 2010). LST is one of the key parameters in the physics of land-surfaceprocesses on regional and global scales, combining the results of all interactions andenergy fluxes between the atmosphere and the ground (Mannstein 1987, Sellers et al.1988). LST is controlled by the surface energy balance and the state of theatmosphere, as well as the thermal properties of the surface and subsurface, and is animportant parameter in many environmental models (Becker and Li 1990, Srivastavaet al. 2009) (see http://www.sciencedirect.com/science/article/pii/S0273117709000714 - aff1).

Research into LSTs shows that this partitioning of heat fluxes, and thussurface energy response, is a function of varying surface soil water content andvegetation cover (Owen et al. 1998). For non-vegetated areas, LST measurementstypically represent the radiometric temperatures of sunlit non-vegetated surfaces,such as bare soil. As the amount of vegetation cover increases, the radiativetemperature recorded by a sensor approximates more closely to the temperaturesof green leaves, and the canopy temperature at spectral vegetation maximum orcomplete canopy cover (Goward et al. 2002, Weng and Lu 2008). In the absenceof a dense network of land-based meteorological stations, the spatio-temporaldistribution of LSTs from remote sensing imagery can be used as a parameter tosupport sustainable management, including water resource management andlandscape planning, as well as in-depth agriculture and agro-environmentalstudies. One of the most important potential applications of the LST retrievedfrom satellite data is to validate and improve the global meteorological modelprediction after appropriate aggregation and parameterisation (Price 1982, Diakand Whipple 1993). Besides its necessity in the LST retrieval, the surfaceemissivity can be used to discriminate senescent vegetation (French et al. 2000).LST derived from remote sensing imagery has been used in LULC analysis(Ehrlich and Lambin 1996, Lambin and Ehrlich 1997). LST can be also used tomonitor drought and estimate surface soil moisture (Feldhake et al. 1996,McVicar and Jupp 1998), to evaluate water requirements of wheat (Jackson et al.1977), and to determine frosts in orange groves (Caselles and Sobrino 1989, Wanet al. 2002).

However, ground measurements of LTS over a range of space and time scales arevery difficult to obtain due to the time and cost involved (Hong et al. 2005). Incomplex terrain such as the Sahand Mountain area of Iran, meteorological stationsand ground surveys are usually sparsely or irregularly distributed, and also tend tobe biased since they often favour agricultural areas. The application of traditionalgeospatial interpolation methods in complex terrains remains challenging (Steina-cker et al. 2006, Neteler 2010). However, remote sensing data have become morewidely available and the spatial distribution of energy balance components such assurface temperature and albedo can be analysed (Taschner and Ranzi 2002, Ranziet al. 2004, Mihalcea et al. 2008). The thermal remote sensing satellite image

2 B. Feizizadeh et al.

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processing would allow the relationship between LST and biophysical descriptors tobe examined (Feizizadeh and Blaschke 2012b). The LST is the energy balance of landsurface energy and has been extensively studied with the technology development ofeffective sensors for thermal remote sensing (Lv and Zhuo 2011). LST is a keyboundary condition in many remote sensing-based land surface modelling schemes(Wan et al. 2002). Progress in remote sensing technology and multi-sensor datasets,higher resolution and new image processing techniques have recently improved theaccuracy of satellite thermal sensing, allowing LST evaluation (Fuqin Li et al. 2004).A number of studies have attempted to estimate LTS from different satellite sensors,including Landsat Enhanced Thematic Mapper Plus (Landsat ETMþ) (Bastiaanssenet al. 2005, Hendrickx and Hong 2005, Allen et al. 2007, Hong 2008), the AdvancedSpaceborne Thermal Emission and Reflection Radiometer (ASTER) (French et al.2002), the Advanced Very High Resolution Radiometer (AVHRR) (Seguin et al.1991), the Moderate Resolution Imaging Spectroradiometer (MODIS), (Nishidaet al. 2003, Hong et al. 2005) or the Geostationary Orbiting Environmental Satellite(GOES) (Mecikalski et al. 1999, Hong 2008). Thermal infrared (TIR) remote sensingis the only possible approach to retrieve LSTs over large portions of the Earth’ssurface at different spatial resolutions and periodicities (Coll et al. 2005) over largeportions of the Earth’s surface at different spatial resolution and periodicities.Several factors need to be quantified in order to retrieve LST from satellite TIR data,such as sensor radiometric calibrations (Wukelic et al. 1989), atmospheric correction(Cooper and Asrar 1989), surface emissivity correction (Norman et al. 1990),characterisation of spatial variability over land cover, and the combined effects ofviewing geometry, background and fractional vegetative cover (Srivastava et al.2009).

