NASA Technical Reports Server (NTRS) 20140011018: Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

4.6 MB·English

by  NASA Technical Reports Server (NTRS)

ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72
ContentslistsavailableatSciVerseScienceDirect
ISPRS Journal of Photogrammetry and Remote Sensing
journal homepage: www.elsevier.com/locate/isprsjprs
Estimation of the relationship between remotely sensed anthropogenic heat
discharge and building energy use
Yuyu Zhoua,⇑, Qihao Wengb, Kevin R. Gurneyc, Yanmin Shuaid,e, Xuefei Huf
aJointGlobalChangeResearchInstitute,PacificNorthwestNationalLaboratory,5825UniversityResearchCourt,Suite3500,CollegePark,MD20740,USA
bCenterforUrbanandEnvironmentalChange,DepartmentofEarthandEnvironmentalSystems,IndianaStateUniversity,TerreHaute,IN47809,USA
cSchoolofLifeSciences,ArizonaStateUniversity,P.O.Box874501,Tempe,AZ85287-4501,USA
dEarthResourcesTechnologyInc.,Laurel,MD20707,USA
eNASAGoddardSpaceFlightCenter,Greenbelt,MD20771,USA
fRollinsSchoolofPublicHealth,EmoryUniversity,Atlanta,GA30322,USA
a r t i c l e i n f o a b s t r a c t
Articlehistory: Thispaperexaminedtherelationshipbetweenremotelysensedanthropogenicheatdischargeandenergy
Received30May2011 usefromresidentialandcommercialbuildingsacrossmultiplescalesinthecityofIndianapolis,Indiana,
Receivedinrevisedform19August2011 USA.Theanthropogenicheatdischargewasestimatedwitharemotesensing-basedsurfaceenergybal-
Accepted17October2011
ancemodel,whichwasparameterizedusinglandcover,landsurfacetemperature,albedo,andmeteoro-
Availableonlinexxxx
logicaldata.ThebuildingenergyusewasestimatedusingaGIS-basedbuildingenergysimulationmodel
inconjunctionwithDepartmentofEnergy/EnergyInformationAdministrationsurveydata,theAssessor’s
Keywords:
parceldata,GISfloorareasdata,andremotesensing-derivedbuildingheightdata.Thespatialpatternsof
Anthropogenicheatdischarge
anthropogenicheatdischargeandenergyusefromresidentialandcommercialbuildingswereanalyzed
Buildingenergyuse
andcompared.Quantitativerelationshipswereevaluatedacrossmultiplescalesfrompixelaggregationto
Multi-scale
Urbanheatisland censusblock.Theresultsindicatethatanthropogenicheatdischargeisconsistentwithbuildingenergy
Urbanremotesensing useintermsofthespatialpattern,andthatbuildingenergyuseaccountsforasignificantfractionof
anthropogenic heat discharge. Theresearch also implies thatthe relationshipbetween anthropogenic
heatdischargeandbuildingenergyuseisscale-dependent.Thesimultaneousestimationofanthropo-
genicheatdischargeandbuildingenergyuseviatwoindependentmethodsimprovestheunderstanding
of the surface energy balance in anurban landscape. The anthropogenic heat discharge derived from
remotesensingandmeteorologicaldata may beableto serveas aspatial distribution proxy forspa-
tially-resolved building energy use, and even for fossil-fuel CO emissions if additional factors are
2
considered.
(cid:2)2011InternationalSocietyforPhotogrammetryandRemoteSensing,Inc.(ISPRS)Allrightsreserved.
1.Introduction pacts on the urban environment, a key issue in global environ-
mental change.
The urban heat island (UHI) phenomenon is formed when Themethodsusedtoestimateanthropogenicheatdischargecan
higher atmospheric and surface temperatures in urbanized areas be grouped into three major categories: inventory approaches,
are observed over the surrounding rural areas (Voogt and Oke, micrometeorologically-basedenergybudgetclosuremethods,and
2003).Basedonthesurfaceenergybalancetheory,UHIismainly buildingenergymodelingapproaches(Sailor,2011).Forexample,
caused by the combination of anthropogenic heat discharge due usingainventoryapproachLeeetal.estimatedanthropogenicheat
to energy consumption, increased impervious surface area, and emissionsintheGyeong-InregionofKoreain2002basedontheen-
decreased vegetation and water area (Kato and Yamaguchi, ergyconsumptionstatisticsdata(Leeetal.,2009).KatoandYamag-
2005).Humaninducedenergydischargehasanimportantimpact uchi separated the contribution of anthropogenic heat discharge
ontheurbanenvironmentintermsofthesurfaceenergybalance. andheatradiationduetosolarinputtothesensibleheatfluxby
Quantification of each heat flux in the energy balance, especially using an energy balance method (Kato and Yamaguchi, 2005).
the human induced anthropogenic heat discharge and its spatial HeipleandSailor(2008)estimatedthehourlyenergyconsumption
pattern,isimportanttoimprovetheunderstandingofhumanim- fromresidentialandcommercialbuildingsat100mspatialresolu-
tioninHouston,Texasusingthebuildingenergymodelingmethod.
⇑ Eachofthesemethodshasitsstrengthsandlimitations.Estimation
Correspondingauthor.Tel.:[phone].
E-mailaddress:[email protected](Y.Zhou). of anthropogenic heat emissions using the inventory approach is
0924-2716/$-seefrontmatter(cid:2)2011InternationalSocietyforPhotogrammetryandRemoteSensing,Inc.(ISPRS)Allrightsreserved.
doi:10.1016/j.isprsjprs.2011.10.007
66 Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72
basedontherealenergyconsumptiondataatthespatialscaleof
utility service territories (Ichinose et al., 1999; Klysik, 1996; Lee
etal.,2009;SailorandLu,2004;Taha,1997).Itisdifficulttoquan-
tify thespatial distributionofenergy consumption atfine spatial
and temporal scales. Spatial and temporal downscaling can only
beachievedwhenadditionaldatasuchaslanduseareemployed.
