Research Article The Impact Analysis of Water...

Research ArticleThe Impact Analysis of Water Body Landscape Pattern onUrban Heat Island A Case Study of Wuhan City

Bohan Yang Fen Meng Xinli Ke and Caixue Ma

College of Land Management Huazhong Agricultural University Wuhan 430079 China

Correspondence should be addressed to Xinli Ke kexligsnrraccn

Received 17 September 2014 Revised 4 March 2015 Accepted 5 March 2015

Academic Editor Hongbo Su

Copyright copy 2015 Bohan Yang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Based on the LST and the landscape metrics of water body with remote sensing technique and spatial analysis the relationshipbetween the mean LST and the attributes of water body was revealed via Pearsonrsquos correlation analysis and multiple stepwiseregression analysis Result showed that in 32 class-based metrics we selected the proportion of water body average water bodysize the isolation and fragmentation of water body and other eight metrics have high correlation with the LST As a resultant forcethe quantity shape and spatial distribution of water body affect the forming of temperature We found that the quantity and spatialpattern of city water body could be allocated reasonably to maximize its cooling effect

1 Introduction

With the rapid development of urbanization since industrialrevolution land use and land cover (LULC) of the earth haschanged dramatically because of human activities and naturaldisasters At the same time LULC not only changed the landsurface condition but also reduced the stability of the earthsurface ecosystem As a result LULC has affected the climatechange directly and indirectly [1] and hence has becomemoreandmore important study area for environmental researchers[2ndash4]

One relative mature research is the study of the relation-ship between LULC and urban heat island (UHI) [3 4] Stud-ies have proved that the urban built-up land has positiveinfluence on UHI [5] while green landscape has negativeinfluence on UHI [3] In addition water body is another landuse type with negative influence [6 7] as the water bodies incity achieve cooling effect by transpiration and heat exchangewith the surrounding environment Compared with the stud-ies of built-up green landscape land use types the research onurban water body is relatively less at present There are threeaspects in this research (1) the relationship between waterbody and land surface temperature the temperature of wateris lower than other kinds of land uses [7] (2) the impact ofspatial characteristics (eg shape) on UHI [8] (3) the config-uration of water body and its relationship with UHI [9]

The land surface temperature (LST) as one importantindicator of the UHI [1] has been used in many researches torepresent climate effects This paper studies the relationshipbetween the land surface temperature and the water bodyfrom the view of landscape pattern

Wuhan the capital of Hubei province in central China isfamous for the title of ldquoone of four furnaces in Chinardquo And ithas another name ldquothe city of hundreds lakesrdquo whose waterarea covers around 25 of the territory and the coverageof water area ranks first in the whole country In recentyears Wuhan city has experienced rapid urbanization andeconomic development As a result many urbanwater bodiesinWuhan city have been disappeared because of the pressureto gain extra land profit in the process of urban development[10] At the same time the land usecover change has a greateffect on its local climate Therefore Wuhan city is chosen asthe case area to estimate the relationship between LST and thewater pattern from the view of landscape ecology

Based on existing research this paper used the LandsatThematic Mapper (TM) data to retrieve LST and land usecover for water Then considering the number of samplesand computational burden Wuhan city was divided into 736sample areas whose size is 3000m lowast 3000m Then the land-scape metrics of water and the mean LST of each samplearea were calculated by using ArcGIS 100 With the help of

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2015 Article ID 416728 7 pageshttpdxdoiorg1011552015416728

2 Advances in Meteorology

China mainlandHubei province

114∘09984000998400998400E 114

∘30

9984000998400998400E 115

∘09984000998400998400E

31∘30

9984000998400998400N

30∘30

9984000998400998400N

31∘09984000998400998400N

30∘09984000998400998400N

Study area Wuhan

0 5 10 20 30 40

(km)

N

Figure 1 Wuhan city the research area

statistical analysis theory and methods Pearsonrsquos correlationcoefficients were used to determine the effect of the spatialstructure of water on the LST Finally the computed experi-mental samples could be fitted to multiple linear regressionmodel by stepwise regressions

2 Study Area and Data Sources

21 Study Area Wuhan city is situated in central China andlocated between 113∘411015840ndash115∘051015840 E and 29∘581015840ndash31∘221015840N shownin Figure 1Wuhan is located at a subtropical humidmonsoonclimate zone with an annual mean precipitation about10961mm The average annual temperature ranges from158∘C to 175∘C and sometimes in summer the maximumtemperature can soar to 40∘C

