Urban Forestry & Urban Greening

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ContentslistsavailableatSciVerseScienceDirect

Urban

Forestry

&

Urban

Greening

j o ur na l ho m e p a g e :w w w . e l s e v i e r . d e / u f u g

Tree

and

impervious

cover

change

in

U.S.

cities

David

J. Nowak

∗

, Eric

J. Greenﬁeld

USDAForestService,NorthernResearchStation,5MoonLibrary,SUNY-ESF,Syracuse,NY13210,UnitedStates

a

r

t

i

c

l

e

i

n

f

o

Keywords: Citytrees Ecosystemservices Forestmonitoring Urbanforestry Urbangreening Urbantrees

a

b

s

t

r

a

c

t

Pairedaerialphotographswereinterpretedtoassessrecentchangesintree,imperviousandothercover typesin20U.S.citiesaswellasurbanlandwithintheconterminousUnitedStates.Nationalresults indicatethattreecoverinurbanareasoftheUnitedStatesisonthedeclineatarateofabout7900ha/yr or4.0milliontreesperyear.Treecoverin17ofthe20analyzedcitieshadstatisticallysignificantdeclines intreecover,while16citieshadstatisticallysignificantincreasesinimperviouscover.Onlyonecity (Syracuse,NY)hadastatisticallysignificantincreaseintreecover.Citytreecoverwasreduced,onaverage, byabout0.27percent/yr,whileimpervioussurfacesincreasedatanaveragerateofabout0.31percent/yr. Astreecoverprovidesasimplemeanstoassessthemagnitudeoftheoverallurbanforestresource, monitoringoftreecoverchangesisimportanttounderstandhowtreecoverandvariousenvironmental benefitsderivedfromthetreesmaybechanging.Photo-interpretationofdigitalaerialimagescanprovide asimpleandtimelymeanstoassessurbantreecoverchangetohelpcitiesmonitorprogressinsustaining desiredurbantreecoverlevels.

Published by Elsevier GmbH.

Introduction

Treecoverincitiesisconstantlychangingduetovariousnatural andanthropogenicforces.Naturalforcesforchangeincludenatural regeneration,treegrowthandtreemortalityfrominsectsand dis-easesoroldage.Anthropogenicfactorsthatinﬂuencetreecover includetreeplantingandtreemortalityorremovalfromeither directorindirecthumanactionssuchasdevelopmentandair pollu-tion(Nowak,1993).Thecombinationofthesefactorsthroughtime determinesexistingandfuturetreecoverlevels.

Animportantquestionforcitymanagersishowtheirlocaltree coveriscurrentlychangingaspresent-daybeneﬁtsderivedfrom urbanforestsarerelatedtotheamountoftreecoverincities.As manyurbanforestecosystemservicesaredirectlyrelatedtothe amountofhealthyandfunctioningleaves,treecoverbecomesa simplemeasureoftheextentoftheurbanforestandconsequently themagnitudeofservicesprovidedbytheforest.Tohelpsustain treecoverincities,variouscityprogramsareplantinglarge num-bers oftrees (e.g.,Cityof New York,2011;CityofLosAngeles, 2011),protectingexistingtrees(e.g.,TownofChapelHill,2011; CityofPasadena,2011)anddevelopingtreecanopygoals(e.g.,City ofSeattle,2011;MarylandDepartmentofNaturalResources,2011). Thoughtreecoverincitiesisconstantlychanging,limited stud-ieshave investigated how overall tree cover in cities hasor is

∗Correspondingauthor.Tel.:+13154483212;fax:+13154483216.

E-mailaddresses:dnowak@fs.fed.us(D.J.Nowak),ejgreenﬁeld@fs.fed.us

(E.J.Greenﬁeld).

changing.Nowak(1993)illustratedthroughananalysisof histori-calimageryanddocumentsthatthetreecoverinOakland,CA,has increasedfromapresettlementtreecoverofapproximately2 per-centin1850sto19percentin1991.Landcovermapshavebeen usedtoquantifyhowvariouscoverclasseshavechangedthrough time,butassessmentsoftreecoverchangewithincitiesare lim-ited(e.g.,Zhouetal.,2008).InSeattle,treecoverwasestimatedto changefrom22.5percentin2002to22.9percentin2007by com-paringdigital landcovermapsdevelopedfrom0.6mresolution imagery(Parlin,2009).However,theaccuracyofthemap classiﬁ-cationisunknownandcomparingcovermapstoestimatechange canleadtofalsechangesduetomisclassiﬁcationofcovertypeson eithermap.

Variouslandcoverchangeanalyseshavebeenconductedusing satellite-basedapproaches.ModerateResolutionImaging Spectro-radiometer(MODIS)data(250-m)andLandsatdata(30-m)have andarebeingusedtoestimatechangesinlandcoverand imper-vioussurfacecover(e.g.,Yangetal.,2003; Lunettaetal.,2006; U.S.EPA,2011).MODISdata(500-m)alsohastheabilityto esti-matechangeinpercenttreecoveracrosstheglobe(Hansenetal., 2003;Schwarzetal.,2006).Thesesatellite-basedapproacheshave limitationsbasedonimageresolutionandinaccuraciesofimage classiﬁcations.Photo-interpretationofhighresolutionimagesto detect cover changes hastheability to overcomethese limita-tions,butlackstheabilitytodevelopdetailedcomprehensivecover changemaps.

Treesand impervious surfacesprovidenumerous ecosystem servicesandvaluestoacommunity,butalsohavevariouseconomic orenvironmentalcosts.Treesprovidevariousbeneﬁtsassociated

1618-8667/$–seefrontmatter.Published by Elsevier GmbH. doi:10.1016/j.ufug.2011.11.005

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withairandwaterquality,buildingenergyconservation,coolerair temperatures,reductionsinultravioletradiation,andmanyother environmentalandsocialbeneﬁts(e.g.,Dwyeretal.,1992;Kuoand Sullivan,2001;Westphal,2003;Wolf,2003;NowakandDwyer, 2007).Costsassociatedwithtreesarebotheconomic(e.g.,planting andmaintenanceandincreasedbuildingenergycosts)and envi-ronmental(e.g.,pollenandvolatileorganiccompoundemissions) (NowakandDwyer,2007).

Likewise,imperviouscoverplaysanimportantroleinthe land-scape,particularlyinurbanareas.Thesesurfaces,suchasroads, buildings, sidewalks, and parking lots, facilitate transportation andprovideshelter,butalsocannegativelyimpactthe environ-ment.Increasedimpervioussurfacesenhancelocaltemperatures and heat islands (Oke, 1989; Heisler and Brazel, 2010), which consequently affects building energy use, human comfort and health, ozone production, and pollutant emissions in cities. In addition,impervioussurfacessigniﬁcantlyaffecturbanhydrology (e.g.,streamﬂowandwaterquality)(e.g.,U.S.EPA,1983;National ResearchCouncil,2008).

As development occurs in forests, tree cover will decrease tomake space for buildings and other impervious surfaces. In non-forestregions, treecover can increase due tourbanization (unpublished data). Thus, urbanization as a process will alter regionaltree cover.As treecover changes in cities, sowillthe associatedecosystemservicesandtheireffectsonenvironmental qualityandhumanhealth. Unfortunately,withinexisting cities, ratesand directionof changein treeand imperviouscover are largelyunknown.Thispaperinvestigatestreeandimperviouscover changein urbanareasand selectcitiesacrosstheUnitedStates usingasimpleandrepeatablemeasurethatcanbeusedworldwide wherepairedmulti-yeardigitalaerialimageryexists.The objec-tiveofthispaperistodeterminethecurrentdirectionandrateof treeandimperviouscoverchangeinU.S.citiestohelpguidecities insustainingdesiredtreecoverlevelsandassociatedecosystem services.