LST observations acquired by remote sensing technologies have been used todevelop models of land surface-atmosphere exchange and to analyse therelationship between temperature and LULC (Voogt and Oke 2003, Amiriet al. 2009). Some studies investigated the effect of biophysical factors on LST bymaking use of fundamental surface descriptors, such as vegetation fraction,instead of qualitative LULC classes (Gallo and Tarpley 1996, Owen et al. 1998,Dousset and Gourmelon 2003). The vegetation index-LST relationship has beenused by Carlson et al. (1994) to retrieve surface biophysical parameters, byKustas et al. (2003) to extract sub-pixel thermal variations, and by Lambin andEhrlich (1996) to analyse land cover dynamics. Many investigators have observeda negative relationship between vegetation and LST. This finding stimulatedfurther research into two major pathways, namely, statistical analysis of therelationship and the temperature/vegetation index (TVX) approach. TVX, bydefinition, is a multi-spectral method of combining LST and a vegetation index(VI) in a scatterplot to observe their associations (Quattrochi and Luvall 2004,Amiri et al. 2009).

The objectives of this research were (1) to calculate LSTs for the investigated areain north-western Iran from Landsat ETMþ imagery, and (2) to analyse the resultingtemporal and spatial variations and their relationships to LULC. A statisticalmethod was used based on thermal infrared imagery, in combination with near-infrared and visible ETMþ imagery, and with in situ data, using image processingtechniques. For this purpose, we employed the Surface Energy Balance Algorithmfor Land, which is one of several remote sensing algorithms used to extract thermalinformation from satellite data.

Journal of Environmental Planning and Management 3

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2. Case study area

The study area was Maraqeh County (whose capital is Maraqeh City: 378230 N,468160 E), which is located in the East Azerbaijan Province of north-western Iran (seeFigure 1). This county has an area of 2192 km2; it includes four cities and 178 villagesand has an overall population of about 635,000 (Iranian Census Center 2007).Elevations range from 1320 to 3700 m above sea level. Maraqeh County experienceswarm summers and cold winters, and has an average annual temperature of 128C. It hasan annual precipitation of 310 mm, which falls mostly in winter and spring. Because ofthe semi-arid climate, bare soil is commonly exposed in between the sparse vegetation.Dense vegetation cover is restricted to plains where surface water or groundwater isavailable, which are commonly agricultural areas. The study area is characterised bydifferent types of soil and topography that makes it suitable for a variety of land usesand typical rural economic activities. Fruit tree cultivation is a particularly importantagricultural activity in the East Azerbaijan Province of Iran, while dry-farming isdominant on those slopes of the Sahand Mountain that are suitable. The increasingpopulation has, however, led to a high demand for agricultural products that can nolonger be satisfied by the relatively extensive dry-farming practices.

3. Materials and methods

3.1. SEBAL

Surface Energy Balance Algorithm for Land (SEBAL) is a physically basedanalytical image processing method that evaluates the components of the energybalance and determines the LST as the residual. SEBAL is based on the computationof energy balance parameters from multi-spectral satellite data (Bastiaanssen et al.1998, Morse et al. 2000, Allen et al. 2007, Hong, 2008). ‘‘SEBAL is a single-sourcemodel requiring minimal amount of ancillary data’’ (Gowda et al. 2008, p. 4). LSTretrieval uses the thermal bands of TM/ETMþ data. The heat fluxes were estimatedusing the SEBAL model to facilitate the application of the radiance transfer equation(Qin et al. 2001) to Landsat satellite images. To implement SEBAL, images areneeded with information on reflectance in the visible, near infrared and mid-infraredbands, as well as emissions in the thermal infrared band (Hong 2008). The energybalance equation is as follows:

Rn� G�H ¼ lET ð1Þ

where Rn is the net incoming radiation flux density (Wm), G is the ground heat fluxdensity (Wm72), H is the sensible heat flux density (Wm72), lET is the latent heatflux density (Wm). The net radiation (Rn) was computed for each pixel from theradiation balance using surface albedo obtained from short-wave radiation andusing emissivity estimated from the long-wave radiation (Allen et al. 1998,Bastiaanssen et al. 1998, Morse et al. 2000). Soil heat flux (G) was estimated fromnet radiation together with other parameters such as Normalised DifferenceVegetation Index (NDVI), surface temperature and surface albedo (Clothier et al.1986, Choudhury et al. 1987, Daughtry et al. 1990). Sensible heat flux (H) wascalculated from wind speed, estimated surface roughness for momentum transport,and air temperature differences between two heights (0.1 and 2 m) using an iterativeprocess based on the Monin–Obukhov similarity theory (Brutsaert 1982, Morse et al.2000, Tasumi 2003, Hong et al. 2009).


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Figure

1.

Locationofcase

studyareawithin

north-w

estern

Iran(colouronline).


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3.2. Pre-processing, atmospheric correction and calibration of Landsat thermal data

In order to estimate the LST using the SEBAL algorithm, an ETMþ image from31 August 2010 was used, together with meteorological data from the Maraqehmeteorological station. Since satellite sensors have different spatial, spectral andradiometric resolutions, the consistency of LST estimates from different satellitesby SEBAL needs to be certified (Hong 2008). PCI Geomatica software was usedfor pre-processing and the decision tree algorithm in ENVI software, which isdescribed in detail since it provides new options to objectively derive para-meters used in SEBAL, was deployed for a major part of the SEBAL processingsteps.

