monitoring forest restoration effectiveness on galiano ...i monitoring forest restoration...

MonitoringForestRestorationEffectivenessonGalianoIsland,British

Columbia:ConventionalandNewMethods

QuirinVascoHohendorfB.Eng.,HochschuleWeihenstephan-Triesdorf,2015

AThesissubmittedinPartialFulfillmentoftheRequirementsfortheDegreeof

MASTEROFSCIENCE

IntheSchoolofEnvironmentalStudies

ÓQuirinVascoHohendorf,2018UniversityofVictoria

Allrightsreserved.Thisthesismaynotbereproducedinwholeorinpart,byphotocopyorothermeans,withoutthepermissionoftheauthor.

MonitoringForestRestorationEffectivenessonGalianoIsland,British

Columbia:ConventionalandNewMethods

QuirinVascoHohendorfB.Eng.,HochschuleWeihenstephan-Triesdorf,2015

SupervisoryCommitteeDr.EricHiggs,SupervisorSchoolofEnvironmentalStudiesDr.CecilC.Konijnendijk,additionalmemberProfessorinurbanforestry,UniversityofBritishColumbia

Abstract

Icomparedforeststructuralparametersoftreatedanduntreatedplotsonaforestrestoration

siteonGalianoIsland,BritishColumbia.ThesitewasreplantedwithDouglas-fir(Pseudotsuga

menziesii(mirb.)Franco)afterbeingintensivelyloggedinthe1970sandthenthinnedintheearly

2000s.Iusedexistingbaselinedatafrom8permanentplots(5treated,3control)andcompared

itwithforestassessmentdatacollectedinthefieldinthesummerof2017.Additionally,Iused16

temporaryplots(8treated,8control).Iassessedvegetationpercentagecoverbyplot,coarse

woodydebrisbyplot,treediameter,speciesandstatus(n=846),height(n=48)anddiameter

growth(n=271).Ifoundthattreatedplotsshowedimprovedmeasuresofstructuraldiversity

likediametergrowth,crownratiosandplantdiversity,butIwasunabletorelatetheincreased

diametergrowthtotherestorationtreatments.Myfindingssuggestthattocreatealasting

impact,restorationthinningwillhavetobemorefrequentorcreatelargergaps.

Ithenreviewedthecurrentstudieswithunmannedaerialvehicles(UAV)inecological

restoration.IevaluatedpotentialuseofhobbyistUAVsforsmallorganizationsandnot-for-profits

andfoundthatifappliedcorrectly,UAVscanincreasetheamountofavailabledatabefore,

duringandafterrestoration.Reproducibleandreliableresultsrequiretrainedpersonneland

calibratedsensors.UAVscanincreaseaccesstoremoteareasanddecreasedisturbanceof

sensitiveecosystems.Regulations,limitedflighttimeandprocessingtimeremainimportant

restrictionsonUAVuseandhobbyistUAVshavealimitavailabilityofsensorsandflight

performance.

Finally,IusedimagestakenfromahobbyistUAVtoassessforeststructureoftherestorationsite

onGalianoIslandandcomparedmyresultswiththegroundmeasurements.Ifoundacanopy

heightmodel(CHM)fromUAVimagesunderestimatedmeantreeheightvaluesforthestudysite

onaverageby10.2metres,whilealsoseverelyunderestimatingmeanstemdensities.Usinga2

metrethreshold,Idelineatedcanopygapswhichaccountedfor6%ofthecanopy.UAVimages

andtheresultingCHMrepresentanewvisualizationofthestudysite’sstructureandcanbea

helpfultoolinthecommunicationofrestorationoutcomestoawideraudience.Theyarenot,

however,sufficientformonitoringorscientificapplications.

TableofContents

Abstract..................................................................................................................................ii

ListofTables...........................................................................................................................v

ListofFigures.........................................................................................................................vi

ListofAbbreviations..............................................................................................................viii

Acknowledgements................................................................................................................ix

Dedication..............................................................................................................................xi

Chapter1:Introduction...........................................................................................................11.1Ecologicalrestoration..................................................................................................................11.2EcologicalRestorationofForests.................................................................................................31.3TheCoastalDouglas-firzone........................................................................................................41.4TheGalianoConservancyAssociationandRestorationofaDouglas-firplantation.......................61.5RemotesensingandUnmannedAerialVehicles...........................................................................91.6ConceptualFoundationandOrganizationoftheThesis.............................................................11

Chapter2:RestorationeffectivenessinaYoungDouglas-firForest.......................................130. Abstract..................................................................................................................................131. Introduction............................................................................................................................132.Methods......................................................................................................................................18

2.1.StudySite.....................................................................................................................................182.2.Permanentplots..........................................................................................................................202.3.FieldMethods.............................................................................................................................212.4.Analysis.......................................................................................................................................22

3.Results........................................................................................................................................243.1.CoarseWoodyDebris.................................................................................................................263.2.UnderstoryVegetation................................................................................................................273.3.Diameter,Height,Density,BasalAreaandGrowth....................................................................28

4.Discussion...................................................................................................................................325.Conclusion..................................................................................................................................366.References..................................................................................................................................37

Chapter3:ThePotentialforHobbyistUnmannedAerialVehiclesinEcologicalRestoration...400. Abstract..................................................................................................................................401. Introduction............................................................................................................................402. CurrentUAVtechnologyanduse.............................................................................................43

2.1SeveraltypesofUAVsfordifferentpurposes...............................................................................452.2Temporalandspatialflexibility.....................................................................................................462.3.AffordabilityandAccessibility.....................................................................................................472.4.Availabilityofopensourcesoftwareandplatforms....................................................................472.5.Widerangeofsensors.................................................................................................................482.6.MultipleUAVimageanalysissoftware........................................................................................51

3. ReliabilityandconcernswithUAVuse....................................................................................534. Futuredevelopments..............................................................................................................56

5. UAVsinEcologicalRestoration................................................................................................576. Conclusion..............................................................................................................................606.References..................................................................................................................................62

Chapter4:AssessingCanopyStructureUsingaHobbyistUAVand‘StructurefromMotion’TechnologyinaRestoredDouglas-firForest..........................................................................67

0.Abstract.......................................................................................................................................671.Introduction................................................................................................................................672.MaterialsandMethods...............................................................................................................713.Results........................................................................................................................................77

3.1TreeheightsandDensity..............................................................................................................773.2.CanopyGaps................................................................................................................................793.3TreeLocations...............................................................................................................................80

4.Discussion...................................................................................................................................805.Conclusions.................................................................................................................................836.References..................................................................................................................................84

Chapter5:Conclusion............................................................................................................885.1Summaryoffindings..................................................................................................................885.2GreaterContext.........................................................................................................................905.3LimitationsofthisResearch.......................................................................................................915.4SuggestionsforFutureResearch................................................................................................91

References............................................................................................................................93

AppendixA:DesignofPermanentPlots................................................................................99Essentialinformation........................................................................................................................100Baselinetreedata.............................................................................................................................100Coarsewoodydebris........................................................................................................................100Vegetation........................................................................................................................................101

ListofTablesTable2-1:Summaryofallmeasuresofstandstructureanddiversitybytreatments.Fd=Douglas-

fir,Dr=RedAlder.Valuesaregroupmeans..........................................................................25Table4-1:Ecosystemtypesonthestudysite................................................................................71Table4-2:CharacteristicsoftheDJIMavicProconsumergradeUnmannedAerialVehicle

(https://www.dji.com/mavic/info#specs)..............................................................................73Table4-3:Meanandrangeoftreeheightanddensityfromfieldmeasurementsof111treesand

predictionsfromacanopyheightmodel(CHM)usingimagesgatheredbyanunmannedaerialvehicle..........................................................................................................................77

Table4-4:Proportionofcanopygapsofvarioussizes...................................................................80Table0-1:Treestatus(Dallmeier,1992)......................................................................................102

ListofFiguresFigure1-1:OverviewofBritishColumbiawiththeCoastalDouglas-firzone(green).......................5Figure2-1:(a)LocationofGalianoIslandinWesternCanadaandstudysiteonGalianoIsland,

BritishColumbia,Canada.(b)Overviewofthestudysitewithpermanentandtemporaryplots.......................................................................................................................................18

Figure2-2:Layoutofpermanentplotsandassessmentoftreelocation,accordingtotheprotocolsuggestedbyRoberts-PichetteandGillespie(1999)..............................................................20

Figure2-3:Comparisonofvolumesofcoarsewoodydebris(CWD).CO=untreatedcontrol,TR=treated.(a)BoxplotofCWDbytreatments.Thelowerandupperhingescorrespondtothefirstandthirdquartiles(the25thand75thpercentiles).Whiskersextend1.5*IQRfromhinge.(b)VolumeofCWDbysurveyyear.Eachdotrepresentsoneplot..............................26

Figure2-4:(a)Abundanceof12mostcommonplantspeciesinthestudyplots.(b)Speciescountbytreatment..........................................................................................................................27

Figure2-5:Comparisonoftreeheightsbytreatmentandsurveyyear.(a)Treeheightbytreatmentin2007(grey)and2017(beige).Thelowerandupperhingescorrespondtothefirstandthirdquartiles(the25thand75thpercentiles).Whiskersextend1.5*IQRfromhinge.(b)TreeheightbycrownratioofPseudotsugamenziesiitrees..................................29

Figure2-6:Density,basalareaandsnagsofallspeciesbytreatmentin2007(grey)and2017(beige).Thelowerandupperhingescorrespondtothefirstandthirdquartiles(the25thand75thpercentiles).Whiskersextend1.5*IQRfromhinge.(a)Densitybytreatment.(b)basalareabytreatment.(c)Numberofsnagsbytreatment..........................................................30

Figure2-7:Thelowerandupperhingescorrespondtothefirstandthirdquartiles(the25thand75thpercentiles).Whiskersextend1.5*IQRfromhinge.(a)Boxplotofdiameteratbreastheightin2007(grey)and2017(beige)bytreatment;(b)Diametergrowthperyearbytreatment.maxCO=1.28cma-1,meanCO=0.347975cma-1,maxTR=2.66cma-1,meanTR=0.54cma-1....................................................................................................................31

Figure3-1:TwoexamplesofcommonUAVs.(a)DJIInspire2multi-rotorUAV.(b)SenseFlyeBeeClassicfixed-wingUAV.Imageswereobtainedfromthemanufacturers'websites...............45

Figure3-2:RBGcanopyphotoofaDouglas-firforestthatwastakentoassessrestorationeffectiveness..........................................................................................................................50

Figure4-1:Locationandcontourmapofthe61.5hastudysiteonGalianoIsland,BritishColumbia................................................................................................................................72

Figure4-2:Workflowusedintreetopandcanopygapdetection.................................................75Figure4-3:(a)MeanplotheightmeasuredonthegroundvsmeanplotheightderivedfromCHM.

Eachdotrepresentsone20x20msurveyplot;(b)DensitymeasuredonthegroundvsdensityderivedfromCHM.................................................................................................................77

Figure4-4:Mapoftreeheightsobtainedfromunmannedaerialvehicleimages(polygons)anddiscretefieldmeasurementsofindividualtreesin18squaresurveyplots(squares)............78

Figure4-5:Mapoftreedensityobtainedfromunmannedaerialvehicleimages(polygons)anddiscretefieldmeasurementsofindividualtreesin18squaresurveyplots(squares)............78

Figure4-6:Canopygapslowerthanthe2-meterthresholdappliedtoourCHM..........................79Figure4-7:Imageobtainedbyanunmannedaerialvehicleshowingthreeplots(greenpolygon)

withtreetops(reddots)andactuallocationoftrees(bluedots).Lightergreyrepresentshigherelevationwhiledarkgreyrepresentslowelevation...................................................80

Figure0-1:Layoutofpermanentplots(Roberts-Pichette&Gillespie,1999)................................99Figure0-2:DecayclassesasdefinedbytheMinistryofEnvironmentCanada(MOE,2010).......101

ListofAbbreviationsBVLOS beyondvisualline-of-sightCHM canopyheightmodelCWD coarsewoodydebrisDBH diameteratbreastheightDEM digitalelevationmodelDTM digitalterrainmodelEVLOS extendedvisualline-of-sightGCP groundcontrolpointGIS geographicinformationsystemGPS globalpositioningsystemIQR innerquartilerangeRBG Red-green-blue.Primarycoloursrepresenting

visuallightSfM Structure-from-motiontechnologyUAV unmannedaerialvehicleVLOS visualline-of-sight

Acknowledgements

IwouldliketoacknowledgetheLkwungen-speakingpeoplesonwhosetraditionalterritorythe

UniversityofVictoriastandsandtheSonghees,EsquimaltandWSÁNEĆpeopleswhosehistoric

relationshipswiththelandcontinuetothisday.

MyresearchwasfocusedonwhatisnowknownasDistrictLot63,GalianoIsland.Iwouldliketo

acknowledgethatmyworkwasconductedintheshared,assertedanduncededterritoryofthe

Penelakut,theLamalcha,andtheHwlitsumNations,otherHul'qumi'numspeakingpeoples,

SENĆOŦENandWSÁNEĆspeakingpeoples,andanyotherswithrightsandresponsibilitiesinand

aroundwhatisnowknownasGalianoIsland.Iwouldliketoacknowledgethatmyworkwas

conductedonthecededterritoryoftheTsawassenFirstNation.Iamverygratefulforthe

privilegeofhavingbeenabletoconductmyworkwithinthesesharedtraditionalterritories.

Iwouldliketoexpressmygratitudetoeveryonewhosupportedmeonthisjourney:Tomy

graduatesupervisorDr.EricHiggsandcommitteememberCecilC.Konijnendijkforsupporting

meandallowingmethefreedomtoturnmyideasintothisproject.Thankyoutoeveryoneatthe

GalianoConservancyAssociationandespeciallyKeithErickson.Keith,alongwithHerbHammond

wereparticipantsintheoriginaltreatmentsandhelpedmeunderstandthethinkingbehindit.

Thankyoutomylabgroup,mycohortandtheSchoolofEnvironmentalStudiesformakingmy

twoyearsinVictoriasuchanunforgettableexperience.ThankyoutotheUniversityofVictoriafor

financiallysupportingmygraduatestudiesandtotheLoreneKennedyGraduateStudent

ResearchAwardcommitteeforsupportingmyfieldworkonGalianoIsland.Lastbutnotleast,I

wouldliketothankmypartner,myfriendsandmyfamilywhokeptmemotivatedalongtheway.

“Damngoodcoffee!”-DaleCooper,TwinPeaks

Dedication

InmemoryofKenMillardwhowastheheartoftherestorationtreatmentsonDL63andinspired

usalltoworkhardforconservationandrestoration.

Chapter1:Introduction1.1Ecologicalrestoration

Thestandarddefinitionofecologicalrestoration,“istheprocessofassistingtherecoveryofan

ecosystemthathasbeendegraded,damaged,ordestroyed”(SER,2004,p.3).Inthelightof

decreasingbiodiversityandlandloss,itismoreimportantthanevertorestoredegradedsystems

andecologicalrestorationbecomesincreasinglyrecognizedasanimportanttoolinprotecting

theenvironment(AronsonandAlexander,2013).Ecologicalrestorationisnoreplacementfor

conservationbutanadditionalmeasurethatneedstobetakengloballytocounteract

degradationanddestructionofnaturalsystems(AronsonandAlexander,2013;Keenleysideetal.,

2012;Suding,2011).

Ecologicalrestorationfirstevolvedasadisciplineinthe1980s,butitsrootsinNorth

Americadatebackatleasttothe1930s,whenAldoLeopoldconductedthefirstdocumented

restorationprojectattheUniversityofWisconsin-Madison(Greenwood,2017).Manynewideas

andconceptsinecologyalsoinfluencedrestorationecologyandthefieldevolvedfromasimple

“bringbackwhatwasbefore”toacomplexdiscipline,dealingwithachangingclimate(Falkand

Millar,2016),heavilyalteredandnovelecosystems(Hobbsetal.,2013),andalieninvasive

species(Headetal.,2015).

Tobesuccessful,restorationprojectsneedtobeeffective,efficientandengaging

(Keenleysideetal.,2012).Ecologicalrestorationiseffectivewheninterventionsre-establish

ecosystemstructure,functionandcompositionintheshortandlong-termbyincreasingthe

resilienceagainstfuturedisturbanceandencouragingecological,socialandculturalsustainability

oftheproject.Efficientrestorationconsidersdifferentscales,enhancestheecosystemservices

providedbytherestoredecosystemandensureslongtermmaintenanceandmonitoring.

Availableresourcesareusedsothattheyhavethemostpossibleimpact.Ecologicalrestorationis

engagingwhenprojectplannerscollaboratewithlocalcommunities,scientistsandother

stakeholdersthroughoutthewholeprojectandwhenmonitoringresultsarecommunicated

effectivelytoallstakeholders.Thisincreasesthesupportforrestorationprojects,improves

monitoringandbuildscapacityandunderstandingforecologicalprocesses(Keenleysideetal.,

2012).

Restorationmustnotonlymeetecologicalneeds,butalsoconsidersocialandcultural

needstobesuccessful(Perringetal.,2015;WiensandHobbs,2015).Servicesprovidedby

restoredecosystemsoftenincludesocialandculturalbenefitslikerecreation,foodresourcesor

cleanwater(Keenleysideetal.,2012).Theseshouldbeincorporatedinthegoalsetting,planning

andmonitoringregimeinaquantifiableway.

Inearlyrestoration,monitoringwasoftenneglectedwhichcomplicatedtheassessment

ofrestorationsuccess(Wortleyetal.,2013).Thisresultedinmanyprojectswithlowsuccessand

decliningsupportfromfundersandlocalcommunities.Areviewofscientificpaperson

restorationsuccessin2013showedthatmonitoringofrestorationsuccessisbecoming

increasinglyimportant.Theauthorsfound301publicationsthatevaluaterestorationoutcomesin

the28yearscoveredbythestudy,withmoststudiespublishedbetween2008and2012(Wortley

etal.,2013).Theauthorsrelatethisdevelopmenttoincreasingmaturityofrestorationprojects.

Monitoringcanimproverestorationsuccessbycontributingtoadaptivemanagement(AM).AM

usesaniterativeprocessofmanagementdecisionsasameansofdealingwithuncertaintyinthe

process.AnimportantpartofAMislearningaboutthesystemwhilemanagingitandsofurther

improvefuturemanagement.Itfollowssixstepstomanageaproject.Assessment,design,

implementation,monitoring,evaluation,adjustmentandrepeatedassessment(Murray&

Marmorek,2003).The“EcologicalRestorationforProtectedAreas”IUCNguidelinesrecommend

aseven-phaseprocesstoecologicalrestorationwhichincludesAMasitsmainelement

(Keenleysideetal.,2012).AMhasbeenrecognizedasanexcellentstrategyforsuccessful

restoration(Dellasalaetal.,2013;Gayloretal.,2002),andisbeingimplementedmanyprojects

aroundtheglobe,forexampleinfederalforestsintheUSA(Dellasalaetal.,2013;Franklinand

Johnson,2012)andtherestorationofSpringbrookworldheritagerainforestinAustralia

(Keenleysideetal.,2012).

1.2EcologicalRestorationofForests

Deforestationandforestdegradationarethesecondlargestsourceofanthropogeniccarbon

emissions(IPCC,2007).Theeffectsofelevatedamountsofcarbonintheearth’satmosphereon

biodiversityandhumanlivelihoods,haveledtoanincreasedrecognitionforcountermeasures

likere-forestationandforestrestoration(Ciccareseetal.,2012).Additionally,intactand

functioningforestecosystemsarecriticalforimportantecosystemservices,suchascleanwater,

air,firewoodandtimbersupply(Ciccareseetal.,2012).Ecosystemswithlong-livedspeciesare

especiallyhardtorestore,duetolongplanningperiodsandhighuncertaintiesaboutfuture

environmentalconditions(Golladayetal.,2016;HamannandWang,2006).Thisisespeciallytrue

forforests,duetotheslowgrowthandlonglifetimesoftrees.Wecannotpredictpreciselyhow

theclimatewillhavechangedin50orevenin200years,whenanowyoungstandwillhave

reachedamaturestateandforeststhereforeforestmanagementhastodealwithadegreeof

uncertainty(IPCC,2007).Whilemostyoungforestswilleventuallyundergosuccessiontowards

old-growthstands,thegoalofforestrestorationistohelpthesuccessionandacceleratethe

process(ParksCanadaAgencies,2008).