Methods

Todeterminethepercenttree/shrubcover(hereafterreferred toastreecoverorcanopy)andimperviouscoverchangeincitiesin theUnitedStates,20citiesfromacrossthenationwereselected

(Table 1).Some citieswere selectedbased onexisting projects (Syracuse,NY; Baltimore,MD;Spokane, WA).Othercitieswere selectedbypickingmajorcitiesscatteredthroughoutthe contermi-nousUnitedStateswherepairedimagerycouldbeobtained.Two citieswerespeciﬁcallyselectedtodeterminetheeffectofrecent suspectedtreecoverchange:(1)NewOrleans,LA(effectof2005 HurricaneKatrina),and(2)Detroit,MI(effectofrecentinfestation ofemeraldashborer(Agrilusplanipennis)).Foreach city,paired digitalaerialphotographswereobtainedforthemostrecentdate possibleandimageryascloseto5yrpriortothemostcurrentdate aspossible.

In18ofthe20cities,1000randompointswerelaidand inter-pretedacrossthecitytoprovideamaximumstandarderrorof1.6 percentifallpointsareclassiﬁed(LindgrenandMcElrath,1969). Intwocities,morepointswerelaidandinterpreted(Baltimore: 2500points;Spokane,WA:2000points).Citygeographic bound-ariesweredeterminedusing censusincorporatedor designated placesboundaries(U.S.CensusBureau,2007).Eachpointwaslaid inthesamegeographicpositiononbothsetsoftemporalimagesin thecity,andpairedimageinterpretationwasconducted(i.e., inter-preterclassiﬁedeachpointpairbycontrastingandclassifyingthe imagepointsinsequence).Incasesofmisregistrationoftheimage orpoint,theinterpretercorrectedthepointlocationtoensurethe exactsamelocationwasinterpreted.Forexample,sometimesthe pointswouldshiftpositionslightlybetweenimagesduetoissues ofimagemisregistration.Inthesecases,theinterpretermovedthe pointonthemostrecentimagebacktothepositionontheoldest imagetomaketheinterpretationofchangeatthesamepointon bothimages.

Insomecases,notallofthepointscouldbeclassified. Non-classificationoccurredwhenoneoftheimagesweremissingpart ofthecityarea(incompleteimagery)orhadcloudcover.Allcities hadgreaterthan97.2percentofthepointsinterpreted.Assome citieshavesubstantialamountsofwaterwithintheircityboundary (Table1),coverestimateswereonlybasedonpointsthatwerenot classifiedaswaterinbothyears.Thatis,permanentwaterpoints weredeletedfromthesamplesothatcoverestimateswerebased oncitylandarea,notcitytotalarea.

Forthephoto-interpretation,trained photointerpreters with experience interpreting leaf-off and leaf-on imagery classiﬁed each point as toeither: trees/shrubs (woodyvegetation), grass

Table1

Resolutionandyearofimageryfor20analyzedcities.Percentofcityareaclassiﬁedaswaterinbothyears(%Water)wasremovedfromanalysissothatcoverestimatescould bebasedonlandarea.Humanpopulationdensitychange(#/ha)betweenyear1andyear2isbasedonU.S.Censusestimates(1).

City Year1 Res.a_(m) _Leaf_on/off _Year₂ _Res.a_(m) _Leaf_on/off _%interpb _%Water _n _Change_(#/ha)

Albuquerque,NM 2006 0.15 Off 2009 1 On 100 0.2 998 0.6 Atlanta,GA 2005 2 On 2009 1 On 99.5 0.4 991 1.7 Baltimore,MD 2001 1 On 2005 1 On 99.9 12.6 2184 −0.2 Boston,MA 2003 1 On 2008 1 On 99.9 13.6 863 2.3 Chicago,IL 2005 2 On 2009 1 On 100 0.8 992 0.5 Denver,CO 2005 1 On 2009 1 On 100 1.6 984 1.2 Detroit,MI 2005 1 On 2009 1 On 99.9 0.3 996 −0.3 Houston,TX 2004 1 On 2009 1 On 99.5 1.6 979 1.4 KansasCity,MO 2003 1 On 2009 1 On 100 1.5 985 0.4 LosAngeles,CA 2005 1 On 2009 1 On 100 0.2 998 0.3 Miami,FL 2003 1 On 2009 0.3 On 100 9.3 907 6.3 Minneapolis,MN 2003 1 On 2008 1 On 98.9 7.1 919 0.3

Nashville,TN 2003 0.15 Off 2008 0.15 Off 100 0.7 993 0.3

NewOrleans,LA 2005 2 On 2009 1 On 97.2 38.4 563 −2.1

NewYork,NY 2004 0.15 On 2009 1 On 98.1 2.9 953 2.8

Pittsburgh,PA 2004 1 On 2008 1 On 99.5 4.8 947 −0.6

Portland,OR 2005 1 On 2009 1 On 100 1.6 984 1.0

Spokane,WA 2002 0.15 On 2007 0.15 On 100 1.0 1980 0.3

Syracuse,NY 2003 0.3 Off 2009 0.3 Off 99.6 2.0 976 −0.7

Tacoma,WA 2001 0.15 On 2005 0.15 On 100 8.6 914 −0.1

a_Image_(pixel)_resolution.

b _Percent_of_original_points_(land_and_water)_that_were_able_to_be_classified_on_both_images._n_–_sample_size_–_number_of_points_not_classified_as_permanent_water_points (classifiedaswaterinbothyears).

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or herbaceous cover, bare soil, water, impervious (buildings), impervious (roads), or impervious (other). For the analysis of Albuquerque,NM,only,aneighthclassofscrub/shrubwasadded duetothedifferentvegetationcovermorphologyofthatregion. Thisclasswasincludedinthetree/shrubcoverclassiﬁcation,but thescrub/shrubclassresultswerealsoreportedseparately.Within Syracuse,whichwasoneoftheﬁrstcitiesanalyzed,impervious other and impervious road categories were combined by the interpreter as wasthe grass/herbaceous and soil categories. In subsequentcityanalysesthesecategorieswereseparated.

Ininterpretingchangefromaerialimagery,imageparallax(tall objectsappearingtoleanontheimage)andseasonalchangescan appeartocausechanges,butinfactarenotactualchanges.Inthese casestheinterpretercouldusejudgmenttodetermineifactual changedidoccur.Incasesoftallobjectparallax,theinterpreter’s classificationwasbasedontheoldestimageandiftherewasno change,bothdatesofimagerywereclassifiedthesame.For exam-ple,tallobjects(e.g.,buildingsandtrees)mayleantotheleftinthe firstimage,butleantotherightinthesecondimageandapoint maylandontheobjectinthefirstimage,butmisstheobjectin thesecondimage.Thepointclassificationwouldappeartochange class,butnoactualchangewouldhaveoccurred.Alsoagricultural fieldscanchangecoverclassdependingontimeofyear(herbaceous covervs.baresoildependingupontimeofimagery).Thesetypes ofseasonalchangeswereclassifiedasnochangeandclassifiedas herbaceouscover.Byconductingpaired-pointimageanalysis,the interpretercancorrectthesefalsechangestonochangeinthe anal-ysis.Afive-percentrandomsample ofpointswasreinterpreted byanotherphoto-interpretertocheckforclassificationaccuracy. Overall,thetwointerpreterswereinagreementon97percentof theclassifications.

Withineach city,thepercentageof eachcover class(p)was calculatedasthenumberof samplepoints(x)hittingthecover attribute divided by the total number of interpretable sample points(n)withintheareaofanalysis(p=x/n).Thestandarderrorof theestimate(SE)wascalculatedasSE=

p×(1−p)/n(Lindgren andMcElrath,1969).Thismethodhasbeenusedtoassesscanopy coverinmanycities(e.g.,Nowaketal.,1996).

Ifchangesincoverclasseswereobservedatanypointonthe imagethenitisknownthatcoverclassesarechangingwithinthe city (i.e.,nostatisticaltest is needed todetermineif changeis greaterthanzero).However,asacoverclasscanbothgainandlose coverthroughtimeandspace,theMcNemartest(SokalandRohlf, 2003)wasusedtodetermineifthenetchangeincoverwas differ-entfromzero(alphalevels0.90and0.95).Pearsonproductmoment correlationwasusedtotestforarelationshipbetweenchangein percenttreecoverandchangeinpopulationdensityamongthe18 cities.

