LandscapeManagementthroughIntegrationof
ExistingToolsandEmergingTechnologies
and diversityinherentin managing
aturalresource
professionals
Alandscape
approach
toforest
management
facethestaggering
taskofas- forestsoverlargeareas(Morganand
must
consider
theimplications
ofalternative
maytake
similating
andmobilizing
vast Henrion1990).Uncertainty
scenarios
across
stands
andthrough
time.
(Raup
amountsof information.Changing theformof naturaldisturbances
TheLandscape
Management
System,
a
marketandsocialvalues,increasing 1964), changingmarketvaluesfor
population
growth,andtheglobaliza- wood commodities(Oliver 1995), or
computer
program,
facilitates
implementation
tion
of
trade
andtransportation
con- shifting
cultural
values
attached
toforofthisapproach
byintegrating
forest
inventinually
reshape
the
demands
placed
ests
(Oliver
1992).
Diversity
is
charactories,
spatial
information,
growth
models, on the world's forest resources. Conteristic of both landowners and the forvisualization,
summarization,
andanalysis.
currently,
the evolutionof ecosystem eststructures
on theland.A landscape
Acase
study
withthree
scenarios--no
science
andlandscape
ecology
andthe of moderatesize (< 10,000 hectares)
harvest,
clearcut,
andthinning--exposes
the
in the UnitedStates
recognition
of theroleof disturbancesnearlyanywhere
complex
tradeoffs
inherent
inforest
manage- in forestdynamicshaveemphasized mayincludeforests
managed
by nonment
andhighlights
theneed
forcompara- thecomplex
tradeoffs
in values
associ- industrialprivateforestowners,forest
tiveanalysis
tools.
atedwith managing
theseresources industry,
andstateor federalagencies.
(Oliver et al. 1998). Successful
man- Standstructures
varywithin and beagement
requires
lookingfurtherinto tweenlandscapes;
management
objecthe
future
and
more
broadly
across
the
tives
and
methods
vary
by
ownership.
ByJamesB.McCarter,JeremyS.
landscape.
Coordinating
management Diverse stand structures reflect the
Wilson,PatrickJ.Baker,Jeffrey
activitiesandpredictinginteractions diverse
patternsof previous
manageL. Moffett, and Chadwick D.
withinandamongspatial
andtempo- mentpractices,
forestgrowth,andnatOliver
At the landscape
ralscales
isthemajorchallenge
facing ural disturbances.
forestmanagement
professionals.
scale,this diversityprovidesforest
The landscapemanagement
ap- managers
withthe flexibilityto meet
proach(Oliver1992;Boyce1995)ad- the changing
demands
on forestredresses
the complexity
of forestman- sources.
Thus, maintainingdiverse
agement.To coordinatethe vast standstructures
across
the landscape
amountof informationat the many affords
someinsurance
against
theunspatialand temporalscales,
we have certaintyinherentin forestmanagedesigned
a computer
programto im- ment (0liver 1995).
plementthe landscape
management Traditional
forest
planning
addresses
approach.
It allowsforestmanagers varying
spatial
andtemporal
scales.
For
andpolicymakers
to develop
andeval- largeforest
organizations
therearethree
uatestand-andlandscape-scale
silvi- general
levels
ofplanning:
strategic,
tacticulturaloptionsfor both short-and cal,andoperational
(Weintraub
andBare
long-term
planning.
1996). Strategicplanningconsiders
whole forestareas,from thomandsto
LandscapeManagement
Our approach
uses
a richhistoryof
Peer-Reviewed
millionsof acres,overlongplanning
horizons.
Thisscale
requires
considerable
research and data collection in forest
datareduction.
Tactical
planning
considecology,
mensuration,
andsilviculture erssmaller
spatial
scales,
suchaswateratthestandscale.
Thisknowledge
pro- sheds,on an annualbasisoveroneor two
videsthe basisfor classifying
forest decades.
Operational
planningimplestandsand predictingfuturecondi- ments
tactical
plans
during
each
year.
tionsat the landscape
scale.The apOur approach
combines
aspects
of
proachaggregates
thesetechniques
to tacticaland operational
planningfor
thelandscape
scale.
bothspace
andtime.Instead
of explicThe landscape
management
ap- itly definingthreedistinctlevelsof
proachacknowledges
theuncertainty planningthatarevaguely
linked,the
Journalof Forestry • 7
landscape
management
approach
considers
multipleplanning
levels
simultaneously.