Energybalancemethodhasbeenappliedtoestimateenergyfluxes
inurbanareasfromsiteandneighborhoodscaletocityscale(Belan
etal.,2009;Massonetal.,2002;Oke,1988;Okeetal.,1999;Pigeon
etal.,2007;Pigeonetal.,2008).Highspatialresolutionanthropo-
genicheatdischargecanbeestimatedbasedonmicrometeorolog-
ically-based energy budget closure method by the combined use
of remote sensing and meteorological data (Bastiaanssen et al.,
1998a,b; French et al., 2005; Kato and Yamaguchi, 2005; Kato
etal.,2008;Schmuggeetal.,1998;vanderKwastetal.,2009).How-
ever,astheestimationofanthropogenicheatdischargeisbasedon
the residual of other components in the surface energy balance,
eachcomponentintroducesuncertaintiesandpropagateserrorsto-
wardthefinalestimatedanthropogenicheatflux.Byintegratingthe
geospatial data and simulated temporal pattern of energy con-
sumptionfor representativebuildings, energy consumption in all
buildings can be estimated using the building energy modeling
method (Heiple and Sailor, 2008; Ichinose et al., 1999; Zhou and
Gurney,2010).However,therepresentativebuildingsmaynotbe
easily categorized and the use of representative buildings may Fig.1. ThestudyareaofIndianapolisCity,MarionCounty,Indiana,USA.
introduce biasin the estimationof spatial and temporalpatterns
ofenergyuseforsomebuildingsasthesebuildingsmayhavediffer- FallCreek,nearthecenterofthestate.Itpresentlyextendsintonine
ent behaviors of energy consumption in terms of magnitude and MarionCountyTownships,includingPike,Washington,Lawrence,
temporalpatterncomparedtotherepresentativebuildings. Wayne, Center, Warren, Decatur, Perry and Franklin. The city is
Althoughanthropogenicheatdischargewasstudiedusingdiffer- locatedonaflatplainandisrelativelysymmetrical,havingpossibil-
ent methods in previous studies, there was limited research for itiesofexpansioninalldirections,andthecitycenterhasexpanded
cross-examinationofthespatialpatternofanthropogenicheatdis- into adjacent agricultural and rural lands through encroachment
charge.Eventhoughpreviousstudieshaveprovidedinsightsinto (WengandHu,2008).Indianapolisisahighlyurbanizedcitywith
thehumanimpactsontheurbanenvironmentdirectlyorindirectly, spatiallyheterogeneoushumanactivities,whichservesasanideal
simultaneous estimation of anthropogenic heat discharge using areaforthepurposeofthisstudy.
multiplemethodswillhelptoreducetheuncertaintiesinthestudy
ofurbanheatfluxesandimprovetheunderstandingoftheroleofhu- 2.2.Datasets
manactivitiesinthesurfaceenergybalanceanditscontributionto
UHI.Thisstudyaimstoimprovetheunderstandingoftheurbansur- AnthropogenicheatdischargeatnoontimeonJune16th,2001
faceenergybalanceandclarifythespatialpatternofenergyrelated (Saturday)wasestimatedusingasurfaceenergybalancemodeling
humanactivitieswiththehelpofremotesensingdata,technologies approach(Fig.2).Thedetailsoftheapproachwerediscussedinthe
andbuildingenergysimulationinahighlyurbanizedarea.Specifi- methodology section. The major data used in the energy balance
cally,anthropogenicheatdischargeandenergyusefromresidential method include meteorological data, land cover, albedo and land
and commercial buildings were investigated using two different surfacetemperature(LST).Impervioussurfacearea(ISA)wasalso
methods in the metropolitan Indianapolis, Indiana region. First, developed and utilized as a mask in the analysis. Meteorological
anthropogenicheatdischargewasestimatedusingasurfaceenergy data including air temperature, shortwave radiation, and wind
balancemethod.Thenenergyusefromresidentialandcommercial speed at noon time on June 16th, 2001 were retrieved from 3-
buildingswascalculatedusingabuildingenergymodelingmethod. hourlyMAT3FXCHMdataproductattheModelingandAssimilation
Finally, the relationship between remotely sensed anthropogenic DataandInformationServicesCenter(http://disc.sci.gsfc.nasa.gov/
heatdischargeandbuildingenergyusewasexaminedacrossmulti- MDISC/).Thedataprocessingoflandcover,albedo,LSTandISAis
plescalesfrompixelaggregationstocensusblockgroups. discussedbelowindetails.