Longtermplanningundertheseconditionsischallenging,butthereissignificantconsensusthat

especiallyinforestsadaptivemanagementstrategiesareagoodwayofrespondingtothe

challenge(Golladayetal.,2016;Hiersetal.,2016),andamongothers,ParksCanada(2008)and

Keenleysideetal.(2012),suggestusingadaptivemanagementintheirguidelinesforecological

restoration.Sincethepublicationoftheguidelines,adaptivemanagementhasbecomeeven

morepopular(Hobbs,2016).

1.3TheCoastalDouglas-firzone

MystudysiteonislocatedonGalianoIsland,oneofthesouthernGulfIslands,betweenthe

LowerMainlandandVancouverIslandinBritishColumbia,Canada.Thestudysiteisintheheart

ofthemoist-maritimeCoastalDouglas-firbiogeoclimaticzone(CDF)(Nuszdorferetal.,1991).

TheCDFzonecoverslessthanonepercentofBritishColumbiaandappearsonlyatelevationsup

to260m(figure1-1)(Nuszdorferetal.,1991).Theclimateiscoolmesothermal,withmildwet

winters(800mmprecipitation)andwarmanddrysummers(200mmofprecipitation)

(Nuszdorferetal.,1991).Meantemperaturesrangefrom3°Cto17°Cwithanannualmeanof

10°C(Nuszdorferetal.,1991).Douglas-fir(Pseudozugamenzesii(Mirb.)Franco)isthemost

commontreespeciesthroughoutthezone(Nuszdorferetal.,1991).Arbututs(Arbutusmenziesii)

PurshandGarryoak(Quercusgarryana)DouglasexHook.arelesscommonbutalmost

exclusivelyoccurintheCDFzoneinCanada(Nuszdorferetal.,1991).Only3%oftheCDFzoneis

protected,withmostlysmall,isolated,andpatchesandfewlargeprotectedareas(>250ha)

(Nuszdorferetal.,1991).AlmostonethirdoftheCDFhasbeentransformedfromforesttosome

otherformoflanduse(Nuszdorferetal.,1991).Onlyabout10%oftheforestismorethan120

yearsoldandlessthan1%isold-growth(Nuszdorferetal.,1991).Landtransformation,invasive

speciesintroductionandthechangeofecologicalprocesseshaveledtothelistingofmany

speciesasendangered(Nuszdorferetal.,1991).TheCDFzonehasaverylimitedextent,buthas

significantspeciesrichnessanddistinctiveecologicalcommunitiesthatmakewell-connectedand

betterprotectedmanagementnecessary(Nuszdorferetal.,1991).

Figure1-1:OverviewofBritishColumbiawiththeCoastalDouglas-firzone(green)

1.4TheGalianoConservancyAssociationandRestorationofaDouglas-firplantation

TheGalianoConservancyAssociation(GCA)isalocallandtrustthatwasformedin1989.Formed

outofadesiretostopunsustainableloggingpracticesonGalianoIslandinthe1970’s,Forest

conservationandrestorationhasalwaysbeenacoreconcernoftheGCA.Withclear-cutlogging

happeningallovertheislandinthe1970’sthecommunitystartedtostandupagainstlogging

companiestoprotecttheirisland´secosystems,whichconsequentlyledtotheformationofthe

GCAasalandtrust.

In1998,earlyinitshistory,theGCAacquiredahighly-degradedforestlot(DistrictLot63,

orDL63)thatwouldbecomepartoftheMidGalianoIslandProtectedAreaNetwork.TheMid

GalianoIslandProtectedAreaNetworkcovers616hectaresandspansfromwesttoeastroughly

inthemiddleofthelongandnarrowisland.Thesitewaspartiallyclear-cutin1967andagainin

1978andonlyabout4%ofthe61.5hawereleftintact(Gayloretal.,2002).Thefirstcutremoved

alltreesfrom20%ofthelandareaandallremainingwoodybiomasswaspiledandburnedto

createaneasierenvironmentforplanting(Gayloretal.,2002).Afterthesecondcut,slashand

topsoilwerepiledinwindrowsandburned.Thiswasdonepartlytofightlaminatedrootrot,a

fungaldiseasecausedbyPhellinusweirii-1(Murrill)R.L.Gilbertson,butthelargewindrowsdid

notfullycombust(Gayloretal.,2002).Thisleftcoarsewoodydebrisinvarioussizesanddegrees

ofcombustion.Afterbothcutstheopenareaswerere-plantedwithDouglas-firseedlingsfrom

off-islandprovenance(Gayloretal.,2002).

TherestorationoftheDouglas-firplantationstartedin2003bytheGCAwiththehelpof

manyvolunteers(Scholzetal.,2004).Alltherestorationworkwasdonewithouttheuseof

powertoolsorcombustionenginesasanodtolowimpacttechniques.Fortheerectionofsnags,

movingofbiglogs,andthepullingoftrees,theGCAusedchainhoistsandskylines,techniques

specificallydesignedfortheproject(Scholzetal.,2004).Thetreatmentsincludeddispersalofthe

coarsewoodydebris(CWD)formerlypiledinwindrows,erectionoflargesnagstomimicwildlife

trees,controlofinvasivespecies,oflooseningcompactedsoilonroadsandtimberlandings,

pulling,topping,andgirdlingoftrees,andplantingofnativeplantspecies(Scholzetal.,2004).

TherestorationofDL63isauniquerestorationprojectbecauseofitslow-impactapproach.The

projectisofspecialimportancetotheGCA:manyofitsearlymembersweredirectlyinvolvedin

therestorationeffortsandthelow-impactapproachdirectlyreflectsvaluesheldbymany

members.

BeforestartingtherestorationofDistrictLot63,theGCAcollectedextensivebaseline

data.TheGCAdividedtheforestinto47polygonsofvaryingsizesaccordingtoecosystemtypes,

byassessingaerialphotographsandlaterconfirmingandcorrectingtheextendofthepolygons

bygroundsampling.Thecreekattheeastsideoftheproperty,andabufferof20monboth

sides,wereexcludedfromthesamplingandtreatments.Dependingontheirrelativesize,each

polygonwassampledwithonetoeighttemporary20x20msamplingplots.Theplotswere

randomlydistributed,butlocationsweremanuallycorrectedtoavoidedgeeffects,roadsand

openings.

TheGCAthenestablishedeightpermanentplotsonthestudysite–fiveinareaswhere

restorationtreatmentstookplace,andthreecontrolplotsoutsidethetreatmentareas.

Additionally,theGCAestablishedtwopermanentplotsinaneighbouringmatureDouglas-fir

forest.Thoseplotsarepartofa1-hectareSI/MABplot.TheSI/MABplotisaninternationallyused

monitoringplotforbiodiversityrecommendedbytheSmithsonianInstitute(SI)andtheUNESCO

ProgramonManandtheBiosphere(MAB)(Roberts-PichetteandGillespie,1999).TheGCAlaid

outallpermanentplotsusingtheguidelinesdescribedbyRoberts-PichetteandGillespiein

TerrestrialVegetationBiodiversityMonitoringProtocols(Roberts-PichetteandGillespie,1999).

Theplotswere20x20massuggestedforyoung,even-agedstands.Theplotswerelaidout

squaretothegeneralslope,andallcornersA-Dweremarkedwithmetalpins(Figure2).Iwasnot

abletofindsomeofthesemetalpinsandhadtoreestablishseveralcornersusingacompassand

measuringtapes.EachquadratbearsanindividualIDandallfourcornersweremarkedwithGPS

pointsandareavailableasashapefileforGISuse.Forplotsonaslope,theGCAusedslope

correctiontosetupanexact20x20msquareintheplane.

Monitoringstrategieswereincludedintheoriginal“RestorationPlan”(Gayloretal.,2002)

andthe“MonitoringBaseline”(Scholzetal.,2005).TheGCAdesignedanadaptivearrayof

monitoringstrategiestoassurethatmonitoringwillpersistinthefuture,evenwiththe

uncertaintiesthatbesetasmallnon-profitcharitableorganization(Scholzetal.,2005).However,

monitoringwasnotexecutedasplanned.Twostudents,onegraduateandoneundergraduate,

didsubsequentlycollectdataaboutstandstructure,soilnutrients,andspeciescompositionas

partoftheirthesiswork(Harrop-Archibald,2010;Meidl,2013).

CanadahascommittedundertheUnitedNationsFrameworkConventiononClimate

Change(UNFCCC)totakeactionstolimitclimatechange(GovernmentofCanada,2010).These

actionsincludethepromotionof“…sustainabledevelopmentapproaches(e.g.promotethe

conservationandenhancementofsinksandreservoirsofallGHGs,andtakeintoaccountclimate

changeineconomicandenvironmentaldecisionmaking)”(GovernmentofCanada,2010,p.2)

andregularupdatesontheprogressinfulfillingthesecommitments(GovernmentofCanada,

2010).OneofthesemeasuresofpromotionistheEcoActionCommunityFundingProgram,which

helpedfinancecommunitybasedclimateactiononconservedforestland.In2010Canada

reportedaboutsuccessfulprojectsandincludedtherestorationoftheprovinciallyandglobally

endangeredCoastalDouglas-FirforestonDistrictLot63,undertakenbytheGCAonGaliano

Island,BC(GovernmentofCanada,2010).“Restorationeffortsundertakenwillincreasecarbon

sequestrationonthesite.Thiswillhelpreducetheimpactsofclimatechange.Restorationwill

alsoincreasebiodiversity,improveecosystemhealthandenhancethesite’sabilitytoadaptto

theimpactsofachangingclimate.”(GovernmentofCanada,2010,p.134).Theprojectisalso

explicitlymentionedasasuccessofCanadasrestorationeffortsontheIUCNhostedwebsite

www.infoflr.org.Untilnow,thesuccessoftheDL63restorationprojecthasnotbeenevaluated.

Thisthesisisthefirstcomprehensiveevaluationoftheeffectsoftheforestrestorationon

GalianoIsland,andwillcontributetothecontinuingadaptivemanagementofthesite.

1.5RemotesensingandUnmannedAerialVehicles

Environmentalremotesensing,thepracticeofrecordingelectromagneticwavesfromadistance

togatherinformationaboutobjectsontheearth’ssurface,startedwiththeinventionof

airplanesandcameras,butdidonlygainaglobalimportanceafterthelaunchofthefirstsatellites

inthe1950sand1960swhenitwasfirstcoined“remotesensing”bytheUnitedStatesOfficeof

NavalResearch(Cracknell,2007,Khorrametal.,2012).Remotesensingcanbeusedtodetect

anykindelectromagneticenergy,fromgammatoradiowaves.However,mostcommonlyusedis

visibleandinfraredlight(Khorrametal.,2012).Thetechnologywasquicklyadaptedformilitary

reconnaissanceduringWorldWarOneandremotesensingdatasoonbecamepopularforcivilian

applicationsbecauseofitsabilitytoprovidedataforlargeareaswithrelativehighspatialand

temporalresolution(Rees,2013).

Unmannedaerialvehicles(UAVs),commonlyknownasdrones,arethenewestdevelopmentin

remotesensing(Adãoetal.,2017).UAVsaresmall,remotelycontrolledsystems,capableof

autonomouslyfollowingapre-programmedflightpathandusuallycarryoneormoresensors,

mostcommonlydigitalcameras.BothUAV’sandtheirsensorsareaffordablecomparedwith

manyotherremotesensingtechnologies,andhavegainedpopularityforrecreational,

commercial,andmilitaryapplicationsandresearch.ManyclassificationsofUAVsexist,butfor

UAVsinecologyAndersonandGaston(2013)describefourcategories:Large,Medium,Smalland

Mini,andMicroandNano.LargeUAVsweighabout200kg,areaslargeassmallairplanes,

requirearunwayfortakeoffandfullaviationclearing.However,theyallowforanoperating

rangeofabout500kmandflighttimesofuptotwodays.MediumUAVsweightabout50kg,

havesimilarstartandlandingrequirementstolargeUAVs,butarecheaperandeasiertohandle

duetotheirreducedsize.TheiroperatingrangeissimilartolargeUAVs,butflighttimesareonly

about10hours(AndersonandGaston,2013).SmallandminiUAVsweighlessthan30kg(small)

andlessthan5kg(mini),canonlybeflownwithinline-of-sight,requiresmallopenareasand

minimalequipmentfortakeoffandlanding,andcanbecontrolledbyflightplanningsoftwareor

directlybyradiocontrol.Withanoperatingrangeoflessthan10kmandaflighttimeoflessthan

twohours,theirapplicationislimitedtosmallerareas(AndersonandGaston,2013).Microand

nanoUAVsweighlessthan5kg,requirebarelyanyspacefortakeoffandlandingandareflown

withinlineofsight,controlledbyflightplanningsoftwareordirectradiocontrol.Operatingrange

issimilartosmallUAVs,butflighttimesareevenshorter(<1hour).Inthisthesis,Ifocusedon

microUAVs.Theyarecurrentlythemostcommonbecauseoftheiraffordabilityandeasy

handling(AndersonandGaston,2013).

RegulationsforUAVusevaryfromcountrytocountry.Technicaldevelopmentsare

occurringrapidly,cost/performanceislowering.MostcountriesrequirepermissionswhenUAV

areusedforcommercialorscientificapplications,andoftenrequireregistrationoftheUAVand

insurancefordamagecausedbythevehicle(Stöckeretal.,2017).Inaddition,themaximum

flightheight,theweightoftheUAVincludinganyattachmentsanddistancetosensitiveairspace

likeairportsorhospitalsarerestrictedinmostcountries(Stöckeretal.,2017).Usually,operation

ofUAVhastobewithinvisuallineofsight(VLOS).IntheUS,UK,Italy,SpainandSouthAfricathe

useofanextendedvisuallineofsight(EVLOS),whereanadditionalobserverhelpskeepingvisual

contacttotheUAV,ispossible(Stöckeretal.,2017).Flyingbeyondvisuallineofsight(BVLOS)are

almostalwayssubjecttohigherlevelregulationsandrequireexceptionalapprovalorspecial

flightconditions(Stöckeretal.,2017).

1.6ConceptualFoundationandOrganizationoftheThesis

MyresearchfocusedonassessingtheeffectivenessofaforestrestorationprojectonGaliano

Island,whichIexploreindepthinchapter2.Myprojectispartofanongoingmonitoringeffort

thathadbeenlargelyheldbackbyinsufficientresourcessincetheinceptionoftherestorationin

2003.Iexploredalternativewaysofmonitoringrestorationeffectsbecauseoftheuncertaintyof

availablefunding.InitialexperimentationwithaUAVforcanopygapmappingledmetofocuson

UAVapplicationsinecologicalrestorationandtheirfuturepotentialinareviewofcurrent

literatureinchapter3.IconceivedandexecutedatrialofUAVderivedimagesforthemonitoring

ofrestorationeffectivenessonmystudysiteonGalianoIsland(chapter4).

Ihavewrittenuptheresultsasthreemanuscriptsforpotentialpublication.(chapter2to4).

Workingalongsidemycommitteeincomingmonths,Iproposetosubmitchapter2tothejournal

EcologicalRestoration,chapter3toRestorationEcology,andchapter4toForests.Formattingis

accordingtojournalstandardsandthereforediffersslightlybetweenchapters.

Chapter2:RestorationeffectivenessinaYoungDouglas-firForest0. Abstract

Weassessedtheoutcomesoftherestorationofa40-year-oldDouglas-fir(Pseudotsugamenziesii

(Mirb.)FrancoplantationinBritishColumbia,Canada.Themainrestorationprocesses

undertakenbetween2003and2006werethinningbypulling,topping,andgirdlingtrees.We

usedexistingbaselinedatafrom8permanentplots(5treated,3control)andcompareditwith

forestassessmentdatacollectedinthefieldinthesummerof2017.Additionally,weused16

temporaryplots(8treated,8control)tocoverrestorationeffectsinareasoftheforestthatwere

notcoveredbythepermanentplots.Weassessedtreediameter,speciesandstatus(n=846),

height(n=48)anddiametergrowth(n=271).Wealsoassessedunderstorypercentagecoverof

vascularplantsbyspeciesandallpiecesofcoarsewoodydebriswithdiameterslargerthan7.5

cminthe8permanentplots.Analysiswithgeneralizedmixedeffectlinearmodelsshowedthat

treatedareasdisplayedincreaseddiameters,higherdiametergrowth,increasedplantdiversity,

increasedcrownratio,andmoresnags,butlowerbasalarea,treeheights,anddensity.Control

plotsshowedastrongerincreaseinvolumesofcoarsewoodydebrisbutvolumeswerestilllower

thantreatedplots.Wewereunabletorelatetheincreaseddiametergrowthtotherestoration

treatments.Ourfindingssuggestthattocreatealastingimpact,restorationthinningwillhaveto

bemorefrequentorcreatelargergaps.

1. Introduction

Callsforre-forestationandforestrestorationhavebecomemoreurgent,withtwobillionhaof

degradedforestglobally(Minnemayeretal.,2011),continuingglobaldeforestation,aworldwide

lossofbiodiversity,anddirectionalclimatechange(Ciccareseetal.,2012;Mansourianetal.,

2005).Moreover,threatstoforestsareincreasing.Ariseinglobaltemperaturesposesa

significantthreattofutureforestsastreespecieswithsmallpopulationsorfragmentedranges

maynotbeabletomigratefastenoughtokeepupwiththechangingconditions(Aitkenetal

2008).Invasiveinsectsandmammalsposeanadditionalthreattotrees,especiallyincombination

withweatherextremesweakeningthetrees(Dumroese,2014).

Intactandfunctioningforestecosystemsarecriticaltotheprovisionofecosystem

servicessuchascleanwater,air,opportunitiesforrecreation,andperhapsmostimportantlyin

thecontextofclimatechange,carbonsequestration(Ciccareseetal.,2012).Oncedegraded,

forestsareespeciallychallengingtorestore,duetolongplanningperiods,slowtreegrowth,and

uncertaintiesaboutfutureenvironmentalconditions(Golladayetal.,2016;HamannandWang,

2006).

Withincreasingthreats,itisnolongerenoughtoconserveforests.Thereisalsoaneedto

activelyrestoreforeststore-createhabitatforspeciesthatrelyonold-growthstructures(Halme

etal.,2013).Internationally,severalcommitmentstosustainableforestmanagementandforest

restorationhavebeenagreed.TheseincludetheNewYorkDeclarationonForests(UNClimate

Summit,2014),theBonnChallenge((IUCN)InternationalUnionforConservationofNature,

2018),theAichiBiodiversityTargets(specificallyTarget15)(UNEnvironment,2018),theUnited

NationsCollaborativeProgrammeonReducingEmissionsfromDeforestationandForest

DegradationinDevelopingCountries(REDD+)(UN-REDDProgramme,2016),andtheUnited

NationsFrameworkConventiononClimateChange(UNFCCC)(Protocol,1997).Canadahas

committedundertheUNFCCCtotakeactionstolimitclimatechange(Kingsberryetal.,2010).

Thoseactionsincludeecologicalrestoration,suchasforexamplethefederallyfundedrestoration

ofaprovinciallyandgloballyendangeredcoastalDouglas-firecosystemonGalianoIsland,BC

(Kingsberryetal.,2010).Globalcommitmentshaveincreasedawarenessof,andattentionfor

forestrestoration,butresourcesfortreatmentsremainlimitedsincethereisnoimmediate

financialbenefit.

Forestrestorationincreasinglyfocusesonlandscapelevelapproachesthatmaybemore

appropriatethantraditionalapproachestoaddressthelargescaleoftheproblem(Stanturfetal

2014a).TheprobablymostprominentapproachisForestLandscapeRestoration(FLR)asdefined

bytheIUCN(IUCNandWRI,2014),aconceptthatfocusesonrestoringforestedlandscapes

ratherthanindividualsites.Landscape-levelthinkingrequiresthebalancingofdifferentlanduses

andstakeholders.TheFLRapproachfocusesontherestorationofecologicalfunctionand

strategiesarenotlimitedtotraditionalrestorationtoa“natural”statebutcanincludeanyother

combinationofspeciesandland.Restoredlandscapesincreaseecosystemgoodsandservicesfor

localcommunitiesandbuthaveglobalimplicationswithincreasedcarbonstoragecapacities.