Managers
maycompareand
contrast
standstructures
in a giventime
or over multiple decades.Likewise,
standstructures
in a giventimeperiod
canbeexamined
across
a largearea.
Resources,
University
of Washington boundaries,and a digital elevation
modelfor the landscape.
At the indiNorthwest Research Station.
vidualstandscale,
growthmodels
proLMSisa Microsoft
Windows
appli- jectstands
intothefuture.Specific
silcation that coordinates the flow of invicultural
treatments,
suchasthinning,
formation among growth models, regeneration
planting,and clearcutcomputervisualization
software,
and ting, can be modeled.The same
analysis
tools(seeMcCarteret al. 1996 process
canbeapplied
atthelandscape
Overviewof the System
andMcCarter1997).LMS organizes scale,eitheron a stand-by-stand
basis
TheLandscape
Management
System activities
thatincludeapplication
man- or as a uniform whole.
(LMS)provides
landmanagers
witha agement,
growthsimulation,
silviculLMS hasbeendeveloped
to incortoolforevaluating
management
alter- ture and disturbance simulation, and poratesomeexistingforestmodels
natives
byintegrating
thelargeamounts outputprocessing.
Preferred
manage- andcomputer
tools(McCarter1997).
of information
necessary
fordesigning ment scenarios
are developeditera- Data and estimated future conditions
complex
landscape
plans.The system tivelyin LMS by criticallyevaluating canbeanalyzed
andevaluated
byavarequires
knowledge
andinformation
at andrefining
multipleprojections.
rietyof methods,
ranging
fromtables
the standscaleto projectchanges
in
Filterprograms
translate
datafrom andgraphs
to three-dimensional
stand
landscape-scale
processes.
An under- oneformatto another,
generating
the and landscapevisualizations
(SVS,
standing
of silvics,
foreststanddynam- systemnetworkthat ultimatelylinks McGaughey
1997;andUVIEW, Ager
ics,growth
models,
silviculture,
andge- inputsto outputs.(fig.1). Incorporat- andMcGaughey1997).Further,the
ographic
information
systems
(GIS) is ing newgrowthmodelsor inventory benefits
andrisks
estimated
foragiven
necessary
for creatingandevaluating dataformats
intoLMSrequires
chang- alternative, such asstand structure diforestplans.Withoutthisknowledge ing specificfilter programs,not the versityversussprucebudwormsustheprogram
wouldsimply
provide
out- general
architecture
of thesystem
(Me- ceptibility
(Wilsonet al.,in press),
can
Carter 1997).
putwithoutcontext.
beassessed
eitherby usingalgorithms
The development
of LMS ispartof
Landscapes
in LMSareorganized
as withinLMSorbyexporting
outputto
a cooperative
effortbetween
theSilvi- portfolios
tools.Additionalexamcontaining
foreststandin- otheranalysis
cultureLaboratory,
Collegeof Forest ventorydata,a growthmodel,stand plesof interpreting
LMS outputinand the USDA Forest Service, Pacific
GIS
spatial
information•"/
Forest
stand
inventory•"•
information (ARC/INFO)
Landscape Management System
Integrafiveprogramautomatinggrowth
projection,analysis,and visualization
I
(•.gdSilvicultural
operations
'•
LMS portfolio
inventoryand spatial
.,thinning,
regeneration.
information for stands
isturbance
simulation
J
in a landscape
Growt
mode
(FVS-PN variant)
snag-logsimulator
(cwdsim.exe)
/((e
External
analysis
•'x'/
5Summary
and
•'x'•
5Landscap
visualizat
(
.g.,wind
hazard
model
in ]
]
[
analysis
tables]
(e.g.,summaries
and]
•k•,
Micrøsøft
Acess)
J •tructural
{k•.(UVIEW))
classificationS//
Stand
visualization
(SVS)
Figure/.Theflowofinformation
intoandthroughtheLandscape
Management
System.
Eachconnecting
linerepresents
oneor
morefilter programs
that formatinformationbeingtransferredaccording
to the client'srequirements.
Theshadedareaidentifies the corecomponentsof LIqS.
18 June 1998
dude assessmentof crown-fire hazard
Larson 1996), and evaluation of fi-
uralResources.
Approximately
halfthe
landscape
iscomposed
of youngplantations;theremainder
ismostly60- to
80-year-old
second-growth.