LandcoverdatafromUSGeologicalSurveyNationalLandCover
2.Studyareaanddatasets Dataset(NLCD)2001werecollectedtoretrieveparametersforthe
estimationofsurfaceheatfluxes(Fig.3a).Thelandcoverclassifica-
2.1.Studyarea tionwasachievedbyusingaclassificationanddecisiontreemethod
withLandsatimageryandancillarydata(Homeretal.,2004).Main
Inthisstudy,Indianapolis/MarionCounty,Indianawaschosenas landcovertypesinthestudyareaincludeopenwater,urban,ever-
theexperimentarea(Fig.1).IndianapolisisthecapitaloftheStateof greenforest,deciduousforest,shrub,grassland,croplandandwet-
Indiana,andwaslistedasthe14thlargestcityintheUSAin2008, land.UrbanisthedominantlanduseinIndianapolisandaccounts
with a population of 798,382 (US Census Bureau, 2009). The city formorethan3/4ofthetotalarea.Thisdatasetwasutilizedtocreate
hasatemperateclimatewithoutpronouncedwetordryseasons, alookuptablebetweenlandcovertypeandmodelingparameters,
buthasobviousseasonalchanges.Thetotalannualheatingdegree including coefficient for ground heat flux, roughness length and
day value is 5521F, and the cooling degree day value is 1042F emissivity, which were developed in previous studies (Kato and
(NationalWeatherService,2002).Thesynopticweathercondition Yamaguchi,2005;Katoetal.,2008;Snyderetal.,1998).Landcover
during the study period is typical of mid-latitude summer time. basedparametersusedintheestimationofheatfluxeswillbefur-
ThecityofIndianapolisliesontheWhiteRiveratitsconfluencewith therdiscussedinthemethodologysection.
Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72 67
Fig.2. Methodologyusedtoestimateanthropogenicheatdischargeandenergyusefromresidentialandcommercialbuildings.
Fig.3. ModelinputdatainthestudyareaofIndianapolisincludinglandcover(a),LST(b),albedo(c),andISA(d).
Theadvancedspacebornethermalemissionandreflectionradi- NASA(Fig.3b).ThealgorithmforconvertingASTERthermalinfra-
ometer (ASTER) on-demand L2 surface kinetic temperature data redmeasurementstoLSTshasbeenreportedbytheASTERtemper-
with 90m resolution of June 16th, 2001 were purchased from ature/emissivityworkinggroup(Gillespieetal.,1999),withwhich
68 Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72
surfacekinetictemperaturewasdeterminedbyapplyingPlanck’s Yamaguchi, 2005; Kato et al., 2008; Oke, 1988; Xu et al., 2008).
Lawusingthe emissivityvaluesfromthetemperature-emissivity ThemethodisillustratedintheleftpanelofFig.2.Thesurfaceen-
separation(TES)algorithm.LSTvaluescalculatedusingthisproce- ergybalanceduetosurfacepropertiesandanthropogenicheating
dureareexpectedtohaveanabsoluteaccuracyof1–4Kandrela- inthenear-surfaceinanurbanareacanbeexpressedasfollows:
tiveaccuracyof0.3K,andsurfaceemissivityvaluesareexpectedto Q ¼HþLEþG(cid:2)R ð1Þ
have an absolute accuracy of 0.05–0.1 and relative accuracy of f n
0.005(Gillespieetal.,1999). whereQ istheanthropogenicheatdischarge(Wm(cid:2)2),Histhesen-
f
Landsurfacealbedomapin30mspatialresolutionwasgener- sibleheat(Wm(cid:2)2),LEisthelatentheatflux(Wm(cid:2)2),andGisthe
atedfromLandsatsurfacereflectanceandanisotropyinformation ground heat flux (Wm(cid:2)2), and R is the net radiation (Wm(cid:2)2).
n
basedonconcurrentmoderateresolutionimagingspectroradiom- Basedonthesurfaceenergybalancemodel,anthropogenicheatdis-
eter(MODIS)500mobservationsthroughthealgorithmdeveloped chargeQ canbeestimatedastheresidualterm.
f
byShuaietal.(2011)(Fig.3c).Thelevel1Landsat7enhancedthe- In the energy balance equation, net radiation (R ) is the sum
n
maticmapperplus(ETM+)imagecollectedonJuly11th,2001with of short and long wave radiation at the land surface (Kato and
goodqualitywas firstconvertedto30mlandsurface reflectance Yamaguchi,2005).Itiscalculatedas
through Landsat ecosystem disturbance adaptive processing sys- R ¼ð1(cid:2)aÞR þe eR (cid:2)eR ð2Þ
tem,anautomaticcalibrationandatmosphericcorrectionprocess n S a d u
(Maseketal.,2006).Thentheclassification-based 500mBidirec- where R is the shortwave radiation (Wm(cid:2)2) which is retrieved
s
tional reflectance distribution function (BRDFs) for the homoge- fromthemeteorologicaldata.aisthesurfacealbedowhichisde-
nous area were extracted from the concurrent MODIS BRDF rivedfromtheremotesensingdata,andthecreationof30mspatial
product (Schaaf et al., 2002) with the highest inversion quality resolutionalbedodataisdiscussedinthedatasection.eisthesur-
(Shuai et al., 2008), followed by the calculation of spectral albe- faceemissivity,anditisdeterminedbasedonlandtypefromNLCD
do-to-reflectance ratios for each class at the MODIS pixel scale. landcoverdata(Snyderetal.,1998).e istheatmosphericemissiv-
a
Next,thespectralLandsatalbedoswereinversedbyapplyingthese ity,anditisestimatedusinganempiricalequationusedbyKatoand
ratiosintoLandsatsurfacereflectance.Accordingtothevalidation Yamaguchi(2005)
with ground measurements, the Landsat albedo meets the accu- (cid:2) (cid:3)
racy required by regional climate modeling. Compared with the e ¼1:24 ea 1=7 ð3Þ
MODIS500mproducts,italsoprovidesadetailedlandscapetex- a Ta
ture and improved accuracy at the validation stations (Shuai
whereT is the atmospherictemperature (K) from meteorological
etal.,2011).Itwasassumedthattherewasnosignificantchange a
data,ande istheatmosphericwatervaporpressure(hPa),which
ofalbedobetweenJune16thandJuly11th,2001. a
isestimatedusingthemethodbyAllenetal.(1998)
ISAwasestimatedonJune16th,2001usingthemethoddevel-
opedbyWengetal.(2009)(Fig.3d).Throughthismethod,imper- RH e(cid:3)ðT Þþe(cid:3)ðT Þ
e ¼ mean min max ð4Þ
vious surface was calculated based on the relationship between a 100 2
the reflectance of two endmembers (high- and low-albedo) and
wheree(cid:3)(T ) is the saturationvapor pressure at daily minimum
the reflectance of the impervious areas. By examining the rela- min
temperature(hPa),e(cid:3)(T )isthesaturationvaporpressureatdaily
tionships between impervious surfaces and four endmembers, it max
maximum temperature (hPa), and RH is the mean relative
isfoundthatimpervioussurfaceswerelocatedonorneartheline mean
humidity(%).
