i
MonitoringForestRestorationEffectivenessonGalianoIsland,British
Columbia:ConventionalandNewMethods
by
QuirinVascoHohendorfB.Eng.,HochschuleWeihenstephan-Triesdorf,2015
AThesissubmittedinPartialFulfillmentoftheRequirementsfortheDegreeof
MASTEROFSCIENCE
IntheSchoolofEnvironmentalStudies
ÓQuirinVascoHohendorf,2018UniversityofVictoria
Allrightsreserved.Thisthesismaynotbereproducedinwholeorinpart,byphotocopyorothermeans,withoutthepermissionoftheauthor.
i
MonitoringForestRestorationEffectivenessonGalianoIsland,British
Columbia:ConventionalandNewMethods
by
QuirinVascoHohendorfB.Eng.,HochschuleWeihenstephan-Triesdorf,2015
SupervisoryCommitteeDr.EricHiggs,SupervisorSchoolofEnvironmentalStudiesDr.CecilC.Konijnendijk,additionalmemberProfessorinurbanforestry,UniversityofBritishColumbia
ii
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.
iii
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
iv
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
v
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
vi
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
vii
Figure0-1:Layoutofpermanentplots(Roberts-Pichette&Gillespie,1999)................................99Figure0-2:DecayclassesasdefinedbytheMinistryofEnvironmentCanada(MOE,2010).......101
viii
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
ix
Acknowledgements
IwouldliketoacknowledgetheLkwungen-speakingpeoplesonwhosetraditionalterritorythe
UniversityofVictoriastandsandtheSonghees,EsquimaltandWSÁNEĆpeopleswhosehistoric
relationshipswiththelandcontinuetothisday.
MyresearchwasfocusedonwhatisnowknownasDistrictLot63,GalianoIsland.Iwouldliketo
acknowledgethatmyworkwasconductedintheshared,assertedanduncededterritoryofthe
Penelakut,theLamalcha,andtheHwlitsumNations,otherHul'qumi'numspeakingpeoples,
SENĆOŦENandWSÁNEĆspeakingpeoples,andanyotherswithrightsandresponsibilitiesinand
aroundwhatisnowknownasGalianoIsland.Iwouldliketoacknowledgethatmyworkwas
conductedonthecededterritoryoftheTsawassenFirstNation.Iamverygratefulforthe
privilegeofhavingbeenabletoconductmyworkwithinthesesharedtraditionalterritories.
x
Iwouldliketoexpressmygratitudetoeveryonewhosupportedmeonthisjourney:Tomy
graduatesupervisorDr.EricHiggsandcommitteememberCecilC.Konijnendijkforsupporting
meandallowingmethefreedomtoturnmyideasintothisproject.Thankyoutoeveryoneatthe
GalianoConservancyAssociationandespeciallyKeithErickson.Keith,alongwithHerbHammond
wereparticipantsintheoriginaltreatmentsandhelpedmeunderstandthethinkingbehindit.
Thankyoutomylabgroup,mycohortandtheSchoolofEnvironmentalStudiesformakingmy
twoyearsinVictoriasuchanunforgettableexperience.ThankyoutotheUniversityofVictoriafor
financiallysupportingmygraduatestudiesandtotheLoreneKennedyGraduateStudent
ResearchAwardcommitteeforsupportingmyfieldworkonGalianoIsland.Lastbutnotleast,I
wouldliketothankmypartner,myfriendsandmyfamilywhokeptmemotivatedalongtheway.
“Damngoodcoffee!”-DaleCooper,TwinPeaks
xi
Dedication
InmemoryofKenMillardwhowastheheartoftherestorationtreatmentsonDL63andinspired
usalltoworkhardforconservationandrestoration.
1
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
2
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
3
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
4
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)
5
(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)
6
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,
7
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
8
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,
9
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
10
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
11
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
12
literatureinchapter3.IconceivedandexecutedatrialofUAVderivedimagesforthemonitoring
ofrestorationeffectivenessonmystudysiteonGalianoIsland(chapter4).
Ihavewrittenuptheresultsasthreemanuscriptsforpotentialpublication.(chapter2to4).
Workingalongsidemycommitteeincomingmonths,Iproposetosubmitchapter2tothejournal
EcologicalRestoration,chapter3toRestorationEcology,andchapter4toForests.Formattingis
accordingtojournalstandardsandthereforediffersslightlybetweenchapters.
13
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.,
14
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
15
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
16
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
17
“…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.
18
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
(a)
(b)
Figure2-1:(a)LocationofGalianoIslandinWesternCanadaandstudysiteonGalianoIsland,BritishColumbia,Canada.(b)Overviewofthestudysitewithpermanentandtemporaryplots
19
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.
20
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)
21
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
22
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
23
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
24
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.
25
CW
D
Vol [m
^3 ha^-1]
CW
D
Dia [cm
]
Cover
Herb
[%]
Cover
Shrubs [%
]
Height
Fd [m]
DB
H Fd
[cm]
DB
H D
r [cm
]
Basal
Area
[m^2
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
NaN
NaN
8.501.00
51.8186.60
71.5088.32
311.56133.11
Estim
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
0.00
Table2-1:Summaryofallm
easuresofstandstructureanddiversitybytreatments.Fd=Douglas-fir,Dr=Redalder.Allvaluesaregroupm
eans.Table2-1:Sum
maryofallm
easuresofstandstructureanddiversitybytreatments.Fd=Douglas-fir,Dr=RedAlder.Valuesaregroupm
eans.
26
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).
(a)
(b)
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.
27
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)).
(a)
(b)
Figure2-4:(a)Abundanceof12mostcommonplantspeciesinthestudyplots.(b)Speciescountbytreatment.
28
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.
29
(a)
(b)
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).
30
(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.
31
(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).
32
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
33
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
34
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
35
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.
36
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
37
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.
41
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
42
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
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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
44
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
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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).
(a)
(b)
Figure3-1:TwoexamplesofcommonUAVs.(a)DJIInspire2multi-rotorUAV.(b)SenseFlyeBeeClassicfixed-wingUAV.Imageswereobtainedfromthemanufacturers'websites(https://www.dji.com/;https://www.sensefly.com/)
46
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.
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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
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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
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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
day.
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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
51
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
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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
53
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.
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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
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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).
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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.
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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
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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
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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.
60
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
61
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.
62
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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
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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
69
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
70
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
71
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
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(a)
(b)
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).
73
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
74
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).
75
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.
76
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.
77
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.
78
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).
79
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
80
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.
81
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
82
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
83
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
84
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.
89
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
90
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
91
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
92
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
93
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)
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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),
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andmosscoverperpieceofCWD.Ithencalculatedthetotalvolumeandaveragediameterof
CWD.
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
102
standardcircumferencetape,forheightmeasurementsalaserrangefinder(figure
0-3).Inaddition,Irecordedobvioussignsofwildlifeuse,damagetothetrees,and
thetreestatusaccordingtotheBCMinistryofForestsandRange(2010)(table0-
1).
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
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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