TheForest
Vegetation
Simulator-Pacific
Northwest Variant (FVS-PN) (Donnelly
agementregimesdiffer considerably
(fig.3, p. 20).Theno-harvest
scenario
results
in a landscape
of densestands
withlargetrees
andnoharvest
openings
(inventory
andGISdatawereprovided
by theWashington
StateDNR). The
nancial returns.
1996;Tecket al. 1996) wasusedasthe
dearcut scenario creates a mosaic of
(Wilsonand Baker,in press)and
windhazard(Wilsonin prep.),dassification of stand structures(e.g.,
Careyand Elliott 1994; Oliver and
Thesystem
hasbeendesigned
asan growthmodelforallstandprojections. openings
anddense
youngplantations.
interface
to growthmodels
thatoperThreemanagement
alternatives
were The thinningscenario
produces
fewer
ate at the individual tree, distance- projected
overfivedecades.
Thefirstal- openings,
less-dense
stands,
andgreater
independent
level,for two reasons. ternative excluded harvestFirst,mostgrowthmodelsoperateat ing. The seconddearcuta
this resolution(Vanday1994). Sec- portionof thelandscape
in
ond, mostforestsamplingmethods each decade; the harvested
currently
provide
thislevelofinforma- standswere replantedand
tion or canbe readilymodifiedto do precommericallythinned
tance-independentlevel provides
after two decades. The third
combined commercial thin-
within-standsizedistributions,it offers
ningandlimiteddearcutting
a compromise
between
spatialresolutionanddataintensity
for evaluating
landscape-scale
processes
andmanage-
in the older stands.
ment alternatives.
parethe management
sce-
Tobeapplied
at thelandscape
scale,
modelingat the individualtreelevel
with spatiallyexplicitdatawouldrequireprohibitively
largeamounts
of
information.
Thecostofobtaining
tree
locations
ishigh,andthespatial
competitionindices
theypredict
rarelyper-
narios:wood volume, stand
structure, and windthrow
and cut volume in different
mation are available.
Stand Structure Classifica-
so. Becausethe individual tree, dis-
Stand initiation
Forsimplicit)•
onlythree
criteria were used to com-
II :1
susceptibility.
The standing
Stem
exclusion
sizeclasses
of woodbeing
harvested
fromthelandscape
wereprovidedin an LMS
form better than stand-scale measures output table.Foreststruc(Vanclay1994;Wimberlyand Bare tureclasses
provided
a sub1996).In addition,fewforestgrowth jective classificationof
models
thatincorporate
spatial
infor- wildlife habitat(see"Oliver's
Modeling
atthestandscale
doesnot tion," p. 22). Figure2 prerequirethelargeamounts
of datathat sents visualizations of each
individualtree, distance-dependentforest structure dassification.
modelsdo; however,in an environment
Forests
on the Olympic
of changing
management
goals,stand Peninsula have been affected
averages
donotalways
provide
enough by catastrophic
windstorms
information
to address
diverse
ques- (Lynottand Cramer1966;
tionseitherspadallyor temporally. Hendersonet al. 1989).The
Standaveraging
limitstheapplicability wind hazard model for Clalof themodelasnewquestions
orcrite- lamBayincorporates
comria ariseby adoptinga classificationplexspatial
andtemporal
inscheme
earlyin themodeling
process. formationto comparethe
susceptibility
of standsto
windstorms (see "Wind
The ClallamBay Portfolio
A casestudy,comparing
threeman- Hazard Model," p. 20).
agementscenarios,
showssomefea- Windhazardratings
for the
turesof LMS. Forpurposes
of illustra- entire landscapeare estition we restrictedthe exampleto a matedforeachtimeperiod.
Understory reinitiation
Old-growth
small area and few measurable criteria.
The casestudylandscape
is a 400hectare
basinnearClallamBayon the
OlympicPeninsula
managedby the
Washington
StateDepartment
ofNat-
CaseStudy Results
At the end of five decades
theprojected
landscape
con-
Figure2. Representativestandvisualizationof
the four foreststructuresbeingusedto evaluate the landscapein this analysis.
ditions under the three man-
Journalof Forestry 19
variation of tree sizes.
No-harvest
Clearcut
Standing
andcutvolumeson theClallamBay
landscape
vary through
timeandamongscenarios(figs.4, 5). With no
harvesting,
the volume
ofbothstanding
sawtimberandlargesawtimber
sawtimber
and largesawtimber
size
class.Standing
volumeremainsrelativelyconstant
butis considerably
less
than the no-harvest scenario. The har-
vestlevels
in thethinningscenario
producelessstanding
volume
thanthenoharvest
scenario
but resultin a higher
proportion
of largesawtimber.