connectingthe low- and high-albedo endmembers in the feature
R andR usedtocalculatenetradiation(R )arethedownward
space.Anestimationprocedurewasthusdevelopedbasedonthis anddupwardublackbodyradiation(Wm(cid:2)2),andntheyarecalculated
relationship by adding the fractions of high- and low-albedo
usingthefollowingequations(Xuetal.,2008)
endmembers.
Energyusefromcommercialandresidentialbuildingswasesti- R ¼rT4 ð5Þ
d a
matedusingabuildingenergysimulationmethod(rightpanelof R ¼rT4 ð6Þ
Fig.2).Inthismethod,thesimulationofbuildingenergyusewas u s
drivenbythetypicalmeteorologicalyeardata.Baseondataavail- whereristheStefan–Boltzmannconstant(5.67E(cid:2)08Wm(cid:2)2K(cid:2)4),
abilitynoontimeofJune15th,2002(Saturday)waschosentorep- andT isthesurfacekinetictemperature(K).Theprocesstoderive
s
resent building energy use for the typical weekend during the thesurfacetemperaturewasdiscussedinthedatasection.
summertimetobecomparabletotheremotelysensedanthropo- Sensibleheatflux(H)isestimatedbasedonthefollowingequa-
logic heat discharge. The major data used in the building energy tion (Allen et al., 1998; Kato and Yamaguchi, 2005; Kato et al.,
modelingincludesaGISdatabaseofbuildingfootprints,theCounty 2008;Xuetal.,2008)
Assessor’sparceldatabase,andthebuildingheightdata.AlocalGIS
databaseofbuildingfootprintswascollectedandusedtoretrieve H¼qC To(cid:2)Ta ð7Þ
p r
thefloorareaofeachbuilding.TheCountyAssessor’sparceldata- a
basewasaMicrosoftAccessbaseddataset.Itwasusedtoretrieve whereqistheairdensity(kgm(cid:2)3),C isthespecificheatofairat
p
thebuildingtypeandageandtolinkwiththeGISbuildingfoot- constant pressure (Jkg(cid:2)1K(cid:2)1), and T is the surface aerodynamic
o
print. Building heights were estimated from a remote sensing- temperature.AsT isdifficulttoobtain,remotesensingderivedsur-
o
baseddigitalsurfacemodelandadigitalelevationmodelandused face temperature was used to approach T (Kato et al., 2008). r
o a
tocalculatefloornumbersforeachbuilding. (sm(cid:2)1) is the aerodynamic resistance which is calculated using
thefollowingsimplerelation(Allenetal.,1998)
(cid:4) (cid:5) (cid:4) (cid:5)
3.Methodology ln zm(cid:2)d ln zh(cid:2)d
r ¼ z0m z0h ð8Þ
a k2u
3.1.Estimationofanthropogenicheatdischarge z
wherez istheheightofwindmeasurements(m),z istheheightof
m h
In this study, anthropogenic heat discharge was estimated humidity measurements (m), d is the zero plane displacement
based on a surface energy balance modeling method (Kato and height (m), z is the roughness length governing momentum
0m
Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72 69
transfer(m),z istheroughnesslengthgoverningtransferofheat modelusingthemethodbyZhouandGurney(2010).Withthecom-
0h
and vapor (m), k is the von Karman’s constant (0.4), and u is the binationofbuildingareafromGISfootprintdata,buildingtypefrom
z
windspeed(ms(cid:2)1)atheightzfromthemeteorologicaldata. theAssessor’sparceldata,floornumbersfrombuildingheightdata,
Latentheatflux(LE)isestimatedusingthefollowingequation and electric and non-electric EUIs for building prototypes, energy
(KatoandYamaguchi,2005) use in all residential and commercial buildings was estimated in
LE¼qCPcðers(cid:2)þeraÞ ð9Þ thestudyarea.
a s
c 4.Results
wheree isthesaturationvaporpressure(hPa), isthepsychromet-
s
ric constant (hPaK(cid:2)1), and r is the stomatal resistance (sm(cid:2)1)
s 4.1.Anthropogenicheatdischarge
which is estimated based on land type from the NLCD land cover
data(Katoetal.,2008).
The anthropogenic heat discharge in the study area at noon
Groundheatflux(G)isestimatedusingthefollowingempirical
timeonJune16th,2001wasestimatedusingtheenergybalance
equationbyKatoandYamaguchi(2005)
method.Toreducetheuncertaintiesintheevaluationofanthropo-
G¼CgRn ð10Þ genic heat discharge, especially in less developed areas, ISA data
wereusedtoexcludepixelswiththeISAlessthan25%.Theresult
whereC is afixedcoefficient basedontheNLCDland covertype
g ofanthropogenicheatdischargeestimationisshowninFig.4.The
andseason(KatoandYamaguchi,2005).
resultshowsthespatialpatternandmagnitudeofanthropogenic
heatdischargeintheCityofIndianapolisinatypicalsummertime.
