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2016SeattleTreeCanopyAssessment

Seattlehasalong‐standingcommitmenttoitsurbantrees.Becauseofthemanysocial,environmental,andeconomicbene itsurbantreesprovide,theyareessentialtoenhancingthecommunity’squalityoflife,especiallyasSeattlegrows.

TheUrbanForestStewardshipPlan(UFSP),adoptedbyCouncilin2013,hasagoaltoreach30%treecanopycoverby2037.Theplanalsooutlinesadditionalgoalsforathrivingurbanforestthatincludeahealthydiversityoftreespeciesandagesaswellastheimportanceofcommunityparticipationincaringforandplantingtrees.MeasuringtreecanopyTreecanopycoveristhelayerofbranches,stems,andleavesoftreesthatcoverthegroundwhenviewedfromabove.CanopycoverassessmentstellustheextentofSeattle’streesandwheretheyarelocated;andhelpusinformurbanforestryworkplanning,management,andinvestments.

TherehavebeenseveraltreecanopycoverassessmentsinSeattleoverthelastdecade,eachusingvaryingmethodologiesandyieldingdifferentresults.Duetothedifferingtechnologiesandmethodologies,resultscannotbecomparedbetweenstudies.The2016LiDARassessmentTheCityofSeattleobtainedLiDAR(lightdetectionandranging)datain2016toassessprogresstowardsachievingour30%canopycovergoal.ThisstudyrepresentsthemostaccurateaccountingofSeattle’surbancanopytodateandshowsSeattlehas28%canopycover.

Theassessmentprovidesthefoundationforunderstandingthequantity,distribution,andcon igurationoftreecanopyinSeattle.Thetrue

valueofthisstudywillberealizedwhentheresultsareusedtoguideurbanforestrypolicyandmanagementeffortssuchasestablishinglocalizedcanopygoalsandtargetedplantingandconservationeffortstomaximizelimitedresources.

Thestudyfocusedonansweringseveralresearchquestionsincluding:progressachievingSeattle’s30%canopycovergoalcitywide,ineachoftheeightUFSPmanagementunits,andineachofthe27streettreemanagementunits;canopycoverlevelsinSeattleneighborhoodsandEquityandEnvironmentInitiativefocusareas;coniferoustodeciduoustreeratio;locationofthecity’slargesttreesandtreegroves;heatislandeffecthotspots;impactsfromdevelopment;andvolumeofvegetativematerialthatfallswithintheminimum10ftclearancedistancesofSCLdistributionandtransmissionsystems.Theresultswillbeanalyzedtoinformurbanforestryprioritiesandactionsmovingforward.2016LiDARstudy indingsThestudyexaminedourresearchquestionsaboutSeattle’scanopycovertohelpinformfutureurbanforestryworkandactions.Notable indingsinclude: Themajorityofoururbantreesarefoundin

twolocations:residentialareas(representing67%ofthelandwith72%ofSeattle’streecanopy),andintheright‐ofwaywhichisinterspersedthroughoutthecity(representing27%ofthelandand22%ofthecanopy).

Largertreesandtreegrovesareoftenpresentinparksbutarealsoonresidentialandinstitutionallands.

Canopyexceedstargetsindevelopedparks,naturalareas,multi‐family,andinstitutionalareas;isclosetotargetinsingle‐family,downtown,andcommercialareas;andisbelowtargetinindustrialareas.

ExecutiveSummary

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Canopycoverdiffersacrossthecitybasedonlanduse,thepresenceofparksandnaturalareas,andsocio‐economicfactors.Censustractswherethepopulationtendstoberesidentsofcolorandpeoplewithlowerthanaverageincomealsohaveloweramountsoftreecanopy.

72%ofSeattle’streecanopyisdeciduousand28%isconiferous.Mostoftheconifersareonsingle‐familyland(52%).

Thepresenceoftreescanreduceurbanheatislandeffect(surfacetemperature),especiallyinland,andmitigateextremeheatimpacts.

6%ofSeattle’streecanopyiswithintheclearancedistanceSeattleCityLightmaintainsforsafetyandreliabilityofthepowergrid.

ImpactsofdevelopmentInanefforttounderstanddevelopmentimpactsonSeattle’scanopy,ourconsultantexaminedadditionaldatausinghistoricalGoogleEarthimagerytodoamini‐assessmentof80randomparcelsthatunderwentdevelopmentcomparingbeforeandafterimages.

Althoughnotstatisticallyvalid,thisexercisefoundparcelsinthedowntown,industrial,single‐andmulti‐familyareassawcanopycoverlosswhilecommercial,institutional,anddevelopedparkssawagainafterdevelopment(likelyaresultofretainedtreesmaturingovertime).