The total territorial area of Wuhan is about 849441 km2and the water area is about one-fourth of the cityrsquos totalarea The famous rivers and lakes in Wuhan city are YangtzeRiver Han River East Lake South Lake and so on Theseriverslakes mentioned above together with other waterbodies of Wuhan are scattered and distributed in the studyarea And they have a big influence on humidity purifyingatmosphere of Wuhan climate

Wuhanrsquos economy strength has grown rapidly and levelsof the industrialization and urbanization were improved aswell By the end of 2005 the resident population of Wuhancity was 858 million and the annual GDP per capita reached26238 Yuan

Data preprocessing

Retrieval of LSTSupervised

classification

LST Land use map

Average LST in one grid Water-based metrics

Multiple stepwise regression analysis

Correlation relationship between water metrics and LST

Spot and TM images

Result

DataProcess

Gridding (3km lowast 3km)

Figure 2 Research framework of landscapemetrics of water on landsurface temperature

22 Data Sources This paper utilized three types of datasocial-economy statistical data Wuhan land use data andLST data derived from remote sensing images (1)The social-economy statistical data such as populationannual GDP percapita in this paper came from Wuhan Statistical Yearbookand Wuhan City Water Resources Bulletin (2) A Landsat5 TM imagery on September 11 2005 (patchrow 12239)was selected to quantitatively measure LST and the situationof LULC All bands of TM images except band 6 have aspatial resolution of 30m while band 6 (thermal infraredband) has a spatial resolution of 60m Before getting LULCinformation from the raw image image preprocessing such asgeometric rectification relative radiometric correction andatmospheric correction was completed In addition a Spotimage on December 15 2005 with a spatial resolution of 4mwas used to verify results of supervised classification in thestudy area

3 Methods and Models

31 Research Framework The basic idea of analysis forthe impacts of landscape metrics of water on land surfacetemperature is illustrated in Figure 2 Based on the Landsat

Advances in Meteorology 3

Temperature (∘C)438738

0

0 5 10 20

(km)

N

(a)

0 5 10 20

(km)

Land use typesWater bodyWoodland

Cultivated land

Built-up landOther land

N

(b)

Figure 3 Land surface temperature (a) and land use map (b) in Wuhan city

5 TM imagery the land use information was extractedby the method of supervised classification and the LSTwas retrieved by using the single-channel algorithm Thenconsidering number of statistical samples and the computa-tional burden Wuhan was divided into 736 sample regionsThe mean LST and landscape metrics of water in eachsample region as sample data would be used to do Pearsonrsquoscorrelation analysis andmultiple stepwise regression analysisBy comparing relationship between the landscape metrics ofwater and the LST it is meaningful to reveal the impactsof the spatial structure of water on LST which can helpimproving urban environment through rational allocation ofwater resources

32 Acquisition of Land Use Map In order to calculatelandscape metrics of water a land cover classification isnecessary to obtain the land use map in study area In thispaper five land use classes were established urban forestcropland bare land and water It should be pointed outthat water in here mainly includes rivers lakes and pondsThere are three steps to derive land use map First region ofinterest (ROI)was selected on the true color imagewhichwasproduced by combining bands 5 4 and 3 of the TM imagesThey were used as training areas after passing separable testSecond neural net classificationwas employed to perform theclassificationThird for each land category at least 60 samples

were selected randomly to check the accuracy of the classifiedmaps by using confusion matrices In addition the true ROIwas identified on the Spot image used as ground surveys inaccuracy assessment The overall accuracy is approximately80 and the overall kappa statistics value nearly reached 08which met the recommended value Fourthly a 3 lowast 3 movingwindow in clump process was used to do postclassificationprocess which can eliminate the unnecessary image noisesfrom visual interpretation After completing these abovesteps the land use map was obtained as shown in Figure 3(b)

33 Retrieval of LST In order to acquire the LST of Wuhancity the brightness temperature should be derived first Amethod composed by three equations was proposed byQin etal (2001) to derive brightness temperature from TM images[11]

The first equation can convert digital number of band 6into radiation luminance (119871TM6 m times W times cm

minus2

times srminus1) asfollows

119871TM6 =DN times (119871max minus 119871min)