Asthe overall time frame ofchange in cover varied among citiesfrombetween3and 6yr,changeresultswereannualized for comparativepurposesamong cities.Results werecombined withcityareaand populationdata fromtheyearof theoldest photodate(U.S.CensusBureau,2011)todetermineactualtreeand imperviouscoverchange(ha)andcoverchangepercapitaineach city.Resultsofpercentchangewerereportedasabsolutechange (percentofcityareathatchanged=coverchange/cityarea)and rel-ativechange(percentofexistingcoverclassthatchanged=cover change/originalcoverarea).For example,acitywith30percent treecoverthatchangedto20percenttreecoverwouldhavea10 percentabsolutechange,buta33percentrelativechange.

Asthe20analyzedcitiesarenotatrulyrandomsample,an anal-ysisofchangeintreeandimperviouscoverinurbanareasacrossthe conterminousUnitedStateswasconductedusingGoogleEarth®

(Google,2011)imagerytodeterminetherelativemagnitudeofnet changeinurbantreeandimperviouscover.Urbanlandwasdeﬁned basedonpopulationdensityasdelimited usingtheU.S.Census

Bureau’s(2007)deﬁnition:allterritory,population,andhousing unitslocatedwithinurbanizedareasorurbanclusters.Urbanized areaandurbanclusterboundariesencompassdenselysettled ter-ritories,whicharedescribedbyoneofthefollowing:

•oneormore blockgroups orcensusblocks witha population densityofatleast386.1people/km2₍₁₀₀₀_people/mile2_),

•surroundingcensusblockswithaminimumpopulationdensity of193.1people/km2₍₅₀₀_people/mile2_),_or

•lessdenselysettledblocksthatformenclavesorindentations,or areusedtoconnectdiscontinuousareas.

In the conterminous United States, 1000 points randomly located within urban land were interpreted based on paired imageryfromGoogleusingtheimageswiththemostrecentdate andthenextoldestinterpretableimagerywiththegoaloftrying togetthesecondsetofimageryabout5yrapartfromtheﬁrstset. Imagerydatealongwithcoverclasswasrecordedforeachpoint. ThistypeofanalysisofchangewithGoogleimageryhasvarying dateissuesthatwerenotencounteredwiththepairedcityimagery, butdoesgiveageneralindicationofdirectionandmagnitudeof changenationally.AnalysisofGoogleimagerywassimilartothe cityimageryintermsofnon-interpretableimagesandadjustingfor misregisteredimages.However,Googleimagerycouldalsonotbe interpretedinsomelocationsduetopoorimageresolution.Overall, 97percentofthepointscouldbeinterpretedusingGoogleimagery.

Results

Ofthe20citiesanalyzed,treecoverrangedfrom53.9percentin Atlantato9.6percentinDenver;buildingimperviouscoverranged from27.1percentinChicagoto4.8percentinKansasCity;road andotherimperviouscoverrangedfrom36.2percentinMiamito 12.3percentinNashville;andtotalimperviouscovervariedfrom 61.1percentinNewYorkCityto17.7percentinNashville(Table2). Twocoverclasses–tree/shrubandbaresoilgenerallyexhibiteda reductioninpercentcover,whiletheotherlandclassesgenerally exhibitedanincreaseincover.

Change in tree cover during thevarying periods of analysis rangedfromreductioninpercenttreecoverof−9.6inNewOrleans toanincreaseinpercenttreecoverof1.0inSyracuse(Table3). Nineteen of the 20 cities analyzed showed a reduction in tree cover,17ofthosecitieshadastatisticallysigniﬁcantnet reduc-tion.Average changewascalculated forall20 citiesand for18 cities–excludingthetwo cities(NewOrleansandDetroit)that weretargetedduetoanexpectedlossintreecover.Percenttree coverdroppedonaverageby1.1percentduringthevarying peri-odsofanalysis(1.5percentfor20cityaverage)withthegreatest decreasesin percenttreecover in NewOrleans (−9.6 percent), Houston(−3.0percent)andAlbuquerque(−2.7percent).The rel-ativereductionintreecoverwasashighas−29.2percentinNew Orleans,butaveraged−3.8percent(−5.0percentfor20city aver-age).

Citieswiththegreatest annual lossin treecover wereNew Orleans(averageof−1120ha/yr),Houston(−890ha/yr)and Albu-querque (−420ha/yr) (Table 3). Tree cover losses per capita weregreatestinNewOrleans(−24.6m2_/person/yr),_Albuquerque

(−8.3m2_/person/yr) _and _Nashville ₍₋_5.3_m2_/person/yr) _with_an

average loss of −1.9m2_/person/yr ₍₋_3.0_m2_/person/yr _for ₂₀

city average). Average annual loss in percent tree cover was

−0.27percent/yr(−0.37percent/yr for20 cityaverage).Relative annuallossintreecoverwas−0.90percent/yr(−1.29percent/yr for 20 city average). Loss of treecover wasslightly correlated toincreasedpopulationdensityinthe18cities(Pearsonproduct momentcorrelationcoefﬁcient(r)=−0.31).

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Table2

Changeofpercentofcitylandareaoccupiedbyvariouscoverclassesin20U.S.cities.

City 1styearcoverclass 2ndyearcoverclass 1styear

Grass/herba _Tree/shrub _Imp._bldgb _Imp._roadc _Imp._otherd _Water _Soil _Total _SE

Albuquerque,NM(2006–2009)e _Grass/herb _8.8 _0.1 _0.1 _0.0 _0.1 _0.0 _0.0 _9.1 _0.9 Tree/shrub 0.4 38.0 0.0 0.0 0.4 0.0 2.0 40.8 1.6 Imp.bldg 0.1 0.0 11.9 0.0 0.0 0.0 0.0 12.0 1.0 Imp.road 0.0 0.0 0.0 9.4 0.0 0.0 0.0 9.4 0.9 Imp.other 0.0 0.0 0.0 0.0 13.9 0.0 0.0 13.9 1.1 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.4 0.0 0.5 0.3 0.5 0.0 13.0 14.7 1.1 2ndyeartotal 9.7 38.1 12.5 9.7 14.9 0.0 15.0 2ndyearSE 0.9 1.5 1.0 0.9 1.1 0.0 1.1 Net(2006–2009) 0.6 −2.7 0.5 0.3 1.0 0.0 0.3 Atlanta,GA(2005–2009) Grass/herb 15.1 0.4 0.1 0.0 0.3 0.0 0.6 16.5 1.2 Tree/shrub 1.0 51.6 0.4 0.1 0.3 0.0 0.5 53.9 1.6 Imp.bldg 0.0 0.0 9.6 0.0 0.1 0.0 0.1 9.8 0.9 Imp.road 0.0 0.0 0.0 7.4 0.0 0.0 0.0 7.4 0.8 Imp.other 0.0 0.0 0.1 0.0 9.2 0.0 0.0 9.3 0.9 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 1.3 0.1 0.2 0.2 0.2 0.0 1.1 3.1 0.6 2ndyeartotal 17.5 52.1 10.4 7.7 10.1 0.0 2.3 2ndyearSE 1.2 1.6 1.0 0.8 1.0 0.0 0.5 Net(2005–2009) 0.9 −1.8 0.6 0.3 0.8 0.0 −0.8 Baltimore,MD(2001–2005) Grass/herb 22.2 0.1 0.2 0.0 0.7 0.0 0.3 23.5 0.9 Tree/shrub 0.9 28.4 0.4 0.1 0.5 0.0 0.1 30.4 1.0 Imp.bldg 0.0 0.0 15.3 0.0 0.0 0.0 0.3 15.6 0.8 Imp.road 0.0 0.0 0.0 10.9 0.0 0.0 0.0 11.0 0.7 Imp.other 0.0 0.0 0.0 0.0 16.8 0.0 0.2 17.1 0.8 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.1 0.0 0.3 0.0 0.3 0.0 1.6 2.3 0.3 2ndyeartotal 23.2 28.5 16.3 11.0 18.5 0.0 2.5 2ndyearSE 0.9 1.0 0.8 0.7 0.8 0.0 0.3 Net(2001–2005) −0.4 −1.9 0.7 0.0 1.3 0.0 0.2 Boston,MA(2003–2008) Grass/herb 17.8 0.5 0.1 0.0 0.6 0.0 0.1 19.1 1.3 Tree/shrub 0.6 27.1 0.6 0.2 0.1 0.0 0.2 28.9 1.5 Imp.bldg 0.1 0.0 16.5 0.0 0.1 0.0 0.0 16.7 1.3 Imp.road 0.0 0.0 0.0 12.5 0.0 0.0 0.0 12.5 1.1 Imp.other 0.2 0.2 0.0 0.1 18.4 0.0 0.0 19.0 1.3 Water 0.1 0.0 0.0 0.0 0.0 0.0 0.7 0.8 0.3 Soil 0.5 0.1 0.1 0.1 0.5 0.0 1.7 3.0 0.6 2ndyeartotal 19.4 27.9 17.3 13.0 19.7 0.0 2.8 2ndyearSE 1.3 1.5 1.3 1.1 1.4 0.0 0.6 Net(2003–2008) 0.2 −0.9 0.6 0.5 0.7 −0.8 −0.2 Chicago,IL(2005–2009) Grass/herb 20.0 0.0 0.0 0.0 0.3 0.1 0.4 20.8 1.3 Tree/shrub 0.3 18.0 0.1 0.0 0.0 0.0 0.1 18.5 1.2 Imp.bldg 0.4 0.0 26.5 0.0 0.1 0.0 0.1 27.1 1.4 Imp.road 0.0 0.0 0.0 12.1 0.0 0.0 0.0 12.1 1.0 Imp.other 0.0 0.0 0.2 0.0 19.1 0.0 0.0 19.3 1.3 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.0 0.0 0.0 0.0 0.1 0.1 2.0 2.2 0.5 2ndyeartotal 20.7 18.0 26.8 12.1 19.6 0.2 2.6 2ndyearSE 1.3 1.2 1.4 1.0 1.3 0.1 0.5 Net(2005–2009) −0.1 −0.5 −0.3 0.0 0.3 0.2 0.4 Denver,CO(2005–2009) Grass/herb 41.1 0.0 0.1 0.0 0.3 0.0 0.9 42.4 1.6 Tree/shrub 0.1 9.6 0.1 0.0 0.1 0.0 0.0 9.9 1.0 Imp.bldg 0.0 0.0 12.8 0.0 0.0 0.0 0.1 12.9 1.1 Imp.road 0.0 0.0 0.1 12.5 0.0 0.0 0.0 12.6 1.1 Imp.other 0.2 0.0 0.2 0.1 13.9 0.0 0.1 14.5 1.1 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.8 0.0 0.1 0.1 1.0 0.2 5.5 7.7 0.9 2ndyeartotal 42.2 9.6 13.4 12.7 15.3 0.2 6.6 2ndyearSE 1.6 0.9 1.1 1.1 1.1 0.1 0.8 Net(2005–2009) −0.2 −0.3 0.5 0.1 0.8 0.2 −1.1 Detroit,MI(2005–2009) Grass/herb 27.9 0.1 0.0 0.0 0.2 0.0 0.3 28.5 1.4 Tree/shrub 0.1 22.3 0.1 0.2 0.4 0.0 0.1 23.2 1.3 Imp.bldg 0.1 0.0 17.1 0.0 0.0 0.0 0.0 17.2 1.2 Imp.road 0.0 0.0 0.0 14.7 0.0 0.0 0.0 14.7 1.1