CanopycovertrendanalysisWehadoriginallyrequestedananalysisofcanopycoverchangeovertimecomparing2001LiDARto2016LiDAR.Unfortunately,the2001data,whilecutting‐edgeatthetime,isofsuchlowresolutionthatisunsuitableforcomparisontothe2016data.Togetthecanopycovertrendwedecidedtousethei‐TreeCanopyprotocol,asample‐basedapproachusinghistoricalimageryfromGoogleEarth,tocomparecanopycoverfrom2007,2010,and2015.ThisapproachfoundthatSeattlehadexperiencedapproximately2%canopycoverlossoverthespanoftheeightyearstudyperiod.Thismethodhasa+/‐3%marginoferrorandcanopycoverextent(coverage)isnotcomparabletothe2016LiDARassessment.ConclusionInsightsfromthisstudywillbeusedtoupdatetheUrbanForestStewardshipPlanandtorevisitpolicies,goals,managementefforts,andstrategiestogrowSeattle’surbanforest.Thetablebelowshowssomeofthestudy indingsbyUrbanForestStewardshipPlanManagementUnit.Localizedcanopygoalsandtargetedplantingandconservationeffortswillhelpmaximizelimitedresources.Nowthatwehaveareliablebaseline,itwillbeimportanttoperformanotherLiDAR‐basedtreecanopyassessmenttodeterminechangestocanopycoveroverthenext iveyears.

Management Unit # of acres Land area ( % of city)

% canopy cover % contribu on to

city’s canopy cover % conifer

contribu on

Single‐Family Residen al

29,918 56% 32% 63% 52%

Mul ‐family Residen al

5,646 11% 23% 9% 5%

Commercial/Mixed‐use

4,522 8% 14% 4% 2%

Downtown 815 1% 10% <1% 0.2%

Industrial 6,191 11% 6% 2% 1.3%

Ins tu onal 1,101 2% 25% 2% 2%

Developed Parks 2,578 4% 34% 6% 7.5%

Parks’ Natural Areas 2,356 7% 89% 14% 30%

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UrbantreesinSeattleareanimportantassetthatprovidesmanysocial,environmental,andeconomicbene itsthatenhancethequalityoflifeforthecommunity.Seattle’sUrbanForestStewardshipPlan(UFSP),adoptedbyCouncilin2013,hasagoaltoreach30%treecanopycoverby2037.Theplanalsooutlinesadditionalgoalsforathrivingforestthatincludeahealthydiversityoftreespeciesandages.

Canopycoverassessmentsareanimportanttoolfortrackingprogress.ForSeattletoeffectivelymanageourtreecanopyandenactpolicies,plans,andinitiativestohelpensurearobusturbanforestforgenerationstocome,acomprehensiveunderstandingofourtreecanopyisvital.TheSeattleOf iceofSustainability&Environment(OSE),commissionedthisstudytotheUniversityofVermontSpatialAnalysisLabwhichincludessomeoftheworld’sforemostexpertsinurbantreecanopymapping.AlthoughtreecanopyforSeattlehasbeenmappedbefore,the2016LiDARtreecanopyassessmentrepresentsthemostaccurateaccountingoftreecanopytodate.

Manyofthebene itsurbantreesprovidearerelatedtothesizeandstructureofthetreecanopy,whichisthelayerofbranches,stems,andleavesoftreesthatcoverthegroundwhenviewedfromabove.

Understandingtreecanopyisanimportantstepinurbanforestryplanning.Atreecanopyassessmentprovidesanestimateoftheamountoftreecanopycurrentlypresentinacommunity,wherethecanopyislocated,andtheamountoftreecanopythatcouldtheoreticallybeestablished.Thetreecanopyassessmentcanbeusedbyabroadrangeofstakeholderstohelpcommunitiesplanagreenerfuture.

OSEinpartnershipwiththeUrbanForestryTeammembers(SeattleCityLight,SeattleDepartmentofConstructionandInspections,SeattleDepartmentofTransportation,SeattleParksandRecreation,andSeattlePublicUtilities)ledtheefforttoobtain2016LiDARdatatoperformacanopycoverassessmentfortheCityofSeattleinsupportoftheUFSPgoals.AtOSE’srequest,theSpatialAnalysisLaboratoryanalyzedthedatatorespondtoprojectresearchquestionsusingtheUSDAForestServices'TreeCanopyAssessmentprotocols.

Introduction

WhyisTreeCanopyImportant?

Figure 1. Study area for this project, which is the full City of Seattle boundary.

Figure 2. Tree Canopy derived from high-resolution imagery and LiDAR.