255

+ 119871min(1)

where DN is the digital number of TM image 119871max and 119871minrepresent the maximum and minimum radiation luminancecorresponding to the DNmax (255) and DNmin (0) According


One sample

One corresponded sample

Temperature

Spatial structure of water

Figure 4 Water spatial structure and its corresponding mean temperature in sample areas

to Li [12] the 119871max is 1896m timesW times cmminus2

times srminus1 times 120583mminus1 and119871min is 01534m timesW times cm

minus2

times srminus1 times 120583mminus1Then brightness temperature can be derived by two equa-

tions as follows

119871120582=

119871TM6119887

119879 =

1198962

ln (1198961119871120582+ 1)

(2)

where 119887 is 1239 120583m and represents the effective spectralrange 1198701 = 60766m timesW times cmminus2 times srminus1 times 120583mminus1 and 1198702 =126056K and they are prelaunch calibration constants ofLandsat TM5

The brightness temperature obtained above just repre-sents blackbody temperature However the blackbody is notexistent in the real world So it is necessary to convert thebrightness temperature into LST by using the following for-mula [13]

119879119878=

119879

1 + (120582 lowast 119879120588) ln 120576 (3)

where 119879119904represents the LST 119879 represents brightness tem-

perature and 120582 is the effective wavelength whose value is115 120583m 120588 = ℎ119888120590 = 1438 lowast 10minus2mk Boltzmann constant120590 = 138 lowast 10

minus23 JK Planck constant ℎ = 6626 lowast 10minus34 J lowast Sspeed of light 119888 = 2998lowast108ms [14] As emissivity is difficultto obtain this paper uses Vanrsquos empirical formula [15] whenNDVI le 0157 120576 = 0923 when 0157 lt NDVI lt 0727120576 = 10094 + 0047 ln(NDVI) and the emissivity of water is09925

After finishing these above steps the LST image wasretrieved shown in Figure 3(a)

34 Calculation of Spatial Structure of Water Body by UsingLandscape Metrics From the land use map of Wuhan cityit can be intuitively identified that water body is evenlydistributed in the whole study area Unlike other metropolisin China such as Beijing and Shanghai the correlationbetweenmean LST andwater-based landscapemetric ismoresignificant inWuhan because of the large quantity and specialspatial structure of water body in study area [9] as it shownin Figure 4


As we all know landscape metrics are often used forquantitative spatial model building in biological habitat andlandscape ecological contexts [16] And they are used toanalyze and characterize urban land use and structure types[17] In this paper 32 class-based metricsrsquo landscape was usedfor quantitative expression of the spatial structure of waterbody

Considering the relatively low time resolution of TMimages it is not convenient to get a series of sample datain the view of time and the sample data would be chosenin space Wuhan was divided into 736 sample regions whosesize is 3000m lowast 3000m (the value is determined by severalexperiments) The mean LST of each sample region wascalculated by zonal statistics in ArcGIS 100 and the value ofMoranrsquos I was acquired tomeasure the spatial autocorrelationof the mean LST Then the landscape metrics of water bodywere got usingPatchAnalyst 5 PatchAnalyst 5 is an extensionto ArcGIS system that can facilitate the spatial analysis oflandscape patches and is used for spatial pattern analysis

Then Pearsonrsquos correlation coefficients between the land-scape metrics and mean LST were calculated It is a signif-icance test between spatial structure of water body and themean LST

Stepwise multiple regression analysis was designed tofind the most parsimonious set of predictors which are mosteffective in predicting the dependent variable [18] Analyseswere performed using IBM SPSS Statistics

4 Results and Discussion

41 The Land Use Types and Their Corresponding Mean LSTIn this paper in order to study the land surface temperaturerelationships of different land use types the mean tempera-tures of different land use types were calculated by averagingall corresponding pixel values (Table 1)

The results shown in Table 1 suggested that the built-uparea is corresponding to relatively high temperature while thewater bodywith lowest temperaturemay affect the LSTwhichwill be considered as potential mitigating factors Becausethe urban underlying surface is made up of different landuse types it is reasonable to analyze the relationship betweenland use and LST through considering other land use typesas potential factors