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Table2(Continued)

Imp.other 0.0 0.0 0.1 0.0 14.5 0.0 0.0 14.6 1.1 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.2 0.1 0.1 0.0 0.3 0.0 1.2 1.9 0.4 2ndyeartotal 28.3 22.5 17.4 14.9 15.4 0.0 1.6 2ndyearSE 1.4 1.3 1.2 1.1 1.1 0.0 0.4 Net(2005–2009) −0.2 −0.7 0.2 0.2 0.8 0.0 −0.3 Houston,TX(2004–2009) Grass/herb 28.7 0.0 0.5 0.0 0.5 0.2 0.2 30.1 1.5 Tree/shrub 1.4 27.4 0.3 0.1 0.2 0.1 0.8 30.3 1.5 Imp.bldg 0.0 0.0 13.5 0.0 0.0 0.0 0.2 13.7 1.1 Imp.road 0.0 0.0 0.0 12.0 0.0 0.0 0.0 12.0 1.0 Imp.other 0.1 0.0 0.1 0.0 11.8 0.0 0.1 12.2 1.0 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.4 0.0 0.0 0.0 0.1 0.0 1.2 1.7 0.4 2ndyeartotal 30.6 27.4 14.4 12.1 12.7 0.3 2.6 2ndyearSE 1.5 1.4 1.1 1.0 1.1 0.2 0.5 Net(2004–2009) 0.5 −3.0 0.7 0.1 0.5 0.3 0.8

KansasCity,MO(2003–2009) Grass/herb 48.5 0.5 0.2 0.3 0.7 0.1 0.3 50.7 1.6

Tree/shrub 1.1 27.5 0.0 0.1 0.0 0.0 0.5 29.2 1.4 Imp.bldg 0.0 0.0 4.6 0.0 0.2 0.0 0.0 4.8 0.7 Imp.road 0.0 0.0 0.0 6.3 0.0 0.0 0.0 6.3 0.8 Imp.other 0.0 0.0 0.2 0.1 6.8 0.0 0.0 7.1 0.8 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.2 0.0 0.0 0.0 0.7 0.0 1.0 1.9 0.4 2ndyeartotal 49.8 28.0 5.0 6.8 8.4 0.1 1.8 2ndyearSE 1.6 1.4 0.7 0.8 0.9 0.1 0.4 Net(2003–2009) −0.8 −1.2 0.2 0.5 1.3 0.1 −0.1

LosAngeles,CA(2005–2009) Grass/herb 21.0 0.0 0.3 0.0 0.2 0.0 0.3 21.8 1.3

Tree/shrub 0.4 20.6 0.2 0.0 0.3 0.0 0.0 21.5 1.3 Imp.bldg 0.0 0.0 21.0 0.0 0.2 0.0 0.0 21.2 1.3 Imp.road 0.0 0.0 0.0 14.7 0.0 0.0 0.0 14.7 1.1 Imp.other 0.0 0.0 0.4 0.0 15.8 0.0 0.1 16.3 1.2 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.3 0.0 0.5 0.2 0.2 0.0 3.1 4.3 0.6 2ndyeartotal 21.7 20.6 22.4 14.9 16.7 0.0 3.5 2ndyearSE 1.3 1.3 1.3 1.1 1.2 0.0 0.6 Net(2005–2009) −0.1 −0.9 1.2 0.2 0.4 0.0 −0.8 Miami,FL(2003–2009) Grass/herb 14.2 0.2 0.1 0.1 0.6 0.0 0.1 15.3 1.2 Tree/shrub 1.1 21.2 0.4 0.2 0.1 0.1 0.1 23.3 1.4 Imp.bldg 0.3 0.0 23.5 0.0 0.0 0.0 0.1 23.9 1.4 Imp.road 0.0 0.0 0.0 18.0 0.0 0.0 0.0 18.0 1.3 Imp.other 0.2 0.2 0.6 0.0 17.0 0.0 0.1 18.1 1.3 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.0 0.0 0.2 0.0 0.2 0.0 1.0 1.4 0.4 2ndyeartotal 15.9 21.6 24.8 18.3 17.9 0.1 1.4 2ndyearSE 1.2 1.4 1.4 1.3 1.3 0.1 0.4 Net(2003–2009) 0.6 −1.7 0.9 0.3 −0.2 0.1 0.0 Minneapolis,MN(2003–2008) Grass/herb 18.6 0.2 0.0 0.0 0.3 0.0 0.4 19.6 1.3 Tree/shrub 1.0 33.7 0.1 0.2 0.1 0.0 0.0 35.1 1.6 Imp.bldg 0.0 0.0 14.4 0.0 0.1 0.0 0.1 14.6 1.2 Imp.road 0.0 0.1 0.0 12.3 0.0 0.0 0.0 12.4 1.1 Imp.other 0.0 0.0 0.3 0.0 15.6 0.0 0.0 15.9 1.2 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.2 0.0 0.1 0.0 0.1 0.2 1.7 2.4 0.5 2ndyeartotal 19.8 34.1 14.9 12.5 16.2 0.2 2.3 2ndyearSE 1.3 1.6 1.2 1.1 1.2 0.2 0.5 Net(2003–2008) 0.2 −1.1 0.3 0.1 0.3 0.2 −0.1 Nashville,TN(2003–2008) Grass/herb 28.3 0.4 0.1 0.1 0.0 0.0 0.3 29.2 1.4 Tree/shrub 0.7 49.4 0.1 0.2 0.1 0.0 0.5 51.1 1.6 Imp.bldg 0.0 0.0 5.4 0.0 0.0 0.0 0.0 5.4 0.7 Imp.road 0.0 0.0 0.0 5.6 0.0 0.0 0.0 5.6 0.7 Imp.other 0.0 0.0 0.0 0.0 6.7 0.0 0.0 6.7 0.8 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.4 0.0 0.2 0.0 0.3 0.1 0.9 1.9 0.4 2ndyeartotal 29.4 49.8 5.8 5.9 7.2 0.1 1.7