ProjectPartners

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TreecanopywasassessedusingtheUSForestServiceUrbanTreeCanopy(UTC)Assessmentprotocols.Twodistinct,complementaryremotelysenseddatasetswereusedtomapSeattle’scanopy:LiDAR(lightdetectionandranging),acquiredunderleaf‐offconditionsin2016(Figure3A)and2015leaf‐onimagery,obtainedthroughtheUSDANationalAgriculturalImageryProgram(NAIP)(Figures3Band3C).WhileslightlylessaccuratethanLiDAR,thisimageryhasthebene itofprovidingcolorinformationthatisusefulinseparatingvegetatedfromnon‐vegetatedsurfaces.Treecanopymappingwasaccomplishedusingasemi‐automatedapproachinwhichtrees’featureswereautomaticallyextracted,thenmanuallyreviewedandeditedatascaleof1:2500.Anyobservableerrorswerecorrectedandincorporatedintothe inalresults.TheUTCAssessmentprotocolsaretheacceptedstandardformappingtreecanopyandhavebeenappliedbytheUniversityofVermontSpatialLabtoover80communitiesinNorthAmerica.Aformalaccuracyassessmentwasnotperformedforthestudy,butsimilarstudieshaveshowstohaveamarginoferrorof+/‐1%.

This2016treecanopyassessmentrepresentsthemostaccurateaccountingoftreecanopyeverdoneforSeattle,withtreesassmallaseightfeetinheightmapped(Figure3D).Datasetsaresuitableforsummarizingtheareaandpercentareaoftreecanopydowntotheindividualpropertyparcellevel.The2016LiDARdatasetwillserveasthefoundationfortrackingtreecanopychangesovertimefortheCityofSeattle.

HowwastheTreeCanopyMapped?

Figure 3. Example of the input data used to extract tree canopy data. LiDAR is on the left, true color imagery, CIR Imagery, and then tree canopy data on the right.

A B C D

Previous projects have mapped Sea le’s

tree canopy, but as this project used a

combina on of superior source data and

methodologies, any comparisons between

the various studies are not valid. Of

par cular note is the fact that prior canopy

cover es mates should not be used to

draw conclusions with respect to changes

in tree canopy over me.

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TreecanopymetricsaregeneratedbytheUSDAForestService’streecanopymetricstool(Figure4).ThistooltakespropertyparcelsandCensustractsandcomputestheamountofExistingTreeCanopyandPossibleTreeCanopyineachgeography.ExistingTreeCanopyistheamountoftreecanopythatcurrentlyexistsbasedon2016conditions.Itiscomputedbydividingtheamountoftreecanopyineachgeographicunitbythelandarea.PossibleTreeCanopyistheamountoflandthatistheoreticallyavailablefortheestablishmentofnewtreecanopy.Itisimportanttonotethatjustbecauselandisavailableforestablishingnewtreecanopyitdoesnotmeanthatitwouldbeeithersociallydesirablenor inanciallyfeasibletoestablishtreecanopyonsuchareas.Figure5showsanexampleoftheTreeCanopymetricscomputedatthepropertyparcellevel.Theparcelboundaries(top,blacklines)representthegeographicunits.TheExistingTreeCanopy(middle)istherelativepercentageoftreecanopyineachparcel.PossibleTreeCanopy(bottom)indicatestherelativepercentageoflandineachparcelthatistheoreticallyavailablefortheestablishmentofnewtreecanopy.

HowisTreeCanopyCalculated?

Figure 5. Parcel-based tree canopy metrics.

Parcels

PossibleTreeCanopy

ExistingTreeCanopy

Figure 4. Graphical user interface for the tree canopy metrics tool. The tree canopy metrics tool is an ArcGIS-based geoprocessing model that summarizes tree canopy information based on the input polygon boundaries

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WhatisourProgressonAchieving30%TreeCanopyCover?

Methods Results

Figure 6. Tree Canopy derived from high-resolution imagery and LiDAR. Figure 7. Tree Canopy at the city-scale derived from high-resolution imagery and LiDAR

Treecanopyiscomputedbymappingalllandcoverthendividingtheamountoftreecanopybytheamountofland.Excludingwatergivesamoremeaningfulpercentagethatcanbecomparedtoothercities.

