Evaluation of Airborne LiDAR as a Tool for Obtaining Sustainable Forest Management of Maine's

75 Pages·2017·2.79 MB·English

The University of Maine
DigitalCommons@UMaine
Electronic Theses and Dissertations Fogler Library
12-2014
Evaluation of Airborne LiDAR as a Tool for
Obtaining Sustainable Forest Management of
Maine's Forests
Rei Hayashi
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Recommended Citation
Hayashi, Rei, "Evaluation of Airborne LiDAR as a Tool for Obtaining Sustainable Forest Management of Maine's Forests" (2014).
Electronic Theses and Dissertations. 2223.
http://digitalcommons.library.umaine.edu/etd/2223
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EVALUATIONOFAIRBORNELIDARASATOOLFOROBTAINING
SUSTAINABLEFORESTMANAGEMENTOFMAINE’SFORESTS
By
ReiHayashi
B.S.OregonStateUniversity,2003
M.S.UniversityofGeorgia,2006
ATHESIS
SubmittedinPartialFulfillmentofthe
RequirementsfortheDegreeof
MasterofScience
(inForestResources)
TheGraduateSchool
TheUniversityofMaine
December2014
AdvisoryCommittee:
StevenA.Sader,ProfessorofForestResources,Co-Advisor
AaronWeiskittel,AssociateProfessorofForestBiometricsandModeling,
Co-Advisor
CynthiaLoftin,AssociateProfessorofWildlifeEcologyandUnitLeader,
U.S.GeologicalSurvey,MaineCooperativeFishandWildlifeResearchUnit
LIBRARYRIGHTSSTATEMENT
Inpresentingthisthesisinpartialfulfillmentoftherequirementsforanadvanceddegree
atTheUniversityofMaine,IagreethattheLibraryshallmakeitfreelyavailableforinspection.
I further agree that permission for “fair use” copying of this thesis for scholarly purposes may
be granted by the Librarian. It is understood that any copying or publication of this thesis for
financialgainshallnotbeallowedwithoutmywrittenpermission.
Signature: Date:
ReiHayashi
EVALUATIONOFAIRBORNELIDARASATOOLFOROBTAINING
SUSTAINABLEFORESTMANAGEMENTOFMAINE’SFORESTS
ByReiHayashi
ThesisCo-Advisors: Dr. StevenSader
Dr. AaronWeiskittel
AnAbstractoftheThesisPresented
inPartialFulfillmentoftheRequirementsforthe
DegreeofMasterofScience
(inForestResources)
December2014
The first objective of this study was to evaluate the applicability of using a low density
(ca. 1pointm−2)discrete-returnLiDARforpredictingmaximumtreeheight,stemdensity,basal
area, quadratic mean diameter, and stem volume using an area-based approach. The research
was conducted at the Penobscot Experimental Forest in central Maine, where a range of stand
structures and species composition is present and generally representative of northern Maines
forests. UsingavarietyofhighdimensionalLiDARmetrics,apredictionmodelwasdeveloped
usingrandomforest,anonparametricapproach,basedonreferencedatacollectedinfixedradius
circular plots. For comparison, the volume model used two sets of reference data with one
being fixed radius circular plots and the other were variable sampling plots. Prediction biases
were evaluated with respect to five silvicultural treatments and softwood species composition
basedonthecoefficientofdetermination(R2),rootmeansquareerror,andmeanbiasaswellas
residual scatter plots. LiDAR tended to underestimate forest inventory attributes, regardless of
silviculturaltreatmentsandspeciescomposition. However,theunmanagedunitshadparticularly
larger prediction biases, while the prediction biases also tended to be larger when softwood
species composition was greatest. The maximum tree height model had the largest R2 (86.9%)
followed by the volume model (72.1%), while the stem density had the smallest (R2) (28.7%).
Referencedatacollectedinthe0.08-hafixedradiuscircularplotsresultedinavolumeprediction
model with a larger R2. While it was difficult to develop models with a large (R2) owing to
complexities of Maines forest structures and species composition, low density LiDAR with the
area-basedapproachcanbeusedasasupportingtoolinforestmanagementforthisregion.