There are distortions and degradations in the raw remote sensing images due tomany reasons, such as the sensors, the platform and the atmospheric conditionswhen images were taken. Before classification, the raw remote sensing images needsome sort of correction (Yan 2003) which may be summarised as image pre-processing. The ETMþ image was a level 1G product, which means that it waspartly geo-registered and radiometrically corrected. However, the data were notcorrected for atmospheric effects. The digital numbers (DNs) for all bands can becalibrated to sensor radiance using Ik ¼ a þ bDN, where Ik is the radiance at thesensor, a is the offset, b is the gain. If information about the atmospheric profile(especially water vapour) is available, this satellite based radiance can be corrected toground-based radiance using a radiative transfer model (Li et al. 2004). Applying aproper radiometric correction procedure is a necessary step for extracting reliableLULC classes and estimating LSTs. For this purpose ‘‘Dark object subtraction’’(Srivastava et al. 2009, p. 12) algorithms considering Rayleigh scattering andatmospheric transmittance known as DOS3 (Song et al. 2001, Srivastava et al. 2009)were used for atmospheric correction of the ETMþ image.

Due to the Scan Line Corrector (SLC) failure of the Landsat ETMþ sensorsince 2003, the bad line replacement technique needs to be implemented for thoseimages affected by this technical problem. However, as Figure 2 shows, our studyarea’s ETMþ satellite images were not influenced by bad lines. Consequently, thegeometric correction was performed as the next step. The objective of anygeometric correction is to compensate for the distortions and degradations causedby the errors due to the variation in altitude, velocity of the sensor platform,variation in scan speed and in the sweep of the sensors’ field of view, Earthcurvature and relief displacement (Yan 2003). In our study, 1:25,000 scale digitaltopographic maps and ground control points were collected by GPS in fieldoperations used to correct the Landsat ETMþ image for geometric errors. In orderto reach an acceptable geometric accuracy, the image was georeferenced to theUTM co-ordinate system of the topographic maps. The root-mean-square-error(RMSE) was estimated to be 0.38 of the pixel. Because of the importance of linearfeatures, a linear re-sampling method was used in order to preserve the lineardetails.

3.3. NDVI

The NDVI is widely used as an indicator for biomass and greenness (Chen andBrutsaert 1998, Boone and Galvin 2000, Myneni et al. 2001). When standardised,it may also be used as a method for comparing vegetation greenness betweensatellite images (Gillies et al. 1997, Weng and Lo 2001). The index value is


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sensitive to the presence of vegetation on the Earth’s land surface and alsocorrelates well with climatic variables such as precipitation (Schmidt and Karnieli2000). Many studies have revealed temperature to be the main climatic factorinfluencing vegetation greenness in the northern hemisphere (Tucker et al. 2001,Zhou et al. 2001, Ichii et al. 2002, Lucht et al. 2002, Xiao and Moody 2005).Others have related the NDVI to ecological quantities, such as terrestrial netprimary productivity (Kaufmann et al. 2004), and soil organic carbon (SOC) atlocal, regional and global scales (Asrar et al. 1984, Fung et al. 1987). We haveused the NDVI to examine the relationship between LST and greenness. TheNDVI was calculated as the ratio between measured reflectance in the red (R)and near infrared (NIR) spectral bands of the ETMþ images, using thefollowing formula:

Figure 2. ETMþ satellite image of Maraqeh County (colour online).


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NDVI ¼ Near IR band� Red band

Near IR bandþ Red bandð2Þ

The index value may range from 7 1.0 to 1.0, with higher values associated withhigher levels of healthy vegetation cover. Green vegetation commonly has higherreflectance in the near infrared than in the visible spectrum, while clouds, water andsnow have higher reflectance in the visible spectrum than in the near infrared. For barerock and bare soil there is little difference between the two. The NDVI values forvegetation typically range from 0.1 to 0.75, with the higher values associated with agreater density and greenness of the plant canopy. The NDVI values for soil and rockare close to zero, while water bodies such as rivers and dams have negative index values(Tucker et al. 1986, Xiao and Weng, 2007). Figure 2 shows the distribution of theNDVI values that we obtained for our research area. Orchards had the highest averageNDVI, followed by agricultural land, and results were similar for all seasons, with onlyslight variations detected over the length of the study period.

3.4. SEBAL method for calculation of surface temperature

Both LST and reflective data were extracted from thermal IR bands acquired by theLandsat 7 ETMþ sensor on 31 August 2010. The other ETMþ bands were used toextract LULC classes. The SEBAL method estimates surface temperatures from thecorrected thermal radiance (Allen et al. 2002). In order to calculate the correctedthermal radiance, the emissivity in the thermal band must first be calculated, forwhich the spectral radiance (Ll), the reflectivity values in each band (rl), and thesurface albedo (a) are required.