Restorationstrategiesarebasedonlocalconditions,knowledgeandtraditionallanduse.FLR

activelyengagesandinvolvesstakeholdersandgoalsandpracticesarealignedwiththeirvalues

toimprovelivelihoods.Restoredlandscapesexplicitlyincludemanylandusessuchas

agroforestry,managedforestsandprotectedland(IUCNandWRI,2014).

Thinningiscommonlyusedinforestrestorationtoincreasespatialheterogeneityand

improveecologicalfunction(Fajardoetal.,2007;Versluijsetal.,2017).Anotherrestoration

strategywithgrowingimportanceisthere-establishmentoffireregimesinforeststhat

historicallyhadfrequentlowintensityfires,butwherefireshavebeensuppressedinthepast

decades.Thisoftenincludesremovaloffuelandmechanicalthinningtoreducefuelloadsbefore

prescribedburning,whichmayotherwiseleadtounwantedhighintensityfires.

Measurestoprepareforestsforfutureconditionsortransformingdegradedforest

ecosystemstofunctioningsystemscanincludeassistedmigrationoftreespeciesandeven

introductionofnon-nativespeciesthatcanfulfillsimilarfunctionstohistoricspeciesthatmaynot

beabletopersistintothefutureduetoclimatechange.InCanada,assistedmigrationisbeing

testedandconsideredforPinusalbicaulis(Whitebarkpine)(MclaneandAitken,2017).

Focusingonecologicalfunctioncanhelpavoidunsustainablegoalsandobjectivesinthe

lightofclimatechange(Stanturf2014).Justasinecologicalrestorationmoregenerally,ecological

forestrestorationismovingawayfromtheideaofahistoricalbaseline,anditisbecoming

increasinglycommontoworktowardsafunctioningecosystemthatfulfillsaspecificsetof

functions.Thismayincludeplantingnon-nativegenotypesorspeciesandcanincludesilvicultural

managementstrategies(e.g.,restorationforestry)sincetherecanbelargeoverlapbetween

silvicultureandforestrestoration.Methodsforforestrestorationaremainlybasedonplanting,

butincreasingfocusisplacedonsoil,hydrology,andfireregimes.Especiallyindeveloping

countriesthatarepartoftheREDD+thereisanincreasingfocusonsocialaspectsofrestoration

onecologicalfunctionslikefoodproductionandfirewood.

Uncertaintyremainsaboutwhethercommonforestmanagementmethodslikethinning

areeffectiveinimprovingstructuraldiversity,especiallyifmodelsystemsarelacking.Herewe

focusonarestorationprojectinaprovinciallyandgloballyendangeredcoastalDouglas-fir

ecosystemonGalianoIsland,BritishColumbia(Kingsberryetal.,2010).Therestorationaimedto

“…increasecarbonsequestrationonthesite[…]increasebiodiversity,improveecosystemhealth

andenhancethesite’sabilitytoadapttotheimpactsofachangingclimate.”(Kingsberryetal.,

2010,p.134).In2002,thelocallandtrust,theGalianoConservancyAssociation(GCA)createda

restorationplanfora61.5-hectarepropertyitowned,andrestorationtreatmentshappenedin

2003and2006.Managementincludedpre-andpost-assessmentsofthesiteandthe

establishmentofpermanentplotsforcontinuedmonitoring(Gayloretal.,2002).Thesite

providesanopportunitytoassesstheeffectsofsmall-scalerestorationonforeststanddynamics.

Intheabsenceofmonitoring,itremainedunknownhoweffectivethisrestorationprojectwasin

increasingbiodiversity,improvingecosystemhealth,andenhancingthesite’sabilitytoadaptto

theimpactsofachangingclimate.

Weinvestigatedtheperformanceoftherestorationtreatmentsinprovidingimproved

structuraldiversitybyassessingthepresentplantcompositionandforestcanopystructureofthe

restorationforestandadjacentcontrolareas.Wehypothesizedthat:1)thetreatedareaswill

showelevatedstandheight,increaseddiametergrowth,lowerstemdensity,higherdiversityin

understoryplantspecies,highervolumeanddiametersofcoarsewoodydebris(CWD),and

higherpercentagecoverofunderstoryvegetationthantheun-treatedareas;wegenerally

expectedahigherspatialvariabilityinthetreatedareas;and2)bothun-treatedandtreated

areaswillshowlowerdiversity,volumeanddiametersofCWD,andpercentagecoverof

understoryvegetationthanthereferencestand.

2.Methods

2.1.StudySite

ThestudyareaislocatedalongtheStraitofGeorgia,amajorinletofthePacificOceanbetween

VancouverandVancouverIslandonCanada’sWestCoast(figure2-1).Thestudyareaissituated

intheheartofthemoist-maritimeCoastalDouglas-firbio-geoclimaticzone(CDFmm)(Krakowski

etal.,2009).Relativelysteepslopesandelevationsfromsealeveluptoabout140mcharacterize

thetopographyofthearea.

Oldforestsintheareaarecharacterizedbyamoderatelyopentoclosedcanopyof

Pseudotsugamenziesii(Mirb.)Franco(Douglasfir),withsomeAbiesgrandis(DouglasexD.Don)

Lindl.(grandfir)andThujaplicata(DonnexD.)Don(Westernredcedar).Theunderstoryis

dominatedbyMahonianervosa(Pursh)Nutt.(dullOregon-grape),GaultheriashallonPursh

(Salal),Holodiscusdiscolor(Pursh)Maxim.(oceanspray),RubusursinusCham.&Schltdl.(Pacific

trailingblackberry),TrientalisborealisHook.(broad-leavedstarflower),Polystichummunitum

(Kaulf.)C.Presl(swordfern),andPteridiumaquilinum(L.)Kuhn(brackenfern).Themosslayeris

Figure2-1:(a)LocationofGalianoIslandinWesternCanadaandstudysiteonGalianoIsland,BritishColumbia,Canada.(b)Overviewofthestudysitewithpermanentandtemporaryplots

dominatedbyEurhynchiumoreganum(Sull.)A.Jaeger(Oregonbeaked-moss),Rhytidiadelphus

triquetrus(Hedw.)Warnst.(electrifiedcat’s-tailmoss)andHylocomiumsplendens(Hedw.)B.S.G.

(stepmoss)(GreenandKlinka1994).Sitesarerelativelydryandsoilswithverypoortomedium

nutrientregimes(Pojaretal.,2004).

Thestudysitewaspartiallyclear-cutloggedin1967andthenagainin1978.Onlyabout4

%ofthe61.5hawereleftintactafterthetwoforestrypasses(Gayloretal.,2002b).Remaining

coarsewoodydebriswasbulldozedintopiles(windrows),setonfire.butdidnotcombustfully.

Thesewindrowswerenotreplantedandsomeremainvisibleonthesite.

Afterbothcutstheopenareaswerere-plantedwithP.menziesiiseedlingsfromoff-island

(Gayloretal.,2002b).ThecanopynowconsistsofP.menziesiiwithsomeAlnusrubraBong.(red

alder),AcermacrophyllumPursh(bigleafmaple),A.grandis,andT.plicata.Therestoration

treatmentswereplannedcarefullywiththehelpofaforestmanagerandcarriedoutentirelyby

hand.Treatmentsincludedpullingoftreestomimicnaturalsoildisturbanceandgapcreation,

toppingtreestocreategapsandestablishsnags.Girdlingtreescausedaslowerdeathofsome

treesandcreatedfoodtreesforwildlifeaswellasdelayedgapswhichwereintendedtoextend

theeffectsofthetreatmentslongerintothefuture.Abouthalfthestudysitewasrestored

between2003andearly2006.Intreatmentareasabout50%ofthetreeswereculled(min40%,

max60%)bygirdling,pulling,ortopping.

2.2.Permanentplots

WeusedeightpermanentplotsestablishedbytheGCA.Fiveplotswereinareaswhere

restorationtreatmentstookplace(TR1–TR5),andthreecontrolplotsoutsidethetreatment

areas(CO1–CO3).Asareference,weusedtwopermanentplotsinaneighbouringmature

Douglas-firforest(MA1andMA23)thatarepartofa1-hectarebiodiversitymonitoringplotthat

waslaidoutbytheGCAfollowingtheTerrestrialVegetationMonitoringProtocolbytheEcological

MonitoringandAssessmentNetwork(Roberts-PichetteandGillespie,1999).Allplotsinthestudy

were20x20massuggestedforyoung,even-agedstands

(Roberts-PichetteandGillespie,1999).QuadratsideA-B

wasplacedsquaretothegeneralslope(paralleltothe

overallcontourlines),andallcornersA-Dweremarked

withmetalpins(figure2-2).Thecoordinatesofthe

permanentplotswererecordedbytheGCAwitha

TRIMBLEhandheldGPSdevice.Photographsofthesites

helpedwithre-identificationofthesites.Alltreeswere

taggedwithauniqueIDforidentificationduringtheinstallationoftheoriginalplots.Forplots

wherewewerenotabletofindallfourmetalpins,were-installedthemissingmarkerusingtwo

measuringtapesandacompass.AdditionaltothetreemappingaccordingtoRoberts-Pichette

andGillespie(1999)theGCAcollecteddataonsoiltype,vegetationpercentagecoverbyspecies,

slope,andcoarsewoodydebris(CWD).

Figure2-2:Layoutofpermanentplotsandassessmentoftreelocation,accordingtotheprotocolsuggestedbyRoberts-PichetteandGillespie(1999)

2.3.FieldMethods

Werepeatedafullassessmentofalltenpermanentplots.Wemeasuredthediameteratbreast

height(DBH)ofalltrees,estimatedvegetationpercentagecoverbylayer,assessedlengthand

diameterofallpiecesofcoarsewoodydebris(CWD)withadiameterlargerthan7.5cm,and

retrievedsixdepthmeasurementsforL,F,andHlayer(B.C.MinistryofForestsandRangeand

B.C.MinistryofEnvironment,2010).

Asthenumberofpermanentplotswasrelativelysmall,wesetupanothersixteen

temporarysamplingplotsinotherpartsofthepropertywithcomparableecologicalsite

conditions;eightplotsthatweretreatedinthesamewayandatthesametimeasthetreated

permanentplots(NTR1–NTR8)andeightcontrolplotsinuntreatedareasofthestudysite

(NCO1–NCO8).Thesesampleshadasimplifiedsamplingdesign(noCWDdataandDBH

categories,insteadofexactdiameter).Werandomlydistributedthetemporaryplotsinpre-

mappedtreatmentandcontrolareas,usingQGIS’"randompoints”tool(QGISDevelopment

team,2018).

WemeasuredlengthandthecenterdiameterofallpiecesofCWDwithdiameterslarger

than7.5cm(B.C.MinistryofForestsandRangeandB.C.MinistryofEnvironment,2010).The

samplingofunderstoryvegetationfollowedtheguidelinesdescribedin(B.C.MinistryofForests

andRangeandB.C.MinistryofEnvironment,2010).Weassessedspeciesbylayerandpercent

areacoverintheplot.

TheDBHofalltreeswasobtainedinthesampleplots.Wemeasureddiametersofsnags,

butdidnotincludethesemeasurementsinthebasalareacalculations.Were-sampledaboutfive

treesperplotforheight,crownwidth,anddepth,toestimatethelivecrownpercentage,with

theexactnumberdependingonthepreviousassessments.Inplotswheremanyofthepreviously

measuredtreeshaddied,wereplacedthetreeswithtreesofsimilarsize.DBHweremeasured

withastandardcircumferencetape,treeheightwithaNikonForestryProlaserrangefinder.In

addition,werecordedtreestatusaccordingto(B.C.MinistryofForestsandRangeandB.C.

MinistryofEnvironment,2010).

2.4.Analysis

Fourdatasetswereusedintheanalysis.A“temporary”datasetincludedalldatapointsofthe

permanentplotsin2017anddatafromall16temporaryplots(nPlotTR=13,nPlotCO=11),a

“permanent”datasetincludedeightpermanentplots(nPlotTR=5,nPlotCO=3)onthestudysiteand

datapointsfrom2007(shortlyafterrestorationtreatments)and2017.A“height”datasetwith

42heights(nFd=35,nDr=7)wasusedfortheanalysisoftreeheightsandfinallya“vegetation

datasetwithpercentagecoverbyspeciesforallvascularplantsinthepermanentplots(nPlotTR=

5,nPlotCO=3).ThepermanentdatasetwasusedforcalculationofDBHgrowthandCWD

calculations.Thepermanentdatasetthereforeisasubsetofthetemporarydataset.The

temporarydatasetonlyincludesdiameter,height,status,andspeciesoftrees,andvegetation

percentagecoverbylayer.Thetemporarydatasetallowedassessmentofdiameterdistribution,

vegetationanalysisandtreeheights.

AllstatisticalanalysiswasdoneusingRstatisticalsoftware(RCoreTeam,2017).ForCWD,

wecomparedCWDvolumeandnumberofCWDpiecesperplotusinganANOVA.AShapiro-Wilk

testfornormalityofvolumesandcountofCWDpiecesdidnotleadustorejectthehypothesis

thatthesamplescomefromanormaldistribution(pVol=0.7193,pNo=0.3642),andavisual

inspectionofthedistributionconfirmedthisassumption.Wethereforeusedsimplelinear

regressionmodelswithvolume(count)asourresponsevariableandtreatment,plotIDandyear

ofassessmentasexplanatoryvariables.Wedidnotadjustfortheunequalsamplingsize(5

treated,3control).

VegetationdatawereexaminedwithR’smvabundpackageusingtheManyGLMfunction

(ManyGLM;R-package,(Wangetal.,2012)).Mvabundaddressesthemean-variancerelationship

ofmultivariatedatabyfittingageneralizedlinearmodel(GLM)toeveryplantspecies

individually.Assumptionsofthemodelarealsoeasiertointerpretinamodel-basedframework.

Anegative-binomialdistributionwasusedtoaccountforthehighnumberofzerosinthe

vegetationdata.Theresidualsshowedanevenspread.WecalculatedtheShannonIndexfor

eachplotindividuallyandaveragedthevaluebytreatment.Thisdidnotaddresstheuneven

samplesize.

Totestforeffectsoftreatmentsoncanopystructure,wecomparedtreeheight,density

(numberoflivingtreesperplot),diameter,andbasalareabetweentreatmentswithmixedeffect

linearregressionmodels,afterusingtheShapiro-Wilktestfornormalityandvisualinspectionof

thevariables.Toavoidpseudoreplicationandtoaccountfortheunbalancedsamplingdesign,

thePlotIDwasincludedasarandomeffectinthemodels.DBHwasmodelledonlyforthetwo

mostcommonspeciesP.menziesii(nFd=725)andA.rubra(nDr=40)individuallyandheightwas

modelledwiththesmallersubsampleofaboutfivetreesperplot(nFd=35,nDr=7).Sampling

sizesvariedstronglybetweentreespecies(seeFigure2-4(b)below)andwouldhaveaffectedthe

modeloutcomes.AlltreespeciesotherthanP.menziesiiandA.rubrahadsamplesizesthatwere

toosmallforstatisticalanalysisanddidnotappearinallplots.Plotbaseddata(basalarea,

numberofsnagsanddensity)wasmodelledincludingalltreespecies.

Tomakepredictionsabouttheeffectsoftreatmentsongrowthwecalculatedthechange

indiameterbetween2007and2017(“DBHgrowth”)forP.menziesii(nFd=178)andA.rubra(nDr

=40).Sincethereareonlyhistoricaldataforpermanentplots,diametergrowthanalysiswas

limitedtotreesintheeightpermanentplotsonthestudysite.Alldeadtreeswereexcludedfrom

theanalysisbecauseofuncertaintyofmortalityyear.EffectsoftreatmentsonDBHgrowth,were

modelledusingageneralizedlinearmixedeffectmodel.Toaccountforunbalancedsamples

(nPlotCO=3,nPlotTR=5)andavoidpseudo-replication,weincludedtheplotIDasarandomeffectin

ourmodel.Calculationsweredonewiththe‘nmle’packageinthestatisticalsoftwareR(Pinheiro

etal.,2017).Allotherindividualtreebasedanalysiswasdoneusingonlythetwomostcommon

treespeciesP.menziesiiandA.rubrawithtwoindividualmodels.

3.Results

Treatedareasshowedahigherdiversityandhighercoverofunderstoryplants,weremore

structurallydiverse,andhadhighervolumesofCWD.Wewerehowevernotabletoconnectallof

thesedifferencestorestorationtreatments.Treeheightsandbasalareaintreatedareaswere

lowerthanexpected.Table2-1summarizesallresults.

Vol [m

^3 ha^-1]

Dia [cm

Shrubs [%

Height

Fd [m]

ha^-1]

Density

[ha^-1]Snags

[ha^-1]

Treated

192.8113.79

6.624.53

25.2424.62

13.7835.58

800.44568.10

Control

155.6919.41

5.274.18

26.6223.30

16.2342.78

1073.88407.14

Mature

Reference

8.501.00

51.8186.60

71.5088.32

311.56133.11

ate4.1412

-11.2123-2.5155

4.22-2.83

8.349313.0036

Std. Error

2.22072.0787

0.961.98

1.27932.1252

t-value1.8650

-1.21004.39

-1.436.5270

6.1190

p-value0.10

0.000.23

0.000.15

0.100.00

Table2-1:Summaryofallm

easuresofstandstructureanddiversitybytreatments.Fd=Douglas-fir,Dr=Redalder.Allvaluesaregroupm

eans.Table2-1:Sum

maryofallm

easuresofstandstructureanddiversitybytreatments.Fd=Douglas-fir,Dr=RedAlder.Valuesaregroupm

3.1.CoarseWoodyDebris

VolumeofCWDhasincreasedforCOandTRinthelasttenyears(figure2-3(b)).COplotsshowed

astrongincreaseinCWD,butvolumeswerestilllowerthaninTRplots(figure2-3(a)).Results

weresimilarforthenumberofpiecesofCWD.BothCOandTRshowedasteadyincreasein

numberofpiecesandtheyhaveverysimilarnumbers.TheANOVAshowedasignificant

differenceinvolumeofCWDbytreatment(meanSq=83.539,F=14.1640,p=0.004461)anda

significantdifferenceonthenumberofpieces(MeanSq=2030.6,F=6.9740,p=0.0268767).

Figure2-3:Comparisonofvolumesofcoarsewoodydebris(CWD).CO=untreatedcontrol,TR=treated.(a)BoxplotofCWD by treatments. The lower and upper hinges correspond to the first and third quartiles (the 25th and 75thpercentiles).Whiskersextend1.5*IQRfromhinge.(b)VolumeofCWDbysurveyyear.Eachdotrepresentsoneplot.

MostpiecesofCWDhadsmalldiameters,anddifferencesindiameterdistribution

betweentreatmentswerenegligible.TheproportionofCWDwithsmalldiameter(10-30cm)

showedanincreaseforbothCOandTR.

3.2.UnderstoryVegetation

Allspeciesfoundinthestudyarespeciescommontothearea.Cytisusscoparius(L.)Link(scotch

broom),acommoninvasivespeciesintheareawaspresent,butonlyinverysmallnumbers.The

orchidspeciesEpipactishelleborine(L.)Crantz(broadleafhelleborine),acommonexoticspecies,

waspresentaswell.Cirsiumarvense(L.)Scop.(Canadathistle)andCirsiumvulgare(Savi)Ten.

(bullthistle),bothexoticthistles,werepresent.SingleindividualsofIlexaquifoliumL.(English

holly)anotherexoticspecies,werepresentintwoplots.

MostspeciesappearedinbothCOandTRplots,withsimilarabundances.M.nervosa

showedasimilarmeanbuthigherabundancesinCOplots,Prunusemarginata(DouglasexHook.)

D.Dietr.(bittercherry)wasmoreabundantinCOandGaliumaparineL.(cleavers)wasless

abundantinCO(Fig.2-4(a)).Alltwelvemostabundanttreeandshrubspecieswerecommon

species.Ofthesixtreespecies,P.menziesiiwasthemostabundantinallplots(figure2-4(b)).