42 Relationship between the LST and Landscape of WaterBody The mean temperature of each sample area is shownin Figure 5 and the value of Moranrsquos I is 05969 Moranrsquos I isa measure of spatial autocorrelation Therefore it was foundthat the temperature has a strong spatial autocorrelation Inthis paper we used a covariate to control the autocorrelationof the mean temperature

Since we select 32 class-based metrics not every land-scape metric has high correlation with temperature Wechoose the landscape metrics whose Pearsonrsquos correlationcoefficient with temperature is higher than 04The landscapemetrics shown in Table 2 are highly related to the landscapemetrics of water body

Through multiple stepwise regression analysis the mosteffective landscape metrics in predicting the dependent

Table 1 Land use types and their corresponding mean LST inWuhan city

Land use types Mean LST (∘C)Water body 2529Woodland 2846Cultivated land 2833Built-up land 3278Other land 2994

Mean LST3925830

0 5 10 20

(km)

N

Figure 5 The mean land surface temperature in 3000m lowast 3000msample region in Wuhan city

variable (the mean LST) were selected and multiple linearregression equation was calculated

119910 = 07041199091minus 0557119909

2minus 0055119909

3+ 0128119909

4+ 0051119909

5

+ 00641199096+ 0076119909

7+ 0036119909

8

(4)

where 1199091represents surrounding temperature accessed from

the weight matrix via Generate Spatial Weight Matrix ofArcGIS tools The threshold of its distance is 6000m doubleof spatial resolution The weight matrix can be used toexplain the impact of surrounding pixelsrsquo temperatures onthe center pixel In this study it can also eliminate the spatialautocorrelation when analyzing the impact of waterrsquos factorson temperature 119909

2ndash1199098represent TCAMCA TCAI CACVI

IJI MPI and MNN respectively


Table 2 Pearsonrsquos correlation coefficient between landscapemetricsof water and LST

Landscape metrics ValueClass proportion minus0604lowastlowast

MPI minus046lowastlowast

CA minus0604lowastlowast

MPS minus05lowastlowast

PSSD minus0515lowastlowast

TCA minus0613lowastlowast

MCA minus0436lowastlowast

CASD minus0515lowastlowast

TCAI minus0408lowastlowast

LPI minus061lowastlowast

MCAI minus0474lowastlowastlowastlowastCorrelation is significant at the 001 level

Table 3 Landscape metrics of water and their correspondingmeaning according to the Patch Analyst Help of ArcGIS

Landscapemetrics Meaning

Classproportion The proportion of class

MPI Measure of the degree of isolation and fragmentationCA Sum areas of all patches belonging to a given classMPS Average patch sizePSSD Patch size standard deviationTCA The total size of disjunct core patchesMCA The average size of disjunct core patchesCASD Measure of variability in core area sizeTCAI Measure of amount of core area in the landscape

LPI The LPI is equal to the percent of the total landscapethat is made up by the largest patch

MCAI The average core area per patchIJI Measure of patch adjacencyMNN Measure of patch isolation

Themodel is significant at the 005 level and the adjusted1198772 reached 0814 All independent variables are significant at

005 levels whose meaning is shown in Table 3 [19 20]According to the result it was found that the LST

has spatial autocorrelation with water body And the sur-rounding temperature calculated through weight matrix canhelp reduce the space correlation The results of Pearsonrsquoscorrelation analysis show that the proportion of water bodyaveragewater body size and the percent of the total landscapehave relationship with the mean LST They are made up bythe largest water body patch and the isolation and fragmen-tation of water body While the quantity shape and spatialdistribution of water body affect the forming of temperatureas resultant force the result of multiple stepwise regressionanalysis reveals how the attributes of water body influencetemperature

5 Conclusions and Discussions

Rational allocation of land resources not only maintains thesustainable development of social economy but alsomitigatesclimate change This research field has become one of thehottest issues in government and academy society Despitethe fact thatWuhan has abundant surface water resource theamount of water resources is under severe threat MeanwhileWuhan is facing problems caused by urban heat island

The UHI types can be generally categorized into airtemperature UHI and surface UHI [21 22] Air temperatureUHIs are generally stronger and exhibit greatest spatialvariations at night whereas the greatest difference in surfaceUHIs usually occurs during the daytime [21ndash23] In this studywe focus on remotely sensed land surface temperature (LST)surface UHI