3.2.Estimationofenergyusefromresidentialandcommercial The anthropogenic heat discharge ranges from 0 to 400Wm(cid:2)2,
buildings withameanvalueof32Wm(cid:2)2.Thehighanthropogenicheatdis-
chargeoccursinthedenseresidentialandcommercialareassuch
Energyusefromresidentialandcommercialbuildingswasesti- asthecenterofthecityandtheresidentialareasinthenorthwest
mated using a building energy modeling method, which is illus- cornerofthecity.ThetownshipofCenterhasthehighestanthro-
trated in the right panel of Fig. 2. The method for CO2 emissions pogenicheatdischargeof78Wm(cid:2)2asithashighdensityofcom-
quantification developed by Zhou and Gurney (2010), in which mercial buildings while the Franklin township with large rural
on-sitefossil-fuelCO2emissionsfromresidentialandcommercial areasobservesthelowestvalue.Therearenegativevaluesin the
buildings were quantified by downscaling the county-level com- anthropogenicheatdischargeestimation,althoughittheoretically
mercial and residential fossil fuel CO2 emissions, was modified should be greater than 0Wm(cid:2)2. These negative values are re-
and extended. Using the modified method energy use from resi- gardedasestimationerrors,andweresetto0Wm(cid:2)2inthefinal
dentialandcommercialbuildingswasestimatedforeachbuilding. result.
First, a building typology was developed to define the building
typesforresidentialandcommercialbuildingstockinIndianapolis.
4.2.Buildingenergyuse
Atotalof30buildingprototypes,22commercialandeightresiden-
tial,weredefinedbasedonthebuildingtype,size,andagewiththe
Using the building energy modeling approach, the energy use
helpoftheMarionCountyAssessor’sparceldatabase.
fromresidentialandcommercialbuildingsineachbuildingatnoon
Second,hourlybuildingEnergyUseIntensity(EUI)–theenergy
timeonJune15th,2002wasestimatedinthestudyarea.Thespa-
used per unit floor area – was simulated using the Quick Energy
tialdistributionoftheenergyusefromresidentialandcommercial
SimulationTool(eQUEST)foreachbuildingprototype(USDepart-
buildingsatare-sampled90m spatialresolutionisillustratedin
mentofEnergy,2009).ThemajorparametersusedineQUESTsim-
ulation including building shell characteristics (e.g., R value),
internalload(e.g.,lighting)andHeating,Ventilating,andAirCon-
ditioningsystemcharacteristicswerederivedfromHuangetal.for
theNorthCentralUScensusregion(Huangetal.,1991).Indianap-
oliswasspecifiedasthelocationineQUESTsimulationtoretrieve
thetypicalweatherinformationforthisregion.TheEUIsandthe
ratio of the non-electric to electric EUI values from the eQUEST
simulation were calibrated using the US Department of Energy’s
EnergyInformationAdministrationdatafortheEastNorthCentral
Census Division from its residential energy consumption survey
and commercial building energy consumption survey (US Energy
InformationAdministration,2001;USEnergyInformationAdmin-
istration,2003).
Finally,energyuseineachbuildingwasestimatedfromthecal-
ibratednon-electricandelectricEUIs,buildingareaandnumberof
floorsasfollows:
(cid:4) (cid:5)
EbuildingðiÞ¼ EUIet;lbeuildingðiÞþEUInt;obnueilldeingðiÞ AbuildingðiÞNFbuildingðiÞ ð11Þ
where EUIele is the electric energy use intensity of building
t;buildingðiÞ
typetforbuildingi,EUInonele isthenon-electricenergyuseinten-
t;buildingðiÞ
sityofbuildingtypetforbuildingi,AbuildingðiÞisthefootprintareaof
buildingi(whichwasretrievedfromalocalGISdatabaseofbuilding
footprints), and NFbuildingðiÞ is the number of floors for building i
whichwasestimatedbasedonbuildingheightcalculatedfromare-
mote sensing-based digital surface model and a digital elevation Fig.4. Thespatialdistributionofanthropogenicheatdischarge.
70 Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72
Fig.5.Tobeconsistentwiththeanthropogenicheatdischargeesti- anthropogenicheatdischargeandbuildingenergyuse.Itisworth
mation,thebuildingenergyusewasexaminedwiththeexclusion noting that there are uncertainties in both datasets of anthropo-
ofpixelswiththeISAlessthan25%.Theenergyusefromresidential genicheatdischargeandbuildingenergyusewhichmightcontrib-
andcommercialbuildingsrangesfrom0to85Wm(cid:2)2withamean utetothediscrepancybetweenthesetwovariablestobediscussed
valueof5.5Wm(cid:2)2.Thespatialvariationofbuildingenergyuseis inthenextparagraph.Theuncertaintyintheestimatedanthropo-
mainlydeterminedbybuildingdensity,heightandtype.Thespatial genicheatdischargemainlycamefromeachcomponentinthesur-
patternshowsthatthehighestbuildingenergyuseoccursatthecity faceenergybalance.Theinputparametersofmeteorologicaldata,
center,andthatthetownshipofCenterpossessesthehighestenergy land cover, land surface temperature, and albedo may introduce
useof36Wm(cid:2)2.Thebuildingenergyuseisalsohighindenseresi- theuncertaintytowardsthefinalestimationofanthropogenicheat
dentialareasassociatedwithsuburbandevelopmentintheouter discharge. The uncertainty in the estimated building energy use
ringofHighway465inIndianapolisasshowninFig.5. mainly came from EUIs using eQUEST simulation and building
heightextractedfromadigitalsurfaceandadigitalelevationmod-
el. The uncertainty analysis of fossil-fuel CO emissions which is
2
4.3.Spatialrelationship,uncertaintyandscaleeffect
based on non-electric building energy use can be found in the
studybyZhouandGurney(2010)usingaMonteCarloapproach.