Figure2-4:(a)Abundanceof12mostcommonplantspeciesinthestudyplots.(b)Speciescountbytreatment.

ThemeanShannonIndexwashigherforTRplots(0.92)thanitwasforCO(0.78)and

highestforMAplots(1.49).

3.3.Diameter,Height,Density,BasalAreaandGrowth

ThemostcommoncanopytreespecieswasP.menziesii,withsomeA.rubraandfewArbutus

menziesii(arbutus),P.emarginata,A.grandis,A.macrophyllum,andT.plicata(Fig.2-4(b)).

3.3.1.TreeHeight

TreeheightforP.menziesiiincreasedforallplotsbetween2007and2017.TRplotsshoweda

widerrangeoftreeheightsandalowermeantreeheight(figure2-5(a)).Theresultsofalinear

mixedeffectsmodelsuggestastrongnegativeeffectoftreatmentsontreeheight(Estimate=-

5.14695,p=0.005294).DBHwasanotherstrongpredictorofheight(Estimate=0.43165,p=

6.462e-13).

Crownratio(𝐶𝑟𝑅𝑡 = &'((*(+,-./0'123-*(+,-.&'((*(+,-.

)wasonaveragesmallerintheCOplots,

andTRsupportedlowerlivebranches(figure5(b)).Theanalysiswithalinearmixedeffectsmodel

showedasmallbutinsignificantnegativeeffectoftreatmentsoncrownratio,however(estimate

=-0.0538480,p=0.557812).TheonlysignificantpredictorofcrownratiowasDBH(estimate=

0.0091957,p=0.000487).EffectsofDBHwereminimal.ThecorrelationbetweenDBHandcrown

rationwasstrongerfortreesinTRplots,thanfortreesinCOplots.

Figure2-5:Comparisonoftreeheightsbytreatmentandsurveyyear.(a)Treeheightbytreatmentin2007(grey)and2017(beige).Thelowerandupperhingescorrespondtothefirstandthirdquartiles(the25thand75thpercentiles).Whiskersextend1.5*IQRfromhinge.(b)TreeheightbycrownratioofPseudotsugamenziesiitrees.

3.3.2.Density,BasalAreaandSnags

MeandensityforTRwas800.44trees/haand1073.88trees/haforCOplots.Densitydecreased

forbothtreatments,itwaslowerforTRthanCOplotsin2007andremainedlowerin2017

(figure2-6(a)).Densitiesbytreatmentsweremoresimilarin2017thantheywerein2007.Basal

areadifferedstronglyin2007(shortlyafterthetreatments)sincemanytreeswereculledinTR

plots(figure2-6(b)).Basalareaincreasedforbothtreatments,buttheincreasewasstrongerfor

TR(from21.91m2ha-1to39.97m2ha-1forTRandfrom31.74m2ha-1to44.09m2ha-1forCO).

Themeannumberofsnagsperplotdecreasedfrom2007to2017forbothtreatments

andspreaddecreasedaswell(figure2-6(c)).Diameterofsnagsincreasedforbothtreatments

(from10.67cmto11.93cmforTRandfrom7.97cmto11.88cmforCO).

(a) (b) (c)

Figure2-6:Density,basalareaandsnagsofallspeciesbytreatmentin2007(grey)and2017(beige).Thelowerandupperhingescorrespondtothefirstandthirdquartiles(the25thand75thpercentiles).Whiskersextend1.5*IQRfromhinge.(a)Densitybytreatment.(b)basalareabytreatment.(c)Numberofsnagsbytreatment

3.3.3.DiameterDistributionandGrowth

MeanDBHincreasedforbothtreatments.MeanDBHwashigherforTRplotsthanforCOin2017,

butwaslowerin2007(figure2-7(a)).ThisincreaseinmeanDBHexplainstheincreaseofbasal

areainTRplotsevenwithadecreaseindensity.

MeandiametergrowthdifferedbetweenTRandCOplots(GrowthCOmean=0.35cma-1,

GrowthTRmean0.54cma-1).Themeanforbothtreatmentswasverysimilarbutthereweresome

treeswithveryhighgrowthratesinTRplots(figure2-7(b)).Overall,diametergrowthwashigher

fortreeswithlargerdiameter.

(a) (b)

Figure2-7:The lowerandupperhingescorrespond to the firstand thirdquartiles (the25thand75thpercentiles).Whiskers extend 1.5*IQR from hinge. (a) Boxplot of diameter at breast height in 2007 (grey) and 2017 (beige) bytreatment;(b)Diametergrowthperyearbytreatment.maxCO=1.28cma-1,meanCO=0.347975cma-1,maxTR=2.66cma-1,meanTR=0.54cma-1

Wewereunabletofitamodelthatproperlyexplainedthevariationindiametergrowth.

Inthegeneralizedlinearmixedeffectsmodels,treatmentonlyhadaverysmallandstatistically

insignificanteffectonP.menziesii(Estimate=0.1770389,p=0.408)andasmallbutsignificant

effectonA.rubra(Estimate=-1.90892,p=0.010706).ForP.menziesii,thepreviousdiameterin

2007hadtheonlysignificanteffectondiametergrowth(Estimate=0.0870893,p=3.97e-06).

ThediametergrowthofA.rubrawasmainlyinfluencedpositivelybypercentagecoverof

substratewater(Estimate=2.97826,p=0.000158)andnegativelybytheslopegradient

(Estimate=-0.21722,p=0.001688).

4.Discussion

Wefoundthattreatedareasshowedahigherdiversityandcoverofunderstoryplants,were

morestructurallydiverse,andhadhighervolumesofCWD.Wewerehowevernotableto

connectallofthesedifferencestorestorationtreatments.Moreover,treeheightsintreated

areaswerelowerthanexpected.

EventhoughwefoundalowerdensityforTRplots,lowerbasalarea,alargercrownratio

(longercrowns),higherdiametergrowth,highervolumesofCWDahigherpercentagecoverof

understoryplants,andahigherdiversityofplantspecies,thesedifferenceswererelativelysmall

andinmostcasesnotstatisticallysignificant.Theparameterswereclosertovaluesinour

referencestand(MA)inTRplotsthantheywereinCOanddiameteranddiametergrowthhada

widerrangeforTRplotsthanforCOplots,whichisasignofincreasedstructuraldiversity,which

mayhintatpositiveeffectsofthetreatments,butcouldnotbeconfirmedbystatisticalmodels.

Otherstructuralparameterswerenotshowingtheexpectedresults.Meantreeheightswere

lowerintreatedplotsthaninthecontrol.

EventhoughvolumesofCWDwerestillhigherinTRplots,controlplotsgainedlarge

amountsofCWDvolumeinthelast10yearswhereasvolumesinTRonlyshowedasmall

increase.Thisisasignthatthestandunderwentitsstemexclusionphase,wheredominanttrees

out-shadesub-dominanttreesandultimatelyresultsinahighertreemortality(SpiesandCline,

1988).Restorationtreatmentsmayhavesloweddownthisdevelopment,decreasingtherateof

dyingtreesandconsequentlyCWDonthegroundforTRplots.ThehighervolumesinTRare

mostlikelyduetoremainingdebrisfromthewindrowsthatwerere-distributedthroughoutthe

TRplotsaspartoftheoriginalrestorationefforts.Generally,CWDvolumeincreaseswiththeage

oftheforestandtheproductivity,andCWDvolumesintheneighbouringmatureforestwere

indeedhigher.WeconsideredthehighervolumeofCWDinTRplotsthereforeasasuccess.

AccordingtoFeller(Feller,2003)therearenostudiesonCWDvolumeinCDFold-growthforests,

andthereforewewerenotabletocomparethemeasuredamountswith“ideal”values.The

numberofsnagsdecreasedforbothtreatments,mostlikelycausedbydecayofsmalldiameter

snagswhichwerenowpartoftheCWDontheground.

TRplotsshowedsignificantlymoretreesofA.rubra.A.rubraisanitrogenfixerandits

leaflitterhelpsimprovesoilqualitybyincreasingnitrogencontent(TarrantandMiller,1963).

MixedleaflitterofP.menziesiiandA.rubradecomposesfasterthatlitteralone(FylesandFyles,

1993).ThehighernumberofA.rubratreesisnotaresultoftherestorationtreatments:thetrees

werealreadypresentbeforethetreatments.

ThebasalareaofbothTRandCOplotsincreased,buttreatmentsincreasedthebasalarea

ofTRplotsmorethanintheCOplots.DensityoftreesdecreasedforbothTRandCO,which

supportedlowerlifebranchesandthereforelongercrowns.Ourresultsareinlinewithother

studiesinavarietyofforestecosystemsthathavefoundthatthinningdecreasestreedensityand

basalarea(Battagliaetal.,2010;Fajardoetal.,2007;Harrodetal.,2009;Stephensand

Moghaddas,2005;Vaillantetal.,2009).BaileyandTappeiner(BaileyandTappeiner,1998)found

thatlivecrownratiowassignificantlyhigherinthinnedDouglas-firstandsthaninun-thinned

stands,whichcorrespondswithourfindingsoflongercrownsinTRplots.Otherstudieson

thinningtreatmentsinDouglas-firforestsfoundthatthinninghadnoeffectonbasalareaofP.

menziesii(Wilsonetal.,2009).WesawsimilarresultsthanWilsonandPuettmann(Wilsonand

Puettmann,2007)whoshowedthatthinninginyoungP.menziesiistandsinwesternOregonand

Washington,UnitedStatesincreasedspatialvariability,supportedlowerlivebranchesandhad

greatergrowth.

Unexpectedly,diametersofthedominanttreespeciesP.menziesiiwereonlyslightly

higher,andmeantreeheightofallspecieswaslowerforTRplotsthanitwasforCO.Other

studieshavefoundthatthinningincreaseddiameter(Harrodetal.,2009;Vaillantetal.,2009)

andheight(Battagliaetal.,2010;Harrodetal.,2009;StephensandMoghaddas,2005;Vaillantet

al.,2009).Thinningincreasestheamountofresourcesavailabletoremainingtreeswhichis

expectedtoincreasetheirgrowth.Thiseffectappearstonothavebeenstrongenoughtobe

reflectedinourresults.

WeidentifiedahigherdiversityofvascularplantsinTRplotsbutdidnotfindanyold-

growthassociatedunderstoryplantsinTRorCOplots.AstudybyLindhandMuir(2004)found

thatthinningofyoungDouglas-firforestsincreasedthecoverofold-growthassociated

understoryplants,butdidhavenoeffectonbasalareaofP.menziesii(Wilsonetal.,2009),an

effectwewerenotabletoconfirm.

Inthelightofourhypotheses,weweresurprisednottoseestrongersignalsacrossmost

indicesforthetreatedplots.Thismayhaveseveralreasons.First,withfivepermanenttreatment

plotsandthreepermanentcontrolplots,thestudydesignwasunbalanced.Theoutcomesmay

havebeenaffected,eventhoughwetriedtoaccountfortheunbalanceddesignbychoosing

appropriatemodels.Wedidnotreanalyzethedatausingaweightedapproachtotheunbalanced

design,butthiswillbeundertakenpriortoanyfurtherpublicationoftheseresults.Apreliminary

re-examinationofthedatasuggeststherestorationresponsemanyinfactbehigherthan

accountedforinthepresentanalysis.

Second,thetreatmentsdidnotshowasignificanteffectonthediametergrowtheven

thoughthediametergrowthmeanwassignificantlyhigherinTRplots.Thismayhavebeen

causedbyapoormodelfit.Noneofourincludedvariableswereabletoexplainthevariationin

DBHgrowthwell.Higherdiametergrowthmaybecausedbybettersoilormoistureconditionsin

theTRplots,insteadofthethinningtreatments.TRandCOplotsdifferedintheirstructural

diversitybeforetherestorationtreatments.Particularlymeandiameter,densityandspecies

distributiondifferedsignificantlybetweenCOandTRbeforethetreatmentsandmadeitharder

tofitappropriatemodels.

Third,eventhoughthedataspannedtenyears,thetimedifferencemaynothavebeen

enoughtoshowsignificantdifferences.Forestsareverylonglivedecosystemsthatreactslowly

tochanges.Consequently,wemayseestrongereffectsovertime(WilsonandPuettmann,2007).

Ontheotherhand,youngforeststandsaredynamicsystems,thatreactquicklyto

disturbances.Young,densestandsundergo“self-thinning”,aprocessthatsignificantlyreduces

stemdensityintheyearsaftercanopyclosure.Onourstudysite,naturaldeathoftrees

significantlyreducedstemdensitybetween2007and2017onuntreatedcontrolsites(figure2-6

(a)).ManyofthecanopygapstheGCAcreatedwererelativelysmallandwereclosedby

surroundingtreesrelativelyquickly.Gapsizesoftherestorationtreatmentsmaythereforenot

havebeenlargeenough.Thisissupportedbyanoverallsimilaritybetweentreatments.

5.Conclusion

Paststudieshavesuggestedthatrestorationcannotalwaysreturnecosystemstoaprevious

“natural”state(Benayasetal.,2009;Jonesetal.,2018).Ifpossible,effortsshouldbefocusedon

themostimportantareasandmosteffectivetreatments,butwhenresourcesforrestoration

treatmentsarelimited,itmaybeprudenttosimplyremovethedisturbanceandletnatural

successiondoitswork.

Giventherightconditions,naturalregenerationorpassiverestoration,canprovide

ecologicalandsocialbenefitsatsignificantlylowercoststhanactiverestoration(Chazdonetal.,

2016).Thisishoweverlimitedbypolitical,socialandeconomicbarriersanddependsonthe

severityofthedisturbance(Chazdonetal.,2016).Additionally,passiverestorationallowsforless

engagementoflocalstakeholdersinintherestorationprocess,andthereforeremovesthe

possibilityofcreatingjobsandadeeperunderstandingoftheecologicalprocessesinvolvedinthe

restorationtreatments.

Basedonourfindingsweconcludethatevenmoderatepre-commercialthinningwith

intensitiesofapproximately50%oftreesinyoungDouglas-firforestscanimprovestructural

diversityandbiodiversity,butsingletreatmentsatayoungagearenotenough.Youngforest

standsshowfastgrowthandhighflexibilitytowardsdisturbances.Especiallywhenresourcesfor

restorationtreatmentsarelimiteditmaythereforebebeneficialtofocusonthecreationof

largergapsandleavetheremainingstanduntreated.Thiscreatesaheterogeneousmatrixand

gapcreationhavebeenshowntoimprovebiodiversity(Muscoloetal.,2014).Ourstudycanhelp

focusoftenlimitedresourcesinecologicalrestorationtowheretheycanhavethemostimpact.

Giventhatthelastrestorationtreatmentshappenedmorethantenyearsagoandthattheforest

isstillrelativelyyoung,acontinuationoftreatmentscouldfurtherimprovethestructural

diversityofthestudysite.

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Chapter3:ThePotentialforHobbyistUnmannedAerialVehiclesinEcologicalRestoration

0. Abstract

Weexplorethepotentialofrelativelyinexpensivehobbyistunmannedaerialvehicles(UAV)asa

toolininecologicalrestorationforsmallandnot-for-profitorganizations.First,wesummarize

existingUAVtechnology,currentcommercialandscientificapplicationsandfuture

developments.ThenUAVsareevaluatedfortheirapplicationinimprovingrestorationoutcomes.

SensorsavailableforthesmallestclassofUAVsincludedigitalcameras,infraredcameras,multi-

andhyperspectralcamerasandLiDARsensors.Ifappliedcorrectly,UAVscanincreasetheamount

ofavailabledatabefore,duringandafterrestorationandthereforehelpimprovescientific

understandingofecologicalprocessesinvolvedinrestoration.Thiscanhelpinsettingmore

effective,efficientandengagingrestorationgoalsandbettermonitorifthesegoalshavebeen

met.UAVscanincreaseaccesstoremoteareasanddecreasedisturbanceofsensitive

ecosystems.Regulations,limitedflighttimeandprocessingtimeremainimportantrestrictionson

UAVuse.Thelossoffieldexpertiseandhands-onexperiencecanbeaseriousconcernfor

volunteereducation.ResultingdataandavailablesensorsforhobbyistUAVspresentlylimittheir

applicationformonitoringandscientificresearch.

1. Introduction

Remotesensingandaerialphotographyprovideaccesstolargerspatialcoverageanddetailed

analysesinecology(Aplin,2005).Sincethe1970,satellite-baseddatahaveprovidedimproved

resolution,widertemporalandspatialcoverage,multipledatatypes,andrelativeaffordability.

Consequently,remotesensinghasbecomeanintegralpartofecologicalresearchandinformed

restorationplanning(Lovittetal.,2018).Unmannedaerialvehicles(UAVs),commonlyknownas

drones,arethenewestdevelopmentinremotesensing(Adãoetal.,2017).UAVsaresmall,

remotelycontrolledsystems,capableofautonomouslyfollowingapre-programmedflightpath

andusuallycarryoneormoresensors,mostcommonlydigitalcameras.Both,UAV’sandtheir

sensors,areaffordablecomparedwithmanyremotesensingtechnologies.Unmannedaerial

systems(UAS)usuallyconsistofoneormoreUAVs,equippedwithsensorsandagroundcontrol

station(Páduaetal.,2017).TheresolutionofimagesobtainedwithUAV’siscomparableor

betterthanthatobtainedwithtraditionalremotesensinginstruments,whichmakesupforthe

lackofvastlandscapecoverage(AndersonandGaston,2013).Manyremotesensingdataanalysis

softwarecanbeusedtoanalyzeUAVdata,whilespecialsoftwareisavailabletoextractthefull

potentialofUAVimages.

SinceUAV’sareeasytouseandofferimprovedspatialandtemporalresolutionatavery

lowcost(Anderson&Gaston,2016),theyareemployedforcommercialapplicationssuchas

surveying,agriculture,construction,photo-andvideography,replacingorenhancingother

remotesensingmethods(DroneDeploy,2018).UAVshavebeenusedforresearchinfieldsas

variedashydrologyandgeology,measuringstreamflow(Tauroetal.,2016),waterlevels

(Bandinietal.,2017),andvolcanicactivities(Amicietal.,2013).SomestudieshaveusedUAVsin

ecologicalresearch(ReifandTheel,2017).Eventhoughecologyrepresentsamuchsmaller

marketforUAVproductsthanforestryoragricultureand,hardwareandsoftwareapplications

canandhavebeenadaptedforecologicalresearch(AndersonandGaston,2013;Crutsingeret

al.,2016).RelativelyinexpensiveUAVsforhobbyistshaveriseninpopularityinthelastyearsand

arenowwidelyavailable.ThishascreatedinterestinusingaUAVwithsmallerandnot-for-profit

organizations.

Ecologicalrestorationisnotlimitedtoaspecificecosystemandcantakeplaceinanykind

ofsystem,fromcoralreefs(e.g.Rinkevich,2014),tograsslands(Barretal2017),wetlands(Kelly

etal2011),rivers(Palmeretal.2005),tropical,temperateandborealforests(Zahawietal,2013,

Dumroese,2015,Hekkala2014).Goalsofecologicalrestorationarenotjustbasedoncurrent

conditions,butareinformedbyhistoricalandfuturebioticandabioticconditions(Sudingetal.

2015).Planningrestorationprojectsthereforerequiresarangeofinformationaboutbiotic,

abiotic,socialandculturalfactorsaffectingtheecosystemthatistoberestored.

Keenleysideetal.(2012)describethreeprinciplesofsuccessfulecologicalrestoration:

effectiveness,efficiencyandengagement(Keenleysideetal.,2012).Beforestartingarestoration

ofadisturbedsite,itisimportanttosetrealisticandachievablegoals,whicharethenfurther

refinedbymeasurableobjectives(Keenleysideetal.,2012).Thegoalswilltheninformplanning,

implementationandmonitoringoftheprojectandallowforquantitativeassessmentofthe

projectsuccess.Goalscanbeeffectivewhenfocusingonprojectspecificvalues,efficientby

consideringspecificconstraintsontheproject,andengagingwhenconsideringthat

understandingandsupportfromlocalstakeholdersarecrucialforthelong-termsuccessofthe

project.Successfulprojectsoftenrequireadaptivemanagement,wheremonitoringallowsfor

detectionofpotentialproblemsandrevisionofrestorationstrategies.Therequiredmonitoring

cantakemanyformsanddependsonrestorationgoalsandobjectives.