Remotely sensed LST records the radiative energy emit-ted from the ground surface including building roofs pavedsurfaces vegetation bare ground and water [21 24] There-fore the pattern of land cover in urban landscapes maypotentially influence LST [21 25] This study took Wuhancity as the case area and investigated the impact of waterlandscape pattern on UHI Based on the mean LST andlandscapemetrics ofwater body via remote sensing techniqueand spatial analysis the relationship between the mean LSTand the attributes of water body was revealed by Pearsonrsquoscorrelation analysis andmultiple stepwise regression analysis

The results showed the following (1) Compared to otherland use types water body is corresponding to lower LST(2) In 32 class-based metrics we selected the proportionof water body average water body size the isolation andfragmentation of water body and other eight metrics haveobvious correlation with the LST whose Pearsonrsquos correlationcoefficient with temperature is higher than 04 Among the11 metrics class proportion CA TCA and LPI are of veryhigh correlation with LST The proportion of water bodyhas a negative correlation with the mean LST It means thatin a fixed region the lower the ratio of water body thehigher themean LSTMeanwhile the averagewater body sizethe percent of the total landscape which is made up by thelargest water body patch and the isolation and fragmentationof water body all have negative correlation with the meanLST (3) The LST is the result of comprehensive effectsof multifactors The result of multiple stepwise regressionanalysis indicated that the total size and amount of corewater patches have a larger effect on the LST except forthe variable of surrounding temperature calculated throughweight matrix The core water patches represent the interiorwater body with small external influence

By analyzing the relationship between the LST andthe attributes of water body it was found that the spatialcharacteristics of water body have impact on LST thusproviding decision support for governments to allocate waterresources

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Acknowledgments

This research was financially supported by the NationalKey Program for Developing Basic Science of China (Grantno 2010CB950904) the National Science Foundation ofChina (Grant no 41101098) the Ministry of EducationResearch of Social Sciences Youth funded Projects (Grant no10YJC790121) and the National Department Public BenefitResearch Foundation of the Ministry of Land and Resourcesof China (Grant no 201311001-5)

References

[1] H Liu and QWeng ldquoScaling effect on the relationship betweenlandscape pattern and land surface temperature a case study ofIndianapolis United Statesrdquo Photogrammetric Engineering andRemote Sensing vol 75 no 3 pp 291ndash304 2009

[2] W Zhang and B Huang ldquoLand use optimization for a rapidlyurbanizing city with regard to local climate change Shenzhen asa case studyrdquo Journal of Urban Planning and Development vol141 no 1 Article ID 05014007 2015

[3] L Huang D Zhao J Wang J Zhu and J Li ldquoScale impacts ofland cover and vegetation corridors on urban thermal behaviorinNanjing ChinardquoTheoretical and Applied Climatology vol 94no 3-4 pp 241ndash257 2008

[4] J P Connors C S Galletti and W T L Chow ldquoLandscapeconfiguration and urban heat island effects assessing the rela-tionship between landscape characteristics and land surfacetemperature in Phoenix Arizonardquo Landscape Ecology vol 28no 2 pp 271ndash283 2013

[5] X-L Chen H-M Zhao P-X Li and Z-Y Yin ldquoRemotesensing image-based analysis of the relationship between urbanheat island and land usecover changesrdquo Remote Sensing ofEnvironment vol 104 no 2 pp 133ndash146 2006

[6] Y Sun Q Guo J Liu and R Wang ldquoScale effects on spatiallyvarying relationships between urban landscape patterns andwater qualityrdquo Environmental Management vol 54 no 2 pp272ndash287 2014

[7] E A Hathway and S Sharples ldquoThe interaction of rivers andurban form in mitigating the Urban Heat Island effect a UKcase studyrdquo Building and Environment vol 58 pp 14ndash22 2012

[8] W Yue and L Xu ldquoThermal environment effect of urban waterlandscaperdquo Acta Ecologica Sinica vol 33 no 6 pp 1852ndash18592013

[9] H Wu L-P Ye W-Z Shi and K C Clarke ldquoAssessing theeffects of land use spatial structure on urban heat islands usingHJ-1B remote sensing imagery in Wuhan Chinardquo InternationalJournal of Applied Earth Observation and Geoinformation vol32 pp 67ndash78 2014

[10] Y Liu Q Wang and H Chen ldquoThe development protectingand planning envision of lakes in Wuhan Cityrdquo EconomicGeography vol 24 no 2 pp 192ndash196 2004