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Table2(Continued)

2ndyearSE 1.4 1.6 0.7 0.8 0.8 0.1 0.4

Net(2003–2008) 0.2 −1.2 0.4 0.3 0.4 0.1 −0.2

NewOrleans,LA(2005–2009) Grass/herb 22.7 0.0 0.0 0.0 0.2 0.4 0.7 24.0 1.8

Tree/shrub 6.6 23.3 0.0 0.9 1.1 0.4 0.7 32.9 2.0 Imp.bldg 1.4 0.0 14.6 0.0 0.4 0.0 0.4 16.7 1.6 Imp.road 0.0 0.0 0.0 15.5 0.0 0.0 0.0 15.5 1.5 Imp.other 0.2 0.0 0.0 0.0 9.1 0.0 0.0 9.2 1.2 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.2 0.0 0.0 0.0 0.2 0.0 1.4 1.8 0.6 2ndyeartotal 31.1 23.3 14.6 16.3 10.8 0.7 3.2 2ndyearSE 2.0 1.8 1.5 1.6 1.3 0.4 0.7 Net(2005–2009) 7.1 −9.6 −2.1 0.9 1.6 0.7 1.4

NewYork,NY(2004–2009) Grass/herb 14.9 0.1 0.2 0.0 0.6 0.1 0.6 16.6 1.2

Tree/shrub 1.2 19.3 0.0 0.2 0.2 0.0 0.0 20.9 1.3 Imp.bldg 0.0 0.0 24.4 0.0 0.1 0.0 0.0 24.6 1.4 Imp.road 0.0 0.0 0.0 16.1 0.0 0.0 0.0 16.1 1.2 Imp.other 0.0 0.3 0.2 0.1 18.5 0.0 0.0 19.1 1.3 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 Soil 0.5 0.0 0.3 0.0 0.1 0.0 1.8 2.7 0.5 2ndyeartotal 16.6 19.7 25.2 16.4 19.5 0.1 2.5 2ndyearSE 1.2 1.3 1.4 1.2 1.3 0.1 0.5 Net(2004–2009) 0.0 −1.2 0.6 0.3 0.4 0.0 −0.2 Pittsburgh,PA(2004–2008) Grass/herb 16.9 0.0 0.0 0.0 0.1 0.0 0.1 17.1 1.2 Tree/shrub 0.2 41.6 0.1 0.0 0.0 0.0 0.0 41.9 1.6 Imp.bldg 0.0 0.0 14.7 0.0 0.1 0.0 0.1 14.9 1.2 Imp.road 0.0 0.0 0.0 13.3 0.0 0.0 0.1 13.4 1.1 Imp.other 0.0 0.0 0.1 0.0 11.6 0.0 0.0 11.7 1.0 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.1 0.0 0.0 0.0 0.0 0.0 0.8 1.0 0.3 2ndyeartotal 17.2 41.6 14.9 13.3 11.8 0.0 1.2 2ndyearSE 1.2 1.6 1.2 1.1 1.0 0.0 0.3 Net(2004–2008) 0.1 −0.3 0.0 −0.1 0.1 0.0 0.2 Portland,OR(2005–2009) Grass/herb 21.4 0.3 0.3 0.1 0.3 0.0 0.2 22.7 1.3 Tree/shrub 0.7 30.4 0.1 0.0 0.3 0.0 0.0 31.5 1.5 Imp.bldg 0.2 0.1 14.4 0.0 0.0 0.0 0.0 14.7 1.1 Imp.road 0.0 0.0 0.0 12.5 0.0 0.0 0.0 12.5 1.1 Imp.other 0.0 0.1 0.1 0.0 15.8 0.0 0.0 16.0 1.2 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 0.6 0.0 0.2 0.1 0.5 0.0 1.2 2.6 0.5 2ndyeartotal 23.0 30.9 15.1 12.7 16.9 0.0 1.4 2ndyearSE 1.3 1.5 1.1 1.1 1.2 0.0 0.4 Net(2005–2009) 0.3 −0.6 0.4 0.2 0.9 0.0 −1.2 Spokane,WA(2002–2007) Grass/herb 24.0 0.7 0.2 0.1 0.4 0.0 1.7 27.1 1.0 Tree/shrub 0.5 20.6 0.2 0.0 0.1 0.0 1.1 22.4 0.9 Imp.bldg 0.0 0.0 12.0 0.0 0.1 0.0 0.0 12.1 0.7 Imp.road 0.0 0.0 0.0 11.1 0.0 0.0 0.1 11.1 0.7 Imp.other 0.1 0.0 0.0 0.0 10.5 0.0 0.1 10.6 0.7 Water 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 Soil 1.3 0.5 0.5 0.2 0.7 0.0 13.6 16.7 0.8 2ndyeartotal 25.9 21.8 12.8 11.4 11.6 0.0 16.5 2ndyearSE 1.0 0.9 0.8 0.7 0.7 0.0 0.8 Net(2002–2007) −1.2 −0.6 0.8 0.3 1.0 −0.1 −0.2 Syracuse,NY(2003–2009)f _Grass/herb _21.7 _1.6 _0.1 _0.6 _na _0.0 _na _24.1 _1.4 Tree/shrub 0.5 25.0 0.1 0.3 na 0.0 na 25.9 1.4 Imp.bldg 0.7 0.0 18.9 0.1 na 0.0 na 19.7 1.3 Imp.road 0.6 0.3 0.2 29.2 na 0.0 na 30.3 1.5 Imp.other na na na na na 0.0 na na na Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil na na na na na 0.0 na na na 2ndyeartotal 23.6 26.9 19.3 30.2 na 0.0 na 2ndyearSE 1.4 1.4 1.3 1.5 na 0.0 na Net(2003–2009) −0.5 1.0 −0.4 −0.1 na 0.0 na Tacoma,WA(2001–2005) Grass/herb 24.8 1.2 0.1 0.1 0.8 0.0 0.1 27.1 1.5 Tree/shrub 1.8 21.3 0.1 0.0 0.8 0.0 0.4 24.4 1.4

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Table2(Continued)

Imp.bldg 0.2 0.0 13.2 0.0 0.3 0.0 0.2 14.0 1.1 Imp.road 0.0 0.0 0.0 12.5 0.0 0.0 0.0 12.5 1.1 Imp.other 0.0 0.1 0.1 0.1 13.8 0.0 0.1 14.2 1.2 Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Soil 1.3 0.3 0.3 0.1 2.0 0.3 3.4 7.8 0.9 2ndyeartotal 28.1 23.0 13.9 12.8 17.6 0.3 4.3 2ndyearSE 1.5 1.4 1.1 1.1 1.3 0.2 0.7 Net(2001–2005) 1.0 −1.4 −0.1 0.3 3.4 0.3 −3.5

Average20citiesg _Grass/herb _23.0 _0.3 _0.1 _0.0 _0.4 _0.0 _0.4 _24.3 _na

Tree/shrub 1.1 27.8 0.2 0.1 0.3 0.0 0.4 29.9 na Imp.bldg 0.2 0.0 15.2 0.0 0.1 0.0 0.1 15.6 na Imp.road 0.0 0.0 0.0 12.1 0.0 0.0 0.0 12.1 na Imp.other 0.1 0.1 0.1 0.0 13.6 0.0 0.0 13.9 na Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 na Soil 0.5 0.1 0.2 0.1 0.4 0.1 3.0 4.3 na 2ndyeartotal 24.7 28.2 15.9 12.3 14.8 0.1 4.0 2ndyearSE na na na na na na na Averagenet 0.5 −1.5 0.3 0.3 0.8 0.1 −0.3