28%ofSeattle’slandiscoveredbytreecanopy91,776‐38,271=53,505thisisthetotallandareawithoutwater15,167/53,505=28%dividingthetreecanopyareaintothelandareagivesusthepercenttreecanopycover

91,776acresTotalareaofthecity(includeswater)38,271acresAreaofwaterwithinthecityboundary15,167acresTotalareaoftreecanopy

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Becauseofthedifferencesbetweendevelopedproperty,streetscapes,parklands,remnantforests,andotherareas,Seattle’surbanforestcannotbeviewedasasingleunitformanagementpurposes.TheUFSPde ineseightmanagementunits(MUs)thatcoverallthelandtypesinthecity.Usingtheseland‐usetypesallowsforeasycoordinationofGISmappinglayersandforrelatedplanninginitiatives.Theunitsincludeeightdistinctareasthatwereselectedbaseduponphysicalcharacteristics.AninthMU,theright‐of‐way,overlaystheeightunitssoisnotaseparatelandarea.Canopycoverisamajorindicatorofthebreadthoftheurbanforestanditsoverallhealthandvitality.TodeterminethecanopycoverofeachMU,theirindividuallandareawasdeterminedandthetreecanopypercentineachMUwascalculated.TreecanopywascomputedbothintermsoftotalareaandasapercentageofthelandareawithineachMU.

Table1identi iesthepercentageofthecity’slandmass,currentcanopycover,targetsbyMU,aswellaseachMU’scontributiontothecity’soverallcanopycover.Figure8showsMUscontributionto

overallcanopycover.CanopycoverexceedstargetsinDevelopedParks,Parks’NaturalAreas,Multif‐familyResidential,andInstitutionalmanagementunits;isclosetotargetintheSingle‐familyResidential,Downtown,andCommercial/Mixed‐usemanagementunits;andisbelowtargetintheIndustrialmanagementunit.Themajorityofoururbantreesresideintwolocations:residentialareas(representing67%ofthelandandhousing72%ofSeattle’streecanopy),andintheRight‐of‐way(ROW),whichrepresents27%ofthelandthatisinterspersedthroughoutallManagementUnits,andhouses22%ofthecanopy.

WhatisourProgressonAchievingCanopyCoverTargetsontheUrbanForestStewardshipPlanManagementUnits?

Methods

Results

Management Unit Land area ( % of city)

2016 canopy cover

2037 canopy goal (set in 2007)

% contribu on to city’s canopy cover

Single‐Family Residen al 56% 32% 33% 63%

Mul ‐family Residen al 11% 23% 20% 9%

Commercial/Mixed‐use 8% 14% 15% 4%

Downtown 1% 10% 12% <1%

Industrial 11% 6% 10% 2%

Ins tu onal 2% 25% 20% 2%

Developed Parks 4% 34% 25% 6%

Parks’ Natural Areas 7% 89% 80% 14%

City total 100% 28% 30% 100%

Right‐of‐way (runs through all other MUs)

27% 23% 24% 22%

Figure 8. Contribution to the city’s overall canopy cover by Management Unit.

Table 1. Percentage of city’s landmass,, current canopy cover, targets by MU and MU contribution to city’s canopy cover

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TheamountofExistingTreeCanopywascomputedforthirteenneighborhoods,whoseboundariesdonotpreciselyalignwiththeactuallandarea.Thisresultsinthe“water”neighborhoodhavingasmallamountoftreecanopyduetothepresenceoftreesalongthewater’sedge.

Theamountoftreecanopyineachareaisre lectiveoflanduseandurbanization,withindustrialareasandtheurbancorehavingtheleastamountoftreecanopy(Figure9).Downtownhas10%canopycover,mostlyintheROW.GreaterDuwamish,themostheavilyindustrialarea,has15%canopycover.Delridge,withitslargepatchesofconnectedtreecanopyisabovethecityaveragewith38%canopycover.TheNorthneighborhoodhasarobustamountoftreecanopywithinitsresidentialareas(35%).TheCentral,East,Northeast,Northwest,Southeast,andSouthwestneighborhoodsallhaveatleast30%canopycover.Thiscanlargelybetracedtorobusttreecanopyofresidentiallandscoupledwithlargerexpansesoftreecanopyinparks,theROW,andotherprotectedareas(Figure10).

WhatistheCanopyCoverinDifferentAreasofSeattle?

Methods

Results

Figure 9. Map of existing tree canopy for each of Seattle’s neighborhoods.

Figure 10 Percent of neighborhoods’ land that is covered by tree canopy.

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TheSeattleDepartmentofTransportation(SDOT)isinventoryingstreettreesaspartoftheStreetTreeManagementPlan.SDOTdividedthecityinto27StreetTreeManagementUnits.Havingcleargeographicboundariessupportsestablishmentandtrackingofperformancemeasures.Inventorydatawillinformtreemaintenanceandreplacementeffortsandwillhelpbalanceresourcesmoreequitably.

CanopycoverwascalculatedforeachunitandfurthersubdividedintowhetherornotitfellwithintheROW.Itisimportanttonotethata

limitationofLiDARanalysisisthatthelocationofthetreestemcanbeobscuredduetothetopdownnatureofthetechnology,makingtheanalysisoftreeswithintheROWlessprecise.