3.4.1. Spectral radiance (Ll):

The spectral radiance is the radiance energy at the top of the atmosphere, which isdetected by the satellite sensors. We calculated the spectral radiance for each bandusing the following formula (Allen et al. 2002):

Ll ¼ Lmax� Lmin

255�DNþ Lmin ð3Þ

where DN is the degree of greyness of the pixels and Ll is in (W/m2/sr/mm). Lmaxand Lmin are the calibration constants of the sensor, equal to the maximum andminimum values of the spectral radiance (in W/m2/sr/ mm) detectable for each band,by the sensor and the ETMþ sensor, as shown in (Table 1) (NASA 2006).

If gain and offset values exist in the header file, the radiance can be estimated bythe following formula:

Ll ¼ gain �DNþ offset ð4Þ

3.4.2. Reflectivity of hemisphere (rl)

The reflectivity of a surface is the ratio of the reflected energy to the amount ofenergy striking the surface. The amount of reflection is calculated for each bandusing the following formula (Allen et al. 2002):


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rl ¼ pLlESUNl � cos y � dr ð5Þ

where rl is the spectral reflectivity for each band, ESUNl is the average for eachband of solar radiation striking the top of the atmosphere in (W/m2/ mm). TheESUNl values for the ETMþ sensor are shown in Table 2, which were obtainedfrom Landsat 7 science data users handbook (NASA 2006). The incident angle of thesun’s radiation (y) is computed as follows:

y ¼ 90� b ð6Þ

in which b is the sun’s elevation, which is obtained from the header file of theLandsat satellite image, and available for every scene. The sun’s elevation dependson geographical location and in this study it was equal to 54.3277460 for our studyarea (scene 168–34 of Landsat satellite images).

The inverse of the square distance between the Earth and the sun (dr), iscalculated using the Duffie and Beckman (1980) formula (Allen et al. 2002):

dr ¼ 1þ 0:033Cos DOY2p365

� �ð7Þ

where DOY is the sequential day within a calendar year which, for the image date of30 August in our study, is calculated at about 243.

3.4.3. The surface albedo (a)

The albedo is the proportion of incident electromagnetic radiation from the sun thatis reflected from soil and plant surfaces. The albedo for a surface is calculated usingthe following formula (Allen et al. 2002):

Table 1. Lmax and Lmin for Landsat ETMþ.

Band Lmin Lmax

1 76.2 293.72 76.4 300.93 75.0 234.44 75.1 241.15 71.0 47.576(1) 0.000 17.047 70.350 16.548 74.7 243.1

Source: NASA (2006).

Table 2. The ESUNl values for the ETMþ.

Band 1 2 3 4 5 7 8

ESUNl 1969.000 1840.000 1551.000 1044.000 225.7.00 82.07 1368.000

Source: NASA (2006).


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a ¼ atoa�apath�radiancet2sw

ð8Þ

where atoa is the top-of-atmosphere albedo, apath 7 radiance is the albedo that iscaused by the effective radiance, and t2sw is the atmospheric, transmission.The apath 7 radiance is the average of the incident radiances that are transmitted bythe atmosphere; apath 7 radiance reveal absorption and dispersion events that haveoccurred in the atmosphere. In our research for apath 7 radiance the value of 0.03 isconsidered based on Bastiaanssen (2000). Since the effect is available for bothincident and reflected radiances, the surface albedo is taken to be the square of theatmospheric transitivity (tsw) under a clear sky and dry weather conditions, which isobtained using the following formula (Allen et al. 2002):

tsw ¼ 0:75þ 2 � 10�5 � z ð9Þ

in which z is the elevation above sea level in metres, i.e. the regional elevation, suchas the elevation of a meteorological station (Allen et al. 2002). For our investigationswe used a figure of 1477 m, this being the elevation of the meteorological station ofMaraqeh city. The top-of-the-atmosphere albedo (atoa) is obtained using the formulabelow (Allen et al. 2002):

atoa ¼Xðol � rlÞ ð10Þ

In which rl is the spectral reflectivity for each band and ol is the scaled coefficientfor non-thermal bands, which is calculated using the following function:

ol ¼ ESUNlPESUNl

ð11Þ

The ol values for non-thermal bands from ETMþ data are shown in Table 3.Surface radiation is the ratio of the thermal energy emitted from a surface to the

thermal energy emitted from a black body of the same temperature. In the SEBALmethod two surface radiations are used: the first for the thermal energy emitted in anarrow thermal band (eNB) of 10.4 to 12.5 microns; and the second in a broadthermal range (e0) of 6 to 14 microns. The eNB figure is used to calculate the surfacetemperature (Ts). In our model, surface radiations are calculated using the followingexperimental functions:

If NDVI 4 0 then the following two situations are distinguished:(a) For Leaf Area Index (LAI), LAI 5 3eNB ¼ 0.97 þ 0.0033*LAIe0 ¼ 0.95 þ 0.01*LAI

Table 3. Calculated values for ol (scaled coefficients for the non-thermal bands).