Figure
dcompares
theproportion
of
increases over time. Harthelandscape
in eachstructural
stage
vestingin the clearcut amongscenarios.
The no-harvest
opscenario
generates
an in- tiongenerates
old-growth
structure
by
creasing
flowof cutvol- the fourth decade;however,without
ume,particularly
in the anycutting,thestandinitiationstruc-
scenario
scenario
ture is lost in the same decade. The
clearcut
scenario
generates
littleunderstoryreinitiation
andnoold-growth.
Baycasestudylandscape. Stands
arecycled
between
standinitiaFigure3. Landscape
visu-
alization
of the Clallam
The landscapesare depictedduringthe fifth
Thinning scenario
decade of each scenario.
Wind
Hazard
tion and stem exclusion. In the thin-
ningscenario
old-growth
structure
is
created
by thethirddecade.
The lim-
Model
matrix(modifiedfrom Mitchell1995).
Exposure
isa measureof a stand's
topographic
positionrelative to upwindstands(wind direction is variablein the
model). Severeexposure= ridges,mid- and upper-slope
standswith aspectsparallelto the wind,and upper slopes
with windwardaspects.Moderate exposure= upper-slope
standsprotected by higher elevationsupwind,mid-slope
standswith windwardaspects,
andbottom-slopestandshavingaspectsparallelto stormwinds.Low exposure= mid- or
respondto changing
standconditions
(Cremeret al. 1982; bottom-slopestandsprotectedby higherelevationsupwind.
Soil describes the effect of a stand's soil attributes on
Becqueyand Riou-Nivert 1987; Lohmanderand Helles
we used
1987).Siteandstandratingsare combined
to providea wind windthrowpotential.For the ClallamBaylandscape
a soilwindthrowhazardcodedevelopedbytheWashington
hazardrating.
Soilsreceivea seInsteadof explicitlypredictingandsimulating
disturbance StateDepartmentof NaturalResources.
events,
thisapproachratesthe susceptibility
of eachstandto vere,medium,or low rankingbasedon their maximumrootwind. Hatrices for soil, exposure,and stand rankingsare ingdepth and soildrainagerates.
Mitchell(I 995) proposeda simplemodelfor combining
site
andstandhazardratingsinto an overallwind hazardrating.
Sitehazardsindexrootingdepth,soilmoisture,topographic
exposure,and other environmental
conditionsthat are not
generally
alteredbyforestmanagement
(Cremeret al. 1982;
Mitchell 1995;Quine 1995).Standcharacteristics,
suchas
tree heightanddiameter,crownsize,species,
trees per area,
and the conditionof neighboringupwindstands,are determinedbythe individual
trees in the standandlandscape
and
combined into an overall wind hazard value. Values for site
hazard from the first matrix are used for site in the second
Site Hazard
Exposure
low
Soil
moderate
severe
low
moderate
severe
low
low
moderate
low
moderate
severe
moderate
severe
severe
Stand
severe
low
low
low
moderate
moderate
low
moderate
severe
severe
severe
severe
moderate
:
30 meters
low < 80, moderate _<90, severe > 90
Site
moderate
low < 15 meters, moderate _<30 meters, severe >
2. Height to diameter ratio (sameunits) of the largest
250 trees per hectare:
Wind Hazard
low
Stand refers to the conditions of trees in a stand and rel-
ativeconditionsof upwindneighbors.
Standfactorsandtheir
weightinghavebeen developedfrom a reviewof the wind
hazardliterature (Cremer et al. 1982;Becqueyand RiouNivert 1987;Lohmanderand Helles 1987;Mitchell 1995).
Eachof thesefactorsis givenequalrating.
I. Heightof the largest250 trees per hectare:
3. Upwindneighborheight/focus
standheightratio:
low > .75,moderate_<.75,severe< .50 (minimum
20 percentof focusstandborder)
4. Percentof trees retainedin thinningsduringprevious
decade:
low > 80, moderate -< 80, severe _<60
20 June1998
No-harvestscenario
Clearcutscenario
Thinningscenario
40,000
' 35,000
'•E
• 30,000
25,000
15,ooo
••20,000
• 5,000
! :ß ß ß
0
1991
2001
2011
2021
2031
m
2041
1991
pole
2001
•
2011
2021
sawtimber
• !ß •
m
2031
2041
•
1991
2001
2011
2021
2031
2041
large sawt•mber
Figure4. Standingvolumeby yearand sizeclassin eachof the three scenarios.
itedclearcut
harvesting
in thethinning
scenariocontinuesto generatethe
Clearcut scenario
Thinning scenario
standinitiationstructure
throughout
the50-yearprojection.