TheamountoftreecanopyineachSDOTStreetTreeManagementUnit,alongwiththeproportionofthetreecanopythatfallswithintheROW,differsubstantiallyfromunittounit(Figures11and12).Unit18hasthehighestpercentoftreecanopyat39%,with15%beingintheROW.Atthelowend,Unit27hasthelowesttreecanopyat10%withthe70%ofthecanopybeingintheROW.

WhataretheCanopyCoverlevelsinSDOT’sStreetTreeManagementUnitsandtheRight‐of‐Way?

Methods

Results

Figure 11. Percent of existing tree canopy for each of the SDOT’s 27 street tree management units.

Figure 12. Percent of tree canopy as ROW or Not ROW in relation to each of the SDOT’s 27 street tree management units.

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In2015,MayorEdMurraylaunchedtheEquityandEnvironmentInitiative(EEI)todeepenSeattle'scommitmenttoraceandsocialjusticeinenvironmentalworkalsocreatingtheEnvironmentalActionAgenda(EEA).TheEEAisaseriesofcommunity‐developedgoalsandstrategiesthataddressenvironmentalinequitiesandcreateopportunitiesforcommunitiesofcolor,refugees,peoplewithlowincomes,andlimitedEnglishpro iciencyindividualstobecomeleadersinSeattle'senvironmentalmovement.Twoenvironmentalequitymeasureswereselectedforanalysis:peopleofcolorandpeoplewithin200%ofthepovertylevel.Thesetwofactorswereanalyzed,mapped,andgraphedalongsidetreecanopy.Itshouldbenotedthatalthoughdemographicfactorsmaybecorrelatedwithtreecanopy,thesecorrelationsdonotnecessarilyequalcausation.

Thereisastatisticallysigni icantinverserelationshipbetweentreecanopyandbothpeopleofcolorandpeoplewithin200%ofthepovertylevel.TheanalysisfoundthatinCensustractswithloweramountsoftreecanopymoreofthepopulationtendstobepeopleofcolorandhavelowerincomes.Incensustractswithhighnumbersofpeopleofcolor,treecanopyisaslowas11%whileinareaswithnotmanypeopleofcolorthereis55%canopycover(Figure14).Itisimportanttonotethatalthoughthereisageneralinverserelationship,therearenumerousexceptions.SomelocationswithinSeattlethathavethehighestconcentrationsofpeopleofcolorandresidentsunder200%ofthepovertylevelhavearelativelyhighpercentageoftreecanopyduetothepresenceofparksandstreettrees.

WhatistheRelationshipBetweenTreeCanopyandEnvironmentalEquity?

Methods Results

Figure 13. Map of percent people of color and percent tree canopy for each Census tract.

Figure 14. Figure describing percent tree canopy in relation to people of color. Each dot represents an EEA polygon.

The analysis found that in Census

tracts with lower amounts of tree

canopy more of the popula on

tends to be people of color and

have lower incomes.

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Coniferoustreesprovidegreaterenvironmentalbene itsbecausetheytendtomaintaintheircanopyyear‐round,includingduringtherainyseason,helpingslowdownandreducestormwaterrun‐off.Theyalsoabsorbmorecarbondioxideandairpollutants.Achallengeconiferspresentisthatduetotheirlongerlifespans,theytendtoachievegreatersizerendingthemnotviableinmanylocations.Homeownersareoftenreluctanttoplantconifersintheiryardsandtheyarenotalwaysagood itforstreettrees.Tocalculatethedeciduoustoconiferoustreeratio,treecanopywasseparatedintoapproximateindividualtreecanopiesusingatreecanopysegmentationroutinedevelopedforthisproject.Eachtreecanopyshapewasthenclassi iedaseitherdeciduousorconiferousbasedonitsmorphologicalpropertiesintheLiDARdata.

Coniferstendtobeconicalinshape,whiledeciduousspecies,morerounded.TheoptimalmethodofseparatingoutevergreenspeciesistouseacombinationofLiDARandleaf‐offimagery,butnosuchimagerywasavailabletobeusedintheproject.Theprojectteamdecidedtoaccountforconiferousasaproxyforevergreenspecies.Thedeciduous/coniferousbreakdownwasthensummarizedforeachManagementUnitandthenbasedonwhetherornotthecanopywasinsideoroutsideoftheROW.

Citywide,72%oftreesaredeciduousand28%areconiferous.17%oftheconifersareintheROW.Parks’NaturalAreasistheonlymanagementunitwithamajorityofconifers(60%).Downtownhadthelowestpercentofconiferoustreesatonly10%,whichdemonstratesthechallengewefaceaccommodatingconifersintheright‐of‐way.

WhatistheDeciduoustoConiferousTreeRatioCitywide,byManagementUnit,andtheRight‐of‐Way?