The second objective of this thesis was to investigate the applicability of low density
(ca. 3 pulses m−2) LiDAR data to deploy an individual tree-based approach. Specifically, this
study focused on species classifications as well as total height and volume predictions for stem
mapped trees. The research was conducted at the Penobscot Experimental Forests in central
Maine, where a range of stand structures and species composition is present and generally
representative of northern Maine’s forests. First, a random forest technique classified species
type and softwood species based on LiDAR metrics. Second, the random forest technique was
employedtocalibratedindividualtreeheightandvolumepredictionmodels. Classificationerrors
for species were evaluated with a confusion matrix, while height and volume prediction biases
were evaluated based on the coefficient of determination R2, root mean square error, and mean
bias, aswellasresidualscatterplotswithrespecttothreesilviculturaltreatmentsandsoftwood
species composition. Overall, both species type and softwood species classifications had poor
classificationaccuracy,inferringthatcalibrationofLiDARpulseintensityisnecessary. Also,the
heightandvolumemodelshadsmallR2valuesof0.38and0.30,respectively. Thislimitedaccu-
racy of both models likely is caused by low LiDAR pulse density, which prevents an accurate
representationoftreesinsubcanopypositions.
THESISACCEPTANCESTATEMENT
On behalf of the Graduate Committee for Rei Hayashi, I affirm that this manuscript is
thefinalandacceptedthesis. SignaturesofallcommitteemembersareonfilewiththeGraduate
SchoolattheUniversityofMaine,42StodderHall,Orono,Maine.
Date:
Dr. StevenSader
ProfessorofForestResources
Date:
Dr. AaronWeiskittel
AssociateProfessorofForestBiometricsandModeling
ii
ACKNOWLEDGMENTS
First and foremost, I would like to thank my chair advisors, Dr. Steven Sader and Dr.
Aaron Weiskittel, for guiding me through this thesis but more so for inspiring me to perform
mybestthroughoutmygraduateeducation. Also,IwasgladthatIcouldsuccessfullypublisha
journal article based on research conducted for Chapter 1 of my thesis. Without your patience
throughmultiplemanuscriptrevisions,Icouldnothaveaccomplishedthis. Additionally,Ihave
learned extensively from a number of researchers at several conferences (NESAF and NEMO),
a workshop (ISPRS Summer LiDAR School) and an internship (NASA) as well as other semi-
nars thanks to your funding. I am also grateful to Dr. Weiskittel for supporting my graduate
assistantship for an extra two years. Due to your extended guidance and patience, I had the
opportunitytopublishanotherarticle,currentlyunderreview,inadditiontomythesistopics.
Of course, I would also like to thank Dr. Cynthia Loftin for graciously serving on my
advisorycommittee. Iappreciatethatyouprovidedmewithvaluableadviceandsuggestionsfor
mythesisandatmydefense. Furthermore,Ihavelearnedkeyconceptsaboutlandscapeecology
fromseveralofthearticlesyousuggestedformygraduateeducation.
I would like to thank Dr. Andrew Finley at Michigan State University and Dr. Jeremy
WilsonattheHarrisCenterforConservationEducationforprovidingmewithvaluableadvice.
I would like to send a special thanks to Dr. John Kershaw at the University of New
Brunswick for providing me with priceless datasets and research topics. My hope is to publish
atleastonepaper,currentlyunderreview,inthenearfuture.
IwouldliketothankDaveSandilandsandSamGrimmattheSchoolofForestResources
for helping me revise my resume and cover letters. While I was working on research, your
supportenabledmetocompletenumerousjobapplications.