[11] Z-H Qin M-H Zhang A Karnieli and P Berliner ldquoMono-window algorithm for retrieving land surface temperature fromLandsat TM6 datardquo Acta Geographica Sinica vol 56 no 4 pp456ndash466 2001

[12] J Li ldquoStudy of relation between land cover condition and tem-perature based on LandsatTM datardquo Remote Sensing Technol-ogy and Application vol 13 no 1 pp 18ndash28 1998

[13] W-Z Yue J-H Xu and L-H Xu ldquoAn analysis on eco-environmental effect of urban land use based on remote sensingimages a case study of urban thermal environment and NDVIrdquoActa Ecologica Sinica vol 26 no 5 pp 1450ndash1460 2006

[14] B L Markham and J L Barker ldquoSpectral characterization ofthe Landsat thematic Mapper sensorsrdquo International Journal ofRemote Sensing vol 6 no 5 pp 697ndash716 1985

[15] A A VanDeGriend andMOwe ldquoOn the relationship betweenthermal emissivity and the normalized difference vegetationindex for natural surfacesrdquo International Journal of RemoteSensing vol 14 no 6 pp 1119ndash1131 1993

[16] NWeber D Haase and U Franck ldquoZooming into temperatureconditions in the city of Leipzig how do urban built and greenstructures influence earth surface temperatures in the cityrdquoScience of The Total Environment vol 496 pp 289ndash298 2014

[17] R M Chefaoui ldquoLandscape metrics as indicators of coastalmorphology a multi-scale approachrdquo Ecological Indicators vol45 pp 139ndash147 2014

[18] R F Kokaly and R N Clark ldquoSpectroscopic determination ofleaf biochemistry using band-depth analysis of absorption fea-tures and stepwise multiple linear regressionrdquo Remote Sensingof Environment vol 67 no 3 pp 267ndash287 1999

[19] K McGarigal and B J Marks FRAGSTATS Spatial PatternAnalysis Program for Quantifying Landscape Structure ForestScience Department Oregon State University Corvallis OreUSA 1995

[20] K H Riitters R V OrsquoNeill C T Hunsaker et al ldquoA factor anal-ysis of landscape pattern and structure metricsrdquo LandscapeEcology vol 10 no 1 pp 23ndash39 1995

[21] A J Arnfield ldquoTwo decades of urban climate research a reviewof turbulence exchanges of energy and water and the urbanheat islandrdquo International Journal of Climatology vol 23 no 1pp 1ndash26 2003

[22] J E Nichol W Y Fung K-S Lam and M S Wong ldquoUrbanheat island diagnosis using ASTER satellite images and lsquoin sitursquoair temperaturerdquo Atmospheric Research vol 94 no 2 pp 276ndash284 2009

[23] C M Frey E Parlow R Vogt M Harhash and M M AbdelWahab ldquoFlux measurements in Cairo Part 1 in situ measure-ments and their applicability for comparisonwith satellite datardquoInternational Journal of Climatology vol 31 no 2 pp 218ndash2312011

[24] J A Voogt and T R Oke ldquoThermal remote sensing of urbanclimatesrdquoRemote Sensing of Environment vol 86 no 3 pp 370ndash384 2003

[25] R T T Forman Land Mosaics The Ecology of Landscape andRegions CambridgeUniversity Press NewYork NYUSA 1995

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of


EarthquakesJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering


GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of


OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


MineralogyInternational Journal of


MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in


China mainlandHubei province

114∘09984000998400998400E 114

∘30

9984000998400998400E 115

∘09984000998400998400E

31∘30

9984000998400998400N

30∘30

9984000998400998400N

31∘09984000998400998400N

30∘09984000998400998400N

Study area Wuhan

0 5 10 20 30 40

(km)

N

Figure 1 Wuhan city the research area

statistical analysis theory and methods Pearsonrsquos correlationcoefficients were used to determine the effect of the spatialstructure of water on the LST Finally the computed experi-mental samples could be fitted to multiple linear regressionmodel by stepwise regressions

2 Study Area and Data Sources

21 Study Area Wuhan city is situated in central China andlocated between 113∘411015840ndash115∘051015840 E and 29∘581015840ndash31∘221015840N shownin Figure 1Wuhan is located at a subtropical humidmonsoonclimate zone with an annual mean precipitation about10961mm The average annual temperature ranges from158∘C to 175∘C and sometimes in summer the maximumtemperature can soar to 40∘C