Band 1 2 3 4 5 7

ol 0.246 0.23 0.194 0.131 0.028 0.171


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(b) For LAI � 3

eNB ¼ 0:98; e0 ¼ 0:98 ð12Þ

For water and snow, the eNB and e0 filters are used.Water:

NDVI <0; a < 0:47! eNB ¼ 0:99; e0 ¼ 0:985 ð13ÞSnow:

NDVI < 0; a � 0:47! eNB ¼ 0:99; e0 ¼ 0:985 ð14Þ

In the above equations a is the (i.e. the surface’s albedo).The LAI depends on the production type and on the geographical position

calculated on the basis of the NDVI-LAI relation mean, experimentally derived inthe Czech Republic (Tewari et al. 2003). The NDVI and LAI calculated fromLandsat ETM data are shown (see Figures 3 and 4) respectively, while thecorrelation between the two values is shown (see Figure 5).

We used the decision tree method in the ENVI software to derive threshold levelsfor estimating surface radiation. In order to define these threshold levels, bandwiseinformation for the LAI, the NDVI and the surface albedo was used for the decisiontree. The structure is shown in Figure 6.The decision tree classification results werethen converted into vector data and exported to ArcGIS. After the process ofdissolving, converting to raster and reclassifying, the value for each class was savedas a separated image in the form of Boolean functions: a value of 1 was assigned tothe class area and a value of zero to other areas for each resulting image. These fileswere then imported to ENVI and surface radiation values calculated for each class.

3.4.4. Corrected thermal radiance (Rc)

The corrected thermal radiance is the actual emitted radiance after it has beencorrected for atmospheric effects. The following relationship is offered by Wukelicet al. (1989) for correcting emitted thermal radiance:

Rc ¼ LNB�RP

tNBð1�eNBÞRSky ð15Þ

where LNB is the radiance of the thermal band, RP is the course radiance of thethermal band, the RSky is the radiance in the thermal band for clear sky conditions,and tNB is the atmospheric transition capability in the thermal band. The Rsky canbe calculated by the experimental formula given below, which is based on an Idso-Jackson style of empirical formula, as applied by Wukelic et al. (1989). Due to theinequality between pixel sizes in the thermal band and those in other bands, thethermal data needed to be resized (to 28.5 m) before applying this formula.

Rsky ¼ ð1:807� 10� 10ÞTa4½1� 0:26 � exp ð�7:777 � 10� 4½273:15� Ta�2Þ� ð16Þ

where Ta is Ta the air temperature near surface at the time of passing of the satellite.The tNB and Rp values (see equation 15) were set to 1 and 0, respectively(Ahmadian 2006).


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3.4.5. Land surface temperature

The LST was calculated by using surface temperature (Ts) formula:

Ts ¼K2

eNB � k1RC

þ 1

� � ð17Þ

Where eNB is the narrow band surfaces emissivity, RC is the corrected thermalradiance from the surface and K is the spectral radiance for thermal band. Theresulting values of k1 and k2 were 666.09 and 1282.71, respectively. The Band Mathoption in ENVI was used for all of the processing steps required to calculate surfacetemperatures, except for the decision tree portion, while the final processing steps

Figure 3. NDVI map of Maraqeh County.


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were performed in ArcGIS. The resulting LST map for Maraqeh County is shown inFigure 7.

3.4.6. LST Validation

SEBAL model estimates have been evaluated by comparison of their respective rootmean square error (RMSE), relative root mean square error (RelRMSE) and relativedeviation (RD), with the measurement systematic error (MSE) associated with the insitu measurement. Those estimates were considered satisfactory whenever the valuesof RelRMSE or RD were lesser than or equal to MSE. The RMSE, RelRMSE andRD were calculated as (Maria Paiva 2011):

Figure 4. LAI map of Maraqeh County.


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RMSE ¼PN

i¼1 ðFsebal�i � FTowerÞ2N

" #12

ð18Þ

RMSE ¼ ðRMSEÞFTower

ð19Þ

RDi ¼ 100Fsabal�i � FTower

FTower

� �ð20Þ

where RMES is the root means square error (Wm72); N is the number of cloudlesspixels; Fsebal 7 i represent each one of the components of the energy balance obtainedthrough the SEBAL model, in each cloudless pixel, in Wm72; subscript I refers to ithpixel and varies from 1 to 9; FTower represent each one of the energy balancecomponents measured in situ in Wm72 (Maria Paiva 2011).

Figure 5. Correlation of NDVI and LAI.

Figure 6. Decision rules of the classification process based on NDVI to estimate surfaceradiation.