Afterdefininggoals,restorationpractitionersandresearchersencountermanychallenges

achievingthem,andoftenthereisnoonedefinitewayofachievingarestorationgoal.Basedon

eightrecentstudiespublishedinRestorationEcology,Matzeketal.(2017)suggestedfiveoverall

directivesthatcanhelprestorationprojectsinachievingtheirgoalsmoreeffectively.Theauthors

suggest1)tofollowecologicaltheory,2)harnesstechnologicaladvances,3)rejectdogma,4)

encourageself-critiqueand5)respectstakeholders’limitationstoimprovefutureperformanceof

restorationprojects.

Inthisarticle,wereviewthecharacteristicsofcurrenthobbyistUAVtechnologyand

highlighttheroleofUAV’sinrecentecologicalrestorationstudies.Wewillalsoexaminehow

relativelyinexpensiveUAV’scansupporttheapplicationoffivedirectivesforsuccessful

restorationprojectsasproposedbyMatzeketal.(2017)asmentionedabove.Finally,wewill

discussthereliabilityofhobbyistUAVdataandfuturedevelopmentsinthefield.Inourreview,

wewillfocusonmicroUAV’s.MicroUAV’saredefinedbyweightsoflessthan5kg,whereasmini

UAVsweightupto30kgandlarge,usuallytactical,UAVsweighupto150kg(Ballarietal.,2016).

MicroUAVs(hereaftersimply‘UAVs’)areidealforecologicalresearchsincetheyareaffordable

andaccessibleplatformsthatareeasytohandle,transport,andset-up.

2. CurrentUAVtechnologyanduse

BenefitsofUAVsaretheirhighspatialandtemporalresolution,flexibility,accessibility,andlow

operationalcost.Theycanfillthegapbetweensatelliteorairplaneremotesensingthatcovers

largeareaswithcoarseresolutionandtraditionalgroundmeasurements,whichareusefulfor

verysmallareas.UAV’scansurveyareasofafewkm2withrelativeease,whilelargerareasare

bettersuitedforotherremotesensingtechnologies(Cordelletal.,2017;Cruzanetal.,2016).In

fact,UAVremotesensingislikelytoaddtoorreplacetraditionalmethodsinmanyfieldsand

offernewopportunitiesforecologicalassessments(Linchantetal.,2015;Páduaetal.,2017).

UAVscanbeusedinmostclimaticzonesandweatherconditionsalthoughrainandstrong

windspreventflights.Baenaetal.(2017)describetheirsuccessfuluseofUAVsforplant

conservationindifferentregionsoftheworldandecosystems,“…rangingfromPeru'shyper-arid

vegetationtothedryforestsoftheCaribbeanandfinallytothehumidforestofSouthAfricaand

theBrazilianAmazon.”(Baenaetal.,2017).UAVshavebeenusedtostudythemicro-topography

ofAntarcticmossbeds(Lucieeretal.,2012),forsearchandrescueoperationsinmountain

environments(Silvagnietal.,2017),andarcheologicalmappingintheAmazonianrainforest

(Khanetal.,2017).

However,UAVsarestillaveryyoungtechnologyandtheycomewithinherentlimitations.

CitizenstendtobeconcernedaboutprivacyinfringementsbyUAVuse(Winteretal.2016,Finn

etal2014),whichrequiresopencommunicationofUAVapplicationstothelocalcommunities

whenworkinginpopulatedareas.Duetotheiroverheadorbirds-eyeperspective,UAVsare

limitedtosurveysofparametersthatarevisiblefromaboveandnotblockedbytreecanopyor

othercovers.Newersensorscanpenetratecanopy,butlimitationsofthebirds-eyeperspective

remain.UAVsensorsarealsolimitedtodatabasedonelectromagneticwavesreflectedfroma

surface.Thisexcludesacousticorchemicalanalysisofthestudysite.DirectimpactsoftheUAV

alsoneedtobeconsidered,especiallywhenflyingclosetothegroundandwhenstudying

wildlife,whichmayshowstressreactionstothevehicle.UAVsarebecomingincreasinglymore

affordable,butespeciallysensorsotherthanstandarddigitalcamerasarestillexpensivein

acquisition.Currentquickdevelopmentoftechnologymakestechnologyobsoletequickly.UAV

technologyisdiverseandwesummarizecurrenttechnologyinrespecttotheirapplicationto

ecologicalrestoration:

2.1SeveraltypesofUAVsfordifferentpurposes

UAVscanbeclassifiedintwogeneralcategories:Fixed-wingandmulti-rotorpoweredUAVs.

Fixed-wingsystemscancoverlargerareasduetotheirlongerflightlengthandfasterspeeds.

Theyaregenerallysusceptibletovibrations(Wallaceetal.,2011),butareespeciallyusefulwhen

largerareasneedtobecapturedandtherequiredflighttimesarelonger(TothandJóźków,

2016).Thismakesthemespeciallyusefulinagricultureandforestryapplications.

Multi-rotorUAVsarecurrentlyonlyabletoflyfor15-30minbutaremorestableinflight,

moreflexiblewhenflightspaceislimited,andcandeliverhigherresolutionimages(Cruzanetal.,

2016;Páduaetal.,2017).Theyareespeciallyusefulinareaswithlimitedstartandlandingarea

sincetheycantakeofvertically,andwhenstableimagesofsmallerareasarerequired.Multi-

rotorUAVsaremostusefulforinspection,surveying,construction,emergencyresponse,law

enforcementandcinematography,andstillimages(Páduaetal.,2017).

Figure3-1:TwoexamplesofcommonUAVs.(a)DJIInspire2multi-rotorUAV.(b)SenseFlyeBeeClassicfixed-wingUAV.Imageswereobtainedfromthemanufacturers'websites(https://www.dji.com/;https://www.sensefly.com/)

2.2Temporalandspatialflexibility

UAVscanprovideimagesatahigherspatialandtemporalresolutionthanotherremotesensing

technologies.Theparametersforspatialandtemporalresolutionarealmostcompletelysetby

theuserandarenotconstrainedbysatelliterevisitingperiodsorpre-determinedspatial

resolution(AndersonandGaston,2013).UAVshavebeenusedinecologicalstudieswithsub-

centimeterresolutionofintertidalreefsinAustralia(Murfittetal.,2017),andcantheoreticallybe

usedforconstantmonitoringwhenseveralUAVsareused(Fetisovetal.,2012;Merinoetal.,

2012).Mostresearchstudiescurrentlyuseaspatialimageresolutionof1-10cmperpixel(Ballari

etal.,2016;Cordelletal.,2017;Lovittetal.,2018),ascomparedtothefreelyavailablesatellite

datawhichusuallyhasaresolutionof10-60mperpixelformultispectraldataand<2mperpixel

forortho-photographs(Díaz-delgado,2017).Therearestillfewstudiesoffrequentlyrepeated

assessmentsalthoughVegaetal.(2015)flewUAVsatfourdifferentdatesthroughoutthe

croppingseason.Similarly,Dempewolf(2017)determinedgrowthoftreeterminalshootsin

GermanywithUAV’sflyingrepeatedlyatfourtimesthroughoutthegrowingseason.

Imageprocessingbecomesincreasinglyfaster,andnear-real-timecreationof3-Dmodels

isalreadyavailableforcommercialapplications(Stefaniketal.,2011,LockheedMartin,2018).

Thiswillallowforprocessingofthedatawhileitisbeingcollectedandinsufficientdataquality

duetobadimagequalitycouldbecorrectedwhileresearchersarestillinthefieldinsteadof

havingtowaittoprocessimagesintheoffice.Near-real-timeobjectdetectionhasbeentestedin

avalancheresponse(Bejigaetal.,2017),butcouldalsobeusedinwildlifemonitoringasawayof

detectingnearbyanimalswiththermalimagesensor.

2.3.AffordabilityandAccessibility

UAVsareveryaffordablecomparedtootherremotesensingmethodsandevenconsumergrade

modelscanbeplatformsforscientificstudies(Cruzanetal.,2016;Dempewolfetal.,2017;

Marteauetal.,2017;Surovýetal.,2018).Moreover,aUAVisrelativelyeasytouseanddoesnot

requireextensivetraining(Crutsingeretal.,2016).Infact,Smithetal.(2015)acknowledgethat

UAV’shavedriventhediffusionofremotesensingduetotheiraffordabilityandeasyusage.

RegulationscanlimittheuseofUAVs,withmanycountriesnowrequiringpermitsorlimit

theareaswhereUAVscanbeused.Regulationsarenecessaryforairspacesafety.However,

regulationsarelaggingbehindrapidtechnologicaldevelopment(Stöckeretal.,2017b).

Regulationsvarybycountryandareinmanycasesstillindevelopment.Acurrentsummaryof

regulationscanbefoundinStöckeretal.(2017b),butitremainsnecessarytostayinformed

aboutlocalregulationsbeforeapplyingUAVsforecologicalresearch.

2.4.Availabilityofopensourcesoftwareandplatforms

SeveralopensourcekitsareavailableinadditiontocommerciallyavailableUAVs.Opensource

softwaremakesprocessingofUAVderiveddatawidelyaccessibleandcanimprovethe

reproducibilityofanalysis.OpensourceflightcontrolsoftwarelikeArduCopter(RoboticsInc.;

http://ardupilot.org/copter/)allowforspecializedset-upsandDIYsolutions.Zahawietal.(2015)

usedlowbudgetUAVswithEcosynth(http://ecosynth.org/)open-sourcesoftwareandan

arducopter-basedplatformtomonitortropicalforestrecoveryinCostaRica.Arelatively

inexpensive(<$1500US)UAVwasusedtoquantifyforeststructuremetrics.Zahawietal.(2015)

foundthatmodeledtreeheightfromUAVdatawasastrongpredictoroftreeheightmeasuredin

thefield.AccuracywascomparabletosimilarstudiesusingLiDARdata.Theauthorsestimated

above-groundbiomassandpredictedfrugivorousbirdabundanceusingtheircanopyheightdata.

Lehmannetal.(2017)usedhobbyistgradeUAVstomapinvasivespeciesinasavannah

typeecosysteminBahiaState,Brazilandusedfreelyavailablesoftware(ArduPilotMega2.6

(APM2.6;http://ardupilot.com);VisualSfMsoftware(Wu,2013);CloudCompare

(http://www.danielgm.net/cc/);QuantumGIS(https://www.qgis.org/))tomanage3-Ddatafor

pointcloudcreation.Theywantedtoencourageinvasivespeciesmappingbyshowingthe

possibilitiesofaUAVworthlessthan$2000.Similarly,Dandois&Ellis(2013)usedopensource

softwareEcosynth(http://ecosynth.org/)andBundler(http://www.cs.cornell.edu/~snavely/

bundler/)tomapvegetationspectraldynamics.HopefullysoftwareforUAVimageprocessingwill

continuetodevelopandrepresentatruealternativetocommercialsoftwareasithasalready

happenedingeographicinformationsystems(QuantumGIS(https://www.qgis.org/))and

statisticalsoftware(Rstatisticalsoftware(RCoreTeam,2017)).

2.5.Widerangeofsensors

UAVscanbeequippedwithmanytypesofsensors.Whileweightofsensorsusedtobea

limitation,recentdevelopmentsresultinginminiaturizationmakesitpossibleforUAV’stocarry

severalsensorsandtakeimageswithdifferentbandwidthsandchannelssimultaneously(Pádua

etal.,2017).Digitalcamerasforvisible(RGB)andnear-infrared(NIR)lightwereusedmost

commonlyinthestudiescitiedinthisarticle.RGBimagescoverthespectrumvisibletothe

humaneye(400–700nm)whileNIRsensorscapturelightwithlongerwavelengthsfrom800nm

to2500nm.Mostconventionaldigitalcamerascandetectinfraredlightafterremovingthe

infraredfilter.ThiscanbeusedtoexpandthebandwidthofRGBcamerastoincludenearinfrared

light(e.g.Honkavaaraetal.,2013).

SeveralstudieshaveshownthatmultispectraldatafromUAVscanbeusedinrestoration

monitoring.Multispectralsensorscommonlyincludethevisiblespectrumandaportionof

infraredlight,categorizedin5-12bands.Theinclusionofinfraredlightallowsforthecalculation

ofvegetationindicesliketheNormalizedDifferenceVegetationIndexscores(NDVI)orthe

EnhancedVegetationIndex(EVI)becauseplantsreflecttheinfraredspectrumdifferentlythan

mostothersurfaces.Michezetal.(2016)describetheuseofvisibleandnear-infrared

orthopohotosandasupervisedclassificationalgorithminassessmentsofinvasiveplantspecies

abundanceintworiparianforestsinBelgium.Lishawaetal.(2017)fieldobservationsandUAV

datainastudyofTypharemovalintheGreatLakeswasassessedusingNDVI,blueband

reflectanceandvegetationheightthatwerewellcorrelatedtofieldobservations(Lishawaetal.

2017).Lehmannetal.(2017)detectedoaksplendourbeetle(Agrilusbiguttatus(Fabricius))

infectionsbycomparingNDVIdatafromacompactdigitalcameramodifiedtodetectNIR

reflection.Theauthorsusedamulti-resolutionsegmentationandsubsequentobject-based

classificationtodistinguishbetweenhealthyandinvestedbranchesandfoundthatthe

classificationmatchedapreviousfieldsurveywell.Romero-Triguerosetal.(2017)measured

citrustreeshealthinagriculturalplantationswithamultispectralcamerausedseveralflightsper

Hyperspectraldatacanbeusedforinspectionofforestryoperations,wildfiredetection,

healthmonitoring,andforestpreservation(Colomina&Molina2014).Hyperspectralsensors

coverhundredsorthousandsofbandsinnarrowbandwidths(5-20nm)comparedtoonly5-12

bandsinmultispectraldata.Multispectralandvisiblelightdatathereforelackspectralprecision

andbandwidthandarethereforenotsuitedfortheanalysisofchemicalandphysicalproperties

(Adãoetal.2017).However,highdatavolumescomplicateanalysisandstorageofhyperspectral

data(Adãoetal.2017).

Figure3-2:RBGcanopyphotoofaDouglas-firforestthatwastakentoassessrestorationeffectiveness.

LightDetectionandRanging(LiDAR)laserscannersareusedinmappingofterrainand

plantcoverbecausetheycanpenetrateplantcover.LiDARsensorsonUAVsarearecent

developmentandarestillrelativelyexpensiveanduncommon.LiDARhasbeencommonlyused

asaremotesensingtoolfromairplanes,buttheacquisitionisexpensiveandcantaketime.

Wallace,Musk&Lucieer(2014)testedtheuseofUAVlaserscannersforforestinventory.After

mergingpointcloudsfromupto19flightsforsixplotstheauthorscomparedplotlevelmetrics

fortreeheight,andindividualtreeheightandstemposition.TheirresultsshowedthatUAVlaser

scanningdeliversresultscomparabletogroundmeasurements,whilebeingfasterandbeingable

tocoverahighernumberoftreesthanrealisticallypossiblefromtheground.

Thermalimagescanbeusedforwaterstressassessmentwhencombinedwith

multispectraldata(Anderson&Gaston2013).Santestebanetal.(2017)determinedwaterstress

ingrapevinesbyusinganopensourceUAVplatformequippedwithathermalcamerawitha

pixelresolutionof13x13cm.Bernietal.(2009)foundthatUAVthermaldatacandetermine

waterstressinolivetreesinthesouthofSpainbycomparingitwithfieldmeasurementsof

temperatureandleafconductanceandremotelysensedcanopytemperaturedatafromairplane.

UAVimageswerebetterindistinguishingtreecrownsbecauseofahigherresolutionthanimages

fromairplanes.Similarmethodscouldbeusedinmonitoringofplantrecoveryafterrestoration

treatments.

2.6.MultipleUAVimageanalysissoftware

UAVimageryoftenrequirespost-processingtobemeaningfulfortheassessmentofecological

metricslikewaterstatus,plantvigour,biomass,ordiseasemonitoringofplants.Differentsensor

typesallowfordifferentapplicationsandrequiredifferentpre-processing.UAVdatacanbeused

tocreate3-Dpointclouds,rasterimages,falsecolourimageswithdifferentspectralfootprints,

stitchedOrthophotosorthermalmaps.

Orthophotosareaerialimagesthathavebeenorthorectifiedtorepresentageographical

location.InUAVs,orthophotosoftenconsistofmanyphotosthathavebeenmergedintoone

image,usingimagestitchingsoftware.Orthophotoscanbeusedinmonitoringofrewilding

projectsliketheKneppwildernessintheUK(KneppWilderness,ThomsonEcology2016)orin

wildlifestudies(Reyetal.2017).

3-Dgroundorcanopymodelscanbecreatedfrom2-DimagesusingStructure-from-

Motiontechnology(SfM)(Dandois&Ellis2013).Thistechnology,originallyintendedforground-

basedphotographyisnowusedtocalculatebiomassandelevationmappingfromUAVdata(Nex

&Remondino2014).InSfManimagefeaturedetectionalgorithmdetectsfeaturesacrossseveral

imagesusingimagefeaturedescriptors(Dandoisetal.2017).Thosefeaturesarethen

representedasapointwithx,yandzcoordinatesandthefinalresultoftheSfMalgorithmisa‘3-

Dpointcloud’.A3-dimensionalpointcloud(hereafter‘pointcloud’)isasetofpointswith3-

dimensinalspatialinformation(x,y,andzcoordinates)thatrepresentaphysicalsurface

(Weinmann2016).SfMishighlydependentonthequalityoftheimages,andthequalityof

resultscanvarywidely.3-Dmodelsareusefulforvolumeestimatesorelevationmodels,for

analysisofcanopystructureorinrestorationplanning(Dandois&Ellis2013;Lovittetal.2018;

Zahawietal.2015).Elevationmodelscanbeconvertedtorasterimagestobeusedfortree

crowndetectionusingawatershedanalysis(Mongus&Žalik2015).Dufouretal.(2013)

compared3-DmodelsderivedfromLiDAR,radarandUAVimagesforriparianvegetation

monitoringinthenorthwestofFrance.TheyfoundthatUAVsallowedforassessmentsbefore

andafterrestorationtreatmentsandcandeliver3-Dsurfacemodelswithaveryhighresolution.

UAVimagerywascheaper,fasterandeasiertoprocesscomparedtoLiDARandradar,butspatial

coveragewaslimited.UAVscanbeusedtodeterminepastconditionswithmethodsusedin

archeology(Çabuketal.2007;Lambersetal.2007;Oczipkaetal.2009;Verhoeven2009;

Chiabrandoetal.2011;Rinaudoetal.2012).Wallaceetal(2016)usedaUAVtomapcanopy

structurewithSfMinAustralia.TheirresultsshowedthatUAVderiveddataarecomparablewith

LiDAR3-Dpointclouds.Thesedatacanbeusedindirectlyforassessmentsofhydrology,

microclimate,andbiodiversity.Lovittetal(2018)foundthatseismiclinesgenerallyshowlower

elevationandmoremoisturethanthesurroundingforestinastudyoftheeffectsonseismiclines

onborealpeatlandsmicrotopographywithUAVderived3-Dterrainmodels.Itistherefore

unlikelythattheseismiclineswillrecoverwithoutactiverestoration.

Specificobjectsliketreecrownsorbreedingbirdscanbedetectedfrom2-Dimageswith

visuallightormultispectralproperties.Objectbasedimagesegmentationalgorithmscanbeused

forautomaticorsemi-automaticobjectdetection(Carleetal.2014).Michezetal(2016)describe

theuseofUASinassessmentofinvasiveplantspeciesabundanceusingvisibleandnear-infrared

orthopohotosandasupervisedclassificationalgorithmintwostudysitesinBelgium.Theyfound

thatinvasivespeciesdetectionishighlyspeciesdependent.ResultsforHeracleum

mantegazzianumreachedthebestaccuracieswitha97%detectionrate,whereastheothertwo

species(Fallopiasachalinensis/FallopiajaponicaandImpatiensglandulifera)inthestudyonly

reached68%and72%.Theapplicabilityofthemethodthereforedependsonthetargetspecies.