Methods

Results

Figure 15. Percent of deciduous to coniferous tree ratio by each UFSP Management Unit

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Theprojectteamde inedlargetreesandtreegrovesbasedondiameteratbreastheight(DBH).Forthepurposeofthisstudy,largetreesmusthaveatleasta30‐inchDBHandtreegroveshavetocontainaminimumofeighttrees,eachwitha12‐inchminimumDBHandformingacontiguouscanopy(excludingstreettrees).AsDBHcannotbedirectlymeasuredfromoverheadremotesensingdata,weusedheightinformationfromtheLiDARdatasetasaproxybasedonequationsdevelopedbyJenkinsetal(2003).

Seattleisestimatedtohave6,338largetrees(Figure17)and3,188treegroves(Figure18).Notsurprisingly,thelargetreesandtreegrovesareoftenpresentinparksandotherforestedareasthroughoutthecity.Althoughthelargetreesandtreegrovesarelargelyabsentfromthemosturbanizedareassomedoexistonresidentialandinstitutionallands.

WherearetheLargestTreesandTreeGrovesLocated?

Methods Results

Figure 16. Locations of the largest trees at the city scale. Figure 17. Locations of tree groves at the city scale.

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Mappingtreecanopychangeovertimerequiresconsistent,high‐qualitydata,withoutwhichitisnotpossibletodetermineiftheresultingtrendisduetoactualcanopychangeorduetoanomaliesinthesourcedata.Theoriginalstudyscopeincludedcomparing2001LiDARwith2016LiDARtodeterminecanopycoverchangeovertime.Unfortunately,the2001dataset,whileconsideredcutting‐edgetechnologyatthetime,isofverylowresolutionwhichpreventedreliablecomparisontothenew,higher‐qualitydatafrom2016. However,in2015,OSEcommissionedananalysisusingUSDAForestServicei‐TreeCanopyprotocols,whichconsistofasample‐basedapproachthatestimatescanopybydeterminingthepresence/absenceoftreecanopyatagivenlocationusingaerialorsatelliteimagery.Theamountofchangewasdeterminedthroughthemanualinterpretationof4,000strati iedrandompointsgeneratedforthreeyears:2007,2010and2015.

The2015analysisestimateda2%lossoverthespanoftheeight‐yearstudyperiodwitha+/‐3%marginoferror(Figure18).The2016LiDARdatasetwillserveasthefoundationfortrackingtreecanopychangesovertimefortheCityofSeattlemovingforward.Anumberoffactorscouldcausedifferencesincanopyestimatesincludinglimitationsofusingasample‐basedapproachandthathistoricalimageryfromGoogleEarthisnotcollectedatthesametimeofday,causingshiftsoftreecanopylocation.Treecanopylosscouldinitiallybecausedbyincreaseofimpervioussurfaceduetodevelopment.Naturalgrowthofmaturetreesandtreeplantingstendtooffsetcanopyloss.

HowhasSeattle’sCanopyCoverChangedsince2007?

Methods Results

Thedifferencesinthe2015approachandthe2016LiDARmakesthestudies’resultsnotdirectlycomparabletoone

another.Priorestimatesoftreecanopyshouldnotbeusedto

drawconclusionswithrespecttochangesintreecanopyovertime.

Figure 18. Percent tree canopy cover for each UFSP Management Unit in 2007, 2010 and 2015. The gray area represents the standard error.

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Developmentimpactontreecanopywasexploredbyrandomlyselecting10developmentpointsfromeachUFSPManagementUnittotaling80points.Using2007‐2017historicalimageryfromGoogleEarth,treecanopywasmappedbyparcelforbeforeandafterdevelopment(Figure19).Percenttreecanopywascalculatedbydividingthetotalareaoftreecanopybythetotalareaoftheparcels.

Althoughthispieceisnotstatisticallyvalid,itisadetailedanalysisofhowdevelopmentimpactedtreecanopyateachidenti iedpointandprovidesgeneralinsightsintohowdevelopmentcanimpactcanopycoverovertime..DowntownandSingleFamilyhadthemosttreecanopyloss.(Figure20).

WhatImpactcanDevelopmentHaveonTreeCanopy?

Results

Figure 19. Example of the impacts of development in a single family management unit parcel. The left image is from 2011 and the right is 2016.

Methods

Figure 20. Percent tree canopy cover for each management unit before and after development occurred.

Land Area (% of city)

Single Family 2011 Single Family 2016

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Theurbanheatislandeffectisproducedbydenseconcentrationsofbuildings,pavement,andothersurfacesthatabsorbandretainheat.Thisincreasesairpollution,costsrelatedtoairconditioning,andheat‐relatedhealthconditionsdisproportionallyimpactingvulnerablepopulations.Treecanopyhelpsreduceheatislandeffectmitigatingimpacts.