• 5,000
The proportion
of thelandscape
in
various
windhazard
classes
iscompared '•E4,000
amongscenarios
in figure7. In the
'• 3.000
clearcut
scenario,
a largeproportion
of
• 2.000
1,000
thelandscape
ismaintained
in thelow
0
hazardclassthroughthe creationof
1991 2001 2011
2021 2031 2041
1991 2001 2011 2021 2031
openings
andtheyoungstands
thatdepole
•
sawtimber
•
large sawtimber
velopin them.Severe
windhazard
ona
smallproportion
of the landscape
reFigure5. Cut volumeby yearand sizeclassin eachharvestingscenario.
sultsfromthecreation
of openings
up-
No-harvest
Clearcut
scenario
scenario
2041
Thinning scenario
100%
60
8ø
l
40
20
o
1991
20;3120'112021 2031
•
2041
standinitiation
1991
2011 2(•212031 20411991 2001 2011 2021 20'312041
2001
-- stem exclusion
•
understoryreinitiation
•
old-growth
FiRure6. Proportionsof the landscape
in differentforeststructuralstagesthroughouteachmanagementscenario.
No-harvest
Clearcut
scenario
scenario
Thinning scenario
100%
60
20
1
0
1991
20;312011 20'2120'31 2041 1991 2001 2011 2•21 2•31 2041 1991 2001 2011 2021 2031 2041
low
I•
moderate
•
severe
Figure7. Proportions
of the landscape
in differentwindhazardclasses
throughouteachmanagement
scenario.
Journalof Forestry 21
Oliver's
Stand
Structure
Classification
The classification
of standstructureusedfor the ClallamBaycasestudyis
basedon Oliver's(1981)fourstructuralstages:
old-growth,
understory
reinitiation,stemexclusion,
andstandinitiation.
Althoughthere are manyclassifications(e.g.,Oliver 1981;Brown1985;FEMAT1993;CareyandElliott1994),we
choseOliver's(1981)because
it iswidelyapplicable
in westernWashington.
Standswere evaluatedfor structuralclassin the order presentedbelow;if a
standdidnot meetthe requirements
of one structuralclass,it waspassed
to
canbe expected
fromnaturaldisturbances.
Theclearcut
scenario
produces
thelandscape
mosaic
withthelowest
cumulative
windhazard
rating;
thenoharvestandthinningscenarios
have
higherratings.Because
windhazard
ratingsaredetermined
for individual
stands,
managers
canplanto reduce
stand-specific
windhazardassociated
the next one down.
with certain sites or allow stand struc-
Old-growth (OGDTG
1986)
2 or morespecies
20 or moretreesper hectare> 81.3 centimeters
dbh
30 or moreshade-tolerant
treesper hectare>40.6 centimeters
dbh
7 or moreconifersnags
perhectare>51 centimeters
dbhand>4.5 meterstall
10or morelogsper hectare>61 centimeters
diameterand> 15meterslong
Canopyclosure> 30 percent
Understory reinitiation
Averagedbhof the largest250 treesper hectare_>51 centimeters
Canopyclosure> 40 percent
Stem
exclusion
turesthatarerelatively
moresuscepubleto windthrow
to growin protected
sites(Wilson,in prep.).
Forourexample
wechose
arelatively
smalllandscape
andjustthreeanalysis
criteria.
Realmanagement
problems
are
morecomplex,
andincluding
otherobjectives-balanced
timberflow,aesthetics,or recreation--would
require
developmentof additional
management
scenarios and measurable criteria. Never-
Averagedbhof the largest250 treesper hectare< 51 centimeters
Canopyclosure> 60 percent
theless,
eventhis limitedcasestudy
shows
thatLMS efficiently
automates
themanyrepetitive
routines
necessary
Stand
to use stand-scaleinformation for land-
initiation
Anystandthat doesnot fit in anothercategory
scape-level
projections.
Projecting
the
ClallamBaylandscape
(26stands)
one
timeperiodusingFVS-PN takesapwind of uncutstands.
As treesgrow thetradeoffs
among
theClallam
Bayal- proximately
15seconds
using
aPentium
taller in the no-harvestscenario,more ternatives.