Average18citiesh _Grass/herb _22.7 _0.3 _0.2 _0.0 _0.4 _0.0 _0.4 _24.0 _na

Tree/shrub 0.8 28.4 0.2 0.1 0.2 0.0 0.4 30.0 na Imp.bldg 0.1 0.0 15.1 0.0 0.1 0.0 0.1 15.4 na Imp.road 0.0 0.0 0.0 11.7 0.0 0.0 0.0 11.7 na Imp.other 0.1 0.1 0.2 0.0 13.8 0.0 0.0 14.2 na Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 na Soil 0.5 0.1 0.2 0.1 0.4 0.1 3.2 4.6 na 2ndyeartotal 24.2 28.8 15.9 12.0 15.0 0.1 4.2 2ndyearSE na na na na na na na Averagenet 0.1 −1.1 0.4 0.2 0.8 0.0 −0.4

SE–standarderror.Net–netdifferencebetweentheyears(2ndyear−1styear). a_Grass_and_other_herbaceous_ground_cover.

b_Impervious_cover_occupied_by_buildings. c _Impervious_cover_occupied_by_roads.

d _Other_impervious_cover_(e.g.,_sidewalks,_driveways,_and_parking_lots).

e_{Scrub/shrub/chaparral}_was_a_cover_class_only_measured_in_Albuquerque,_NM,_and_is_included_in_tree/shrub_cover._This_cover_class_occupied_31.6_percent_of_the_city_area in2006anddroppedto29.4percentin2009,alossof2.2percentofthecityarea.

f _Soil_cover_is_included_in_grass_and_herbaceous_cover;_impervious_other_is_included_in_impervious_road.

g_Results_from_Syracuse_are_not_included_in_average_of_{grass/herbaceous,}_impervious_road,_impervious_other_or_soil_(see_table_footnote_f).

h _Average_result_not_including_New_Orleans_or_Detroit_as_these_cities_were_{speciﬁcally}_selected_due_to_expected_losses_from_hurricane_and_emerald_ash_borer_damage respectively.ResultsfromSyracusearenotincludedinaverageofgrass/herbaceous,imperviousroad,imperviousotherorsoil(seetablefootnotef).

Mostofthelossof treecover convertedtograss/herbaceous cover(47percent),followedbyconversionstoimperviouscover(29 percent)andbaresoil(23percent)(Table2).Likewise,newcover mostoftenconvertedfromgrass/herbaceouscover(68percent), followedbyimperviouscover(17percent)andbaresoil(14 per-cent).Onlyonecity(Syracuse)exhibitedanoverallincreaseintree cover,withmostofthisincreasecomingfromgrass/herbaceous cover.

Changeinpercentimperviouscoverduringthevarying peri-odsofanalysisrangedfromanincreaseof3.6percentinTacoma toa decrease in percentimpervious cover of −0.5 in Syracuse (Table3).Seventeenofthe20citiesanalyzedshowedanincrease in net impervious cover, 16 of those cities had a statistically signiﬁcant increase. Four cities exhibited small changes in net imperviouscoverthatwerenotstatisticallysigniﬁcantfromzero (Syracuse,Chicago,Pittsburgh,NewOrleans).Percentimpervious coverincreasedonaverageby1.4percentduringthevarying peri-odsofanalysis(1.3percentfor20cityaverage)withthegreatest increasesinpercentimperviouscoverinTacoma(3.6percent), Bal-timore(2.1percent) and KansasCityand Spokane (2.0percent each).Therelativeincreaseinimperviouscoverwasashighas11.2 percentinKansasCity,butaveraged3.9percent(3.7percentfor20 cityaverage).

Citieswiththegreatestannual increasein imperviouscover wereLosAngeles(averageof550ha/yr),Houston(400ha/yr)and Albuquerque(280ha/yr)(Table3).Imperviouscoverincreasesper capita weregreatest in Tacoma (6.0m2_/person/yr), _Kansas _City

(5.9m2_/person/yr) _and _Albuquerque _(5.5_m2_/person/yr) _with_an

averageincreaseof2.2m2_/person/yr_(2.1_m2_/person/yr_for₂₀_city

average). Average annual increase in percentimpervious cover was 0.31percent/yr (0.30percent/yr for 20 city average). Rel-ative annual increase in impervious cover was 0.87percent/yr (0.82percent/yrfor20cityaverage).

Theanalysisofthe20citiesshowsagenerallossintreecoverand increaseinimperviouscoverinthemidtolate2000s.Thisoverall trendofchangewasalsoexhibitedintheresultsofnationalurban landcoverchangeusingGoogleEarthimagery.Ofthe1000 ran-dompaired-pointslaidthroughouttheconterminousurbanUnited States,970pointswereinterpretable,withaveragelengthoftime betweenpointsof6.4yr.Themostrecentimageryhad an aver-ageyearof2009,butrangedbetween2004and2011.Theolder pairedimageyearaveraged2002witharangeof1990–2006.Tree coverincreasesbetweenimagesaveraged2.1percent(SE=0.5 per-cent)withaveragelossesof−2.3percent(SE=0.5percent)foran averagenetchangeintreecoverof−0.2percent.Imperviouscover increasesbetweenimagesaveraged3.2percent(SE=0.6percent)

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Table3

Percentnetandannualizednetabsoluteandrelativetreeandimperviouscoverchangein20U.S.cities.Absolutepercentchangeisbasedoncitylandareabetweentheyears (percentofcitylandinyear1minuspercentofcitylandinyear2).Relativepercentchangeisbasedonamountofcoverinyear1(percentofcityinyear1minuspercent ofcityinyear2dividedbypercentofcityinyear1).Annualizedchangeispercentchangeduringtimeperiodonanannualbasis.Percapitachangeestimatesarebasedon populationintheﬁrstyearofanalysis.

City Net Treecoverchange Imperviouscoverchange Annualizednet

Absolutechange Relativechange ha/yrc _m2_/cap/yrd _ha/yrc _m2_/cap/yrd _Absolute_change _Relative_change