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3.4.7. Land use and land cover classification

Remotely sensed data have been widely used to provide land use land cover (LULC)information and to derive secondary information on, for example, degradation levelsof forests and wetlands, rates of urbanisation, the intensity of agricultural activities,and on other human-induced changes (Alrababah and Alhamad 2006). Satelliteimagery has been effectively utilised to distinguish different land cover conditionsand generate LULC maps (Vogelmann et al. 2001, Lobo et al. 2004, Cohen andGoward 2004, Musaoglu et al. 2005). Landsat images are among the most frequentlyused data sources when mapping and monitoring natural resources. In this study wecarried out an LULC classification for the study area, in order to examine therelationship between the LST results and recent land use. The supervised

Figure 7. Resulting LST map of Maraqeh County (colour online).


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classification was followed by a knowledge-based expert classification using referencemaps, in order to estimate and improve the accuracy of the classification process(Berberoglu et al. 2007, Xiaoling et al. 2006). For this purpose, we used a Landsat 7ETMþ image from 31 August 2010. Following georeferencing and atmosphericcorrection, the image was classified using a Maximum Likelihood Classification(MLC) algorithm in a per-pixel classification approach. Six land use/land cover typeswere identified, these being (1) urban areas, (2) orchards, (3) croplands, (4) pasturelands, (5) water bodies and (6) rock outcrops and bare soils. As is widely known, thepixel-based image classification approach classifies remote sensing images accordingto the spectral information in the image ‘pixel by pixel’ (see Weng 2010). Althoughobject based image analysis approaches are becoming increasingly widely used(Blaschke 2010), the majority of studies documented in scientific literature use the‘per pixel approach’ for unsupervised and supervised classifications (Yan 2003,Seetha 2005, Weng 2010, Feizizadeh and Helali 2010).

In order to evaluate the accuracy of the classified image, the Accuracy Assessmenttool in ENVI was used, which is based on the random sampling method: 120 pointswere automatically created, based on GPS points. The authors are aware of somelimitations to the ‘per pixel approach’ (see Blaschke 2010 for an overview), but in thiscase the MLC produced very satisfactory results. The overall accuracy and KappaCoefficient were calculated to be 89.21% and 0.86, respectively. A geodatabase wassubsequently created in ArcGIS by calculating the geometric characteristics for eachland use class. Final results of the LULC classification are shown in Figure 8.

4. Results

4.1. LST results and validation

The main outcome of our research has been the production of a map of absoluteland surface temperatures for the area of investigation. The computed LST map isshown in Figure 6. The derived LST values reveal surface temperatures rangingbetween 20–458C.

In the context of accuracy assessment, the obtained LST map was compared withthe LST value which was measured in the meteorological station. Due to non-availability of LST field data, it is well known that the reliability of LST resultsderived from the TIR sensor may not always be possible to validate using groundsurface temperature (Srivastava et al., 2009). However, the LST measured in asynoptic meteorological station within Maraqeh County leads to validation of LSTresults by ground measurement. Thus, in the second part of this study, estimatedLST values obtained through the SEBAL method were compared with LSTsmeasured at the Maraqeh County meteorological station, which is located in asuburb of Maraqeh City in the croplands area (this was the only meteorologicalstation data available).At the time and date of the ETMþ satellite image (10:09 am on 31 August 2010), themeasured LST at the meteorological station was 27.428C, while the LST calculatedfrom the satellite image using the SEBAL method was 29.118C, an overestimation of1.698C. We consider that the LST that we derived from emissivity and single channelequations correlates well with the true ground temperature. The accuracy of theestimated LST was + 1.698C, which means that the maximum difference betweenLandsat-derived LST and the measured data was only 1.698C. Temperaturesobviously vary significantly during the daytime between different land surfaces, but


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this variation is negligible in the early hours of the morning. Surface temperatureschange more rapidly than those at a depth of 5 cm: from midday to 2 pm the surfacetemperature is about 10 degrees higher than the temperature at 5 cm depth, while atsunset the temperatures are almost equal. Temperatures at 5 cm depth in soilincrease during the night (Vazquez et al. 1997). In addition, attempts to convertobserved radiances to LST necessarily involve a number of assumptions andapproximations. There are three main sources of error: the first relates to the sensorproperties (calibration, assumption from broad band to single band); the secondarises from the atmospheric correction (in this instance, radiative transfer modelMODTRAN 4.1 and water vapour); and the third source of error results fromestimating surface emissivity. All three of these factors influence the results of LSTestimation from satellite images.

Figure 8. Land use/cover map of Maraqeh County (colour online).