Bothharvesting
scenarios 133MHz desktop
computer.
stands move into the moderate and seChanges
in forestmanagement
obproduce
substantial
cutvolumes
in the
verewind hazardclass.By the fifth sawtimber
andlargesawtimber
cate- jectivesgenerate
newsuitesof quesand thinning tions. Consequently,
decade
only10percent
ofthelandscapegories.The no-harvest
analysistools
is in the lowest wind-hazard class.The
scenarios
produce
old-growth
structure needto bereadily
adaptable
tovariable
thinningscenario
produces
similarre- onthelandscape,
environments.
LMS inalthough
thethinning management
suits. Increasesin wind risk becauseof
scenario
creates
old-growth
structure cludes
several
analysis
programs,
such
develops
moreunderstory
reini- assummary
the creation
of upwindopenings
and sooner,
standstatistics,
landscape
thinningin stands
areoffset
byreduced tiation structure, and maintains some timbervaluation(standing
or cutvolheight-to-diameter
ratios
resulting
from stand initiation structure. These three ume), and harvestadjacency
conthinningand the creationof young structures
havebeenidentified
assup- straints.
At thesame
time,LMSoutput
stands,which havelowerwind risk.
porting
thehighest
relative
diversity
of is sufficiently
genericthat it canbe
wildlife(Franklinet al. 1986).
readilytransferred
to a widerangeof
Discussion
In theabsence
of large-scale
distur- analysis
tools.Forexample,
thewind
The integration
of growthmodels, banceor harvesting,
thestandinitia- hazard
ratingmodule
wasdeveloped
m
inventorydata,geographic
informa- tionstructure
willeventually
disappear a database
program,
andstandstructural classifications have been develtion,andanalysis
toolsin LMScreates
a fromthelandscape.
If landscape-scale
framework
to helpusers
compare
man- hazards--wind, fires, insect infesta- opedin a spreadsheet
program.
The substandresolution of LMS inagement
scenarios.
Table1 summarizestions-arehigh,somestandinitiation
formationallowsfor detailedanalysis
For example,
in our standstructure
classification,
theold-growth
category
requires
anestimate
of thenumberof
treesof shade-tolerant
species
greater
than40.6 centimeters
dbh.Aggregationof stand-scale
information
might
obscure this level of resolution. In con-
trastto optimization
methodologies,
a
desired stand structure distribution for
22 Junel998
a landscape
isobtained
iteratively
with
multipleprojections;
however,optimizationtechniques
canbeusedto developscenarios
in conjunctionwith
1986.Interim
definitions
j•r old-growth
Douglas-fir
and
mixed-coni•r
j•rests
inthePacific
Northu•st
andCali]br-
scape
visualization.
General
Technical
Report
PNWGTR-397. Portland,OR: USDA ForestService,Pacific Northwest ResearchStation.
nia. Research
Note PNW-447. Portland,OR: USDA
Forest
Service,
Pacific
Northwest
Research
Station.
BECQUEY,
J., andP.RIou-NIVERT.
1987.L'existence
de
C.D. 1981.Forestdevelopment
in North
zones
destabilit•
despeuplements.
Consequences
sup OLIVER,
lagestion.
Revue
Foresti?re
Franfaise
39:323-34.
America
following
major
disturbances.
Forest
Ecology
andManagement
3:15348.
BOYCE,
S.G.
1995.
Landscapej•restry.
New
York:
John
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Conclusion
The landscape
management
approachaddresses
theuncertainty,
complexity,and tradeoffs
associated
with
managing
forestresources.
LMSallows
forestmanagers
to synthesize
andintegrate data and informationfrom
growthmodels,
forestinventories,
and
GIS databases
to implement
thelandscapemanagement
approach.
By provldinggraphical,tabular,and visual
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Research
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current
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Moj•
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user}
manual,
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1.5 Seattle:
as well as outreach and education. As
assistants
andChadwick
University
of Washington,
College
of Forest
Re- j•tt areresearch
the demands on the forestsof the world
sources,
Landscape
Management
Project.
D. Oliverisprojõssor,
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forest
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dynam- Resources,
continue
to increase,
managers
needthe McG^UGHEY,
Universityof Washington,
means to create and assessinnovative
management
options.We believethat
LMSisa strong
steptowardthisgoal.
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DEFINITION
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Microcomputer
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Seattle, 98195. LMS can be obtained
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(http://silvae.
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Thesoftware
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Journalof Forestry 2:3