Although energy use from residential and commercial build-
Theuncertaintyofnon-electricenergyuseineachtownisapprox-
ings represents only one component of anthropogenic heat dis-
imatelywithin10%ofthetotalenergyuse.
charge, it shows an obvious consistency in terms of spatial
Thediscrepancybetweenbuildingenergyuseandanthropogenic
distribution with remote sensing-derived anthropogenic heat
heatdischargeiscausedbyseveralfactors.First,anthropogenicheat
discharge. Visual comparison between anthropogenic heat dis-
dischargeincludesnotonlyenergyusefromresidentialandcom-
charge (Fig. 4) and building energy use (Fig. 5) shows that they
mercialbuildings,butalsoothersourcessuchasenergyusefrom
both are higher in the dense residential and commercial areas
industrialbuildingsandtransportation.Thequantitativerelation-
and lower in the rural areas. In order to evaluate the quantita-
shipconfirmsthatbuildingenergyuseaccountsforpartofanthro-
tive relationship two variables were compared at pixel levels
pogenic heat discharge. Second, energy use from residential and
as well as at the census block level. First, the building energy
commercialbuildingswasestimatedbasedonthetypicalweather
use and anthropogenic heat discharge data were re-sampled to
informationatnoontimeonJune15th,2002whiletheanthropo-
a variety of spatial resolutions of 90m, 180m, 270m, 360m
genic heat discharge was estimated at noon time on June 16th,
and 450m. Furthermore, the pixels with 0.5% of the ranked
2001. The different dates of the input data may have introduced
building energy use in the both ends, which are considered as
inconsistencybetweentwovariables.Third,asonlylowresolution
outliers,wereexcludedin the analysis. Therelationshipbetween
meteorologicaldatasuchaswindspeedandairtemperatureused
thesetwovariablesacrossthesescalesandtheadditionalcensus
intheenergybalancemethod,thespatialvariationofimpactfrom
block level is shown in Fig. 6a–f. The result indicates that the
meteorologicalfactorswasnotconsideredduetodatalimitation.Fi-
correlation between building energy use and anthropogenic heat
nally,imageregistrationofdatausedintheestimationoftwovari-
discharge is statistically significant at all spatial scales
ables may also bring in uncertainties in the investigation of the
(p<0.001), and that the relationship between two variables var-
relationship.Itisoneofthereasonsthatthecorrelationbetween
ieswithscales. Theexplainedvariance ofbuildingenergy use by
anthropogenicheatdischargeandbuildingenergyusevarieswith
anthropogenic heat discharge increases with the decreasing spa-
thespatialresolution.Withthedecreasingspatialresolution,theer-
tial resolution.
rorofthespatialregistrationbetweentwoimagesdecreases.There-
Apotentialapplicationofthisstudyistoreducetheuncertainty
forethecorrelationbetweenbuildingenergyuseandanthropogenic
inurbanenergybalancestudythroughindependentestimationof
heatdischargeincreasesasindicatedbyFig.6.
5.Implications
There are still challenges to obtaining accurate estimation of
eachcomponentofheatfluxintheurbanenergybalancesuchas
the ground heat flux. Accurate estimation of anthropogenic heat
dischargewillhelptobetterunderstandtheurbanenergybalance
and separate the contribution of anthropogenic heat discharge
fromthatofgroundheatflux.Bycombiningwithenergyusefrom
othersourcessuchasindustrialbuildingsandtransportation,the
spatially-resolved energy use from residential and commercial
buildings will improve the estimation of anthropogenic heat dis-
charge,andtherefore,helptoreducetheuncertaintiesinthestudy
ofurbanenergybalance.
The relationship between building energy use and anthropo-
genic heat discharge indicates that anthropogenic heat discharge
may be able to serve as a spatial distribution proxy for the spa-
tially-resolvedenergyuseinanurbanlandscape.Inaddition,be-
cause of the relationship between energy use and fossil-fuel CO
2
emissions, anthropogenic heat discharge,which is relatively easy
toobtainwiththehelpofremotelysenseddata,providesthepo-
tentialtodevelopspatially-resolvedfossil-fuelCO emissionsfrom
2
building sectors with additional considerations. Together with
Fig. 5. The spatial distribution of energy use from commercial and residential point source and mobile emissions, the spatially-resolved fossil-
buildings. fuel CO2 emissions dataset will help to improve the scientific
Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72 71
Fig.6. Therelationshipbetweenanthropogenicheatdischargeandenergyusefromresidentialandcommercialbuildingsat90m(a),180m(b),270m(c),360m(d),450m
(e)spatialresolution,andtheUScensusblocklevel(f).
understandingofthecontributionofhumaninducedemissionsto confirm that the energy use from residential and commercial
atmosphericconcentrationthroughtransportmodelingintheur- buildings represents only partial anthropogenic heat discharge
bandomain. whichincludesadditionalcomponentsofenergyusesuchasthose
fromindustrialbuildingsandtransportation.