Treea _Imp.b _Treea _Imp.b _Treea _Imp.b _Treea _Imp.b

NewOrleans,LA(2005–2009) −9.6** _0.4 ₋_29.2** _0.9 ₋₁₁₂₀ ₋_24.6 ₄₀ _0.9 ₋_2.49 _0.09 ₋_8.27 _0.21 Houston,TX(2004–2009) −3.0** _1.3** ₋_9.8** _3.5** ₋₈₉₀ ₋_4.3 ₄₀₀ _1.9 ₋_0.60 _0.26 ₋_2.03 _0.69 Albuquerque,NM(2006–2009) −2.7** _1.8** ₋_6.6** _5.1** ₋₄₂₀ ₋_8.3 ₂₈₀ _5.5 ₋_0.91 _0.60 ₋_2.26 _1.67 Baltimore,MD(2001–2005) −1.9** _2.1** ₋_6.3** _4.7** ₋₁₀₀ ₋_1.5 ₁₁₀ _1.7 ₋_0.48 _0.51 ₋_1.62 _1.16 Atlanta,GA(2005–2009) −1.8** _1.7** ₋_3.4** _6.5** ₋₁₅₀ ₋_3.1 ₁₅₀ _3.1 ₋_0.46 _0.43 ₋_0.85 _1.58 Miami,FL(2003–2009) −1.7** _1.0* ₋_7.1** _1.7* ₋₃₀ ₋_0.8 ₂₀ _0.5 ₋_0.28 _0.16 ₋_1.22 _0.27 Tacoma,WA(2001–2005) −1.4** _3.6** ₋_5.8** _8.9** ₋₅₀ ₋_2.6 ₁₁₇ _6.0 ₋_0.36 _0.89 ₋_1.49 _2.15 KansasCity,MO(2003–2009) −1.2** _2.0** ₋_4.2** _11.2** ₋₁₆₀ ₋_3.5 ₂₇₀ _5.9 ₋_0.20 _0.34 ₋_0.71 _1.78 Nashville,TN(2003–2008) −1.2** _1.1** ₋_2.4** _6.2** ₋₃₀₀ ₋_5.3 ₂₇₀ _4.8 ₋_0.24 _0.22 ₋_0.48 _1.21 NewYork,NY(2004–2009) −1.2** _1.4** ₋_5.5** _2.3** ₋₁₈₀ ₋_0.2 ₂₁₀ _0.3 ₋_0.23 _0.27 ₋_1.13 _0.45 Minneapolis,MN(2003–2008) −1.1** _0.8** ₋_3.1** _1.8** ₋₃₀ ₋_0.8 ₂₀ _0.5 ₋_0.22 _0.15 ₋_0.63 _0.35 Boston,MA(2003–2008) −0.9* _1.7** ₋_3.2* _3.6** ₋₂₀ ₋_0.3 ₄₀ _0.7 ₋_0.19 _0.35 ₋_0.65 _0.71 LosAngeles,CA(2005–2009) −0.9** _1.8** ₋_4.2** _3.4** ₋₂₇₀ ₋_0.7 ₅₅₀ _1.4 ₋_0.23 _0.45 ₋_1.06 _0.85 Detroit,MI(2005–2009) −0.7** _1.2** ₋_3.0** _2.6** ₋₆₀ ₋_0.7 ₁₁₀ _1.2 ₋_0.18 _0.30 ₋_0.77 _0.64 Portland,OR(2005–2009) −0.6 1.5** ₋_1.9 _3.5** ₋₅₀ ₋_0.9 ₁₃₀ _2.4 ₋_0.15 _0.38 ₋_0.49 _0.87 Spokane,WA(2002–2007) −0.6 2.0** ₋_2.5 _5.8** ₋₂₀ ₋_1.0 ₆₀ _3.0 ₋_0.11 _0.39 ₋_0.50 _1.14 Chicago,IL(2005–2009) −0.5** _0.0 ₋_2.7** _0.0 ₋₇₀ ₋_0.2 ₀ _0.0 ₋_0.13 _0.00 ₋_0.69 _0.00 Pittsburgh,PA(2004–2008) −0.3* _0.0 ₋_0.8* _0.0 ₋₁₀ ₋_0.3 ₀ _0.0 ₋_0.08 _0.00 ₋_0.19 _0.00 Denver,CO(2005–2009) −0.3* _1.4** ₋_3.1* _3.6** ₋₃₀ ₋_0.5 ₁₄₀ _2.5 ₋_0.08 _0.35 ₋_0.78 _0.88 Syracuse,NY(2003–2009) 1.0* ₋_0.5 _4.0* ₋_1.0 ₁₀ _0.7 ₋₆ ₋_0.4 _0.17 ₋_0.09 _0.65 ₋_0.17 20cityaverage −1.5 1.3 −5.0 3.7 −3.0 2.1 −0.37 0.30 −1.29 0.82 18cityaveragee ₋_1.1 _1.4 ₋_3.8 _3.9 ₋_1.9 _2.2 ₋_0.27 _0.31 ₋_0.90 _0.87

a_Percent_tree_and_shrub_cover_(including_{shrub/scrub/chaparral}_cover_in_Albuquerque,_NM). b _Percent_impervious_surfaces_(building,_roads_and_other_combined).

c _Average_annual_change_in_hectares_per_year.

d _Average_annual_change_in_square_meters_per_capita_per_year.

e_Average_result_not_including_New_Orleans_or_Detroit_as_these_cities_were_{speciﬁcally}_selected_due_to_expected_losses_from_hurricane_and_emerald_ash_borer_damage respectively.

* _Change_{signiﬁcantly}_different_from_zero_at_alpha₌_0.90. ** _Change_{signiﬁcantly}_different_from_zero_at_alpha₌_0.95.

withaveragelossesof−0.4percent(SE=0.2percent)foranaverage netchangeinimperviouscoverof+2.8percent.

Discussion

Whilecitiesexpendresourcestoplantmillionsofnewtrees, landdevelopment,storms,old ageand otherfactorsare reduc-ingthenumberofolder,establishedtreesincities.Thoughcurrent plantingcampaignsmayincreasetreecovernowandinthefuture, recenttrendsindicatethattreecoverisdecreasinginmanyU.S. cities.Treecoverisdecreasingatarateofabout0.27percentofthe citylandareaperyear,whichisequivalenttoabout0.9percentof theexistingtreecoverbeinglostannually.

Thetreecoverlossintheanalyzedcitieswashigherthanthe averagetreecoverlossforurbanlandintheconterminousUnited Statesbyafactorofabout6(1.1vs.0.2percentoverthevarying timeframes).Thisdifferenceislikelybecausetheseanalyzedcities donotrepresenttheentireurbanarea.Theselectedcitiesare rel-ativelymajorcitieswithincreasedpopulationdensitiesandlikely increaseddevelopmentpressureswhencomparedwiththe aver-ageurbanlandscape,whichincludesmanysmaller,lessdensely populatedareas.Thesecityboundaries,whichareofteninforested regions,canalsoincludenon-urbanlandsthatmayhaveahigh likelihoodfordevelopmentandthereforelossoftreecover and increased impervious cover. The change effects in these cities arelikelymorerepresentativeofchangeinmajorcitiesthanthe nationalurbanchangeestimates.

Usingthenationaltreecover lossestimateof0.2 percentof urbanlandoverabouta6yrperiod,whichequatestoabout1/30 ofapercentperyear,aﬁrstorderapproximationoftreecoverloss

inurbanareasoftheconterminousUnitedStatesisalossrateof about7900haofurbantreecoverperyear.Givenanaveragetree densityperunitofurbantreecoverofapproximately508trees/ha (averagefromCummingetal.,2007;Nowaketal.,2007,in press-a,inpress-b;NowakandGreenﬁeld,2008;unpublisheddata),this lossequates toanannualnetlossofabout4.0milliontreesper yearinurbanareasoftheconterminousUnitedStates.This esti-mateofnumberoftreeslostmaybeexcessiveasmuchofcanopy lossmaybeduetolossofmaturetreesthatwouldhavealowertree densityperunitcanopythantheaverageurbanforest,butfurther researchisneededtounderstandthecompositionandsizeclass distributionofthecanopyloss.Althoughtreeplantingand natu-ralregenerationareoccurringinurbanareas,nettreecoverison ageneraldeclineinurbanareasoftheUnitedStates.Treecanopy lossofmaturetrees,forwhateverreason(storms,insects, devel-opment,old age),cancreaterelativelylargegapsinthecanopy coverthatwillrequirenewtreeplantingsorregenerationandtime toﬁll.

Itisapparentthattreeplantingandnaturalregenerationare insufﬁcienttooffsetthecurrentlossesofestablishedurbantree canopies.However,withoutvarioustreeplantingeffortsincities, treecoverlosswouldbehigher.Effortstofacilitatemorenatural regenerationincities(e.g.,limitsonmowing)mayalsobeneeded tosustaintreecover.Naturalregenerationmaynotworkinall loca-tions(e.g.,waterlimitedareas)orproducedesiredtreespecies,but itcanprovideforrelativelylowcosttree/shrubestablishment. Sim-ilarly,treeplantingmaynotbeappropriateinallcities(e.g.,water limitedareas)duetotheresourcecostsofmaintainingvegetation (e.g.,water).Sustainingtreecovernotonlyincludesestablishing newtrees,butalsolimitingthelossofexistingcanopy,particularly

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largetreesthatprovidesubstantialamountsofcanopypertree. Sustainingtreehealthandprotectionofhealthytreecanopiesfrom humanremoval(e.g.,development)ornaturalmortalityforces(e.g., insectsanddiseases)canalsohelpsustainexistingtreecoverand associatedenvironmentalservices.

Thoughthecurrenttrendisadeclineincanopycover,notall citiesarelosingtreecover.Oneofthe20citiesanalyzed(Syracuse, NY)hadanabsoluteincreaseincanopycoverofonepercent,or 0.2percentincreaseperyear,withmostofthetreecoverincrease occurringingrass/herbaceous areas.Thisincrease in treecover matchesﬁelddataestimatesofurbanforestchangeinSyracuse (U.S.ForestService,unpublisheddata)thatshowsthatthenumber oftrees(woodyplantswithstemdiameterat1.37mgreaterthan 2.54cm)areincreasing.ThisincreaseisdominatedbyEuropean buckthorn (Rhamnus cathartica L.),aninvasive smalltree/shrub fromEurope.Thus,thecoverincreaseinSyracuseismostlikely duetonaturalregenerationinconcertwithlimiteddevelopment oractivitiesthatwouldtendtoreduceregeneration.