3. ReliabilityandconcernswithUAVuse

Moreresearchisneededcomparingfieldbasedmethodsandremotesensing,especiallywhen

usinghobbyistUAVs.Dufouretal.(2013)pointedoutthatfewstudiescomparedfieldbased

approachesandremotelysenseddata.Theauthorsconcludedthatremotelysenseddatacannot

completelyreplacefieldbasedassessments,especiallyforunderstoryassessmentsinareaswith

densecanopycover,treeage,orsoilproperties.

UAVscanaffectthebehaviouroftargetspecies.Barnasetal.(2018)researchedthe

effectsoffixed-wingUAVflightsonnestingbehaviouroflessersnowgeese(Ansercaerulescens)

andfoundthatsurveyflightssignificantlyaffectedthebehaviourofthegeese.Thebirdswere

moreactiveandspentlesstimerestingcomparedtoacontrolgroup.Borelle&Fletcher(2017)

foundthatUAVflightsalwayshaveaneffectonnestingbirdsafterexaminingelevenstudieson

shorebirdsconductedwithUAVsandtheirrecordedeffectsonbehaviourofnestingbirds.This

willhavetobeconsideredwhenmonitoringtheeffectsofrestorationonwildlifewithUAVs.Itis

alsonecessary,aswitheverysamplingmethod,tobeawareofpossibleeffectsthesamplinghas

onthesubject.Ontheotherhand,UAVsurveyscanreduceinterferenceanddisturbance

comparedtodirectsurveysdoneontheground(Jonesetal.2006;Sarda-Palomera,Francescet

al.2012).

3-DpointcloudsderivedfromSfMvaryinqualityandmayneedtobecombinedwith

groundproofingordatafusionwithotherremotesensingdataifhighprecisionisrequired.

Tomastiketal.(2017)assessedtheaccuracyofSfMderivedpointcloudsbycomparing

coordinatesofthederivedpointcloudandcoordinatesofgroundcontrolpointmeasuredinthe

field.Theirmodelsreceivedasub-decimetreaccuracy.Dandoisetal.(2017)wentastepfurther

andassessedtheaccuracyofindividualpointsofthepointcloud.Theyreportedthatthefeature

detectionalgorithmhasasignificanteffectonthesamplingqualityandmoreattentionshouldbe

paidtothedevelopmentofthese.Mlamboetal.(2017)assessedtheapplicationofSfMfor

measuringgreenhousegasemissionsinthecontextorREDD+forestrestorationefforts.The

authorsassessedtheaccuracyoftreeheightsmeasuredfromSfMderivedpointcloudsand

comparedthemtoLiDARderivedmodelsandgroundmeasuredtreeheights.TheUAVderived

modelswerestronglycorrelatedwithLiDARdatainanopencanopyforestbutperformedpoorly

inclosedcanopyforests.TheauthorsconcludethatSfMpointcloudsarewellsuitedforthe

assessmentforestwithsparsecanopies,butarenotyetabletoperformwellinclosedcanopy

forestsincetheSfMtechniqueisnotabletoaccuratelymaptheground.

Sensorcalibrationanddataprocessingareimportantstepsinavoidingerrorintheresults

fromUAVderiveddata.Spectraldatavaluesdifferunderdifferentlightingconditions,anditis

thereforenecessarytoeithercontrolenvironmentalconditionsorcorrectnoiseresultingfrom

environmentalconditionsinthepre-processingphase(Adãoetal.2017).Pre-flightcalibrationof

hardwareincludingsatellitenavigationsystemandspectralsensorscanincreasedataquality

significantly.ConventionalnavigationgradeGPSisnotpreciseenoughforgeo-referencingwith

anerrorlowenoughforresearchapplications.Toimprovetheprecisionofgeo-referencing

groundcontrolpoints(GCPs)arenecessary.GCPsarehighlyvisiblemarkersthatareplaced

aroundtheedgesofthestudysiteandwhichlocationismeasuredonthegroundwithahigh-

precisionGPS.ThoseknowGPSlocationscanthemhelptocorrectlygeo-referenceUAVimages.

Newer,better,directgeo-referencing(GlobalNavigationSatelliteSystem(GNSS)andInertial

NavigationSystem(INS))canmaketheuseofGCPslessimportant(Adãoetal.2017).Pre-

processingafterdatacollectionhelpsimprovedataqualityandcorrectsforuncalibratedsensors

andvaryingenvironmentalconditions.Spectralcalibration(Lucieeretal.2012)andgeometric

corrections(Hruskaetal.2012)usetargetsofknowreflectanceinthefield.Asopposedto

remotelysenseddatafromsatelliteorairplane,thereisnoneedforatmosphericcorrection

(Adãoetal.2017).

4. Futuredevelopments

UAVsconsistingofseveralUAVsandacontrolstationwillbeincreasinglyusedinmonitoring,

withfirstapplicationsinnearrealtimeforestfirereporting(Merinoetal.2012).Thismayinclude

‘droneswarms’,agroupofidenticalUAVsthatcanreplaceeachotheroncethebatteriesneedto

berecharged.ThereforeatleastoneUAVcancontinuouslybeinflight,deliveringaconstant

monitoring.

Sensorswillbecomeincreasinglysmallandlight,whichwillallowforamorecommonuse

ofLiDAR,thermal,multispectralandhyperspectralsensoronsmallUAVs.Flighttimeswill

increase,safetymechanismsonboardwillbeimprovedandUAVswillbecomeincreasinglydust

andweatherproof(Adãoetal.2017;Crutsingeretal.2016).Increasingpossibilitiesforsoftware

developmentcoulddrivetheuseof“crowd-sourced”UAVimageryformonitoringorsamplingof

largerareas(Crutsingeretal.2016).

NewclassesofUAVslike‘ornithocopters’,whichmimictheflightmechanicsofbirdsare

stillexperimentalbutmaybecomeusefulinmonitoringofareaswheredisturbancethrough

biggerUAVsisunwanted(Anderson&Gaston2013).

UAVaerialsampling(e.g.Randomtransects)willgetincreasinglystandardizedandtobe

transferableandcomparablebetweenstudies.Thiswillrequirestandardmethodsandsampling

protocolsaswellasstandardizedsensorcalibration.DataqualityisaproblematicissuewithUAV

datasincemanyapplicationsinecologyarestillinanearlyorexperimentalstage(Reif&Theel

2017).Cameracalibrationanddatanormalizationareimportantstepstoavoidunreliabledata.

5. UAVsinEcologicalRestoration

Respectingthefivedirectives(1)tofollowecologicaltheory,2)harnesstechnologicaladvances,

3)rejectdogma,4)encourageself-critiqueand5)respectstakeholders’limitations)forsuccessful

restorationbyMatzeketal.(2017),UAVswiththeirversatilenature,quickanduncomplicated

use,butalsotheirlimitations,willcontributetosuccessfulrestorationinseveralways.

AdaptingUAVapplicationswillharnesstechnologicaladvances.UAVsthemselvesarea

relativelynewtechnologyinecologicalrestoration,andtheycanprovidescientificresearchwith

morefrequentandfinerscaleassessmentsaswellascarrysensorthatarealreadyavailablefrom

otherremotesensingsourcesbuthavebeentooexpensive.UAVretrieveddataincombination

withnewstatisticalmodellingprocessesandanalysistoolscanhelpintheplanningofecological

restoration.Freelyavailableopen-sourcesoftwareandaffordableUAVplatformsincreasethe

availabilityofsuchdataandallowforhighlyindividualizedmonitoringregimeswithrelatively

littleeffort.Imagesderivedfromabirds-eyeperspectivehavecertainlimitationsasmentioned

above,butdoallowforanewandunusualperspectiveonrestorationprojects.Thiscanhelpin

communicatingrestorationgoalsandmonitoringresultstostakeholdersbyprovidinganintuitive

wayunderstandingspatialdata.

UAVsareabletoprovidemoredataandhigherspatialandtemporalresolutionthanit

waspossiblewithotherformsofremotelysensedimagery.Thiscanhelpinprovidingscientific

evidenceabouttheeffectivenessofrestorationtreatments.Whileevidencecanbehelpfulin

challengingconventionalbeliefs,itisunlikelythatUAVswillbehelpfulinrejectingdogma,as

definedbyMatzeketal.(2017,111)as“…restorationprinciplesthataregenerallyregardedas

true,butthatshouldnotbeslavishlyobeyed”.Researchaboutclimatechangedenialhasfound

thatprovidingmoreaccuratefactsdoesnotresultinachangeofopinionaspeopleselectively

searchforevidencethatsupportstheirownopinionsandrejectopposingevidence,evenifitis

moreconvincing(VanderLinden,2015).

Abetterunderstandingofecologicalprocessesandeasierassessmentofecological

experimentscaninformthemoveawayfromlongheldbelieveswithoutrejectingscientific

evidence.Collectingmoredataonexistingandnewrestorationprojectswillhelptotestbeliefs

aboutthebestmethodsandcan,whennecessary,informnewmethods.Matzeketal.(2017)

writeabouttheexampleofincludingnon-nativespeciesinrestorationtreatmentstorestore

ecologicalfunctioninsteadoflimitingthespeciesselectiontonativespeciesalone.UAVscould

forexamplebeusedtocloselyandregularlymonitorthenon-nativespecies’spreadand

thereforedrawresultsaboutbenefitsofnon-nativespecies.Thiscouldhelpproveorrejectthe

longheldbelieveofseeingnon-nativespeciesaspurelynegative.

FrequentandcomprehensivemonitoringwithUAVswillencourageself-critique.

Monitoring,whichhistoricallyhasbeenlackinginecologicalrestoration(Wortleyetal.2013),is

simplifiedandsignificantlyreducedincostcomparedtotraditionalgroundmeasurementswhen

usingUAVs.Thismonitoringwillneedtobegroundtruthedandstandardsamplingmethodswill

havetobedevelopedandrepeatabilityofassessmentswillneedtobesecuredtocreatereliable

monitoringresults.

Inexpensive,easyandfastUAVassessmentsrespectstakeholderandpractitioner

limitationsbydecreasingcostsandfocusingintensiveeffortsonareasthataremostinneedof

restorationtreatments.Suchassessmentsofcurrentecologicalconditionscanincreasethe

efficientuseofresourcesandoptimizethelimitedresource,makingsurelandmanagersand

restorationpractitionersgetthemostvalueoutoftheirlimitedbudget.Rapiddigitalmappingin

combinationwithGISalsoallowsforsimpleinclusionoftheinterestsofseveralstakeholders.

Monitoring,oftenlackinginecologicalrestoration,issimplifiedandsignificantlyreducedincost

comparedtotraditionalon-the-groundassessments.However,restorationmonitoringwith

traditionalgroundmeasurementscanbequickerandmoreefficientthanintroducingahigh-tech

solutionlikeUAVs.Mostgroundmeasurementshavebeenproventodeliverrepeatableresults

withagoodaccuracyandarecarriedoutwithrelativelysimpletools.Thismakestraditional

methodsmoreaccessibleforvolunteerswithoutspecifictrainingandlesspronetotechnological

failureorweatherconditions.UAVsarethereforemostusefulforprojectsthathavearelatively

largespatialextentanddoesnothaveanestablishedvolunteergroup.UAVremotesensingcan

beaveryusefultool,butshouldremainjustoneofmany.

UAVscanmakefieldworksafer,especiallywhenusedinremoteareasandareasthatare

hardtoaccess.Traditionalfieldworkoftenisindirty,dullanddangerousconditionsoreven

inaccessible(Watts,Ambrosia,andHinkley2012).UAVsaremostusefulforsmalltomedium

sizedareasofuptoseveralhectares,areaswithhighspatialvariability,applicationsthatneed

frequentorfastmonitoringandcanbeusedunderacloudcoverwhichisnotpossiblewith

satellitephotography.

Ifappliedwell,UAVassessmentswillhelptomakerestorationprojectsmoreeffectiveby

increasingtheavailabledatafortheassessmentofrestorationoutcomes,efficientbysavingtime

andresourcesandengagingbyprovidingintuitivenewperspectiveonrestorationprojectsand

offermorefrequentupdatesofmonitoringdata.

Ontheotherhand,whenrelyingentirelyonremotelysenseddata,thereisnochancefor

groundproofingthedata.RetrievingfieldmeasurementsfromUAVimagesremovesthehands-

onexperienceofcollectingthedataandremovesthestepofcriticallythinkingaboutdata

quality.Whenmeasuringdatainthefieldbyhand,outliersormeasuringerrorscanoftenbe

distinguishedwithcommonsense.Thisstepcanbemorechallengingwhenmetricsarederived

fromdigital3-Dmodelsthatarehardertointuitivelyunderstand.Increaseduseofairspaceby

microUAVshascausedconflictwithcivilianaircrafts.Dystopianvisionsoftotalsurveillance

causedbywidespreaduseofUAVsmaybescience-fiction,butprivacyissuescanbeproblematic

whenusingUAVs.Withmoremonitoringdonewithremotesensingmethods,theriskof

accidentallydocumentingpeople’sactivitiesincreases.NormalizationofUAVsinpublicwill

increasetheriskofabusingthistechnologybyhackingthedronesofothersorusingdronesasa

toolinillegalactivities.TherecentattackontheVenezuelanpresidentwithanamateurdrone

demonstratesthisveryseriousconcern.Madurowasattackedwithwhatappearedtobea

makeshiftexplosiveattachedtoamicroUAV(Herrero&Casey,2018).AsaffordableUVAs

becomeincreasinglywidespread,regulationsaroundtheiruseanddatacollectionbecome

increasinglyimportant.

6. Conclusion

Ecologicalrestorationprojectsareoftenunsuccessfulinreachingtheirgoalsandobtainingthe

expectedresultsbecauseofunclearorunspecificgoals,unrealisticexpectations,andnoorlittle

monitoring(Keenleysideetal.2012).Oneofthebiggestchallengesforecologicalrestorationnow

andinthefuture,isconsistentmonitoringaftertreatments.UAVscanhelptoestablishbaseline

databeforerestorationtreatmentsandincombinationswithgeographicinformationsystems

helpintheplanningprocessoftreatments.Afterthetreatments,UAVscanhelpinmany

monitoringapplications,andbecauseitcanbedoneregularlyandquickly,adaptivemanagement

(reactingtochangesorunexpecteddevelopments)canbeimprovedbymanagers.

UAVsallowforaplethoraofapplicationsinrestorationecologyofwhichsomehave

alreadybeenestablishedascommontechniquesandothershavebeentried.FieldswhereUAVs

arecommonlyappliednow(e.g.forestryoragriculture)canhelpcontributetoanunderstanding

ofecologicalprocessesandimprovedplanningofecologicalrestorationprojects.

WithincreasingminiaturizationandaffordabilityofsensorstheuseofUAVsinrestoration

ecologywillgrowinfutureyears.Duetotheirlimitationsmentionedabove,itisunlikelythat

UAVswillreplaceregulargroundmeasurementscompletely,buttheycanmakefieldworkeasier

andfaster.UAVscanalsoallowforrestorationplanning,executionandmonitoringinareasthat

werepreviouslyinaccessible,orwherefunds(especiallyformonitoring)arelimited.

Inrewildingprojectswherewemaywanttoexcludehumanstocreatewildernessareas,

theuseofUAVsformonitoringofvegetationrecoveryandspeciesabundanceofanimalsand

plantscouldbeawayofminimizinghumanimpact.EffectsoflowflyingUAVsonanimal

behaviourwillhavetobeconsidered.

UAV’s,justlikeanyotherremotesensingtechnologycanalwaysonlybeatoolinworking

towardsarestorationgoal.Definingclearandmeasurablegoalsremainsthemostimportant

factorinplanningandexecutingasuccessfulrestorationgoal.

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Chapter4:AssessingCanopyStructureUsingaHobbyistUAVand‘StructurefromMotion’TechnologyinaRestoredDouglas-firForest0.Abstract

Wecomparedforeststructuralmetricsfromaerialimagesderivedfromahobbyistunmanned

aerialvehicle(UAV)andgroundmeasurementstodemonstratetheapplicabilityofUAVsfor

restorationmonitoring.Wefoundacanopyheightmodel(CHM)fromUAVimages

underestimatedmeantreeheightsonaverageby10.64mcomparedtogroundmeasurements

butbothdatashowedastatisticallysignificantcorrelation.StemdensitiesforUAVdatawere

underestimatedby375stemsha-1onaverageandbothdatasourcesshowednocorrelation.

Canopygapsaccountedfor6%ofthecanopy,withanaveragegapsizeof58m2.Mostgapswere

smallerthan20m2.UAVimagesandtheresultingCHMrepresentanewvisualizationofthestudy

siteforthecommunicationofrestorationoutcomestoawideraudiencebutdidnotmeet

requirementsformonitoringofresultsorscientificstudies.Changesinthesamplingmethods

suchasabetterdigitalelevationmodelandtheuseofgroundcontrolpointswouldimprovethe

results.However,itisunlikelythathobbyistUAVsareabletoproducereliableandreproducible

results.

1.Introduction

Regularevaluationofrestorationoutcomesthroughmonitoringcanhelpimprovepracticesand

allowforthewiseuseoflimitedresources(Jonesetal.2018).Wedemonstratedtheapplicability

ofaconsumergradeunmannedaerialvehicles(UAV)inforestrestorationmonitoringbytesting

theaccuracyofmeantreeheightandtreedensitymeasuresagainstgroundmeasurementdata

frompermanentplots.TheinvestigationfocusedonwhetheraUAVsurveyisaccurateenoughto

provideusefulinformationforrestorationpractitioners.

Thinningisacommonmethodinforestrestorationtoimproveecologicaldiversityand

function(Fajardoetal.2007;Versluijsetal.2017).Thecreationofadiversecanopyandgaps

playsanimportantroleinrecreatingoldgrowthstructures.Parametersadaptedfromforest

managementsuchasdensity,canopyheight,basalarea,canopyclosureandbiomassare

commonlyusedinmonitoringofforestrestoration(Ruiz-Jaen&Aide2005;Zahawietal.2015).

Theseparametersareespeciallyusefulforplanningwhenforestrestorationisincorporated

withinsilviculturaltreatments.Forexample,Getzinetal.(2012)usedveryhighresolutionUAV

derivedortho-rectifiedphotographstoexaminetherelationshipbetweenfloristicbiodiversity

andcanopygapsizeinbeechdominatedmixedforests.Theyfoundthatfinescalespatial

informationofgapswasstronglycorrelatedwithplantbiodiversity.Untilrecently,such

monitoringofcanopystructurewastimeconsumingandlabourintensive,becauseithadtorely

ontransects(Runkle1992)oronvisualassessmentofthecanopycover(Seischabetal.1993).

Visualassessmentsarequickbutoftensubjectiveandimprecise(Coopsetal.2007).Withthe

increasedavailabilityofremotesensingandespeciallyUAVdata,gapassessmentscanbedone

quickandassistedbyalgorithmsthatdelineatecanopygaps(Zielewska-Büttneretal.2016).

Themostimportantadvancesinmonitoringinthelastdecadearelinkedtotheincreasing

availabilityofremotelysenseddata.Manysatellite-basedremotesensingdataarenowfreely

available(e.g.Landsat,Sentinel)inresolutionsofupto10m/pixel.Thisincludesvisiblelight,

multispectral,hyperspectral,LiDARandradardata.Theincreasedavailabilityandaffordabilityof

UAVs,commonlyknownasdrones,haveaddedveryhighresolutionaerialimagestothetoolkit

ofrestorationscientistsandpractitioners.Withitseasyandrelativelyinexpensivedeployment,

UAV-basedmonitoringislikelytocontributetotheimplementationofsuccessfuladaptive

management,astrategythatrequireslong-termmonitoring.Adaptivemanagementhasbeen

identifiedasthebeststrategyforasuccessfulrestorationproject;however,itisrarely

successfullyimplementedsincerequiringconsiderableresources(Perringetal.2015).

ThelowcostofUAVsformonitoringhasresultedinvariousapplicationsforagriculture(e.g.