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4.2. LST relationship to land use/land cover

To better understand the relationship between LST and land cover, and thusvegetation abundance indicators (vegetation fraction and NDVI), the thermalsignature of each LULC type must be investigated (Weng et al. 2004). Thus, in thesecond part of the study, for the comparison between LST and LULC, we selectedsampling points representing different LULC classes within the study area, in orderto compare them with the LST values. In doing so, the mean temperature of everyLULC type was calculated by averaging all corresponding pixels of a given LULCtype. Table 4 shows the difference in temperature between different LULC types. AsFigure 6 shows, the minimum LST value of 20.218C was observed for reflective waterbodies, while a maximum of approximately 458C was observed for rock outcrops.Based on the results of our research, the cold anchor pixels were observed in waterbodies and croplands under irrigation. Areas of dense vegetation, such as orchards,also have lower surface temperatures during daytime. The hottest pixels were foundfor rock outcrops, bare soils and built-up areas. The surface temperatures for coldand hot pixels ranged from approximately 20–458C. Water bodies and orchards hadthe lowest temperatures, agricultural croplands and pasture lands showed moderatetemperatures, while bare soils and urbanised areas had relatively high temperatures.The comparison reveals the rock bodies had moderately lowest temperatures (31–388C) because the highest temperature (38–458C) was found in areas of rockoutcrops. A clear relationship can be seen between LST and LULC. While orchardsand water bodies had the lowest LSTs among the data from the satellite image, theyalso correlated with the same LULC types, rock bodies, bare soils, rural and urbansettlements.

LSTs are generally lower in flood plains, where socio-economic activities arehighly clustered, and become greater with increasing elevation, where LULC isdominated by rock outcrops and bare soils. Our results indicate that theabsorption and loss of radiated heat of LST correspond closely to the LULCcharacteristics. Each surface component in the landscape (e.g. agricultural lands,bare soils, urban areas, water bodies, etc.) exhibits unique radiative properties. Tobetter analyse the relation between LST and LULC, correlation coefficientsbetween LST and NDVI, by LULC type, were calculated (see Table 5). NDVIhas been widely used as an indicator of vegetation abundance to estimate LST instudies (Xiao and Weng 2007). The NDVI and LST were found to be closelycorrelated in several LULC categories, especially in vegetated lands. Thecorrelation coefficient reached as high as 0.71 in orchard areas. Therefore, theimpact of LULC on LST may be examined by an analysis of the changes inNDVI (Xiao and Weng 2007).

Table 4. LST value for the different LULC classes.

LULC class Min LST (8C) Max LST (8C) Mean LST (8C) Standard deviation

Water bodies 20.209999 20.709999 20.459999 0.712126Croplands 22. 299999 35. 599998 28.9499985 3.793377Orchards 18.400000 42.599998 30.5999995 3.694276Pasture 14.200000 41.599998 27.899999 3.050296Urban areas 31.900000 35.500000 33.700000 1.440244Bare soils 31.124500 38.1642350 34.64443675 1.680164Rock bodies 38.103000 45.000000 41.551500 16.230471


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5. Discussion

The improved availability of satellite data with high temporal resolutions offers newopportunities for remote sensing. The gap that previously existed between high spatialresolution (typically with low temporal resolution) and high temporal resolution(initially with low spatial resolution, now significantly enhanced, for example, forETM and for Aster) has now been reduced. In this research, we have examined thespatial and temporal dynamics of LST in relation to LULC, in theMaraqeh County ofnorth-western Iran, through the use of Landsat data. The temperatures derived fromaggregated LST data were very similar to those derived from the meteorologicalstation: no systematic shift or bias could be observed. Using the SEBAL method forthe extraction of LST from Landsat ETM, we have shown that the difference betweenthe actual temperature at the Maraqeh meteorological station and the estimatedsurface temperature was less than two degrees. The advantage of using LST dataderived from satellite imagery is that the study area can be covered in its entirety, andthat each LST map pixel time series can be considered as a virtual meteorologicalstation for temperature data. The results are potentially very useful for a variety ofapplications, including climatology, hydrology, ecology, geology, the design andimprovement of transport networks and, in particular, for agriculture, within theMaraqeh County. Using this information, we can estimate how changes in LULCpattern may have contributed to the regional temperature. Our research resultsindicated the LST is sensitive to vegetation and soil moisture and, hence, can be usedto detect changes in LULC over time. Regarding the effects of land use changes,especially urbanisation, deforestation and soil erosion in this area, it can be anticipatedthat LSTs in Maraqeh County will increase in future, since low temperature land usetypes have decreased while high temperature land use types have increased.

Due to a population increase in Maraqeh city over the last two decades, a largeamount of land around the city has been redeveloped by the government or privatecompanies for the purpose of the construction of new residential, shopping andindustrial facilities. Based on the rate of pollution growth, for a future 10-year scenario,we assumed that urban built-up areas would be increased by the same proportion as thepast 10 years from 2000 to 2010. Increasing population numbers, (particularly indeveloping countries) intensify the pressure on both natural and agricultural resources(Feizizadeh and Blaschke 2012a). Therefore, it can be anticipated that the increasedbuilt-up areas will come from the other LULC types. It is well known that the changesin LULC would have resulted in changes in LST (Xiao and Weng 2007). Further,changes in LULC include changes in biotic diversity, actual and potential primaryproductivity, soil quality, runoff and sedimentation rates (Steffen et al. 1992), andcannot be well understood without the knowledge of land use change that drives them.