NewOrleans,asexpected,hadasigniﬁcantreductionintree cover(−9.6percentabsolutereductionor−29.2percentrelative reduction),whichismostlikelyduetothedevastationof Hurri-caneKatrinain2005(e.g.,Chapmanetal.,2008).Incontrast,the lossintreecoverduetotheemeraldashborerinDetroitwaslower thanexpected.Since2002,thisbeetlehaskilledmorethan30 mil-lionashtreesinSoutheasternMichigan(USForestServiceetal., 2011).However,thelossoftreecoverinDetroit(−0.18percent absoluteannualreductionor−0.77percentrelativeannual reduc-tion)waslessthantheaveragelossfromthesampledcities(−0.27 percentabsoluteannualreductionor−0.90percentrelativeannual reduction).Thisdifferencecouldbeduetoashtreesnot compris-ingamajorcomponentofoveralltreecoverinDetroitand/ornew treesbeingestablishedthroughtreeplantingprogramsornatural regenerationthathelpoffsetthelossofashandothertrees.

Overall,mostofthetreelossesconvertedtograss/herbaceous cover(47percent)orimperviouscover(29percent),whilemostof thegainofnewtreecoveralsocamefromgrass/herbaceouscover (68percent)orimperviouscover(17percent).Someofthe conver-sionsfromtreetoimperviouscoverareduetodevelopment,butare alsoduetoimperviouscoverbeingbeneathtrees.Whentreesare removed,thegroundsurfacebeneaththetreesswitchestothenew coverclass.Likewise,astreescovergroundsurfaces,additionaltree covercantendtoreduceimperviouscoverestimateswhentrees growovertheimpervioussurfaces.

Oftheoverallaverageincreaseinimperviouscover,about29 percentofthatchangewasduetochangeswithlossoftreecover. That29percentofnewlyclassiﬁedimperviouscoverisa combi-nationofnewdevelopmentandexposureofexistingimpervious coverbeneathtrees.However,atleast71percentofthe imper-viouscoverincrease wasduetonewdevelopment.Somecities (i.e.,Chicago,Pittsburgh)exhibitednonetchangeinimpervious cover during theanalysisperiod, but didexhibit increases and decreasesin impervious cover that offset each other. Syracuse exhibitedadecreaseinimperviouscover,whichmaybe,inpart,due totheoverallincreaseintreecover.However,mostofthechanges inimperviouscoverinSyracuseoccurredwithgrass/herbaceous cover.NewOrleansalsolostasubstantialamountofbuildingcover (2.1percentabsolutereduction),mostlikelyduetodamagefrom HurricaneKatrina(e.g.,Katesetal.,2006).

Abetterunderstandingofhowtreecoverandtreepopulations are changing can aid managers in developing regeneration or canopyprotectionplanstosustainadequatetreecoverthrough timeandspace.Photo-interpretationofpaireddigitalimagesoffers arelativelyeasy,quickandlow-costmeanstostatisticallyassess changes amongvarious cover types.To helpin quantifyingthe covertypeswithinanarea,afreetool(i-TreeCanopy)isavailable (www.itreetools.org)thatallows userstophoto-interpreta city

usingGoogleimages. Thisprogramautomaticallyquantiﬁesthe percentcoverandassociatedstandarderrorforeachcoverclass basedonuserinterpretations.Coverdata ona citycanprovide a baselinefor developingmanagement plans,settingtreecover goals, andformonitoringchangethroughtime.Futureanalyses oncoverdistributionorchangebyland usetype orgeographic regionareneededtoinvestigatepatternsandcausesoftreeand imperviouscoverchangesbetweenandwithincities.

Thepaireddigitalimageanalysisoffersarelativelyquick,easy andcost-effectivemeanstoassesscoverchange,butitdoeshave somelimitations.ThoughGoogleoffershigh-resolutionimageryin manypartsoftheworld,pairedimageanalysiswithGoogleimages islimitedbythevaryingdatesamongimagesandvaryingimage resolution.Inurbanareas,manyoftheGoogleimagesareof sufﬁ-cientresolutionforaccuratephoto-interpretationandimagesare continuallyupdated.Obtaining localdigitalimageswithknown andconsistentdatesacrossanareaofanalysiscanovercomethe problemsassociatedwithvaryingdatesacrossastudyarea. Some-timespairedcitydataalsohaddifferentimageresolutionbetween years,butmostimageswere1morless.Asimageinterpretation waspaired,informationfromthehigherresolutionimagecould aidininterpretingthelowerresolutionimage.Anotherlimitation ofthepairedimageapproachis theabilityoftheinterpreterto correctlyclassifysamplepoints.Interpretererrorcanleadto inac-curateresults,butpropertrainingandtestingcanproduceaccurate results.Satellitecovermapsalsohaveinherentinaccuraciesdueto classiﬁcationerrorsandcancosttensofthousandsofdollarsto pro-duceacovermapforacity.Thepairedphoto-interpretationmethod offersamorecosteffectivemeanstoassesschange,butdoesnot produceadetailedmapofcoverattributesorcoverchangeacross acity.

Theresultsofthis studyillustraterecentchangesintreeand imperviouscover in citiesand urbanareasthat canbeused to informplannersandpolicymakers.Todeterminewhether simi-lartrendsoccurredinthe1990sorearly2000s,andwhetherthese trendswillcontinueinthefuture,morepairedimageanalysescan beconductedusingolderpairedimageryorbycomparingfuture imagerywithcontemporaryimages.Morepairedimageanalyses canhelpbetterdeterminebothspatialandtemporalpatternsand ratesoflandscapecoverchange.Photo-interpreteddataoncover inurbanareasandelsewherecanprovideanaccuratemeansof assessingcovertypesandchangesincoverthroughtimetohelp managersandplannersmakeinformeddecisionsonhowtobetter improvelocallandscapesandtheenvironment.

Conclusion

Treecoverprovidesasimplemeanstoassessthemagnitudeof theoverallurbanforestanditsenvironmentaleffects.Despite vari-ousandlikelylimitedtreeplantingandprotectioncampaigns,tree covertendstobeonthedeclineinU.S.citieswhileimperviouscover isontheincrease.Whiletheseindividualcampaignsarehelpingto increaseorreducethelossofurbantreecover,morewidespread, comprehensiveandintegratedprogramsthatfocusonsustaining overalltreecanopymaybeneededtohelpreversethetrendof decliningtreecoverincities.Nettreecoverchangeistheresultof thecombinedinﬂuencesoftreeplantingandnaturalregeneration, treegrowthandtreemortality.Developingcoordinatedhealthy treecanopy programsacross variousland ownershipscanhelp sustaindesiredtreecoverlevelsandbettermanagecoverchange. Monitoringoftreecoverchangesisessentialtodeterminecurrent trendsandwhetherdesiredcanopylevelsorprogrameffectsare beingattained.Photo-interpretation ofdigital aerialimagescan provideasimpleandtimelymeanstoassessurbantreecoverand howitischanging.

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Acknowledgments

Fundingforthisprojectwasprovided,inpart,bytheU.S. For-estService’sRPAAssessmentStaffandState&PrivateForestry’s UrbanandCommunityForestryProgramandtheNationalScience Foundation(NSFgrantsDEB-0423476andBCS-0948952)through the Baltimore Ecosystem Study-Long Term Ecological Research (BES-LTER) and the Syracuse Urban Long-term Research Area ExploratoryAward(ULTRA-Ex).Theuseoftrade,ﬁrm,or corpo-rationnamesinthisarticleisfortheinformationandconvenience ofthereader.Suchusedoesnotconstituteanofﬁcialendorsement orapprovalbytheU.S.DepartmentofAgricultureForestServiceof anyproductorservicetotheexclusionofothersthatmaybe suit-able.WethankMikeBoarman,AllisonBodineandTianZhoufor photo-interpretation.WealsothankJohnStanovickforhis statis-ticalassistanceandreview,andMikeGalvinandJackieLufortheir commentsonadraftmanuscript.

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