The total territorial area of Wuhan is about 849441 km2and the water area is about one-fourth of the cityrsquos totalarea The famous rivers and lakes in Wuhan city are YangtzeRiver Han River East Lake South Lake and so on Theseriverslakes mentioned above together with other waterbodies of Wuhan are scattered and distributed in the studyarea And they have a big influence on humidity purifyingatmosphere of Wuhan climate

Wuhanrsquos economy strength has grown rapidly and levelsof the industrialization and urbanization were improved aswell By the end of 2005 the resident population of Wuhancity was 858 million and the annual GDP per capita reached26238 Yuan

Data preprocessing

Retrieval of LSTSupervised

classification

LST Land use map

Average LST in one grid Water-based metrics

Multiple stepwise regression analysis

Correlation relationship between water metrics and LST

Spot and TM images

Result

DataProcess

Gridding (3km lowast 3km)

Figure 2 Research framework of landscapemetrics of water on landsurface temperature

22 Data Sources This paper utilized three types of datasocial-economy statistical data Wuhan land use data andLST data derived from remote sensing images (1)The social-economy statistical data such as populationannual GDP percapita in this paper came from Wuhan Statistical Yearbookand Wuhan City Water Resources Bulletin (2) A Landsat5 TM imagery on September 11 2005 (patchrow 12239)was selected to quantitatively measure LST and the situationof LULC All bands of TM images except band 6 have aspatial resolution of 30m while band 6 (thermal infraredband) has a spatial resolution of 60m Before getting LULCinformation from the raw image image preprocessing such asgeometric rectification relative radiometric correction andatmospheric correction was completed In addition a Spotimage on December 15 2005 with a spatial resolution of 4mwas used to verify results of supervised classification in thestudy area

3 Methods and Models

31 Research Framework The basic idea of analysis forthe impacts of landscape metrics of water on land surfacetemperature is illustrated in Figure 2 Based on the Landsat



0

0 5 10 20

(km)

N

(a)

0 5 10 20

(km)


Cultivated land


N

(b)







minus2



255

+ 119871min(1)



One sample


Temperature





minus2


tions as follows

119871120582=

119871TM6119887

119879 =

1198962

ln (1198961119871120582+ 1)

(2)



119879119878=

119879

1 + (120582 lowast 119879120588) ln 120576 (3)



















Mean LST3925830

0 5 10 20

(km)

N



119910 = 07041199091minus 0557119909

2minus 0055119909

3+ 0128119909

4+ 0051119909

5

+ 00641199096+ 0076119909

7+ 0036119909

8

(4)




































Acknowledgments


References



































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in



0

0 5 10 20

(km)

N

(a)

0 5 10 20

(km)


Cultivated land


N

(b)







minus2



255

+ 119871min(1)



One sample


Temperature





minus2


tions as follows

119871120582=

119871TM6119887

119879 =

1198962

ln (1198961119871120582+ 1)

(2)



119879119878=

119879

1 + (120582 lowast 119879120588) ln 120576 (3)



















Mean LST3925830

0 5 10 20

(km)

N



119910 = 07041199091minus 0557119909

2minus 0055119909

3+ 0128119909

4+ 0051119909

5

+ 00641199096+ 0076119909

7+ 0036119909

8

(4)




































Acknowledgments


References



































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


One sample


Temperature





minus2


tions as follows

119871120582=

119871TM6119887

119879 =

1198962

ln (1198961119871120582+ 1)

(2)



119879119878=

119879

1 + (120582 lowast 119879120588) ln 120576 (3)



















Mean LST3925830

0 5 10 20

(km)

N



119910 = 07041199091minus 0557119909

2minus 0055119909

3+ 0128119909

4+ 0051119909

5

+ 00641199096+ 0076119909

7+ 0036119909

8

(4)




































Acknowledgments


References



































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in














Mean LST3925830

0 5 10 20

(km)

N



119910 = 07041199091minus 0557119909

2minus 0055119909

3+ 0128119909

4+ 0051119909

5

+ 00641199096+ 0076119909

7+ 0036119909

8

(4)




































Acknowledgments


References



































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in
































Acknowledgments


References



































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in










Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

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