Thespatialconsistencybetweenanthropogenicheatdischarge
6.Conclusions and energy use from residential and commercial buildings indi-
catesthat remotelysensedanthropogenicheat dischargemay be
Inthisstudy,theanthropogenicheatdischargeandenergyuse abletoserveasaspatialproxytoderivethespatialpatternofen-
from residential and commercial buildings were estimated using ergyuse,andevenfossil-fuelCO2emissionswithconsiderationof
twoindependentmethodssimultaneouslyintheCityofIndianap- additionalfactors.Asthespatialrelationshipbetweenbuildingen-
olis in the summer time. The anthropogenic heat discharge was ergy use and anthropogenic heat discharge varies with spatial
estimated using a remote sensing method, while the energy use scales,appropriatespatialresolutionshouldbeidentifiedinfuture
from residential and commercial buildings was estimated with a studies to aid the development of spatially-resolved energy use
GIS-based simulationmethod. The mean anthropogenic heat dis- datasets.
chargeis32Wm(cid:2)2whiletheenergyusefromresidentialandcom- Theindependentestimationofbuildingenergyuseandanthro-
mercial buildings is 5.5Wm(cid:2)2. The visual comparison and pogenicheatdischargewillalsohelptoimprovetheestimationof
quantitative analysis show consistency between anthropogenic otherheatfluxesandtheunderstandingofenergybalanceinthe
heatdischargeandbuildingenergyuseintermsofthespatialpat- urbanareas.Inordertoreducetheuncertaintiesinthestudyofur-
tern and magnitude. The spatial relationship between anthropo- banenergybalance,furtherstudiesofenergyusefromotherhu-
genic heat discharge and building energy use varies with the man activities, especially industrial activities and transportation,
spatial scales, and the explained variation of the building energy shouldbecombinedwiththeestimationofenergyusefromresi-
usebyanthropogenicheatdischargeincreaseswiththedecreasing dentialandcommercialbuildingstoobtainamorecompletepic-
spatialresolution.Theresultsofthequantitativerelationshipalso tureofhumanenergyuseinanurbandomain.
72 Y.Zhouetal./ISPRSJournalofPhotogrammetryandRemoteSensing67(2011)65–72
References Pigeon,G.,Legain,D.,Durand,P.,Masson,V.,2007.Anthropogenicheatreleaseinan
old European agglomeration (Toulouse, France). International Journal of
Climatology27(14),1969–1981.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration
Pigeon,G.,Moscicki,M.A.,Voogt,J.A.,Masson,V.,2008.Simulationoffallandwinter
Guidelines for Computing Crop Water Requirements. Food and Agriculture
surface energy balance over a dense urban area using the TEB scheme.
Organization,Rome,Italy.
MeteorologyandAtmosphericPhysics102(3),159–171.
Bastiaanssen,W.G.M.,Menenti,M.,Feddes,R.A.,Holtslag,A.A.M.,1998a.Aremote
Sailor, D.J., 2011. A review of methods for estimating anthropogenic heat and
sensingsurfaceenergybalancealgorithmforland(SEBAL).Part1:Formulation.
moisture emissions in the urban environment. International Journal of
JournalofHydrology212–213,198–212.
Climatology31,189–199(SpecialIssue).
Bastiaanssen,W.G.M.,Pelgrum,H.,Wang,J.,Ma,Y.,Moreno,J.F.,Roerink,G.J.,van
Sailor, D.J., Lu, L., 2004. A top-down methodology for developing diurnal and
derWal,T.,1998b.Aremotesensingsurfaceenergybalancealgorithmforland
seasonal anthropogenic heating profiles for urban areas. Atmospheric
(SEBAL).Part2:Validation.JournalofHydrology212–213,213–229.
Environment38(17),2737–2748.
Belan,B.,Pelymskii,O.,Uzhegova,N.,2009.Studyoftheanthropogeniccomponent
Schaaf, C.B., Gao, F., Strahler, A.H., Lucht, W., Li, X.W., Tsang, T., Strugnell, N.C.,
ofurbanheatbalance.AtmosphericandOceanicOptics22(4),441–445.
Zhang,X.Y.,Jin,Y.F.,Muller,J.P.,Lewis,P.,Barnsley,M.,Hobson,P.,Disney,M.,
French,A.N.,Jacob,F.,Anderson,M.C.,Kustas,W.P.,Timmermans,W.,Gieske,A.,Su,
Roberts, G., Dunderdale, M., Doll, C., D’entremont, R.P., Hu, B.X., Liang, S.L.,
Z.,Su,H.,McCabe,M.F.,Li,F.,Prueger,J.,Brunsell,N.,2005.Surfaceenergyfluxes
Privette, J.L., Roy, D., 2002. First operational BRDF albedo nadir reflectance
with the advanced spaceborne thermal emission and reflection radiometer
productsfromMODIS.RemoteSensingofEnvironment83(1–2),135–148.
(ASTER)attheIowa2002SMACEXsite(USA).RemoteSensingofEnvironment
Schmugge,T.J.,Kustas,W.P.,Humes,K.S.,1998.Monitoringlandsurfacefluxesusing
99(1–2),55–65.
ASTERobservations.IEEETransactionsonGeoscienceandRemoteSensing36
Gillespie,A.,Rokugawa,S.,Hook,S.,Matsunaga,T.,Kahle,A.,1999.Temperature/
(5),1421–1430.
emissivity separation algorithm theoretical basis document (version 2.4),
Shuai,Y.,Masek,J.G.,Gao,F.,Schaaf,C.B.,2011.Analgorithmfortheretrievalof30-
<http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/ASTER/
msnow-freealbedofromLandsatsurfacereflectanceandMODISBRDF.Remote
atbd-ast-03.pdf>(accessed14.8.2011).
SensingofEnvironment115(9),2204–2216.