Table 5. Correlation coefficients between LST and NDVI by LULC type.

Land use/land cover class

Urban area 70.66Orchard 0.71Croplands 0.67Water bodies 70.11Pasture lands 0.55Bare soils 0.33Rock bodies 0.16


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Therefore, LULC changes have environmental implications at local and regional levels,and are possibly linked to the global environmental process. Because of the interrelatednature of the elements of the natural environment, the direct effects on one elementmay cause indirect effects on others (Weng 2001). Urbanisation is thus an importantchange in land use within Maraqeh County. The conversion of agricultural land intourban/built-up land has contributed to the increased LST. The new built-up areas,along with supporting infrastructures, were frequently located in high quality suburbagricultural lands or orchards. These changes have reduced agricultural areas andincreased LST. In the past, agricultural or orchard areas could provide a buffer zonebetween the urban and rural areas to absorb excess heat generated by population andurban facilities (Xiao and Weng 2007).

Urban areas are hot spots that drive environmental change on a multiple scale(Grimm et al. 2008). Urbanisation transforms the natural land surfaces to modernLULC such as buildings, roads and other impervious surfaces, making urbanlandscapes fragmented and complex which affects the inhabitability of cities (Albertiand Marzluff 2004). The enormous changes with LULC lead to the urban heat island(UHI) (Jiang and Tian 2010). Usually, LST in urban areas is 2–58C higher than inrural surroundings (Ackerman 1985). Urbanisation has a great impact on climate bycovering landscapes with buildings, roads and other impervious surfaces (Weng2001). Therefore, urban areas tend to experience a relatively higher temperaturecompared with the surrounding rural areas. This thermal difference, in conjunctionwith waste heat released from urban houses, transportation and industry,contributes to the development of the UHI. The temperature difference betweenurban and rural areas is usually modest, but occasionally rises to several degreesdifference when urban, topographical and meteorological conditions are favourablefor the UHI to develop (Mather 1986, Weng 2001). The rapid urbanisation processbrought about many eco-environmental problems, such as the drastic change of landuse and development of urban heat islands (Jiang and Tian 2010). The climate in andaround cities and other built-up areas is altered due to changes in LULC, and theanthropogenic activities of urbanisation. The most imperative problem in urbanareas is an increase in surface temperature due to the alteration and conversion ofvegetated surfaces to impervious surfaces. These changes lead to environmentalimpacts and affect the absorption of solar radiation, surface temperature,evaporation rates, storage of heat and wind turbulence, and can drastically alterthe conditions of the near-surface atmosphere over cities (Mallick et al. 2008). Basedon current LULC changes in the study area it can be anticipated that the furtherchanges that occur as a result of urbanisation or desertification may therefore have asignificant impact on landscape quality in Maraqeh County.

6. Conclusions

This study has examined the relationship between LST values and the spatialdistribution of LULC types within Maraqeh County. Results show the LST derivedthrough satellite data may have errors up to 1.698C with actual groundmeasurements. While the accuracy of LST retrieved for satellite data greatlydepends on satellite sensor measurements, it can be further improved with accuratemeasurements of surface emissivity and estimates of atmospheric parameters(Srivastava et al. 2009). It is well known that the availability of direct measurementsof LST from meteorological stations is limited, but TIR remotely sensed imagery of


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the Earth’s surface allows the spatial distribution of LSTs to be modelled andestimated for any area of interest. In particular, in complex terrain (such as theSahand Mountain area of Iran) in the absence of meteorological stations TIR datacan be widely used for LST mapping. Based on the results of our research, LST washighly influenced by the LULC, and very sensitive to vegetation and soil moisture.Therefore, we found a strong relationship between LST and LULC. In particular,the amount of vegetation was found to be the key to this relationship while the morevegetation a LULC class has the lower the LST. Since we did not calculate theabsolute quantity of vegetation, or biomass, we can only state that there is a negativerelationship between the density of vegetation in the various LULC categories andthe LST. The LST values were also dependent on the human activities. In particular,settlement areas and several areas of bare and exposed soils showed relatively highLST values, while the maximum LST values were from rock outcrops. The results ofthis research will be important for decision makers and, in particular, forgovernment departments such as the Ministry of Agriculture, the Ministry of WaterResource Management, and the Ministry of Natural Resources of the EastAzerbaijan Province of Iran. Initial discussions have already been triggered abouthow to further enhance the level of detail portrayed in the LULC maps. Thisenhancement will be carried out for Maraqeh County in collaboration with thedecision-making authorities. It will include actions to designate specific areas inagricultural land use planning. However, in cultural landscapes the ecological andsocio-economic realms are intricately linked (Blaschke 2006).

Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful and constructivecomments on earlier versions of the manuscript, and the department of Geoinformatics(Z_GIS) University of Salzburg for partial financial support.

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monitoring land surface temperature relationship to land use/land cover from satellite imagery in...

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