Torres-Sánchezetal.,2015),construction(e.g.Bangetal.,2017),forestry(e.g.TangandShao,

2015)andincreasinglyecologicalresearch(e.g.DandoisandEllis,2013).Forexample,UAVshave

beenusedforthemonitoringofriparianvegetationrestoration(Dufouretal.2013),bog

restoration(Knothetal.2013),invasivespeciesremoval(Lishawaetal.2017),tropicalforest

recovery(Zahawietal.2015)andpost-fireforestrecovery(Aicardietal.2016).UAVshavealso

beenusedinthemonitoringofsmallandpatchyecosystemssuchasoakforestsinGermanythat

arenotwellsuitedfortraditionalremotesensingtechnologieswhichrequirelargeareasfor

optimumresults(Lehmannetal.2015).Canopyheightmodels(CHM)derivedfromairborne

stereophotographyproduceaccurateestimatesoftimbervolumeandbasalareaofforeststands

(Straubetal.2013;Wangetal.2015)anddetectionofgaps(Bettsetal.2005).CHMsderived

fromUAVimageryarenowbeingused(Otaetal.2017).Suchmethodsdevelopedforforest

managementcanbeusedforthemonitoringofforestrestorationprojectsforestimationof

canopystructureandbiomass.

UAVdatacanbecombinedwithotherremotesensingtools.UAVremotelysenseddataare

usuallylimitedtorelativelysmallareas.However,highresolutionUAVdataincombinationwith

lowresolutionsatellitedatacanworkasapromisingwayofmonitoringlargerareasofforest(e.g.

Pulitietal.,2018).

With2billionhectaresofforestinneedofrestorationglobally,newwaysofthinkingabout

restorationprojectsarenecessary(Stanturf,2014).Moreandmoreprojectsareplannedata

landscapescale,withanincreasingfocusonsocialandculturalvaluesofseverallandownersand

stakeholders.UAVscanhelpbyprovidingappealingdatavisualizationandreducingtimeand

resourcesneededtomonitorremoteareasthataredifficulttoaccess(e.g.ReifandTheel,2017).

Keenleysideetal.(2012;alsoMcDonaldetal.2016)describethreeprinciplesforsuccessful

restorationofprotectedareas.Projectsneedtobeeffective,efficientandengaging.Engagement

requirescollaborationwithlocalcommunitiesandcommunicationofrestorationtreatmentsand

effectstothem.Communicatingtheresultsofrestorationmonitoringtostakeholders,local

communities,otherscientists,practitionersandthegeneralpublicisanimportantpartof

ecologicalrestorationandcontributestothesuccessofaproject(McDonaldetal.,2016).

Communicationcanhappenusingimagebasedremotesensingproductssuchasortho-

photographsorcanopyheightmodels(CHM),allowingawideaudiencetointuitivelyunderstand

restorationresults.Thebirds-eyeviewprovidedfromalowflyingUAVcansparkinterestand

helpstakeholdersunderstandscientificresults(Davidetal.2016).

Weusedcurrentunmannedaerialvehicle(UAV)technologytomonitorforeststructural

parametersofarestorationprojectinthecoastalDouglas-firzone(CDF)inBritishColumbia.The

intentwastoassessanoff-the-shelfconsumer(or“prosumer”)grademicroUAVtodemonstrate

theirapplicationinrestorationmonitoringbypresentingatypicalworkflowforUAVimage

processingandcomparingresultsformeantreeheightsanddensitytogroundmeasurements

fromseventeenplots.Additionally,theUAVimageswereusedtoderivecanopygapsasanother

measureofcanopystructure.

2.MaterialsandMethods

ThestudyareaislocatedonGalianoIsland,BritishColumbia,Canada(48°56'47.4"N,

123°29'36.6"W)alongtheSalishSea,amajorinletofthePacificOceanbetweenVancouverand

VancouverIsland(figure4-1).The61.5hasiteisintheheartofthemoist-maritimeCoastal

Douglas-firbiogeoclimaticzone(CDFmm)(Krakowskietal.2009).Relativelysteepslopesand

elevationsfromsealeveluptoabout140mcharacterizethetopographyoftheareaandasmall

creekrunsfromsouthtonorthacrosstheeasternsideoftheproperty.Vegetation,soiland

moistureregimedifferacrossthesiteandecosystemtypeswerepreviouslydelineatedwith50

individualpolygons(Gayloretal.2002)(table4-1).

Table4-1:Ecosystemtypesonthestudysite

ECOSYSTEMTYPE Stage Area(Ha.) %TotalArea

Douglas-fir–Salal Pole/Sapling 19.1 32.4

Douglas-fir–Salal YoungForest 1.5 2.5

Douglas-fir,Grandfir–Oregongrape Shrub/Herb 0.4 0.7

Douglas-fir,Grandfir–Oregongrape TallShrub 0.2 0.3

Douglas-fir,Grandfir–Oregongrape Pole/Sapling 13.6 23

Douglas-fir,Grandfir–Oregongrape YoungForest 11.3 19.2

Douglas-fir,Grandfir–Oregongrape MatureForest 1.1 1.9

WesternRedCedar,Grandfir–Foamflower Shrub/Herb 0.6 1

WesternRedCedar,Grandfir–Foamflower Pole/Sapling 2.4 4

WesternRedCedar,Grandfir–Foamflower YoungForest 2.8 4.7

WesternRedCedar–Skunkcabbage TallShrub 1.5 2.5

WesternRedCedar–Skunkcabbage YoungForest 1 1.7

Other 3.6 6.1

Figure4-1:Locationandcontourmapofthe61.5hastudysiteonGalianoIsland,BritishColumbia.

Thelocallandtrust,theGalianoConservancyAssociation,conductedrestorationthinningon

theyoung,coniferousforesttoincreasestructuraldiversityandbiodiversitystartingin2004.

Restorationthinningwasdeemednecessaryaftertheforestwaspartiallyclear-cutloggedin

1967and1978withonlyapproximately4%ofthearealeftintactin1978(Gayloretal.2002).

Remainingcoarsewoodydebriswerebulldozedintopilesorwindrowsandsetonfire,butdid

notcombustfully.Thesewindrowswerenotreplantedandarestillvisible.

Inthefollowingseason,theopenareaswerere-plantedwithPseudotsugamenziesii(Mirb.)

Franco(Douglas-fir)seedlingsfromnon-localstock(Gayloretal.2002).Abouthalfthestudysite

wasrestoredin2004andearly2005.InanassessmentofthesitebeforethetreatmentstheGCA

foundseveralecosystemtypesindifferentstages(Table1).Restorationconsistedofthinning

andcreationofsmallgapswhere40-60%oftreeswereculledbygirdling,pullingortopping.The

canopynowconsistsmainlyofP.menziesii,withminorcontributionsofAlnusrubraBong.(red

alder),AcermacrophyllumPursh(bigleafmaple),Abiesgrandis(DouglasexD.Don)Lindl.(grand

fir),andThujaplicata(DonnexD.)Don(Westernredcedar).

Treeheightanddensityestimateswerederivedfrom(1)UAVderivedimagesobtainedin

latesummer2017,and(2)traditionalforestrymethodscollectedwithalaserrangefindersand

treecountsfromthegroundinearlysummer2017.

AerialimagesweretakenwithaDJIMavicPro(https://www.dji.com/mavic;consumergrade

UAVwithstandardcamera;table4-2).Theimageswereoriginallyintendedforthecreationofa

stitchedortho-photo.WeusedDJI’sflightplanningsoftwareDJIGSPro

(https://www.dji.com/ground-station-pro)toplantheflight.Horizontaloverlapwassetto90%

andsideoverlapto60%ataflightaltitudeof85metersabovelaunchpoint.Thesoftwareallows

forquickflightplanningandflightplanscanbechangedinthefieldifnecessary.Thesurveyarea

canbemanuallyselectedonanofflinemapandflightpathsarecalculatedautomatically

accordingtothementionedpre-setparameters(imageoverlap,flightaltitude).Thesoftware

doesnotallowforcorrectionoftheflightheightaccordingtothegroundtopography.Becauseof

thisandbecauseoflimitedbatterytime,weflewthepropertyinfourseparateflights,always

startingthehighestpossiblepointthatwasaccessibleandsettingtheflightheightto85mabove

ground.Theactualheightabovegroundvarieddependingonthetopography.

Table4-2:CharacteristicsoftheDJIMavicProconsumergradeUnmannedAerialVehicle(https://www.dji.com/mavic/info#specs).

Weight(Battery&PropellersIncluded) 734g(excludegimbalcover)MaxSpeed 65kphinSportmodewithoutwindOverallFlightTime 21minutes(Innormalflight,15%remainingbatterylevel)SatellitePositioningSystems GPS/GLONASSSensor 1/2.3”(CMOS),Effectivepixels:12.35M(Totalpixels:12.71M)Lens FOV78.8°28mm(35mmformatequivalent)f/2.2

Distortion<1.5%Focusfrom0.5mto∞

ISORange photo:100-1600

ElectronicShutterSpeed 8s-1/8000sImageSize 4000×3000

Groundmeasurementswerecollectedbymeasuringtreeheightsofrandomlyselectedtrees

(onaveragefivetreesperplot)inseventeen20mx20mplotsforatotalof111trees.Three

heightmeasurementspertreewithalaserrangefinderwereaveragedtoreceiveaheightvalue.

Andersenetal.(2006)achievedaprecisionof+-0.27mwithalaserrangefinderbycomparingthe

measurementswithheightmeasurementsbytotalstations.Luomaetal.(2017)foundastandard

deviationof0.5mwhencomparingtreemeasurementsbyuserswithdifferentlevelsof

experienceusingaclinometer.Sibonaetal.(2017)reportedsimilarprecisionforlaser

rangefindersinacomparisonofLiDAR,rangefinderanddirectmeasurementsafterfelling.We

thereforeconsideredvaluesmeasuredonthegroundasaccuratetoatleast0.5m.Wecounted

alltreesinthoseplotsandcalculateddensitiesbyhectare.For42trees,wealsorecordedthe

exactlocationbymeasuringthedistancetotwoplotcorners(Roberts-Pichette&Gillespie1999).

AstandardphotogrammetricandStructurefromMotion(SfM)approachsimilartoLisein

(2013)wasusedtocreateacanopyheightmodel(CHM)fromUAVdata(figure4-2).Flightpaths

produced1313RGBimagesofthestudysiteinAgisoftPhotoScanProsoftware

(www.agisoft.com,AgisoftLLC,St.Petersburg,Russia)toaligntheimagesusingthefollowing

settings:mediumaccuracy,referencepreselection,40,000keypointlimitand10,000tiepoint

limit.PhotoScanProautomaticallyusesGPSimagepositionstoalignphotos.TheinternalGPSof

theUAVwasusedforimagealignmentandortho-rectificationwhichiscommonlyreferredtoas

directgeoreferencing(Uysaletal.2015).

Thesamesoftwarewasusedto

calculateadensepointcloudfromoverlapping

photosusingthehighqualityandmedium

depthfilteringsettings,toremovepointswith

extremelydifferentvaluesthantheir

surroundingpoints.PhotoScanProusesaSfM

approachtocreate3-dimensionalpointclouds

from2-dimensionalphotosbydetecting

featuresacrossseveralimagesandmatching

them.Thesoftwarethenappliesiterative

adjustmentstoestimatethecamera

orientationandposition,andfinallythe3-

dimensionalpositionsofthefeatures.

Twocanopyheightmodelswerecreatedaftermanuallydeletingartifactsfromthepoint

cloud.PhotoScanProoffersadigitalelevationmodel(DEM)function.Thefirstmodelwascreated

usingpointsclassifiedasgroundpointstocreateaDEMwitharesolutionof6.02cm/pixeland

oneusingallpointsclassifiedashighvegetationtocalculateamodeloftheearth’ssurface

includingthecanopy,commonlyknownasdigitalsurfacemodel(DSM).JensenandMathews

(2016)testedtheaccuracyofDEMfromSfMpointcloudsinopencanopywoodlandsystems.

TheyconcludedthatSfMproductsdeliveracomparableaccuracytoairbornelaserscanningwith

lightdetectionandranging(LiDAR)products.However,thedetectionofgroundpointsfrom

standarddigitalimagesunderclosedcanopyischallenging(Zahawietal.2015).Duetothedense

Figure4-2:Workflowusedintreetopandcanopygapdetection.

canopyandsmallgapsizesofourarea,thegroundmodelshowedlargegaps,whichnecessitated

usingaDEMderivedfrom10mcontourlinesinstead.Wethencalculatedacanopyheightmodel

(CHM)bysubtractingtheDEMfromtheDSM.

ByautomaticallydetectinglocalmaximaintheCHMrasterimageanalgorithmdetectedtree

tops.Toavoiderrorscausedbyindividualtreebranches,the‘CHMsmoothing’functionwasused

intherLiDARpackage(Silva,C.A.,Crookston,N.L.,Hudak,A.T.,andVierling2015)withstandard

settings(Filter=Gaussian,windowsize=5pixel,sigma=0.67)tosmooththeCHMbefore

applyingthedetectionalgorithm.TreetopsweredetectedfromtheCHMrasterfileusingthe

‘vwf’functionintheForestToolsR-package(Plowright2018).The‘vwf’functiondetectstree

crownsintherasterdatabyapplyingavariablewindowfilteralgorithmdevelopedbyPopescu

andWynne(2004).

TheCHMrasterdatawasusedtodelineatecanopygaps.Allrastercellswereconsidereda

gapwhentheelevationvaluewaslowerthan2m.Thethresholdof2mwasusedforsimilar

purposesbyBrokaw(1982)andZielewska-Buettneretal.(2016)Subsequently,allgapssmaller

than10m2wereexcludedasdemonstratedbyZielewska-Buettneretal.(2016).The10m2

thresholdwaschosensomewhatarbitrarilyduetoalackofagenerallyacceptedminimalgap

size,butitvaguelyrepresentedhalfthemeantreeheight.

SimilartoLehmannetal.(2017),linearregressionmodelswereusedtoassessthe

relationshipbetweenUAV-derivedtreeheight(“predicted”)withfieldinventorydataoftree

height(“measured”),andtherelationshipbetweenpredictedandmeasuredstanddensity.

3.Results

3.1TreeheightsandDensity

TreeheightsderivedfromtheCHMrangedfrom7.00–46.96m.withameanof16.92meters(sd=

2.0).Treedensitywasestimatedat860.25stemsha-1(sd=119.9).Meantreeheightanddensity

fromfieldmeasurementswere25.40m(sd=3.2)and904.50stemsha-1(sd=269.9)respectively

(Table4-3).

Treeheightsmeasuredinthefieldwereonaverage10.64mhigherthanvaluesderivedfrom

UAVswithdifferencesbetweenplotmeansrangingfrom1.93mto19.98m.

Table4-3:Meanandrangeof treeheightanddensity fromfieldmeasurementsof111treesandpredictions fromacanopyheightmodel(CHM)usingimagesgatheredbyanunmannedaerialvehicle.

MeanHeight MinHeight MaxHeight SDHeight MeanDensity

Modelprediction 15.1(8.9-26.0) 10.9(5.1-21.1) 18.6(11.7-28.4) 2.0(0.6-3.3) 508.3Fieldmeasurements 25.7(20.0–30.5) 21.8(14.4–29.7) 29.5(21.4–34.9) 3.2(1.3–7.0) 890.3

Therewasasignificantcorrelationbetweentreeheightmeasurementsandtreeheight

estimationsfromtheCHM(r=0.67,p=0.01),buttherewasnocorrelationbetweentreedensity

measurementsandtreedensityestimationsbyCHM(figure4-4(a)and(b)).

(a) (b)

Figure4-3:(a)MeanplotheightmeasuredonthegroundvsmeanplotheightderivedfromCHM.Eachdotrepresentsone20x20msurveyplot;(b)DensitymeasuredonthegroundvsdensityderivedfromCHM.

ValuesforbothtreeheightanddensitydifferedstronglybetweenfieldmeasuresandSfM

derivedvalues(figure4-4,figure4-5).ThetreedensityvaluesestimatedbytheCHMusingUAV

deriveddataunderestimatedtreedensityinallplotsbyanaverageof15treesperplot(table4-3).

Figure 4-4:Map of tree heights obtained fromunmanned aerial vehicle images (polygons) and discrete fieldmeasurements ofindividualtreesin18squaresurveyplots(squares).

Figure 4-5:Map of tree density obtained from unmanned aerial vehicle images (polygons) and discrete fieldmeasurements ofindividualtreesin18squaresurveyplots(squares).

3.2.CanopyGaps

AplotoftheCHMrastervisualizedseveralimportantfeaturesoftheforeststructureincluding

areaswithlowornotreecover.Thewindrows,wherenotreeswerere-plantedafterlogging,were

visibleaslong,narrowgapsinthecentralpartorthesite(figure4-7).Oldskiddertrailswerevisibleas

longstraightgapsinthecanopy,aswellasalargelandingsiteinthesouthwestcornerofthesite.

Alongthecreekontheeastsideoftheproperty,thecanopywasmoreopen,treeswerehigherand

someoftheremainingmaturetreeswereclearlyvisibleinfigure4-7.Atthefareastofthesite,the

bordertotheneighboringmatureforestwasclearlyvisiblewithfewerbutfartallertrees.

Thecanopygapswereevenlyspreadacrossthestudysitewithmostgapslocatedinthecenter

oftheproperty(figure4-6).Canopygapsaccountedfor6%ofthecanopy,withanaveragegapsizeof

58m2.Mostofthegapswerebelow20m2withcloseto75%ofgapsbelow50m2.Therewereonly

threegapslargerthan500m2(Table4-4).

Figure4-6:Canopygapslowerthanthe2-meterthresholdappliedtoourCHM

Table4-5:Proportionofcanopygapsofvarioussizes.

3.3TreeLocations

Thelocationoftreessubjecttofieldmeasurementscouldnotbealignedwiththoserepresentedin

theimagesfromtheUAV.Figure4-7showspredictedandmeasuredtreetopsforthreeplots.We

wereunabletomatchupthetreesfromeachdataset.Becauseofthepoorfit,anaccuracy

assessmentwasnotfeasible.

4.Discussion

DeterminingtreeheightsfromUAVimageswithoutaDEMthatisofsimilarresolutionasthe

UAV-derivedDHMdeliveredunsatisfyingresults.Themodelprovidedrelativeheightdifferences

betweendifferentpartsofthestudysiteandthereforeanestimateofstandstructure.Theimage

canbehelpfulindetectingareaswithbettergrowthandareaswithmoregapsandthereforebe

helpfulinrestorationplanning,evenifindividualtreeheightsareunderestimated.Wewere

lackingahigh-qualityDEMforthecreationofourCHM.DEMscanbecreatedfrompoints

Gapsize[m2] 10-20 20-50 50-100 100-200 200-500 >500

Proportionoftotalgapsize[%] 41.6 30.6 14.1 7.6 5.2 0.9

Figure4-7:Imageobtainedbyanunmannedaerialvehicleshowingthreeplots(greenpolygon)withtreetops(reddots)andactuallocationoftrees(bluedots).Lightergreyrepresentshigherelevationwhiledarkgreyrepresentslowelevation.

classifiedasgroundinthedensepointcloud,butinourcase,wedidnothaveenoughground

pointstocreateagoodmodel,mainlybecausethecanopywastoodensefortheUAVtotake

picturesoftheground.LiDARdataprovidesbetterdataandisneededforprecisesurfacemodels,

butisexpensive.CurrentminiaturizationofLiDARsensorsassociatedwithlowerpricescouldcan

becarriedbyUAVs,andwillbecomeincreasinglyaffordable.

DensityestimatesfromUAVdataweresignificantlybelowdensitiesmeasuredonthe

ground,becausethetreetopdetectionalgorithmdidnotdetectalltrees.Densecanopiesmake

itdifficulttodetectnon-dominanttreesasnotedbyLiseinetal.(2013)whichcoincidedwithour

findings.Densitieswereunderestimatedthemostinareaswithdense,homogenouscanopy

cover.

Relativeheightsfromourmodelcanbeusedindetectingareaswithbettergrowthandareas

withmoregapsandthereforebehelpfulinrestorationplanning.Wecouldidentifymanysmall

canopygapsandveryfewlargerones.BradshawandSpies(1992)usedtransectsamplingforgap

detectionandfoundgapdistributionssimilartoourstudyformatureDouglas-firforestsin

OregonandWashington,withmostgapshavingsmallersizes.Theauthorsfoundthatold-growth

Douglas-firforestsshowedgenerallylargergapsthanmaturestandsinthestudy.Whiteetal.