Heiple, S., Sailor, D.J., 2008. Using building energy simulation and geospatial
Shuai,Y.,Schaaf,C.B.,Strahler,A.H.,Liu,J.,Jiao,Z., 2008.Qualityassessment of
modeling techniques to determine high resolution building sector energy
BRDF/albedo retrievals in MODIS operational system. Geophysical Research
consumptionprofiles.EnergyandBuildings40(8),1426–1436.
Letter35,L05407.
Homer,C.,Huang,C.,Yang,L.,Wylie,B.,Coan,M.,2004.Developmentofa2001
Snyder,W.C.,Wan,Z.,Zhang,Y.,Feng,Y.-Z.,1998.Classification-basedemissivityfor
National Land-Cover Database for the United States. Photogrammetric
land surface temperature measurement from space. International Journal of
EngineeringandRemoteSensing70(7),829–840.
RemoteSensing19(14),2753–2774.
Huang,Y.J.,Akbari,H.,Rainer,L.,Ritschard,R.,1991.481PrototypicalCommercial
Taha,H.,1997.Urbanclimatesandheatislands:albedo,evapotranspiration,and
Buildingsfor20UrbanMarketAreas.LawrenceBerkeleyLaboratory,Berkeley,
anthropogenicheat.EnergyandBuildings25(2),99–103.
CA,USA.
US Department of Energy, 2009. eQUEST, <http://www.doe2.com> (accessed
Ichinose,T.,Shimodozono,K.,Hanaki,K.,1999.Impactofanthropogenicheaton
15.8.2011).
urbanclimateinTokyo.AtmosphericEnvironment33(24–25),3897–3909.
US Energy Information Administration, 2001. Residential Energy Consumption
Kato,S.,Yamaguchi,Y.,2005.Analysisofurbanheat-islandeffectusingASTERand
Survey.EnergyInformationAdministration,Washington,DC,USA.
ETM+ data: separation of anthropogenic heat discharge and natural heat
US Energy Information Administration, 2003. Commercial Building Energy
radiationfromsensibleheatflux.RemoteSensingofEnvironment99(1–2),44–
ConsumptionSurvey.EnergyInformationAdministration,Washington,DC,USA.
54.
US Census Bureau, 2009. Annual Estimates of the Resident Population for
Kato,S.,Yamaguchi,Y.,Liu,C.-C.,Sun,C.-Y.,2008.Surfaceheatbalanceanalysisof
IncorporatedPlacesover100,000,<http://www.census.gov/popest/cities/SUB-
TainanCityonMarch6,2001usingASTERandFormosat-2data.Sensors8(9),
EST2008.html>(accessed15.8.2011).
6026–6044.
vanderKwast,J.,Timmermans,W.,Gieske,A.,Su,Z.,Olioso,A.,Jia,L.,Elbers,J.,
Klysik,K.,1996.Spatialandseasonaldistributionofanthropogenicheatemissions
Karssenberg, D., de Jong, S., 2009. Evaluation of the surface energy balance
inLodz,Poland.AtmosphericEnvironment30(20),3397–3404.
system(SEBS)appliedtoASTERimagerywithflux-measurementsattheSPARC
Lee,S.H.,Song,C.K.,Baik,J.J.,Park,S.U.,2009.Estimationofanthropogenicheat
2004site(Barrax,Spain).HydrologyandEarthSystemSciencesDiscussion6(1),
emissionintheGyeong-InregionofKorea.TheoreticalandAppliedClimatology
1165–1196.
96(3),291–303.
Voogt, J.A., Oke, T.R., 2003. Thermal remote sensing of urban climates. Remote
Masek,J.G.,Vermote,E.f.,Saleous,N.,Wolfe,R.,Hall,F.G.,Huemmrich,F.,Gao,F.,
SensingofEnvironment86(3),370–384.
Kutler,J.,Lim,T.K.,2006.ALandsurfacereflectancedatasetforNorthAmerica,
Weng,Q.,Hu,X.,2008.Mediumspatialresolutionsatelliteimageryforestimatingand
1990–2000.GeoscienceandRemoteSensingLetters3(1),68–72.
mapping.UrbanImperviousSurfacesUsingLSMAandANN46(8),2397–2406.
Masson, V., Grimmond, C.S.B., Oke, T.R., 2002. Evaluation of the town energy
Weng,Q.,Hu,X.,Liu,H.,2009.Estimatingimpervioussurfacesusinglinearspectral
balance(TEB)schemewithdirectmeasurementsfromdrydistrictsintwocities.
mixture analysis withmultitemporalASTER images. InternationalJournal of
JournalofAppliedMeteorology41(10),1011–1026.
RemoteSensing30(18),4807–4830.
NationalWeatherService,2002.Climatologicalnormalsfortheperiod1971–2000,
Xu,W.,Wooster,M.J.,Grimmond,C.S.B.,2008.Modellingofurbansensibleheatflux
<www.crh.noaa.gov/ind/print_localdata.php?loc=txtdat&data=climatenormals.txt>
at multiple spatial scales: a demonstration using airborne hyperspectral
(accessed15.8.2011).
imagery of Shanghai and a temperature-emissivity separation approach.
Oke,T.R.,1988.Theurbanenergybalance.ProgressinPhysicalGeography12(4),
RemoteSensingofEnvironment112(9),3493–3510.
471–508.
Zhou,Y.,Gurney,K.,2010.Anewmethodologyforquantifyingon-siteresidential
Oke,T.R.,Spronken-Smith,R.A.,Jáuregui,E.,Grimmond,C.S.B.,1999.Theenergy
and commercial fossil fuel CO2 emissions at the building spatial scale and
balanceofcentralMexicoCityduringthedryseason.AtmosphericEnvironment
hourlytimescale.CarbonManagement1(1),45–56.
33(24–25),3919–3930.