Lastbutnotleast,IwouldliketothankmanyformerandcurrentSFRgraduatestudents
and research associates, Dr. Andrew Nelson, Ben Rice, Brianne Du Clos, Jereme Frank, Josh
Puhlick,KaseyLegaard,Dr. MattRussell,PatClune,andPatrickHiesl.
iii
TABLEOFCONTENTS
ACKNOWLEDGMENTS..............................................................................iii
LISTOFTABLES ....................................................................................... vi
LISTOFFIGURES..................................................................................... vii
CHAPTER
1.ASSESSINGTHEFEASIBILITYOFLOWDENSITYLIDARFORSTAND
INVENTORYATTRIBUTEPREDICTIONSINCOMPLEXAND
MANAGEDFORESTSOFNORTHERNMAINE,USA ..................................1
1.1.Introduction................................................................................................ 1
1.2.ExperimentalSection ................................................................................... 3
1.2.1.StudyArea........................................................................................ 3
1.2.2.InventoryAttributesData..................................................................... 4
1.2.3.LiDARSystemSpecifications............................................................... 8
1.2.4.LiDARDataProcessingandModelCalibrationPredictions........................ 8
1.3.Results .....................................................................................................10
1.3.1.MaximumTreeHeight........................................................................11
1.3.2.StemDensity....................................................................................11
1.3.3.QuadraticMeanDiameter...................................................................11
1.3.4.BasalArea .......................................................................................14
1.3.5.StemVolume....................................................................................14
1.4.Discussion.................................................................................................24
1.4.1.PredictorvariablesinLiDARmetrics....................................................24
1.4.2.Silviculturaltreatmentsandspeciescomposition......................................24
1.4.3.MaximumTreeHeight........................................................................25
1.4.4.StemDensity....................................................................................26
1.4.5.QuadraticMeanDiameter...................................................................26
iv
1.4.6.BasalArea.......................................................................................26
1.4.7.StemVolume....................................................................................27
1.5.Conclusion................................................................................................28
2.PREDICTION OF INDIVIDUAL TREE ATTRIBUTES USING LOW
DENSITY LIDAR DATA ACROSS A RANGE OF SILVICULTURAL
TREATMENTSINCENTRALMAINE,USA...............................................30
2.1.Introduction...............................................................................................30
2.2.Methods....................................................................................................33
2.2.1.StudyArea.......................................................................................33
2.2.2.InventoryAttributesData....................................................................33
2.2.3.LiDARSystemSpecifications..............................................................37
2.2.4.LiDARDataProcessingandModelCalibrationPredictions.......................37
2.3.Results.....................................................................................................39
2.3.1.SpeciesTypeClassificationandSoftwoodSpeciesClassification................39
2.3.2.IndividualTreeHeightPrediction.........................................................41
2.3.3.StemVolumePrediction......................................................................47
2.4.Discussion.................................................................................................53
2.4.1.SpeciesTypeClassificationandSoftwoodSpeciesClassification................53
2.4.2.IndividualTreeHeightPrediction.........................................................55
2.4.3.StemVolumePrediction......................................................................56
2.4.4.Conclusion.......................................................................................57
REFERENCES............................................................................................58
BIOGRAPHYOFTHEAUTHOR..................................................................64
v
LISTOFTABLES
Table1.1.Descriptionofsilviculturaltreatmentsinmanagementunitsinthestudyarea......... 5
Table1.2.Examinedattributesbymanagementunit........................................................ 7
Table1.3.Developedpredictionmodelswiththethreemostkeypredictors........................21
Table1.4.Biasevaluationsbysilviculturaltreatments,andspeciescomposition..................22
Table1.5.Predictionmodelsbasedonresearch-andoperational-gradeplotdata .................23
Table1.6.Bias evaluations in research- and operational-grade volume models by silvi-
culturaltreatments,andspeciescomposition...................................................23
Table2.1.Descriptionofsilviculturaltreatmentsinmanagementunits ..............................34
Table2.2.Examinedattributesbymanagementunit.......................................................36
Table2.3.Developedpredictionmodelswiththethreemostimportantpredictors................40
Table2.4.Theresultsofspeciestypeclassification........................................................42
Table2.5.Theresultsofthesoftwoodclassification.......................................................43
Table2.6.BiasinamodelbasedonLiDARmetrics.......................................................44
Table2.7.BiasinamodelbasedonLiDARmetricswithspeciestype...............................44
Table2.8.BiasinamodelbasedonLiDARmetricswithsoftwoodspecies ........................47
vi