SurfacetemperatureobtainedfromLandsat8satellitethermalimagerycollectedonSeptember20,2016wasusedtoestimatetheurbanheatislandeffect.Landsat8sensesthermalenergyataresolutionof60‐meters.Surfacetemperaturewasintegratedwiththe2016LiDARtreecanopydatatoanalyzehowthepresenceoftreecanopyaffectstheurbanheatisland.

DeterminingtheurbanheatislandinSeattleischallenginggiventhatmuchofthecityissurroundedbywater,whichdampenstheurbanheatislandeffect.Nevertheless,intheinlandareastreesclearlyhelpreducesurfacetemperature,therebyreducingtheurbanheatisland.ThisstudyproducedGISshape ilesforexistingtreecanopy,thermalimagery,andpossibletreecanopy(Figure21)thatcanbeusedtoidentifytreeplantinglocationstominimizetheimpactsofurbanheatislandhotspotsbyreducingsurfacetemperatures,aswellastoprioritizetreeplantingeffortstomitigateequityissues.

HowdoesTreeCanopyReducetheUrbanHeatIslandEffect?

Methods Results

Figure 21. Seattle’s urban heat island hot spots correspond to low tree canopy areas..

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SeattleCityLightmaintainsvegetationclearances(pruningtreesawayfrompowerlines)forsafetyandelectricalservicesystemreliability.SCLmaintainsclearancesinaccordancewithutilitybestmanagementpracticesandtree‐industrystandards.ToisolatecanopythatintersectsSCL’selectricalinfrastructure,a10‐footbufferwascreatedaroundallprimaryandsecondarydistributionlines,transmissionsupportstructures,and115kV‐

230kVoverheadwires(Figure22).BiomassequationsfromJenkinsetal.(2003),wereusedtocomputevolume.Thebiomassequationsrequiresaconsiderationoftreediameteratbreastheight(DBH)andspecies.AsDBHwasnotavailable,heightwasusedasproxy.Averageswereobtainedforthemostcommonconiferousanddeciduousspecies:blackcottonwood,redalder,westernredcedar,DouglasFir,bigleafmaple,Scouler’swillow,westernhemlock,andbeakedhazelnut.Densitieswere540kg/m3and470kg/m3fordeciduousandconiferoustrees,

respectively.Diameter‐at‐breast‐heighttotreeheightmodels(Figure23)wascreatedbycombiningminimum,maximum,andmeandataforeachparameterfromseveraljournals(FierkeandKauffman,2005;Garmanetal.,1995;andHanusetal.1999).

Thevolumeoftreecanopythatintersectstheminimum10‐footclearancedistancesforSeattleCityLight’sdistributionandtransmissionsystemsis1,328,000ft3or6%ofSeattle’stotaltreecanopyvolume(Table2).SCLwillusethisinformationandadditionalanalysisthatisbeingperformedoutsidethescopeofthisstudytoinformvegetationmanagementefforts.

WhatistheVolumeofTreeCanopythatIntersectstheMinimum10ftClearanceDistancesforSCL’sElectricalSystem?

Methods

Results

Figure 22. Coniferous and deciduous trees intersecting the 10ft minimum distance of SCL infrastructure.

Figure 23. DBH by height regression to produce a simple polynomial to best fit the model. This model incorporates data of all species before coniferous/deciduous differentiation.

Table 2. Volume (cubic feet) of Seattle trees and trees that intersect within 10ft of SCL infrastructure.

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Treecanopy inSeattle isavitalasset thatprovidesmultipleecosystemservicessuchasstormwaterrunoffreduction,improvedairquality,decreasedcarbonfootprint,enhancedqualityoflife,savingson energybills,andhabitatforwildlife.

The2016LiDARassessmentrepresentsthemostaccurate,comprehensive,anddetailedaccountingofSeattle’scanopycovertodate.Itprovidesthefoundationforunderstandingthequantity,distribution,andcon igurationoftreecanopywithinSeattleandestablishesthebaselineforfuturecanopycoverchangeanalysis.

ThemajorityofSeattle’surbantreesarefoundintwolocations:residentialareas(representing67%ofthelandwith72%ofSeattle’streecanopy),andintheright‐of‐way,interspersedthroughoutthecity(representing27%ofthelandwith22%ofthecanopy).

Largertreesandtreegrovesareoftenpresentinparksbutarealsoonresidentialandinstitutionallands.

Canopyexceedstargetsindevelopedparks,naturalareas,multi‐family,andinstitutionalareas;isclosetotargetinsingle‐family,downtown,andcommercialareas;andisbelowtargetinindustrialareas.