(2018)foundthatgapdetectionusingpointcloudsfromstereophotographyonmannedaircrafts

imagesdeliveredpoorresultscomparedtoairbornelaserscanning.Pointcloudsderivedfrom

UAVSfMdeliverbetterresults,butarenotasreliableasLiDARdata(Wallaceetal.2016).

ThequalityoftreedetectionandheightestimatesfromUAVdatahighlydependsoncanopy

density.Densityofthecanopyandtheinstrumentationbothaffectestimationsbymodels.Birdal

etal.(2017)weresuccessfulatobtaininggoodestimationsoftreeheightsusingamoving

windowfilteralgorithmonadigitalelevationmodelinayoung,openconiferousforestinTurkey.

Theauthorsachievedarootmeansquareerrorof28cmfortreeheightscomparedtoground

measurements.However,indensecanopyconditions,precisetreeheightestimatesareharderto

achieveandmayrequireadditionaldatalikemultispectralimages(Dandoisetal.2015a;

Panagiotidisetal.2017).Mengetal.(2017)usedobject-orientedclassificationensemble

algorithmstoimprovequalityofDTMunderdensevegetation.Thismethodusesanadditional

steptoimprovethequalityofgroundpointsundervegetationbycomparingthemtosurrounding

groundpointsintheopen.

TherelativelylargeareaofourstudysitewouldbebettersuitedforaUAVwithextended

batterylifeorafixed-wingUAV.Thesevehiclesallowforlongerflighttimesandfasterflight

speeds,andarebettersuitedtocoverourwholesiteinoneflight.Therearedefinitedrawbacks

incoveringthesiteinseveralflights.Forexample,achangeinlightingconditionscanaffectthe

qualityofphotogrammetricdata(Dandoisetal.2015).

Wedidnothavegroundcontrolpoints(GCP)inourimagesbecausetheimageswerenot

originallyintendedtobegeoreferenced.GCPsareusuallyclearlyvisiblerectangularmarkersof

whichcoordinatesarerecordedinthefieldwithahigh-qualityGPS.Additionally,imageoverlap

variedbetweenimagesandareasofthestudysitebecauseofthehillyterrainandtheconstant

flightheightimposedbytheflightplanningsoftware.Thiscausedsomewarpsandfragmentsin

partsofthemodel.

Thetimerequiredtocollectthedatawasdramaticallylongerforgroundmeasurements.The

fieldcrewspentseveraldaysmeasuringtreeheightsandcountingstems,whereasacquiringall

UAVimagestookjustoneday.ProcessingtimesforUAVimagesarehigher,dependonavailable

computerhardware,butwillneedatleastafullworkday.Forsmallrestorationsites,ground

measurementsmaythereforeremainthemostefficientmethodtoacquirestructuralforestdata.

5.Conclusions

Itispossibletoobtaingeoreferenceddigitalimageswithsufficientqualitytocreate3-

dimensionalmodelsofthecanopy,buttheresultingdataqualityisnotsufficientformonitoring

orscientificuse.TheUAVdidnotdeliverreasonableestimatesforstructuralcanopymetricsthat

canbeusedasmeasuresforrestorationsuccess.Densecanopyandhomogenouscovermay

requirebetterUAVs,trainedpilotsandmoresophisticatedpre-andpost-processing.

Evenwithourlowaccuracyofrelativetreeheightresults,restorationpractitionerscanuse

theseasanindicatorofbettertreegrowthandstructuraldiversity,butaconfirmationofthe

resultswithgroundmeasurementsisnecessary.ImagestakenfromUAVsandmapsproduced

fromtheseimagesallowforauniqueperspectiveontheprojectandaquickoverview.Our

resultscanbeahelpfulvisualizationforthecommunicationofrestorationmonitoringresultsand

allowforanalmostinstantunderstandingofgeneralcanopystructure.

Additionally,allremotelysensedandparticularlyUAVderiveddataisgeospatial,which

meansthat“…observedareasandobjectsarereferencedaccordingtotheirgeographiclocation

inageographiccoordinatesystem.”(Khorrametal.2012,2).SpatiallyexplicitUAVdataallows

foraspatialandtemporalresolutionthatisnotpossibletoachievewithanyothermethod.This

makesUAVdataanimportantspatialplanningtool,andcanbeusedforrestorationplanningin

theofficetodefineareasinneedoftreatments.Areasofinterestcanbemarkedand

geographicalcoordinatesuseddirectlytoinputintoaGPSdeviceforfieldwork.UAVscan

thereforebeusedasasupportingtoolinrestorationplanningaswellasamonitoringtool.While

ecologicalsamplingalwaysonlydeliversanaverageperplot/polygon/site,UAVmappingcan

deliverafullmappingofthestudysiteandthereforedeliveramorepreciseassessment.While

thisremainstrueforhobbyistUAVs,thedataqualityonlyallowsforafirstassessmentofasite

andmoreprecisemeasurementsrequirebettertechnologyortheuseofconventionalground

measurements.Thedevelopmentofsensorsystems,UAVtechnologies,andsoftwareis

advancingsorapidlythatitisreasonablylikelythatprofessionalqualityfeaturessuitabletosite-

levelrestorationmonitoringwillbeavailablewithinafewyears.Thus,UAVsmaysoonbebotha

powerfulandaffordabletoolforsmallerandnot-for-profitorganizationsthatconductrestoration

monitoringandscientificresearch.

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Chapter5:Conclusion

5.1Summaryoffindings

IaskedifforestrestorationeffortsattheGalianoConservancyAssociation’sDistrictLot63

restorationsiteweresuccessfulandhowUAVscouldimprovelabourandtimeintensiveground

measurementsandcontributetosuccessfulecologicalrestoration.IthenappliedaUAVimage

analysisworkflowtoimagesoftherestorationsitetodemonstrateapotentialapplicationin

restorationmonitoring.

Themainfindingsofchapter2werethatareasofrestorationtreatmentshowedahigher

diversityandcoverofunderstoryplants,weremorestructurallydiverse,andhadhighervolumes

ofCWD.However,Iwasnotabletoconnectallofthesedifferencestothetreatments

themselves.Treeheightsintreatedareaswerelowerthanexpected.Theresultsshowsome

positiveeffectsoftherestorationtreatmentsonforeststructureandplantdiversity,butalso

highlighttheimportanceofappropriatemonitoringstrategiesandaneedforappropriatedesign

ofmonitoringplots.

Themainfindingsofchapter3werethatUAVscanhelptocreatebetterrestorationgoals,

helpintheplanningoftreatmentsandimprovemonitoringafterthetreatments.However,

positiveeffectsofUAVusearehighlydependentonindividualprojectsandstakeholders

involved.NegativeeffectsofUAVsonsomewildlifespecieshavealreadybeenprovenand

technical,socialandlegalrestrictionsofUAVslimittheiruseinecologicalrestoration.

Beingarelativelynewtechnologicaldevelopmentwithappropriatestandardsstillunder

development,UAVsareincreasinglyusedbyecologiststorefinetheavailabledataonecosystem

recoveryandeffectsofrestorationtreatments.Thismayhelpvalidateorrejectlongheld

hypothesesandtheories.Monitoringcanbedonemoreoftenandrestorationpractitionerscan

reacttoproblemsfaster.Restorationoutcomesandmonitoringresultscanbecommunicated

fasterandbetterwiththehelpofUAVderivedimageproducts.UAVScanincreasesafetyof

fieldworkinremoteandhardtoaccessenvironments.Limitationsincludelegalregulations,

weatherconditions,limitedflighttimeandtheneedfortrainedpersonnel.UAVsensorsare

limitedtoelectromagneticradiationthatcanbesensedfromabove.Chemicalanalysislikesoil

samplingareatleastcurrentlynotpossibleandwillneedtobedonebyfieldcrewsonthe

ground.Additionally,dataqualityiscurrentlynotalwaysconsistentandstandardswillneedtobe

established.EventhoughthecostofUAVshasdecreaseddramaticallyinrecentyears,initial

investmentsarestillhigherthanfortraditionalequipmentliketapemeasuresorcompasses.Cost

ofmaintenanceofUAVsishighanddamagetotheUAVduringuseiscommon.Additionally,

increaseduseofUAVscouldleadtoalossofexpertiseinprovengroundbasedmethodsand

analysisofUAVderiveddatarequiresspecialsoftware,expertiseintheuseofthissoftwareand

canconsumesignificantamountsoftime.

Themainfindingsofchapter4werethatUAVimagescanhelpingettinganoverviewof

canopystructure,butsurveysneedtobecarriedoutwithcaretoreceivepreciseresults.This

includesimageoverlapandflightheightaccordingtothecanopydensity,timeofdayandthe

correctseason.Especiallyinhomogenousforeststheuseofgroundcontrolpointsmaybe

necessarytoachievegoodresults.Apre-existingDEMisnecessaryunderdensecanopyto

receivegoodresultsfortreeheightsbecauseincontrasttolaserscanners,photogrammetry

usingvisiblelightisnotabletopenetratecanopycover.Canopyheightmodelscanhowever

deliveragoodestimateofrelativecanopyheightandbeausefultoolinquicklyvisuallyassessing

canopystructuralmeasuresliketreedensity,canopygapsandmeanheight,bothimportant

measuresofstructuraldiversity.Technologyischangingrapidly,anditislikelythatwithinafew

yearsthequalityofdatagatheredwithrelativelyinexpensivehobbyistUAVswillbesufficientfor

monitoringandscientificuse.

5.2GreaterContext

Treatmentsforforestrestorationcanvarygreatly,dependingonthepreviousdisturbance,the

ecosystem,theinvolvedstakeholdersandtheavailableresources.Sometreatmentslikewire

fencingtopreventgrazingorcanopythinninghavebeenfoundsuccessfulovermanyecosystems;

otherssuchasapplyingfertilizersorprescribedfireshowedmixedeffects.Someprovedharmful

likethinning(Agraetal.,2018).Forestrestorationcanbeassimpleasrelyingonsuccessional

processesforthereturnofamatureforest.However,rapidlychangingclimateconditionsmay

requireustoactivelyprepareforestsforunprecedentedclimateconditionswithmethods

includingassistedmigrationandsupportingnewspeciesassemblages.Intemperateclimates,

creatingdiversityandthereby“spreadingtherisk”seemstobethebeststrategytoprepare

forestforthefuture.

Achangingclimatemakesadaptivemanagementmoreimportantthaneverbeforein

ecologicalrestoration.Thenecessarymonitoringwillcontinuetorelyontraditionalforestry

methodslikediametertapesandlaserrangefinders,butanincreaseduseofremotesensing

technologiesandespeciallyUAVsislikely.Thesenewtechnologieswillincreasetheamountof

availabledatabutdataqualitystandardswillhavetogetestablishedtomakegainedknowledge

transferable.

5.3LimitationsofthisResearch

Iwasnotabletofullyrelaterestorationtreatmentstoimprovedecologicalconditionsinthe

studysitetreatmentareas.Anincreasedsamplingsizemayhaveimprovedthestatistical

robustnessoftheanalysisanddeliveredclearerresults.Additionally,usingadjustedweightsin

theanalysisoftreedatawouldimprovethestatisticalpoweroftheresultsandcouldhelp

detectingeffectsofthetreatments.Unfortunately,pastdataonlyexistedfortheeight

permanentplots,whichlimitedthepossiblecomparisonofbeforeandafterdata.

TheUAVimagesusedtoanalyzethecanopystructureinchapter4wereofsufficient

qualityforarelativecomparisonofstructureacrossthesite,butdataqualityandcomparability

couldhavebeensignificantlyimprovedbyahigherimageoverlap,higherimageresolutionand

theuseofgroundcontrolpoints.Especiallyahigherimageoverlapcouldhaveincreasedthe

numberofgroundpoints,improvedmyDEMandthereforethecanopyheights.Duetotime

constraints,Iwasnotabletotakemoreimagesduringthe2017fieldseason.

5.4SuggestionsforFutureResearch

Theresultsofchapter2wereinconclusive,whichpointstoreanalysisofthedatatoweightmore

effectivelytheunbalanceddata.Italsoencouragesfurtherinvestigationofeffectsofthinning

treatmentsonforeststructureinthecomingyearsbutalsotheassessmentofotherindicatorsof

old-growthstructureslikebiomassaccumulationandtreeregeneration.Newthinningtreatments

onthestudysiteandsubsequentmonitoringoftheeffectscouldgiveinsightintheeffectiveness

ofrepeatedthinningtreatments.Along-termstudyondifferentthinningtreatmentsofyoung

Douglas-firforestsintheAmericanPacific-Northwestfoundthathomogenousthinningoverthe

wholestanddoesonlyinsignificantlyincreasethediametergrowthoftreeswithbiggerdiameters

unlessremainingdensitieswereextremelylow(Puettmannetal.,2016).Thisisconsistentwith

myresults,anditsuggeststhatfuturetreatmentsshouldconsistofthinningwithvarying

intensities,includinggapsandareaswithextremelylowremainingdensitiestoincreasegrowth

oflargertrees.AccordingtotoPuettmannetal.(2016)extremethinningdoesnotaffectthe

carbonsequestrationoftheremainingstand,butanassessmentofcarbonsequestrationonmy

studysitecouldgivevaluableinsightintheseprocesses.Gapswillalsoallowfornatural

regenerationandfurtherthestructuraldiversity.Thesuccessofseedlinggrowthwilldependon

theexclusionofhyper-abundantherbivorousdeer.

Thesizeofmystudysiterequiredmetoflythesiteinseveralseparateflightstokeep

visualcontactandtoaccountfortheshortflighttimesoftheUAV.Thiscomplicatedthecreation

ofacanopyheightmodel,butcouldbeavoidedbyusingUAVsampling,ratherthanafull

assessmentofthewholesite.Justlikeconventionalgroundmeasurements,imagescanbetaken

alonganeasilyaccessibleandvisibletransectlineorbelimitedtosamplingplots.Thisreduces

thetimerequiredforimageacquisitionandprocessing.Asamplingworkflowrepresentsamore

feasibleoptionofsupportingtherestorationmonitoringbyacharitableorganizationlikethe

GCA.DuetothenatureofUAVimages,theyarebestsuitedforassessmentsofcanopygapsand

treeheights.Ifthesedataarecombinedwithgroundmeasurementsoftreediametersand

understoryvegetation,acomprehensiveassessmentofrestorationsuccesscanbeachieved.The

relativelylabourintensiveandtimeconsumingassessmentofcoarsewoodydebriscouldbe

replacedwithanestimatebasedontreesthathavefallenandarenomorevisibleintheUAV

images.

TheapplicabilityofUAVstomonitorforestrestorationintemperateforestsneedsmore

research.Comparablestandardsandstandardizedmethodsareneededtobeabletocompare

resultsbetweenstudies.Inareaswherenohigh-resolutionDEMfromLiDARexists,other

methodsarenecessary.Datafusion,thecombinationofseveraltypesofremotesensingdatato

generatenewdata,maybeapromisingapproachofovercomingthoselimitationsofUAVdata

butneedsfurtherinvestigation.

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AppendixA:DesignofPermanentPlots

AllpermanentplotswerelaidoutusingtheguidelinesdescribedbyRoberts-Pichetteand

GillespieinTerrestrialVegetationBiodiversityMonitoringProtocols(Roberts-Pichetteand

Gillespie,1999).Theplotshaveasizeof20x20massuggestedforyoung,even-agedstands

(Roberts-PichetteandGillespie,1999).Theplotswerelaidoutsquaretothegeneralslope,and

allcornersA-Dweremarkedwithmetalpins(Figure0-1).Iwasnotabletofindsomeofthese

metalpins,howeverandhadtoreestablishthemissingcornerswithacompassandmeasuring

tape.EachquadratbearsanindividualIDandallfourcornersaremarkedwithGPSpointsandare

availableasashapefileforGISuse.Forplotsonaslope,TheGCAusedslopecorrectiontosetup

anexact20x20msquareintheplane.

Figure0-1:Layoutofpermanentplots(Roberts-Pichette&Gillespie,1999)

Forallplots,theGCAcollectedthefollowingdataRoberts-Pichette&Gillespie(1999):

Essentialinformation• nameofstandandnumberofplotsorstand-alonequadrats• mapofstandshowingtheplotlocation(s),theirrelationshiptoanyprominentfeature,

andtheroutetofindtheplotorplotarea• latitudeandlongitudeofone-hectareplotcentrestake• latitudeandlongitudeandelevationofallcorners• compassbearingofLineA-D-thebasereferenceline(BRL)• numberofeachplotorstand-alonequadrat• planofhectareplotwithallquadratsnumbered• averagestandheightandcanopydepth• writtendescriptionofaccessroutetostandandtotheplot(s)

Baselinetreedata

• tagnumberandspeciesofalllivingandstandingtrees10cmDBHandover• locationofallnumberedtrees(plottedonamap)• DBHofallnumberedtrees• conditionofallnumberedtrees• heightofaboutfivetreesperspeciesandplot• heighttolowestlivingbranchofaboutfivetreesperspeciesandplot• ageofstand(determinedfromoff-plottrees)• photographsfromstandardpositionsatstandardtimesanddates• degreeofcanopyclosure(byquadrat)

Additionally,tothetreemappingtheGCAcollecteddataonsoiltype,vegetationpercentage

coverbyspecies,slope,andcoarsewoodydebris.Thepermanentplotsarepartofthelong-term

monitoringstrategyfortherestorationprojectandallowadetaileddescriptionofthechange

overtime.

Coarsewoodydebris

Imeasuredlengthandthediameteratthecentreofeachpieceofcoarsewoodydebris(CWD)

largerthan7.5cmindiameter.ThisdiffersfromthetransectsamplingsuggestedbytheMinistry

ofEnvironmentCanada(2010).Irecordedthespecies(ifpossible),thedecayclass(figure0-2),

andmosscoverperpieceofCWD.Ithencalculatedthetotalvolumeandaveragediameterof

Figure0-2:DecayclassesasdefinedbytheMinistryofEnvironmentCanada(MOE,2010)

Vegetation

ThesamplingfollowedtheguidelinesdescribedbytheBCMinistryofForestsandRange(2010),

exceptforthetreelayer(seebelow).Iassessedspeciesbylayerandpercentareacoverinthe

plot.Icollectedanyunknownspeciesandverifiedthemwiththehelpofanexpert.

A. Treelayer(A1,A2,A3):Irepeatedthemethodsusedinthebaselineassessment,

thatdifferfromthestandardassessmentmethodfortreemensurationdescribed

bytheBCMinistryofForestsandRange(2010).Iassessedthespeciesand

measuredtheDBHofalltrees.Imeasuredsnags,butdidnotincludetheminthe

basalareacalculations.Ire-sampledaboutfivetreesperplotforheight,crown

widthanddepth,toestimatethelivecrownpercentage,withtheexactnumber

dependingonthepreviousassessments.FormeasurementoftheDBHIuseda

standardcircumferencetape,forheightmeasurementsalaserrangefinder(figure

0-3).Inaddition,Irecordedobvioussignsofwildlifeuse,damagetothetrees,and

thetreestatusaccordingtotheBCMinistryofForestsandRange(2010)(table0-

Figure0-3:HowtomeasureDBH(Roberts-Pichette&Gillespie,1999)

Table0-1:Treestatus(Dallmeier,1992)

Standingalive AS

Standingdead DS

Brokenalive AB

Brokendead DB

Leaningalive AL

Leaningdead DL

Fallen/pronealive AF

Fallen/pronedead DF

Standingalivedeadtop AD

A. Shrublayer(B1,B2):Alltreeandshrubspeciesincludingwoodyplantsbetween

10mand0.15mareincludedinthislayer.Iestimatedpercentagecoverper

species.

B. HerbaceousPlantslayer(C):Allherbaceousspeciesincludingwoodyplantsless

than15cmtallareincludedinthislayer.Iestimatedpercentagecoverper

species.

C. Moss,lichen,liverwort,andseedlinglayer(D):Thislayerincludesallmosses,

terrestriallichensandliverworts,andtreeseedlings(seedlingsaretreesyounger

than2years,i.e.treesthatdoonlyshowoneyearofgrowth).Iestimatedthetotal

percentagecoverofthislayerandrecordallspecies.Seedlingswereofspecial

interestfortheassessmentofpotentialfordiversification

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