Canopycoverdiffersacrossthecitybasedonlanduse,thepresenceofparksandnaturalareas,andsocio‐economicfactors.Censustractswherethepopulationtendstoberesidentsofcolorandpeoplewithlowerthanaverageincomealsohaveloweramountsoftreecanopy.

72%ofSeattle’streecanopyisdeciduousand28%isconiferous.Mostoftheconifersareonsingle‐familyareas.

Thepresenceoftreescanreduceurbanheatisland(surfacetemperature),especiallyinland.

6%ofSeattle’streecanopyiswithintheclearancedistanceSeattleCityLightmaintainsforsafetyandreliabilityofthepowergrid.

Althoughnotstatisticallyvalid,amini‐assessmentof80randomparcelsfoundthatdevelopmentledtocanopycoverloss.

Aseparatesample‐basedsatelliteimagerytrendanalysisdonein2015suggestsanoverallcanopycoverlossofaround2%from2010to2015,withamarginoferrorof+/‐3%.

1. Thevalueofthisstudywillberealizedwhentheresultsareusedtoguideurbanforestrypolicyandmanagementefforts.

2. Preservingtreecanopyisasimportantasnewtreeplantinginitiatives.Establishedmaturetreegrowthcontributestocanopycovergain.

3. Studydatacanbeusedtoestablishlocalizedcanopygoalsandtargetedplantingsandconservationeffortstomaximizelimitedresources.Selectingaspeci icbene ittobuildanengagementcampaigncanincreasethesuccessintreeplantingandconservationactions,particularlywhenanaudienceisalreadygalvanizedaroundaparticularissue(e.g.engagingresidentsconcernedaboutairqualityissuesinaspeci icneighborhoodintreeplantingeffortsinthatarea).

4. ItisrecommendthattheCityofSeattleupdateitsUrbanForestryStewardshipPlanwithinformationderivedfromthisstudy.Theinformationcanbeusedtorevisitexistinggoalsandtargets.

5. AnotherLiDAR‐basedassessmentshouldbeplannedtodeterminechangestothetreecanopyinSeattlewithinthenext iveyears.Suchassessmentcanprovideinformationonhoweffectivetreeplantingandpreservationeffortshavebeen,inadditiontounderstandinghowotherfactors(e.g.development,drought,pests,etc.)maybeimpactingtreecanopy.Futureassessmentswillonlybemadepossibleifcontinuedinvestmentsinhigh‐resolutionremoteimageryandLiDARdataacquisitionaremade.UndertakingLiDAR‐basedassessmentsinthefuturewillallowfortrendanalysiswithcomparabletreecanopydatatobemade.

Conclusions

Study indings

Usingthe indings

18

Additional Information Preparedby:JarlathO’Neil‐DunneUniversityofVermontSpatialAnalysisLaboratoryjoneildu@uvm.edu802.656.3324

Formore info on theUrban Tree Canopy Assessmentpleasevisithttp://nrs.fs.fed.us/urban/UTC/

TreeCanopyAssessmentTeam:NoahAhles,LukeBan,JarrettBarbuto,NoahBell,JasonBlack,ErnieBuford,EmmaButter ield,JosePabloBrenesCoto,JacobCiof i,KristineCorey,KaiDarke,TaylerEngel,EmmaEstabrook,NathanielFuchs,MikeFranck,LindseyFreitag,DanGordon,JacobKing,SeanMacFaden,JaredMaher,ElizabethMcElwee,OwenMoseley,JarlathO’Neil‐Dunne,AnnaRoyar,KellySchulze,MatthiasSirch,ConnorSullivan,PatrickSullivan,andJamesRambone.

Figure 25. 2016 LiDAR for Seattle. The acquisition of such data in the future will be crucial for tracking tree canopy change over time.

Fierke,M.K.andJ.B.Kauffman.2005.Structuraldynamicsofriparianforestsalongablackcottonwoodsuccessionalgradient.Garman,S.L.,Acker,S.A.,Ohmann,J.L.,andT.A.Spies.1995.Asymptoticheight‐diameterequationsfortwenty‐fourtreespeciesinWest‐

ernOregon.ResearchContribution,10:5‐22.Hanus,M.L.,Marshall,D.D.,andD.W.Hann.1999.Height‐diameterequationsforsixspeciesintheCoastalRegionsofthePaci icNorth‐

west.ResearchContribution,25:5‐11.Jenkins,J.C.,Chojnacky,D.C.,Heath,L.S.andR.A.Birdsey.2003.National‐ScaleBiomassEstimatorsforUnitedStatesTreeSpecies.Forest

Science,49:12‐35.

Figure 24. Seattle canopy cover by Urban Forest Stewardship Plan management unit..

2016 Sea le Canopy Cover